From 2efc63ea15294da5f8e7d5dc37c0f3e3db735034 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 17 Oct 2024 18:38:02 -0700 Subject: [PATCH 001/147] Initial sae code --- electrolyte_fm/data_modules/__init__.py | 3 +- electrolyte_fm/data_modules/sae_dataset.py | 245 +++++++++++++++++++++ electrolyte_fm/models/__init__.py | 3 +- electrolyte_fm/models/model_utils.py | 16 +- electrolyte_fm/models/sae.py | 231 +++++++++++++++++++ electrolyte_fm/utils/ckpt.py | 17 +- electrolyte_fm/utils/metrics.py | 85 ++++++- submit/default.jsonnet | 1 + submit/dgx.jsonnet | 4 +- submit/sae.jsonnet | 49 +++++ 10 files changed, 641 insertions(+), 13 deletions(-) create mode 100644 electrolyte_fm/data_modules/sae_dataset.py create mode 100644 electrolyte_fm/models/sae.py create mode 100644 submit/sae.jsonnet diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index 3648b03f..d144cb5c 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -1,4 +1,5 @@ from .molnet_dataset import MolNetDataModule -from .roberta_dataset import RobertaDataSet from .property_prediction_dataset import PropertyPredictionDataModule +from .roberta_dataset import RobertaDataSet +from .sae_dataset import HiddenStateDataModule from .tmqm_dataset import tmQMDataModule diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py new file mode 100644 index 00000000..094277db --- /dev/null +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -0,0 +1,245 @@ +import re +from math import floor +from pathlib import Path +from typing import Optional, Union + +import pytorch_lightning as pl +import torch +from datasets import Dataset, load_dataset +from torch.utils.data import DataLoader +from transformers import DataCollatorWithPadding, PreTrainedModel + +from ..models.model_utils import load_encoder +from ..utils.tokenizer import load_tokenizer +from .roberta_dataset import maybe_shard_dataset + + +def extract_hidden_state( + input_ids, + attention_mask, + encoder: PreTrainedModel = None, + collate: DataCollatorWithPadding = None, + layer: Union[int, float] = 0.5, + device="cpu", +): + batch = collate({"input_ids": input_ids, "attention_mask": attention_mask}) + attention_mask = batch["attention_mask"] + batch = batch.to(encoder.device) + + # Disable gradients + with torch.inference_mode(): + enc = encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) + + if isinstance(layer, float): + layer = floor(len(enc["hidden_states"]) * layer) + + # Flatten hidden states + hs = enc["hidden_states"][layer].to(device) + hidden_state = [] + d_model = hs.shape[-1] + assert attention_mask.shape[0] == hs.shape[0], "batch size mismatch" + assert attention_mask.shape[1] == hs.shape[1], "seq. length mismatch" + for bdx in range(hs.shape[0]): + hs_molecule = hs[bdx][attention_mask[bdx] > 0] + assert hs_molecule.shape == (len(input_ids[bdx]), d_model) + hidden_state.append(hs_molecule) + + return {"hidden_state": hidden_state} + + + + +class HiddenStateDataModule(pl.LightningDataModule): + def __init__( + self, + name_or_path: str, + path: str, + layer: Union[int, float] = 0.5, + tokenizer: Optional[str] = None, + batch_size: int = 64, + val_batch_size: Optional[int] = None, + encoder_batch_size: Optional[int] = None, + encoder_device: str = "cuda", + return_molecule: bool = False, + canonical=False, + ): + super().__init__() + + # Locate Tokeniser and dataset + self.name_or_path = name_or_path + self.layer = layer + self.tokenizer = load_tokenizer(tokenizer or name_or_path) + self.encoder_device = torch.device(encoder_device) + self.vocab_size = len(self.tokenizer) + self.path: Path = Path(path) + self.return_molecule = return_molecule + assert self.path.is_dir() or self.path.is_file() + + self.batch_size = batch_size + self.val_batch_size = val_batch_size or batch_size + self.encoder_batch_size = encoder_batch_size or batch_size + self.hparams["tokenizer"] = tokenizer + self.save_hyperparameters(logger=False, ignore=["encoder_device"]) + self.data_collator = DataCollatorWithPadding(self.tokenizer, "longest") + + + def prepare_data(self): + self.dataset + + @property + def dataset(self): + if hasattr(self, "_dataset"): + return self._dataset + self._dataset = load_dataset( + "text", + name=str(self.path.name), + data_files={ + "train": str(self.path.joinpath("data/train/*.txt")), + "validation": str(self.path.joinpath("data/val/*.txt")), + "test": str(self.path.joinpath("data/test/*.txt")), + }, + keep_in_memory=False, + streaming=True, + save_infos=True, + ) + return self._dataset + + def setup(self, stage: str) -> None: + self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) + ds = maybe_shard_dataset(self.trainer, self.dataset) + ds = ds.map( + self.tokenizer, + batched=True, + input_columns="text", + remove_columns="text", + ) + tok_columns = ["input_ids", "attention_mask", ] + ds = ds.select_columns(tok_columns) + + # Extract per molecule hidden states + ds = ds.map( + extract_hidden_state, + batched=True, + batch_size=self.encoder_batch_size, + fn_kwargs={ + "encoder": self.encoder, + "layer": self.layer, + "collate": self.data_collator, + }, + input_columns=tok_columns, + ) + ds = ds.map( + lambda x: {"hidden_state": torch.cat(x, dim=0)}, + batched=True, + input_columns=["hidden_state"], + remove_columns=tok_columns, + ) + + self.train_dataset: Dataset = ds["train"].shuffle( + buffer_size=10 * self.encoder_batch_size + ) + self.val_dataset: Dataset = ds["validation"] + self.test_dataset: Dataset = ds["test"] + + def train_dataloader(self): + return DataLoader( + self.train_dataset, + collate_fn=self.collate_fn, + batch_size=self.batch_size, + pin_memory=True, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + collate_fn=self.collate_fn, + batch_size=self.val_batch_size, + pin_memory=True, + ) + + def test_dataloader(self): + return DataLoader( + self.test_dataset, + collate_fn=self.collate_fn, + batch_size=self.val_batch_size, + ) + + @classmethod + def collate_fn(cls, batch): + return torch.stack([x["hidden_state"] for x in batch]).detach() + +class FeatureTaggerDataModule(HiddenStateDataModule): + def __init__(self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs): + kwargs["return_molecule"] = True + self.features = features + super().__init__(name_or_path, path, **kwargs) + + def setup(self, stage: str) -> None: + self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) + ds = maybe_shard_dataset(self.trainer, self.dataset) + ds = ds.map( + self.tokenizer, + batched=True, + input_columns="text", + fn_kwargs={"return_offsets_mapping": True}, + ) + tok_columns = ["input_ids", "attention_mask", "offsets_mapping"] + ds = ds.select_columns(tok_columns) + + # Extract per molecule hidden states + ds = ds.map( + extract_hidden_state, + batched=True, + batch_size=self.encoder_batch_size, + fn_kwargs={ + "encoder": self.encoder, + "layer": self.layer, + "collate": self.data_collator, + }, + input_columns=tok_columns, + ) + + ds = ds.map( + tag_features, + batched=False, + input_columns=tok_columns, + fn_kwargs={ + "features": self.features, + }, + ) + + # Tag features + self.train_dataset: Dataset = ds["train"].shuffle( + buffer_size=10 * self.encoder_batch_size + ) + self.val_dataset: Dataset = ds["validation"] + self.test_dataset: Dataset = ds["test"] + super().setup(stage) + self.train_dataset + + +def tag_features(obs:dict, features:dict): + token_overlap = torch.zeros(len(features), len(obs["input_ids"]), dtype=torch.bool) + offsets_mapping = obs["offsets_mapping"] + for fdx, (name, pattern) in features.items(): + m = pattern.search(obs["text"]) + if m is None: + continue + for idx, (start, end) in enumerate(offsets_mapping): + if start <= m.span[0] and end >= m.span[1]: + token_overlap[idx, fdx] = True + + return {"features": token_overlap.T.detach()} + + + + + + + + diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index 02d72aaf..4ae80285 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -1,5 +1,6 @@ from .lm_finetuning import LMFinetuning +from .prod_finetune import MISTFinetuned, MISTMultiTask from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer -from .prod_finetune import MISTFinetuned, MISTMultiTask +from .sae import SAE diff --git a/electrolyte_fm/models/model_utils.py b/electrolyte_fm/models/model_utils.py index 7eb85b96..75727700 100644 --- a/electrolyte_fm/models/model_utils.py +++ b/electrolyte_fm/models/model_utils.py @@ -1,10 +1,24 @@ +from pathlib import Path + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint -from torchmetrics import MetricCollection from lightning.pytorch.loggers import WandbLogger +from torchmetrics import MetricCollection from ..utils.ckpt import SaveConfigWithCkpts +def load_encoder(name_or_path: str): + if Path(name_or_path).exists(): + return DeepSpeedMixin.load(name_or_path).get_encoder() + else: + from transformers import AutoModel + + return AutoModel.from_pretrained( + name_or_path, + trust_remote_code=True, + ) + + class DeepSpeedMixin: @staticmethod def load(checkpoint_dir, **kwargs): diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py new file mode 100644 index 00000000..49145746 --- /dev/null +++ b/electrolyte_fm/models/sae.py @@ -0,0 +1,231 @@ +from math import sqrt +from typing import Optional, Union + +import pytorch_lightning as pl +import torch +from pytorch_lightning.cli import LRSchedulerCallable, OptimizerCallable +from torch import nn +from torch.nn import functional as F +from torchmetrics import MetricCollection + +from ..utils.ckpt import get_hidden_size +from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity +from .model_utils import load_encoder + + +def init_bias(bias, w): + fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(w) + bound = 1 / sqrt(fan_in) + nn.init.uniform_(bias, -bound, bound) + + +def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: + """Count the average number of active features for tensor (*, F), where F is are the feature activations""" + return x.detach().count_nonzero() / x.shape[:-1].numel() + + +class GatedSAE(nn.Module): + def __init__(self, hidden_size: int, expansion: int = 4, device=None, dtype=None): + super().__init__() + factory_kwargs = {"device": device, "dtype": dtype} + n_features = hidden_size * expansion + self.w_gate = nn.Parameter( + torch.empty((n_features, hidden_size), **factory_kwargs) + ) + self.b_gate = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.w_dec = nn.Parameter( + torch.empty((hidden_size, n_features), **factory_kwargs) + ) + self.b_dec = nn.Parameter(torch.empty(hidden_size, **factory_kwargs)) + self.r_mag = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.b_enc = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.reset_parameters() + + def reset_parameters(self): + for w in [self.w_gate, self.w_dec]: + nn.init.kaiming_uniform_(w, nonlinearity="relu") + + init_bias(self.b_gate, self.w_gate) + init_bias(self.b_dec, self.w_dec) + # kaiming init for r_mag + nn.init.normal_(self.r_mag, mean=0.0, std=sqrt(2 / self.r_mag.shape[0])) + init_bias(self.b_enc, self.w_gate) + + def forward(self, x): + x_centered = x - self.b_dec + x_enc = x_centered.matmul(self.w_gate.T) + gate = (x_enc + self.b_gate) > 0 + x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) + return x_mag * gate + + def reconstruct(self, features): + return F.linear(features, self.w_dec, self.b_dec) + + @torch.compile + def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + x_centered = x - self.b_dec + x_enc = x_centered.matmul(self.w_gate.T) + pi_gate = x_enc + self.b_gate + pi_rect = F.relu(pi_gate) + loss_sparsity = l1_coef * pi_rect.abs().sum() + + x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) + gate = pi_gate > 0 + features = x_mag * gate + x_hat = F.linear(features, self.w_dec, self.b_dec) + loss_recon = F.mse_loss(x_hat, x) + + x_hat_detach = F.linear(pi_rect, self.w_dec.detach(), self.b_dec.detach()) + loss_aux = F.mse_loss(x_hat_detach, x) + + return { + "loss": loss_sparsity + loss_aux + loss_recon, + "features": features, + } + + +class TiedBiasSAE(nn.Module): + def __init__(self, hidden_size: int, expansion: int = 4): + super().__init__() + n_features = hidden_size * expansion + self.encoder = nn.Linear(hidden_size, n_features) + self.decoder = nn.Linear(n_features, hidden_size) + + def forward(self, x): + return F.relu(self.encoder(x - self.decoder.bias)) + + def reconstruct(self, features): + return self.decoder(features) + + @torch.compile + def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + f = F.relu(self.encoder(x - self.decoder.bias)) + x_hat = self.decoder(f) + f_act = f * self.decoder.weight.norm(p=2, dim=0) + loss = F.mse_loss(x_hat, x) + l1_coef * f_act.abs().sum() + return {"loss": loss, "features": f} + + +class SAE(pl.LightningModule): + def __init__( + self, + hidden_size: Union[int, str], + sae: str = "gated", + expansion: int = 4, + l1_coef: float = 0.01, + optimizer: OptimizerCallable = torch.optim.AdamW, + lr_schedule: LRSchedulerCallable | None = None, + ): + super().__init__() + if isinstance(hidden_size, str): + hidden_size = get_hidden_size(hidden_size) + + if sae == "gated": + self.sae = GatedSAE(hidden_size, expansion) + elif sae == "tied_bias": + self.sae = TiedBiasSAE(hidden_size, expansion) + else: + raise ValueError(f"Unknown sae type: {sae}") + + self.l1_coef = l1_coef + self.optimizer = optimizer + self.lr_schedule = lr_schedule + self.hidden_size = hidden_size + self.num_features = hidden_size * expansion + self.save_hyperparameters(ignore=["sae"]) + + metrics = MetricCollection( + { + "alive_features": AliveFeatures(self.num_features), + "max_feature_density": MaxFeatureDensity(self.num_features), + "feature_density": FeatureDensity(self.num_features), + } + ) + print(metrics) + self.train_metrics = metrics.clone(prefix="train/") + self.val_metrics = metrics.clone(prefix="val/") + self.test_metrics = metrics.clone(prefix="test/") + + def setup(self, stage: str): + if isinstance(self.logger, pl.loggers.WandbLogger): + + def define_metric(*args, **kwargs): + self.logger.experiment.define_metric(*args, **kwargs) + + # Add summary metrics for loss + for stage in ["train", "val", "test"]: + for s in ["step", "epoch"]: + define_metric( + f"{stage}/loss_{s}", summary="best,min,last", goal="minimize" + ) + + define_metric(f"{stage}/alive_features", summary="max,last") + define_metric(f"{stage}/max_feature_density", summary="min,max,last") + + def stage_step(self, stage: str, batch): + out = self.sae.loss(batch, self.l1_coef) + self.log_dict( + { + f"{stage}/loss": out["loss"], + f"{stage}/avg_l0_loss": avg_l0_norm(out["features"]), + }, + sync_dist=True, + on_step=True, + on_epoch=True, + ) + return out + + def backward(self, loss, *args, **kwargs) -> None: + # Retain graph during backprop + loss.backward(retain_graph=True) + + def training_step(self, batch): + out = self.stage_step("train", batch) + self.train_metrics.update(out["features"]) + return out["loss"] + + def validation_step(self, batch): + out = self.stage_step("val", batch) + self.val_metrics.update(out["features"]) + return out["loss"] + + def test_step(self, batch): + out = self.stage_step("test", batch) + self.test_metrics.update(out["features"]) + return out["loss"] + + def _log_feature_metrics(self, metric, stage: str) -> None: + m = metric.compute() + feature_density = m.pop(stage + "/feature_density", None) + self.log_dict(m, on_epoch=True, sync_dist=True) + if ( + isinstance(self.logger, pl.loggers.WandbLogger) + and feature_density is not None + ): + self.logger.log_table( + stage + "/feature_density", + data=list( + zip(feature_density.bin_centers, feature_density.density) + ), + columns=["bin_center", "density"], + ) + + metric.reset() + + def on_train_epoch_end(self): + self._log_feature_metrics(self.train_metrics, "train") + + def on_validation_epoch_end(self): + self._log_feature_metrics(self.val_metrics, "val") + + def on_test_epoch_end(self): + self._log_feature_metrics(self.test_metrics, "test") + + def configure_optimizers(self): + optimizer = self.optimizer(self.parameters()) + if schedule := self.lr_schedule: + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, + } + return optimizer diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index bd1ad05b..0e13aa60 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -1,8 +1,8 @@ import importlib import json import os -from typing import Optional from pathlib import Path +from typing import Optional from jsonargparse import Namespace from lightning.pytorch import Callback, LightningModule, Trainer @@ -165,6 +165,21 @@ def get_ckpt_tokenizer(path: str | Path) -> str: return config["data"]["init_args"]["tokenizer"] +def get_hidden_size(name_or_path: str) -> int: + config_path = Path(name_or_path).parent.parent.joinpath("config.json") + if Path(config_path).is_file(): + with open(config_path, "r") as fid: + config = json.load(fid) + return config["model"]["init_args"]["hidden_size"] + + # Special Case models + elif name_or_path.startswith("ibm/MoLFormer-XL-both-10pct"): + return 768 + + else: + raise ValueError(f"Could not find hidden size for {name_or_path}") + + def norm_class_config(config: dict, class_path: Optional[str] = None) -> (str, dict): """Parse a dictionary of hparams for a class name and init args""" init_args = dict() diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index 7a4299b1..aef609b3 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -1,10 +1,10 @@ -from typing import Union, Dict, Optional, Any, Literal +from dataclasses import dataclass +from typing import Any, Dict, Literal, Optional, Union import torch -from torchmetrics import Metric +from numpy import geomspace +from torchmetrics import Metric, MetricCollection from torchmetrics import MetricCollection as TmMetricCollection -from torchmetrics.wrappers import BootStrapper -from torchmetrics.wrappers.classwise import ClasswiseWrapper as TmClasswiseWrapper from torchmetrics.classification import ( AUROC, AveragePrecision, @@ -12,12 +12,15 @@ ) from torchmetrics.regression import ( MeanAbsoluteError, - MeanSquaredError, - R2Score, MeanAbsolutePercentageError, + MeanSquaredError, PearsonCorrCoef, + R2Score, ) - +from torchmetrics.wrappers import BootStrapper +from torchmetrics.wrappers.abstract import WrapperMetric +from torchmetrics.wrappers.classwise import ClasswiseWrapper +from torchmetrics.wrappers.classwise import ClasswiseWrapper as TmClasswiseWrapper """ Target Value to indicate missing data """ IGNORE_INDEX = -100 @@ -415,3 +418,71 @@ def bootstrap_collection(metrics: MetricCollection, **kwargs) -> MetricCollectio mc[k] = BootStrapper(v, **kwargs) return MetricCollection(mc) + + +class FeaturesUtilization(Metric): + """Base Metric for tracking feature utilization""" + + def __init__(self, num_features: int, **kwargs): + super().__init__(**kwargs) + self.add_state( + "feature_counts", + torch.zeros(num_features, dtype=torch.int64), + dist_reduce_fx="sum", + ) + self.add_state( + "total_tokens", + torch.tensor(0, dtype=torch.int64), + dist_reduce_fx="sum", + ) + + def update(self, features: torch.Tensor) -> None: + assert features.ndim == 2 + self.feature_counts = features.count_nonzero(0) + self.total_tokens += features.shape[0] + + +class AliveFeatures(FeaturesUtilization): + higher_is_better = True + + def compute(self): + return self.feature_counts.count_nonzero() / self.feature_counts.numel() + + +@dataclass +class Histogram: + """Dataclass for storing histogram data, to avoid flattening by MetricCollection""" + + density: torch.Tensor + bin_edges: torch.Tensor + + @property + def bin_centers(self): + return (self.bin_edges[:-1] + self.bin_edges[1:]) / 2 + + +class FeatureDensity(FeaturesUtilization): + def __init__(self, *args, log_density: bool = True, **kwargs): + super().__init__(*args, **kwargs) + self.log_density = log_density + + def compute(self): + feature_density = self.feature_counts / self.total_tokens + nbins = int(max((2 * self.total_tokens).pow(1 / 3).ceil(), 10)) + if self.log_density: + left_edge = float(feature_density[feature_density > 0].min()) + right_edge = float(feature_density[feature_density > 0].max()) + nbins = geomspace(min(left_edge, 1e-3), right_edge, num=nbins + 1) + nbins = torch.tensor(nbins, dtype=feature_density.dtype) + + density, bin_edges = torch.histogram( + feature_density.to("cpu"), nbins, density=True + ) + return Histogram(density, bin_edges) + + +class MaxFeatureDensity(FeaturesUtilization): + higher_is_better = False + + def compute(self): + return self.feature_counts.max() / self.total_tokens diff --git a/submit/default.jsonnet b/submit/default.jsonnet index b4d43733..896d3c76 100644 --- a/submit/default.jsonnet +++ b/submit/default.jsonnet @@ -3,5 +3,6 @@ // Set cache directories to the working directory TORCH_EXTENSIONS_DIR: '${PWD}/.cache/torch_extensions', HF_HOME: '${PWD}/.cache/huggingface', + TOKENIZERS_PARALLELISM: true, }, } diff --git a/submit/dgx.jsonnet b/submit/dgx.jsonnet index 7be66084..eaf8c97e 100644 --- a/submit/dgx.jsonnet +++ b/submit/dgx.jsonnet @@ -1,6 +1,6 @@ { - nodes: 5, - gpus_per_node: 8, + nodes: 1, + gpus_per_node: 1, container: '/lustre/fs0/awadell/sqsh-files/0535844560745234+mist+latest.sqsh', train: { data: { diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet new file mode 100644 index 00000000..c3b57b79 --- /dev/null +++ b/submit/sae.jsonnet @@ -0,0 +1,49 @@ +{ + train: { + tags: ['sae', 'debug'], + model: { + class_path: 'electrolyte_fm.models.SAE', + init_args: { + sae: 'gated', + hidden_size: $.train.data.init_args.name_or_path, + expansion: 16, + l1_coef: 1e-5, + optimizer: { + class_path: 'torch.optim.AdamW', + init_args: { + lr: 1e-3, + betas: [0.0, 0.999], + }, + }, + lr_schedule: { + class_path: 'electrolyte_fm.utils.lr_schedule.RelativeCosineWarmup', + init_args: { + num_training_steps: 10000, + num_warmup_steps: 'beta2', + }, + }, + + }, + }, + data: { + class_path: 'electrolyte_fm.data_modules.HiddenStateDataModule', + init_args: { + name_or_path: 'ibm/MoLFormer-XL-both-10pct', + batch_size: 1024, + encoder_batch_size: 256, + val_batch_size: 4 * self.batch_size, + }, + }, + trainer: { + max_steps: $.train.model.init_args.lr_schedule.init_args.num_training_steps, + val_check_interval: 100, + limit_val_batches: 50, + precision: 'bf16-true', + enable_progress_bar: false, + strategy: "ddp", + }, + }, + env: { + TOKENIZER_PARALLELISM: 'true', + }, +} From 093f4f55f149e2dee2622d73fe67fd07b565d490 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sun, 20 Oct 2024 16:43:12 -0700 Subject: [PATCH 002/147] add nvtx --- poetry.lock | 3310 +++++++++++++++++++++--------------------------- pyproject.toml | 26 +- 2 files changed, 1425 insertions(+), 1911 deletions(-) diff --git a/poetry.lock b/poetry.lock index e4f6cd3c..9fd68fbc 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,127 +1,100 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. - -[[package]] -name = "aiohappyeyeballs" -version = "2.4.3" -description = "Happy Eyeballs for asyncio" -optional = false -python-versions = ">=3.8" -files = [ - {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"}, - {file = "aiohappyeyeballs-2.4.3.tar.gz", hash 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[extras] tokenizers = ["apetokenizer", "gensim", "rdkit", "smilespe"] @@ -4159,4 +3681,4 @@ tokenizers = ["apetokenizer", "gensim", "rdkit", "smilespe"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.14" -content-hash = "19e434807eb8279d0f82d43bea81aee744af28b4e37d34b45585d5f9680a27ac" +content-hash = "1a206fe49bb041d58814bb779b58822e88f80cdb7c13d98b7beb274fe8876136" diff --git a/pyproject.toml b/pyproject.toml index c55bc3fa..304e93e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ readme = "README.md" [tool.poetry.dependencies] python = ">=3.10,<3.14" torch = {version = "~2.3"} -torchmetrics = "^1.6.0" +torchmetrics = "^1.4.0" lightning = "~2.3" jsonargparse = {extras = ["signatures", "jsonnet"], version = "^4.27"} transformers = "^4.40" @@ -20,14 +20,11 @@ pyspark = "^3.5.0" rich = "^13" pydoe2 = "^1.3" jsonnet = "^0.20.0" -scikit-learn = "^1.5.1" -selfies = "^2.1.0" +scikit-learn= "^1.5.1" +selfies= "^2.1.0" rdkit = "^2024.3.5" -smirk = {git = "git@github.com:BattModels/smirk.git", tag = "v0.1.1" } -safetensors = "^0.5.1" - -# Pin pytorch_lightning too: https://github.com/Lightning-AI/pytorch-lightning/issues/20000 -pytorch-lightning = "~2.3" +smirk = {git = "git@github.com:BattModels/smirk.git", rev = "8061286a219420571d606d98083dd900aaa21d02" } +nvtx = "^0.2.10" # Dependencies to support additional tokenizers smilespe = { version = "==0.0.3", optional = true } @@ -42,14 +39,6 @@ pytest = "^8" pre-commit = "^3.7" ruff = "^0.5.4" -[tool.poetry.group.plotting.dependencies] -# Optional Dependency for plotting -matplotlib = "~3.9" - -[tool.poetry.group.tokenizers.dependencies] -# Optional Dependencies for third-party tokenizers -smilespe = "0.0.3" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" @@ -59,5 +48,8 @@ testpaths = ["test"] pythonpath = [".", "test"] addopts = "--color yes" +[tool.ruff.lint] +ignore = ["F401"] + [tool.ruff.lint.extend-per-file-ignores] -"**/__init__.py" = ["F401"] +"test_*.py" = ["F811"] From 244e3c24851a1a83c358ab5a7c0c8e27dd85fa8d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 21 Oct 2024 15:05:38 -0700 Subject: [PATCH 003/147] perf: add multi-processing to HiddenStateDataModule --- electrolyte_fm/data_modules/sae_dataset.py | 73 ++++++++++++++-------- submit/sae.jsonnet | 1 + 2 files changed, 48 insertions(+), 26 deletions(-) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 094277db..5f3a1da7 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -3,6 +3,8 @@ from pathlib import Path from typing import Optional, Union +import nvtx +import torch import pytorch_lightning as pl import torch from datasets import Dataset, load_dataset @@ -27,31 +29,43 @@ def extract_hidden_state( batch = batch.to(encoder.device) # Disable gradients - with torch.inference_mode(): - enc = encoder( - batch["input_ids"], - attention_mask=batch["attention_mask"], - return_dict=True, - output_hidden_states=True, - ) + with nvtx.annotate("encoder"): + with torch.inference_mode(): + enc = encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) if isinstance(layer, float): layer = floor(len(enc["hidden_states"]) * layer) # Flatten hidden states - hs = enc["hidden_states"][layer].to(device) + hidden_state = flatten_hidden_states( + enc["hidden_states"][layer], + attention_mask, + device=device, + ) + return {"hidden_state": hidden_state} + + +@nvtx.annotate() +def flatten_hidden_states(hs, attention_mask, device="cpu"): + hs = hs.to(device) hidden_state = [] - d_model = hs.shape[-1] assert attention_mask.shape[0] == hs.shape[0], "batch size mismatch" assert attention_mask.shape[1] == hs.shape[1], "seq. length mismatch" for bdx in range(hs.shape[0]): hs_molecule = hs[bdx][attention_mask[bdx] > 0] - assert hs_molecule.shape == (len(input_ids[bdx]), d_model) hidden_state.append(hs_molecule) - return {"hidden_state": hidden_state} + return hidden_state +@nvtx.annotate() +def collate_hidden_states(hidden_states): + return {"hidden_state": torch.cat(hidden_states, dim=0)} class HiddenStateDataModule(pl.LightningDataModule): @@ -63,6 +77,8 @@ def __init__( tokenizer: Optional[str] = None, batch_size: int = 64, val_batch_size: Optional[int] = None, + num_workers: int = 1, + prefetch_factor: int = 4, encoder_batch_size: Optional[int] = None, encoder_device: str = "cuda", return_molecule: bool = False, @@ -87,7 +103,6 @@ def __init__( self.save_hyperparameters(logger=False, ignore=["encoder_device"]) self.data_collator = DataCollatorWithPadding(self.tokenizer, "longest") - def prepare_data(self): self.dataset @@ -118,7 +133,10 @@ def setup(self, stage: str) -> None: input_columns="text", remove_columns="text", ) - tok_columns = ["input_ids", "attention_mask", ] + tok_columns = [ + "input_ids", + "attention_mask", + ] ds = ds.select_columns(tok_columns) # Extract per molecule hidden states @@ -134,14 +152,14 @@ def setup(self, stage: str) -> None: input_columns=tok_columns, ) ds = ds.map( - lambda x: {"hidden_state": torch.cat(x, dim=0)}, + collate_hidden_states, batched=True, input_columns=["hidden_state"], remove_columns=tok_columns, ) self.train_dataset: Dataset = ds["train"].shuffle( - buffer_size=10 * self.encoder_batch_size + buffer_size=10 * self.batch_size ) self.val_dataset: Dataset = ds["validation"] self.test_dataset: Dataset = ds["test"] @@ -149,6 +167,9 @@ def setup(self, stage: str) -> None: def train_dataloader(self): return DataLoader( self.train_dataset, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, + multiprocessing_context="spawn", collate_fn=self.collate_fn, batch_size=self.batch_size, pin_memory=True, @@ -157,6 +178,8 @@ def train_dataloader(self): def val_dataloader(self): return DataLoader( self.val_dataset, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, collate_fn=self.collate_fn, batch_size=self.val_batch_size, pin_memory=True, @@ -165,16 +188,22 @@ def val_dataloader(self): def test_dataloader(self): return DataLoader( self.test_dataset, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, collate_fn=self.collate_fn, batch_size=self.val_batch_size, ) @classmethod + @nvtx.annotate() def collate_fn(cls, batch): return torch.stack([x["hidden_state"] for x in batch]).detach() + class FeatureTaggerDataModule(HiddenStateDataModule): - def __init__(self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs): + def __init__( + self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs + ): kwargs["return_molecule"] = True self.features = features super().__init__(name_or_path, path, **kwargs) @@ -215,7 +244,7 @@ def setup(self, stage: str) -> None: # Tag features self.train_dataset: Dataset = ds["train"].shuffle( - buffer_size=10 * self.encoder_batch_size + buffer_size=2 * self.encoder_batch_size ) self.val_dataset: Dataset = ds["validation"] self.test_dataset: Dataset = ds["test"] @@ -223,7 +252,7 @@ def setup(self, stage: str) -> None: self.train_dataset -def tag_features(obs:dict, features:dict): +def tag_features(obs: dict, features: dict): token_overlap = torch.zeros(len(features), len(obs["input_ids"]), dtype=torch.bool) offsets_mapping = obs["offsets_mapping"] for fdx, (name, pattern) in features.items(): @@ -235,11 +264,3 @@ def tag_features(obs:dict, features:dict): token_overlap[idx, fdx] = True return {"features": token_overlap.T.detach()} - - - - - - - - diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index c3b57b79..fffb6f68 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -1,4 +1,5 @@ { + container: '/lustre/fs0/awadell/sqsh-files/0535844560745234+mist+08e9e89.sqsh', train: { tags: ['sae', 'debug'], model: { From 2e165b45c8516c7d592eafcbba86b567451915a7 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 29 Oct 2024 15:56:12 -0700 Subject: [PATCH 004/147] wip: start enumerating possible features --- electrolyte_fm/data_modules/feature_tagger.py | 80 +++++++++++++++++++ test/test_featurizer.py | 48 +++++++++++ 2 files changed, 128 insertions(+) create mode 100644 electrolyte_fm/data_modules/feature_tagger.py create mode 100644 test/test_featurizer.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py new file mode 100644 index 00000000..1d50ab40 --- /dev/null +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -0,0 +1,80 @@ +import re +import torch +import smirk +from rdkit import Chem + +# fmt: off +ELEMENT_SYMBOLS = [ + "H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", + "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", + "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", + "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", + "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", + "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", + "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", + "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", + "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", + "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", + "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds", + "Rg", "Cn", "Nh", "Fl", "Mc", "Lv", "Ts", "Og", +] +# fmt: on + +BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] + +REGEX_FEATURES = { + "chiral_tags": re.compile(r"@{1,2}(?:[A-Z]{2}\d{1,2})?"), + "bracked_atom": re.compile(r"\[[^]]+]"), + "charged_atom": re.compile(r"\[[^\]]+?[+-]{1,2}\d{0,2}]"), + "chiral_center": re.compile(r"\[[^\]]+?@[^\]]*?]"), + "aromatic_atom": re.compile(r"(?:b|c|o|p|se?|as)|(?:\[[a-z]{1,2}[^\]]+?])"), +} + +ELEMENT_GROUPS = [ + ["Li", "Na", "K", "Rb", "Cs", "Fr"], # Alkali Metals (Group 1) + ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) + ["Sc", "Y", "Lu", "Lr"], # Scandium Group (Group 3) + ["Ti", "Zr", "Hf", "Rf"], # Titanium Group (Group 4) + ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) + ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) + ["Mn", "Tc", "Re", "Bh"], # Manganese Group (Group 7) + ["Fe", "Ru", "Os", "Hs"], # Iron Group (Group 8) + ["Co", "Rh", "Ir", "Mt"], # Cobalt Group (Group 9) + ["Ni", "Pd", "Pt", "Ds"], # Nickel Group (Group 10) + ["Cu", "Ag", "Au", "Rg"], # Copper Group (Group 11) + ["Zn", "Cd", "Hg", "Cn"], # Zinc Group (Group 12) + ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) + ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) + ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) + ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) + ["F", "Cl", "Br", "I", "At", "Ts"] # Halogens (Group 17) + # Lanthanides (f-block) + ["La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu"], + # Actinides (f-block) + ["Ac", "Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", "Md", "No", "Lr"] +] + +ELEMENT_SETS = { + "alkali_metals": ELEMENT_GROUPS[0], + "alkaline_earth_metals": ELEMENT_GROUPS[1], + "nobel_gases": ["He"] + ELEMENT_GROUPS[17], + "s_block": ["H"] + ELEMENT_GROUPS[1] + ELEMENT_GROUPS[1], + "f_block": [*ELEMENT_GROUPS[17:18]], + "d_block": [*ELEMENT_GROUPS[2:11]], + "p_block": [*ELEMENT_GROUPS[12:16]], + "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki + "liquid_metals": ["Ga", "Hg", "Rb", "Cs", "Fr"], + "semi_metalalic": ["As", "Sb", "Bi", "Sn"], + "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 +} + +# Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html +SMARTS_FEATURES = { + "ketone": "[#6][CX3](=O)[#6]", # Daylight + "aldehyde": "[CX3H1](=O)[#6]", # Daylight + "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight + "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight + "hydroxyl": "[OX2H]", # Daylight + "phenol": "[OX2H][cX3]:[c]", # Daylight + "rotable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight +} diff --git a/test/test_featurizer.py b/test/test_featurizer.py new file mode 100644 index 00000000..0d230e4d --- /dev/null +++ b/test/test_featurizer.py @@ -0,0 +1,48 @@ +import pytest +from electrolyte_fm.data_modules.feature_tagger import REGEX_FEATURES + + +REGEX_TESTS = [ + { + "feature": "chiral_tags", + "positive": ["@SP1", "@OH32", "@TB2", "@", "@@"], + "negative": ["C", "O", "c", "[Rb]"], + }, + { + "feature": "bracked_atom", + "positive": ["[C]", "[C-]", "[C+]", "[C@]", "[C@@]", "[C@H]", "[C@H-]"], + "negative": ["C", "O", "c", "F"], + }, + { + "feature": "charged_atom", + "positive": ["[C+]", "[C-]", "[C++]", "[Rb@OH2+3]"], + "negative": ["C", "O", "c", "[Rb@OH2]"], + }, + { + "feature": "chiral_center", + "positive": ["[C@]", "[C@@]", "[C@H]", "[C@H-]"], + "negative": ["C", "O", "c", "[Rb]"], + }, + {"feature": "aromatic_atom", + "positive": ["[te+2]", "b", "c", "o", "p", "se", "as"], + "negative": ["C", "O", "[Rb]"], + }, +] + + +@pytest.mark.parametrize( + "feature,negatives", + ((x["feature"], x["negative"]) for x in REGEX_TESTS if "negative" in x), +) +def test_regex_negatives(feature: str, negatives: list[str]): + for neg in negatives: + assert REGEX_FEATURES[feature].match(neg) is None, f"{feature} should not match {neg}" + + +@pytest.mark.parametrize( + "feature,positives", + ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), +) +def test_regex_negatives(feature: str, positives: list[str]): + for pos in positives: + assert REGEX_FEATURES[feature].match(pos) is not None, f"{feature} should match {pos}" From 6099f616b6d7216c2a5095ed05171a2c1e24c9b5 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 30 Oct 2024 12:43:04 -0700 Subject: [PATCH 005/147] Element and Regex features are working --- electrolyte_fm/data_modules/feature_tagger.py | 233 +++++++++++++++--- electrolyte_fm/data_modules/sae_dataset.py | 66 ----- test/test_featurizer.py | 85 ++++++- 3 files changed, 279 insertions(+), 105 deletions(-) diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index 1d50ab40..a179e104 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -1,7 +1,15 @@ import re -import torch +from abc import abstractmethod +from typing import Iterable, Optional +import itertools + import smirk -from rdkit import Chem +import torch + + +def flatten(*iterables) -> list: + return list(itertools.chain(*iterables)) + # fmt: off ELEMENT_SYMBOLS = [ @@ -23,11 +31,11 @@ BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] REGEX_FEATURES = { - "chiral_tags": re.compile(r"@{1,2}(?:[A-Z]{2}\d{1,2})?"), - "bracked_atom": re.compile(r"\[[^]]+]"), - "charged_atom": re.compile(r"\[[^\]]+?[+-]{1,2}\d{0,2}]"), - "chiral_center": re.compile(r"\[[^\]]+?@[^\]]*?]"), - "aromatic_atom": re.compile(r"(?:b|c|o|p|se?|as)|(?:\[[a-z]{1,2}[^\]]+?])"), + "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", + "bracked_atom": r"\[[^]]+]", + "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", + "chiral_center": r"\[[^\]]+?@[^\]]*?]", + "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", } ELEMENT_GROUPS = [ @@ -35,46 +43,205 @@ ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) ["Sc", "Y", "Lu", "Lr"], # Scandium Group (Group 3) ["Ti", "Zr", "Hf", "Rf"], # Titanium Group (Group 4) - ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) - ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) + ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) + ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) ["Mn", "Tc", "Re", "Bh"], # Manganese Group (Group 7) ["Fe", "Ru", "Os", "Hs"], # Iron Group (Group 8) ["Co", "Rh", "Ir", "Mt"], # Cobalt Group (Group 9) ["Ni", "Pd", "Pt", "Ds"], # Nickel Group (Group 10) ["Cu", "Ag", "Au", "Rg"], # Copper Group (Group 11) ["Zn", "Cd", "Hg", "Cn"], # Zinc Group (Group 12) - ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) - ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) - ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) - ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) - ["F", "Cl", "Br", "I", "At", "Ts"] # Halogens (Group 17) - # Lanthanides (f-block) - ["La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu"], - # Actinides (f-block) - ["Ac", "Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", "Md", "No", "Lr"] + ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) + ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) + ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) + ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) + ["F", "Cl", "Br", "I", "At", "Ts"], # Halogens (Group 17) + ["Ne", "Ar", "Kr", "Xe", "Rn", "Rg"], # Noble Gases (Group 18) +] + +F_BLOCK = [ + [ + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + ], + [ + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + ], ] -ELEMENT_SETS = { +ELEMENT_FEATURES = { "alkali_metals": ELEMENT_GROUPS[0], "alkaline_earth_metals": ELEMENT_GROUPS[1], - "nobel_gases": ["He"] + ELEMENT_GROUPS[17], - "s_block": ["H"] + ELEMENT_GROUPS[1] + ELEMENT_GROUPS[1], - "f_block": [*ELEMENT_GROUPS[17:18]], - "d_block": [*ELEMENT_GROUPS[2:11]], - "p_block": [*ELEMENT_GROUPS[12:16]], - "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki + "nobel_gases": ["He"] + ELEMENT_GROUPS[16], + "s_block": flatten(["H"], *ELEMENT_GROUPS[0:1]), + "f_block": flatten(*F_BLOCK), + "d_block": flatten(*ELEMENT_GROUPS[2:11]), + "p_block": flatten(*ELEMENT_GROUPS[12:15]), + "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki "liquid_metals": ["Ga", "Hg", "Rb", "Cs", "Fr"], "semi_metalalic": ["As", "Sb", "Bi", "Sn"], - "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 + "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 + "radioactive": flatten( # Elements that have no stable isotopes + ["Tc", "Po", "At", "Rn", "Pm"], + [group[-1] for group in ELEMENT_GROUPS], + F_BLOCK[1], + ), + "opensmiles_aromatic": [ + "b", + "c", + "n", + "o", + "p", + "s" "se", + "as", + ], } # Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html SMARTS_FEATURES = { - "ketone": "[#6][CX3](=O)[#6]", # Daylight - "aldehyde": "[CX3H1](=O)[#6]", # Daylight - "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight - "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight - "hydroxyl": "[OX2H]", # Daylight - "phenol": "[OX2H][cX3]:[c]", # Daylight - "rotable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight + "ketone": "[#6][CX3](=O)[#6]", # Daylight + "aldehyde": "[CX3H1](=O)[#6]", # Daylight + "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight + "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight + "hydroxyl": "[OX2H]", # Daylight + "phenol": "[OX2H][cX3]:[c]", # Daylight + "rotatable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight } + + +class Feature: + requires_smirk = False + + def __init__(self, name: str, tokenizer: Optional[str] = None): + self.name = name + self.tokenzier = tokenizer or smirk.SmirkTokenizerFast() + self.smirk_tokenizer = ( + smirk.SmirkTokenizerFast() if tokenizer is not None else self.tokenzier + ) + + def featurize(self, smi: str, encoding: Optional[dict] = None) -> torch.BoolTensor: + encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) + assert "offset_mapping" in encoding + + kwargs = {} + if self.requires_smirk: + smirk_encoding = self.smirk_tokenizer( + smi, + return_offsets_mapping=True, + add_special_tokens=False, + ) + kwargs["smirk_encoding"] = smirk_encoding + + return self._featurize(smi, encoding, **kwargs) + + @abstractmethod + def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: + """Identify tokens in the input SMILES encoding expressing the feature""" + + @classmethod + @abstractmethod + def from_named(cls, name: str, **kwargs) -> "Feature": + """Create a feature from a named feature""" + + def align_tokens(self, encoding: dict, span: tuple[int, int]) -> Iterable[int]: + """Identify tokens overlapping span""" + token_offsets = encoding["offset_mapping"] + start, end = span + if end < start: + return # Non-matching span + + for i, t in enumerate(token_offsets): + if t[1] <= t[0]: # Token is empty + continue + + # Spans don't include the end index + elif not (end <= t[0] or t[1] <= start): + print(f"token: {t}, span: {start}, {end}") + yield i + + def align_embeddings( + self, active: torch.BoolTensor, embedding: dict, other: dict + ) -> torch.BoolTensor: + if embedding == other: + return active + raise NotImplementedError() + + def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: + """Convert a list of indices to a one-hot encoding""" + active = torch.zeros(n, dtype=torch.bool) + active[indices] = True + return active + + +class RegexFeature(Feature): + def __init__(self, name: str, regex: [str, re.Pattern], **kwargs): + super().__init__(name, **kwargs) + self.regex = re.compile(regex) + + @classmethod + def from_named(cls, name: str, **kwargs): + return cls(name, REGEX_FEATURES[name], **kwargs) + + def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: + n_groups = self.regex.groups + active = [] + for m in self.regex.finditer(smi): + if n_groups == 0: + active.extend(self.align_tokens(encoding, m.span())) + else: + for i in range(1, n_groups + 1): + active.extend(self.align_tokens(encoding, m.span(i))) + + return self.onehot(active, len(encoding["input_ids"])) + + +class ElementFeature(Feature): + requires_smirk = True + + def __init__(self, name: str, elements: list[str], **kwargs): + super().__init__(name, **kwargs) + self.elements = list(set(elements)) + self.element_ids = torch.tensor( + [ + self.smirk_tokenizer.encode(f"[{e}]", add_special_tokens=False)[1] + for e in self.elements + ] + ).reshape(-1, 1) + + @classmethod + def from_named(cls, name: str, **kwargs) -> "ElementFeature": + return cls(name, ELEMENT_FEATURES[name], **kwargs) + + def _featurize( + self, smi: str, encoding: dict, smirk_encoding: dict + ) -> torch.BoolTensor: + enc = torch.tensor(smirk_encoding["input_ids"]) + active = enc.eq(self.element_ids).any(dim=0) + return self.align_embeddings(active, encoding, smirk_encoding) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 5f3a1da7..6d651d6b 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -198,69 +198,3 @@ def test_dataloader(self): @nvtx.annotate() def collate_fn(cls, batch): return torch.stack([x["hidden_state"] for x in batch]).detach() - - -class FeatureTaggerDataModule(HiddenStateDataModule): - def __init__( - self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs - ): - kwargs["return_molecule"] = True - self.features = features - super().__init__(name_or_path, path, **kwargs) - - def setup(self, stage: str) -> None: - self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) - ds = maybe_shard_dataset(self.trainer, self.dataset) - ds = ds.map( - self.tokenizer, - batched=True, - input_columns="text", - fn_kwargs={"return_offsets_mapping": True}, - ) - tok_columns = ["input_ids", "attention_mask", "offsets_mapping"] - ds = ds.select_columns(tok_columns) - - # Extract per molecule hidden states - ds = ds.map( - extract_hidden_state, - batched=True, - batch_size=self.encoder_batch_size, - fn_kwargs={ - "encoder": self.encoder, - "layer": self.layer, - "collate": self.data_collator, - }, - input_columns=tok_columns, - ) - - ds = ds.map( - tag_features, - batched=False, - input_columns=tok_columns, - fn_kwargs={ - "features": self.features, - }, - ) - - # Tag features - self.train_dataset: Dataset = ds["train"].shuffle( - buffer_size=2 * self.encoder_batch_size - ) - self.val_dataset: Dataset = ds["validation"] - self.test_dataset: Dataset = ds["test"] - super().setup(stage) - self.train_dataset - - -def tag_features(obs: dict, features: dict): - token_overlap = torch.zeros(len(features), len(obs["input_ids"]), dtype=torch.bool) - offsets_mapping = obs["offsets_mapping"] - for fdx, (name, pattern) in features.items(): - m = pattern.search(obs["text"]) - if m is None: - continue - for idx, (start, end) in enumerate(offsets_mapping): - if start <= m.span[0] and end >= m.span[1]: - token_overlap[idx, fdx] = True - - return {"features": token_overlap.T.detach()} diff --git a/test/test_featurizer.py b/test/test_featurizer.py index 0d230e4d..c82a699c 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -1,6 +1,17 @@ +import re +from random import choice +from itertools import chain + import pytest -from electrolyte_fm.data_modules.feature_tagger import REGEX_FEATURES +import torch +from electrolyte_fm.data_modules.feature_tagger import ( + ELEMENT_FEATURES, + ELEMENT_SYMBOLS, + REGEX_FEATURES, + RegexFeature, + ElementFeature, +) REGEX_TESTS = [ { @@ -23,9 +34,10 @@ "positive": ["[C@]", "[C@@]", "[C@H]", "[C@H-]"], "negative": ["C", "O", "c", "[Rb]"], }, - {"feature": "aromatic_atom", - "positive": ["[te+2]", "b", "c", "o", "p", "se", "as"], - "negative": ["C", "O", "[Rb]"], + { + "feature": "aromatic_bracket_atom", + "positive": ["[te+2]", "[b]", "[c@@]"], + "negative": ["C", "O", "[Rb]", "[Sn]", "[Cn]"], }, ] @@ -35,8 +47,9 @@ ((x["feature"], x["negative"]) for x in REGEX_TESTS if "negative" in x), ) def test_regex_negatives(feature: str, negatives: list[str]): + f = re.compile(REGEX_FEATURES[feature]) for neg in negatives: - assert REGEX_FEATURES[feature].match(neg) is None, f"{feature} should not match {neg}" + assert f.match(neg) is None, f"{feature} should not match {neg}" @pytest.mark.parametrize( @@ -44,5 +57,65 @@ def test_regex_negatives(feature: str, negatives: list[str]): ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), ) def test_regex_negatives(feature: str, positives: list[str]): + f = re.compile(REGEX_FEATURES[feature]) for pos in positives: - assert REGEX_FEATURES[feature].match(pos) is not None, f"{feature} should match {pos}" + assert f.match(pos) is not None, f"{feature} should match {pos}" + + +def generate_examples(): + for x in REGEX_TESTS: + yield RegexFeature, x["feature"], x["positive"], x["negative"] + + elements = set(ELEMENT_SYMBOLS) + for name, positive in ELEMENT_FEATURES.items(): + pos_examples = [f"[{e}]" for e in positive] + neg_examples = [f"[{e}]" for e in elements - set(positive)] + yield ElementFeature, name, pos_examples, neg_examples + + +@pytest.mark.parametrize( + "cls,feature,positive,negative", + generate_examples(), +) +def test_positive_feature(cls, feature, positive, negative): + f = cls.from_named(feature) + for pos in positive: + active = f.featurize(pos) + assert active.any(), "{} should match {}".format(feature, pos) + + +@pytest.mark.parametrize( + "cls,feature,positive,negative", + generate_examples(), +) +def test_negative_feature(cls, feature, positive, negative): + f = cls.from_named(feature) + for neg in negative: + active = f.featurize(neg) + assert not active.any(), "{} should not match {}".format(feature, neg) + + +@pytest.mark.parametrize("cls,feature,positive,negative", generate_examples()) +def test_alignment(cls, feature, positive, negative): + f = cls.from_named(feature) + pos = choice(positive) + neg = choice(negative) + active = f.featurize(pos) + inactive = f.featurize(neg) + assert active.any() and not inactive.any() + check_active(pos + neg, torch.cat([active, inactive]), f.featurize(pos + neg)) + check_active(pos + pos, torch.cat([active, active]), f.featurize(pos + pos)) + check_active( + neg + pos + neg, + torch.cat([inactive, active, inactive]), + f.featurize(neg + pos + neg), + ) + + +def check_active(smi, expected, actual): + print(f"smi: {smi}") + print(f"expected: {expected}") + print(f"actual: {actual}") + assert isinstance(actual, torch.BoolTensor) + assert isinstance(expected, torch.BoolTensor) + assert all(actual == expected) From e7eb40d7e3d12fe4e93a05493d107785d2db7b93 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 30 Oct 2024 17:28:05 -0700 Subject: [PATCH 006/147] first pass at using SMARTS to featurize Bonds are not properly handled (Can tag atoms but not the bonds) --- electrolyte_fm/data_modules/feature_tagger.py | 112 +++++++++++++++--- test/test_featurizer.py | 62 +++++++++- 2 files changed, 152 insertions(+), 22 deletions(-) diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index a179e104..ca951d82 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -1,7 +1,8 @@ import re -from abc import abstractmethod +from abc import abstractmethod, ABC from typing import Iterable, Optional import itertools +from rdkit import Chem import smirk import torch @@ -125,18 +126,24 @@ def flatten(*iterables) -> list: } # Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html +# rdkit.Chem.Lipinski from: https://github.com/rdkit/rdkit/blob/master/rdkit/Chem/Lipinski.py SMARTS_FEATURES = { "ketone": "[#6][CX3](=O)[#6]", # Daylight - "aldehyde": "[CX3H1](=O)[#6]", # Daylight + "aldehyde": "[$([CX3H2](=O)),$([CX3H1](=O)[#6])]", # Daylight, plus branch for Formaldehyde "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight - "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight + "amid": "[$([NX3][CX3](=[OX1])[#6]),$(NC=O)]", # Daylight, plus branch for Formamide "hydroxyl": "[OX2H]", # Daylight - "phenol": "[OX2H][cX3]:[c]", # Daylight - "rotatable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight + "phenol": "[OH]c1ccccc1", + "rotatable_bond": "[!$(*#*)&!D1]-&!@[!$(*#*)&!D1]", # rdkit.Chem.Lipinski + "carboxyl_group": "[CX3]=[OX1]", # Daylight + "h_donor": "[$([N;!H0;v3]),$([N;!H0;+1;v4]),$([O,S;H1;+0]),$([n;H1;+0])]", # rdkit.Chem.Lipinski + "h_acceptor": "[$([O,S;H1;v2]-[!$(*=[O,N,P,S])]),$([O,S;H0;v2]),$([O,S;-]),$([N;v3;!$(N-*=!@[O,N,P,S])]),$([nH0,o,s;+0])]", # rdkit.Chem.Lipinski + "NH_or_OH_lipinski": "[#8H1,#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski + "NO_lipinski": "[#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski } -class Feature: +class Feature(ABC): requires_smirk = False def __init__(self, name: str, tokenizer: Optional[str] = None): @@ -149,22 +156,19 @@ def __init__(self, name: str, tokenizer: Optional[str] = None): def featurize(self, smi: str, encoding: Optional[dict] = None) -> torch.BoolTensor: encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) assert "offset_mapping" in encoding - - kwargs = {} - if self.requires_smirk: - smirk_encoding = self.smirk_tokenizer( - smi, - return_offsets_mapping=True, - add_special_tokens=False, - ) - kwargs["smirk_encoding"] = smirk_encoding - + kwargs = self.preprocess(smi) return self._featurize(smi, encoding, **kwargs) @abstractmethod def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: """Identify tokens in the input SMILES encoding expressing the feature""" + def preprocess(self, smi: str) -> dict: + """Shared preprocessing steps for all features to be provided to `self._featurize` + Will be called once per feature class + """ + return {} + @classmethod @abstractmethod def from_named(cls, name: str, **kwargs) -> "Feature": @@ -196,7 +200,9 @@ def align_embeddings( def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: """Convert a list of indices to a one-hot encoding""" active = torch.zeros(n, dtype=torch.bool) - active[indices] = True + if len(indices) > 0: + print(indices) + active[indices] = True return active @@ -223,8 +229,6 @@ def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: class ElementFeature(Feature): - requires_smirk = True - def __init__(self, name: str, elements: list[str], **kwargs): super().__init__(name, **kwargs) self.elements = list(set(elements)) @@ -239,9 +243,79 @@ def __init__(self, name: str, elements: list[str], **kwargs): def from_named(cls, name: str, **kwargs) -> "ElementFeature": return cls(name, ELEMENT_FEATURES[name], **kwargs) + def preprocess(self, smi: str) -> dict: + return { + "smirk_encoding": self.smirk_tokenizer(smi, return_offsets_mapping=True) + } + def _featurize( self, smi: str, encoding: dict, smirk_encoding: dict ) -> torch.BoolTensor: enc = torch.tensor(smirk_encoding["input_ids"]) active = enc.eq(self.element_ids).any(dim=0) return self.align_embeddings(active, encoding, smirk_encoding) + + +class RdkitFeature(Feature): + atomwise = re.compile(r"\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p") + + def preprocess(self, smi: str) -> dict: + mol = Chem.MolFromSmiles(smi, sanitize=False) + s_flags = Chem.SanitizeFlags.SANITIZE_NONE + s_flags |= Chem.SanitizeFlags.SANITIZE_SETAROMATICITY + s_flags |= Chem.SanitizeFlags.SANITIZE_SETHYBRIDIZATION + s_flags |= Chem.SanitizeFlags.SANITIZE_SETCONJUGATION + s_flags |= Chem.SANITIZE_PROPERTIES + Chem.SanitizeMol(mol, s_flags) + atom_spans = [m.span() for m in self.atomwise.finditer(smi)] + + # Validate rdkit -> smi mapping + for idx, atom in enumerate(mol.GetAtoms()): + smi_atom = smi[atom_spans[idx][0] : atom_spans[idx][1]] + smi_atom_mol = Chem.MolFromSmiles(smi_atom, sanitize=False) + Chem.SanitizeMol(smi_atom_mol, s_flags) + + assert atom.GetSymbol() == smi_atom_mol.GetAtomWithIdx(0).GetSymbol() + + # atom_smi = atom.GetSmarts() + # smi_atom_rdkit = Chem.MolToSmiles(smi_atom_mol) + # assert ( + # atom_smi == Chem.MolToSmiles(smi_atom_mol) + # ), f"Expected {atom_smi} and {smi_atom_rdkit} to match. Input atom: {smi_atom}" + + return {"rdkit_molecule": mol, "atom_spans": atom_spans} + + def align_atoms( + self, + atom_idx: int, + encoding: dict, + atom_spans: list[tuple[int, int]], + ) -> Iterable[int]: + """Map atom indices to token indices""" + span = atom_spans[atom_idx] + return self.align_tokens(encoding, span) + + +class SMARTSFeature(RdkitFeature): + def __init__(self, name: str, smarts: str, **kwargs): + super().__init__(name, **kwargs) + self.smarts = Chem.MolFromSmarts(smarts) + + @classmethod + def from_named(cls, name: str, **kwargs): + return cls(name, SMARTS_FEATURES[name], **kwargs) + + def _featurize( + self, + smi: str, + encoding: dict, + atom_spans: list[tuple[int, int]], + rdkit_molecule: Chem.Mol, + ) -> torch.BoolTensor: + # TODO: Handle Bonds + matches = rdkit_molecule.GetSubstructMatches(self.smarts) + atom_indices = set(flatten(*matches)) + active: list[int] = [] + for idx in atom_indices: + active.extend(self.align_atoms(idx, encoding, atom_spans)) + return self.onehot(active, len(encoding["input_ids"])) diff --git a/test/test_featurizer.py b/test/test_featurizer.py index c82a699c..be723ec6 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -11,6 +11,7 @@ REGEX_FEATURES, RegexFeature, ElementFeature, + SMARTSFeature, ) REGEX_TESTS = [ @@ -41,6 +42,53 @@ }, ] +SMARTS_TESTS = [ + { + "feature": "ketone", + "positive": ["CC(=O)C", "C[C@@H]1CCCCCCCCCCCCC(=O)C1"], + "negative": ["C=O", "OCC"], + }, + { + "feature": "aldehyde", + "positive": ["C=O", "O=CC", "CC=O"], + "negative": ["CC(=O)C", "OCC(O)CO"], + }, + { + "feature": "carboxylic_acid", + "positive": ["O=CO", "CC(=O)O", "C(=O)O", "O=C(O)C"], + "negative": ["CC(=O)C", "OCC"], + }, + { + "feature": "amid", + "positive": ["O=CN", "CC(=O)N", "C(=O)NC", "O=C(N)C"], + "negative": ["CC(=O)C", "OCC"], + }, + { + "feature": "hydroxyl", + "positive": ["CO", "CCO", "C(O)C"], + "negative": ["CC(=O)C", "C=O"], + }, + { + "feature": "phenol", + "positive": [ + "c1ccc(cc1)O", + "Oc1ccccc1", + "Oc0ccccc0Cc0cc(C1)c(O)c(c0)Cc0c(O)ccc(c0)Cc0ccc(O)c(c0)Cc0c(O)ccc(c0)Cc0c(O)ccc(c0)Cc0c(O)c(C2)cc(c0)Cc0c(O)ccc(c0)Cc(c0O)cc2cc0Cc0cc(Cc2ccc(O)cc2)c(O)c(c0)Cc0c(O)ccc(c0)C1", + ], + "negative": [ + "CC(=O)C", + "C=O", + "c1ccccc1-c2ccccc2", + "c1ccc(cc1)C[C@@H](C(=O)O)N", + ], + }, + { + "feature": "rotatable_bond", + "positive": ["CC-CC", "c1ccccc1-c2ccccc2"], + "negative": ["CC(=O)C", "C=O", "c1ccccc1"], + }, +] + @pytest.mark.parametrize( "feature,negatives", @@ -72,6 +120,9 @@ def generate_examples(): neg_examples = [f"[{e}]" for e in elements - set(positive)] yield ElementFeature, name, pos_examples, neg_examples + for x in SMARTS_TESTS: + yield SMARTSFeature, x["feature"], x["positive"], x["negative"] + @pytest.mark.parametrize( "cls,feature,positive,negative", @@ -81,7 +132,7 @@ def test_positive_feature(cls, feature, positive, negative): f = cls.from_named(feature) for pos in positive: active = f.featurize(pos) - assert active.any(), "{} should match {}".format(feature, pos) + assert active.any(), "{} should match {}: {}".format(feature, pos, active) @pytest.mark.parametrize( @@ -92,10 +143,15 @@ def test_negative_feature(cls, feature, positive, negative): f = cls.from_named(feature) for neg in negative: active = f.featurize(neg) - assert not active.any(), "{} should not match {}".format(feature, neg) + assert not active.any(), "{} should not match {}: {}".format( + feature, neg, active + ) -@pytest.mark.parametrize("cls,feature,positive,negative", generate_examples()) +@pytest.mark.parametrize( + "cls,feature,positive,negative", + [e for e in generate_examples() if e[0] != SMARTSFeature], +) def test_alignment(cls, feature, positive, negative): f = cls.from_named(feature) pos = choice(positive) From a984e6aeffe85ddccfa4b20c13365c73543744cb Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 19 Nov 2024 14:54:05 -0800 Subject: [PATCH 007/147] wip: more sae pipeline --- electrolyte_fm/data_modules/feature_tagger.py | 96 ++++++++++-- electrolyte_fm/data_modules/sae_dataset.py | 7 +- electrolyte_fm/models/sae.py | 20 ++- electrolyte_fm/utils/ckpt.py | 3 +- electrolyte_fm/utils/featurizer.py | 144 ++++++++++++++++++ electrolyte_fm/utils/metrics.py | 45 +++++- opt/FeatureMiner/Project.toml | 11 ++ opt/FeatureMiner/src/FeatureMiner.jl | 29 ++++ opt/FeatureMiner/src/identification.jl | 74 +++++++++ opt/FeatureMiner/src/stats.jl | 45 ++++++ submit/nsys.yaml | 1 + submit/sae_features.libsonnet | 3 + test/test_featurizer.py | 5 +- test/test_sae.py | 99 ++++++++++++ 14 files changed, 557 insertions(+), 25 deletions(-) create mode 100644 electrolyte_fm/utils/featurizer.py create mode 100644 opt/FeatureMiner/Project.toml create mode 100644 opt/FeatureMiner/src/FeatureMiner.jl create mode 100644 opt/FeatureMiner/src/identification.jl create mode 100644 opt/FeatureMiner/src/stats.jl create mode 100644 submit/sae_features.libsonnet create mode 100644 test/test_sae.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index ca951d82..fe08864b 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -31,14 +31,6 @@ def flatten(*iterables) -> list: BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] -REGEX_FEATURES = { - "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", - "bracked_atom": r"\[[^]]+]", - "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", - "chiral_center": r"\[[^\]]+?@[^\]]*?]", - "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", -} - ELEMENT_GROUPS = [ ["Li", "Na", "K", "Rb", "Cs", "Fr"], # Alkali Metals (Group 1) ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) @@ -97,6 +89,14 @@ def flatten(*iterables) -> list: ], ] +REGEX_FEATURES = { + "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", + "bracked_atom": r"\[[^]]+]", + "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", + "chiral_center": r"\[[^\]]+?@[^\]]*?]", + "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", +} + ELEMENT_FEATURES = { "alkali_metals": ELEMENT_GROUPS[0], "alkaline_earth_metals": ELEMENT_GROUPS[1], @@ -144,8 +144,6 @@ def flatten(*iterables) -> list: class Feature(ABC): - requires_smirk = False - def __init__(self, name: str, tokenizer: Optional[str] = None): self.name = name self.tokenzier = tokenizer or smirk.SmirkTokenizerFast() @@ -187,7 +185,6 @@ def align_tokens(self, encoding: dict, span: tuple[int, int]) -> Iterable[int]: # Spans don't include the end index elif not (end <= t[0] or t[1] <= start): - print(f"token: {t}, span: {start}, {end}") yield i def align_embeddings( @@ -195,17 +192,89 @@ def align_embeddings( ) -> torch.BoolTensor: if embedding == other: return active + return active raise NotImplementedError() def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: """Convert a list of indices to a one-hot encoding""" active = torch.zeros(n, dtype=torch.bool) if len(indices) > 0: - print(indices) active[indices] = True return active +class FeatureCollection(Feature): + def __init__(self, features: dict[str, Feature], **kwargs): + super().__init__("FeatureCollection", **kwargs) + self.features = features + preprocess_steps = {} + for f in features.values(): + preprocess_steps[f.__class__] = f + self.preprocess = list(preprocess_steps.values()) + + @property + def names(self): + return list(self.features.keys()) + + def __call__(self, smi: str) -> dict: + return {"proxy_activations": self.featurize(smi)} + + def featurize( + self, smi: str, encoding: Optional[dict] = None, return_dict: bool = False + ) -> torch.BoolTensor: + encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) + kwargs = {} + for f in self.preprocess: + kwargs.update(f.preprocess(smi)) + + act = [] + for idx, f in enumerate(self.features.values()): + act.append(f._featurize(smi, encoding, **kwargs)) + + if return_dict: + return {name: act for name, act in zip(self.features.keys(), act)} + + return torch.stack(act, dim=0) + + def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: + raise RuntimeError(f"{self} should be called using featurize") + + @classmethod + def from_named(cls, feature_names: str | list[str] = "all", **kwargs): + if isinstance(feature_names, str): + feature_names = [feature_names] + + features = [] + for name in feature_names: + if name == "all": + for k in ELEMENT_FEATURES.keys(): + features.append(ElementFeature.from_named(k, **kwargs)) + for k in REGEX_FEATURES.keys(): + features.append(RegexFeature.from_named(k, **kwargs)) + for k in SMARTS_FEATURES.keys(): + features.append(SMARTSFeature.from_named(k, **kwargs)) + + elif name in ELEMENT_FEATURES.keys(): + features.append(ElementFeature.from_named(name, **kwargs)) + + elif name in REGEX_FEATURES.keys(): + features.append(RegexFeature.from_named(name, **kwargs)) + + elif name in SMARTS_FEATURES.keys(): + features.append(SMARTSFeature.from_named(name, **kwargs)) + + else: + raise ValueError(f"Unknown named feature: {name}") + + feature_map = {} + for f in features: + if f.name in feature_map.keys(): + raise ValueError(f"Duplicate feature name: {f.name}") + feature_map[f.name] = f + + return cls(feature_map, **kwargs) + + class RegexFeature(Feature): def __init__(self, name: str, regex: [str, re.Pattern], **kwargs): super().__init__(name, **kwargs) @@ -249,7 +318,7 @@ def preprocess(self, smi: str) -> dict: } def _featurize( - self, smi: str, encoding: dict, smirk_encoding: dict + self, smi: str, encoding: dict, smirk_encoding: dict, **kwargs ) -> torch.BoolTensor: enc = torch.tensor(smirk_encoding["input_ids"]) active = enc.eq(self.element_ids).any(dim=0) @@ -311,6 +380,7 @@ def _featurize( encoding: dict, atom_spans: list[tuple[int, int]], rdkit_molecule: Chem.Mol, + **kwargs, ) -> torch.BoolTensor: # TODO: Handle Bonds matches = rdkit_molecule.GetSubstructMatches(self.smarts) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 6d651d6b..99c22078 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -14,6 +14,7 @@ from ..models.model_utils import load_encoder from ..utils.tokenizer import load_tokenizer from .roberta_dataset import maybe_shard_dataset +from .utils import MolEncoding, encode_molecules def extract_hidden_state( @@ -82,7 +83,7 @@ def __init__( encoder_batch_size: Optional[int] = None, encoder_device: str = "cuda", return_molecule: bool = False, - canonical=False, + encoding: Optional[str | MolEncoding] = "smiles", ): super().__init__() @@ -94,11 +95,14 @@ def __init__( self.vocab_size = len(self.tokenizer) self.path: Path = Path(path) self.return_molecule = return_molecule + self.encoding = MolEncoding(encoding) assert self.path.is_dir() or self.path.is_file() self.batch_size = batch_size self.val_batch_size = val_batch_size or batch_size self.encoder_batch_size = encoder_batch_size or batch_size + self.num_workers = num_workers + self.prefetch_factor = prefetch_factor self.hparams["tokenizer"] = tokenizer self.save_hyperparameters(logger=False, ignore=["encoder_device"]) self.data_collator = DataCollatorWithPadding(self.tokenizer, "longest") @@ -127,6 +131,7 @@ def dataset(self): def setup(self, stage: str) -> None: self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) ds = maybe_shard_dataset(self.trainer, self.dataset) + ds = encode_molecules(ds, "text", encoding=self.encoding) ds = ds.map( self.tokenizer, batched=True, diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 49145746..de5831c9 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -80,6 +80,7 @@ def loss(self, x, l1_coef: torch.FloatTensor = 0.01): return { "loss": loss_sparsity + loss_aux + loss_recon, + "loss_reconstruction": loss_recon, "features": features, } @@ -102,8 +103,13 @@ def loss(self, x, l1_coef: torch.FloatTensor = 0.01): f = F.relu(self.encoder(x - self.decoder.bias)) x_hat = self.decoder(f) f_act = f * self.decoder.weight.norm(p=2, dim=0) - loss = F.mse_loss(x_hat, x) + l1_coef * f_act.abs().sum() - return {"loss": loss, "features": f} + loss_reconstruction = F.mse_loss(x_hat, x) + loss_sparsity = l1_coef * f_act.abs().sum() + return { + "loss": loss_reconstruction + loss_sparsity, + "loss_reconstruction": loss_reconstruction, + "features": f, + } class SAE(pl.LightningModule): @@ -137,11 +143,14 @@ def __init__( metrics = MetricCollection( { "alive_features": AliveFeatures(self.num_features), + "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), + "alive_features_10k": AliveFeatures( + self.num_features, threshold=10_000 + ), "max_feature_density": MaxFeatureDensity(self.num_features), "feature_density": FeatureDensity(self.num_features), } ) - print(metrics) self.train_metrics = metrics.clone(prefix="train/") self.val_metrics = metrics.clone(prefix="val/") self.test_metrics = metrics.clone(prefix="test/") @@ -167,6 +176,7 @@ def stage_step(self, stage: str, batch): self.log_dict( { f"{stage}/loss": out["loss"], + f"{stage}/loss_reconstruction": out["loss_reconstruction"], f"{stage}/avg_l0_loss": avg_l0_norm(out["features"]), }, sync_dist=True, @@ -204,9 +214,7 @@ def _log_feature_metrics(self, metric, stage: str) -> None: ): self.logger.log_table( stage + "/feature_density", - data=list( - zip(feature_density.bin_centers, feature_density.density) - ), + data=list(zip(feature_density.bin_centers, feature_density.density)), columns=["bin_center", "density"], ) diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index 0e13aa60..29fdf71d 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -108,7 +108,8 @@ def instantiate(config_path: Path) -> LightningModule: config["lightning_module"], class_path=config.get("class_path", None), ) - model_config["vocab_size"] = config["datamodule"]["vocab_size"] + if "vocab_size" in config["datamodule"]: + model_config["vocab_size"] = config["datamodule"]["vocab_size"] else: cls_name, model_config = norm_class_config(config) diff --git a/electrolyte_fm/utils/featurizer.py b/electrolyte_fm/utils/featurizer.py new file mode 100644 index 00000000..bb38ec4b --- /dev/null +++ b/electrolyte_fm/utils/featurizer.py @@ -0,0 +1,144 @@ +import json +import logging +from math import floor +from pathlib import Path +from typing import Optional + +import torch +from datasets import load_dataset +from rdkit import Chem +from torch import nn +from torch.utils.data import DataLoader +from transformers import DataCollatorWithPadding +from torchmetrics import MetricCollection + +from ..data_modules.sae_dataset import extract_hidden_state +from ..data_modules.feature_tagger import FeatureCollection +from ..data_modules.utils import MolEncoding, encode_molecules +from ..models.model_utils import load_encoder +from ..models.sae import SAE +from .tokenizer import load_tokenizer +from .metrics import FeatureCorrelation + + +class FeatureExtractor(nn.Module): + def __init__(self, encoder, tokenizer, sae, layer: int): + super().__init__() + self.encoder = encoder + self.tokenizer = tokenizer + self.sae = sae + self.layer = layer + + def forward(self, batch): + if isinstance(batch, list) and isinstance(batch[0], str): + batch = self.tokenize(batch) + + # Extract hidden states + with torch.inference_mode(): + enc = self.encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) + if isinstance(self.layer, float): + layer = floor(len(enc["hidden_states"]) * self.layer) + else: + layer = self.layer + + hidden_state = enc["hidden_states"][layer] + + # Compute features activations + B, S, D = hidden_state.shape + feature_act = self.sae.forward(hidden_state.reshape(B * S, D)).reshape(B, S, -1) + feature_act *= batch["attention_mask"].unsqueeze(-1) + + return feature_act + + def tokenize(self, batch): + return self.tokenizer( + batch, + return_offsets_mapping=True, + truncation=True, + max_length=self.encoder.config.max_position_embeddings, + ) + + @classmethod + def from_checkpoint(cls, sae_checkpoint: str): + sae_checkpoint = Path(sae_checkpoint) + config_path = sae_checkpoint.parent.parent.joinpath("config.json") + sae_config = json.loads(config_path.read_text()) + + # Load components + encoder_args = sae_config["data"]["init_args"] + encoder = load_encoder(encoder_args["name_or_path"]) + tokenizer = encoder_args["tokenizer"] or encoder_args["name_or_path"] + tokenizer = load_tokenizer(tokenizer) + + # Load just the Sparse Autoencoder + sae = SAE.load_from_checkpoint( + sae_checkpoint, **sae_config["model"]["init_args"] + ).sae + layer = sae_config["data"]["init_args"]["layer"] + + return cls(encoder, tokenizer, sae, layer) + + +def collate_fn(batch, encoder_collate): + encoder_input = {k: batch[k] for k in ["input_ids", "attention_mask"]} + batch.update(encoder_collate(encoder_input)) + return batch + + +class FeaturePipeline: + def __init__( + self, sae_ckpt: str, dataset_path: str, features: Optional[list[str]] = None + ): + self.sae_ckpt = sae_ckpt + self.dataset_path = dataset_path + self.features = FeatureCollection.from_named(features or ["all"]) + + @property + def feature_names(self): + return self.features.names + + def collate_fn(self, batch): + tokens = [{k: x[k] for k in ["input_ids", "attention_mask"]} for x in batch] + out = self.token_collator(tokens) + for k in out.keys(): + if k not in ["input_ids", "attention_mask"]: + out[k] = batch[k] + return out + + def setup(self): + self.miner = FeatureExtractor.from_checkpoint(self.sae_ckpt) + + # Load dataset + dataset_path = Path(self.dataset_path) + ds = load_dataset( + "text", + data_files={"val": str(dataset_path.joinpath("data/val/*.txt"))}, + keep_in_memory=False, + streaming=True, + save_infos=True, + ) + ds = encode_molecules(ds, "text", encoding=MolEncoding.KEUKLE_SMILES) + ds = ds.map(self.miner.tokenize, batched=True, input_columns="text") + ds = ds.map(self.features, batched=False, input_columns="text") + self.dataset = ds + self.token_collator = DataCollatorWithPadding(self.miner.tokenizer, "longest") + + def iter(self, split: str = "val", **kwargs): + dl = DataLoader(self.dataset[split], collate_fn=self.collate_fn, **kwargs) + miner = self.miner.to("cuda") + for batch in dl: + x = { + "input_ids": batch["input_ids"].to("cuda"), + "attention_mask": batch["attention_mask"].to("cuda"), + } + f_act = miner(x) + yield { + "feature_activations": f_act.to("cpu"), + "attention_mask": batch["attention_mask"], + "proxy_activations": x["proxy_activations"], + } diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index aef609b3..54c9dcb8 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -3,7 +3,7 @@ import torch from numpy import geomspace -from torchmetrics import Metric, MetricCollection +from torchmetrics import Metric from torchmetrics import MetricCollection as TmMetricCollection from torchmetrics.classification import ( AUROC, @@ -17,9 +17,10 @@ PearsonCorrCoef, R2Score, ) +from torchmetrics.regression.pearson import ( + _final_aggregation as final_pearson_aggregation, +) from torchmetrics.wrappers import BootStrapper -from torchmetrics.wrappers.abstract import WrapperMetric -from torchmetrics.wrappers.classwise import ClasswiseWrapper from torchmetrics.wrappers.classwise import ClasswiseWrapper as TmClasswiseWrapper """ Target Value to indicate missing data """ @@ -445,7 +446,15 @@ def update(self, features: torch.Tensor) -> None: class AliveFeatures(FeaturesUtilization): higher_is_better = True + def __init__(self, *args, threshold: Optional[int] = None, **kwargs): + super().__init__(*args, **kwargs) + self.threshold = threshold + def compute(self): + if self.threshold: + min_freq = 1 / self.threshold + return (self.feature_counts >= min_freq).sum() / self.feature_counts.numel() + return self.feature_counts.count_nonzero() / self.feature_counts.numel() @@ -486,3 +495,33 @@ class MaxFeatureDensity(FeaturesUtilization): def compute(self): return self.feature_counts.max() / self.total_tokens + + +class FeatureCorrelation(PearsonCorrCoef): + def compute(self): + if (self.num_outputs == 1 and self.mean_x.numel() > 1) or ( + self.num_outputs > 1 and self.mean_x.ndim > 1 + ): + mean_x, mean_y, var_x, var_y, corr_xy, n_total = final_pearson_aggregation( + self.mean_x, + self.mean_y, + self.var_x, + self.var_y, + self.corr_xy, + self.n_total, + ) + else: + mean_x = self.mean_x + mean_y = self.mean_y + var_x = self.var_x + var_y = self.var_y + # corr_xy = self.corr_xy + n_total = self.n_total + + return { + "avg_active": mean_x, + "avg_inactive": mean_y, + "var_active": var_x, + "var_inactive": var_y, + "n_total": n_total, + } diff --git a/opt/FeatureMiner/Project.toml b/opt/FeatureMiner/Project.toml new file mode 100644 index 00000000..58a56987 --- /dev/null +++ b/opt/FeatureMiner/Project.toml @@ -0,0 +1,11 @@ +name = "FeatureMiner" +uuid = "83091abd-1720-4da7-94ca-5ff8667e187f" +version = "0.1.0" + +[deps] +ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63" +DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" +OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" +OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl new file mode 100644 index 00000000..520ef207 --- /dev/null +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -0,0 +1,29 @@ +module FeatureMiner + +using ArgParse +using PythonCall: Py, pyimport +using OnlineStats: OnlineStats, KHist, Variance, Series, fit! +using OnlineStatsBase: OnlineStatsBase, OnlineStat, EqualWeight, smooth, bessel, nobs +using StatsBase: StatsBase + + +function FeatureExtractor(ckpt_path::String) + cls = pyimport("electrolyte_fm.models.sae.FeatureExtractor") + return cls.from_checkpoint(ckpt_path) +end + +function FeaturizedSmiles(path::String, miner::Py) + cls = pyimport("electrolyte_fm.data_modules.sae_dataset.FeaturizedSmiles") + return cls(path, miner) +end + +function split_dataset_by_node(dataset::Py, rank::Int, size::Int) + m = pyimport("datasets.distributed") + return m.split_dataset_by_node(dataset, rank, size) +end + +include("identification.jl") +include("stats.jl") + +end + diff --git a/opt/FeatureMiner/src/identification.jl b/opt/FeatureMiner/src/identification.jl new file mode 100644 index 00000000..e138fa45 --- /dev/null +++ b/opt/FeatureMiner/src/identification.jl @@ -0,0 +1,74 @@ +struct FeatureStats{TD,M,I} + feature_activations::Vector{TD} + positive_proxy::Vector{M} + negative_proxy::Vector{M} + proxy_count::Vector{I} + n::Ref{I} +end + +function FeatureStats(n_feature::Integer, n_proxy::Integer) + f_act = map(_ -> Series(; hist=KHist(100), var=Variance()), 1:n_feature) + pos_proxy = map(_ -> ElementwiseVariance(Float32, n_feature), 1:n_proxy) + proxy_count = zeros(UInt64, n_proxy) + neg_proxy = map(_ -> ElementwiseVariance(Float32, n_feature), 1:n_proxy) + count = zero(UInt64) + TD = eltype(f_act) + M = eltype(pos_proxy) + I = eltype(proxy_count) + FeatureStats{TD,M,I}(f_act, pos_proxy, neg_proxy, proxy_count, count) +end + + +""" + fit!(stats, f_act, mask, proxy_act) + +Update feature statistics `stats` with the provided activations for a single observation. + +`f_act`: activations of the features (seq_len, n_features) +`mask`: mask of the observations (seq_len,) +`proxy_act`: activations of the proxies (seq_len, n_proxies) + +""" +function OnlineStatsBase.fit!(stats::FeatureStats, f_act::AbstractMatrix, proxy_act::AbstractMatrix, mask::AbstractVector,) + stats.n[] += 1 + + # Update f activation statistics + for i in axes(f_act, 2) + f_act_masked = f_act[mask, i] + OnlineStats.fit!(stats.feature_activations[i], Float64.(f_act_masked)) + end + + # Update proxy activation statistics + for fdx in axes(proxy_act, 2) + for tdx in axes(proxy_act, 1) + mask[tdx] || continue # Skip if token is masked + fa = @view f_act[tdx, :] + proxy_active = false + # Maintain separate statistics for positive and negative proxies + pa = proxy_act[tdx, fdx] + if pa + proxy_active = true + OnlineStats.fit!(stats.positive_proxy[fdx], fa) + else + OnlineStats.fit!(stats.negative_proxy[fdx], fa) + end + stats.proxy_count[fdx] += proxy_active + end + end + + return nothing +end + +function tabulate_features(pipeline::Py; batch_size::Integer=64) + stats = FeatureStats(512, 32) + for batch in pipeline.iter("val"; batch_size) + f_act = pyconvert(Array, batch["feature_activations"]) + mask = pyconvert(Matrix, batch["attention_mask"].T) + proxy_act = pyconvert.(Matrix{Bool}, batch["proxy_activations"]) + for idx in axes(f_act, 1) + # Update activation statistics + fit!(stats, selectdim(f_act, 1, idx), selectdim(mask, 2, idx), proxy_act[idx, :]) + end + end + return stats +end diff --git a/opt/FeatureMiner/src/stats.jl b/opt/FeatureMiner/src/stats.jl new file mode 100644 index 00000000..db4e7829 --- /dev/null +++ b/opt/FeatureMiner/src/stats.jl @@ -0,0 +1,45 @@ +struct ElementwiseVariance{T,S,W} <: OnlineStat{T} + σ2::S + μ::T + weight::W + n::Ref{Int} +end + +function ElementwiseVariance(T::Type{<:Number}, n::Integer; weight=EqualWeight()) + s = Vector{T}(undef, n) + m = Vector{T}(undef, n) + s .= zero(T)^2 / one(T) + m .= zero(T) / one(T) + ElementwiseVariance{Vector{T},typeof(s),typeof(weight)}(s, m, weight, 0) +end + +OnlineStatsBase.fit!(o::ElementwiseVariance, x::AbstractVector{T}) where {T} = (OnlineStatsBase._fit!(o, x); return o) +function OnlineStatsBase._fit!(o::ElementwiseVariance, x) + μ = o.μ + γ = o.weight(o.n[] += 1) + @. o.μ = smooth(o.μ, x, γ) + @. o.σ2 = smooth(o.σ2, (x - o.μ) * (x - μ), γ) + return o +end + +function OnlineStatsBase._merge!(o::ElementwiseVariance, o2::ElementwiseVariance) + γ = o2.n / (o.n += o2.n) + @. δ = o2.μ - o.μ + @. o.σ2 = smooth(o.σ2, o2.σ2, γ) + δ^2 * γ * (1.0 - γ) + @. o.μ = smooth(o.μ, o2.μ, γ) + return o +end + +function OnlineStatsBase.value(o::ElementwiseVariance) + if nobs(o) > 0 + return @. o.σ2 * bessel(o) + else + return NaN + end +end + +StatsBase.var(o::ElementwiseVariance) = OnlineStatsBase.value(o) +StatsBase.mean(o::ElementwiseVariance) = o.μ +StatsBase.nobs(o::ElementwiseVariance) = o.n[] + + diff --git a/submit/nsys.yaml b/submit/nsys.yaml index b12c85f7..df2c4971 100644 --- a/submit/nsys.yaml +++ b/submit/nsys.yaml @@ -2,6 +2,7 @@ nsys: trace: cuda,cudnn,cublas,nvtx o: "nsys_multinode_%q{JOBID}_%q{NODE_RANK}" sampling-period: 16000000 + duration: 200 # sample: none # Disable CPU Profiling # Uncomment to profile GPU memory usage diff --git a/submit/sae_features.libsonnet b/submit/sae_features.libsonnet new file mode 100644 index 00000000..9c81f8f4 --- /dev/null +++ b/submit/sae_features.libsonnet @@ -0,0 +1,3 @@ +{ + aromatic_elements: "[a-z]{1,2}" +} diff --git a/test/test_featurizer.py b/test/test_featurizer.py index be723ec6..e3d06db7 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -84,7 +84,10 @@ }, { "feature": "rotatable_bond", - "positive": ["CC-CC", "c1ccccc1-c2ccccc2"], + "positive": [ + "CC-CC", + "c1ccccc1-c2ccccc2", + ], "negative": ["CC(=O)C", "C=O", "c1ccccc1"], }, ] diff --git a/test/test_sae.py b/test/test_sae.py new file mode 100644 index 00000000..24efa9f0 --- /dev/null +++ b/test/test_sae.py @@ -0,0 +1,99 @@ +import pytest +import torch +from transformers import DataCollatorWithPadding + +from electrolyte_fm.data_modules.sae_dataset import ( + HiddenStateDataModule, + extract_hidden_state, +) +from electrolyte_fm.models.model_utils import load_encoder +from electrolyte_fm.models.sae import GatedSAE, TiedBiasSAE, avg_l0_norm +from electrolyte_fm.utils.tokenizer import load_tokenizer + + +@pytest.mark.parametrize("sae_cls", [GatedSAE, TiedBiasSAE]) +class TestSAE: + B = 3 + H = 4 + E = 2 + + @classmethod + def setup_class(cls): + torch.manual_seed(0) + + @property + def feature_shape(self): + return (self.B, self.H * self.E) + + def input(self): + return torch.rand(self.B, self.H) + + def test_init(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + for p in sae.parameters(): + assert p.isfinite().all() + assert not p.isnan().any() + + def test_forward(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + features = sae(self.input()) + assert features.shape == self.feature_shape + assert features.isfinite().all() + assert not features.isnan().any() + + def test_loss(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + x = self.input() + out = sae.loss(x) + for v in out.values(): + assert v.isfinite().all() + assert not v.isnan().any() + + # Check output + assert out["loss"].shape == () + assert out["features"].shape == self.feature_shape + assert out["features"].isclose(sae(x)).all() + # assert out["x_hat"].shape == x.shape + # assert out["x_hat"].isclose(sae.reconstruct(sae(x))).all() + + +def test_avg_l0_norm(): + x = torch.tensor([[1, 0, 0], [0, 5, 0]]) + assert avg_l0_norm(x) == 1 + assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) + + +def test_dataloader(): + ckpt_path = "ibm/MoLFormer-XL-both-10pct" + path = "/lustre/fs0/awadell/realspace" + dm = HiddenStateDataModule(ckpt_path, path) + dm.prepare_data() + dm.setup("fit") + for batch in dm.train_dataloader(): + assert isinstance(batch, torch.Tensor) + assert batch.shape == (dm.batch_size, 768) + assert not batch.requires_grad + break + + +def test_extract_hidden_state(): + ckpt_path = "ibm/MoLFormer-XL-both-10pct" + encoder = load_encoder(ckpt_path) + d_model = 768 + tok = load_tokenizer(ckpt_path) + smiles = [ + "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", + "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", + ] + tokens = [tok(smi) for smi in smiles] + input_ids = [x["input_ids"] for x in tokens] + attention_mask = [x["attention_mask"] for x in tokens] + batch = extract_hidden_state( + input_ids, + attention_mask, + encoder=encoder, + collate=DataCollatorWithPadding(tok), + layer=0.5, + ) + assert "hidden_state" in batch + assert batch["hidden_state"].shape == (len(smiles), d_model) From 5b359dca020407bf1709460c309688e48f87f6dd Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 3 Mar 2025 12:38:28 -0800 Subject: [PATCH 008/147] wip: rework sae to inject coders into the model --- .github/workflows/CI.yaml | 5 +- electrolyte_fm/data_modules/__init__.py | 1 - electrolyte_fm/models/__init__.py | 2 +- electrolyte_fm/models/sae.py | 370 ++++++++++++++++++------ pyproject.toml | 3 + submit/sae.jsonnet | 26 +- test/test_sae.py | 284 ++++++++++++++---- 7 files changed, 516 insertions(+), 175 deletions(-) diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index c4b37152..360cf2f1 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -2,7 +2,6 @@ on: push: pull_request: workflow_dispatch: - jobs: test: runs-on: ubuntu-latest @@ -12,10 +11,10 @@ jobs: - uses: actions/setup-python@v4 with: python-version: 3.11.8 - cache: 'poetry' + cache: poetry - name: Install deploy key for BattModels/smirk uses: webfactory/ssh-agent@dc588b651fe13675774614f8e6a936a468676387 #v0.9.0 with: ssh-private-key: ${{ secrets.SMIRK_PRIVATE_KEY }} - run: poetry install - - run: source activate && pytest + - run: source activate && pytest -m "not gpu" diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index d144cb5c..d53b5976 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -1,5 +1,4 @@ from .molnet_dataset import MolNetDataModule from .property_prediction_dataset import PropertyPredictionDataModule from .roberta_dataset import RobertaDataSet -from .sae_dataset import HiddenStateDataModule from .tmqm_dataset import tmQMDataModule diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index 4ae80285..7bf952a3 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -3,4 +3,4 @@ from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer -from .sae import SAE +from .sae import LightningSAE diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index de5831c9..a27a9799 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -1,16 +1,17 @@ -from math import sqrt -from typing import Optional, Union +from math import sqrt, floor +from typing import Callable, Optional, Literal +from contextlib import contextmanager -import pytorch_lightning as pl +import lightning.pytorch as pl import torch -from pytorch_lightning.cli import LRSchedulerCallable, OptimizerCallable +from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from torch import nn from torch.nn import functional as F from torchmetrics import MetricCollection +from transformers import PreTrainedModel, AutoModel -from ..utils.ckpt import get_hidden_size -from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity from .model_utils import load_encoder +from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity def init_bias(bias, w): @@ -24,8 +25,177 @@ def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: return x.detach().count_nonzero() / x.shape[:-1].numel() -class GatedSAE(nn.Module): - def __init__(self, hidden_size: int, expansion: int = 4, device=None, dtype=None): +def hf_cross_entropy(logits: torch.FloatTensor, target: torch.IntTensor): + """F.cross_entropy but for logits of `(B, T, C)` and target of `(B, T)`""" + return F.cross_entropy(logits.view(-1, logits.shape[-1]), target.view(-1)) + + +class AbstractSAE(nn.Module): + def forward(self, x: torch.Tensor): + return self.decode(self.encode(x)) + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """Encode x into a feature representation""" + raise NotImplementedError() + + def decode(self, features: torch.Tensor) -> torch.Tensor: + """Encode features into a reconstruction of x""" + raise NotImplementedError() + + def forward_with_loss(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Forward pass with loss calculation -> `(self.forward(x), loss)`""" + return self.forward(x), torch.tensor(0.0) + + +InjectedCoderState = Literal["null", "dense", "sparse"] + + +class InjectedCoder(nn.Module): + def __init__(self, dense_model: nn.Module, coder: AbstractSAE): + super().__init__() + self.dense_model = dense_model + self.coder = coder + self.loss = torch.tensor(0.0) + self.state: InjectedCoderState = "sparse" + + def forward(self, *args, **kwargs): + if self.state == "null": + return (*args,) + + output = self.dense_model(*args, **kwargs) + if self.state == "dense": + return output + + assert self.state == "sparse" + hs = output[0] + if self.training: + hs, loss = self.coder.forward_with_loss(hs) + self.loss = loss + + else: + hs = self.coder.forward(hs) + + return (hs, *output[1:]) + + +class SparsifiedModel(nn.Module): + def __init__(self, model: nn.Module, coders: list[InjectedCoder]): + super().__init__() + self.model = model + self.coders = coders + + @classmethod + def from_huggingface( + cls, model: PreTrainedModel, coder: AbstractSAE, layer: float | int = 0.5 + ): + layers = model.base_model.encoder.layer + if isinstance(layer, float): + n_layers = len(layers) + layer = floor(layer * n_layers) + + coders = [cls.inject_sparse_coder(layers, coder, layer)] + return cls(model, coders) + + @staticmethod + def inject_sparse_coder(layers: nn.ModuleList, coder: AbstractSAE, layer: int): + m = InjectedCoder(layers[layer], coder) + layers[layer] = m + return m + + def set_sparsity(self, enable: bool = True): + """Enable or disable sparse coders""" + assert isinstance(enable, bool) + for coder in self.coders: + coder.state = "sparse" if enable else "dense" + + @contextmanager + def sparse(self, enable: bool = True): + sparsity = [] + for coder in self.coders: + sparsity.append(coder.state) + coder.state = "sparse" if enable else "dense" + + try: + yield self + finally: + for coder in self.coders: + coder.state = sparsity.pop() + + @contextmanager + def nullcoders(self): + state = [] + for coder in self.coders: + state.append(coder.state) + coder.state = "null" + + try: + yield self + finally: + for coder in self.coders: + coder.state = state.pop() + + def forward( + self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + return self.model.forward(input_ids, attention_mask)[0] + + def forward_with_loss(self, *args, **kwargs): + y = self.model.forward(*args, **kwargs)[0] + device = self.coders[0].loss.device + loss = torch.tensor(0.0, device=device) + for coder in self.coders: + loss += coder.loss + + return y, loss + + def sparse_parameters(self): + """Return an iterator over the parameters of the sparse autoencoders""" + for coder in self.coders: + yield from coder.coder.parameters() + + def sparse_named_parameters(self): + for coder in self.coders: + yield from coder.coder.named_parameters() + + @torch.no_grad() + def loss_recovered( + self, + target: torch.Tensor, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, + sparse_output: Optional[torch.Tensor] = None, + ): + """Compute the fraction of the loss recovered by the SAE relative to an null-embedding""" + training = self.training + self.eval() + if sparse_output is None: + with self.sparse(True) as self: + sparse_output = self.forward(input_ids, attention_mask=attention_mask) + loss_sparse = lossfn(sparse_output, target) + + with self.sparse(False) as self: + y_dense = self.forward(input_ids, attention_mask=attention_mask) + loss_dense = lossfn(y_dense, target) + + with self.nullcoders() as self: + y_null = self.forward(input_ids, attention_mask=attention_mask) + loss_null = lossfn(y_null, target) + + self.train(training) + + return 1 - (loss_sparse - loss_dense) / (loss_null - loss_dense) + + +class GatedSAE(AbstractSAE): + def __init__( + self, + hidden_size: int, + expansion: int = 4, + l1_coef: float = 0.01, + device=None, + dtype=None, + ): super().__init__() factory_kwargs = {"device": device, "dtype": dtype} n_features = hidden_size * expansion @@ -39,6 +209,11 @@ def __init__(self, hidden_size: int, expansion: int = 4, device=None, dtype=None self.b_dec = nn.Parameter(torch.empty(hidden_size, **factory_kwargs)) self.r_mag = nn.Parameter(torch.empty(n_features, **factory_kwargs)) self.b_enc = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.l1_coef = torch.tensor(l1_coef, requires_grad=False, **factory_kwargs) + + # Loss parameters + self.loss = torch.tensor(0.0, **factory_kwargs) + self.reset_parameters() def reset_parameters(self): @@ -51,23 +226,25 @@ def reset_parameters(self): nn.init.normal_(self.r_mag, mean=0.0, std=sqrt(2 / self.r_mag.shape[0])) init_bias(self.b_enc, self.w_gate) - def forward(self, x): + def forward(self, x: torch.Tensor): + return self.decode(self.encode(x)) + + def encode(self, x: torch.Tensor): x_centered = x - self.b_dec x_enc = x_centered.matmul(self.w_gate.T) gate = (x_enc + self.b_gate) > 0 x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) return x_mag * gate - def reconstruct(self, features): + def decode(self, features: torch.Tensor): return F.linear(features, self.w_dec, self.b_dec) - @torch.compile - def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + def forward_with_loss(self, x: torch.Tensor): x_centered = x - self.b_dec x_enc = x_centered.matmul(self.w_gate.T) pi_gate = x_enc + self.b_gate pi_rect = F.relu(pi_gate) - loss_sparsity = l1_coef * pi_rect.abs().sum() + loss_sparsity = self.l1_coef * pi_rect.abs().sum() x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) gate = pi_gate > 0 @@ -78,82 +255,86 @@ def loss(self, x, l1_coef: torch.FloatTensor = 0.01): x_hat_detach = F.linear(pi_rect, self.w_dec.detach(), self.b_dec.detach()) loss_aux = F.mse_loss(x_hat_detach, x) - return { - "loss": loss_sparsity + loss_aux + loss_recon, - "loss_reconstruction": loss_recon, - "features": features, - } + loss = loss_sparsity + loss_aux + loss_recon + return x_hat, loss -class TiedBiasSAE(nn.Module): - def __init__(self, hidden_size: int, expansion: int = 4): +class TiedBiasSAE(AbstractSAE): + def __init__(self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01): super().__init__() n_features = hidden_size * expansion self.encoder = nn.Linear(hidden_size, n_features) self.decoder = nn.Linear(n_features, hidden_size) + self.l1_coef_coef = torch.tensor(l1_coef) - def forward(self, x): - return F.relu(self.encoder(x - self.decoder.bias)) + def encode(self, x: torch.Tensor): + return self.encoder(x - self.decoder.bias) - def reconstruct(self, features): + def decode(self, features): return self.decoder(features) - @torch.compile - def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + def forward_with_loss(self, x: torch.Tensor): f = F.relu(self.encoder(x - self.decoder.bias)) x_hat = self.decoder(f) f_act = f * self.decoder.weight.norm(p=2, dim=0) loss_reconstruction = F.mse_loss(x_hat, x) - loss_sparsity = l1_coef * f_act.abs().sum() - return { - "loss": loss_reconstruction + loss_sparsity, - "loss_reconstruction": loss_reconstruction, - "features": f, - } + loss_sparsity = self.l1_coef_coef * f_act.abs().sum() + loss = loss_reconstruction + loss_sparsity + return x_hat, loss -class SAE(pl.LightningModule): +def pretrained_sae( + name_or_path: str, sae: str = "gated", layer: int | float = 0.5, expansion: int = 2 +): + model = AutoModel.from_pretrained(name_or_path, trust_remote_code=True) + hidden_size = model.config.hidden_size + if sae == "gated": + sae = GatedSAE(hidden_size=hidden_size, expansion=expansion) + + +class LightningSAE(pl.LightningModule): def __init__( self, - hidden_size: Union[int, str], - sae: str = "gated", - expansion: int = 4, + name_or_path: str, + sae_type: str = "gated", + layer: int | float = 0.5, l1_coef: float = 0.01, + expansion: int = 2, + lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, optimizer: OptimizerCallable = torch.optim.AdamW, lr_schedule: LRSchedulerCallable | None = None, ): super().__init__() - if isinstance(hidden_size, str): - hidden_size = get_hidden_size(hidden_size) - if sae == "gated": - self.sae = GatedSAE(hidden_size, expansion) - elif sae == "tied_bias": - self.sae = TiedBiasSAE(hidden_size, expansion) + encoder = load_encoder(name_or_path) + hidden_size = encoder.config.hidden_size + if sae_type == "gated": + sae = GatedSAE( + hidden_size=hidden_size, expansion=expansion, l1_coef=l1_coef + ) else: - raise ValueError(f"Unknown sae type: {sae}") + raise ValueError(f"Unknown sae type: {sae_type}") - self.l1_coef = l1_coef + self.sparse_model = SparsifiedModel.from_huggingface(encoder, sae, layer) self.optimizer = optimizer self.lr_schedule = lr_schedule - self.hidden_size = hidden_size - self.num_features = hidden_size * expansion - self.save_hyperparameters(ignore=["sae"]) - - metrics = MetricCollection( - { - "alive_features": AliveFeatures(self.num_features), - "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), - "alive_features_10k": AliveFeatures( - self.num_features, threshold=10_000 - ), - "max_feature_density": MaxFeatureDensity(self.num_features), - "feature_density": FeatureDensity(self.num_features), - } - ) - self.train_metrics = metrics.clone(prefix="train/") - self.val_metrics = metrics.clone(prefix="val/") - self.test_metrics = metrics.clone(prefix="test/") + self.lossfn = lossfn + self.save_hyperparameters(ignore=["sparse_model"]) + + # metrics = MetricCollection( + # { + # "alive_features": AliveFeatures(self.num_features), + # "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), + # "alive_features_10k": AliveFeatures( + # self.num_features, threshold=10_000 + # ), + # "max_feature_density": MaxFeatureDensity(self.num_features), + # "feature_density": FeatureDensity(self.num_features), + # } + # ) + # self.train_metrics = metrics.clone(prefix="train/") + # self.val_metrics = metrics.clone(prefix="val/") + # self.test_metrics = metrics.clone(prefix="test/") def setup(self, stage: str): if isinstance(self.logger, pl.loggers.WandbLogger): @@ -171,38 +352,44 @@ def define_metric(*args, **kwargs): define_metric(f"{stage}/alive_features", summary="max,last") define_metric(f"{stage}/max_feature_density", summary="min,max,last") - def stage_step(self, stage: str, batch): - out = self.sae.loss(batch, self.l1_coef) - self.log_dict( - { - f"{stage}/loss": out["loss"], - f"{stage}/loss_reconstruction": out["loss_reconstruction"], - f"{stage}/avg_l0_loss": avg_l0_norm(out["features"]), - }, - sync_dist=True, - on_step=True, - on_epoch=True, - ) - return out + def stage_step(self, stage: str, batch, recovered_loss: bool = False): + input_ids = batch["input_ids"] + attention_mask = batch["attention_mask"] + y, loss = self.sparse_model.forward_with_loss(input_ids, attention_mask) + logdict = {f"{stage}/loss": loss} + + if self.lossfn: + target = batch["target"] if "target" in batch else batch["labels"] + y_loss = self.lossfn(y, target) + logdict[f"{stage}/target_loss"] = y_loss + + if recovered_loss: + logdict[f"{stage}/recovered_loss"] = self.sparse_model.loss_recovered( + target, + input_ids, + attention_mask, + lossfn=self.lossfn, + sparse_output=y, + ) - def backward(self, loss, *args, **kwargs) -> None: - # Retain graph during backprop - loss.backward(retain_graph=True) + return loss, logdict def training_step(self, batch): - out = self.stage_step("train", batch) - self.train_metrics.update(out["features"]) - return out["loss"] + loss, logdict = self.stage_step("train", batch) + self.log_dict(logdict, on_epoch=True, on_step=True) + return loss def validation_step(self, batch): - out = self.stage_step("val", batch) - self.val_metrics.update(out["features"]) - return out["loss"] + loss, logdict = self.stage_step("val", batch, recovered_loss=True) + logdict = {k + "_epoch": v for k, v in logdict.items()} + self.log_dict(logdict, on_epoch=True, on_step=False) + return loss def test_step(self, batch): - out = self.stage_step("test", batch) - self.test_metrics.update(out["features"]) - return out["loss"] + loss, logdict = self.stage_step("test", batch, recovered_loss=True) + logdict = {k + "_epoch": v for k, v in logdict.items()} + self.log_dict(logdict, on_epoch=True, on_step=False) + return loss def _log_feature_metrics(self, metric, stage: str) -> None: m = metric.compute() @@ -220,17 +407,8 @@ def _log_feature_metrics(self, metric, stage: str) -> None: metric.reset() - def on_train_epoch_end(self): - self._log_feature_metrics(self.train_metrics, "train") - - def on_validation_epoch_end(self): - self._log_feature_metrics(self.val_metrics, "val") - - def on_test_epoch_end(self): - self._log_feature_metrics(self.test_metrics, "test") - def configure_optimizers(self): - optimizer = self.optimizer(self.parameters()) + optimizer = self.optimizer(self.sparse_model.sparse_parameters()) if schedule := self.lr_schedule: return { "optimizer": optimizer, diff --git a/pyproject.toml b/pyproject.toml index 304e93e8..1b9383d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -48,6 +48,9 @@ testpaths = ["test"] pythonpath = [".", "test"] addopts = "--color yes" +[tool.pytest.ini_options.markers] +gpu = "tests that require a GPU to run" + [tool.ruff.lint] ignore = ["F401"] diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index fffb6f68..73f97820 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -1,19 +1,18 @@ { - container: '/lustre/fs0/awadell/sqsh-files/0535844560745234+mist+08e9e89.sqsh', + container: '/lustre/fs0/shared/sqsh-files/mist+pytorch+25.01+v2.sif', train: { tags: ['sae', 'debug'], model: { - class_path: 'electrolyte_fm.models.SAE', + class_path: 'electrolyte_fm.models.LightningSAE', init_args: { - sae: 'gated', - hidden_size: $.train.data.init_args.name_or_path, - expansion: 16, + name_or_path: 'ibm/MoLFormer-XL-both-10pct', + sae_type: 'gated', + expansion: 4, l1_coef: 1e-5, optimizer: { class_path: 'torch.optim.AdamW', init_args: { - lr: 1e-3, - betas: [0.0, 0.999], + lr: 5e-4, }, }, lr_schedule: { @@ -27,21 +26,22 @@ }, }, data: { - class_path: 'electrolyte_fm.data_modules.HiddenStateDataModule', + class_path: 'electrolyte_fm.data_modules.RobertaDataSet', init_args: { - name_or_path: 'ibm/MoLFormer-XL-both-10pct', - batch_size: 1024, - encoder_batch_size: 256, + path: '/lustre/fs0/awadell/realspace', + batch_size: 128, val_batch_size: 4 * self.batch_size, + num_workers: 8, + prefetch_factor: 8, }, }, trainer: { max_steps: $.train.model.init_args.lr_schedule.init_args.num_training_steps, val_check_interval: 100, limit_val_batches: 50, - precision: 'bf16-true', + precision: '32', enable_progress_bar: false, - strategy: "ddp", + strategy: 'auto', }, }, env: { diff --git a/test/test_sae.py b/test/test_sae.py index 24efa9f0..9facd59a 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -1,16 +1,56 @@ +from datasets import features +from numpy import zeros_like import pytest import torch -from transformers import DataCollatorWithPadding - -from electrolyte_fm.data_modules.sae_dataset import ( - HiddenStateDataModule, - extract_hidden_state, +from transformers import ( + AutoModel, + AutoTokenizer, + DataCollatorWithPadding, + RobertaPreLayerNormConfig, + RobertaPreLayerNormForMaskedLM, + RobertaPreLayerNormModel, ) + +# from electrolyte_fm.data_modules.sae_dataset import ( +# HiddenStateDataModule, +# extract_hidden_state, +# ) from electrolyte_fm.models.model_utils import load_encoder -from electrolyte_fm.models.sae import GatedSAE, TiedBiasSAE, avg_l0_norm +from electrolyte_fm.models.sae import ( + AbstractSAE, + GatedSAE, + InjectedCoder, + SparsifiedModel, + TiedBiasSAE, + avg_l0_norm, +) from electrolyte_fm.utils.tokenizer import load_tokenizer +def get_default_device(): + if torch.cuda.is_available(): + return torch.device("cuda") + else: + return torch.device("cpu") + + +DEVICE = get_default_device() + + +@pytest.fixture() +@pytest.mark.cuda +def roberta_tokenzier(): + tokenizer = load_tokenizer("smirk") + config = RobertaPreLayerNormConfig( + vocab_size=len(tokenizer), + hidden_size=256, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=512, + ) + return RobertaPreLayerNormForMaskedLM(config).to(DEVICE), tokenizer + + @pytest.mark.parametrize("sae_cls", [GatedSAE, TiedBiasSAE]) class TestSAE: B = 3 @@ -34,66 +74,188 @@ def test_init(self, sae_cls): assert p.isfinite().all() assert not p.isnan().any() - def test_forward(self, sae_cls): + def test_forward(self, sae_cls: AbstractSAE): sae = sae_cls(hidden_size=self.H, expansion=self.E) - features = sae(self.input()) - assert features.shape == self.feature_shape + assert isinstance(sae, AbstractSAE) + x = self.input() + features = sae.forward(x) + assert features.shape == x.shape assert features.isfinite().all() assert not features.isnan().any() def test_loss(self, sae_cls): sae = sae_cls(hidden_size=self.H, expansion=self.E) + assert isinstance(sae, AbstractSAE) x = self.input() - out = sae.loss(x) - for v in out.values(): - assert v.isfinite().all() - assert not v.isnan().any() - - # Check output - assert out["loss"].shape == () - assert out["features"].shape == self.feature_shape - assert out["features"].isclose(sae(x)).all() - # assert out["x_hat"].shape == x.shape - # assert out["x_hat"].isclose(sae.reconstruct(sae(x))).all() - - -def test_avg_l0_norm(): - x = torch.tensor([[1, 0, 0], [0, 5, 0]]) - assert avg_l0_norm(x) == 1 - assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) - - -def test_dataloader(): - ckpt_path = "ibm/MoLFormer-XL-both-10pct" - path = "/lustre/fs0/awadell/realspace" - dm = HiddenStateDataModule(ckpt_path, path) - dm.prepare_data() - dm.setup("fit") - for batch in dm.train_dataloader(): - assert isinstance(batch, torch.Tensor) - assert batch.shape == (dm.batch_size, 768) - assert not batch.requires_grad - break - - -def test_extract_hidden_state(): - ckpt_path = "ibm/MoLFormer-XL-both-10pct" - encoder = load_encoder(ckpt_path) - d_model = 768 - tok = load_tokenizer(ckpt_path) - smiles = [ - "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", - "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", - ] - tokens = [tok(smi) for smi in smiles] - input_ids = [x["input_ids"] for x in tokens] - attention_mask = [x["attention_mask"] for x in tokens] - batch = extract_hidden_state( - input_ids, - attention_mask, - encoder=encoder, - collate=DataCollatorWithPadding(tok), - layer=0.5, + y, loss = sae.forward_with_loss(x) + assert loss.isfinite() and loss.shape == () + assert y.shape == x.shape + assert y.isfinite().all() + + +# def test_avg_l0_norm(): +# x = torch.tensor([[1, 0, 0], [0, 5, 0]]) +# assert avg_l0_norm(x) == 1 +# assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) + + +# def test_dataloader(): +# ckpt_path = "ibm/MoLFormer-XL-both-10pct" +# path = "/lustre/fs0/awadell/realspace" +# dm = HiddenStateDataModule(ckpt_path, path) +# dm.prepare_data() +# dm.setup("fit") +# for batch in dm.train_dataloader(): +# assert isinstance(batch, torch.Tensor) +# assert batch.shape == (dm.batch_size, 768) +# assert not batch.requires_grad +# break + + +def test_injected_coder(roberta_tokenzier): + roberta, _ = roberta_tokenzier + hidden_size = roberta.config.hidden_size + sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) + injected = InjectedCoder(roberta.base_model.encoder.layer[2], sae) + injected.eval() + assert isinstance(injected, InjectedCoder) + dense_model = injected.dense_model + assert not injected.training and not dense_model.training + x = torch.rand(1, 5, hidden_size, device=DEVICE) + + # Check dense + y_ref = dense_model(x)[0] + injected.state = "dense" + y_dense = injected(x)[0] + print(y_ref, y_dense) + assert y_dense.equal(y_ref) + + # Check null + injected.state = "null" + y_null = injected(x)[0] + assert y_null.equal(x) + + # Check sparse + injected.state = "sparse" + y_sparse = injected(x)[0] + assert y_sparse.shape == y_dense.shape + + +def test_instrumented(roberta_tokenzier): + model = AutoModel.from_pretrained( + "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True + ) + tokenizer = AutoTokenizer.from_pretrained( + "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True + ) + model, tokenizer = roberta_tokenzier + hidden_size = model.config.hidden_size + sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) + sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=2).to(DEVICE) + model = model.to(DEVICE) + sparse_model.eval() + model.eval() + + batch = tokenizer("CNCCC") + input_ids = torch.tensor(batch["input_ids"]).to(DEVICE) + y = model(input_ids)[0] + assert y.equal(model(input_ids)[0]) + print(y) + + with sparse_model.nullcoders() as sparse_model: + y_null = sparse_model(input_ids)[0] + print(y_null) + + with sparse_model.sparse(False) as sparse_model: + y_dense = sparse_model(input_ids)[0] + print(y_dense) + assert y_dense.equal(y) + + assert False + + +def test_sparse_model(roberta_tokenzier): + roberta, tokenizer = roberta_tokenzier + sae = GatedSAE(hidden_size=roberta.config.hidden_size, expansion=2) + sparse_model = SparsifiedModel.from_huggingface(roberta, sae, layer=2).to(DEVICE) + robert = sparse_model.model.base_model + assert isinstance(robert, RobertaPreLayerNormModel) + assert isinstance(robert.encoder.layer[2], InjectedCoder) + assert robert.encoder.layer[2] is sparse_model.coders[0] + + collate = DataCollatorWithPadding(tokenizer) + batch = collate([tokenizer("CNCCC")]) + input_ids = batch["input_ids"].to(DEVICE) + attention_mask = batch["attention_mask"].to(DEVICE) + y_sparse = sparse_model.forward(input_ids, attention_mask) + robert.eval() + y_dense = roberta(input_ids, attention_mask)[0] + assert y_sparse.shape == y_dense.shape + assert y_sparse.shape == (1, 5, len(tokenizer)) + + # Check null features + with sparse_model.nullcoders() as model: + y_null = model.forward(input_ids, attention_mask) + assert y_null.shape == y_sparse.shape + + # Check dense features + with sparse_model.sparse(False) as model: + # Run model in eval model to be deterministic + model.eval() + robert.eval() + y_dense = roberta(input_ids, attention_mask)[0] + y_dense_context = model.forward(input_ids, attention_mask) + robert.train() + model.train() + assert y_dense_context.shape == y_dense.shape + e = (y_dense_context.detach() - y_dense.detach()).abs() + assert (e < 1e-6).all() + + # Check non-encoder parameters don't get gradients + assert len(list(sparse_model.sparse_parameters())) > 0 + assert len(dict(sparse_model.sparse_named_parameters())) > 0 + + # Check_gradient + y, loss = sparse_model.forward_with_loss(input_ids, attention_mask) + loss.backward() + for k, v in sparse_model.sparse_named_parameters(): + assert v.grad is not None + assert (v.grad != 0).any() + + # Check Recovered Loss + rc = sparse_model.loss_recovered( + input_ids, input_ids, attention_mask, sparse_output=y_sparse ) - assert "hidden_state" in batch - assert batch["hidden_state"].shape == (len(smiles), d_model) + print(rc) + assert isinstance(rc, torch.Tensor) and rc.shape == () + assert rc.isfinite() and not rc.isnan() + assert False + + +# def test_extract_hidden_state(): +# ckpt_path = "ibm/MoLFormer-XL-both-10pct" +# encoder = load_encoder(ckpt_path) +# d_model = 768 +# tok = load_tokenizer(ckpt_path) +# smiles = [ +# "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", +# "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", +# ] +# tokens = [tok(smi) for smi in smiles] +# input_ids = [x["input_ids"] for x in tokens] +# attention_mask = [x["attention_mask"] for x in tokens] +# batch = extract_hidden_state( +# input_ids, +# attention_mask, +# encoder=encoder, +# collate=DataCollatorWithPadding(tok), +# layer=0.5, +# ) +# assert "hidden_state" in batch +# assert batch["hidden_state"].shape == (len(smiles), d_model) + + +# def test_wrapped_sae(): +# encoder = load_encoder("ibm/MoLFormer-XL-both-10pct") +# coder = GatedSAE(hidden_size=768, expansion=4) +# model = WrappedSAE(encoder, coder, 2) From e3ec515da538d92d39983e3b6dc5e1f1f40937fe Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 7 Mar 2025 14:21:41 -0500 Subject: [PATCH 009/147] add more sae types --- electrolyte_fm/data_modules/sae_dataset.py | 21 +- electrolyte_fm/models/sae.py | 209 +- poetry.lock | 3484 ++++++++++---------- pyproject.toml | 1 - submit/sae.jsonnet | 17 +- test/test_sae.py | 194 +- 6 files changed, 2075 insertions(+), 1851 deletions(-) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 99c22078..c8b1e7b6 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -1,10 +1,7 @@ -import re from math import floor from pathlib import Path from typing import Optional, Union -import nvtx -import torch import pytorch_lightning as pl import torch from datasets import Dataset, load_dataset @@ -30,14 +27,13 @@ def extract_hidden_state( batch = batch.to(encoder.device) # Disable gradients - with nvtx.annotate("encoder"): - with torch.inference_mode(): - enc = encoder( - batch["input_ids"], - attention_mask=batch["attention_mask"], - return_dict=True, - output_hidden_states=True, - ) + with torch.inference_mode(): + enc = encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) if isinstance(layer, float): layer = floor(len(enc["hidden_states"]) * layer) @@ -51,7 +47,6 @@ def extract_hidden_state( return {"hidden_state": hidden_state} -@nvtx.annotate() def flatten_hidden_states(hs, attention_mask, device="cpu"): hs = hs.to(device) hidden_state = [] @@ -64,7 +59,6 @@ def flatten_hidden_states(hs, attention_mask, device="cpu"): return hidden_state -@nvtx.annotate() def collate_hidden_states(hidden_states): return {"hidden_state": torch.cat(hidden_states, dim=0)} @@ -200,6 +194,5 @@ def test_dataloader(self): ) @classmethod - @nvtx.annotate() def collate_fn(cls, batch): return torch.stack([x["hidden_state"] for x in batch]).detach() diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index a27a9799..41925c30 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -1,8 +1,9 @@ from math import sqrt, floor -from typing import Callable, Optional, Literal +from typing import Callable, Optional, Literal, override from contextlib import contextmanager import lightning.pytorch as pl +from numpy import minimum import torch from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from torch import nn @@ -25,12 +26,28 @@ def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: return x.detach().count_nonzero() / x.shape[:-1].numel() -def hf_cross_entropy(logits: torch.FloatTensor, target: torch.IntTensor): +def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): """F.cross_entropy but for logits of `(B, T, C)` and target of `(B, T)`""" + if isinstance(logits, tuple): + logits = logits[0] + elif not isinstance(logits, torch.Tensor): + logits = logits.logits + return F.cross_entropy(logits.view(-1, logits.shape[-1]), target.view(-1)) class AbstractSAE(nn.Module): + def __init__(self, hidden_size: int, expansion: int) -> None: + super().__init__() + self.hidden_size = hidden_size + self.num_features = hidden_size * expansion + self.num_batches_not_active = torch.zeros(self.num_features) + + def update_inactive_features(self, f: torch.Tensor): + f_act = f.sum(dim=list(range(f.ndim)[:-1])) + self.num_batches_not_active += f_act == 0 + self.num_batches_not_active[f_act > 0] = 0 + def forward(self, x: torch.Tensor): return self.decode(self.encode(x)) @@ -58,24 +75,22 @@ def __init__(self, dense_model: nn.Module, coder: AbstractSAE): self.loss = torch.tensor(0.0) self.state: InjectedCoderState = "sparse" - def forward(self, *args, **kwargs): - if self.state == "null": - return (*args,) - - output = self.dense_model(*args, **kwargs) + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.dense_model(x) if self.state == "dense": - return output + return x + + elif self.state == "null": + return torch.zeros_like(x) - assert self.state == "sparse" - hs = output[0] if self.training: - hs, loss = self.coder.forward_with_loss(hs) + x_hat, loss = self.coder.forward_with_loss(x) self.loss = loss else: - hs = self.coder.forward(hs) + x_hat = self.coder.forward(x) - return (hs, *output[1:]) + return x_hat class SparsifiedModel(nn.Module): @@ -98,8 +113,8 @@ def from_huggingface( @staticmethod def inject_sparse_coder(layers: nn.ModuleList, coder: AbstractSAE, layer: int): - m = InjectedCoder(layers[layer], coder) - layers[layer] = m + m = InjectedCoder(layers[layer].output.dense, coder) + layers[layer].output.dense = m return m def set_sparsity(self, enable: bool = True): @@ -134,13 +149,11 @@ def nullcoders(self): for coder in self.coders: coder.state = state.pop() - def forward( - self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - return self.model.forward(input_ids, attention_mask)[0] + def forward(self, *args, **kwargs): + return self.model.forward(*args, **kwargs) def forward_with_loss(self, *args, **kwargs): - y = self.model.forward(*args, **kwargs)[0] + y = self.model.forward(*args, **kwargs) device = self.coders[0].loss.device loss = torch.tensor(0.0, device=device) for coder in self.coders: @@ -161,30 +174,38 @@ def sparse_named_parameters(self): def loss_recovered( self, target: torch.Tensor, - input_ids: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, + *args, lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, sparse_output: Optional[torch.Tensor] = None, + **kwargs, ): """Compute the fraction of the loss recovered by the SAE relative to an null-embedding""" training = self.training self.eval() if sparse_output is None: with self.sparse(True) as self: - sparse_output = self.forward(input_ids, attention_mask=attention_mask) + sparse_output = self(*args, **kwargs) + assert sparse_output is not None loss_sparse = lossfn(sparse_output, target) with self.sparse(False) as self: - y_dense = self.forward(input_ids, attention_mask=attention_mask) + y_dense = self(*args, **kwargs) loss_dense = lossfn(y_dense, target) with self.nullcoders() as self: - y_null = self.forward(input_ids, attention_mask=attention_mask) + y_null = self(*args, **kwargs) loss_null = lossfn(y_null, target) self.train(training) - return 1 - (loss_sparse - loss_dense) / (loss_null - loss_dense) + print( + { + "loss_sparse": loss_sparse, + "loss_dense": loss_dense, + "loss_null": loss_null, + } + ) + return 1 - ((loss_sparse - loss_dense) / (loss_null - loss_dense)) class GatedSAE(AbstractSAE): @@ -196,7 +217,7 @@ def __init__( device=None, dtype=None, ): - super().__init__() + super().__init__(hidden_size, expansion) factory_kwargs = {"device": device, "dtype": dtype} n_features = hidden_size * expansion self.w_gate = nn.Parameter( @@ -259,16 +280,63 @@ def forward_with_loss(self, x: torch.Tensor): return x_hat, loss +def init_column_fixed_l2(tensor: torch.Tensor, l2: float = 0.1): + """init a column of a tensor to a fixed l2""" + col_l2 = l2 / tensor.norm(2, dim=0) + with torch.no_grad(): + tensor.copy_(tensor * col_l2) + + +class VanillaSAE(AbstractSAE): + def __init__( + self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01 + ) -> None: + super().__init__(hidden_size, expansion) + self.encoder = nn.Linear(hidden_size, hidden_size * expansion) + self.decoder = nn.Linear(hidden_size * expansion, hidden_size) + self.l1_coef_coef = torch.tensor(l1_coef) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + init_column_fixed_l2(self.decoder.weight) + self.decoder.bias.zero_() + self.encoder.weight.copy_(self.decoder.weight.T) + self.encoder.bias.zero_() + + def encode(self, x: torch.Tensor): + return F.relu(self.encoder(x)) + + def decode(self, features: torch.Tensor) -> torch.Tensor: + return self.decoder(features) + + def forward_with_loss(self, x: torch.Tensor): + f = self.encode(x) + x_hat = self.decode(f) + loss = ( + F.mse_loss(x_hat, x) + + self.l1_coef_coef * (self.decoder.weight.norm(2, dim=0) * f).sum() + ) + return x_hat, loss + + class TiedBiasSAE(AbstractSAE): def __init__(self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01): - super().__init__() + super().__init__(hidden_size, expansion) n_features = hidden_size * expansion self.encoder = nn.Linear(hidden_size, n_features) self.decoder = nn.Linear(n_features, hidden_size) self.l1_coef_coef = torch.tensor(l1_coef) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + self.encoder.weight.copy_(self.decoder.weight.T) + self.decoder.bias.zero_() + self.encoder.bias.zero_() def encode(self, x: torch.Tensor): - return self.encoder(x - self.decoder.bias) + return F.relu(self.encoder(x - self.decoder.bias)) def decode(self, features): return self.decoder(features) @@ -283,23 +351,63 @@ def forward_with_loss(self, x: torch.Tensor): return x_hat, loss -def pretrained_sae( - name_or_path: str, sae: str = "gated", layer: int | float = 0.5, expansion: int = 2 -): - model = AutoModel.from_pretrained(name_or_path, trust_remote_code=True) - hidden_size = model.config.hidden_size - if sae == "gated": - sae = GatedSAE(hidden_size=hidden_size, expansion=expansion) +def topk(x: torch.Tensor, k: int, dim: int = -1): + k = minimum(x.shape[dim], k) + vi = torch.topk(x, k, dim=dim, sorted=False, largest=True) + return torch.zeros_like(x).scatter(dim, vi.indices, vi.values) + + +class TopKSAE(AbstractSAE): + def __init__( + self, + hidden_size: int, + expansion: int = 4, + k: int = 10, + alpha: float = 1 / 32, + dead_threshold=1_000_000, + ) -> None: + super().__init__(hidden_size, expansion) + self.encoder = nn.Linear(hidden_size, hidden_size * expansion) + self.decoder = nn.Linear(hidden_size * expansion, hidden_size) + self.k = k + self.alpha = alpha + self.dead_threshold = dead_threshold + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + init_column_fixed_l2(self.decoder.weight) + self.decoder.bias.zero_() + self.encoder.weight.copy_(self.decoder.weight.T) + self.encoder.bias.zero_() + + def encode(self, x: torch.Tensor): + return topk(self.encoder(x), self.k, dim=-1) + + def decode(self, features: torch.Tensor) -> torch.Tensor: + return self.decoder(features) + + def encode_dead_features(self, x: torch.Tensor): + f = self.encoder(x) + dead = self.num_batches_not_active > self.dead_threshold + f = f * dead + return topk(f, self.k, dim=-1) + + def forward_with_loss(self, x: torch.Tensor): + f = self.encode(x) + self.update_inactive_features(f) + x_hat = self.decode(f) + x_dead = self.decode(self.encode_dead_features(x)) + loss = F.mse_loss(x_hat, x) + self.alpha * F.mse_loss(x_dead, x) + return x_hat, loss class LightningSAE(pl.LightningModule): def __init__( self, name_or_path: str, - sae_type: str = "gated", + sae: AbstractSAE, layer: int | float = 0.5, - l1_coef: float = 0.01, - expansion: int = 2, lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, optimizer: OptimizerCallable = torch.optim.AdamW, lr_schedule: LRSchedulerCallable | None = None, @@ -307,35 +415,12 @@ def __init__( super().__init__() encoder = load_encoder(name_or_path) - hidden_size = encoder.config.hidden_size - if sae_type == "gated": - sae = GatedSAE( - hidden_size=hidden_size, expansion=expansion, l1_coef=l1_coef - ) - else: - raise ValueError(f"Unknown sae type: {sae_type}") - self.sparse_model = SparsifiedModel.from_huggingface(encoder, sae, layer) self.optimizer = optimizer self.lr_schedule = lr_schedule self.lossfn = lossfn self.save_hyperparameters(ignore=["sparse_model"]) - # metrics = MetricCollection( - # { - # "alive_features": AliveFeatures(self.num_features), - # "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), - # "alive_features_10k": AliveFeatures( - # self.num_features, threshold=10_000 - # ), - # "max_feature_density": MaxFeatureDensity(self.num_features), - # "feature_density": FeatureDensity(self.num_features), - # } - # ) - # self.train_metrics = metrics.clone(prefix="train/") - # self.val_metrics = 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"1a206fe49bb041d58814bb779b58822e88f80cdb7c13d98b7beb274fe8876136" +content-hash = "9501ed88169a1876d2c43429e5b96bef4db7dd56e95e2ed42cee8613a65ac98a" diff --git a/pyproject.toml b/pyproject.toml index 1b9383d4..9f1d9a0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -24,7 +24,6 @@ scikit-learn= "^1.5.1" selfies= "^2.1.0" rdkit = "^2024.3.5" smirk = {git = "git@github.com:BattModels/smirk.git", rev = "8061286a219420571d606d98083dd900aaa21d02" } -nvtx = "^0.2.10" # Dependencies to support additional tokenizers smilespe = { version = "==0.0.3", optional = true } diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index 73f97820..5b59b723 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -6,13 +6,20 @@ class_path: 'electrolyte_fm.models.LightningSAE', init_args: { name_or_path: 'ibm/MoLFormer-XL-both-10pct', - sae_type: 'gated', - expansion: 4, - l1_coef: 1e-5, + sae: { + class_path: 'electrolyte_fm.models.sae.VanillaSAE', + init_args: { + hidden_size: 768, + expansion: 4, + l1_coef: 1e-5, + }, + }, optimizer: { class_path: 'torch.optim.AdamW', init_args: { - lr: 5e-4, + lr: 5, + beta: [0.9, 0.999], + weight_decay: 0, }, }, lr_schedule: { @@ -42,6 +49,8 @@ precision: '32', enable_progress_bar: false, strategy: 'auto', + gradient_clip_val: 1, + gradient_clip_algorithm: 'norm', }, }, env: { diff --git a/test/test_sae.py b/test/test_sae.py index 9facd59a..5188933a 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -1,9 +1,7 @@ -from datasets import features -from numpy import zeros_like import pytest import torch from transformers import ( - AutoModel, + AutoModelForMaskedLM, AutoTokenizer, DataCollatorWithPadding, RobertaPreLayerNormConfig, @@ -11,18 +9,15 @@ RobertaPreLayerNormModel, ) -# from electrolyte_fm.data_modules.sae_dataset import ( -# HiddenStateDataModule, -# extract_hidden_state, -# ) -from electrolyte_fm.models.model_utils import load_encoder from electrolyte_fm.models.sae import ( AbstractSAE, GatedSAE, InjectedCoder, SparsifiedModel, TiedBiasSAE, - avg_l0_norm, + VanillaSAE, + TopKSAE, + topk, ) from electrolyte_fm.utils.tokenizer import load_tokenizer @@ -30,12 +25,16 @@ def get_default_device(): if torch.cuda.is_available(): return torch.device("cuda") + elif torch.backends.mps.is_available(): + return torch.device("mps") else: return torch.device("cpu") DEVICE = get_default_device() +SAE_CLASSES = [GatedSAE, TiedBiasSAE, VanillaSAE, TopKSAE] + @pytest.fixture() @pytest.mark.cuda @@ -51,10 +50,18 @@ def roberta_tokenzier(): return RobertaPreLayerNormForMaskedLM(config).to(DEVICE), tokenizer -@pytest.mark.parametrize("sae_cls", [GatedSAE, TiedBiasSAE]) +def test_topk(): + x = torch.rand(8, 4, 20) + assert ((x > 0).sum(-1) > 5).all() + x_hat = topk(x, 5) + assert ((x_hat > 0).sum(-1) == 5).all() + + +@pytest.mark.parametrize("sae_cls", SAE_CLASSES) class TestSAE: B = 3 H = 4 + T = 8 E = 2 @classmethod @@ -63,10 +70,10 @@ def setup_class(cls): @property def feature_shape(self): - return (self.B, self.H * self.E) + return (self.B, self.T, self.H * self.E) def input(self): - return torch.rand(self.B, self.H) + return torch.rand(self.B, self.T, self.H) def test_init(self, sae_cls): sae = sae_cls(hidden_size=self.H, expansion=self.E) @@ -74,6 +81,15 @@ def test_init(self, sae_cls): assert p.isfinite().all() assert not p.isnan().any() + def test_encode(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + x = self.input() + f = sae.encode(x) + assert f.shape == self.feature_shape + x_hat = sae.decode(f) + assert x_hat.shape == x.shape + assert sae.forward(x).equal(x_hat) + def test_forward(self, sae_cls: AbstractSAE): sae = sae_cls(hidden_size=self.H, expansion=self.E) assert isinstance(sae, AbstractSAE) @@ -93,109 +109,111 @@ def test_loss(self, sae_cls): assert y.isfinite().all() -# def test_avg_l0_norm(): -# x = torch.tensor([[1, 0, 0], [0, 5, 0]]) -# assert avg_l0_norm(x) == 1 -# assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) - - -# def test_dataloader(): -# ckpt_path = "ibm/MoLFormer-XL-both-10pct" -# path = "/lustre/fs0/awadell/realspace" -# dm = HiddenStateDataModule(ckpt_path, path) -# dm.prepare_data() -# dm.setup("fit") -# for batch in dm.train_dataloader(): -# assert isinstance(batch, torch.Tensor) -# assert batch.shape == (dm.batch_size, 768) -# assert not batch.requires_grad -# break - - -def test_injected_coder(roberta_tokenzier): - roberta, _ = roberta_tokenzier - hidden_size = roberta.config.hidden_size - sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) - injected = InjectedCoder(roberta.base_model.encoder.layer[2], sae) +@pytest.mark.parametrize("sae_cls", SAE_CLASSES) +def test_injected_coder(sae_cls): + hidden_size = 64 + model = torch.nn.Linear(hidden_size, hidden_size) + sae = sae_cls(hidden_size=hidden_size, expansion=2) + injected = InjectedCoder(model, sae).to(DEVICE) injected.eval() assert isinstance(injected, InjectedCoder) dense_model = injected.dense_model assert not injected.training and not dense_model.training - x = torch.rand(1, 5, hidden_size, device=DEVICE) + x = torch.rand(2, 5, hidden_size, device=DEVICE) # Check dense - y_ref = dense_model(x)[0] + y_ref = dense_model(x) injected.state = "dense" - y_dense = injected(x)[0] - print(y_ref, y_dense) + y_dense = injected(x) + assert y_dense.shape == y_ref.shape assert y_dense.equal(y_ref) # Check null injected.state = "null" - y_null = injected(x)[0] - assert y_null.equal(x) + y_null = injected(x) + assert y_null.shape == y_ref.shape + assert y_null.equal(torch.zeros_like(y_dense)) # Check sparse injected.state = "sparse" - y_sparse = injected(x)[0] + y_sparse = injected(x) assert y_sparse.shape == y_dense.shape -def test_instrumented(roberta_tokenzier): - model = AutoModel.from_pretrained( +def test_instrumented(): + model = AutoModelForMaskedLM.from_pretrained( "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained( "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True ) - model, tokenizer = roberta_tokenzier hidden_size = model.config.hidden_size sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) - sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=2).to(DEVICE) + sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=0).to(DEVICE) model = model.to(DEVICE) sparse_model.eval() - model.eval() - - batch = tokenizer("CNCCC") - input_ids = torch.tensor(batch["input_ids"]).to(DEVICE) - y = model(input_ids)[0] - assert y.equal(model(input_ids)[0]) - print(y) + assert not model.training + assert not sae.training + assert not model.training + batch = tokenizer(["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"]) + collate = DataCollatorWithPadding(tokenizer) + batch = collate(batch) + batch = { + k: v.to(DEVICE) + for k, v in batch.items() + if k in ["input_ids", "attention_mask"] + } + batch["return_dict"] = True + y = model(**batch).logits + + assert all([coder.state == "sparse" for coder in sparse_model.coders]) with sparse_model.nullcoders() as sparse_model: - y_null = sparse_model(input_ids)[0] - print(y_null) + assert all([coder.state == "null" for coder in sparse_model.coders]) + y_null = sparse_model(**batch).logits + assert y_null.shape == y.shape + assert y_null.device == y.device + assert y_null.dtype == y.dtype with sparse_model.sparse(False) as sparse_model: - y_dense = sparse_model(input_ids)[0] - print(y_dense) + assert all([coder.state == "dense" for coder in sparse_model.coders]) + y_dense = sparse_model(**batch).logits + assert y_dense.shape == y.shape + assert y_dense.device == y.device + assert y_dense.dtype == y.dtype assert y_dense.equal(y) - assert False + rc = sparse_model.loss_recovered(target=batch["input_ids"], **batch) + assert isinstance(rc, torch.Tensor) and rc.shape == () + assert rc.isfinite() and not rc.isnan() + assert rc <= 1 def test_sparse_model(roberta_tokenzier): roberta, tokenizer = roberta_tokenzier - sae = GatedSAE(hidden_size=roberta.config.hidden_size, expansion=2) + sae = TiedBiasSAE(hidden_size=roberta.config.hidden_size, expansion=2) sparse_model = SparsifiedModel.from_huggingface(roberta, sae, layer=2).to(DEVICE) robert = sparse_model.model.base_model assert isinstance(robert, RobertaPreLayerNormModel) - assert isinstance(robert.encoder.layer[2], InjectedCoder) - assert robert.encoder.layer[2] is sparse_model.coders[0] + assert isinstance(robert.encoder.layer[2].output.dense, InjectedCoder) + assert robert.encoder.layer[2].output.dense is sparse_model.coders[0] collate = DataCollatorWithPadding(tokenizer) batch = collate([tokenizer("CNCCC")]) - input_ids = batch["input_ids"].to(DEVICE) - attention_mask = batch["attention_mask"].to(DEVICE) - y_sparse = sparse_model.forward(input_ids, attention_mask) + batch = { + "input_ids": batch["input_ids"].to(DEVICE), + "attention_mask": batch["attention_mask"].to(DEVICE), + "return_dict": True, + } + y_sparse = sparse_model(**batch).logits robert.eval() - y_dense = roberta(input_ids, attention_mask)[0] + y_dense = roberta(**batch).logits assert y_sparse.shape == y_dense.shape assert y_sparse.shape == (1, 5, len(tokenizer)) # Check null features with sparse_model.nullcoders() as model: - y_null = model.forward(input_ids, attention_mask) + y_null = model(**batch).logits assert y_null.shape == y_sparse.shape # Check dense features @@ -203,8 +221,8 @@ def test_sparse_model(roberta_tokenzier): # Run model in eval model to be deterministic model.eval() robert.eval() - y_dense = roberta(input_ids, attention_mask)[0] - y_dense_context = model.forward(input_ids, attention_mask) + y_dense = roberta(**batch).logits + y_dense_context = model(**batch).logits robert.train() model.train() assert y_dense_context.shape == y_dense.shape @@ -216,46 +234,16 @@ def test_sparse_model(roberta_tokenzier): assert len(dict(sparse_model.sparse_named_parameters())) > 0 # Check_gradient - y, loss = sparse_model.forward_with_loss(input_ids, attention_mask) + out, loss = sparse_model.forward_with_loss(**batch) + # assert out.logits.equal(y_sparse) loss.backward() - for k, v in sparse_model.sparse_named_parameters(): + for _, v in sparse_model.sparse_named_parameters(): assert v.grad is not None assert (v.grad != 0).any() # Check Recovered Loss rc = sparse_model.loss_recovered( - input_ids, input_ids, attention_mask, sparse_output=y_sparse + **batch, target=batch["input_ids"], sparse_output=y_sparse ) - print(rc) assert isinstance(rc, torch.Tensor) and rc.shape == () assert rc.isfinite() and not rc.isnan() - assert False - - -# def test_extract_hidden_state(): -# ckpt_path = "ibm/MoLFormer-XL-both-10pct" -# encoder = load_encoder(ckpt_path) -# d_model = 768 -# tok = load_tokenizer(ckpt_path) -# smiles = [ -# "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", -# "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", -# ] -# tokens = [tok(smi) for smi in smiles] -# input_ids = [x["input_ids"] for x in tokens] -# attention_mask = [x["attention_mask"] for x in tokens] -# batch = extract_hidden_state( -# input_ids, -# attention_mask, -# encoder=encoder, -# collate=DataCollatorWithPadding(tok), -# layer=0.5, -# ) -# assert "hidden_state" in batch -# assert batch["hidden_state"].shape == (len(smiles), d_model) - - -# def test_wrapped_sae(): -# encoder = load_encoder("ibm/MoLFormer-XL-both-10pct") -# coder = GatedSAE(hidden_size=768, expansion=4) -# model = WrappedSAE(encoder, coder, 2) From 9952cbabce5ef6f04c92ff0701d2410ddb3a39ea Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 8 Mar 2025 18:52:58 -0500 Subject: [PATCH 010/147] first pass at linear probe --- .../data_modules/lipinski_dataset.py | 95 +++++++++++++ .../property_prediction_dataset.py | 8 +- electrolyte_fm/data_modules/utils.py | 10 ++ electrolyte_fm/models/linear_probe.py | 133 ++++++++++++++++++ electrolyte_fm/models/sae.py | 6 +- test/test_sae.py | 47 ++++++- 6 files changed, 292 insertions(+), 7 deletions(-) create mode 100644 electrolyte_fm/data_modules/lipinski_dataset.py create mode 100644 electrolyte_fm/models/linear_probe.py diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py new file mode 100644 index 00000000..c0e53177 --- /dev/null +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -0,0 +1,95 @@ +from pathlib import Path + +import torch +from datasets import load_dataset +from rdkit.Chem import Lipinski, MolFromSmiles +from rdkit.Chem.Crippen import MolLogP +from rdkit.Chem.Descriptors import ExactMolWt + +from .molnet_dataset import _URLS +from .property_prediction_dataset import PropertyPredictionDataModule +from .utils import AbstractDataset, MolEncoding, filter_invalid_smi +from .molnet_dataset import train_val_test_split + + +class LipinskiDataModule(PropertyPredictionDataModule): + def __init__(self, name_or_path: str, **kwargs): + self.name_or_path = name_or_path + if not Path(name_or_path).exists(): + # Set default smi_column + assert name_or_path in _URLS.keys() + self.name_or_path = _URLS[name_or_path] + kwargs["smi_column"] = ( + kwargs.get("smi_column", None) or "smiles" + if name_or_path != "bace" + else "mol" + ) + assert isinstance(kwargs["smi_column"], str) + kwargs["additonal_columns"] = [ + "probe_target", + *kwargs.get("additonal_columns", []), + ] + super().__init__(**kwargs) + assert self.encoding != MolEncoding.SELFIES + + def _get_dataset(self) -> AbstractDataset: + # Load the dataset + ds: AbstractDataset = load_dataset( + "csv", + name=self.name, + data_files=[self.name_or_path], + split="train", + keep_in_memory=False, + save_infos=False, + ) # type: ignore + + ds = ds.select_columns(self.smi_column) + ds = filter_invalid_smi(ds, self.smi_column) + + ds = ds.map( + lipinki_rule_of_five, + batched=False, + fn_kwargs={"smi_column": self.smi_column}, + ) + return train_val_test_split(ds) + + def collate_fn(self, batch): + output = super().collate_fn(batch) + output = self.token_collator(batch) + output["probe_target"] = torch.stack( + [torch.tensor(x["probe_target"]) for x in batch] + ) + + return output + + +def lipinki_rule_of_five(x: dict, smi_column: str) -> dict: + smi = x[smi_column] + mol = MolFromSmiles(smi) + assert mol is not None, "invalid smi: %s" % smi + x["num_h_bond_donors"] = Lipinski.NumHDonors(mol) + x["lipinki_h_donor"] = x["num_h_bond_donors"] <= 5 + x["num_h_bond_acceptors"] = Lipinski.NumHAcceptors(mol) + x["lipinki_h_acceptor"] = x["num_h_bond_acceptors"] <= 10 + x["molecular_weight"] = ExactMolWt(mol) + x["lipinki_mwt"] = x["molecular_weight"] <= 500 + x["log_p"] = MolLogP(mol) + x["lipinki_log_p"] = x["log_p"] <= 5 + x["lipinki"] = all(v for k, v in x.items() if k.startswith("lipinki")) + x["probe_target"] = [v for k, v in x.items() if k.startswith("lipinki")] + return x + + +if __name__ == "__main__": + ds = LipinskiDataModule(name_or_path="hiv") + ds = ds.dataset + df = ds.to_pandas() + df.to_csv("lipo.csv") + cols = [ + "lipinki", + "lipinki_h_donor", + "lipinki_h_acceptor", + "lipinki_mwt", + "lipinki_log_p", + ] + print({k: df[k].value_counts() for k in cols}) diff --git a/electrolyte_fm/data_modules/property_prediction_dataset.py b/electrolyte_fm/data_modules/property_prediction_dataset.py index e04499b8..e5fe2bd9 100644 --- a/electrolyte_fm/data_modules/property_prediction_dataset.py +++ b/electrolyte_fm/data_modules/property_prediction_dataset.py @@ -28,6 +28,7 @@ def __init__( target_columns: Optional[List[str]] = None, val_batch_size: Optional[int] = None, encoding: str = MolEncoding.SMILES.value, + additonal_columns: Optional[List[str]] = None, include_encoding: bool = False, ): super().__init__() @@ -38,6 +39,7 @@ def __init__( self.smi_column = smi_column self.target_columns = target_columns + self.additonal_columns = additonal_columns or [] self.encoding = MolEncoding(encoding) self.include_encoding = include_encoding @@ -77,9 +79,11 @@ def setup(self, stage: str) -> None: # Save training dataset for target transformations self.target_dataset = ds["train"].select_columns(["target", "target_mask"]) - ds = ds.select_columns([self.smi_column, "target", "target_mask"]) + ds = ds.select_columns( + [self.smi_column, "target", "target_mask", *self.additonal_columns] + ) else: - ds = ds.select_columns([self.smi_column]) + ds = ds.select_columns([self.smi_column, *self.additonal_columns]) # Tokenize ds = ds.map( diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py index 2addae4e..16792760 100644 --- a/electrolyte_fm/data_modules/utils.py +++ b/electrolyte_fm/data_modules/utils.py @@ -33,6 +33,16 @@ class MolEncoding(Enum): KEKULE = "smiles-kekule" +def filter_invalid_smi( + ds: AbstractDataset, input_column: str, **kwargs +) -> AbstractDataset: + def is_valid(x: dict): + mol = Chem.MolFromSmiles(x[input_column]) + return mol is not None + + return ds.filter(is_valid, batched=False, **kwargs) + + def encode_molecules( ds: AbstractDataset, input_column: str, diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py new file mode 100644 index 00000000..37c31706 --- /dev/null +++ b/electrolyte_fm/models/linear_probe.py @@ -0,0 +1,133 @@ +from typing import Iterable +from collections import defaultdict +import torch +from torch import nn +from torch.nn import functional as F +import lightning.pytorch as pl +from lightning.pytorch.cli import OptimizerCallable, LRSchedulerCallable +from fnmatch import fnmatchcase + + +class LightningProbe(pl.LightningModule): + def __init__( + self, + model: nn.Module, + probes: dict[str, nn.Module], + optimizer: OptimizerCallable = torch.optim.AdamW, + lr_schedule: LRSchedulerCallable | None = None, + ): + super().__init__() + + self.model = model + self.optimizer = optimizer + self.lr_schedule = lr_schedule + self.hookpoints = self._identify_hookpoints(probes.keys()) + self._probes = nn.ModuleList(probes.values()) + self._probe_points = list(probes.keys()) + self.activations = dict() + + @property + def probes(self): + yield from zip(self._probe_points, self._probes) + + def _identify_hookpoints(self, hooks: Iterable[str]): + hooks: set[str] = set() + for name, _ in self.model.named_modules(): + if any((fnmatchcase(name, hook) for hook in hooks)): + hooks.add(name) + + hooks = list(hooks) + hooks.sort() + return hooks + + def _install_hooks(self, results: dict[str, torch.Tensor]): + for name in self.hookpoints: + + def hook(module: nn.Module, input, output: torch.Tensor): + assert isinstance(output, torch.Tensor) + results[name] = output + return None + + self.model.get_submodule(name).register_forward_hook(hook) + + def on_train_start(self): + self.model.to(self.device) + self._install_hooks(self.activations) + + def forward(self, batch: dict): + self.model.eval() + with torch.no_grad(): + self.model(batch) + out = defaultdict(dict) + for name, probe in self.probs.items(): + for hook in self.hookpoints: + if fnmatchcase(name, hook): + act = self.activations[hook] + out[name][hook] = probe(act) + + return out + + def forward_fit(self, batch: dict): + self.model.eval() + target = batch.pop("probe_target") + self.model(**batch) + loss = torch.tensor(0.0) + out = {} + for name, probe in self.probs.items(): + probe_loss = torch.tensor(0.0) + for hook in self.hookpoints: + if fnmatchcase(name, hook): + act = self.activations[hook] + y = probe(act) + probe_loss += F.binary_cross_entropy_with_logits(y, target) + + out[f"{name}-probe-loss"] = probe_loss + loss += probe_loss + + out["loss"] = loss + return out + + def training_step(self, batch): + out = self.forward_fit(batch) + self.log_dict({f"train/{k}": v for k, v in out.items()}) + return out["loss"] + + def validation_step(self, batch): + out = self.forward_fit(batch) + self.log_dict( + {f"train/{k}": v for k, v in out.items()}, on_step=False, on_epoch=True + ) + return out["loss"] + + def configure_optimizers(self): + optimizer = self.optimizer(self._probes.parameters()) + if schedule := self.lr_schedule: + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, + } + return optimizer + + +if __name__ == "__main__": + from transformers import AutoModelForMaskedLM + from ..data_modules.lipinski_dataset import LipinskiDataModule + from lightning.pytorch import Trainer + + name_or_path = "ibm/MoLFormer-XL-both-10pct" + model = AutoModelForMaskedLM.from_pretrained(name_or_path, trust_remote_code=True) + dm = LipinskiDataModule( + name_or_path="hiv", + tokenizer=name_or_path, + encoding="smiles-canonical", + num_workers=4, + ) + hidden_size = model.config.hidden_size + probes = { + f"*.encoder.layer.{layer}.output": nn.Linear(hidden_size, 5) + for layer in range(hidden_size) + } + lm = LightningProbe(model, probes) + + trainer = Trainer() + trainer.fit(lm, datamodule=dm) diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 41925c30..6fd691e1 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -33,7 +33,11 @@ def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): elif not isinstance(logits, torch.Tensor): logits = logits.logits - return F.cross_entropy(logits.view(-1, logits.shape[-1]), target.view(-1)) + return F.cross_entropy( + logits.view(-1, logits.shape[-1]), + target.view(-1), + ignore_index=-100, + ) class AbstractSAE(nn.Module): diff --git a/test/test_sae.py b/test/test_sae.py index 5188933a..ba525df0 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -1,8 +1,11 @@ import pytest import torch +import torch.nn.functional as F from transformers import ( + AutoModel, AutoModelForMaskedLM, AutoTokenizer, + DataCollatorForLanguageModeling, DataCollatorWithPadding, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM, @@ -15,9 +18,10 @@ InjectedCoder, SparsifiedModel, TiedBiasSAE, - VanillaSAE, TopKSAE, + VanillaSAE, topk, + hf_cross_entropy, ) from electrolyte_fm.utils.tokenizer import load_tokenizer @@ -57,6 +61,37 @@ def test_topk(): assert ((x_hat > 0).sum(-1) == 5).all() +@pytest.mark.gpu +def test_hf_crossentropy(): + name = "ibm/MoLFormer-XL-both-10pct" + model = AutoModelForMaskedLM.from_pretrained(name, trust_remote_code=True).to( + DEVICE + ) + tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True) + batch = [ + tok(smi) + for smi in [ + "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", + "CN3[C@H]1CC[C@@H]3C[C@@H](C1)OC(=O)C(CO)c2cc", + ] + ] + collate = DataCollatorForLanguageModeling(tok, mlm=False) + # collate = DataCollatorWithPadding(tok) + batch = collate(batch) + batch = {k: v.to(DEVICE) for k, v in batch.items()} + model.eval() + out = model( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + ) + logits = out.logits + vocab_size = model.config.vocab_size + loss_ref = F.cross_entropy(logits.view(-1, vocab_size), batch["labels"].view(-1)) + loss = hf_cross_entropy(logits, batch["labels"]) + assert loss == loss_ref + + @pytest.mark.parametrize("sae_cls", SAE_CLASSES) class TestSAE: B = 3 @@ -156,13 +191,16 @@ def test_instrumented(): assert not sae.training assert not model.training - batch = tokenizer(["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"]) - collate = DataCollatorWithPadding(tokenizer) + batch = [ + tokenizer(smi) + for smi in ["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"] + ] + collate = DataCollatorForLanguageModeling(tokenizer, mlm=False) batch = collate(batch) batch = { k: v.to(DEVICE) for k, v in batch.items() - if k in ["input_ids", "attention_mask"] + if k in ["input_ids", "attention_mask", "labels"] } batch["return_dict"] = True y = model(**batch).logits @@ -175,6 +213,7 @@ def test_instrumented(): assert y_null.device == y.device assert y_null.dtype == y.dtype + assert all([not coder.dense.training for coder in sparse_model.coders]) with sparse_model.sparse(False) as sparse_model: assert all([coder.state == "dense" for coder in sparse_model.coders]) y_dense = sparse_model(**batch).logits From 2c7838830fffc013bfdfb44d0bf9878934150593 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 10 Mar 2025 07:20:48 -0700 Subject: [PATCH 011/147] initial run over linear probes --- electrolyte_fm/data_modules/feature_tagger.py | 3 +- .../data_modules/lipinski_dataset.py | 2 +- .../property_prediction_dataset.py | 4 +- electrolyte_fm/models/linear_probe.py | 247 ++++++++++++++---- electrolyte_fm/models/prod_finetune.py | 14 +- electrolyte_fm/models/sae.py | 6 +- electrolyte_fm/utils/ckpt.py | 54 ++-- opt/linear-probes/submit_linear_probes.py | 143 ++++++++++ opt/package/__main__.py | 3 +- submit/dgx.j2 | 17 +- 10 files changed, 390 insertions(+), 103 deletions(-) create mode 100644 opt/linear-probes/submit_linear_probes.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index fe08864b..9d4cede3 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -120,7 +120,8 @@ def flatten(*iterables) -> list: "n", "o", "p", - "s" "se", + "s", + "se", "as", ], } diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index c0e53177..f007d518 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -27,7 +27,7 @@ def __init__(self, name_or_path: str, **kwargs): assert isinstance(kwargs["smi_column"], str) kwargs["additonal_columns"] = [ "probe_target", - *kwargs.get("additonal_columns", []), + *(kwargs.get("additonal_columns", None) or []), ] super().__init__(**kwargs) assert self.encoding != MolEncoding.SELFIES diff --git a/electrolyte_fm/data_modules/property_prediction_dataset.py b/electrolyte_fm/data_modules/property_prediction_dataset.py index e5fe2bd9..7f7240a1 100644 --- a/electrolyte_fm/data_modules/property_prediction_dataset.py +++ b/electrolyte_fm/data_modules/property_prediction_dataset.py @@ -33,7 +33,9 @@ def __init__( ): super().__init__() - self.tokenizer = load_tokenizer(tokenizer) + self.tokenizer = ( + load_tokenizer(tokenizer) if isinstance(tokenizer, str) else tokenizer + ) self.token_collator = DataCollatorWithPadding(self.tokenizer) self.vocab_size = len(self.tokenizer) diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 37c31706..0903e536 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -1,18 +1,35 @@ -from typing import Iterable +import logging +from typing import Iterable, Dict, Any, Callable from collections import defaultdict +from jsonargparse import lazy_instance import torch from torch import nn from torch.nn import functional as F import lightning.pytorch as pl from lightning.pytorch.cli import OptimizerCallable, LRSchedulerCallable from fnmatch import fnmatchcase +from torchmetrics import AUROC + + +def per_layer_probe( + hidden_size: int, features: int, n_layers: int, location: str = "output" +) -> dict[str, nn.Module]: + probes = {} + template: str = "*.encoder.layer.{layer}.{location}" + for layer in range(n_layers): + hook_name = template.format(layer=layer, location=location) + probes[hook_name] = nn.Linear(hidden_size, features) + return probes + + +ProbeConfigCallable = Callable[Any, dict[str, nn.Module]] class LightningProbe(pl.LightningModule): def __init__( self, model: nn.Module, - probes: dict[str, nn.Module], + probes: ProbeConfigCallable = per_layer_probe, optimizer: OptimizerCallable = torch.optim.AdamW, lr_schedule: LRSchedulerCallable | None = None, ): @@ -21,84 +38,135 @@ def __init__( self.model = model self.optimizer = optimizer self.lr_schedule = lr_schedule + self.save_hyperparameters() self.hookpoints = self._identify_hookpoints(probes.keys()) + + # Setup probes self._probes = nn.ModuleList(probes.values()) self._probe_points = list(probes.keys()) - self.activations = dict() + self._hooks_installed = dict() + self._activations = {} + + # Add metrics + self.val_metrics = nn.ModuleList( + AUROC(task="binary", thresholds=100) for probe in self._probe_points + ) + + def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + state_dict = checkpoint["state_dict"] + # Remove hooks from model (not picklable)) + for hook in state_dict.pop("_hooks_installed", {}).values(): + hook.remove() + state_dict["_hooks_installed"] = {} - @property - def probes(self): + # Don't save activations + state_dict["_activations"] = {} + + def named_probes(self): yield from zip(self._probe_points, self._probes) - def _identify_hookpoints(self, hooks: Iterable[str]): + def _identify_hookpoints(self, probe_locs: Iterable[str]): hooks: set[str] = set() for name, _ in self.model.named_modules(): - if any((fnmatchcase(name, hook) for hook in hooks)): - hooks.add(name) + for loc in probe_locs: + if fnmatchcase(name, loc): + hooks.add(name) hooks = list(hooks) hooks.sort() return hooks - def _install_hooks(self, results: dict[str, torch.Tensor]): + def _install_hooks(self): for name in self.hookpoints: + if name not in self._hooks_installed: + hook = self._create_act_hook(name, self._activations) + self._hooks_installed[name] = self.model.get_submodule( + name + ).register_forward_hook(hook) + logging.debug(f"Installed hook for %s", name) - def hook(module: nn.Module, input, output: torch.Tensor): - assert isinstance(output, torch.Tensor) - results[name] = output - return None + return self._activations - self.model.get_submodule(name).register_forward_hook(hook) + @staticmethod + def _create_act_hook(name: str, results: dict): + def hook(module: nn.Module, input, output: torch.Tensor): + assert isinstance(output, torch.Tensor) + results[name] = output[:, 0, :].detach() + return None - def on_train_start(self): - self.model.to(self.device) - self._install_hooks(self.activations) + return hook - def forward(self, batch: dict): + def forward(self, *args, **kwargs): self.model.eval() + activations = self._install_hooks() with torch.no_grad(): - self.model(batch) + self.model(*args, **kwargs) out = defaultdict(dict) - for name, probe in self.probs.items(): + for name, probe in self.named_probes(): for hook in self.hookpoints: - if fnmatchcase(name, hook): - act = self.activations[hook] + if fnmatchcase(hook, name): + act = activations[hook] out[name][hook] = probe(act) return out def forward_fit(self, batch: dict): self.model.eval() + activations = self._install_hooks() target = batch.pop("probe_target") self.model(**batch) - loss = torch.tensor(0.0) + loss = [] out = {} - for name, probe in self.probs.items(): - probe_loss = torch.tensor(0.0) + for name, probe in self.named_probes(): + probe_loss = [] for hook in self.hookpoints: - if fnmatchcase(name, hook): - act = self.activations[hook] + if fnmatchcase(hook, name): + act = activations[hook].detach() y = probe(act) - probe_loss += F.binary_cross_entropy_with_logits(y, target) + probe_loss.append( + F.binary_cross_entropy_with_logits(y, target.to(dtype=y.dtype)) + ) - out[f"{name}-probe-loss"] = probe_loss - loss += probe_loss + out[f"{name}-probe-loss"] = sum(probe_loss) + loss.append(sum(probe_loss)) - out["loss"] = loss + out["loss"] = sum(loss) / len(loss) return out def training_step(self, batch): - out = self.forward_fit(batch) - self.log_dict({f"train/{k}": v for k, v in out.items()}) - return out["loss"] - - def validation_step(self, batch): out = self.forward_fit(batch) self.log_dict( {f"train/{k}": v for k, v in out.items()}, on_step=False, on_epoch=True ) return out["loss"] + def validation_step(self, batch): + target = batch.pop("probe_target") + out = self.forward(**batch) + metrics = {} + loss = [] + for probe, probe_metrics in zip(self._probe_points, self.val_metrics): + probe_pred = [] + for hook in self.hookpoints: + if fnmatchcase(hook, probe): + probe_pred.append(out[probe][hook]) + + probe_pred = torch.stack(probe_pred) + probe_pred = probe_pred.view(-1, probe_pred.shape[-1]) + probe_loss = F.binary_cross_entropy_with_logits( + probe_pred, + target.to(dtype=probe_pred.dtype), + ) + probe_metrics.update(probe_pred, target) + metrics[f"val/{probe}-loss"] = probe_loss + metrics[f"val/{probe}-auroc"] = probe_metrics + loss.append(probe_loss) + + metrics["val/loss"] = sum(loss) / len(loss) + self.log_dict(metrics, on_step=False, on_epoch=True) + + return metrics["val/loss"] + def configure_optimizers(self): optimizer = self.optimizer(self._probes.parameters()) if schedule := self.lr_schedule: @@ -110,24 +178,91 @@ def configure_optimizers(self): if __name__ == "__main__": - from transformers import AutoModelForMaskedLM - from ..data_modules.lipinski_dataset import LipinskiDataModule - from lightning.pytorch import Trainer - - name_or_path = "ibm/MoLFormer-XL-both-10pct" - model = AutoModelForMaskedLM.from_pretrained(name_or_path, trust_remote_code=True) - dm = LipinskiDataModule( - name_or_path="hiv", - tokenizer=name_or_path, - encoding="smiles-canonical", - num_workers=4, + import smirk + import json + from jsonargparse import lazy_instance + from lightning.pytorch.cli import ( + LightningCLI, + LightningArgumentParser, + _InstantiatorFn, + _get_module_type, ) - hidden_size = model.config.hidden_size - probes = { - f"*.encoder.layer.{layer}.output": nn.Linear(hidden_size, 5) - for layer in range(hidden_size) - } - lm = LightningProbe(model, probes) - - trainer = Trainer() - trainer.fit(lm, datamodule=dm) + from lightning.pytorch.loggers import WandbLogger + from lightning.pytorch.callbacks import ModelCheckpoint + + logging.basicConfig(level=logging.INFO) + + def mlm_from_pretrained(name_or_path: str) -> nn.Module: + from transformers import AutoModelForMaskedLM + + return AutoModelForMaskedLM.from_pretrained( + name_or_path, trust_remote_code=True + ) + + class MyLightningCLI(LightningCLI): + def add_arguments_to_parser(self, parser: LightningArgumentParser): + parser.add_argument( + "--tags", + type=list, + help="Tags for WandB logger", + default=[], + ) + parser.link_arguments("tags", "trainer.logger.init_args.tags") + + def _add_instantiators(self) -> None: + self.config_dump = json.loads( + self.parser.dump( + self.config, skip_link_targets=False, skip_none=False, format="json" + ) + ) + if "subcommand" in self.config: + self.config_dump = self.config_dump[self.config.subcommand] + + self.parser.add_instantiator( + _InstantiatorFn(cli=self, key="model"), + _get_module_type(self._model_class), + subclasses=self.subclass_mode_model, + ) + self.parser.add_instantiator( + _InstantiatorFn(cli=self, key="data"), + _get_module_type(self._datamodule_class), + subclasses=self.subclass_mode_data, + ) + + cli = MyLightningCLI( + LightningProbe, + save_config_callback=None, + seed_everything_default=42, + trainer_defaults={ + "logger": lazy_instance( + WandbLogger, project="linear-probes", save_code=True + ), + "max_epochs": 1000, + }, + parser_kwargs={"parser_mode": "jsonnet"}, + run=False, + ) + trainer: pl.Trainer = cli.trainer + model: LightningProbe = cli.model + ckpts = [] + for probe, _ in model.named_probes(): + probe_name = probe.replace(".", "-").replace("*", "star") + monitor = f"val/{probe}-loss" + auroc = f"val/{probe}-auroc" + ckpts.append( + ModelCheckpoint( + monitor=monitor, + save_top_k=1, + save_weights_only=True, + auto_insert_metric_name=False, + filename=probe_name + + "--epoch-{epoch}--loss-{" + + monitor + + ":.3f}--auroc-{" + + auroc + + ":.3f}", + ) + ) + trainer.callbacks.extend(ckpts) + + trainer.fit(model, cli.datamodule) diff --git a/electrolyte_fm/models/prod_finetune.py b/electrolyte_fm/models/prod_finetune.py index 32c969f7..9be1cb5b 100644 --- a/electrolyte_fm/models/prod_finetune.py +++ b/electrolyte_fm/models/prod_finetune.py @@ -35,8 +35,8 @@ def __init__(self, encoder, task_network, transform, channels=None): self.transform = transform self.channels = channels - def forward(self, input): - hs = self.encoder(input["input_ids"]).last_hidden_state + def forward(self, **kwargs): + hs = self.encoder(**kwargs).last_hidden_state y = self.task_network(hs) return self.transform.forward(y) @@ -62,14 +62,14 @@ def predict(self, smi: list[str], tokenizer): batch = tokenizer(smi) collate_fn = DataCollatorWithPadding(tokenizer) batch = collate_fn(batch).to(self.encoder.device) - out = self(batch) + out = self(**batch) if self.channels is None: return out return {k: out[:, idx].cpu().detach() for idx, k in enumerate(self.channels)} @classmethod - def from_pretrained(self, save_directory: str): - config = json.loads(Path(save_directory, "config.json").read_text()) + def from_pretrained(cls, name_or_path: str) -> "MISTFinetuned": + config = json.loads(Path(name_or_path, "config.json").read_text()) encoder_config = AutoConfig.for_model( config["encoder"]["model_type"] ).from_dict(config["encoder"]) @@ -80,8 +80,8 @@ def from_pretrained(self, save_directory: str): ) # Instantiate model - model = MISTFinetuned(encoder, task_network, transform, config["channels"]) - load_model(model, save_directory) + model = cls(encoder, task_network, transform, config["channels"]) + load_model(model, name_or_path) return model diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 6fd691e1..6554d930 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -1,5 +1,5 @@ from math import sqrt, floor -from typing import Callable, Optional, Literal, override +from typing import Callable, Optional, Literal from contextlib import contextmanager import lightning.pytorch as pl @@ -31,7 +31,7 @@ def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): if isinstance(logits, tuple): logits = logits[0] elif not isinstance(logits, torch.Tensor): - logits = logits.logits + logits = logits.last_hidden_state return F.cross_entropy( logits.view(-1, logits.shape[-1]), @@ -456,7 +456,7 @@ def stage_step(self, stage: str, batch, recovered_loss: bool = False): logdict[f"{stage}/recovered_loss"] = self.sparse_model.loss_recovered( target, input_ids, - attention_mask, + attention_mask=attention_mask, lossfn=self.lossfn, sparse_output=y, ) diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index 29fdf71d..8a54cced 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -1,9 +1,11 @@ +import logging import importlib import json import os -from pathlib import Path from typing import Optional +from pathlib import Path +import torch from jsonargparse import Namespace from lightning.pytorch import Callback, LightningModule, Trainer from lightning.pytorch.cli import LightningArgumentParser @@ -108,8 +110,7 @@ def instantiate(config_path: Path) -> LightningModule: config["lightning_module"], class_path=config.get("class_path", None), ) - if "vocab_size" in config["datamodule"]: - model_config["vocab_size"] = config["datamodule"]["vocab_size"] + model_config["vocab_size"] = config["datamodule"]["vocab_size"] else: cls_name, model_config = norm_class_config(config) @@ -137,20 +138,36 @@ def load(checkpoint_dir: str | Path, config_path=None) -> LightningModule: config_path = config_path or checkpoint_dir.parent.parent.joinpath( "model_hparams.json" ) - assert ( - checkpoint_dir.is_dir() - ), f"Missing deepspeed checkpoint director {checkpoint_dir}" + assert checkpoint_dir.exists(), ( + f"Missing deepspeed checkpoint directory: {checkpoint_dir}" + ) assert config_path.is_file(), f"Missing model config file {config_path}" model = SaveConfigWithCkpts.instantiate(config_path) + if checkpoint_dir.is_file(): + state = torch.load(checkpoint_dir) + model.load_state_dict(state["state_dict"], strict=True, assign=True) + return model + # Load model weights from the checkpoint - from deepspeed.utils.zero_to_fp32 import ( - get_fp32_state_dict_from_zero_checkpoint, - ) + try: + from deepspeed.utils.zero_to_fp32 import ( + get_fp32_state_dict_from_zero_checkpoint, + ) + + state = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) + model.load_state_dict(state, strict=False, assign=True) + except FileNotFoundError: + logging.error( + "failed to load checkpoint %s, trying to load rank 0 model states", + checkpoint_dir, + ) + file = Path(checkpoint_dir, "checkpoint", "mp_rank_00_model_states.pt") + state = torch.load(file) + logging.info("loaded %s", file) + model.load_state_dict(state["module"], strict=True, assign=True) - state = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) - model.load_state_dict(state, strict=False, assign=True) return model @@ -166,21 +183,6 @@ def get_ckpt_tokenizer(path: str | Path) -> str: return config["data"]["init_args"]["tokenizer"] -def get_hidden_size(name_or_path: str) -> int: - config_path = Path(name_or_path).parent.parent.joinpath("config.json") - if Path(config_path).is_file(): - with open(config_path, "r") as fid: - config = json.load(fid) - return config["model"]["init_args"]["hidden_size"] - - # Special Case models - elif name_or_path.startswith("ibm/MoLFormer-XL-both-10pct"): - return 768 - - else: - raise ValueError(f"Could not find hidden size for {name_or_path}") - - def norm_class_config(config: dict, class_path: Optional[str] = None) -> (str, dict): """Parse a dictionary of hparams for a class name and init args""" init_args = dict() diff --git a/opt/linear-probes/submit_linear_probes.py b/opt/linear-probes/submit_linear_probes.py new file mode 100644 index 00000000..21129791 --- /dev/null +++ b/opt/linear-probes/submit_linear_probes.py @@ -0,0 +1,143 @@ +from pathlib import Path +from copy import deepcopy +import json +import _jsonnet as jsonnet +import subprocess +from submit.submit import render +from submit.utils import dict_product +from transformers import AutoConfig + +# jsonnet +config = """ +function( + encoder_class, + encoder_path, + location, + dataset, + tokenizer="smirk", + encoding="smiles-kekule", + batch_size, + hidden_size, + n_layers, +) + +{ + nodes: 1, + gpus_per_node: 1, + container: '/lustre/fs0/awadell/sqsh-files/mist+pytorch+25.01+v2.sif', + env: { + JOBID: '$SLURM_JOB_ID', + PMIX_MCA_gds: 'hash', + NCCL_TOPO_FILE: '/cm/shared/etc/ndv4-topo.xml', + MELLANOC_VISIBLE_DEVICES: 'all', + }, + program: "-m electrolyte_fm.models.linear_probe", + train: { + trainer: { + max_epochs: 1000, + }, + model: { + model: { + class_path: encoder_class, + init_args: { + name_or_path: encoder_path, + }, + }, + probes: { + class_path: 'electrolyte_fm.models.linear_probe.per_layer_probe', + init_args: { + hidden_size: std.parseInt(hidden_size), + features: 5, + location: location, + n_layers: std.parseInt(n_layers), + }, + }, + }, + data: { + class_path: 'electrolyte_fm.data_modules.lipinski_dataset.LipinskiDataModule', + init_args: { + name_or_path: dataset, + tokenizer: tokenizer, + encoding: encoding, + num_workers: 16, + batch_size: std.parseInt(batch_size), + }, + }, + }, +} +""" + +template = "submit/dgx.j2" + + +def submit(config: dict): + script = render(template, config) + print(script) + subprocess.run("sbatch", input=script, text=True) + + +# Pretrained Models +datasets = ["tox21", "toxcast", "hiv"] +locations = ["output", "intermediate", "output.dense"] +models = [ + { + "encoder_path": "ibm/MoLFormer-XL-both-10pct", + "encoding": "smiles-canonical", + "tokenizer": "ibm/MoLFormer-XL-both-10pct", + }, + { + "encoder_path": "./models/mist-ti624ev1-moleculenet/pretrained", + }, + { + "encoder_path": "./models/mist-1.8B-dh61satti", + "batch_size": 16, + }, +] +models.extend( + [ + { + "encoder_class": "electrolyte_fm.models.prod_finetune.MISTFinetuned.from_pretrained", + "encoder_path": f"./models/mist-ti624ev1-moleculenet/{dataset}", + } + for dataset in ["bace", "qm9", "bbbp", "muv", "qm8", "tmQM"] + ] +) + + +def get_mist_finetune_config(path): + config = json.loads(Path(path, "config.json").read_text()) + return AutoConfig.for_model(config["encoder"]["model_type"]).from_dict( + config["encoder"] + ) + + +runs = [] +for model in models: + run = deepcopy(model) + run.setdefault("encoder_class", "__main__.mlm_from_pretrained") + run.setdefault("batch_size", 64) + if run["encoder_class"] == "__main__.mlm_from_pretrained": + model_config = AutoConfig.from_pretrained( + run["encoder_path"], + trust_remote_code=True, + ) + else: + model_config = get_mist_finetune_config(run["encoder_path"]) + + run.setdefault("n_layers", model_config.num_hidden_layers) + + for c in dict_product({"location": locations, "dataset": datasets}): + run.update(c) + if run["location"] == "intermediate": + hidden_size = model_config.intermediate_size + else: + hidden_size = model_config.hidden_size + + run["hidden_size"] = hidden_size + run_config = jsonnet.evaluate_snippet( + "snippet", + config, + tla_vars={k: str(v) for k, v in run.items()}, + ) + run_config = json.loads(run_config) + submit(run_config) diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 21a1bf35..0ec509d2 100644 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -150,7 +150,8 @@ def pretrained(ckpt: str, name: Optional[str] = None): def export_finetuned(ckpt: str): model = SaveConfigWithCkpts.load(ckpt) - model_config = json.loads(Path(ckpt, "..", "..", "config.json").read_text()) + config_file = Path(ckpt).parent.parent.joinpath("config.json") + model_config = json.loads(config_file.read_text()) return MISTFinetuned( model.encoder, model.task_network, diff --git a/submit/dgx.j2 b/submit/dgx.j2 index ca2dfedd..3b59c662 100644 --- a/submit/dgx.j2 +++ b/submit/dgx.j2 @@ -31,6 +31,9 @@ cd $(git rev-parse --show-toplevel) # Set env variables {{- export_env( env ) }} +TMPDIR=${TMPDIR:-/tmp}/mist-${SLURM_JOB_ID}/ +mkdir -p $TMPDIR + {%- if job_queue is defined %} submit/sweep.py scheduler --num-workers {{ job_queue.tasks }} {{ job_queue.queue }} @@ -56,10 +59,11 @@ export JOB_CONFIG=$(cat $PL_CONFIG) # Write out lightning config export PL_CONFIG=$TMPDIR/lightning.json cat > ${PL_CONFIG}< Date: Mon, 10 Mar 2025 11:43:41 -0400 Subject: [PATCH 012/147] add analysis code for linear probes --- electrolyte_fm/models/linear_probe.py | 5 ++- opt/FeatureMiner/Project.toml | 10 +++++ opt/FeatureMiner/explore_probes.jl | 17 +++++++++ opt/FeatureMiner/src/FeatureMiner.jl | 37 ++++++++++++++++++- opt/FeatureMiner/src/lipinski.jl | 23 ++++++++++++ .../submit_linear_probes.py | 0 6 files changed, 88 insertions(+), 4 deletions(-) create mode 100644 opt/FeatureMiner/explore_probes.jl create mode 100644 opt/FeatureMiner/src/lipinski.jl rename opt/{linear-probes => FeatureMiner}/submit_linear_probes.py (100%) diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 0903e536..8bc745b0 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -57,10 +57,11 @@ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: # Remove hooks from model (not picklable)) for hook in state_dict.pop("_hooks_installed", {}).values(): hook.remove() - state_dict["_hooks_installed"] = {} + state_dict.pop("_hooks_installed", None) + state_dict["_prob_points"] = self._probe_points # Don't save activations - state_dict["_activations"] = {} + state_dict.pop("_activations", None) def named_probes(self): yield from zip(self._probe_points, self._probes) diff --git a/opt/FeatureMiner/Project.toml b/opt/FeatureMiner/Project.toml index 58a56987..863dab58 100644 --- a/opt/FeatureMiner/Project.toml +++ b/opt/FeatureMiner/Project.toml @@ -4,8 +4,18 @@ version = "0.1.0" [deps] ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63" +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" + +[compat] +CSV = "0.10.15" +DataFrames = "1.7.0" +LinearAlgebra = "1.11.0" +Makie = "0.22.2" diff --git a/opt/FeatureMiner/explore_probes.jl b/opt/FeatureMiner/explore_probes.jl new file mode 100644 index 00000000..a21d8afe --- /dev/null +++ b/opt/FeatureMiner/explore_probes.jl @@ -0,0 +1,17 @@ +using Makie +using DataFrames +using CSV +using FeatureMiner: load_fitted_probes + + +df = DataFrame() +for ckpt in readdir(joinpath(@__DIR__, "..", "..", "linear-probes"); join=true) + isdir(ckpt) || continue + isdir(joinpath(ckpt, "checkpoints")) || continue + + ckpt_probes = load_fitted_probes(ckpt) + select!(ckpt_probes, Not(:weight)) + select!(ckpt_probes, Not(:Bias)) + append!(df, ckpt_probes) +end +CSV.write("linear_probes.csv", df) diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl index 520ef207..8f95ddf4 100644 --- a/opt/FeatureMiner/src/FeatureMiner.jl +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -1,11 +1,12 @@ module FeatureMiner using ArgParse -using PythonCall: Py, pyimport +using DataFrames +using PythonCall: Py, pyimport, pyconvert, @pyconst using OnlineStats: OnlineStats, KHist, Variance, Series, fit! using OnlineStatsBase: OnlineStatsBase, OnlineStat, EqualWeight, smooth, bessel, nobs using StatsBase: StatsBase - +using LinearAlgebra: norm, dot function FeatureExtractor(ckpt_path::String) cls = pyimport("electrolyte_fm.models.sae.FeatureExtractor") @@ -22,8 +23,40 @@ function split_dataset_by_node(dataset::Py, rank::Int, size::Int) return m.split_dataset_by_node(dataset, rank, size) end +function load_linear_probes(ckpt) + torch = @pyconst(pyimport("torch")) + data = torch.load(ckpt; map_location=torch.device("cpu")) + + # Extract weights + probe_weights = Dict() + for (k, v) in data["state_dict"].items() + if pyconvert(Bool, k.startswith("_probes")) + probe_weights[pyconvert(String, k)] = pyconvert(Array, v) + end + end + + # Collate probes + probes = [] + location = pyconvert(String, data["hyper_parameters"]["probes"]["init_args"]["location"]) + for idx in range(0; length=fld(length(probe_weights), 2)) + push!(probes, (; + weight=probe_weights["_probes.$idx.weight"], + bias=probe_weights["_probes.$idx.bias"], + location=location, + layer=idx, + )) + end + + meta = (; + name_or_path=pyconvert(String, data["hyper_parameters"]["model"]["init_args"]["name_or_path"]), + ) + + return probes, meta +end + include("identification.jl") include("stats.jl") +include("lipinski.jl") end diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl new file mode 100644 index 00000000..63ee15dd --- /dev/null +++ b/opt/FeatureMiner/src/lipinski.jl @@ -0,0 +1,23 @@ +function load_fitted_probes(ckpt_dir) + probes = [] + for ckpt in readdir(ckpt_dir; join=true) + startswith(basename(ckpt), "star") || continue + m = match(r"layer-(\d+)-(\w+).*?--auroc-([\d\.]+)\.ckpt", basename(ckpt)) + layer = parse(Int, m[1]) + 1 + location = m[2] + auroc = parse(Float64, m[3]) + ckpt_probes, ckpt_meta = load_linear_probes(ckpt) + push!(probes, (; + ckpt_probes[layer]..., + model=ckpt_meta.name_or_path, + auroc, + )) + end + return DataFrame(probes) +end + +function additive_features(w::Matrix) + f_overall = w[end, :] + f_componets = sum(w[1:end-1, :]; dims=1) + return dot(f_overall, f_componets) / (norm(f_overall) * norm(f_componets)) +end diff --git a/opt/linear-probes/submit_linear_probes.py b/opt/FeatureMiner/submit_linear_probes.py similarity index 100% rename from opt/linear-probes/submit_linear_probes.py rename to opt/FeatureMiner/submit_linear_probes.py From 41f01340afbe88f5b64721e5200b16b7b2376978 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 10 Mar 2025 12:33:17 -0700 Subject: [PATCH 013/147] add layerwise similarity and submission script --- .gitignore | 3 ++ activate | 2 +- opt/FeatureMiner/Project.toml | 4 +++ opt/FeatureMiner/explore_probes.jl | 39 ++++++++++++++++--------- opt/FeatureMiner/src/FeatureMiner.jl | 9 ++++-- opt/FeatureMiner/src/lipinski.jl | 43 +++++++++++++++++++++------- opt/FeatureMiner/submit_explore.sh | 18 ++++++++++++ 7 files changed, 92 insertions(+), 26 deletions(-) mode change 100644 => 100755 opt/FeatureMiner/explore_probes.jl create mode 100644 opt/FeatureMiner/submit_explore.sh diff --git a/.gitignore b/.gitignore index 8befc050..075332b1 100644 --- a/.gitignore +++ b/.gitignore @@ -38,3 +38,6 @@ spack.lock # outputs *.json +*.tar.xz +*.tar.gz +*.tar diff --git a/activate b/activate index e64333e3..1516b0fd 100755 --- a/activate +++ b/activate @@ -5,7 +5,7 @@ GIT_ROOT=$(git rev-parse --show-toplevel) # Activate virtual environment -source .venv/bin/activate +source ${GIT_ROOT}/.venv/bin/activate # Add NVIDIA libraries export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${GIT_ROOT}/.venv/lib64:${GIT_ROOT}.venv/lib" diff --git a/opt/FeatureMiner/Project.toml b/opt/FeatureMiner/Project.toml index 863dab58..da3c46fd 100644 --- a/opt/FeatureMiner/Project.toml +++ b/opt/FeatureMiner/Project.toml @@ -7,6 +7,8 @@ ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" @@ -17,5 +19,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [compat] CSV = "0.10.15" DataFrames = "1.7.0" +JLD2 = "0.5.11" +JSON = "0.21.4" LinearAlgebra = "1.11.0" Makie = "0.22.2" diff --git a/opt/FeatureMiner/explore_probes.jl b/opt/FeatureMiner/explore_probes.jl old mode 100644 new mode 100755 index a21d8afe..11c4f773 --- a/opt/FeatureMiner/explore_probes.jl +++ b/opt/FeatureMiner/explore_probes.jl @@ -1,17 +1,30 @@ -using Makie +#!/usr/bin/env -S julia --project=@script --color=yes --startup-file=no using DataFrames -using CSV -using FeatureMiner: load_fitted_probes +using JLD2: JLD2 +using FeatureMiner: FeatureMiner, load_fitted_probes, additive_features - -df = DataFrame() -for ckpt in readdir(joinpath(@__DIR__, "..", "..", "linear-probes"); join=true) - isdir(ckpt) || continue - isdir(joinpath(ckpt, "checkpoints")) || continue - - ckpt_probes = load_fitted_probes(ckpt) +function process_checkpoint(ckpt::String) + ckpt_probes, ckpt_meta = load_fitted_probes(joinpath(ckpt, "checkpoints")) + transform!(ckpt_probes, + :weight => ByRow(additive_features) => :lipinski_alignment + ) + probe_similarity = map(1:5) do idx + FeatureMiner.layerwise_similarity(ckpt_probes.weight, idx) + end + probes = collect(zip(ckpt_probes.weight, ckpt_probes.bias)) select!(ckpt_probes, Not(:weight)) - select!(ckpt_probes, Not(:Bias)) - append!(df, ckpt_probes) + select!(ckpt_probes, Not(:bias)) + ckpt_meta = (; ckpt_meta..., id=basename(ckpt)) + return (; + meta=ckpt_meta, + probes, + probe_stats=ckpt_probes, + probe_similarity, + ) +end + +function (@main)(args::Vector{String}) + ckpt = args[1] + out = process_checkpoint(ckpt) + JLD2.jldsave(joinpath(ckpt, "linear_probes.jld2"); out...) end -CSV.write("linear_probes.csv", df) diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl index 8f95ddf4..f92d9823 100644 --- a/opt/FeatureMiner/src/FeatureMiner.jl +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -2,7 +2,7 @@ module FeatureMiner using ArgParse using DataFrames -using PythonCall: Py, pyimport, pyconvert, @pyconst +using PythonCall: Py, pyimport, pyconvert, @pyconst, GIL using OnlineStats: OnlineStats, KHist, Variance, Series, fit! using OnlineStatsBase: OnlineStatsBase, OnlineStat, EqualWeight, smooth, bessel, nobs using StatsBase: StatsBase @@ -38,17 +38,22 @@ function load_linear_probes(ckpt) # Collate probes probes = [] location = pyconvert(String, data["hyper_parameters"]["probes"]["init_args"]["location"]) + hidden_size = pyconvert(Int, data["hyper_parameters"]["probes"]["init_args"]["hidden_size"]) for idx in range(0; length=fld(length(probe_weights), 2)) push!(probes, (; weight=probe_weights["_probes.$idx.weight"], bias=probe_weights["_probes.$idx.bias"], - location=location, + location, + hidden_size, layer=idx, )) end meta = (; name_or_path=pyconvert(String, data["hyper_parameters"]["model"]["init_args"]["name_or_path"]), + dataset=pyconvert(String, data["datamodule_hyper_parameters"]["init_args"]["name_or_path"]), + encoding=pyconvert(String, data["datamodule_hyper_parameters"]["init_args"]["encoding"]), + tokenizer=pyconvert(String, data["datamodule_hyper_parameters"]["init_args"]["tokenizer"]), ) return probes, meta diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 63ee15dd..4914fc28 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -1,23 +1,46 @@ function load_fitted_probes(ckpt_dir) probes = [] + local ckpt_meta for ckpt in readdir(ckpt_dir; join=true) - startswith(basename(ckpt), "star") || continue + @info realpath(ckpt) + if !startswith(basename(ckpt), "star") + _, ckpt_meta = load_linear_probes(ckpt) + continue + end m = match(r"layer-(\d+)-(\w+).*?--auroc-([\d\.]+)\.ckpt", basename(ckpt)) layer = parse(Int, m[1]) + 1 location = m[2] auroc = parse(Float64, m[3]) - ckpt_probes, ckpt_meta = load_linear_probes(ckpt) - push!(probes, (; - ckpt_probes[layer]..., - model=ckpt_meta.name_or_path, - auroc, - )) + try + ckpt_probes, ckpt_meta = load_linear_probes(ckpt) + push!(probes, (; + ckpt_probes[layer]..., + location, + auroc, + )) + catch e + e isa InterruptException && rethrow() + @error "failed to load $ckpt" e catch_backtrace() + continue + end end - return DataFrame(probes) + return DataFrame(probes), ckpt_meta end +cosine_similarity(a::Vector, b::Vector) = dot(a, b) / (norm(a) * norm(b)) + function additive_features(w::Matrix) f_overall = w[end, :] - f_componets = sum(w[1:end-1, :]; dims=1) - return dot(f_overall, f_componets) / (norm(f_overall) * norm(f_componets)) + f_componets = vec(sum(w[1:end-1, :]; dims=1)) + return cosine_similarity(f_overall, f_componets) end + +function layerwise_similarity(weights::Vector{W}, feature::Int) where {W<:Matrix{T}} where {T} + N = length(weights) + s = Matrix{T}(undef, N, N) + for I in CartesianIndices(s) + s[I] = cosine_similarity(weights[I[1]][feature, :], weights[I[2]][feature, :]) + end + return s +end + diff --git a/opt/FeatureMiner/submit_explore.sh b/opt/FeatureMiner/submit_explore.sh new file mode 100644 index 00000000..dbbd7aa2 --- /dev/null +++ b/opt/FeatureMiner/submit_explore.sh @@ -0,0 +1,18 @@ +#!/bin/bash +#SBATCH --job-name=explore +#SBATCH -p cpuq +#SBATCH -N 1 +#SBATCH -n 16 +#SBATCH -c 4 +#SBATCH --mem-per-cpu 1800M + +GIT_ROOT=$(git rev-parse --show-toplevel) +MODEL_DIR=../../linear-probes/ +source "${GIT_ROOT}/activate" +# julia --project --startup-file=no -e 'using Pkg; Pkg.instantiate()' +find $MODEL_DIR -maxdepth 1 -mindepth 1 -print0 | \ + xargs -0 -P ${SLURM_NTASKS} -i ./explore_probes.jl {} + +# Archive results +find $MODEL_DIR -name '*.jld2' -printf '%P\n' | \ + tar -caf linear_probes.tar.xz -C $MODEL_DIR --files-from=- From 760fdfb1cfec3521d64c4663773ab13cefb8b0b4 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 14 Mar 2025 09:58:23 -0400 Subject: [PATCH 014/147] wip: exploring design rules with MIST --- Project.toml | 2 + opt/design/MIST.jl | 63 +++++++ opt/design/plots.jl | 25 +++ opt/design/reaction.jl | 344 +++++++++++++++++++++++++++++++++++ opt/design/sat_fats.csv | 24 +++ opt/design/saturated_fats.py | 31 ++++ 6 files changed, 489 insertions(+) create mode 100644 opt/design/MIST.jl create mode 100644 opt/design/plots.jl create mode 100644 opt/design/reaction.jl create mode 100644 opt/design/sat_fats.csv create mode 100644 opt/design/saturated_fats.py diff --git a/Project.toml b/Project.toml index 105cbf4f..cf871e3d 100644 --- a/Project.toml +++ b/Project.toml @@ -1,6 +1,7 @@ [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" +CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" FreeTypeAbstraction = "663a7486-cb36-511b-a19d-713bb74d65c9" GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" @@ -8,6 +9,7 @@ JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195" MPIPreferences = "3da0fdf6-3ccc-4f1b-acd9-58baa6c99267" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9" diff --git a/opt/design/MIST.jl b/opt/design/MIST.jl new file mode 100644 index 00000000..6d90d64c --- /dev/null +++ b/opt/design/MIST.jl @@ -0,0 +1,63 @@ +module MIST + +using PythonCall +using DataFrames +using Statistics: mean, std + +function finetuned(ckpt) + MISTFinetuned = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTFinetuned + return MISTFinetuned.from_pretrained(ckpt) +end + +function multitask(ckpt) + MISTMultiTask = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTMultiTask + return MISTMultiTask.from_pretrained(ckpt) +end + +function predict(smi::Vector{String}, model::Py) + py_out = model.predict(PyList(smi)) + out = [] + for py_y in py_out + y = Dict{String,Float64}() + for (k, v) in py_y.items() + y[pyconvert(String, k)] = pyconvert(Float64, v["value"]) + end + push!(out, y) + end + df = DataFrame(out) + insertcols!(df, 1, :smi => smi) + return df +end + +function embed(smi::Vector{String}, model::Py) + pyconvert(Matrix, model.embed(PyList(smi))) +end + +function predict_monte(smi::Vector{String}, model::Py; n=10) + model = model.train() + results = Dict() + for i in 1:n + df_sample = predict(smi, model) + sample = Dict(zip(names(df_sample), eachcol(df_sample))) + pop!(sample, "smi") + for (k, v) in sample + if haskey(results, k) + results[k] = hcat(results[k], vec(v)) + else + results[k] = v + end + end + end + + # Compute statistics + summary = Dict() + for (k, v) in pairs(results) + summary["$(k)_mean"] = vec(mean(v; dims=2)) + summary["$(k)_stderr"] = vec(std(v; dims=2)) / sqrt(n) + end + df = DataFrame(summary) + insertcols!(df, 1, :smi => smi) + return df +end + +end diff --git a/opt/design/plots.jl b/opt/design/plots.jl new file mode 100644 index 00000000..9b96d003 --- /dev/null +++ b/opt/design/plots.jl @@ -0,0 +1,25 @@ +using Makie +using DataFrames +using CSV + +function plot_sat_fats(file) + f = Figure(size=96 .* (4.5, 6)) + df = DataFrame(CSV.File(file)) + df.n_carbon = map(x -> count(==('C'), x), df.smi) + + ax = Axis(f[1, 1], ylabel="Diploe Moment [D]", xlabel="Number of Carbons") + scatter!(ax, df.n_carbon, df.mu) + + ax = Axis(f[2, 1], ylabel="Energy [Ha]", xlabel="Number of Carbons") + scatter!(ax, df.n_carbon, df.u0; marker=:+, label="Internal 0K") + scatter!(ax, df.n_carbon, df.u298; marker=:x, label="Internal at 298.15K") + scatter!(ax, df.n_carbon, df.h298; marker=:circle, label="Enthalpy at 298.15K") + scatter!(ax, df.n_carbon, df.g298; marker=:diamond, label="Gibbs at 298.15K") + axislegend(ax; position=:rb, nbanks=2) + + ax = Axis(f[3, 1], ylabel="HOMO-LUMO Gap", xlabel="Number of Carbons") + scatter!(ax, df.n_carbon, df.gap; marker=:x, label="Direct") + scatter!(ax, df.n_carbon, df.lumo - df.homo; marker=:+, label="Calculated") + axislegend(ax; position=:rb) + return f +end diff --git a/opt/design/reaction.jl b/opt/design/reaction.jl new file mode 100644 index 00000000..7f8e159b --- /dev/null +++ b/opt/design/reaction.jl @@ -0,0 +1,344 @@ + +# include("MIST.jl") +# using .MIST + +using PythonCall +using DataFrames +using Makie +using CategoricalArrays +using ManifoldLearning + + +steps = [ + "CC(C)(CO)C(C(=O)NCCC(=O)O)O", + "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)O)O", + "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)N[C@@H](CS)C(=O)O)O", + "O=C(NCCS)CCNC(=O)[C@H](O)C(C)(C)COP(=O)(O)O", + "CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@@H](O1)N2C=NC3=C(N=CN=C32)N)O)O)[C@H](C(=O)NCCC(=O)NCCS)O", + "O=C(NCCS)CCNC(=O)C(O)C(C)(C)COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n2cnc1c(ncnc12)N)[C@H](O)[C@@H]3OP(=O)(O)O", +] + +function predict_pathway(steps, model, tok) + o = model.predict(PyList(steps), tok) + G = pyconvert(Vector{Float64}, o["g298"]) + f = Figure() + ax = Axis(f[1, 1]; ylabel="Gibbs Free Energy @ 298K [Ha]") + stairs!(ax, G, step=:center) + f +end + +function alkene_chain_gen(n::Int) + smi = [] + for len in range(3, n) + for pos in range(1, len - 1; step=1) + push!(smi, (; + len, + pos, + smi=("C"^pos) * "=" * "C"^(len - pos), + )) + end + end + return DataFrame(smi) +end + +function plot_alkene_chain(n, model, tok) + df = alkene_chain_gen(n) + df.type .= "normal" + df2 = copy(df) + df2.smi = map(smi -> replace(smi, "=" => "/C=C/"), df2.smi) + df2.len .+= 2 + df2.type .= "cis" + df3 = copy(df) + df3.smi = map(smi -> replace(smi, "=" => "/C=C\\"), df2.smi) + df3.len .+= 2 + df3.type .= "trans" + df = vcat(df, df2, df3) + + pred = model.predict(PyList(df.smi), tok) + df.h298 = pyconvert(Vector{Float64}, pred["h298"]) + df.mu = pyconvert(Vector{Float64}, pred["mu"]) + + f = Figure() + ax = Axis(f[1, 1]; + # limits=((0, 1), nothing), + # yscale=log10, + ylabel="H298 [kcal/mol]") + cb = Colorbar(f[1, 2], limits=(3, n + 2), colormap=:viridis, label="Length") + for len in unique(df.len) + for (type, marker) in [("normal", :x), ("cis", :cross), ("trans", :diamond)] + df_len = subset(df, :len => ByRow(==(len)), :type => ByRow(==(type))) + scatter!(ax, + range(1, nrow(df_len)), + df_len.h298; + color=len, + marker, + colorrange=cb.limits, + colormap=cb.colormap, + ) + end + end + return f, df +end + +alkyne(n::Int) = "C#" * "C"^(n-1) +alkene(n::Int) = "C=" * "C"^(n-1) +alkane(n::Int) = "C" ^ n +isoalkane(n::Int) = "C(C)" * "C" ^ (n - 2) +alcohol(n::Int) = "O" * "C" ^ n +aldehyde(n::Int) = "O=" * "C"^n +nitrile(n::Int) = "N#" * "C"^n +dinitrile(n::Int) = "N#" * "C"^n * "#N" +amine(n::Int) = "N" * "C"^ n +carboxylic_acid(n::Int) = "C(=O)" * "C"^(n-1) +halide(n::Int, element::String) = element * "C"^n +halide(element::String) = Base.Fix2(halide, element) +tetra_sub_alkene(n::Int) = "$("C"^n)C(=C($("C"^n))$("C"^n))$("C"^n)" +disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" +triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" +trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" + +function carbon_chains(n::Int) + return DataFrame(vcat( + [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], + # [(; n, type="Isoalkanes", smi=isoalkane(n)) for n in 3:n], + [(; n, type="Alcohols", smi=alcohol(n)) for n in 1:n], + [(; n, type="Nitrile", smi=nitrile(n)) for n in 1:n], + # [(; n, type="Dinitriles", smi=dinitrile(n)) for n in 1:n], + [(; n, type="Amines", smi=amine(n)) for n in 1:n], + [(; n, type="Carboxylic Acids", smi=carboxylic_acid(n)) for n in 2:n], + [(; n, type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], + # [(; n, type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], + # [(; n, type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], + [(; n, type="Tetra subsitued Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], + [(; n, type="Disubsitued Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], + )) +end + +function melt_boil_trends_monte(n::Int, model) + alkanes = [alkane(n) for n in 1:n] + isoalkanes = [isoalkane(n) for n in 3:n] + + df_alkanes = MIST.predict_monte(alkanes, model; n=50) + df_alkanes.n = 1:n + df_isoalkanes = MIST.predict_monte(isoalkanes, model; n=50) + df_isoalkanes.n = 3:n + + f = Figure() + ax = Axis(f[1, 1]; ylabel="Boiling Point [°C]") + errorbars!(ax, df_alkanes.n, df_alkanes.bp_mean, df_alkanes.bp_stderr; label="Alkanes") + errorbars!(ax, df_isoalkanes.n, df_isoalkanes.bp_mean, df_isoalkanes.bp_stderr; label="Isoalkanes") + axislegend(ax; position=:rb) + + ax = Axis(f[2, 1]; ylabel="Melting Point [°C]") + errorbars!(ax, df_alkanes.n, df_alkanes.mp_mean, df_alkanes.mp_stderr; label="Alkanes") + errorbars!(ax, df_isoalkanes.n, df_isoalkanes.mp_mean, df_isoalkanes.mp_stderr; label="Isoalkanes") + + return f +end + +function predict_class(class::Dict, model) + smi = Iterators.flatten(values(class)) + df = MIST.predict(collect(smi), model) + types = map((kv) -> repeat([kv[1]], length(kv[2])), collect(pairs(class))) + df.types = collect(Iterators.flatten(types)) + return df +end + + + +function melt_boil_trends(n::Int, model) + alkanes = + isoalkanes = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] + alchols = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] + df = DataFrame(vcat( + [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], + [(; n, type="Isoalkane", smi=isoalkane(n)) for n in 3:n], + [(; n, type="Alchols", smi=alchol(n)) for n in 1:n], + )) + return df + + + + + model = model.eval() + df_alkanes = MIST.predict(alkanes, model) + df_alkanes.n = 1:n + df_isoalkanes = MIST.predict(isoalkanes, model) + df_isoalkanes.n = 3:n + + f = Figure() + ax_bp = Axis(f[1, 1]; ylabel="Boiling Point [°C]") + ax_mp = Axis(f[2, 1]; ylabel="Melting Point [°C]") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + lines!(ax_bp, df.n, df.bp; label=type) + lines!(ax_mp, df.n, df.bp; label=type) + end + axislegend(ax_bp; position=:rb) + + return f +end + +function dipole_trends(n::Int; qm, dn, kt, mpbp, samples=20) + df = carbon_chains(n) + df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) + select!(df_qm9, Not(:smi)) + df_dn = MIST.predict_monte(df.smi, dn; n=samples) + select!(df_dn, "BF3 affinity_mean" => :donor_number_mean, "BF3 affinity_stderr" => :donor_number_stderr) + df_mpbp = MIST.predict_monte(df.smi, mpbp; n=samples) + select!(df_mpbp, :mp_mean, :mp_stderr, :bp_mean, :bp_stderr) + df_kt = MIST.predict_monte(df.smi, kt; n=samples) + select!(df_kt, Not(:smi)) + rename!(df_kt, + "alpha_mean" => :alpha_kt_mean, + "alpha_stderr" => :alpha_kt_stderr, + "beta_mean" => :beta_kt_mean, + "beta_stderr" => :beta_kt_stderr, + ) + df = hcat(df, df_qm9, df_dn, df_kt, df_mpbp) + + f = Figure() + gl_trends = GridLayout(f[1,1]) + rowgap!(gl_trends, 5) + ax = Axis(gl_trends[1, 1]; ylabel=L"$\mu$ [D]") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.mu_mean; label=type) + errorbars!(ax, df_type.n, df_type.mu_mean, df_type.mu_stderr; color=h.color) + end + + ax = Axis(gl_trends[2, 1]; ylabel=L"$G^{\degree}$ [kJ/mol]") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.h298_mean; label=type) + errorbars!(ax, df_type.n, df_type.h298_mean, df_type.h298_stderr; color=h.color) + end + + ax = Axis(gl_trends[3, 1]; ylabel=L"DN", xlabel=L"$$Number of Carbons") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.donor_number_mean; label=type) + errorbars!(ax, df_type.n, df_type.donor_number_mean, df_type.donor_number_stderr; color=h.color) + end + + ax = Axis(gl_trends[4, 1]; ylabel=L"$\alpha$ [$\alpha_0^2$]", xlabel=L"$$Number of Carbons") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.alpha_mean; label=type) + errorbars!(ax, df_type.n, df_type.alpha_mean, df_type.alpha_stderr; color=h.color) + end + + ax = Axis(gl_trends[5, 1]; ylabel=L"KT $\beta$", xlabel=L"$$Number of Carbons") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.beta_kt_mean; label=type) + errorbars!(ax, df_type.n, df_type.beta_kt_mean, df_type.beta_kt_stderr; color=h.color) + end + + ax = Axis(gl_trends[6, 1]; ylabel=L"Melting Point [$^{°}C$]", xlabel=L"$$Number of Carbons") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.mp_mean; label=type) + errorbars!(ax, df_type.n, df_type.mp_mean, df_type.mp_stderr; color=h.color) + end + + gl_corr = GridLayout(f[1,2]) + ax = Axis(gl_corr[1, 1]; ylabel=L"DN", xlabel=L"Kamlet-Taft $\beta$") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + h = errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.donor_number_stderr, label=type) + errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.beta_kt_stderr; direction=:x, color=h.color) + end + + ax = Axis(gl_corr[2, 1]; ylabel=L"$\alpha$ [$a_0^3$]", xlabel=L"$\mu$ [D]") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + h = errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.alpha_stderr, label=type) + errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.mu_stderr; direction=:x, color=h.color) + end + + Legend(f[2, :], ax; nbanks=2, tellheight=true) + rowgap!(gl_trends, 5) + resize_to_layout!(f) + + return f +end + +labels(x) = x.label[] + +function hard_soft_acid(; qm, kt, samples=20) + df = DataFrame(vcat( + [(; smi, type="Hard Acid") for smi in [ + "FB(F)F", "O=S(=O)=O", "OB(O)O", "O(B(OC)OC)C", + map(triboroester, 1:5)..., + ]], + [(; smi, type="Borderline Acid") for smi in [ + map(aldehyde, 1:2:10)..., + ]], + [(; smi, type="Soft Acid") for smi in [ + map(aldehyde, 1:5:30)..., + ]], + [(; smi, type="Hard Base") for smi in [ + map(amine, 1:5:30)..., + map(alcohol, 1:5:30)..., + ]], + [(; smi, type="Soft Base") for smi in [ + map(alkene, 1:5:30)..., + map(tetra_sub_alkene, 1:10)..., + map(disubstituted_alkyne, 1:3:15)..., + ]], + )) + df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) + select!(df_qm9, Not(:smi)) + df_kt = MIST.predict_monte(df.smi, kt; n=samples) + select!(df_kt, :pKa_mean, :pKa_stderr) + df = hcat(df, df_qm9, df_kt) + df.type = categorical(df.type) + color = map(type -> MISTStyle.CAT_COLORS[levelcode(type)], df.type) + + + f = Figure() + ax = Axis(f[1, 1]; xlabel=L"$\alpha$ [$\alpha^2_0$]", ylabel=L"$$pKa") + xyerrorbars!(ax, df.alpha_mean, df.pKa_mean, df.alpha_stderr, df.pKa_stderr; label=df.type, color) + + ax = Axis(f[2, 1]; xlabel=L"$$LUMO [Har]", ylabel=L"$$pKa") + h = xyerrorbars!(ax, df.lumo_mean, df.pKa_mean, df.lumo_stderr, df.pKa_stderr; label=df.type, color) + + ax = Axis(f[1, 2]) + hidexdecorations!(ax) + hideydecorations!(ax) + emb = MIST.embed(df.smi, qm) + m = fit(TSNE, emb'; p=30, maxoutdim=2) + r = predict(m) + scatter!(ax, r[1, :], r[2, :]; color) + + types = map(enumerate(levels(df.type))) do (i, label) + PolyElement( + color=MISTStyle.CAT_COLORS[i], + label=label + ) + end + + Legend(f[2, 2], types, labels.(types)) + + return f + + +end + +function xyerrorbars!(ax, x, y, errorx, errory; label=nothing, kwargs...) + h = errorbars!(ax, x, y, errory; label, kwargs...) + errorbars!(ax, x, y, errorx; direction=:x, color=h.color) + return h +end diff --git a/opt/design/sat_fats.csv b/opt/design/sat_fats.csv new file mode 100644 index 00000000..14eddae8 --- /dev/null +++ b/opt/design/sat_fats.csv @@ -0,0 +1,24 @@ +smi,mu,alpha,homo,lumo,gap,r2,zpve,u0,u298,h298,g298,cv +O=C(O)CCC,4.769931,71.028,-0.26474136,-0.07180658,0.19366309,1116.128,0.067295,-455.21826,-454.96674,-454.93094,-455.2984,25.358398 +O=C(O)CCCC,4.971653,73.43377,-0.2669614,-0.073890984,0.19384971,1196.3062,0.0732354,-449.44058,-449.31204,-449.23938,-449.54324,25.694302 +O=C(O)CCCCC,5.2371836,75.44909,-0.26635858,-0.07276623,0.19430482,1281.5747,0.079891905,-443.68478,-443.6336,-443.54514,-443.796,26.355312 +O=C(O)CCCCCC,5.5895414,76.27199,-0.26350012,-0.06660598,0.19753295,1344.3341,0.08678694,-439.01654,-439.0433,-438.93744,-439.1521,27.013805 +O=C(O)CCCCCCC,5.649559,76.24556,-0.26225054,-0.060204238,0.20261885,1392.9761,0.09317882,-435.1404,-435.2054,-435.09824,-435.26633,27.856176 +O=C(O)CCCCCCCC,5.2911034,76.77113,-0.2604756,-0.05375296,0.20719764,1487.1074,0.10051406,-430.50854,-430.59537,-430.4845,-430.6233,28.716946 +O=C(O)CCCCCCCCC,5.344325,76.20799,-0.25762165,-0.04362812,0.21440068,1522.6865,0.10760877,-426.41803,-426.52133,-426.4131,-426.52682,29.321398 +O=C(O)CCCCCCCCCC,5.166279,76.36943,-0.2543188,-0.033491634,0.22118333,1555.0681,0.11445395,-422.9598,-423.07117,-422.96436,-423.07388,29.88532 +O=C(O)CCCCCCCCCCC,4.919032,76.11539,-0.2517563,-0.024300717,0.22780345,1547.0695,0.1217105,-419.9719,-420.09467,-419.9814,-420.09427,30.262314 +O=C(O)CCCCCCCCCCCC,4.680345,76.35677,-0.25301033,-0.014943976,0.23838933,1569.8961,0.1287832,-416.5352,-416.67746,-416.56747,-416.66455,30.87184 +O=C(O)CCCCCCCCCCCCC,4.3812184,76.159874,-0.25552556,-0.0074483696,0.24833615,1553.9812,0.13486302,-412.0727,-412.20636,-412.10773,-412.1919,31.143152 +O=C(O)CCCCCCCCCCCCCC,4.0451183,76.726524,-0.25839686,-0.0001775017,0.2584055,1606.5541,0.14232084,-407.17923,-407.2892,-407.22147,-407.24896,31.79684 +O=C(O)CCCCCCCCCCCCCCC,3.9297128,77.519806,-0.2595612,0.0036514928,0.2633514,1667.9335,0.14853513,-405.0917,-405.16525,-405.13257,-405.1035,32.368652 +O=C(O)CCCCCCCCCCCCCCCC,3.804593,79.58518,-0.25875157,0.007440024,0.26625136,1797.5773,0.15650843,-405.41147,-405.47327,-405.46625,-405.358,33.53738 +O=C(O)CCCCCCCCCCCCCCCCC,3.6632895,81.52695,-0.25755906,0.01096091,0.26849225,1999.3899,0.16350242,-403.53384,-403.5899,-403.5829,-403.4258,34.75062 +O=C(O)CCCCCCCCCCCCCCCCCC,3.4002395,83.31501,-0.25374886,0.014807183,0.26848534,2125.2043,0.17007431,-401.65665,-401.6828,-401.6706,-401.51105,35.88625 +O=C(O)CCCCCCCCCCCCCCCCCCC,3.1372354,84.147804,-0.25196138,0.011332356,0.26324683,2245.4724,0.17593305,-400.616,-400.64716,-400.5887,-400.49283,36.77691 +O=C(O)CCCCCCCCCCCCCCCCCCCC,2.9869483,85.180084,-0.24901211,0.0060275695,0.255123,2341.8481,0.18185005,-400.038,-400.08688,-399.95813,-399.9605,37.780804 +O=C(O)CCCCCCCCCCCCCCCCCCCCC,2.8969917,85.900406,-0.24720106,0.0011447165,0.24848019,2374.979,0.18786205,-398.368,-398.41678,-398.2661,-398.28995,38.320934 +O=C(O)CCCCCCCCCCCCCCCCCCCCCC,2.8028266,86.91344,-0.2461817,-0.0023705475,0.24399008,2413.5757,0.1948694,-395.99924,-396.05588,-395.89774,-395.90454,38.907734 +O=C(O)CCCCCCCCCCCCCCCCCCCCCCC,2.7076352,87.758156,-0.24554278,-0.0029556807,0.24283132,2438.045,0.2007028,-393.5421,-393.59503,-393.43665,-393.429,39.42135 +O=C(O)CCCCCCCCCCCCCCCCCCCCCCCC,2.6062937,88.49571,-0.24500887,-0.0012770239,0.2441615,2500.8901,0.2061011,-392.06732,-392.14346,-391.94748,-391.9792,39.92587 +O=C(O)CCCCCCCCCCCCCCCCCCCCCCCCC,2.4823546,89.42716,-0.24477701,0.0033710813,0.248929,2618.2927,0.21180302,-390.44525,-390.58524,-390.31842,-390.42496,40.447372 diff --git a/opt/design/saturated_fats.py b/opt/design/saturated_fats.py new file mode 100644 index 00000000..88b477c1 --- /dev/null +++ b/opt/design/saturated_fats.py @@ -0,0 +1,31 @@ +from argparse import ArgumentParser +import torch +import pandas as pd +from electrolyte_fm.models.prod_finetune import MISTFinetuned +from smirk import SmirkTokenizerFast + +if torch.cuda.is_available(): + device = "cuda" +elif torch.backends.mps.is_available(): + device = "mps" +else: + device = "cpu" + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--model", type=str, default=".") + parser.add_argument("--output", default="sat_fats.csv") + args = parser.parse_args() + + model = MISTFinetuned.from_pretrained(args.model).eval().to(device) + tok = SmirkTokenizerFast() + + smi: list[str] = [] + for n in range(3, 40): + # do nitrile + smi.append("N#" + "C" * n) + + pred = model.predict(smi, tok) + + df = pd.DataFrame(pred, index=smi) + df.to_csv(args.output, index_label="smi") From 761e85443058fff0ff61b7ab7a58cdb4944ddc76 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 14 Mar 2025 18:23:24 -0400 Subject: [PATCH 015/147] rework: refactor design rule plots --- opt/design/MIST.jl | 93 +++++++-- opt/design/Project.toml | 11 ++ opt/design/electrolytes.csv | 95 +++++++++ opt/design/figures.jl | 54 ++++++ opt/design/plots.jl | 71 ++++--- opt/design/reaction.jl | 344 --------------------------------- opt/design/src/DesignRules.jl | 20 ++ opt/design/src/hydrocarbons.jl | 38 ++++ opt/design/src/inference.jl | 79 ++++++++ opt/design/src/plot_utils.jl | 43 +++++ opt/design/src/uq.jl | 22 +++ 11 files changed, 484 insertions(+), 386 deletions(-) create mode 100644 opt/design/Project.toml create mode 100644 opt/design/electrolytes.csv create mode 100644 opt/design/figures.jl delete mode 100644 opt/design/reaction.jl create mode 100644 opt/design/src/DesignRules.jl create mode 100644 opt/design/src/hydrocarbons.jl create mode 100644 opt/design/src/inference.jl create mode 100644 opt/design/src/plot_utils.jl create mode 100644 opt/design/src/uq.jl diff --git a/opt/design/MIST.jl b/opt/design/MIST.jl index 6d90d64c..688d1b0a 100644 --- a/opt/design/MIST.jl +++ b/opt/design/MIST.jl @@ -2,27 +2,46 @@ module MIST using PythonCall using DataFrames -using Statistics: mean, std +# using Makie +using Statistics: Statistics, mean, std +using StatsBase: StatsBase, stderror, mean_and_std function finetuned(ckpt) - MISTFinetuned = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTFinetuned - return MISTFinetuned.from_pretrained(ckpt) + MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned + model = MISTFinetuned.from_pretrained(ckpt) + return model end function multitask(ckpt) - MISTMultiTask = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTMultiTask + MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask return MISTMultiTask.from_pretrained(ckpt) end +struct UQReal{T} + mean::T + std::T + n::Int +end + +Statistics.mean(x::UQReal) = x.mean +Statistics.std(x::Real) = x.std +StatsBase.stderror(x) = x.std / sqrt(x.n) +function Base.show(io::IO, x::UQReal) + μ = mean(x) + se = stderror(x) + if get(io, :compact, false)::Bool + μ = round(μ; sigdigits=5) + se = round(se; sigdigits=5) + end + print(io, "$μ ± $se") +end + function predict(smi::Vector{String}, model::Py) + model = model.to("mps") py_out = model.predict(PyList(smi)) - out = [] - for py_y in py_out - y = Dict{String,Float64}() - for (k, v) in py_y.items() - y[pyconvert(String, k)] = pyconvert(Float64, v["value"]) - end - push!(out, y) + out = Dict{String,Vector{Float64}}() + for (k, v) in py_out.items() + out[pyconvert(String, k)] = pyconvert(valtype(out), v["value"]) end df = DataFrame(out) insertcols!(df, 1, :smi => smi) @@ -34,15 +53,17 @@ function embed(smi::Vector{String}, model::Py) end function predict_monte(smi::Vector{String}, model::Py; n=10) + training = model.training model = model.train() results = Dict() - for i in 1:n - df_sample = predict(smi, model) - sample = Dict(zip(names(df_sample), eachcol(df_sample))) - pop!(sample, "smi") - for (k, v) in sample + py_smi = PyList(smi) + for _ in 1:n + sample = model.predict(py_smi) + for (k, v) in sample.items() + k = pyconvert(String, k) + v = pyconvert(Vector{Float64}, v["value"]) if haskey(results, k) - results[k] = hcat(results[k], vec(v)) + results[k] = hcat(results[k], v) else results[k] = v end @@ -52,12 +73,46 @@ function predict_monte(smi::Vector{String}, model::Py; n=10) # Compute statistics summary = Dict() for (k, v) in pairs(results) - summary["$(k)_mean"] = vec(mean(v; dims=2)) - summary["$(k)_stderr"] = vec(std(v; dims=2)) / sqrt(n) + mu_std = map(mean_and_std, eachrow(v)) + summary[k] = map(x -> UQReal(x..., n), mu_std) end df = DataFrame(summary) insertcols!(df, 1, :smi => smi) + model.train(training) return df end +# @recipe(ErrorLines, x, y, error_y) do scene +# Attributes() +# end +# Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), stderror.(y)) +# +# function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Real},AbstractVector{<:Real}}}) +# lines!(plt, plt.x, plt.y, Makie.shared_attributes(plt, Lines)) +# if !isnothing(plt.error_y) +# errorbars!(plt, plt.x, plt.y, plt.error_y, Makie.shared_attributes(plt, Makie.Errorbars)) +# end +# return plt +# end +# +# @recipe(ErrorCross, x, y, error_x, error_y) do scene +# Attributes() +# end +# +# Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) +# +# function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) +# attrs = Makie.shared_attributes(plt, Errorbars) +# h = errorbars!(plt, plt.x, plt.y, plt.error_y; direction=:y, attrs...) +# errorbars!(plt, plt.x, plt.y, plt.error_x; +# color=h.color, +# colorscale=h.colorscale, +# colormap=h.colormap, +# colorrange=h.colorrange, +# direction=:x, +# attrs... +# ) +# return plt +# end + end diff --git a/opt/design/Project.toml b/opt/design/Project.toml new file mode 100644 index 00000000..ef36dd89 --- /dev/null +++ b/opt/design/Project.toml @@ -0,0 +1,11 @@ +name = "DesignRules" +version = "0.1.0" + +[deps] +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" diff --git a/opt/design/electrolytes.csv b/opt/design/electrolytes.csv new file mode 100644 index 00000000..f4299d01 --- /dev/null +++ b/opt/design/electrolytes.csv @@ -0,0 +1,95 @@ +name,smi +Dimethyl dicarbonate,O=C(OC(=O)OC)OC +Ethylene carbonate,C1COC(=O)O1 +Dimethyl carbonate,COC(=O)OC +Ethyl methyl carbonate,CCOC(=O)OC +Dimethyl carbonate,COC(=O)OC +Diethyl carbonate,CCOC(=O)OCC +"2,2,2-trifluoromethyl ethyl carbonate",CCOC(=O)OC(F)(F)F +"2,2,2-trifluoroethyl methyl carbonate",COC(=O)OCC(F)(F)F +methyl trifluoromethyl carbonate,COC(=O)OC(F)(F)F +"ethyl 2,2,2-trifluoroethyl carbonate",CCOC(=O)OCC(F)(F)F +Bis(trifluoromethyl) ester,C(=O)(OC(F)(F)F)OC(F)(F)F +"2,2,2-trifluoroethyl trifluoromethyl carbonate",C(C(F)(F)F)OC(=O)OC(F)(F)F +"bis(2,2,2-trifluoroethyl) carbonate",C(C(F)(F)F)OC(=O)OCC(F)(F)F +"1,3-bis(trimethylsilyloxy)propane",C[Si](C)(C)OCCCO[Si](C)(C)C +"1,2-bis(trimethylsilyloxy)ethane",C[Si](C)(C)OCCO[Si](C)(C)C +"Chloroform-d",[2H]C(Cl)(Cl)Cl +"Dichloromethane",C(Cl)Cl +"1,2-Dichloroethane",C(CCl)Cl +"1,3-Dichloropropane",C(CCl)CCl +"1,1,2,2-Tetrachloroethane",C(C(Cl)Cl)(Cl)Cl +"1,1,2,2,3,3,4-Heptafluorocyclopentane",C1C(C(C(C1(F)F)(F)F)(F)F)F +"1,1,2,2-Tetrafluoroethyl-2,2,3,3-tetrafluoropropylether",C(C(C(F)F)(F)F)OC(C(F)F)(F)F +Fluorobenzene,C1=CC=C(C=C1)F +"1,2-Difluorobenzene",C1=CC=C(C(=C1)F)F +"1,3-Difluorobenzene",C1=CC(=CC(=C1)F)F +"1,4-Difluorobenzene",C1=CC(=CC=C1F)F +"1,2,3-Trifluorobenzene (123-FB)",C1=CC(=C(C(=C1)F)F)F +"1,2,4-Trifluorobenzene",C1=CC(=C(C=C1F)F)F +"1-Fluoropentane",CCCCCF +Fluorocyclohexane,C1CCC(CC1)F +"DEE (1,2-diethoxyethane)",CCOCCOCC +"F1F0 (1-Ethoxy-2-(2-fluoroethoxy)ethane)",CCOCCOCCF +"F1F1 (Ethane, 1,2-bis(2-fluoroethoxy)-)",C(COCCF)OCCF +F1F2,C(COCC(F)F)OCCF +"F3DEE (1-(2,2,2-Trifluoroethoxy)-2-ethoxyethane)",CCOCCOCC(F)(F)F +"FDMB (2,2,3,3-Tetrafluoro-1,4-dimethoxybutane)",COCC(C(COC)(F)F)(F)F +"F4DEE (1,2-Bis(2,2-difluoroethoxy)ethane)",C(COCC(F)F)OCC(F)F +"F5DEE (1-Difluoroethoxy-2-Trifluoroethoxyethane)",C(COCC(F)(F)F)OCC(F)F +"F6DEE (1,1,1-Trifluoro-2-[2-(2,2,2-trifluoroethoxy)ethoxy]ethane)",C(COCC(F)(F)F)OCC(F)(F)F +ethylene carbonate,C1COC(=O)O1 +fluoroethylene carbonate,C1C(OC(=O)O1)F +difluoroethylene carbonate,C1(C(OC(=O)O1)F)F +cis-difluoroethylene carbonate,O=C1O[C@@H](F)[C@@H](F)O1 +trans-difluoroethylene carbonate 2,O=C1O[C@H](F)[C@@H](F)O1 +trans-difluoroethylene carbonate,O=C1O[C@@H](F)[C@H](F)O1 +trifluoroethylene carbonate,C1(C(OC(=O)O1)(F)F)F +tetrafluoroethylene carbonate,C1(=O)OC(C(O1)(F)F)(F)F +TMS,C1CCS(=O)(=O)C1 +EMS,CCS(=O)(=O)C +BS,CCCCS(=O)(=O)CCCC +FS,CS(=O)(=O)C1=CC=CC=C1F +EVS,CCS(=O)(=O)C=C +MiPS,CC(C)S(=O)(=O)C +EiPS,CCS(=O)(=O)C(C)C +EiBS,CCS(=O)(=O)CC(C)C +iPiBS,CC(C)CS(=O)(=O)C(C)C +iPsBS,CCC(C)S(=O)(=O)C(C)C +BiBS,CCCCS(=O)(=O)CC(C)C +MEMS,COCCS(=O)(=O)C +EMES,CCS(=O)(=O)CCOC +ESEME,CCS(=O)(=O)CCOCCOC +AN,CC#N +MPN,COCCC#N +EPN,CCOCCC#N +FEPN,C(COCC(F)(F)F)C#N +MAN,C(C#N)C#N +SCN,C(CC#N)C#N +GLN,C(CC#N)CC#N +ADN,C(CCC#N)CC#N +PMN,C(CCC#N)CCC#N +SUN,C(CCCC#N)CCC#N +AZN,C(CCCC#N)CCCC#N +SEN,C(CCCCC#N)CCCC#N +TMP,COP(=O)(OC)OC +DMMP,COP(=O)(C)OC +DMMEMP,COCCOCP(=O)(OC)OC +DEMEMP,CCOP(=O)(COCCOC)OCC +HMP,COP1(=NP(=NP(=N1)(OC)OC)(OC)OC)OC +HTP,CCOP1(=NP(=NP(=N1)(OCC)OCC)(OCC)OCC)OCC +HHTP,CCCOP1(=NP(=NP(=N1)(OCCC)OCCC)(OCCC)OCCC)OCCC +METMS,COCCO[Si](C)(C)C +MEETMS,COCCOCCO[Si](C)(C)C +MEEETMS,COCCOCCOCCO[Si](C)(C)C +MEEEETMS,COCCOCCOCCOCCO[Si](C)(C)C +MEEEEETMS,COCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEEEETMS,COCCOCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEEEEETMS,COCCOCCOCCOCCOCCOCCOCCO[Si](C)(C)C +DMC,COC(=O)OC +EMC,CCOC(=O)OC +DEC,O=C(OCC)OCC +DME,COCCOC +DMDC,O=C(OC(=O)OC)OC + + diff --git a/opt/design/figures.jl b/opt/design/figures.jl new file mode 100644 index 00000000..ba3d2e5e --- /dev/null +++ b/opt/design/figures.jl @@ -0,0 +1,54 @@ +function hydrocarbon_trends(df) + f = Figure() + + # Trends with Size + df = rename(df, "BF3 affinity" => :dn) + gl_trends = GridLayout(f[1, 1]) + rowgap!(gl_trends, 5) + axes = [ + :mu => L"$\mu$ [D]", + :h298 => L"$G\degree$\n[kJ/mol]", + :alpha => L"$\alpha$\n[$\alpha_0^2$]", + :mp => L"$$Melt\n[$\degree C$]", + :dn => L"$$DN\n[kJ/mol]", + :pKa_kt => L"pKa", + :alpha_kt => L"KT $\alpha$", + :beta_kt => L"KT $\beta$", + ] + axes = map(enumerate(axes)) do (idx, (col, ylabel)) + is_last = idx == length(axes) + col => Axis(gl_trends[idx, 1]; + xlabel=L"$$Number of Carbons", ylabel, + limits=((1, 25), nothing), + xlabelvisible=is_last, + xticksvisible=is_last, + xticklabelsvisible=is_last, + ) + end |> Dict + foreach(groupby(df, :type)) do gdf + for (col, ax) in pairs(axes) + errorlines!(ax, gdf.n_carbon, gdf[:, col]; label=first(gdf.type)) + end + end + + + # Design Rules + gl_dr = GridLayout(f[1, 2]) + ax = Axis(gl_dr[1, 1]; + xlabel="HOMO [eV]", ylabel="pKa", + ) + cb = Colorbar(gl_dr[1, 2]; + label="DN", + colorrange=(0, 30), + ) + errorcross!(ax, df.homo .* 27.2114, df.pKa_kt; + # color=mean.(df.dn), + color=df.n_carbon, + cb_attrs(cb)..., + ) + + rowgap!(gl_trends, 5) + resize_to_layout!(f) + + return f +end diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 9b96d003..741b00df 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -1,25 +1,50 @@ +using PythonCall + +# Load the MIST models +# Normally, I'd do all of the code loading first. By something's funky +# with loading the models later on (seg-faults) so here we are +MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned +MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask + +mist_qm9 = MISTFinetuned.from_pretrained("../../models/mist-x4i8qzuq-qm9") +mist_kt = MISTMultiTask.from_pretrained("../../models/solvent-properties") +mist_dn = MISTMultiTask.from_pretrained("../../models/donor-number") +mist_solvent = MISTMultiTask.from_pretrained("../../models/mist-solventnet") + +# Generate Plots using Makie +using DesignRules using DataFrames -using CSV - -function plot_sat_fats(file) - f = Figure(size=96 .* (4.5, 6)) - df = DataFrame(CSV.File(file)) - df.n_carbon = map(x -> count(==('C'), x), df.smi) - - ax = Axis(f[1, 1], ylabel="Diploe Moment [D]", xlabel="Number of Carbons") - scatter!(ax, df.n_carbon, df.mu) - - ax = Axis(f[2, 1], ylabel="Energy [Ha]", xlabel="Number of Carbons") - scatter!(ax, df.n_carbon, df.u0; marker=:+, label="Internal 0K") - scatter!(ax, df.n_carbon, df.u298; marker=:x, label="Internal at 298.15K") - scatter!(ax, df.n_carbon, df.h298; marker=:circle, label="Enthalpy at 298.15K") - scatter!(ax, df.n_carbon, df.g298; marker=:diamond, label="Gibbs at 298.15K") - axislegend(ax; position=:rb, nbanks=2) - - ax = Axis(f[3, 1], ylabel="HOMO-LUMO Gap", xlabel="Number of Carbons") - scatter!(ax, df.n_carbon, df.gap; marker=:x, label="Direct") - scatter!(ax, df.n_carbon, df.lumo - df.homo; marker=:+, label="Calculated") - axislegend(ax; position=:rb) - return f -end +using CSV: CSV + +include("../style.jl") + + +# Evaluate hydrocarbons +df_hydrocarbons = DesignRules.predict_all( + DesignRules.simple_hydrocarbons(25), + mist_qm9, + "kt" => mist_kt, + mist_dn, + mist_solvent => [:bp, :mp, :fp]; + n=10 +) + +DesignRules.hydrocarbon_trends(df_hydrocarbons) + +# Evaluate electrolytes +df_electrolyte = DataFrame(CSV.File("electrolytes.csv")) +df_electrolyte = DesignRules.predict_all( + df_electrolyte, + mist_qm9, + "kt" => mist_kt, + mist_dn, + mist_solvent => [:bp, :mp, :fp]; + n=10 +) + + + + + + diff --git a/opt/design/reaction.jl b/opt/design/reaction.jl deleted file mode 100644 index 7f8e159b..00000000 --- a/opt/design/reaction.jl +++ /dev/null @@ -1,344 +0,0 @@ - -# include("MIST.jl") -# using .MIST - -using PythonCall -using DataFrames -using Makie -using CategoricalArrays -using ManifoldLearning - - -steps = [ - "CC(C)(CO)C(C(=O)NCCC(=O)O)O", - "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)O)O", - "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)N[C@@H](CS)C(=O)O)O", - "O=C(NCCS)CCNC(=O)[C@H](O)C(C)(C)COP(=O)(O)O", - "CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@@H](O1)N2C=NC3=C(N=CN=C32)N)O)O)[C@H](C(=O)NCCC(=O)NCCS)O", - "O=C(NCCS)CCNC(=O)C(O)C(C)(C)COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n2cnc1c(ncnc12)N)[C@H](O)[C@@H]3OP(=O)(O)O", -] - -function predict_pathway(steps, model, tok) - o = model.predict(PyList(steps), tok) - G = pyconvert(Vector{Float64}, o["g298"]) - f = Figure() - ax = Axis(f[1, 1]; ylabel="Gibbs Free Energy @ 298K [Ha]") - stairs!(ax, G, step=:center) - f -end - -function alkene_chain_gen(n::Int) - smi = [] - for len in range(3, n) - for pos in range(1, len - 1; step=1) - push!(smi, (; - len, - pos, - smi=("C"^pos) * "=" * "C"^(len - pos), - )) - end - end - return DataFrame(smi) -end - -function plot_alkene_chain(n, model, tok) - df = alkene_chain_gen(n) - df.type .= "normal" - df2 = copy(df) - df2.smi = map(smi -> replace(smi, "=" => "/C=C/"), df2.smi) - df2.len .+= 2 - df2.type .= "cis" - df3 = copy(df) - df3.smi = map(smi -> replace(smi, "=" => "/C=C\\"), df2.smi) - df3.len .+= 2 - df3.type .= "trans" - df = vcat(df, df2, df3) - - pred = model.predict(PyList(df.smi), tok) - df.h298 = pyconvert(Vector{Float64}, pred["h298"]) - df.mu = pyconvert(Vector{Float64}, pred["mu"]) - - f = Figure() - ax = Axis(f[1, 1]; - # limits=((0, 1), nothing), - # yscale=log10, - ylabel="H298 [kcal/mol]") - cb = Colorbar(f[1, 2], limits=(3, n + 2), colormap=:viridis, label="Length") - for len in unique(df.len) - for (type, marker) in [("normal", :x), ("cis", :cross), ("trans", :diamond)] - df_len = subset(df, :len => ByRow(==(len)), :type => ByRow(==(type))) - scatter!(ax, - range(1, nrow(df_len)), - df_len.h298; - color=len, - marker, - colorrange=cb.limits, - colormap=cb.colormap, - ) - end - end - return f, df -end - -alkyne(n::Int) = "C#" * "C"^(n-1) -alkene(n::Int) = "C=" * "C"^(n-1) -alkane(n::Int) = "C" ^ n -isoalkane(n::Int) = "C(C)" * "C" ^ (n - 2) -alcohol(n::Int) = "O" * "C" ^ n -aldehyde(n::Int) = "O=" * "C"^n -nitrile(n::Int) = "N#" * "C"^n -dinitrile(n::Int) = "N#" * "C"^n * "#N" -amine(n::Int) = "N" * "C"^ n -carboxylic_acid(n::Int) = "C(=O)" * "C"^(n-1) -halide(n::Int, element::String) = element * "C"^n -halide(element::String) = Base.Fix2(halide, element) -tetra_sub_alkene(n::Int) = "$("C"^n)C(=C($("C"^n))$("C"^n))$("C"^n)" -disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" -triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" -trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" - -function carbon_chains(n::Int) - return DataFrame(vcat( - [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], - # [(; n, type="Isoalkanes", smi=isoalkane(n)) for n in 3:n], - [(; n, type="Alcohols", smi=alcohol(n)) for n in 1:n], - [(; n, type="Nitrile", smi=nitrile(n)) for n in 1:n], - # [(; n, type="Dinitriles", smi=dinitrile(n)) for n in 1:n], - [(; n, type="Amines", smi=amine(n)) for n in 1:n], - [(; n, type="Carboxylic Acids", smi=carboxylic_acid(n)) for n in 2:n], - [(; n, type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], - # [(; n, type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], - # [(; n, type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], - [(; n, type="Tetra subsitued Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], - [(; n, type="Disubsitued Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], - )) -end - -function melt_boil_trends_monte(n::Int, model) - alkanes = [alkane(n) for n in 1:n] - isoalkanes = [isoalkane(n) for n in 3:n] - - df_alkanes = MIST.predict_monte(alkanes, model; n=50) - df_alkanes.n = 1:n - df_isoalkanes = MIST.predict_monte(isoalkanes, model; n=50) - df_isoalkanes.n = 3:n - - f = Figure() - ax = Axis(f[1, 1]; ylabel="Boiling Point [°C]") - errorbars!(ax, df_alkanes.n, df_alkanes.bp_mean, df_alkanes.bp_stderr; label="Alkanes") - errorbars!(ax, df_isoalkanes.n, df_isoalkanes.bp_mean, df_isoalkanes.bp_stderr; label="Isoalkanes") - axislegend(ax; position=:rb) - - ax = Axis(f[2, 1]; ylabel="Melting Point [°C]") - errorbars!(ax, df_alkanes.n, df_alkanes.mp_mean, df_alkanes.mp_stderr; label="Alkanes") - errorbars!(ax, df_isoalkanes.n, df_isoalkanes.mp_mean, df_isoalkanes.mp_stderr; label="Isoalkanes") - - return f -end - -function predict_class(class::Dict, model) - smi = Iterators.flatten(values(class)) - df = MIST.predict(collect(smi), model) - types = map((kv) -> repeat([kv[1]], length(kv[2])), collect(pairs(class))) - df.types = collect(Iterators.flatten(types)) - return df -end - - - -function melt_boil_trends(n::Int, model) - alkanes = - isoalkanes = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] - alchols = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] - df = DataFrame(vcat( - [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], - [(; n, type="Isoalkane", smi=isoalkane(n)) for n in 3:n], - [(; n, type="Alchols", smi=alchol(n)) for n in 1:n], - )) - return df - - - - - model = model.eval() - df_alkanes = MIST.predict(alkanes, model) - df_alkanes.n = 1:n - df_isoalkanes = MIST.predict(isoalkanes, model) - df_isoalkanes.n = 3:n - - f = Figure() - ax_bp = Axis(f[1, 1]; ylabel="Boiling Point [°C]") - ax_mp = Axis(f[2, 1]; ylabel="Melting Point [°C]") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - lines!(ax_bp, df.n, df.bp; label=type) - lines!(ax_mp, df.n, df.bp; label=type) - end - axislegend(ax_bp; position=:rb) - - return f -end - -function dipole_trends(n::Int; qm, dn, kt, mpbp, samples=20) - df = carbon_chains(n) - df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) - select!(df_qm9, Not(:smi)) - df_dn = MIST.predict_monte(df.smi, dn; n=samples) - select!(df_dn, "BF3 affinity_mean" => :donor_number_mean, "BF3 affinity_stderr" => :donor_number_stderr) - df_mpbp = MIST.predict_monte(df.smi, mpbp; n=samples) - select!(df_mpbp, :mp_mean, :mp_stderr, :bp_mean, :bp_stderr) - df_kt = MIST.predict_monte(df.smi, kt; n=samples) - select!(df_kt, Not(:smi)) - rename!(df_kt, - "alpha_mean" => :alpha_kt_mean, - "alpha_stderr" => :alpha_kt_stderr, - "beta_mean" => :beta_kt_mean, - "beta_stderr" => :beta_kt_stderr, - ) - df = hcat(df, df_qm9, df_dn, df_kt, df_mpbp) - - f = Figure() - gl_trends = GridLayout(f[1,1]) - rowgap!(gl_trends, 5) - ax = Axis(gl_trends[1, 1]; ylabel=L"$\mu$ [D]") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.mu_mean; label=type) - errorbars!(ax, df_type.n, df_type.mu_mean, df_type.mu_stderr; color=h.color) - end - - ax = Axis(gl_trends[2, 1]; ylabel=L"$G^{\degree}$ [kJ/mol]") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.h298_mean; label=type) - errorbars!(ax, df_type.n, df_type.h298_mean, df_type.h298_stderr; color=h.color) - end - - ax = Axis(gl_trends[3, 1]; ylabel=L"DN", xlabel=L"$$Number of Carbons") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.donor_number_mean; label=type) - errorbars!(ax, df_type.n, df_type.donor_number_mean, df_type.donor_number_stderr; color=h.color) - end - - ax = Axis(gl_trends[4, 1]; ylabel=L"$\alpha$ [$\alpha_0^2$]", xlabel=L"$$Number of Carbons") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.alpha_mean; label=type) - errorbars!(ax, df_type.n, df_type.alpha_mean, df_type.alpha_stderr; color=h.color) - end - - ax = Axis(gl_trends[5, 1]; ylabel=L"KT $\beta$", xlabel=L"$$Number of Carbons") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.beta_kt_mean; label=type) - errorbars!(ax, df_type.n, df_type.beta_kt_mean, df_type.beta_kt_stderr; color=h.color) - end - - ax = Axis(gl_trends[6, 1]; ylabel=L"Melting Point [$^{°}C$]", xlabel=L"$$Number of Carbons") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.mp_mean; label=type) - errorbars!(ax, df_type.n, df_type.mp_mean, df_type.mp_stderr; color=h.color) - end - - gl_corr = GridLayout(f[1,2]) - ax = Axis(gl_corr[1, 1]; ylabel=L"DN", xlabel=L"Kamlet-Taft $\beta$") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - h = errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.donor_number_stderr, label=type) - errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.beta_kt_stderr; direction=:x, color=h.color) - end - - ax = Axis(gl_corr[2, 1]; ylabel=L"$\alpha$ [$a_0^3$]", xlabel=L"$\mu$ [D]") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - h = errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.alpha_stderr, label=type) - errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.mu_stderr; direction=:x, color=h.color) - end - - Legend(f[2, :], ax; nbanks=2, tellheight=true) - rowgap!(gl_trends, 5) - resize_to_layout!(f) - - return f -end - -labels(x) = x.label[] - -function hard_soft_acid(; qm, kt, samples=20) - df = DataFrame(vcat( - [(; smi, type="Hard Acid") for smi in [ - "FB(F)F", "O=S(=O)=O", "OB(O)O", "O(B(OC)OC)C", - map(triboroester, 1:5)..., - ]], - [(; smi, type="Borderline Acid") for smi in [ - map(aldehyde, 1:2:10)..., - ]], - [(; smi, type="Soft Acid") for smi in [ - map(aldehyde, 1:5:30)..., - ]], - [(; smi, type="Hard Base") for smi in [ - map(amine, 1:5:30)..., - map(alcohol, 1:5:30)..., - ]], - [(; smi, type="Soft Base") for smi in [ - map(alkene, 1:5:30)..., - map(tetra_sub_alkene, 1:10)..., - map(disubstituted_alkyne, 1:3:15)..., - ]], - )) - df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) - select!(df_qm9, Not(:smi)) - df_kt = MIST.predict_monte(df.smi, kt; n=samples) - select!(df_kt, :pKa_mean, :pKa_stderr) - df = hcat(df, df_qm9, df_kt) - df.type = categorical(df.type) - color = map(type -> MISTStyle.CAT_COLORS[levelcode(type)], df.type) - - - f = Figure() - ax = Axis(f[1, 1]; xlabel=L"$\alpha$ [$\alpha^2_0$]", ylabel=L"$$pKa") - xyerrorbars!(ax, df.alpha_mean, df.pKa_mean, df.alpha_stderr, df.pKa_stderr; label=df.type, color) - - ax = Axis(f[2, 1]; xlabel=L"$$LUMO [Har]", ylabel=L"$$pKa") - h = xyerrorbars!(ax, df.lumo_mean, df.pKa_mean, df.lumo_stderr, df.pKa_stderr; label=df.type, color) - - ax = Axis(f[1, 2]) - hidexdecorations!(ax) - hideydecorations!(ax) - emb = MIST.embed(df.smi, qm) - m = fit(TSNE, emb'; p=30, maxoutdim=2) - r = predict(m) - scatter!(ax, r[1, :], r[2, :]; color) - - types = map(enumerate(levels(df.type))) do (i, label) - PolyElement( - color=MISTStyle.CAT_COLORS[i], - label=label - ) - end - - Legend(f[2, 2], types, labels.(types)) - - return f - - -end - -function xyerrorbars!(ax, x, y, errorx, errory; label=nothing, kwargs...) - h = errorbars!(ax, x, y, errory; label, kwargs...) - errorbars!(ax, x, y, errorx; direction=:x, color=h.color) - return h -end diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl new file mode 100644 index 00000000..6cab3c32 --- /dev/null +++ b/opt/design/src/DesignRules.jl @@ -0,0 +1,20 @@ +module DesignRules + +using PythonCall: PythonCall, Py, PyList, pyconvert +using DataFrames +using Makie +using Statistics: Statistics, mean, std +using StatsBase: StatsBase, stderror, mean_and_std + +export labels, cb_attrs + +include("uq.jl") +include("inference.jl") + +include("hydrocarbons.jl") +include("plot_utils.jl") + +# Helper for getting the label from plot elements +labels(x) = x.label[] + +end diff --git a/opt/design/src/hydrocarbons.jl b/opt/design/src/hydrocarbons.jl new file mode 100644 index 00000000..4d480959 --- /dev/null +++ b/opt/design/src/hydrocarbons.jl @@ -0,0 +1,38 @@ +alkyne(n::Int) = "C#" * "C"^(n - 1) +alkene(n::Int) = "C=" * "C"^(n - 1) +alkane(n::Int) = "C"^n +isoalkane(n::Int) = "C(C)" * "C"^(n - 2) +alcohol(n::Int) = "O" * "C"^n +aldehyde(n::Int) = "O=" * "C"^n +nitrile(n::Int) = "N#" * "C"^n +dinitrile(n::Int) = "N#" * "C"^n * "#N" +amine(n::Int) = "N" * "C"^n +carboxylic_acid(n::Int) = "C(=O)" * "C"^(n - 1) +halide(n::Int, element::String) = element * "C"^n +halide(element::String) = Base.Fix2(halide, element) +tetra_sub_alkene(n::Int) = "$("C"^n)C(=C($("C"^n))$("C"^n))$("C"^n)" +disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" +triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" +trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" +ether(n::Int) = "COC"^Int(n / 2) + +function simple_hydrocarbons(n::Int) + df = DataFrame(vcat( + [(; type="Alkanes", smi=alkane(n)) for n in 1:n], + [(; type="Isoalkanes", smi=isoalkane(n)) for n in 3:n], + [(; type="Alcohols", smi=alcohol(n)) for n in 1:n], + [(; type="Nitrile", smi=nitrile(n)) for n in 1:n], + [(; type="Dinitriles", smi=dinitrile(n)) for n in 1:n], + [(; type="Amines", smi=amine(n)) for n in 1:n], + [(; type="Carboxylic Acids", smi=carboxylic_acid(n)) for n in 2:n], + [(; type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], + [(; type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], + [(; type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], + [(; type="Tetra subsitued Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], + [(; type="Disubsitued Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], + [(; type="Ether", smi=ether(n)) for n in 2:2:n], + )) + transform!(df, :smi => ByRow(smi -> count(==('C'), smi)) => :n_carbon) + return df +end + diff --git a/opt/design/src/inference.jl b/opt/design/src/inference.jl new file mode 100644 index 00000000..b9dba9ca --- /dev/null +++ b/opt/design/src/inference.jl @@ -0,0 +1,79 @@ +function predict(smi::Vector{String}, model::Py) + model = model.to("mps") + py_out = model.predict(PyList(smi)) + out = Dict{String,Vector{Float64}}() + for (k, v) in py_out.items() + out[pyconvert(String, k)] = pyconvert(valtype(out), v["value"]) + end + df = DataFrame(out) + insertcols!(df, 1, :smi => smi) + return df +end + +function embed(smi::Vector{String}, model::Py) + pyconvert(Matrix, model.embed(PyList(smi))) +end + +function predict_monte(smi::Vector{String}, model::Py; n=10) + model = model.to("mps") + model = model.train() + results = Dict() + py_smi = PyList(smi) + for _ in 1:n + sample = model.predict(py_smi) + for (k, v) in sample.items() + k = pyconvert(String, k) + v = pyconvert(Vector{Float64}, v["value"]) + if haskey(results, k) + results[k] = hcat(results[k], v) + else + results[k] = v + end + end + end + + # Compute statistics + summary = Dict() + for (k, v) in pairs(results) + mu_std = map(mean_and_std, eachrow(v)) + summary[k] = map(x -> UQReal(x..., n), mu_std) + end + df = DataFrame(summary) + insertcols!(df, 1, :smi => smi) + return df +end + +model_spec(model::Py) = (nothing, model, Colon()) +model_spec(name_model::Pair{String,Py}) = (first(name_model), last(name_model), Colon()) +model_spec(name_model_cols::Pair{String,Pair{Py,<:Vector}}) = (first(name_model_cols), last(name_model_cols)...) +model_spec(model_cols::Pair{Py,<:Vector}) = (nothing, first(model_cols), last(model_cols)) + +""" +Run all models against a DataFrame of smiles in the `smi_column` + +Models can be specified as: + +- `model::Py` the model to run +- `"name" => model`: Runs model renaming columns as `col * "_" * name` +- `model => [cols...]`: runs the model only returning the selected columns +- `"name" => model => [cols...]`: Runs the model renaming and selecting columns + +By default, a single evaluation is sampled. Increasing `n` will collect multiple samples from the model +""" + +function predict_all(df::DataFrame, models...; smi_column=:smi, n=1) + for spec in models + name, model, cols = model_spec(spec) + if n == 1 + df_predict = predict(df[:, smi_column], model) + else + df_predict = predict_monte(df[:, smi_column], model; n) + end + select!(df_predict, Not(:smi)) + select!(df_predict, cols) + isnothing(name) || rename!(col -> col * "_" * name, df_predict) + df = hcat(df, df_predict) + end + return df +end + diff --git a/opt/design/src/plot_utils.jl b/opt/design/src/plot_utils.jl new file mode 100644 index 00000000..059682b6 --- /dev/null +++ b/opt/design/src/plot_utils.jl @@ -0,0 +1,43 @@ +@recipe(ErrorLines, x, y, error_y) do scene + Attributes() +end +Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), stderror.(y)) +Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:Real}) = (x, y, zero(y)) + +function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Real},AbstractVector{<:Real}}}) + lines!(plt, plt.x, plt.y; Makie.shared_attributes(plt, Lines)...) + if !isnothing(plt.error_y) + errorbars!(plt, plt.x, plt.y, plt.error_y; Makie.shared_attributes(plt, Makie.Errorbars)...) + end + return plt +end + +@recipe(ErrorCross, x, y, error_x, error_y) do scene + Attributes() +end + +Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) + +function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) + attrs = Makie.shared_attributes(plt, Errorbars) + h = errorbars!(plt, plt.x, plt.y, plt.error_y; direction=:y, attrs...) + errorbars!(plt, plt.x, plt.y, plt.error_x; + color=h.color, + colorscale=h.colorscale, + colormap=h.colormap, + colorrange=h.colorrange, + direction=:x, + attrs... + ) + return plt +end + +function cb_attrs(cb::Colorbar) + return (; + colormap=cb.colormap, + colorscale=cb.scale, + colorrange=cb.colorrange, + lowclip=cb.lowclip, + highclip=cb.highclip, + ) +end diff --git a/opt/design/src/uq.jl b/opt/design/src/uq.jl new file mode 100644 index 00000000..4b45e43a --- /dev/null +++ b/opt/design/src/uq.jl @@ -0,0 +1,22 @@ +struct UQReal{T} + mean::T + std::T + n::Int +end + +Statistics.mean(x::UQReal) = x.mean +Statistics.std(x::Real) = x.std +StatsBase.stderror(x) = x.std / sqrt(x.n) +function Base.show(io::IO, x::UQReal) + μ = mean(x) + se = stderror(x) + if get(io, :compact, false)::Bool + μ = round(μ; sigdigits=5) + se = round(se; sigdigits=5) + end + print(io, "$μ ± $se") +end + +Base.:*(x::UQReal, y::Real) = UQReal(x.mean * y, x.std * y, x.n) +Base.:*(x::Real, y::UQReal) = y * x + From db6a9922d3ab0203f69f1ed117c4ecced0c0dd42 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 27 Mar 2025 10:57:49 -0400 Subject: [PATCH 016/147] refactor: Move MistStyle into a proper package --- opt/MISTStyle/Project.toml | 5 ++ opt/{style.jl => MISTStyle/src/MISTStyle.jl} | 52 +++++++++++++++----- opt/MISTStyle/src/errorcross.jl | 17 +++++++ opt/design/Project.toml | 8 +++ opt/design/src/DesignRules.jl | 12 ++--- opt/design/src/plot_utils.jl | 27 ---------- opt/interp_embeddings/Project.toml | 3 ++ opt/interp_embeddings/embedding_figure.jl | 6 +-- opt/synth_access/Project.toml | 5 ++ opt/synth_access/plot.jl | 4 +- 10 files changed, 86 insertions(+), 53 deletions(-) create mode 100644 opt/MISTStyle/Project.toml rename opt/{style.jl => MISTStyle/src/MISTStyle.jl} (70%) create mode 100644 opt/MISTStyle/src/errorcross.jl diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml new file mode 100644 index 00000000..36649c1d --- /dev/null +++ b/opt/MISTStyle/Project.toml @@ -0,0 +1,5 @@ +name = "MISTStyle" +uuid = "0a43f4a4-3db7-4939-b247-26273f54b603" + +[deps] +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" diff --git a/opt/style.jl b/opt/MISTStyle/src/MISTStyle.jl similarity index 70% rename from opt/style.jl rename to opt/MISTStyle/src/MISTStyle.jl index 02d4c85b..dc4f632d 100644 --- a/opt/style.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -2,9 +2,11 @@ module MISTStyle using Makie -const pt = 4 / 3 +const pt = 3 / 4 const inch = 96 +export pt, inch + """ Save duplicate figures for publication and web """ function savefig(name::String, f::Figure; dpi=300, fig_dir="fig") mkpath(fig_dir) @@ -13,6 +15,22 @@ function savefig(name::String, f::Figure; dpi=300, fig_dir="fig") return nothing end +""" Helper function to get the label of an plot element """ +label(x) = x.label[] + +""" Get the colorbar attributes of a plot element """ +function cb_attrs(cb::Colorbar) + return (; + colormap=cb.colormap, + colorscale=cb.scale, + colorrange=cb.colorrange, + lowclip=cb.lowclip, + highclip=cb.highclip, + ) +end + +include("errorcross.jl") + const CAT_COLORS = cgrad( map(x -> RGBf(x ./ 255...), [ (99, 110, 250), @@ -43,6 +61,7 @@ function theme() size=(246, 152), figure_padding=(2, 2, 2, 2), colormap=:lipari, + linewidth=0.5, CairoMakie=(; pt_per_unit=2, px_per_unit=300 / inch @@ -57,28 +76,34 @@ function theme() ), Axis=(; spinewidth=0.5, + xlabelsize=8pt, + ylabelsize=8pt, + yticklabelsize=6pt, + xticklabelsize=6pt, ylabelpadding=1pt, xlabelpadding=1pt, - yticksize=3, + yticklabelpad=2pt, + xticklabelpad=2pt, + yticksize=2pt, ytickwidth=0.5, yminortickwidth=0.5, yminorticksize=2, xtickwidth=0.5, - xticksize=3, + xticksize=2pt, xminortickwidth=0.5, xminorticksize=2, xgridwidth=0.5, ygridwidth=0.5, xminorgridwidth=0.5, yminorgridwidth=0.5, - yticklabelsize=7pt, - xticklabelsize=7pt, ), Legend=(; titlegap=0, + labelsize=8pt, patchsize=(8, 8), - rowgap=2pt, - colgap=8, + patchlabelgap=3pt, + rowgap=1pt, + colgap=3pt, groupgap=4pt, famevisible=true, framewidth=0.5, @@ -90,8 +115,8 @@ function theme() spinewidth=0.5, tickwidth=0.5, ticksize=2, - labelsize=6pt, - ticklabelsize=5pt, + labelsize=8pt, + ticklabelsize=6pt, labelpadding=0pt, ticklabelpad=0pt, ), @@ -99,9 +124,12 @@ function theme() markersize=5pt, marker=:x, ), - ErrrorBar=(; - whiskerwidth=2, - linewidth=0.5, + ErrorLines=(; + whiskerwidth=3, + ), + ErrorCross=(; + markersize=5pt, + whiskerwidth=3, ) ) end diff --git a/opt/MISTStyle/src/errorcross.jl b/opt/MISTStyle/src/errorcross.jl new file mode 100644 index 00000000..6e0f74e4 --- /dev/null +++ b/opt/MISTStyle/src/errorcross.jl @@ -0,0 +1,17 @@ +@recipe(ErrorCross, x, y, error_x, error_y) do scene + Attributes() +end + +function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) + scatter!(plt, plt.x, plt.y; Makie.shared_attributes(plt, Scatter)...) + h = errorbars!(plt, plt.x, plt.y, plt.error_y; + direction=:y, + Makie.shared_attributes(plt, Errorbars)... + ) + errorbars!(plt, plt.x, plt.y, plt.error_x; + color=h.color, + direction=:x, + Makie.shared_attributes(plt, Errorbars)... + ) + return plt +end diff --git a/opt/design/Project.toml b/opt/design/Project.toml index ef36dd89..05ae9690 100644 --- a/opt/design/Project.toml +++ b/opt/design/Project.toml @@ -3,9 +3,17 @@ version = "0.1.0" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" +MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" + +[sources] +MISTStyle = {path = "../MISTStyle"} + diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index 6cab3c32..f271f338 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -1,20 +1,20 @@ module DesignRules -using PythonCall: PythonCall, Py, PyList, pyconvert +using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst using DataFrames using Makie using Statistics: Statistics, mean, std -using StatsBase: StatsBase, stderror, mean_and_std +using StatsBase: StatsBase, stderror, mean_and_std, mean +using MISTStyle: MISTStyle, label, cb_attrs, ErrorCross +using CategoricalArrays: levelcode, categorical -export labels, cb_attrs +const HARTREE_TO_EV = 27.211_386_245_981 include("uq.jl") include("inference.jl") include("hydrocarbons.jl") include("plot_utils.jl") - -# Helper for getting the label from plot elements -labels(x) = x.label[] +include("trends.jl") end diff --git a/opt/design/src/plot_utils.jl b/opt/design/src/plot_utils.jl index 059682b6..4178e74e 100644 --- a/opt/design/src/plot_utils.jl +++ b/opt/design/src/plot_utils.jl @@ -12,32 +12,5 @@ function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Rea return plt end -@recipe(ErrorCross, x, y, error_x, error_y) do scene - Attributes() -end - Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) -function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) - attrs = Makie.shared_attributes(plt, Errorbars) - h = errorbars!(plt, plt.x, plt.y, plt.error_y; direction=:y, attrs...) - errorbars!(plt, plt.x, plt.y, plt.error_x; - color=h.color, - colorscale=h.colorscale, - colormap=h.colormap, - colorrange=h.colorrange, - direction=:x, - attrs... - ) - return plt -end - -function cb_attrs(cb::Colorbar) - return (; - colormap=cb.colormap, - colorscale=cb.scale, - colorrange=cb.colorrange, - lowclip=cb.lowclip, - highclip=cb.highclip, - ) -end diff --git a/opt/interp_embeddings/Project.toml b/opt/interp_embeddings/Project.toml index d449ccb0..1925fb67 100644 --- a/opt/interp_embeddings/Project.toml +++ b/opt/interp_embeddings/Project.toml @@ -5,3 +5,6 @@ CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" + +[sources] +MISTStyle = {path = "../MISTStyle"} diff --git a/opt/interp_embeddings/embedding_figure.jl b/opt/interp_embeddings/embedding_figure.jl index 42a9b8bb..61219520 100644 --- a/opt/interp_embeddings/embedding_figure.jl +++ b/opt/interp_embeddings/embedding_figure.jl @@ -3,11 +3,7 @@ using DataFrames using CairoMakie: CairoMakie using CSV: CSV using CategoricalArrays: categorical, levelcode - -include("../style.jl") -using .MISTStyle: MISTStyle, savefig, inch, pt - -labels(x) = x.label[] +using MISTStyle: MISTStyle, savefig, inch, pt, label function figure_embedding() df_benzene = DataFrame(CSV.File("interp_benzene.csv")) diff --git a/opt/synth_access/Project.toml b/opt/synth_access/Project.toml index 3c46dbdf..9a5f81bb 100644 --- a/opt/synth_access/Project.toml +++ b/opt/synth_access/Project.toml @@ -6,5 +6,10 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" FreeTypeAbstraction = "663a7486-cb36-511b-a19d-713bb74d65c9" GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" +MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" + +[sources] +MISTStyle = {path = "../MISTStyle"} + diff --git a/opt/synth_access/plot.jl b/opt/synth_access/plot.jl index 1dd537dd..58008ec9 100644 --- a/opt/synth_access/plot.jl +++ b/opt/synth_access/plot.jl @@ -5,9 +5,7 @@ using StatsBase: cor, corspearman, corkendall using Clustering: hclust using JSON: JSON using CategoricalArrays: categorical, levelcode, levels - -include("../style.jl") -using .MISTStyle: MISTStyle, savefig, inch, pt +using MISTStyle: MISTStyle, savefig, inch, pt function plot_score_correlation!(f, df; correlation, kwargs...) columns = names(df) From 133d7c3b4cedf8027ff67014f5079b45ef8c8b4f Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 27 Mar 2025 19:27:36 -0400 Subject: [PATCH 017/147] feat: better plots for design trends --- opt/MISTStyle/Project.toml | 4 + opt/MISTStyle/src/MISTStyle.jl | 26 ++- opt/MISTStyle/src/powerlaw.jl | 38 +++++ opt/design/figures.jl | 54 ------ opt/design/plots.jl | 67 +++++--- opt/design/src/DesignRules.jl | 5 +- opt/design/src/hydrocarbons.jl | 27 ++- opt/design/src/inference.jl | 21 +++ opt/design/src/trends.jl | 292 +++++++++++++++++++++++++++++++++ opt/design/src/uq.jl | 5 +- 10 files changed, 444 insertions(+), 95 deletions(-) create mode 100644 opt/MISTStyle/src/powerlaw.jl delete mode 100644 opt/design/figures.jl create mode 100644 opt/design/src/trends.jl diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index 36649c1d..d58c2678 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -2,4 +2,8 @@ name = "MISTStyle" uuid = "0a43f4a4-3db7-4939-b247-26273f54b603" [deps] +CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" + +[compat] +CategoricalArrays = "0.10.8" diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index dc4f632d..8fac19d4 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -1,6 +1,7 @@ module MISTStyle using Makie +using CategoricalArrays: levels const pt = 3 / 4 const inch = 96 @@ -18,6 +19,9 @@ end """ Helper function to get the label of an plot element """ label(x) = x.label[] +categorical_ticks(x) = (1:length(levels(x)), levels(x)) + + """ Get the colorbar attributes of a plot element """ function cb_attrs(cb::Colorbar) return (; @@ -30,6 +34,7 @@ function cb_attrs(cb::Colorbar) end include("errorcross.jl") +include("powerlaw.jl") const CAT_COLORS = cgrad( map(x -> RGBf(x ./ 255...), [ @@ -47,6 +52,12 @@ const CAT_COLORS = cgrad( 10 ) +UM_COLORS = (; + blue=RGBf(0, 39 / 255, 76 / 255), + maize=RGBf(1, 203 / 255, 5 / 255), +) + + const CONTINUOUS_COLORS = :lipari function theme() @@ -85,13 +96,13 @@ function theme() yticklabelpad=2pt, xticklabelpad=2pt, yticksize=2pt, - ytickwidth=0.5, - yminortickwidth=0.5, - yminorticksize=2, - xtickwidth=0.5, + ytickwidth=0.5pt, + yminortickwidth=0.25pt, + yminorticksize=1pt, + xtickwidth=0.5pt, xticksize=2pt, - xminortickwidth=0.5, - xminorticksize=2, + xminortickwidth=0.25pt, + xminorticksize=1pt, xgridwidth=0.5, ygridwidth=0.5, xminorgridwidth=0.5, @@ -100,7 +111,7 @@ function theme() Legend=(; titlegap=0, labelsize=8pt, - patchsize=(8, 8), + patchsize=(8pt, 8pt), patchlabelgap=3pt, rowgap=1pt, colgap=3pt, @@ -110,6 +121,7 @@ function theme() tellheight=false, tellwidth=false, padding=(2pt, 2pt, 2pt, 2pt), + margin=(2pt, 2pt, 2pt, 2pt), ), Colorbar=(; spinewidth=0.5, diff --git a/opt/MISTStyle/src/powerlaw.jl b/opt/MISTStyle/src/powerlaw.jl new file mode 100644 index 00000000..60850ece --- /dev/null +++ b/opt/MISTStyle/src/powerlaw.jl @@ -0,0 +1,38 @@ +Makie.@recipe(Powerlaw, a, b) do scene + Attributes(; + npoints=100, + ) +end + +xint(rect::Makie.Rect) = minimum(rect)[1] .. maximum(rect)[1] + +function Makie.plot!(plt::Powerlaw) + # Get Limits + scene = Makie.parent_scene(plt) + # limits = lift(xint, scene.finallimits) + limits = lift(xint, Makie.projview_to_2d_limits(plt)) + + # Regenerate points when the view / model updates + points = Observable(Point2f[]) + onany(limits, plt[:a], plt[:b], plt[:npoints]) do limits, a, b, npoints + # Sample x over the full plot width, plus a bit extra to avoid + # clipping artifacts at the plot limits + xmin = first(minimum(limits)) + xmax = first(maximum(limits)) + chrome = 2 * (xmax - xmin) / (npoints) + transforms = (Makie.transform_func)(scene) + xinv = Makie.inverse_transform(first(transforms)) + x = xinv.(range(xmin - chrome, xmax + chrome; length=npoints)) + y = map(x -> a * x^b, x) + + # Update points + empty!(points[]) + append!(points[], Point2.(x, y)) + notify(points) + end + + # Plot response, and translate it forward + lines!(plt, points; Makie.shared_attributes(plt, Lines)...) + return plt +end + diff --git a/opt/design/figures.jl b/opt/design/figures.jl deleted file mode 100644 index ba3d2e5e..00000000 --- a/opt/design/figures.jl +++ /dev/null @@ -1,54 +0,0 @@ -function hydrocarbon_trends(df) - f = Figure() - - # Trends with Size - df = rename(df, "BF3 affinity" => :dn) - gl_trends = GridLayout(f[1, 1]) - rowgap!(gl_trends, 5) - axes = [ - :mu => L"$\mu$ [D]", - :h298 => L"$G\degree$\n[kJ/mol]", - :alpha => L"$\alpha$\n[$\alpha_0^2$]", - :mp => L"$$Melt\n[$\degree C$]", - :dn => L"$$DN\n[kJ/mol]", - :pKa_kt => L"pKa", - :alpha_kt => L"KT $\alpha$", - :beta_kt => L"KT $\beta$", - ] - axes = map(enumerate(axes)) do (idx, (col, ylabel)) - is_last = idx == length(axes) - col => Axis(gl_trends[idx, 1]; - xlabel=L"$$Number of Carbons", ylabel, - limits=((1, 25), nothing), - xlabelvisible=is_last, - xticksvisible=is_last, - xticklabelsvisible=is_last, - ) - end |> Dict - foreach(groupby(df, :type)) do gdf - for (col, ax) in pairs(axes) - errorlines!(ax, gdf.n_carbon, gdf[:, col]; label=first(gdf.type)) - end - end - - - # Design Rules - gl_dr = GridLayout(f[1, 2]) - ax = Axis(gl_dr[1, 1]; - xlabel="HOMO [eV]", ylabel="pKa", - ) - cb = Colorbar(gl_dr[1, 2]; - label="DN", - colorrange=(0, 30), - ) - errorcross!(ax, df.homo .* 27.2114, df.pKa_kt; - # color=mean.(df.dn), - color=df.n_carbon, - cb_attrs(cb)..., - ) - - rowgap!(gl_trends, 5) - resize_to_layout!(f) - - return f -end diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 741b00df..a20e102d 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -6,45 +6,58 @@ using PythonCall MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask -mist_qm9 = MISTFinetuned.from_pretrained("../../models/mist-x4i8qzuq-qm9") -mist_kt = MISTMultiTask.from_pretrained("../../models/solvent-properties") -mist_dn = MISTMultiTask.from_pretrained("../../models/donor-number") -mist_solvent = MISTMultiTask.from_pretrained("../../models/mist-solventnet") +models = ( + MISTFinetuned.from_pretrained("../../models/mist-x4i8qzuq-qm9"), + "rand" => MISTFinetuned.from_pretrained("../../models/mist-26.9M-kkgx0omx-qm9"), + "kt" => MISTMultiTask.from_pretrained("../../models/solvent-properties"), + MISTFinetuned.from_pretrained("../../models/mist-26.9M-6hk5coof-dn"), + MISTFinetuned.from_pretrained("../../models/mist-26.9M-b302p09x-bp"), + MISTFinetuned.from_pretrained("../../models/mist-26.9M-y3ge5pf9-mp"), + MISTFinetuned.from_pretrained("../../models/mist-26.9M-cyuo2xb6-fp"), + "lyte" => MISTMultiTask.from_pretrained("../../models/electrolyte-solvent/"), +) # Generate Plots using Makie using DesignRules +using DesignRules: simple_hydrocarbons +using MISTStyle using DataFrames using CSV: CSV -include("../style.jl") - - # Evaluate hydrocarbons df_hydrocarbons = DesignRules.predict_all( DesignRules.simple_hydrocarbons(25), - mist_qm9, - "kt" => mist_kt, - mist_dn, - mist_solvent => [:bp, :mp, :fp]; - n=10 + models...; + n=2 ) -DesignRules.hydrocarbon_trends(df_hydrocarbons) +with_theme(MISTStyle.theme()) do + DesignRules.hydrocarbon_trends(df_hydrocarbons) +end # Evaluate electrolytes df_electrolyte = DataFrame(CSV.File("electrolytes.csv")) -df_electrolyte = DesignRules.predict_all( - df_electrolyte, - mist_qm9, - "kt" => mist_kt, - mist_dn, - mist_solvent => [:bp, :mp, :fp]; - n=10 -) - - - - - - +df_electrolyte.smi .= DesignRules.encode.(df_electrolyte.smi; encoding="smiles-kekule") +df_electrolyte = DesignRules.predict_all(df_electrolyte, models...; n=10) + +with_theme(MISTStyle.theme()) do + DesignRules.electrolyte_trends(df_electrolyte) +end + +# Permutation sensitivity +df_perm = simple_hydrocarbons(25) +f(m) = :smi => ByRow(smi -> DesignRules.sample_encodings(smi, m)) => AsTable +df_perm_ref = transform(df_perm, f(models[1])) # QM9 finetuned on kekule +df_perm_rand = transform(df_perm, f(last(models[2]))) # QM9 finetuned with random + +# Order sensitivity +df_order_rand = DesignRules.alkene_sweep(25, last(models[2])) +df_order = DesignRules.alkene_sweep(25, models[1]) + +with_theme(MISTStyle.theme()) do + DesignRules.figure_permutations( + "Kekule" => df_perm_ref, "Random" => df_perm_rand; + name_df_order=("Kekule" => df_order, "Random" => df_order_rand) + ) +end diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index f271f338..f2b99dbd 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -1,11 +1,12 @@ module DesignRules -using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst +using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport using DataFrames using Makie +using MISTStyle +using MISTStyle: label using Statistics: Statistics, mean, std using StatsBase: StatsBase, stderror, mean_and_std, mean -using MISTStyle: MISTStyle, label, cb_attrs, ErrorCross using CategoricalArrays: levelcode, categorical const HARTREE_TO_EV = 27.211_386_245_981 diff --git a/opt/design/src/hydrocarbons.jl b/opt/design/src/hydrocarbons.jl index 4d480959..8d3e0961 100644 --- a/opt/design/src/hydrocarbons.jl +++ b/opt/design/src/hydrocarbons.jl @@ -1,6 +1,8 @@ alkyne(n::Int) = "C#" * "C"^(n - 1) alkene(n::Int) = "C=" * "C"^(n - 1) +alkene(n::Int, pos::Int) = "C"^pos * "=" * "C"^(n - pos) alkane(n::Int) = "C"^n +arene(n::Int) = "c1ccccc1" * "C"^n isoalkane(n::Int) = "C(C)" * "C"^(n - 2) alcohol(n::Int) = "O" * "C"^n aldehyde(n::Int) = "O=" * "C"^n @@ -15,6 +17,7 @@ disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" ether(n::Int) = "COC"^Int(n / 2) +ester(n::Int) = "C"^n * "C(=O)O" * "C"^n function simple_hydrocarbons(n::Int) df = DataFrame(vcat( @@ -28,11 +31,29 @@ function simple_hydrocarbons(n::Int) [(; type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], [(; type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], [(; type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], - [(; type="Tetra subsitued Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], - [(; type="Disubsitued Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], + [(; type="Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], + [(; type="Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], [(; type="Ether", smi=ether(n)) for n in 2:2:n], + [(; type="Arene", smi=arene(n)) for n in 6:n], + [(; type="Ester", smi=ester(n)) for n in 1:cld(n, 2)], )) - transform!(df, :smi => ByRow(smi -> count(==('C'), smi)) => :n_carbon) + n_carbon!(df) return df end +n_carbon!(df) = transform!(df, :smi => ByRow(smi -> count(==('C'), smi)) => :n_carbon) + +function alkene_sweep(n, model) + smi = String[] + rel_pos = Float64[] + for i in 2:n + for pos in 1:i-1 + push!(smi, alkene(i, pos)) + push!(rel_pos, (2pos-1)/(2i-2)) + end + end + df = predict_monte(smi, model) + n_carbon!(df) + df.rel_pos = rel_pos + return df +end diff --git a/opt/design/src/inference.jl b/opt/design/src/inference.jl index b9dba9ca..dfa13bd9 100644 --- a/opt/design/src/inference.jl +++ b/opt/design/src/inference.jl @@ -1,5 +1,6 @@ function predict(smi::Vector{String}, model::Py) model = model.to("mps") + model.eval() py_out = model.predict(PyList(smi)) out = Dict{String,Vector{Float64}}() for (k, v) in py_out.items() @@ -10,6 +11,13 @@ function predict(smi::Vector{String}, model::Py) return df end +function encode(smi::String; encoding::String="smiles-kekule", random::Bool=false) + utils = @pyconst(pyimport("electrolyte_fm.data_modules.utils")) + encoder = utils.MolEncoding(encoding) + smi_out = random ? encoder.random(smi) : encoder(smi) + return something(pyconvert(Union{String,Nothing}, smi_out), smi) +end + function embed(smi::Vector{String}, model::Py) pyconvert(Matrix, model.embed(PyList(smi))) end @@ -43,6 +51,19 @@ function predict_monte(smi::Vector{String}, model::Py; n=10) return df end +encoding_variants(smi::String, n::Int=100) = unique(encode(smi; encoding="smiles", random=true) for _ in 1:n) + +function sample_encodings(smi::String, model::Py; n=100) + smi_variants = encoding_variants(smi, n) + df = predict(smi_variants, model) + out = Dict() + for col in names(df) + col == "smi" && continue + out[col] = UQReal(df[:, col]) + end + return out +end + model_spec(model::Py) = (nothing, model, Colon()) model_spec(name_model::Pair{String,Py}) = (first(name_model), last(name_model), Colon()) model_spec(name_model_cols::Pair{String,Pair{Py,<:Vector}}) = (first(name_model_cols), last(name_model_cols)...) diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl new file mode 100644 index 00000000..9a9fb5ce --- /dev/null +++ b/opt/design/src/trends.jl @@ -0,0 +1,292 @@ +function hydrocarbon_trends(df) + f = Figure(size=(3.5inch, 3inch)) + # Trends with Size + gl_trends = GridLayout(f[1, 1]) + axes = [ + :u298 => L"$G\degree$\n[kJ/mol]", + :mu => L"$\mu$ [D]", + :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", + :gap => L"Gap\n[eV]$$", + :mp => L"$$Melt\n[$\degree C$]", + :bp => L"$$Boil\n[$\degree C$]", + # :dn => L"$$DN\n[kJ/mol]", + :pKa_kt => L"pKa", + # :alpha_kt => L"KT $\alpha$", + # :beta_kt => L"KT $\beta$", + ] + axes = map(enumerate(axes)) do (idx, (col, ylabel)) + is_last = idx == length(axes) + col => Axis(gl_trends[idx, 1]; + xlabel=L"$$Number of Carbons", ylabel, + limits=((1, 25), nothing), + xticks=LinearTicks(5), + xlabelvisible=is_last, + xticksvisible=is_last, + xminorticksvisible=is_last, + xticklabelsvisible=is_last, + tellwidth=true, + yticks=WilkinsonTicks(5), + yminorticksvisible=true, + xminorticks=IntervalsBetween(5), + ) + end |> Dict + foreach(groupby(df, :type)) do gdf + for (col, ax) in pairs(axes) + y = gdf[:, col] + if col in [:gap] + y .*= HARTREE_TO_EV + end + errorlines!(ax, gdf.n_carbon, y; label=string(first(gdf.type))) + end + end + Legend(gl_trends[length(axes)+1, 1], axes[:mu]; nbanks=3, tellheight=true) + + # Design Rules + gl_dr = GridLayout(f[1, 2]) + ax_dn = Axis(gl_dr[1, 1]; ylabel=L"DN", xlabel=L"HOMO [eV]$$") + ax_alpha_beta = Axis(gl_dr[2, 1]; ylabel=L"KT $\beta$", xlabel=L"KT $\alpha$") + foreach(groupby(df, :type)) do gdf + label = string(first(gdf.type)) + errorcross!(ax_dn, HARTREE_TO_EV .* gdf.homo, gdf.dn; label) + errorcross!(ax_alpha_beta, gdf.alpha_kt, gdf.beta_kt; label) + end + + colgap!(f.layout, 5) + colsize!(f.layout, 1, Relative(2 / 3)) + resize_to_layout!(f) + + return f +end + +function electrolyte_trends(df) + f = Figure(size=(3.5inch, 3inch)) + + ax = Axis(f[1, 1]; ylabel=L"DN$$", xlabel=L"HOMO [eV]$$") + errorcross!(ax, df.homo .* HARTREE_TO_EV, df.dn) + vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r + hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + + ax = Axis(f[2, 1]; ylabel=L"DN$$", xlabel=L"KT $\beta$") + errorcross!(ax, df.beta_kt, df.dn) + hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + + ax = Axis(f[3, 1]; xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$") + errorcross!(ax, df.homo .* HARTREE_TO_EV, df.gap .* HARTREE_TO_EV) + vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r + hlines!(ax, 5; color=:black) # 10.1021/acsenergylett.3c00004 (Really just says 5eV is good + + ax = Axis(f[1, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Flash Point $[\degree C]$") + vlines!(ax, -100; color=:black) + hlines!(ax, 60; color=:black) + errorcross!(ax, df.mp, df.fp) + + ax = Axis(f[2, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Boiling Point $[\degree C]$") + vlines!(ax, -100; color=:black) + hlines!(ax, 60; color=:black) + errorcross!(ax, df.mp, df.bp) + + + return f +end + +function convert_units(y, col) + if col in [:homo, :lumo, :gap, :cv, :zpve] + y .*= HARTREE_TO_EV + end + return y +end + +function figure_permutations(name_df::Pair...; name_df_order) + f = Figure(size=(3.42inch, 2inch), figure_padding=(2, 3, 2, 4)) + gl_trends = GridLayout(f[1, 1]) + axes = [ + :homo => L"HOMO$$", + :gap => L"Gap$$", + :lumo => L"LUMO$$", + :zpve => L"ZPVE$$", + # :cv => L"CV$$", + :h298 => L"$G\degree$", + ] + dfs = [] + for (name, df) in name_df + df = deepcopy(df) + df._plt_name .= name + push!(dfs, df) + end + df = vcat(dfs...) + df.type = categorical(df.type) + df._plt_name = categorical(df._plt_name) + + axes = map(enumerate(axes)) do (idx, (col, ylabel)) + is_last = idx == length(axes) + ax_tend = Axis(gl_trends[idx, 1]; + ylabel, + yscale=log10, + xticklabelrotation=0.55, + xticks=MISTStyle.categorical_ticks(df.type), + xlabelvisible=is_last, + xticksvisible=is_last, + xminorticksvisible=is_last, + xticklabelsvisible=is_last, + tellwidth=true, + yticks=LogTicks(WilkinsonTicks(5)), + yminorticksvisible=true, + xminorticks=IntervalsBetween(5), + ) + ax_scatter = Axis(gl_trends[idx, 2]; + xscale=ax_tend.yscale, + yscale=ax_tend.yscale, + xlabel="Randomized\nUncertainty", + xlabelvisible=is_last, + xticksvisible=false, + xticklabelsvisible=false, + yticksvisible=false, + yticklabelsvisible=false, + ) + return col => (ax_tend, ax_scatter) + end |> Dict + + for (col, (ax, axs)) in axes + y = std.(df[:, col]) + if col in [:homo, :lumo, :gap, :cv, :zpve] + y .*= HARTREE_TO_EV + end + x = df.type + dodge = levelcode.(df._plt_name) + idx = @. !isnan(y) + x = x[idx] + y = y[idx] + dodge = dodge[idx] + boxplot!(ax, levelcode.(x), y; + dodge, + show_outliers=false, + color=dodge, + colormap=MISTStyle.CAT_COLORS, + colorrange=(1, length(MISTStyle.CAT_COLORS)), + ) + scatter!(axs, y[dodge.==2], y[dodge.==1]; + color=MISTStyle.UM_COLORS.blue, + alpha=0.2, + marker=:circle, + ) + linkyaxes!(ax, axs) + powerlaw!(axs, 1, 1; color=:black) + end + + # Legend + elements = map(enumerate(levels(df._plt_name))) do (i, label) + PolyElement( + color=MISTStyle.CAT_COLORS[i], + label=label + ) + end + Legend(gl_trends[end, 1], elements, MISTStyle.label.(elements); + orientation=:horizontal, + halign=:left, + valign=:top, + alignmode=Outside(), + ) + + colsize!(gl_trends, 1, Relative(3 / 4)) + colgap!(gl_trends, 1, 2) + + # Order Sensitivity + n_carbon_range = extrema(last(first(name_df_order)).n_carbon) + gl_order = GridLayout(f[1, 2]) + cb = Colorbar(gl_order[1:length(name_df_order), 2]; + label="Number of Carbons", + colorrange=n_carbon_range, + width=5 + ) + axes = Axis[] + for (idx, (_, df)) in enumerate(name_df_order) + is_last = idx == length(name_df_order) + ax = Axis(gl_order[idx, 1]; + xlabel=L"Double Bond Location$$", + ylabel=L"HOMO [eV]$$", + limits=((0, 1), nothing), + xtickformat="{:.0%}", + xlabelvisible=is_last, + xticksvisible=is_last, + xticklabelsvisible=is_last, + ) + push!(axes, ax) + df = subset(df, :n_carbon => ByRow(>(4))) + # df = subset(df, :n_carbon => ByRow(n -> n % 2 == 0)) + foreach(groupby(df, :n_carbon)) do gdf + n_carbon = gdf.n_carbon[1] + homo = gdf.homo .* HARTREE_TO_EV + h = lines!(ax, gdf.rel_pos, mean.(homo); + color=n_carbon, + MISTStyle.cb_attrs(cb)... + ) + lb = mean.(homo) .- stderror.(homo) + ub = mean.(homo) .+ stderror.(homo) + band!(ax, gdf.rel_pos, lb, ub; + alpha=0.2, + color=h.color, + MISTStyle.cb_attrs(cb)... + ) + end + end + linkyaxes!(axes...) + colgap!(gl_order, 1, 4) + colgap!(f.layout, 1, 3) + colsize!(f.layout, 1, Relative(3 / 4)) + + label_kwargs = (; + fontsize=8pt, + font=:bold, + halign=:right, + tellheight=false, + ) + Label(f[1, 1, TopLeft()], "a)"; padding=(0, 15, 0, 2), label_kwargs...) + Label(gl_order[1, 1, TopLeft()], "b)"; padding=(0, 7, 0, 2), label_kwargs...) + Label(gl_order[2, 1, TopLeft()], "c)"; padding=(0, 7, 0, 7), label_kwargs...) + + resize_to_layout!(f) + + + return f +end + +function figure_order2(name_df::Pair...) + f = Figure(; size=(3.42inch, 2inch), figure_padding=(2, 3, 2, 2)) + n_carbon_range = extrema(last(first(name_df)).n_carbon) + cb = Colorbar(f[2, 1:length(name_df)]; + label="Number of Carbons", + colorrange=n_carbon_range, + vertical=false, + flipaxis=false, + height=5 + ) + for (idx, (name, df)) in enumerate(name_df) + is_first = idx == 1 + ax = Axis(f[1, idx]; + xlabel=L"Location of Double Bond$$", + ylabel=L"HOMO [eV]$$", + limits=((0, 1), nothing), + ylabelvisible=is_first, + yticksvisible=is_first, + yticklabelsvisible=is_first, + ) + df = subset(df, :n_carbon => ByRow(>(4))) + df = subset(df, :n_carbon => ByRow(n -> n % 2 == 0)) + foreach(groupby(df, :n_carbon)) do gdf + n_carbon = gdf.n_carbon[1] + homo = gdf.homo .* HARTREE_TO_EV + h = lines!(ax, gdf.rel_pos, mean.(homo); + color=n_carbon, + MISTStyle.cb_attrs(cb)... + ) + lb = mean.(homo) .- stderror.(homo) + ub = mean.(homo) .+ stderror.(homo) + band!(ax, gdf.rel_pos, lb, ub; + alpha=0.2, + color=h.color, + MISTStyle.cb_attrs(cb)... + ) + end + end + return f +end diff --git a/opt/design/src/uq.jl b/opt/design/src/uq.jl index 4b45e43a..7bdc6db7 100644 --- a/opt/design/src/uq.jl +++ b/opt/design/src/uq.jl @@ -4,9 +4,10 @@ struct UQReal{T} n::Int end +UQReal(x::Vector) = UQReal(mean_and_std(x)..., length(x)) Statistics.mean(x::UQReal) = x.mean -Statistics.std(x::Real) = x.std -StatsBase.stderror(x) = x.std / sqrt(x.n) +Statistics.std(x::UQReal) = x.std +StatsBase.stderror(x::UQReal) = x.std / sqrt(x.n) function Base.show(io::IO, x::UQReal) μ = mean(x) se = stderror(x) From f3e09b5ba9a9fdabdf200993933b4fb797b9c96a Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 15:46:59 -0400 Subject: [PATCH 018/147] add sat/unsat fats figure --- .gitignore | 1 + opt/MISTStyle/Project.toml | 4 + opt/MISTStyle/src/MISTStyle.jl | 21 ++- opt/design/.gitignore | 2 + opt/design/Project.toml | 2 +- opt/design/electrolytes.csv | 188 +++++++++++---------- opt/design/plots.jl | 27 ++- opt/design/sat_fats.csv | 24 --- opt/design/saturated_fats.py | 31 ---- opt/design/src/DesignRules.jl | 4 +- opt/design/src/hydrocarbons.jl | 58 +++++-- opt/design/src/pubchem.jl | 25 +++ opt/design/src/trends.jl | 294 +++++++++++++++++++++++++++++---- 13 files changed, 479 insertions(+), 202 deletions(-) create mode 100644 opt/design/.gitignore delete mode 100644 opt/design/sat_fats.csv delete mode 100644 opt/design/saturated_fats.py create mode 100644 opt/design/src/pubchem.jl diff --git a/.gitignore b/.gitignore index 8befc050..eace05ec 100644 --- a/.gitignore +++ b/.gitignore @@ -38,3 +38,4 @@ spack.lock # outputs *.json +*.jsonl diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index d58c2678..e37484c9 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -2,8 +2,12 @@ name = "MISTStyle" uuid = "0a43f4a4-3db7-4939-b247-26273f54b603" [deps] +CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" [compat] +CairoMakie = "0.13.2" CategoricalArrays = "0.10.8" +GLMakie = "0.11.3" diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 8fac19d4..41014256 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -2,6 +2,8 @@ module MISTStyle using Makie using CategoricalArrays: levels +using GLMakie: GLMakie +using CairoMakie: CairoMakie const pt = 3 / 4 const inch = 96 @@ -11,11 +13,19 @@ export pt, inch """ Save duplicate figures for publication and web """ function savefig(name::String, f::Figure; dpi=300, fig_dir="fig") mkpath(fig_dir) - save(joinpath(fig_dir, name * ".pdf"), f; pt_per_unit=1) - save(joinpath(fig_dir, name * ".png"), f; px_per_unit=dpi / inch) + save(joinpath(fig_dir, name * ".pdf"), f; pt_per_unit=1, backend=CairoMakie) + save(joinpath(fig_dir, name * ".png"), f; px_per_unit=dpi / inch, backend=GLMakie) return nothing end +function savefig(name::String; kwargs...) + function curry_savefig(fig; kwargs...) + savefig(name, fig; kwargs...) + return fig + end + return curry_savefig +end + """ Helper function to get the label of an plot element """ label(x) = x.label[] @@ -32,6 +42,12 @@ function cb_attrs(cb::Colorbar) highclip=cb.highclip, ) end +function cb_attrs(cb::Colorbar, plt) + attrs = cb_attrs(cb) + valid = Makie.attribute_names(plt) + invalid = setdiff(keys(attrs), valid) + return Base.structdiff(attrs, NamedTuple{(invalid...,)}) +end include("errorcross.jl") include("powerlaw.jl") @@ -131,6 +147,7 @@ function theme() ticklabelsize=6pt, labelpadding=0pt, ticklabelpad=0pt, + size=8pt, ), Scatter=(; markersize=5pt, diff --git a/opt/design/.gitignore b/opt/design/.gitignore new file mode 100644 index 00000000..8159e7f2 --- /dev/null +++ b/opt/design/.gitignore @@ -0,0 +1,2 @@ +fig/ + diff --git a/opt/design/Project.toml b/opt/design/Project.toml index 05ae9690..dc3a8421 100644 --- a/opt/design/Project.toml +++ b/opt/design/Project.toml @@ -7,6 +7,7 @@ CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" +JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" @@ -16,4 +17,3 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [sources] MISTStyle = {path = "../MISTStyle"} - diff --git a/opt/design/electrolytes.csv b/opt/design/electrolytes.csv index f4299d01..f1789d21 100644 --- a/opt/design/electrolytes.csv +++ b/opt/design/electrolytes.csv @@ -1,95 +1,93 @@ -name,smi -Dimethyl dicarbonate,O=C(OC(=O)OC)OC -Ethylene carbonate,C1COC(=O)O1 -Dimethyl carbonate,COC(=O)OC -Ethyl methyl carbonate,CCOC(=O)OC -Dimethyl carbonate,COC(=O)OC -Diethyl carbonate,CCOC(=O)OCC -"2,2,2-trifluoromethyl ethyl carbonate",CCOC(=O)OC(F)(F)F -"2,2,2-trifluoroethyl methyl carbonate",COC(=O)OCC(F)(F)F -methyl trifluoromethyl carbonate,COC(=O)OC(F)(F)F -"ethyl 2,2,2-trifluoroethyl carbonate",CCOC(=O)OCC(F)(F)F -Bis(trifluoromethyl) ester,C(=O)(OC(F)(F)F)OC(F)(F)F -"2,2,2-trifluoroethyl trifluoromethyl carbonate",C(C(F)(F)F)OC(=O)OC(F)(F)F -"bis(2,2,2-trifluoroethyl) carbonate",C(C(F)(F)F)OC(=O)OCC(F)(F)F -"1,3-bis(trimethylsilyloxy)propane",C[Si](C)(C)OCCCO[Si](C)(C)C -"1,2-bis(trimethylsilyloxy)ethane",C[Si](C)(C)OCCO[Si](C)(C)C -"Chloroform-d",[2H]C(Cl)(Cl)Cl -"Dichloromethane",C(Cl)Cl -"1,2-Dichloroethane",C(CCl)Cl -"1,3-Dichloropropane",C(CCl)CCl -"1,1,2,2-Tetrachloroethane",C(C(Cl)Cl)(Cl)Cl -"1,1,2,2,3,3,4-Heptafluorocyclopentane",C1C(C(C(C1(F)F)(F)F)(F)F)F -"1,1,2,2-Tetrafluoroethyl-2,2,3,3-tetrafluoropropylether",C(C(C(F)F)(F)F)OC(C(F)F)(F)F -Fluorobenzene,C1=CC=C(C=C1)F -"1,2-Difluorobenzene",C1=CC=C(C(=C1)F)F -"1,3-Difluorobenzene",C1=CC(=CC(=C1)F)F -"1,4-Difluorobenzene",C1=CC(=CC=C1F)F -"1,2,3-Trifluorobenzene (123-FB)",C1=CC(=C(C(=C1)F)F)F -"1,2,4-Trifluorobenzene",C1=CC(=C(C=C1F)F)F -"1-Fluoropentane",CCCCCF -Fluorocyclohexane,C1CCC(CC1)F -"DEE (1,2-diethoxyethane)",CCOCCOCC -"F1F0 (1-Ethoxy-2-(2-fluoroethoxy)ethane)",CCOCCOCCF -"F1F1 (Ethane, 1,2-bis(2-fluoroethoxy)-)",C(COCCF)OCCF -F1F2,C(COCC(F)F)OCCF -"F3DEE (1-(2,2,2-Trifluoroethoxy)-2-ethoxyethane)",CCOCCOCC(F)(F)F -"FDMB (2,2,3,3-Tetrafluoro-1,4-dimethoxybutane)",COCC(C(COC)(F)F)(F)F -"F4DEE (1,2-Bis(2,2-difluoroethoxy)ethane)",C(COCC(F)F)OCC(F)F -"F5DEE (1-Difluoroethoxy-2-Trifluoroethoxyethane)",C(COCC(F)(F)F)OCC(F)F -"F6DEE (1,1,1-Trifluoro-2-[2-(2,2,2-trifluoroethoxy)ethoxy]ethane)",C(COCC(F)(F)F)OCC(F)(F)F -ethylene carbonate,C1COC(=O)O1 -fluoroethylene carbonate,C1C(OC(=O)O1)F -difluoroethylene carbonate,C1(C(OC(=O)O1)F)F -cis-difluoroethylene carbonate,O=C1O[C@@H](F)[C@@H](F)O1 -trans-difluoroethylene carbonate 2,O=C1O[C@H](F)[C@@H](F)O1 -trans-difluoroethylene carbonate,O=C1O[C@@H](F)[C@H](F)O1 -trifluoroethylene carbonate,C1(C(OC(=O)O1)(F)F)F -tetrafluoroethylene carbonate,C1(=O)OC(C(O1)(F)F)(F)F -TMS,C1CCS(=O)(=O)C1 -EMS,CCS(=O)(=O)C -BS,CCCCS(=O)(=O)CCCC -FS,CS(=O)(=O)C1=CC=CC=C1F -EVS,CCS(=O)(=O)C=C -MiPS,CC(C)S(=O)(=O)C -EiPS,CCS(=O)(=O)C(C)C -EiBS,CCS(=O)(=O)CC(C)C -iPiBS,CC(C)CS(=O)(=O)C(C)C -iPsBS,CCC(C)S(=O)(=O)C(C)C -BiBS,CCCCS(=O)(=O)CC(C)C -MEMS,COCCS(=O)(=O)C -EMES,CCS(=O)(=O)CCOC -ESEME,CCS(=O)(=O)CCOCCOC -AN,CC#N -MPN,COCCC#N -EPN,CCOCCC#N -FEPN,C(COCC(F)(F)F)C#N -MAN,C(C#N)C#N -SCN,C(CC#N)C#N -GLN,C(CC#N)CC#N -ADN,C(CCC#N)CC#N -PMN,C(CCC#N)CCC#N -SUN,C(CCCC#N)CCC#N -AZN,C(CCCC#N)CCCC#N -SEN,C(CCCCC#N)CCCC#N -TMP,COP(=O)(OC)OC -DMMP,COP(=O)(C)OC -DMMEMP,COCCOCP(=O)(OC)OC -DEMEMP,CCOP(=O)(COCCOC)OCC -HMP,COP1(=NP(=NP(=N1)(OC)OC)(OC)OC)OC -HTP,CCOP1(=NP(=NP(=N1)(OCC)OCC)(OCC)OCC)OCC -HHTP,CCCOP1(=NP(=NP(=N1)(OCCC)OCCC)(OCCC)OCCC)OCCC -METMS,COCCO[Si](C)(C)C -MEETMS,COCCOCCO[Si](C)(C)C -MEEETMS,COCCOCCOCCO[Si](C)(C)C -MEEEETMS,COCCOCCOCCOCCO[Si](C)(C)C -MEEEEETMS,COCCOCCOCCOCCOCCO[Si](C)(C)C -MEEEEEETMS,COCCOCCOCCOCCOCCOCCO[Si](C)(C)C -MEEEEEEETMS,COCCOCCOCCOCCOCCOCCOCCO[Si](C)(C)C -DMC,COC(=O)OC -EMC,CCOC(=O)OC -DEC,O=C(OCC)OCC -DME,COCCOC -DMDC,O=C(OC(=O)OC)OC - - +name,class,subsitute,smi +"1,1,2,2-Tetrachloroethane",Alkane,Chloro,C(C(Cl)Cl)(Cl)Cl +"1,2-Dichloroethane",Alkane,Chloro,C(CCl)Cl +"1,3-Dichloropropane",Alkane,Chloro,C(CCl)CCl +1-Fluoropentane,Alkane,Fluoro,CCCCCF +Chloroform-d,Alkane,Chloro,[2H]C(Cl)(Cl)Cl +Dichloromethane,Alkane,Chloro,C(Cl)Cl +EMS,Alkane,Sulfone,CCS(=O)(=O)C +EiBS,Alkane,Sulfone,CCS(=O)(=O)CC(C)C +EiPS,Alkane,Sulfone,CCS(=O)(=O)C(C)C +iPiBS,Alkane,Sulfone,CC(C)CS(=O)(=O)C(C)C +iPsBS,Alkane,Sulfone,CCC(C)S(=O)(=O)C(C)C +EVS,Alkene,Sulfone,CCS(=O)(=O)C=C +"1,2,3-Trifluorobenzene (123-FB)",Arene,Fluoro,C1=CC(=C(C(=C1)F)F)F +"1,2,4-Trifluorobenzene",Arene,Fluoro,C1=CC(=C(C=C1F)F)F +"1,2-Difluorobenzene",Arene,Fluoro,C1=CC=C(C(=C1)F)F +"1,3-Difluorobenzene",Arene,Fluoro,C1=CC(=CC(=C1)F)F +"1,4-Difluorobenzene",Arene,Fluoro,C1=CC(=CC=C1F)F +FS,Arene,"Fluoro, Sulfone",CS(=O)(=O)C1=CC=CC=C1F +Fluorobenzene,Arene,Fluoro,C1=CC=C(C=C1)F +Methyl phenyl sulfone,Arene,Sulfone,CS(=O)(=O)C1=CC=CC=C1 +Diethyl carbonate,Carbonate ester,,CCOC(=O)OCC +Dimethyl carbonate,Carbonate ester,,COC(=O)OC +"2,2,2-trifluoroethyl methyl carbonate",Carbonate ester,Fluoro,COC(=O)OCC(F)(F)F +"2,2,2-trifluoroethyl trifluoromethyl carbonate",Carbonate ester,Fluoro,C(C(F)(F)F)OC(=O)OC(F)(F)F +"2,2,2-trifluoromethyl ethyl carbonate",Carbonate ester,Fluoro,CCOC(=O)OC(F)(F)F +"bis(2,2,2-trifluoroethyl) carbonate",Carbonate ester,Fluoro,C(C(F)(F)F)OC(=O)OCC(F)(F)F +"ethyl 2,2,2-trifluoroethyl carbonate",Carbonate ester,Fluoro,CCOC(=O)OCC(F)(F)F +Bis(trifluoromethyl) ester,Carbonate ester,Fluoro,C(=O)(OC(F)(F)F)OC(F)(F)F +DEC,Carbonate ester,,O=C(OCC)OCC +DMC,Carbonate ester,,COC(=O)OC +EMC,Carbonate ester,,CCOC(=O)OC +Ethyl methyl carbonate,Carbonate ester,,CCOC(=O)OC +methyl trifluoromethyl carbonate,Carbonate ester,Fluoro,COC(=O)OC(F)(F)F +"1,1,2,2,3,3,4-Heptafluorocyclopentane",Cycloalkane,Fluoro,C1C(C(C(C1(F)F)(F)F)(F)F)F +Fluorocyclohexane,Cycloalkane,Fluoro,C1CCC(CC1)F +TMS,Cycloalkane,Sulfone,C1CCS(=O)(=O)C1 +HHTP,Cyclophosphazene,,CCCOP1(=NP(=NP(=N1)(OCCC)OCCC)(OCCC)OCCC)OCCC +HMP,Cyclophosphazene,,COP1(=NP(=NP(=N1)(OC)OC)(OC)OC)OC +HTP,Cyclophosphazene,,CCOP1(=NP(=NP(=N1)(OCC)OCC)(OCC)OCC)OCC +DMDC,Dicarbonate,,O=C(OC(=O)OC)OC +Dimethyl dicarbonate,Dicarbonate,,O=C(OC(=O)OC)OC +DEMEMP,Ester,Phosphate,CCOP(=O)(COCCOC)OCC +DMMEMP,Ester,Phosphate,COCCOCP(=O)(OC)OC +DMMP,Ester,Phosphate,COP(=O)(C)OC +EMES,Ester,Sulfone,CCS(=O)(=O)CCOC +ESEME,Ester,Sulfone,CCS(=O)(=O)CCOCCOC +F1F2,Ether,Fluoro,C(COCC(F)F)OCCF +"1,1,2,2-Tetrafluoroethyl-2,2,3,3-tetrafluoropropylether",Ether,Fluoro,C(C(C(F)F)(F)F)OC(C(F)F)(F)F +"1,2-bis(trimethylsilyloxy)ethane",Ether,Silyl,C[Si](C)(C)OCCO[Si](C)(C)C +"1,3-bis(trimethylsilyloxy)propane",Ether,Silyl,C[Si](C)(C)OCCCO[Si](C)(C)C +"DEE (1,2-diethoxyethane)",Ether,,CCOCCOCC +F1F0 (1-Ethoxy-2-(2-fluoroethoxy)ethane),Ether,Fluoro,CCOCCOCCF +"F1F1 (Ethane, 1,2-bis(2-fluoroethoxy)-)",Ether,Fluoro,C(COCCF)OCCF +"F3DEE (1-(2,2,2-Trifluoroethoxy)-2-ethoxyethane)",Ether,Fluoro,CCOCCOCC(F)(F)F +"F4DEE (1,2-Bis(2,2-difluoroethoxy)ethane)",Ether,Fluoro,C(COCC(F)F)OCC(F)F +F5DEE (1-Difluoroethoxy-2-Trifluoroethoxyethane),Ether,Fluoro,C(COCC(F)(F)F)OCC(F)F +"F6DEE (1,1,1-Trifluoro-2-[2-(2,2,2-trifluoroethoxy)ethoxy]ethane)",Ether,Fluoro,C(COCC(F)(F)F)OCC(F)(F)F +"FDMB (2,2,3,3-Tetrafluoro-1,4-dimethoxybutane)",Ether,Fluoro,COCC(C(COC)(F)F)(F)F +DME,Ether,,COCCOC +MEEEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEETMS,Ether,Silyl,COCCOCCOCCOCCO[Si](C)(C)C +MEEETMS,Ether,Silyl,COCCOCCOCCO[Si](C)(C)C +MEETMS,Ether,Silyl,COCCOCCO[Si](C)(C)C +MEMS,Ether,Sulfone,COCCS(=O)(=O)C +METMS,Ether,Silyl,COCCO[Si](C)(C)C +Ethylene carbonate,Ethylene carbonate,,C1COC(=O)O1 +cis-difluoroethylene carbonate,Ethylene carbonate,Fluoro,O=C1O[C@@H](F)[C@@H](F)O1 +difluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(C(OC(=O)O1)F)F +Ethylene carbonate,Ethylene carbonate,,C1COC(=O)O1 +fluoroethylene carbonate,Ethylene carbonate,Fluoro,C1C(OC(=O)O1)F +tetrafluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(=O)OC(C(O1)(F)F)(F)F +"trans-difluoroethylene carbonate, R",Ethylene carbonate,Fluoro,O=C1O[C@H](F)[C@@H](F)O1 +"trans-difluoroethylene carbonate, S",Ethylene carbonate,Fluoro,O=C1O[C@@H](F)[C@H](F)O1 +trifluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(C(OC(=O)O1)(F)F)F +ADN,Alkane,Nitrile,C(CCC#N)CC#N +AN,Alkane,Nitrile,CC#N +AZN,Alkane,Nitrile,C(CCCC#N)CCCC#N +EPN,Ether,Nitrile,CCOCCC#N +GLN,Alkane,Nitrile,C(CC#N)CC#N +MAN,Alkane,Nitrile,C(C#N)C#N +MPN,Ether,Nitrile,COCCC#N +PMN,Alkane,Nitrile,C(CCC#N)CCC#N +SCN,Alkane,Nitrile,C(CC#N)C#N +SEN,Alkane,Nitrile,C(CCCCC#N)CCCC#N +SUN,Alkane,Nitrile,C(CCCC#N)CCC#N +FEPN,Ether,"Fluoro, Nitrile",C(COCC(F)(F)F)C#N +TMP,Ether,Phosphate,COP(=O)(OC)OC +BS,Alkane,Sulfone,CCCCS(=O)(=O)CCCC +BiBS,Alkane,Sulfone,CCCCS(=O)(=O)CC(C)C +MiPS,Alkane,Sulfone,CC(C)S(=O)(=O)C diff --git a/opt/design/plots.jl b/opt/design/plots.jl index a20e102d..4fecbf87 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -27,23 +27,36 @@ using CSV: CSV # Evaluate hydrocarbons df_hydrocarbons = DesignRules.predict_all( - DesignRules.simple_hydrocarbons(25), + DesignRules.simple_hydrocarbons(30), models...; - n=2 + n=10 ) with_theme(MISTStyle.theme()) do DesignRules.hydrocarbon_trends(df_hydrocarbons) -end +end |> MISTStyle.savefig("hydrocarbons") + +df_sat = DesignRules.predict_all( + DesignRules.saturated_fats(24; n_max=9, d_max=6), + models...; + n=3, +) + +with_theme(MISTStyle.theme()) do + DesignRules.figure_fatty_acids(df_sat) +end |> MISTStyle.savefig("saturated-fats") + # Evaluate electrolytes df_electrolyte = DataFrame(CSV.File("electrolytes.csv")) df_electrolyte.smi .= DesignRules.encode.(df_electrolyte.smi; encoding="smiles-kekule") df_electrolyte = DesignRules.predict_all(df_electrolyte, models...; n=10) +df_pc = DesignRules.pubchem_from_jsonl("electrolytes.jsonl") +df_electrolyte = leftjoin(df_electrolyte, df_pc, on=:smi) with_theme(MISTStyle.theme()) do DesignRules.electrolyte_trends(df_electrolyte) -end +end |> MISTStyle.savefig("electrolytes") # Permutation sensitivity df_perm = simple_hydrocarbons(25) @@ -57,7 +70,7 @@ df_order = DesignRules.alkene_sweep(25, models[1]) with_theme(MISTStyle.theme()) do DesignRules.figure_permutations( - "Kekule" => df_perm_ref, "Random" => df_perm_rand; - name_df_order=("Kekule" => df_order, "Random" => df_order_rand) + "Baseline" => df_perm_ref, "Random" => df_perm_rand; + name_df_order=("Baseline" => df_order, "Random" => df_order_rand) ) -end +end |> MISTStyle.savefig("permutations") diff --git a/opt/design/sat_fats.csv b/opt/design/sat_fats.csv deleted file mode 100644 index 14eddae8..00000000 --- a/opt/design/sat_fats.csv +++ /dev/null @@ -1,24 +0,0 @@ -smi,mu,alpha,homo,lumo,gap,r2,zpve,u0,u298,h298,g298,cv -O=C(O)CCC,4.769931,71.028,-0.26474136,-0.07180658,0.19366309,1116.128,0.067295,-455.21826,-454.96674,-454.93094,-455.2984,25.358398 -O=C(O)CCCC,4.971653,73.43377,-0.2669614,-0.073890984,0.19384971,1196.3062,0.0732354,-449.44058,-449.31204,-449.23938,-449.54324,25.694302 -O=C(O)CCCCC,5.2371836,75.44909,-0.26635858,-0.07276623,0.19430482,1281.5747,0.079891905,-443.68478,-443.6336,-443.54514,-443.796,26.355312 -O=C(O)CCCCCC,5.5895414,76.27199,-0.26350012,-0.06660598,0.19753295,1344.3341,0.08678694,-439.01654,-439.0433,-438.93744,-439.1521,27.013805 -O=C(O)CCCCCCC,5.649559,76.24556,-0.26225054,-0.060204238,0.20261885,1392.9761,0.09317882,-435.1404,-435.2054,-435.09824,-435.26633,27.856176 -O=C(O)CCCCCCCC,5.2911034,76.77113,-0.2604756,-0.05375296,0.20719764,1487.1074,0.10051406,-430.50854,-430.59537,-430.4845,-430.6233,28.716946 -O=C(O)CCCCCCCCC,5.344325,76.20799,-0.25762165,-0.04362812,0.21440068,1522.6865,0.10760877,-426.41803,-426.52133,-426.4131,-426.52682,29.321398 -O=C(O)CCCCCCCCCC,5.166279,76.36943,-0.2543188,-0.033491634,0.22118333,1555.0681,0.11445395,-422.9598,-423.07117,-422.96436,-423.07388,29.88532 -O=C(O)CCCCCCCCCCC,4.919032,76.11539,-0.2517563,-0.024300717,0.22780345,1547.0695,0.1217105,-419.9719,-420.09467,-419.9814,-420.09427,30.262314 -O=C(O)CCCCCCCCCCCC,4.680345,76.35677,-0.25301033,-0.014943976,0.23838933,1569.8961,0.1287832,-416.5352,-416.67746,-416.56747,-416.66455,30.87184 -O=C(O)CCCCCCCCCCCCC,4.3812184,76.159874,-0.25552556,-0.0074483696,0.24833615,1553.9812,0.13486302,-412.0727,-412.20636,-412.10773,-412.1919,31.143152 -O=C(O)CCCCCCCCCCCCCC,4.0451183,76.726524,-0.25839686,-0.0001775017,0.2584055,1606.5541,0.14232084,-407.17923,-407.2892,-407.22147,-407.24896,31.79684 -O=C(O)CCCCCCCCCCCCCCC,3.9297128,77.519806,-0.2595612,0.0036514928,0.2633514,1667.9335,0.14853513,-405.0917,-405.16525,-405.13257,-405.1035,32.368652 -O=C(O)CCCCCCCCCCCCCCCC,3.804593,79.58518,-0.25875157,0.007440024,0.26625136,1797.5773,0.15650843,-405.41147,-405.47327,-405.46625,-405.358,33.53738 -O=C(O)CCCCCCCCCCCCCCCCC,3.6632895,81.52695,-0.25755906,0.01096091,0.26849225,1999.3899,0.16350242,-403.53384,-403.5899,-403.5829,-403.4258,34.75062 -O=C(O)CCCCCCCCCCCCCCCCCC,3.4002395,83.31501,-0.25374886,0.014807183,0.26848534,2125.2043,0.17007431,-401.65665,-401.6828,-401.6706,-401.51105,35.88625 -O=C(O)CCCCCCCCCCCCCCCCCCC,3.1372354,84.147804,-0.25196138,0.011332356,0.26324683,2245.4724,0.17593305,-400.616,-400.64716,-400.5887,-400.49283,36.77691 -O=C(O)CCCCCCCCCCCCCCCCCCCC,2.9869483,85.180084,-0.24901211,0.0060275695,0.255123,2341.8481,0.18185005,-400.038,-400.08688,-399.95813,-399.9605,37.780804 -O=C(O)CCCCCCCCCCCCCCCCCCCCC,2.8969917,85.900406,-0.24720106,0.0011447165,0.24848019,2374.979,0.18786205,-398.368,-398.41678,-398.2661,-398.28995,38.320934 -O=C(O)CCCCCCCCCCCCCCCCCCCCCC,2.8028266,86.91344,-0.2461817,-0.0023705475,0.24399008,2413.5757,0.1948694,-395.99924,-396.05588,-395.89774,-395.90454,38.907734 -O=C(O)CCCCCCCCCCCCCCCCCCCCCCC,2.7076352,87.758156,-0.24554278,-0.0029556807,0.24283132,2438.045,0.2007028,-393.5421,-393.59503,-393.43665,-393.429,39.42135 -O=C(O)CCCCCCCCCCCCCCCCCCCCCCCC,2.6062937,88.49571,-0.24500887,-0.0012770239,0.2441615,2500.8901,0.2061011,-392.06732,-392.14346,-391.94748,-391.9792,39.92587 -O=C(O)CCCCCCCCCCCCCCCCCCCCCCCCC,2.4823546,89.42716,-0.24477701,0.0033710813,0.248929,2618.2927,0.21180302,-390.44525,-390.58524,-390.31842,-390.42496,40.447372 diff --git a/opt/design/saturated_fats.py b/opt/design/saturated_fats.py deleted file mode 100644 index 88b477c1..00000000 --- a/opt/design/saturated_fats.py +++ /dev/null @@ -1,31 +0,0 @@ -from argparse import ArgumentParser -import torch -import pandas as pd -from electrolyte_fm.models.prod_finetune import MISTFinetuned -from smirk import SmirkTokenizerFast - -if torch.cuda.is_available(): - device = "cuda" -elif torch.backends.mps.is_available(): - device = "mps" -else: - device = "cpu" - -if __name__ == "__main__": - parser = ArgumentParser() - parser.add_argument("--model", type=str, default=".") - parser.add_argument("--output", default="sat_fats.csv") - args = parser.parse_args() - - model = MISTFinetuned.from_pretrained(args.model).eval().to(device) - tok = SmirkTokenizerFast() - - smi: list[str] = [] - for n in range(3, 40): - # do nitrile - smi.append("N#" + "C" * n) - - pred = model.predict(smi, tok) - - df = pd.DataFrame(pred, index=smi) - df.to_csv(args.output, index_label="smi") diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index f2b99dbd..d2159e35 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -6,13 +6,15 @@ using Makie using MISTStyle using MISTStyle: label using Statistics: Statistics, mean, std -using StatsBase: StatsBase, stderror, mean_and_std, mean +using StatsBase: StatsBase, stderror, mean_and_std, mean, range using CategoricalArrays: levelcode, categorical +using JSON: JSON const HARTREE_TO_EV = 27.211_386_245_981 include("uq.jl") include("inference.jl") +include("pubchem.jl") include("hydrocarbons.jl") include("plot_utils.jl") diff --git a/opt/design/src/hydrocarbons.jl b/opt/design/src/hydrocarbons.jl index 8d3e0961..26d589e7 100644 --- a/opt/design/src/hydrocarbons.jl +++ b/opt/design/src/hydrocarbons.jl @@ -1,41 +1,59 @@ +# Terminal Functional Groups alkyne(n::Int) = "C#" * "C"^(n - 1) -alkene(n::Int) = "C=" * "C"^(n - 1) -alkene(n::Int, pos::Int) = "C"^pos * "=" * "C"^(n - pos) +alkene(n::Int, pos::Int=1) = "C"^pos * "=" * "C"^(n - pos) alkane(n::Int) = "C"^n arene(n::Int) = "c1ccccc1" * "C"^n isoalkane(n::Int) = "C(C)" * "C"^(n - 2) alcohol(n::Int) = "O" * "C"^n aldehyde(n::Int) = "O=" * "C"^n +carboxylic_acid(n::Int) = "O=C(O)" * "C"^(n - 1) nitrile(n::Int) = "N#" * "C"^n dinitrile(n::Int) = "N#" * "C"^n * "#N" amine(n::Int) = "N" * "C"^n -carboxylic_acid(n::Int) = "C(=O)" * "C"^(n - 1) halide(n::Int, element::String) = element * "C"^n halide(element::String) = Base.Fix2(halide, element) + +function fatty_acid(c::Int, d::Int, n::Int) + d == 0 && return carboxylic_acid(c) + @assert n > 0 + @assert c > n + unstat_len = c - n + step = fld(unstat_len, d) + d_locs = range(; stop=unstat_len, step, length=d) + @assert first(d_locs) > 1 "Exceeded Valance of first carbon" + fatty_acid(c, collect(d_locs)) +end +function fatty_acid(c::Int, d_locs::Vector{Int}) + stat_chains = map(alkane, diff(vcat([1], d_locs, [c]))) + return carboxylic_acid(1) * join(stat_chains, "=") +end + +# Branched Functional Group tetra_sub_alkene(n::Int) = "$("C"^n)C(=C($("C"^n))$("C"^n))$("C"^n)" disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" -ether(n::Int) = "COC"^Int(n / 2) -ester(n::Int) = "C"^n * "C(=O)O" * "C"^n + +# Polymers +polyether(n::Int) = "COC"^Int(n / 2) function simple_hydrocarbons(n::Int) df = DataFrame(vcat( [(; type="Alkanes", smi=alkane(n)) for n in 1:n], [(; type="Isoalkanes", smi=isoalkane(n)) for n in 3:n], [(; type="Alcohols", smi=alcohol(n)) for n in 1:n], + [(; type="Aldehydes", smi=aldehyde(n)) for n in 1:n], [(; type="Nitrile", smi=nitrile(n)) for n in 1:n], [(; type="Dinitriles", smi=dinitrile(n)) for n in 1:n], [(; type="Amines", smi=amine(n)) for n in 1:n], - [(; type="Carboxylic Acids", smi=carboxylic_acid(n)) for n in 2:n], + [(; type="Carboxylic acids", smi=carboxylic_acid(n)) for n in 2:n], [(; type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], [(; type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], [(; type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], - [(; type="Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], - [(; type="Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], - [(; type="Ether", smi=ether(n)) for n in 2:2:n], - [(; type="Arene", smi=arene(n)) for n in 6:n], - [(; type="Ester", smi=ester(n)) for n in 1:cld(n, 2)], + [(; type="Alkenes", smi=alkene(n)) for n in 2:n], + [(; type="Alkynes", smi=alkyne(n)) for n in 2:n], + [(; type="Arenes", smi=arene(n)) for n in 6:n], + [(; type="Polyethers", smi=polyether(n)) for n in 2:2:n], )) n_carbon!(df) return df @@ -43,13 +61,29 @@ end n_carbon!(df) = transform!(df, :smi => ByRow(smi -> count(==('C'), smi)) => :n_carbon) +function saturated_fats(max_length::Int; n_max=typemax(Int), d_max=typemax(Int)) + rows = [] + for c in 3:max_length + push!(rows, (; c, d=0, n=0, smi=carboxylic_acid(c))) + for d in 1:min(c, d_max) + nm = c - d - 1 + for n in 1:min(nm, n_max) + push!(rows, (; c, d, n, smi=fatty_acid(c, d, n))) + end + end + end + df = DataFrame(rows) + transform!(df, [:c, :d] => ByRow((c, d) -> d / (c-2)) => :saturation) + return df +end + function alkene_sweep(n, model) smi = String[] rel_pos = Float64[] for i in 2:n for pos in 1:i-1 push!(smi, alkene(i, pos)) - push!(rel_pos, (2pos-1)/(2i-2)) + push!(rel_pos, (2pos - 1) / (2i - 2)) end end df = predict_monte(smi, model) diff --git a/opt/design/src/pubchem.jl b/opt/design/src/pubchem.jl new file mode 100644 index 00000000..6f3d703f --- /dev/null +++ b/opt/design/src/pubchem.jl @@ -0,0 +1,25 @@ +function pubchem_from_jsonl(file::String) + rows = [] + open(file) do f + while !eof(f) + mol = JSON.parse(readline(f)) + min_lowdin = Inf + lowdin = Float64[] + mulliken = Float64[] + for atom in mol["atoms"] + push!(lowdin, atom["properties"]["partial-charge-lowdin"]) + push!(lowdin, atom["properties"]["hs_partial-charge-lowdin"]) + push!(mulliken, atom["properties"]["partial-charge-mulliken"]) + push!(mulliken, atom["properties"]["hs_partial-charge-mulliken"]) + end + push!(rows, (; + smi=mol["smi"], + min_lowdin=minimum(lowdin), + min_mulliken=minimum(mulliken), + range_lowdin=-(extrema(lowdin)...), + range_mulliken=-(extrema(mulliken)...), + )) + end + end + return DataFrame(rows) +end diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 9a9fb5ce..dd416519 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -1,5 +1,29 @@ function hydrocarbon_trends(df) f = Figure(size=(3.5inch, 3inch)) + + func_groups = [ + "Alkanes", + "Alkenes", + "Alkynes", + # "Isoalkanes", + "Arenes", + # "Esters", + # "Ethers", + "Alcohols", + # "Aldehydes", + "Amines", + "Nitriles", + # "Dinitriles", + "Carboxylic acids", + # "Fluoroalkanes", + # "Bromoalkanes", + # "Chloroalkanes", + ] + df = subset(df, :type => ByRow(in(func_groups))) + df.type = categorical(df.type; levels=func_groups) + colormap = :tab10 + colorrange = (1, 10) + # Trends with Size gl_trends = GridLayout(f[1, 1]) axes = [ @@ -36,20 +60,52 @@ function hydrocarbon_trends(df) if col in [:gap] y .*= HARTREE_TO_EV end - errorlines!(ax, gdf.n_carbon, y; label=string(first(gdf.type))) + lines!(ax, gdf.n_carbon, mean.(y); + label=string(first(gdf.type)), + color=levelcode.(gdf.type), + colormap, + colorrange, + ) end end - Legend(gl_trends[length(axes)+1, 1], axes[:mu]; nbanks=3, tellheight=true) + elems = map(enumerate(func_groups)) do (color, label) + PolyElement(; color, label, colormap, colorrange) + end + Legend(gl_trends[length(axes)+1, 1], elems, label.(elems); + nbanks=4, + tellheight=true, tellwidth=true, + ) # Design Rules gl_dr = GridLayout(f[1, 2]) - ax_dn = Axis(gl_dr[1, 1]; ylabel=L"DN", xlabel=L"HOMO [eV]$$") - ax_alpha_beta = Axis(gl_dr[2, 1]; ylabel=L"KT $\beta$", xlabel=L"KT $\alpha$") + ax_dn = Axis(gl_dr[1, 1]; ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"HOMO [eV]$$") + ax_mp_bp = Axis(gl_dr[2, 1]; + xlabel=L"Melting Point [$C\degree$]", + ylabel=L"Boiling Point [$C\degree$]", + ) foreach(groupby(df, :type)) do gdf - label = string(first(gdf.type)) - errorcross!(ax_dn, HARTREE_TO_EV .* gdf.homo, gdf.dn; label) - errorcross!(ax_alpha_beta, gdf.alpha_kt, gdf.beta_kt; label) + kwargs = (; + colormap, + colorrange, + color=levelcode.(gdf.type), + label=string(first(gdf.type)), + marker=:circle + ) + scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), mean.(gdf.dn); kwargs...) + scatter!(ax_mp_bp, mean.(gdf.mp), mean.(gdf.bp); kwargs...) end + ablines!(ax_mp_bp, 0, 1; color=:black, linestyle=:dash) + text!(ax_mp_bp, 20, 20; + text=L"T_m = T_b", + align=(:left, :bottom), + rotation=pi / 4, + markerspace=:data, + fontsize=24, + ) + + # Exceptions to BP > MP + df_except = subset(df, [:mp, :bp] => ByRow((mp, bp) -> mean(mp) > mean(bp))) + @info "Exceptions to BP > MP" df_except[:, [:type, :smi, :mp, :bp]] colgap!(f.layout, 5) colsize!(f.layout, 1, Relative(2 / 3)) @@ -58,33 +114,86 @@ function hydrocarbon_trends(df) return f end + function electrolyte_trends(df) - f = Figure(size=(3.5inch, 3inch)) + f = Figure(size=(3.5inch, 1.7inch)) + df = deepcopy(df) - ax = Axis(f[1, 1]; ylabel=L"DN$$", xlabel=L"HOMO [eV]$$") - errorcross!(ax, df.homo .* HARTREE_TO_EV, df.dn) - vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r - hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + func_groups = [ + "Ethylene carbonate", "Carbonate ester", "Ester", "Ether", + "Dicarbonate", "Alkane", "Alkene", "Arene", + ] + subset!(df, :class => ByRow(in(func_groups))) + df.class = categorical(df.class; levels=func_groups) - ax = Axis(f[2, 1]; ylabel=L"DN$$", xlabel=L"KT $\beta$") - errorcross!(ax, df.beta_kt, df.dn) - hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 - ax = Axis(f[3, 1]; xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$") - errorcross!(ax, df.homo .* HARTREE_TO_EV, df.gap .* HARTREE_TO_EV) - vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r - hlines!(ax, 5; color=:black) # 10.1021/acsenergylett.3c00004 (Really just says 5eV is good + func_groups_mods = Dict( + "Baseline" => :+, + "Chloro" => :pentagon, + "Fluoro" => :hexagon, + "Sulfone" => :diamond, + "Phosphate" => :star5, + "Silyl" => :rect, + "Nitrile" => :utriangle, + ) + df.subsitute .= coalesce.(df.subsitute, "Baseline") + subset!(df, :subsitute => ByRow(in(collect(keys(func_groups_mods))))) + marker = map(df.subsitute) do subsitute + return func_groups_mods[subsitute] + end + color = map(class -> MISTStyle.CAT_COLORS[levelcode(class)], df.class) + + @info sort(combine(nrow, groupby(df, [:class, :subsitute])), :class) - ax = Axis(f[1, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Flash Point $[\degree C]$") - vlines!(ax, -100; color=:black) - hlines!(ax, 60; color=:black) - errorcross!(ax, df.mp, df.fp) + marker_elems = map(collect(pairs(func_groups_mods))) do (label, marker) + label = coalesce(label, "Baseline") + MarkerElement(; label, marker) + end + color_elems = map(enumerate(func_groups)) do (idx, label) + PolyElement(; color=MISTStyle.CAT_COLORS[idx], label) + end + gl = GridLayout(f[1, 1]; default_colgap=3) + Legend(f[1, 2], + [marker_elems, color_elems], + [label.(marker_elems), label.(color_elems)], + ["Substitutions", "Functional Groups"]; + tellwidth=true, + tellheight=true, + valign=:top, + margin=(0, 0, 0, 0), + ) - ax = Axis(f[2, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Boiling Point $[\degree C]$") - vlines!(ax, -100; color=:black) - hlines!(ax, 60; color=:black) - errorcross!(ax, df.mp, df.bp) + ax = Axis(gl[1, 1]; ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"HOMO [eV]$$") + scatter!(ax, mean.(df.homo) .* HARTREE_TO_EV, mean.(df.dn); marker, color) + # vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r + # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + ax = Axis(gl[1, 2]; ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"KT $\beta$") + scatter!(ax, mean.(df.beta_kt), mean.(df.dn); marker, color) + # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + + ax = Axis(gl[1, 3]; ylabel=L"$\mu$ [D]", xlabel=L"Partial Charge Range$$") + scatter!(ax, df.range_lowdin, mean.(df.mu); marker, color) + ax = Axis(gl[1, 4]; ylabel=L"$\mu$ [D]", xlabel=L"Minimum Partial Charge$$") + scatter!(ax, df.min_lowdin, mean.(df.mu); marker, color) + # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + + ax = Axis(gl[2, 1]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Flash Point $[\degree C]$") + # vlines!(ax, -100; color=:black) + # hlines!(ax, 60; color=:black) + scatter!(ax, mean.(df.mp), mean.(df.fp); marker, color) + + ax = Axis(gl[2, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Boiling Point $[\degree C]$") + # vlines!(ax, -100; color=:black) + # hlines!(ax, 60; color=:black) + scatter!(ax, mean.(df.mp), mean.(df.bp); marker, color) + + ax = Axis(gl[2, 3]; xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$") + scatter!(ax, mean.(df.homo) .* HARTREE_TO_EV, mean.(df.gap) .* HARTREE_TO_EV; marker, color) + # vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r + # hlines!(ax, 5; color=:black) # 10.1021/acsenergylett.3c00004 (Really just says 5eV is good + + resize_to_layout!(f) return f end @@ -98,7 +207,9 @@ end function figure_permutations(name_df::Pair...; name_df_order) f = Figure(size=(3.42inch, 2inch), figure_padding=(2, 3, 2, 4)) - gl_trends = GridLayout(f[1, 1]) + gl_order = GridLayout(f[1, 1]) + gl_trends = GridLayout(f[1, 2]) + axes = [ :homo => L"HOMO$$", :gap => L"Gap$$", @@ -192,7 +303,6 @@ function figure_permutations(name_df::Pair...; name_df_order) # Order Sensitivity n_carbon_range = extrema(last(first(name_df_order)).n_carbon) - gl_order = GridLayout(f[1, 2]) cb = Colorbar(gl_order[1:length(name_df_order), 2]; label="Number of Carbons", colorrange=n_carbon_range, @@ -290,3 +400,129 @@ function figure_order2(name_df::Pair...) end return f end + +function figure_fatty_acids(df) + f = Figure(size=(3.42inch, 2inch)) + + gl_trends = GridLayout(f[1, 1]) + gl_cross = GridLayout(f[1, 2]) + + d_max = maximum(df.d) + n_max = maximum(df.n) + df_sat = subset(df, :d => ByRow(==(0))) + df_unsat = subset(df, :d => ByRow(!=(0))) + + # Trends with ω-n + axes = [ + :u298_rand => L"$G\degree$\n[kJ/mol]", + :mu_rand => L"$\mu$ [D]", + # :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", + # :gap => L"Gap\n[eV]$$", + :mp => L"$$Melt\n[$\degree C$]", + :bp => L"$$Boil\n[$\degree C$]", + :fp => L"$$Flash\n[$\degree C$]", + # :dn => L"$$DN\n[kJ/mol]", + # :pKa_kt => L"pKa", + # :beta_kt => L"KT $\beta$", + ] + axes = map(enumerate(axes)) do (idx, (col, ylabel)) + is_last = idx == length(axes) + col => Axis(gl_trends[idx, 1]; + xlabel=L"$\omega$-n Fatty Acids", ylabel, + limits=((-0.1, n_max), nothing), + xticks=0:n_max, + xtickformat = values -> map(n -> n == 0 ? "Sat." : "$(Int(n))", values), + xlabelvisible=is_last, + xticksvisible=is_last, + xminorticksvisible=is_last, + xticklabelsvisible=is_last, + tellwidth=true, + yticks=WilkinsonTicks(5), + yminorticksvisible=true, + ) + end |> Dict + cb = Colorbar(gl_trends[0, 1]; + colorrange=(0, d_max), + label="Number of Double Bonds", + vertical=false, tellwidth=false, + ) + for (col, ax) in pairs(axes) + boxplot!(ax, df_sat.n, mean.(convert_units(df_sat[:, col], col)); + width=1/d_max, + color=df_sat.d, + show_outliers=false, + MISTStyle.cb_attrs(cb, BoxPlot)... + ) + foreach(groupby(df, :n)) do gdf + y = convert_units(gdf[:, col], col) + boxplot!(ax, gdf.n, mean.(y); + dodge=gdf.d, + color=gdf.d, + show_outliers=false, + MISTStyle.cb_attrs(cb, BoxPlot)... + ) + end + end + + n = 2 + ax_bp = Axis(gl_cross[1, 1]; + xlabel=L"Melting Point [$\degree C~$]", + ylabel=L"Flash Point [$\degree C~$]", + xlabelvisible=false, + xticksvisible=false, + xticklabelsvisible=false, + ) + ax_mu = Axis(gl_cross[2, 1]; + xlabel=L"Melting Point [$\degree C~$]", + ylabel=L"$$Dipole Moment [D]", + ) + linkxaxes!(ax_bp, ax_mu) + cb_sat = Colorbar(gl_cross[1:n, 2]; + label="Degree of Saturation", + tickformat="{:.0%}", + colorrange=extrema(df.saturation), + ) + cb_c = Colorbar(gl_cross[n+1, 1]; + label="Saturated Chain Length", + colormap=:imola, + colorrange=extrema(df_sat.c), + vertical=false, tellwidth=false, + flipaxis=false, + ) + + scatter!(ax_bp, mean.(df_unsat.mp), mean.(df_unsat.fp); + color=df_unsat.saturation, + marker=:circle, + alpha=0.8, + MISTStyle.cb_attrs(cb_sat, Scatter)... + ) + lines!(ax_bp, mean.(df_sat.mp), mean.(df_sat.fp); + color=df_sat.c, + linewidth=2, + MISTStyle.cb_attrs(cb_c, Lines)... + ) + + scatter!(ax_mu, mean.(df_unsat.mp), mean.(df_unsat.mu); + color=df_unsat.saturation, + marker=:circle, + alpha=0.8, + MISTStyle.cb_attrs(cb_sat, Scatter)... + ) + lines!(ax_mu, mean.(df_sat.mp), mean.(df_sat.mu); + color=df_sat.c, + linewidth=2, + MISTStyle.cb_attrs(cb_c, Lines)... + ) + + + # foreach(groupby(df, :d)) do gdf + # @info "d" gdf + # boxplot!(ax, gdf.n, mean.(gdf.mp); dodge=gdf.d) + # end + + colgap!(f.layout, 5) + colsize!(f.layout, 1, Relative(2 / 3)) + resize_to_layout!(f) + + return f +end From 9dac8830e7ed6b51778fd780f0645e5ef38bfe5d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 15:48:47 -0400 Subject: [PATCH 019/147] rm unused code --- opt/design/MIST.jl | 118 --------------------------------------------- 1 file changed, 118 deletions(-) delete mode 100644 opt/design/MIST.jl diff --git a/opt/design/MIST.jl b/opt/design/MIST.jl deleted file mode 100644 index 688d1b0a..00000000 --- a/opt/design/MIST.jl +++ /dev/null @@ -1,118 +0,0 @@ -module MIST - -using PythonCall -using DataFrames -# using Makie -using Statistics: Statistics, mean, std -using StatsBase: StatsBase, stderror, mean_and_std - -function finetuned(ckpt) - MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned - model = MISTFinetuned.from_pretrained(ckpt) - return model -end - -function multitask(ckpt) - MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask - return MISTMultiTask.from_pretrained(ckpt) -end - -struct UQReal{T} - mean::T - std::T - n::Int -end - -Statistics.mean(x::UQReal) = x.mean -Statistics.std(x::Real) = x.std -StatsBase.stderror(x) = x.std / sqrt(x.n) -function Base.show(io::IO, x::UQReal) - μ = mean(x) - se = stderror(x) - if get(io, :compact, false)::Bool - μ = round(μ; sigdigits=5) - se = round(se; sigdigits=5) - end - print(io, "$μ ± $se") -end - -function predict(smi::Vector{String}, model::Py) - model = model.to("mps") - py_out = model.predict(PyList(smi)) - out = Dict{String,Vector{Float64}}() - for (k, v) in py_out.items() - out[pyconvert(String, k)] = pyconvert(valtype(out), v["value"]) - end - df = DataFrame(out) - insertcols!(df, 1, :smi => smi) - return df -end - -function embed(smi::Vector{String}, model::Py) - pyconvert(Matrix, model.embed(PyList(smi))) -end - -function predict_monte(smi::Vector{String}, model::Py; n=10) - training = model.training - model = model.train() - results = Dict() - py_smi = PyList(smi) - for _ in 1:n - sample = model.predict(py_smi) - for (k, v) in sample.items() - k = pyconvert(String, k) - v = pyconvert(Vector{Float64}, v["value"]) - if haskey(results, k) - results[k] = hcat(results[k], v) - else - results[k] = v - end - end - end - - # Compute statistics - summary = Dict() - for (k, v) in pairs(results) - mu_std = map(mean_and_std, eachrow(v)) - summary[k] = map(x -> UQReal(x..., n), mu_std) - end - df = DataFrame(summary) - insertcols!(df, 1, :smi => smi) - model.train(training) - return df -end - -# @recipe(ErrorLines, x, y, error_y) do scene -# Attributes() -# end -# Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), stderror.(y)) -# -# function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Real},AbstractVector{<:Real}}}) -# lines!(plt, plt.x, plt.y, Makie.shared_attributes(plt, Lines)) -# if !isnothing(plt.error_y) -# errorbars!(plt, plt.x, plt.y, plt.error_y, Makie.shared_attributes(plt, Makie.Errorbars)) -# end -# return plt -# end -# -# @recipe(ErrorCross, x, y, error_x, error_y) do scene -# Attributes() -# end -# -# Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) -# -# function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) -# attrs = Makie.shared_attributes(plt, Errorbars) -# h = errorbars!(plt, plt.x, plt.y, plt.error_y; direction=:y, attrs...) -# errorbars!(plt, plt.x, plt.y, plt.error_x; -# color=h.color, -# colorscale=h.colorscale, -# colormap=h.colormap, -# colorrange=h.colorrange, -# direction=:x, -# attrs... -# ) -# return plt -# end - -end From c70fa8e14afc4d2a549481cdb51afd90ab4a9842 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 17:29:01 -0400 Subject: [PATCH 020/147] tweak plots --- opt/MISTStyle/src/MISTStyle.jl | 13 +++ opt/design/src/DesignRules.jl | 2 +- opt/design/src/trends.jl | 140 +++++++++++++++++++-------------- 3 files changed, 93 insertions(+), 62 deletions(-) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 41014256..f1b9bc82 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -49,6 +49,19 @@ function cb_attrs(cb::Colorbar, plt) return Base.structdiff(attrs, NamedTuple{(invalid...,)}) end + +function sublabel!(f, letter; left=0, kwargs...) + label_kwargs = (; + fontsize=8pt, + font=:bold, + halign=:right, + tellheight=false, + padding=(0, left, 0, 0), + ) + label_kwargs = merge(label_kwargs, kwargs) + Label(f, "$letter)"; label_kwargs...) +end + include("errorcross.jl") include("powerlaw.jl") diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index d2159e35..44089b2a 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -4,7 +4,7 @@ using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport using DataFrames using Makie using MISTStyle -using MISTStyle: label +using MISTStyle: label, sublabel! using Statistics: Statistics, mean, std using StatsBase: StatsBase, stderror, mean_and_std, mean, range using CategoricalArrays: levelcode, categorical diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index dd416519..d6386614 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -216,8 +216,14 @@ function figure_permutations(name_df::Pair...; name_df_order) :lumo => L"LUMO$$", :zpve => L"ZPVE$$", # :cv => L"CV$$", - :h298 => L"$G\degree$", + :g298 => L"$G\degree$", ] + limits = Dict( + :homo => (nothing, (3e-3, 2)), + :gap => (nothing, (5e-3, 2)), + :zpve => (nothing, (5e-3, 3e-1)), + :g298 => (nothing, (1e-1, 4e1)), + ) dfs = [] for (name, df) in name_df df = deepcopy(df) @@ -240,14 +246,14 @@ function figure_permutations(name_df::Pair...; name_df_order) xminorticksvisible=is_last, xticklabelsvisible=is_last, tellwidth=true, - yticks=LogTicks(WilkinsonTicks(5)), + yticks=LogTicks(WilkinsonTicks(3)), yminorticksvisible=true, xminorticks=IntervalsBetween(5), ) ax_scatter = Axis(gl_trends[idx, 2]; xscale=ax_tend.yscale, yscale=ax_tend.yscale, - xlabel="Randomized\nUncertainty", + xlabel="Comparative\nRobustness", xlabelvisible=is_last, xticksvisible=false, xticklabelsvisible=false, @@ -259,9 +265,7 @@ function figure_permutations(name_df::Pair...; name_df_order) for (col, (ax, axs)) in axes y = std.(df[:, col]) - if col in [:homo, :lumo, :gap, :cv, :zpve] - y .*= HARTREE_TO_EV - end + y = convert_units(y, col) x = df.type dodge = levelcode.(df._plt_name) idx = @. !isnan(y) @@ -280,8 +284,12 @@ function figure_permutations(name_df::Pair...; name_df_order) alpha=0.2, marker=:circle, ) - linkyaxes!(ax, axs) powerlaw!(axs, 1, 1; color=:black) + if haskey(limits, col) + ax.limits[] = limits[col] + axs.limits[] = (last(limits[col]), last(limits[col])) + end + linkyaxes!(axs, ax) end # Legend @@ -303,15 +311,16 @@ function figure_permutations(name_df::Pair...; name_df_order) # Order Sensitivity n_carbon_range = extrema(last(first(name_df_order)).n_carbon) - cb = Colorbar(gl_order[1:length(name_df_order), 2]; + cb = Colorbar(gl_order[1, 1]; label="Number of Carbons", colorrange=n_carbon_range, - width=5 + vertical=false, tellwidth=false, + # flipaxis=false, ) axes = Axis[] for (idx, (_, df)) in enumerate(name_df_order) is_last = idx == length(name_df_order) - ax = Axis(gl_order[idx, 1]; + ax = Axis(gl_order[1+idx, 1]; xlabel=L"Double Bond Location$$", ylabel=L"HOMO [eV]$$", limits=((0, 1), nothing), @@ -340,19 +349,13 @@ function figure_permutations(name_df::Pair...; name_df_order) end end linkyaxes!(axes...) - colgap!(gl_order, 1, 4) colgap!(f.layout, 1, 3) - colsize!(f.layout, 1, Relative(3 / 4)) + colsize!(f.layout, 2, Relative(3 / 4)) - label_kwargs = (; - fontsize=8pt, - font=:bold, - halign=:right, - tellheight=false, - ) - Label(f[1, 1, TopLeft()], "a)"; padding=(0, 15, 0, 2), label_kwargs...) - Label(gl_order[1, 1, TopLeft()], "b)"; padding=(0, 7, 0, 2), label_kwargs...) - Label(gl_order[2, 1, TopLeft()], "c)"; padding=(0, 7, 0, 7), label_kwargs...) + + sublabel!(gl_order[2, 1, TopLeft()], "a"; left=5) + sublabel!(gl_order[3, 1, TopLeft()], "b"; left=5) + sublabel!(gl_trends[1, 1, TopLeft()], "c"; left=13) resize_to_layout!(f) @@ -406,6 +409,8 @@ function figure_fatty_acids(df) gl_trends = GridLayout(f[1, 1]) gl_cross = GridLayout(f[1, 2]) + sublabel!(f[1, 1, TopLeft()], "a"; left=5) + sublabel!(f[1, 2, TopLeft()], "b"; left=5) d_max = maximum(df.d) n_max = maximum(df.n) @@ -415,7 +420,7 @@ function figure_fatty_acids(df) # Trends with ω-n axes = [ :u298_rand => L"$G\degree$\n[kJ/mol]", - :mu_rand => L"$\mu$ [D]", + :mu_rand => L"$\mu$\n[D]", # :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", # :gap => L"Gap\n[eV]$$", :mp => L"$$Melt\n[$\degree C$]", @@ -425,16 +430,16 @@ function figure_fatty_acids(df) # :pKa_kt => L"pKa", # :beta_kt => L"KT $\beta$", ] + x_sat = 0.3 axes = map(enumerate(axes)) do (idx, (col, ylabel)) is_last = idx == length(axes) col => Axis(gl_trends[idx, 1]; xlabel=L"$\omega$-n Fatty Acids", ylabel, - limits=((-0.1, n_max), nothing), - xticks=0:n_max, - xtickformat = values -> map(n -> n == 0 ? "Sat." : "$(Int(n))", values), + limits=((0, nothing), nothing), + xticks=vcat(x_sat, collect(1:n_max)), + xtickformat = values -> map(n -> n == x_sat ? "Sat." : "$(Int(n))", values), xlabelvisible=is_last, xticksvisible=is_last, - xminorticksvisible=is_last, xticklabelsvisible=is_last, tellwidth=true, yticks=WilkinsonTicks(5), @@ -447,12 +452,13 @@ function figure_fatty_acids(df) vertical=false, tellwidth=false, ) for (col, ax) in pairs(axes) - boxplot!(ax, df_sat.n, mean.(convert_units(df_sat[:, col], col)); + boxplot!(ax, x_sat * ones(nrow(df_sat)), mean.(convert_units(df_sat[:, col], col)); width=1/d_max, color=df_sat.d, show_outliers=false, MISTStyle.cb_attrs(cb, BoxPlot)... ) + foreach(groupby(df, :n)) do gdf y = convert_units(gdf[:, col], col) boxplot!(ax, gdf.n, mean.(y); @@ -462,66 +468,78 @@ function figure_fatty_acids(df) MISTStyle.cb_attrs(cb, BoxPlot)... ) end + end n = 2 - ax_bp = Axis(gl_cross[1, 1]; - xlabel=L"Melting Point [$\degree C~$]", + ax_fp_c = Axis(gl_cross[1, 1]; + xlabel=L"Number of Carbons$$", + ylabel=L"Flash Point [$\degree C~$]", + xlabelvisible=false, + xticksvisible=false, + xticklabelsvisible=false, + ) + ax_fp_mu = Axis(gl_cross[1, 2]; + xlabel=L"Dipole Moment [D]$$", ylabel=L"Flash Point [$\degree C~$]", xlabelvisible=false, xticksvisible=false, xticklabelsvisible=false, + ylabelvisible=false, + yticksvisible=false, + yticklabelsvisible=false, ) - ax_mu = Axis(gl_cross[2, 1]; - xlabel=L"Melting Point [$\degree C~$]", - ylabel=L"$$Dipole Moment [D]", + ax_gibbs_c = Axis(gl_cross[2, 1]; + xlabel=L"Chain Length$$", + ylabel=L"$G\degree$ [kJ/mol]", ) - linkxaxes!(ax_bp, ax_mu) - cb_sat = Colorbar(gl_cross[1:n, 2]; + ax_gibbs_mu = Axis(gl_cross[2, 2]; + xlabel=L"Dipole Moment [D]$$", + ylabel=L"$G\degree$ [kJ/mol]", + ylabelvisible=false, + yticksvisible=false, + yticklabelsvisible=false, + ) + linkyaxes!(ax_fp_c, ax_fp_mu) + linkyaxes!(ax_gibbs_c, ax_gibbs_mu) + linkxaxes!(ax_fp_c, ax_gibbs_c) + linkxaxes!(ax_fp_mu, ax_gibbs_mu) + cb_sat = Colorbar(gl_cross[1:n, 3]; label="Degree of Saturation", tickformat="{:.0%}", colorrange=extrema(df.saturation), ) - cb_c = Colorbar(gl_cross[n+1, 1]; - label="Saturated Chain Length", - colormap=:imola, - colorrange=extrema(df_sat.c), - vertical=false, tellwidth=false, - flipaxis=false, - ) + colgap!(gl_cross, 1, 4) - scatter!(ax_bp, mean.(df_unsat.mp), mean.(df_unsat.fp); + sort!(df_unsat, :saturation) + scatter!(ax_fp_c, df_unsat.c, mean.(df_unsat.fp); color=df_unsat.saturation, marker=:circle, - alpha=0.8, + alpha=0.5, MISTStyle.cb_attrs(cb_sat, Scatter)... ) - lines!(ax_bp, mean.(df_sat.mp), mean.(df_sat.fp); - color=df_sat.c, - linewidth=2, - MISTStyle.cb_attrs(cb_c, Lines)... + scatter!(ax_fp_mu, mean.(df_unsat.mu), mean.(df_unsat.fp); + color=df_unsat.saturation, + marker=:circle, + alpha=0.5, + MISTStyle.cb_attrs(cb_sat, Scatter)... ) - scatter!(ax_mu, mean.(df_unsat.mp), mean.(df_unsat.mu); + sort!(df_unsat, :saturation) + scatter!(ax_gibbs_c, df_unsat.c, mean.(df_unsat.g298_rand); color=df_unsat.saturation, marker=:circle, - alpha=0.8, + alpha=0.5, MISTStyle.cb_attrs(cb_sat, Scatter)... ) - lines!(ax_mu, mean.(df_sat.mp), mean.(df_sat.mu); - color=df_sat.c, - linewidth=2, - MISTStyle.cb_attrs(cb_c, Lines)... + scatter!(ax_gibbs_mu, mean.(df_unsat.mu), mean.(df_unsat.g298_rand); + color=df_unsat.saturation, + marker=:circle, + alpha=0.5, + MISTStyle.cb_attrs(cb_sat, Scatter)... ) - - # foreach(groupby(df, :d)) do gdf - # @info "d" gdf - # boxplot!(ax, gdf.n, mean.(gdf.mp); dodge=gdf.d) - # end - - colgap!(f.layout, 5) - colsize!(f.layout, 1, Relative(2 / 3)) + colsize!(f.layout, 1, Relative(0.6)) resize_to_layout!(f) return f From 14affdcf1579fe646aafc49516e36515fe3a6652 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 17:45:12 -0400 Subject: [PATCH 021/147] fix spacing around degC --- opt/design/src/trends.jl | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index d6386614..72238ab3 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -1,5 +1,5 @@ function hydrocarbon_trends(df) - f = Figure(size=(3.5inch, 3inch)) + f = Figure(size=(2.9inch, 2inch)) func_groups = [ "Alkanes", @@ -31,8 +31,8 @@ function hydrocarbon_trends(df) :mu => L"$\mu$ [D]", :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", :gap => L"Gap\n[eV]$$", - :mp => L"$$Melt\n[$\degree C$]", - :bp => L"$$Boil\n[$\degree C$]", + :mp => L"$$Melt\n[$\degree C$ ]", + :bp => L"$$Boil\n[$\degree C$ ]", # :dn => L"$$DN\n[kJ/mol]", :pKa_kt => L"pKa", # :alpha_kt => L"KT $\alpha$", @@ -49,7 +49,7 @@ function hydrocarbon_trends(df) xminorticksvisible=is_last, xticklabelsvisible=is_last, tellwidth=true, - yticks=WilkinsonTicks(5), + yticks=WilkinsonTicks(3), yminorticksvisible=true, xminorticks=IntervalsBetween(5), ) @@ -71,17 +71,17 @@ function hydrocarbon_trends(df) elems = map(enumerate(func_groups)) do (color, label) PolyElement(; color, label, colormap, colorrange) end - Legend(gl_trends[length(axes)+1, 1], elems, label.(elems); + Legend(gl_trends[end+1, 1], elems, label.(elems); nbanks=4, tellheight=true, tellwidth=true, ) # Design Rules gl_dr = GridLayout(f[1, 2]) - ax_dn = Axis(gl_dr[1, 1]; ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"HOMO [eV]$$") + ax_dn = Axis(gl_dr[1, 1]; ylabel=L"$G\degree$ [kJ/mol]", xlabel=L"HOMO [eV]$$") ax_mp_bp = Axis(gl_dr[2, 1]; - xlabel=L"Melting Point [$C\degree$]", - ylabel=L"Boiling Point [$C\degree$]", + xlabel=L"Melting Point [$\degree C$ ]", + ylabel=L"Boiling Point [$\degree C$ ]", ) foreach(groupby(df, :type)) do gdf kwargs = (; @@ -91,7 +91,7 @@ function hydrocarbon_trends(df) label=string(first(gdf.type)), marker=:circle ) - scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), mean.(gdf.dn); kwargs...) + scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), mean.(gdf.g298); kwargs...) scatter!(ax_mp_bp, mean.(gdf.mp), mean.(gdf.bp); kwargs...) end ablines!(ax_mp_bp, 0, 1; color=:black, linestyle=:dash) @@ -178,12 +178,12 @@ function electrolyte_trends(df) scatter!(ax, df.min_lowdin, mean.(df.mu); marker, color) # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 - ax = Axis(gl[2, 1]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Flash Point $[\degree C]$") + ax = Axis(gl[2, 1]; xlabel=L"Melting Point $[\degree C ]$", ylabel=L"Flash Point $[\degree C ]$") # vlines!(ax, -100; color=:black) # hlines!(ax, 60; color=:black) scatter!(ax, mean.(df.mp), mean.(df.fp); marker, color) - ax = Axis(gl[2, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Boiling Point $[\degree C]$") + ax = Axis(gl[2, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Boiling Point $[\degree C ]$") # vlines!(ax, -100; color=:black) # hlines!(ax, 60; color=:black) scatter!(ax, mean.(df.mp), mean.(df.bp); marker, color) @@ -423,9 +423,9 @@ function figure_fatty_acids(df) :mu_rand => L"$\mu$\n[D]", # :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", # :gap => L"Gap\n[eV]$$", - :mp => L"$$Melt\n[$\degree C$]", - :bp => L"$$Boil\n[$\degree C$]", - :fp => L"$$Flash\n[$\degree C$]", + :mp => L"$$Melt\n[$\degree C$ ]", + :bp => L"$$Boil\n[$\degree C$ ]", + :fp => L"$$Flash\n[$\degree C$ ]", # :dn => L"$$DN\n[kJ/mol]", # :pKa_kt => L"pKa", # :beta_kt => L"KT $\beta$", @@ -474,14 +474,14 @@ function figure_fatty_acids(df) n = 2 ax_fp_c = Axis(gl_cross[1, 1]; xlabel=L"Number of Carbons$$", - ylabel=L"Flash Point [$\degree C~$]", + ylabel=L"Flash Point [$\degree C$ ]", xlabelvisible=false, xticksvisible=false, xticklabelsvisible=false, ) ax_fp_mu = Axis(gl_cross[1, 2]; xlabel=L"Dipole Moment [D]$$", - ylabel=L"Flash Point [$\degree C~$]", + ylabel=L"Flash Point [$\degree C$ ]", xlabelvisible=false, xticksvisible=false, xticklabelsvisible=false, From a987e1ab5a8b0796dd7ba2a65b346cd6a8dc381d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 19:01:47 -0400 Subject: [PATCH 022/147] tweak: plot sat fats as a spread Otherwise we miss out on the length dependence --- opt/design/src/trends.jl | 65 +++++++++++++++++++++++++--------------- 1 file changed, 41 insertions(+), 24 deletions(-) diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 72238ab3..55540c52 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -109,6 +109,9 @@ function hydrocarbon_trends(df) colgap!(f.layout, 5) colsize!(f.layout, 1, Relative(2 / 3)) + sublabel!(f[1, 1, TopLeft()], "a"; left=25) + sublabel!(gl_dr[1, 1, TopLeft()], "b"; left=3) + sublabel!(gl_dr[2, 1, TopLeft()], "c"; left=3) resize_to_layout!(f) return f @@ -434,10 +437,7 @@ function figure_fatty_acids(df) axes = map(enumerate(axes)) do (idx, (col, ylabel)) is_last = idx == length(axes) col => Axis(gl_trends[idx, 1]; - xlabel=L"$\omega$-n Fatty Acids", ylabel, - limits=((0, nothing), nothing), - xticks=vcat(x_sat, collect(1:n_max)), - xtickformat = values -> map(n -> n == x_sat ? "Sat." : "$(Int(n))", values), + xlabel=L"Chain Length$$", ylabel, xlabelvisible=is_last, xticksvisible=is_last, xticklabelsvisible=is_last, @@ -451,25 +451,33 @@ function figure_fatty_acids(df) label="Number of Double Bonds", vertical=false, tellwidth=false, ) + local h, hs for (col, ax) in pairs(axes) - boxplot!(ax, x_sat * ones(nrow(df_sat)), mean.(convert_units(df_sat[:, col], col)); - width=1/d_max, - color=df_sat.d, - show_outliers=false, - MISTStyle.cb_attrs(cb, BoxPlot)... - ) - foreach(groupby(df, :n)) do gdf y = convert_units(gdf[:, col], col) - boxplot!(ax, gdf.n, mean.(y); - dodge=gdf.d, + ϵ = randn(nrow(gdf)) * 0.12 + hs = scatter!(ax, gdf.c .+ ϵ, mean.(y); color=gdf.d, - show_outliers=false, + marker=:circle, + alpha=0.5, MISTStyle.cb_attrs(cb, BoxPlot)... ) end - + h = lines!(ax, df_sat.c, mean.(convert_units(df_sat[:, col], col)); + color=:black, + linewidth=1, + label="Saturated", + MISTStyle.cb_attrs(cb, BoxPlot)... + ) end + elems = [h, MarkerElement(color=:black, marker=hs.marker, label="Unsaturated", markersize=hs.markersize)] + Legend(gl_trends[end, 1], elems, label.(elems); + alignmode=Inside(), + labelsize=6pt, + halign=:right, + valign=:bottom, + nbanks=2, + ) n = 2 ax_fp_c = Axis(gl_cross[1, 1]; @@ -478,6 +486,8 @@ function figure_fatty_acids(df) xlabelvisible=false, xticksvisible=false, xticklabelsvisible=false, + yminorticksvisible=true, + yminorticks=IntervalsBetween(5), ) ax_fp_mu = Axis(gl_cross[1, 2]; xlabel=L"Dipole Moment [D]$$", @@ -489,27 +499,34 @@ function figure_fatty_acids(df) yticksvisible=false, yticklabelsvisible=false, ) - ax_gibbs_c = Axis(gl_cross[2, 1]; + ax_mp_c = Axis(gl_cross[2, 1]; xlabel=L"Chain Length$$", ylabel=L"$G\degree$ [kJ/mol]", + xticks=[0, 10, 20], + xminorticks=IntervalsBetween(5), + xminorticksvisible=true, + yminorticksvisible=true, + yminorticks=IntervalsBetween(5), ) - ax_gibbs_mu = Axis(gl_cross[2, 2]; + ax_mp_mu = Axis(gl_cross[2, 2]; xlabel=L"Dipole Moment [D]$$", - ylabel=L"$G\degree$ [kJ/mol]", + ylabel=L"Melting Point [$\degree C$ ]", ylabelvisible=false, yticksvisible=false, yticklabelsvisible=false, + xticks=WilkinsonTicks(3), + xminorticksvisible=true, ) linkyaxes!(ax_fp_c, ax_fp_mu) - linkyaxes!(ax_gibbs_c, ax_gibbs_mu) - linkxaxes!(ax_fp_c, ax_gibbs_c) - linkxaxes!(ax_fp_mu, ax_gibbs_mu) + linkyaxes!(ax_mp_c, ax_mp_mu) + linkxaxes!(ax_fp_c, ax_mp_c) + linkxaxes!(ax_fp_mu, ax_mp_mu) cb_sat = Colorbar(gl_cross[1:n, 3]; label="Degree of Saturation", tickformat="{:.0%}", colorrange=extrema(df.saturation), ) - colgap!(gl_cross, 1, 4) + # colgap!(gl_cross, 1, 4) sort!(df_unsat, :saturation) scatter!(ax_fp_c, df_unsat.c, mean.(df_unsat.fp); @@ -526,13 +543,13 @@ function figure_fatty_acids(df) ) sort!(df_unsat, :saturation) - scatter!(ax_gibbs_c, df_unsat.c, mean.(df_unsat.g298_rand); + scatter!(ax_mp_c, df_unsat.c, mean.(df_unsat.mp); color=df_unsat.saturation, marker=:circle, alpha=0.5, MISTStyle.cb_attrs(cb_sat, Scatter)... ) - scatter!(ax_gibbs_mu, mean.(df_unsat.mu), mean.(df_unsat.g298_rand); + scatter!(ax_mp_mu, mean.(df_unsat.mu), mean.(df_unsat.mp); color=df_unsat.saturation, marker=:circle, alpha=0.5, From 6c4986ebb2e328f816320bd7336d357e60e73b9a Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 19:23:06 -0400 Subject: [PATCH 023/147] restrict to omega-3 fatty acids --- opt/design/plots.jl | 8 +++++--- opt/design/src/trends.jl | 28 +++++++++++++++------------- 2 files changed, 20 insertions(+), 16 deletions(-) diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 4fecbf87..c330a893 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -42,9 +42,11 @@ df_sat = DesignRules.predict_all( n=3, ) -with_theme(MISTStyle.theme()) do - DesignRules.figure_fatty_acids(df_sat) -end |> MISTStyle.savefig("saturated-fats") +for omega in [3, 6, 9] + with_theme(MISTStyle.theme()) do + DesignRules.figure_fatty_acids(df_sat; omega) + end |> MISTStyle.savefig("omega-$omega-saturated-fats") +end # Evaluate electrolytes diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 55540c52..66e3d08f 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -407,7 +407,7 @@ function figure_order2(name_df::Pair...) return f end -function figure_fatty_acids(df) +function figure_fatty_acids(df; omega=3, alpha=0.8) f = Figure(size=(3.42inch, 2inch)) gl_trends = GridLayout(f[1, 1]) @@ -418,7 +418,7 @@ function figure_fatty_acids(df) d_max = maximum(df.d) n_max = maximum(df.n) df_sat = subset(df, :d => ByRow(==(0))) - df_unsat = subset(df, :d => ByRow(!=(0))) + df_unsat = subset(df, :d => ByRow(!=(0)), :n => ByRow(==(omega))) # Trends with ω-n axes = [ @@ -441,8 +441,11 @@ function figure_fatty_acids(df) xlabelvisible=is_last, xticksvisible=is_last, xticklabelsvisible=is_last, + xminorticksvisible=is_last, + xticks=0:5:25, + xminorticks=IntervalsBetween(5), tellwidth=true, - yticks=WilkinsonTicks(5), + yticks=WilkinsonTicks(3), yminorticksvisible=true, ) end |> Dict @@ -453,13 +456,12 @@ function figure_fatty_acids(df) ) local h, hs for (col, ax) in pairs(axes) - foreach(groupby(df, :n)) do gdf + foreach(groupby(df_unsat, :n)) do gdf y = convert_units(gdf[:, col], col) - ϵ = randn(nrow(gdf)) * 0.12 - hs = scatter!(ax, gdf.c .+ ϵ, mean.(y); + hs = scatter!(ax, gdf.c, mean.(y); color=gdf.d, marker=:circle, - alpha=0.5, + alpha=0.8, MISTStyle.cb_attrs(cb, BoxPlot)... ) end @@ -470,7 +472,7 @@ function figure_fatty_acids(df) MISTStyle.cb_attrs(cb, BoxPlot)... ) end - elems = [h, MarkerElement(color=:black, marker=hs.marker, label="Unsaturated", markersize=hs.markersize)] + elems = [h, MarkerElement(color=:black, label="Unsaturated", marker=hs.marker, markersize=hs.markersize)] Legend(gl_trends[end, 1], elems, label.(elems); alignmode=Inside(), labelsize=6pt, @@ -501,7 +503,7 @@ function figure_fatty_acids(df) ) ax_mp_c = Axis(gl_cross[2, 1]; xlabel=L"Chain Length$$", - ylabel=L"$G\degree$ [kJ/mol]", + ylabel=L"Melting Point [$\degree C$ ]", xticks=[0, 10, 20], xminorticks=IntervalsBetween(5), xminorticksvisible=true, @@ -532,13 +534,13 @@ function figure_fatty_acids(df) scatter!(ax_fp_c, df_unsat.c, mean.(df_unsat.fp); color=df_unsat.saturation, marker=:circle, - alpha=0.5, + alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) scatter!(ax_fp_mu, mean.(df_unsat.mu), mean.(df_unsat.fp); color=df_unsat.saturation, marker=:circle, - alpha=0.5, + alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) @@ -546,13 +548,13 @@ function figure_fatty_acids(df) scatter!(ax_mp_c, df_unsat.c, mean.(df_unsat.mp); color=df_unsat.saturation, marker=:circle, - alpha=0.5, + alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) scatter!(ax_mp_mu, mean.(df_unsat.mu), mean.(df_unsat.mp); color=df_unsat.saturation, marker=:circle, - alpha=0.5, + alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) From c353968e033e77e361d526edb7be5a20355da27b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 7 Apr 2025 10:13:52 -0400 Subject: [PATCH 024/147] feat: add tantext and token_embeddings figures --- opt/MISTStyle/src/MISTStyle.jl | 2 + opt/MISTStyle/src/tantext.jl | 34 +++++ opt/design/plots.jl | 6 +- opt/design/src/trends.jl | 26 ++-- opt/interp_embeddings/Project.toml | 8 ++ opt/interp_embeddings/plots.jl | 33 +++++ opt/interp_embeddings/token_embeddings.jl | 148 ++++++++++++++++++++++ 7 files changed, 242 insertions(+), 15 deletions(-) create mode 100644 opt/MISTStyle/src/tantext.jl create mode 100755 opt/interp_embeddings/plots.jl create mode 100644 opt/interp_embeddings/token_embeddings.jl diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index f1b9bc82..2d021d61 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -64,6 +64,7 @@ end include("errorcross.jl") include("powerlaw.jl") +include("tantext.jl") const CAT_COLORS = cgrad( map(x -> RGBf(x ./ 255...), [ @@ -136,6 +137,7 @@ function theme() ygridwidth=0.5, xminorgridwidth=0.5, yminorgridwidth=0.5, + titlegap=2pt, ), Legend=(; titlegap=0, diff --git a/opt/MISTStyle/src/tantext.jl b/opt/MISTStyle/src/tantext.jl new file mode 100644 index 00000000..b2012547 --- /dev/null +++ b/opt/MISTStyle/src/tantext.jl @@ -0,0 +1,34 @@ +Makie.@recipe(TanText, h, x) do scene + Attributes(; + text = "", + fontsize = Makie.theme(scene, :fontsize), + delta = 1, + ) +end + +function Makie.plot!(plt::TanText) + scene = Makie.get_scene(plt) + m = lift(get_grade, plt[:h]) + x = plt[:x] + y = lift(get_position, plt[:h], plt[:x]) + Δx = plt[:delta] + rotation = lift(scene.camera.projection, scene.viewport, x, y, m, Δx) do p, vp, x, y, m, Δx + p1 = Makie.project(scene, :data, :pixel, (x, y)) + p2 = Makie.project(scene, :data, :pixel, (x + Δx, y + m*Δx)) + atan(p2[2] - p1[2], p2[1] - p1[1]) + end + @show rotation + + attrs = Makie.shared_attributes(plt, Makie.Text) + Makie.text!(plt, x, y; rotation, fontsize=plt[:fontsize], attrs...) +end + +get_grade(h::Makie.ABLines) = lift(first, h[2])[] +get_grade(h) = error("$(typeof(h)) is not supported") + + +function get_position(h::Makie.ABLines, x::Number) + b = lift(first, h[1])[] + m = lift(first, h[2])[] + return m*x + b +end diff --git a/opt/design/plots.jl b/opt/design/plots.jl index c330a893..21c78630 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -62,9 +62,9 @@ end |> MISTStyle.savefig("electrolytes") # Permutation sensitivity df_perm = simple_hydrocarbons(25) -f(m) = :smi => ByRow(smi -> DesignRules.sample_encodings(smi, m)) => AsTable -df_perm_ref = transform(df_perm, f(models[1])) # QM9 finetuned on kekule -df_perm_rand = transform(df_perm, f(last(models[2]))) # QM9 finetuned with random +sample_smi(m) = :smi => ByRow(smi -> DesignRules.sample_encodings(smi, m)) => AsTable +df_perm_ref = transform(df_perm, sample_smi(models[1])) # QM9 finetuned on kekule +df_perm_rand = transform(df_perm, sample_smi(last(models[2]))) # QM9 finetuned with random # Order sensitivity df_order_rand = DesignRules.alkene_sweep(25, last(models[2])) diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 66e3d08f..6a79b666 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -94,14 +94,8 @@ function hydrocarbon_trends(df) scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), mean.(gdf.g298); kwargs...) scatter!(ax_mp_bp, mean.(gdf.mp), mean.(gdf.bp); kwargs...) end - ablines!(ax_mp_bp, 0, 1; color=:black, linestyle=:dash) - text!(ax_mp_bp, 20, 20; - text=L"T_m = T_b", - align=(:left, :bottom), - rotation=pi / 4, - markerspace=:data, - fontsize=24, - ) + h = ablines!(ax_mp_bp, 0, 1; color=:black, linestyle=:dash) + MISTStyle.tantext!(ax_mp_bp, h, 25; text=L"T_m = T_b", align=(:center, :bottom)) # Exceptions to BP > MP df_except = subset(df, [:mp, :bp] => ByRow((mp, bp) -> mean(mp) > mean(bp))) @@ -175,9 +169,9 @@ function electrolyte_trends(df) scatter!(ax, mean.(df.beta_kt), mean.(df.dn); marker, color) # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 - ax = Axis(gl[1, 3]; ylabel=L"$\mu$ [D]", xlabel=L"Partial Charge Range$$") - scatter!(ax, df.range_lowdin, mean.(df.mu); marker, color) - ax = Axis(gl[1, 4]; ylabel=L"$\mu$ [D]", xlabel=L"Minimum Partial Charge$$") + # ax = Axis(gl[1, 3]; ylabel=L"$\mu$ [D]", xlabel=L"Partial Charge Range$$") + # scatter!(ax, df.range_lowdin, mean.(df.mu); marker, color) + ax = Axis(gl[1, 3]; ylabel=L"$\mu$ [D]", xlabel=L"Minimum Partial Charge$$") scatter!(ax, df.min_lowdin, mean.(df.mu); marker, color) # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 @@ -192,7 +186,15 @@ function electrolyte_trends(df) scatter!(ax, mean.(df.mp), mean.(df.bp); marker, color) ax = Axis(gl[2, 3]; xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$") - scatter!(ax, mean.(df.homo) .* HARTREE_TO_EV, mean.(df.gap) .* HARTREE_TO_EV; marker, color) + scatter!(ax, mean.(df.homo_rand) .* HARTREE_TO_EV, mean.(df.gap_rand) .* HARTREE_TO_EV; marker, color) + ablines!(ax, 0, -1; color=:black, linestyle=:dash) + # text!(ax, -7, 7; + # text=L"LUMO = 0eV", + # align=(:left, :bottom), + # markerspace=:data, + # rotation=-pi / 4, + # fontsize=0.3, + # ) # vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r # hlines!(ax, 5; color=:black) # 10.1021/acsenergylett.3c00004 (Really just says 5eV is good diff --git a/opt/interp_embeddings/Project.toml b/opt/interp_embeddings/Project.toml index 1925fb67..a5fff9c7 100644 --- a/opt/interp_embeddings/Project.toml +++ b/opt/interp_embeddings/Project.toml @@ -3,8 +3,16 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +EnumX = "4e289a0a-7415-4d19-859d-a7e5c4648b56" GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" +Glob = "c27321d9-0574-5035-807b-f59d2c89b15c" +JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" +SafeTensors = "eeda0dda-7046-4914-a807-2495fc7abb89" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [sources] MISTStyle = {path = "../MISTStyle"} diff --git a/opt/interp_embeddings/plots.jl b/opt/interp_embeddings/plots.jl new file mode 100755 index 00000000..16c3b3a9 --- /dev/null +++ b/opt/interp_embeddings/plots.jl @@ -0,0 +1,33 @@ +#!/usr/bin/env -S julia --color=yes --startup-file=no --project=@script +using MISTStyle: MISTStyle + +include("token_embeddings.jl") +include("embedding_figure.jl") + +models = [ + "Pretrained" => "../../models/mist-ti624ev1-moleculenet/pretrained", + "tmQM" => "../../models/mist-ti624ev1-moleculenet/tmqm", + "QM9" => "../../models/mist-ti624ev1-moleculenet/qm9", + "FreeSolv" => "../../models/mist-ti624ev1-moleculenet/freesolv", + "QM8" => "../../models/mist-ti624ev1-moleculenet/qm8", + "Lipo" => "../../models/mist-ti624ev1-moleculenet/lipo", + "ToxCast" => "../../models/mist-ti624ev1-moleculenet/toxcast", + "BACE" => "../../models/mist-ti624ev1-moleculenet/bace", + "ESOL" => "../../models/mist-ti624ev1-moleculenet/esol", + "HIV" => "../../models/mist-ti624ev1-moleculenet/hiv", + "Tox21" => "../../models/mist-ti624ev1-moleculenet/tox21", + "SIDER" => "../../models/mist-ti624ev1-moleculenet/sider", + "MUV" => "../../models/mist-ti624ev1-moleculenet/muv", + "BBBP" => "../../models/mist-ti624ev1-moleculenet/bbbp", +] +with_theme(MISTStyle.theme()) do + figure_token_embeddings(models[1:8]; emb_models=4) +end |> MISTStyle.savefig("token_embeddings_updates") +with_theme(MISTStyle.theme()) do + figure_token_embeddings(models; last_token=Inf, fig_size=(7inch, 4.5inch), min_update=1e-6) +end |> MISTStyle.savefig("token_embeddings_updates_si") + + +with_theme(MISTStyle.theme()) do + figure_embedding() +end |> MISTStyle.savefig("interp_embeddings") diff --git a/opt/interp_embeddings/token_embeddings.jl b/opt/interp_embeddings/token_embeddings.jl new file mode 100644 index 00000000..f2b5a48d --- /dev/null +++ b/opt/interp_embeddings/token_embeddings.jl @@ -0,0 +1,148 @@ +using Makie +using ManifoldLearning +using SafeTensors: SafeTensors +using JSON: JSON +using Glob: @fn_str +using EnumX: @enumx +using MISTStyle +using LinearAlgebra: norm, dot +using Statistics: mean, std + + +function token_embedding(model::String) + # Get Token embeddings + tensors = SafeTensors.deserialize(joinpath(model, "model.safetensors")) + key = first(filter(x -> occursin(fn"*.word_embeddings.weight", x), keys(tensors))) + emb = tensors[key] + + # Get Token id => symbol mapping + tok_config = JSON.parsefile(joinpath(model, "tokenizer.json")) + + vocab = tok_config["model"]["vocab"] + added_tokens = Dict(tok["content"] => tok["id"] for tok in tok_config["added_tokens"]) + merge!(vocab, added_tokens) + @assert size(emb, 1) == length(vocab) "Missing tokens in vocab" + @assert extrema(values(vocab)) == (0, size(emb, 1) - 1) "Holes in token id space" + sdx = sortperm(collect(values(vocab))) + tokens = collect(keys(vocab))[sdx] + return emb, tokens +end + +@enumx TokenType begin + Element + Bond + Structure + Chiral + Numeric + Other + Special +end + +function token_type(token::String) + if occursin(r"^[A-Za-z][a-z]?", token) + return TokenType.Element + elseif token in ["[", "]", "(", ")", "/", "\\", "%"] + return TokenType.Structure + elseif token in ["=", "#", "\$", ".", ":"] + return TokenType.Bond + elseif occursin(r"\[[A-Z]*]]", token) + return TokenType.Special + elseif token in ["@", "@@"] || occursin(r"[A-Z]{2}", token) + return TokenType.Chiral + elseif occursin(r"[\d\-\+]", token) + return TokenType.Numeric + else + return TokenType.Other + end +end + +cosine_similarity(a, b) = dot(a, b) / (norm(a) * norm(b)) + +zscore(x; kwargs...) = (x .- mean(x; kwargs...)) ./ std(x; kwargs...) +center(x; kwargs...) = (x .- minimum(x; kwargs...)) ./ (maximum(x; kwargs...) .- minimum(x; kwargs...)) + +function figure_token_embeddings(models; last_token=75, emb_models=nothing, fig_size=(3.42inch, 1.5inch), min_update=0.005) + # Token movement during finetuning + ref_emb, ref_tokens = token_embedding(models[1][2]) + emb_movement = [] + for model in last.((models[2:end])) + emb, m_toks = token_embedding(model) + @assert all(m_toks .== ref_tokens) "Tokens don't match" + d = 1 .- map(cosine_similarity, eachrow(ref_emb), eachrow(emb)) + push!(emb_movement, d) + end + emb_movement = reduce(hcat, emb_movement) + @assert 0 <= minimum(emb_movement) && maximum(emb_movement) <= 1 "Unexpected Cos. Dist. Range" + avg_update = vec(mean(emb_movement, dims=2)) + sdx = sortperm(avg_update; rev=true) + emb_movement = emb_movement[sdx, :] + avg_update = avg_update[sdx] + tokens = ref_tokens[sdx] + + # Order models by average token movement + sdx = sortperm(vec(mean(emb_movement; dims=1)); rev=true) + emb_movement = emb_movement[:, sdx] + model_labels = first.(models[2:end])[sdx] + + # Restrict to tokens with a significant movement + @info "tokens with dist > $min_update" searchsortedlast(avg_update, min_update, rev=true) + last_token = Int(min(last_token, length(tokens))) + emb_movement = emb_movement[1:last_token, :] + tokens = tokens[1:last_token] + + # Group tokens by type + tok_class = token_type.(tokens) + sdx = sortperm(tok_class) + emb_movement = emb_movement[sdx, :] + tokens = tokens[sdx] + + # Plot Results + f = Figure(; + size=fig_size, + figure_padding=(2, 2, 2, 3) + ) + gl = GridLayout(f[1, 1]) + MISTStyle.sublabel!(f[1, 1, TopLeft()], "a"; left=15) + ax = Axis(gl[1, 1]; + xticks=(axes(emb_movement, 1), tokens), + xticklabelrotation=pi / 2, + xticklabelsize=5pt, + xticksvisible=false, + yticksvisible=false, + yticks=(axes(emb_movement, 2), model_labels), + ) + h = heatmap!(ax, axes(emb_movement)..., emb_movement; + colorrange=(min_update, 1), + colorscale=log10,) + Colorbar(gl[1, 2], h; + ticks=LogTicks(WilkinsonTicks(3)), + label="Cosine Distance", + size=4pt, + ) + + gl = GridLayout(f[2, 1]) + MISTStyle.sublabel!(f[2, 1, TopLeft()], "b"; left=5) + ax_per_row = 4 + emb_models = isnothing(emb_models) ? length(models) : emb_models + for (idx, (label, model)) in enumerate(models[1:emb_models]) + emb, mtoks = token_embedding(model) + emb = zscore(Float64.(emb); dims=2) + r = predict(fit(TSNE, emb')) + rdx = fld(idx - 1, ax_per_row) + 1 + cdx = (idx - 1) % ax_per_row + 1 + ax = Axis(gl[rdx, cdx]; title=label) + hidedecorations!(ax) + for (tok, pos) in zip(mtoks, eachcol(r)) + tok in tokens || continue + text!(ax, pos[1], pos[2]; + text=tok, + color=Int(token_type(tok)), + colormap=MISTStyle.CAT_COLORS, + colorrange=(0, 9), + ) + end + end + emb_models > 4 && rowsize!(f.layout, 1, 1inch) + resize_to_layout!(f) + return f +end From 30eadc7911d28da8b9af876562b329fa7c02c06d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 9 Apr 2025 22:27:55 -0400 Subject: [PATCH 025/147] fix units and quadrant --- opt/MISTStyle/src/MISTStyle.jl | 11 ++++- opt/MISTStyle/src/quadrant.jl | 49 +++++++++++++++++++ opt/MISTStyle/src/tantext.jl | 48 +++++++++++-------- opt/design/plots.jl | 17 +++---- opt/design/src/DesignRules.jl | 3 ++ opt/design/src/inference.jl | 4 +- opt/design/src/pubchem.jl | 8 ++-- opt/design/src/trends.jl | 86 ++++++++++++++++++---------------- 8 files changed, 150 insertions(+), 76 deletions(-) create mode 100644 opt/MISTStyle/src/quadrant.jl diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 2d021d61..8b295213 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -65,6 +65,7 @@ end include("errorcross.jl") include("powerlaw.jl") include("tantext.jl") +include("quadrant.jl") const CAT_COLORS = cgrad( map(x -> RGBf(x ./ 255...), [ @@ -83,8 +84,8 @@ const CAT_COLORS = cgrad( ) UM_COLORS = (; - blue=RGBf(0, 39 / 255, 76 / 255), - maize=RGBf(1, 203 / 255, 5 / 255), + blue=colorant"#00274C", + maize=colorant"#FFCB05", ) @@ -174,6 +175,12 @@ function theme() ErrorCross=(; markersize=5pt, whiskerwidth=3, + ), + Quadrant=(; + color=UM_COLORS.maize, + linestyle=:solid, + linecolor=UM_COLORS.blue, + alpha=0.2, ) ) end diff --git a/opt/MISTStyle/src/quadrant.jl b/opt/MISTStyle/src/quadrant.jl new file mode 100644 index 00000000..4b652e51 --- /dev/null +++ b/opt/MISTStyle/src/quadrant.jl @@ -0,0 +1,49 @@ +@recipe(Quadrant, x, y, quad) do scene + Attributes(; + color=Makie.inherit(scene, (:Quadrant, :color), :blue), + linecolor=Makie.inherit(scene, (:Quadrant, :linecolor), :black), + linestyle=Makie.inherit(scene, (:Quadrant, :linestyle), :solid), + linealpha=Makie.inherit(scene, (:Quadrant, :linealpha), 1), + linewidth=Makie.inherit(scene, (:Quadrant, :linewidth), Makie.inherit(scene, :linewidth, 1)), + ) +end + +function Makie.plot!(plt::Quadrant) + ax= Makie.current_axis() + limits = ax.finallimits + p = lift(quadpoints, limits, plt[:x], plt[:y], plt[:quad]) + poly!(plt, p; color=plt[:color], Makie.shared_attributes(plt, Poly)...) + + attrs = Makie.shared_attributes(plt, HLines) + attrs[:color] = plt[:linecolor] + attrs[:alpha] = plt[:linealpha] + hlines!(plt, plt[:y]; attrs...) + + attrs = Makie.shared_attributes(plt, VLines) + attrs[:color] = plt[:linecolor] + attrs[:alpha] = plt[:linealpha] + vlines!(plt, plt[:x]; linewidth=5pt, attrs...) + + return plt +end + +quadpoints(hr, x, y, quad) = quadpoints(corner(quad, hr), (x, y)) +function quadpoints(p1::NTuple{2, <:Number}, p2::NTuple{2, <:Number}) + ll_x, ur_x = extrema(first, [p1, p2]) + ll_y, ur_y = extrema(last, [p1, p2]) + return [(ll_x, ll_y), (ur_x, ll_y), (ur_x, ur_y), (ll_x, ur_y)] +end +function corner(quad::Symbol, hr) + if quad == :lt + return tuple(hr.origin .+ (0, hr.widths[2])...) + elseif quad == :rt + return tuple(hr.origin .+ hr.widths...) + elseif quad == :lb + return tuple(hr.origin...) + elseif quad == :rb + return tuple(hr.origin .+ (hr.widths[1], 0)...) + else + error("Quadrant $quad not recognized") + end +end + diff --git a/opt/MISTStyle/src/tantext.jl b/opt/MISTStyle/src/tantext.jl index b2012547..466631d1 100644 --- a/opt/MISTStyle/src/tantext.jl +++ b/opt/MISTStyle/src/tantext.jl @@ -1,34 +1,42 @@ -Makie.@recipe(TanText, h, x) do scene +Makie.@recipe(TanText, h, pos) do scene Attributes(; - text = "", - fontsize = Makie.theme(scene, :fontsize), - delta = 1, + text="", + fontsize=Makie.theme(scene, :fontsize), + delta=1, ) end function Makie.plot!(plt::TanText) scene = Makie.get_scene(plt) - m = lift(get_grade, plt[:h]) - x = plt[:x] - y = lift(get_position, plt[:h], plt[:x]) - Δx = plt[:delta] - rotation = lift(scene.camera.projection, scene.viewport, x, y, m, Δx) do p, vp, x, y, m, Δx - p1 = Makie.project(scene, :data, :pixel, (x, y)) - p2 = Makie.project(scene, :data, :pixel, (x + Δx, y + m*Δx)) + + origin = lift(get_position, plt[:h], plt[:pos]) + h_rot = lift(get_rot2, plt[:h], plt[:pos]) + + rotation = lift(origin, h_rot, scene.camera.projection, scene.viewport) do origin, h_rot, _, _ + p1 = Makie.project(scene, :data, :pixel, origin) + p2 = Makie.project(scene, :data, :pixel, origin .+ h_rot) atan(p2[2] - p1[2], p2[1] - p1[1]) end - @show rotation - attrs = Makie.shared_attributes(plt, Makie.Text) - Makie.text!(plt, x, y; rotation, fontsize=plt[:fontsize], attrs...) + Makie.text!(plt, origin; + fontsize=plt[:fontsize], + rotation, + attrs... + ) + return plt end -get_grade(h::Makie.ABLines) = lift(first, h[2])[] -get_grade(h) = error("$(typeof(h)) is not supported") - +get_rot2(h, ::Number) = get_rot2(h) +get_rot2(h::Makie.ABLines) = (1, lift(first, h[2])[]) +get_rot2(::Makie.HLines) = (1, 0) +get_rot2(::Makie.VLines) = (0, 1) +get_rot2(h) = error("$(typeof(h)) is not supported") function get_position(h::Makie.ABLines, x::Number) - b = lift(first, h[1])[] - m = lift(first, h[2])[] - return m*x + b + m = h[2][] + b = h[1][] + Makie.Point2(x, m * x + b) end + +get_position(h::Makie.HLines, x::Number) = Makie.Point2(x, h[1][]) +get_position(h::Makie.VLines, x::Number) = Makie.Point2(h[1][], x) diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 21c78630..1b5b58e9 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -7,14 +7,15 @@ MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask models = ( - MISTFinetuned.from_pretrained("../../models/mist-x4i8qzuq-qm9"), - "rand" => MISTFinetuned.from_pretrained("../../models/mist-26.9M-kkgx0omx-qm9"), - "kt" => MISTMultiTask.from_pretrained("../../models/solvent-properties"), - MISTFinetuned.from_pretrained("../../models/mist-26.9M-6hk5coof-dn"), - MISTFinetuned.from_pretrained("../../models/mist-26.9M-b302p09x-bp"), - MISTFinetuned.from_pretrained("../../models/mist-26.9M-y3ge5pf9-mp"), - MISTFinetuned.from_pretrained("../../models/mist-26.9M-cyuo2xb6-fp"), - "lyte" => MISTMultiTask.from_pretrained("../../models/electrolyte-solvent/"), + MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-x4i8qzuq-qm9")), + "rand" => MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-26.9M-kkgx0omx-qm9")), + "kt" => MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-26.9M-0vxdbm36-kt/")), + "kt" => MISTMultiTask.from_pretrained(joinpath(@__DIR__, "../../models/solvent-properties")) => [:pKa], + MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-26.9M-6hk5coof-dn")), + MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-26.9M-b302p09x-bp")), + MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-26.9M-y3ge5pf9-mp")), + MISTFinetuned.from_pretrained(joinpath(@__DIR__, "../../models/mist-26.9M-cyuo2xb6-fp")), + "lyte" => MISTMultiTask.from_pretrained(joinpath(@__DIR__, "../../models/electrolyte-solvent/")), ) # Generate Plots diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index 44089b2a..a9102e58 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -11,6 +11,9 @@ using CategoricalArrays: levelcode, categorical using JSON: JSON const HARTREE_TO_EV = 27.211_386_245_981 +const JOULES_TO_CALORIES = inv(4.184) + +sigmoid(x) = 1 / (1 + exp(-x)) include("uq.jl") include("inference.jl") diff --git a/opt/design/src/inference.jl b/opt/design/src/inference.jl index dfa13bd9..f5ba1743 100644 --- a/opt/design/src/inference.jl +++ b/opt/design/src/inference.jl @@ -66,8 +66,8 @@ end model_spec(model::Py) = (nothing, model, Colon()) model_spec(name_model::Pair{String,Py}) = (first(name_model), last(name_model), Colon()) -model_spec(name_model_cols::Pair{String,Pair{Py,<:Vector}}) = (first(name_model_cols), last(name_model_cols)...) -model_spec(model_cols::Pair{Py,<:Vector}) = (nothing, first(model_cols), last(model_cols)) +model_spec(name_model_cols::Pair{String,<:Pair{Py,<:AbstractVector}}) = (first(name_model_cols), last(name_model_cols)...) +model_spec(model_cols::Pair{Py,<:AbstractVector}) = (nothing, first(model_cols), last(model_cols)) """ Run all models against a DataFrame of smiles in the `smi_column` diff --git a/opt/design/src/pubchem.jl b/opt/design/src/pubchem.jl index 6f3d703f..79d568b3 100644 --- a/opt/design/src/pubchem.jl +++ b/opt/design/src/pubchem.jl @@ -1,9 +1,9 @@ function pubchem_from_jsonl(file::String) rows = [] + span(x) = maximum(x) - minimum(x) open(file) do f while !eof(f) mol = JSON.parse(readline(f)) - min_lowdin = Inf lowdin = Float64[] mulliken = Float64[] for atom in mol["atoms"] @@ -16,8 +16,10 @@ function pubchem_from_jsonl(file::String) smi=mol["smi"], min_lowdin=minimum(lowdin), min_mulliken=minimum(mulliken), - range_lowdin=-(extrema(lowdin)...), - range_mulliken=-(extrema(mulliken)...), + max_lowdin=maximum(lowdin), + max_mulliken=maximum(mulliken), + range_lowdin=span(lowdin), + range_mulliken=span(mulliken), )) end end diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 6a79b666..8f567585 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -27,7 +27,7 @@ function hydrocarbon_trends(df) # Trends with Size gl_trends = GridLayout(f[1, 1]) axes = [ - :u298 => L"$G\degree$\n[kJ/mol]", + :g298 => L"$G\degree$\n[eV]", :mu => L"$\mu$ [D]", :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", :gap => L"Gap\n[eV]$$", @@ -56,10 +56,7 @@ function hydrocarbon_trends(df) end |> Dict foreach(groupby(df, :type)) do gdf for (col, ax) in pairs(axes) - y = gdf[:, col] - if col in [:gap] - y .*= HARTREE_TO_EV - end + y = convert_units(gdf[:, col], col) lines!(ax, gdf.n_carbon, mean.(y); label=string(first(gdf.type)), color=levelcode.(gdf.type), @@ -78,7 +75,7 @@ function hydrocarbon_trends(df) # Design Rules gl_dr = GridLayout(f[1, 2]) - ax_dn = Axis(gl_dr[1, 1]; ylabel=L"$G\degree$ [kJ/mol]", xlabel=L"HOMO [eV]$$") + ax_dn = Axis(gl_dr[1, 1]; ylabel=L"$G\degree$ [eV]", xlabel=L"HOMO [eV]$$") ax_mp_bp = Axis(gl_dr[2, 1]; xlabel=L"Melting Point [$\degree C$ ]", ylabel=L"Boiling Point [$\degree C$ ]", @@ -91,7 +88,7 @@ function hydrocarbon_trends(df) label=string(first(gdf.type)), marker=:circle ) - scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), mean.(gdf.g298); kwargs...) + scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), HARTREE_TO_EV .* mean.(gdf.g298); kwargs...) scatter!(ax_mp_bp, mean.(gdf.mp), mean.(gdf.bp); kwargs...) end h = ablines!(ax_mp_bp, 0, 1; color=:black, linestyle=:dash) @@ -161,42 +158,45 @@ function electrolyte_trends(df) ) ax = Axis(gl[1, 1]; ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"HOMO [eV]$$") - scatter!(ax, mean.(df.homo) .* HARTREE_TO_EV, mean.(df.dn); marker, color) - # vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r - # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 + scatter!(ax, mean.(df.homo) .* HARTREE_TO_EV, mean.(df.dn .* JOULES_TO_CALORIES); marker, color) + + ax = Axis(gl[1, 2]; + ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"KT $\beta$", + limits=((0, nothing), nothing), + ) + beta_kt = 2 .* sigmoid.(mean.(df.beta_kt)) + scatter!(ax, beta_kt, mean.(df.dn .* JOULES_TO_CALORIES); marker, color) - ax = Axis(gl[1, 2]; ylabel=L"DN [kcal/mol, BF3]$$", xlabel=L"KT $\beta$") - scatter!(ax, mean.(df.beta_kt), mean.(df.dn); marker, color) - # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 - # ax = Axis(gl[1, 3]; ylabel=L"$\mu$ [D]", xlabel=L"Partial Charge Range$$") - # scatter!(ax, df.range_lowdin, mean.(df.mu); marker, color) ax = Axis(gl[1, 3]; ylabel=L"$\mu$ [D]", xlabel=L"Minimum Partial Charge$$") - scatter!(ax, df.min_lowdin, mean.(df.mu); marker, color) - # hlines!(ax, 10; color=:black) # 10.1021/acsenergylett.3c00004 - - ax = Axis(gl[2, 1]; xlabel=L"Melting Point $[\degree C ]$", ylabel=L"Flash Point $[\degree C ]$") - # vlines!(ax, -100; color=:black) - # hlines!(ax, 60; color=:black) - scatter!(ax, mean.(df.mp), mean.(df.fp); marker, color) + scatter!(ax, df.min_lowdin, mean.(df.pKa_kt); marker, color) + + ax = Axis(gl[2, 1]; xlabel=L"Boiling Point $[\degree C ]$", ylabel=L"Flash Point $[\degree C ]$") + # Fit coefficients from 10.1149/2.0121502jes + b = 0.7099 + a = 26.62 + C_TO_K = 273.15 + a = a + (b * C_TO_K) - C_TO_K + scatter!(ax, mean.(df.bp), mean.(df.fp); marker, color) + h = ablines!(ax, a, b; color=MISTStyle.UM_COLORS.blue, label="Hess et al.") + axislegend(ax, position=:rb, padding=(1, 1, 1, 1), margin=(1, 1, 1, 1)) ax = Axis(gl[2, 2]; xlabel=L"Melting Point $[\degree C]$", ylabel=L"Boiling Point $[\degree C ]$") - # vlines!(ax, -100; color=:black) - # hlines!(ax, 60; color=:black) + quadrant!(ax, 0, 75, :lt) + scatter!(ax, mean.(df.mp), mean.(df.bp); marker, color) + tantext!(ax, ablines!(ax, 0, 1; color=:black, linestyle=:dash), -2; + text=L"T_m = T_b", + align=(:left, :top), + ) - ax = Axis(gl[2, 3]; xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$") + ax = Axis(gl[2, 3]; + xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$", + limits=((-11, -5.5), (3, 10.75)), + ) + @show dme_homo = mean(first(df[df[!, :name].=="DME", :homo]) * HARTREE_TO_EV) + quadrant!(ax, dme_homo, 5.0, :lt) scatter!(ax, mean.(df.homo_rand) .* HARTREE_TO_EV, mean.(df.gap_rand) .* HARTREE_TO_EV; marker, color) - ablines!(ax, 0, -1; color=:black, linestyle=:dash) - # text!(ax, -7, 7; - # text=L"LUMO = 0eV", - # align=(:left, :bottom), - # markerspace=:data, - # rotation=-pi / 4, - # fontsize=0.3, - # ) - # vlines!(ax, -11.444; color=:black) # 10.1021/jz500485r - # hlines!(ax, 5; color=:black) # 10.1021/acsenergylett.3c00004 (Really just says 5eV is good resize_to_layout!(f) @@ -204,8 +204,13 @@ function electrolyte_trends(df) end function convert_units(y, col) - if col in [:homo, :lumo, :gap, :cv, :zpve] + if endswith(string(col), "_rand") + col = Symbol(string(col)[1:end-5]) + end + if col in [:homo, :lumo, :gap, :zpve, :u0, :g298, :u298, :h298] y .*= HARTREE_TO_EV + elseif col in [:dn] + y .*= JOULES_TO_CALORIES end return y end @@ -220,14 +225,12 @@ function figure_permutations(name_df::Pair...; name_df_order) :gap => L"Gap$$", :lumo => L"LUMO$$", :zpve => L"ZPVE$$", - # :cv => L"CV$$", :g298 => L"$G\degree$", ] limits = Dict( :homo => (nothing, (3e-3, 2)), :gap => (nothing, (5e-3, 2)), :zpve => (nothing, (5e-3, 3e-1)), - :g298 => (nothing, (1e-1, 4e1)), ) dfs = [] for (name, df) in name_df @@ -333,6 +336,7 @@ function figure_permutations(name_df::Pair...; name_df_order) xlabelvisible=is_last, xticksvisible=is_last, xticklabelsvisible=is_last, + yticks=WilkinsonTicks(5), ) push!(axes, ax) df = subset(df, :n_carbon => ByRow(>(4))) @@ -358,8 +362,8 @@ function figure_permutations(name_df::Pair...; name_df_order) colsize!(f.layout, 2, Relative(3 / 4)) - sublabel!(gl_order[2, 1, TopLeft()], "a"; left=5) - sublabel!(gl_order[3, 1, TopLeft()], "b"; left=5) + sublabel!(gl_order[2, 1, TopLeft()], "a"; left=15) + sublabel!(gl_order[3, 1, TopLeft()], "b"; left=15) sublabel!(gl_trends[1, 1, TopLeft()], "c"; left=13) resize_to_layout!(f) @@ -424,7 +428,7 @@ function figure_fatty_acids(df; omega=3, alpha=0.8) # Trends with ω-n axes = [ - :u298_rand => L"$G\degree$\n[kJ/mol]", + :g298_rand => L"$G\degree$\n[eV]", :mu_rand => L"$\mu$\n[D]", # :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", # :gap => L"Gap\n[eV]$$", From 03ea1ba699ea8211ac10638dc7b6c7a903a71fdf Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 10 Apr 2025 15:47:26 -0400 Subject: [PATCH 026/147] update plots per victor's comments --- opt/MISTStyle/src/errorcross.jl | 16 ++++++++--- opt/design/plots.jl | 18 ++++++++----- opt/design/src/plot_utils.jl | 11 +++++--- opt/design/src/trends.jl | 47 ++++++++++++++++++--------------- 4 files changed, 56 insertions(+), 36 deletions(-) diff --git a/opt/MISTStyle/src/errorcross.jl b/opt/MISTStyle/src/errorcross.jl index 6e0f74e4..8018fc10 100644 --- a/opt/MISTStyle/src/errorcross.jl +++ b/opt/MISTStyle/src/errorcross.jl @@ -1,14 +1,22 @@ @recipe(ErrorCross, x, y, error_x, error_y) do scene - Attributes() + Attributes(; + n_stds=3, + markervisible=true, + ) end function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) - scatter!(plt, plt.x, plt.y; Makie.shared_attributes(plt, Scatter)...) - h = errorbars!(plt, plt.x, plt.y, plt.error_y; + scatter!(plt, plt.x, plt.y; + visible=plt.markervisible, + Makie.shared_attributes(plt, Scatter)... + ) + error_x = @lift $(plt.error_x) * first($(plt.n_stds)) + error_y = @lift $(plt.error_y) * last($(plt.n_stds)) + h = errorbars!(plt, plt.x, plt.y, error_y; direction=:y, Makie.shared_attributes(plt, Errorbars)... ) - errorbars!(plt, plt.x, plt.y, plt.error_x; + errorbars!(plt, plt.x, plt.y, error_x; color=h.color, direction=:x, Makie.shared_attributes(plt, Errorbars)... diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 1b5b58e9..cd94d98f 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -33,9 +33,11 @@ df_hydrocarbons = DesignRules.predict_all( n=10 ) -with_theme(MISTStyle.theme()) do - DesignRules.hydrocarbon_trends(df_hydrocarbons) -end |> MISTStyle.savefig("hydrocarbons") +for qm_model in ["", "_rand"] + with_theme(MISTStyle.theme()) do + DesignRules.hydrocarbon_trends(df_hydrocarbons; qm_model) + end |> MISTStyle.savefig("hydrocarbons" * qm_model) +end df_sat = DesignRules.predict_all( DesignRules.saturated_fats(24; n_max=9, d_max=6), @@ -43,10 +45,12 @@ df_sat = DesignRules.predict_all( n=3, ) -for omega in [3, 6, 9] - with_theme(MISTStyle.theme()) do - DesignRules.figure_fatty_acids(df_sat; omega) - end |> MISTStyle.savefig("omega-$omega-saturated-fats") +for qm_model in ["", "_rand"] + for omega in [3, 6, 9] + with_theme(MISTStyle.theme()) do + DesignRules.figure_fatty_acids(df_sat; omega, qm_model) + end |> MISTStyle.savefig("omega-$omega-saturated-fats$qm_model") + end end diff --git a/opt/design/src/plot_utils.jl b/opt/design/src/plot_utils.jl index 4178e74e..b454dd57 100644 --- a/opt/design/src/plot_utils.jl +++ b/opt/design/src/plot_utils.jl @@ -1,13 +1,18 @@ @recipe(ErrorLines, x, y, error_y) do scene - Attributes() + Attributes(; + n_stds=3, + ) end -Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), stderror.(y)) +Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), std.(y)) Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:Real}) = (x, y, zero(y)) function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Real},AbstractVector{<:Real}}}) lines!(plt, plt.x, plt.y; Makie.shared_attributes(plt, Lines)...) if !isnothing(plt.error_y) - errorbars!(plt, plt.x, plt.y, plt.error_y; Makie.shared_attributes(plt, Makie.Errorbars)...) + error = @lift $(plt[:error_y]) * $(plt[:n_stds]) + errorbars!(plt, plt.x, plt.y, error; + Makie.shared_attributes(plt, Makie.Errorbars)... + ) end return plt end diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 8f567585..66250154 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -1,4 +1,4 @@ -function hydrocarbon_trends(df) +function hydrocarbon_trends(df; qm_model="") f = Figure(size=(2.9inch, 2inch)) func_groups = [ @@ -27,14 +27,16 @@ function hydrocarbon_trends(df) # Trends with Size gl_trends = GridLayout(f[1, 1]) axes = [ - :g298 => L"$G\degree$\n[eV]", - :mu => L"$\mu$ [D]", - :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", - :gap => L"Gap\n[eV]$$", + Symbol("g298" * qm_model) => L"$G\degree$\n[eV]", + Symbol("alpha" * qm_model) => L"$\alpha$\n$[\alpha_0^3]$", + Symbol("gap" * qm_model) => L"Gap\n[eV]$$", + Symbol("homo" * qm_model) => L"HOMO\n[eV]$$", :mp => L"$$Melt\n[$\degree C$ ]", :bp => L"$$Boil\n[$\degree C$ ]", + # Symbol("mu" * qm_model) => L"$\mu$ [D]", + # Symbol("r2" * qm_model) => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", # :dn => L"$$DN\n[kJ/mol]", - :pKa_kt => L"pKa", + # :pKa_kt => L"pKa", # :alpha_kt => L"KT $\alpha$", # :beta_kt => L"KT $\beta$", ] @@ -57,7 +59,7 @@ function hydrocarbon_trends(df) foreach(groupby(df, :type)) do gdf for (col, ax) in pairs(axes) y = convert_units(gdf[:, col], col) - lines!(ax, gdf.n_carbon, mean.(y); + errorlines!(ax, gdf.n_carbon, y; label=string(first(gdf.type)), color=levelcode.(gdf.type), colormap, @@ -86,10 +88,16 @@ function hydrocarbon_trends(df) colorrange, color=levelcode.(gdf.type), label=string(first(gdf.type)), - marker=:circle + marker=:circle, + # whiskerwidth=2pt, + # markervisible=false, ) - scatter!(ax_dn, HARTREE_TO_EV .* mean.(gdf.homo), HARTREE_TO_EV .* mean.(gdf.g298); kwargs...) + homo = gdf[:, Symbol("homo" * qm_model)] .* HARTREE_TO_EV + g298 = gdf[:, Symbol("g298" * qm_model)] .* HARTREE_TO_EV + scatter!(ax_dn, mean.(homo), mean.(g298); kwargs...) scatter!(ax_mp_bp, mean.(gdf.mp), mean.(gdf.bp); kwargs...) + # errorcross!(ax_dn, homo, g298; kwargs...) + # errorcross!(ax_mp_bp, gdf.mp, gdf.bp; kwargs...) end h = ablines!(ax_mp_bp, 0, 1; color=:black, linestyle=:dash) MISTStyle.tantext!(ax_mp_bp, h, 25; text=L"T_m = T_b", align=(:center, :bottom)) @@ -413,7 +421,7 @@ function figure_order2(name_df::Pair...) return f end -function figure_fatty_acids(df; omega=3, alpha=0.8) +function figure_fatty_acids(df; omega=3, alpha=0.8, qm_model="") f = Figure(size=(3.42inch, 2inch)) gl_trends = GridLayout(f[1, 1]) @@ -428,18 +436,14 @@ function figure_fatty_acids(df; omega=3, alpha=0.8) # Trends with ω-n axes = [ - :g298_rand => L"$G\degree$\n[eV]", - :mu_rand => L"$\mu$\n[D]", - # :r2 => L"$\langle R^2 \rangle$\n$[\alpha_0^2]$", - # :gap => L"Gap\n[eV]$$", + Symbol("g298" * qm_model) => L"$G\degree$\n[eV]", + Symbol("mu" * qm_model) => L"$\mu$\n[D]", + # Symbol("alpha" * qm_model) => L"$\alpha$\n$[\alpha_0^3]$", + # Symbol("gap" * qm_model) => L"Gap\n[eV]$$", :mp => L"$$Melt\n[$\degree C$ ]", :bp => L"$$Boil\n[$\degree C$ ]", :fp => L"$$Flash\n[$\degree C$ ]", - # :dn => L"$$DN\n[kJ/mol]", - # :pKa_kt => L"pKa", - # :beta_kt => L"KT $\beta$", ] - x_sat = 0.3 axes = map(enumerate(axes)) do (idx, (col, ylabel)) is_last = idx == length(axes) col => Axis(gl_trends[idx, 1]; @@ -534,30 +538,29 @@ function figure_fatty_acids(df; omega=3, alpha=0.8) tickformat="{:.0%}", colorrange=extrema(df.saturation), ) - # colgap!(gl_cross, 1, 4) sort!(df_unsat, :saturation) + mu = mean.(df_unsat[:, Symbol("mu" * qm_model)]) scatter!(ax_fp_c, df_unsat.c, mean.(df_unsat.fp); color=df_unsat.saturation, marker=:circle, alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) - scatter!(ax_fp_mu, mean.(df_unsat.mu), mean.(df_unsat.fp); + scatter!(ax_fp_mu, mu, mean.(df_unsat.fp); color=df_unsat.saturation, marker=:circle, alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) - sort!(df_unsat, :saturation) scatter!(ax_mp_c, df_unsat.c, mean.(df_unsat.mp); color=df_unsat.saturation, marker=:circle, alpha, MISTStyle.cb_attrs(cb_sat, Scatter)... ) - scatter!(ax_mp_mu, mean.(df_unsat.mu), mean.(df_unsat.mp); + scatter!(ax_mp_mu, mu, mean.(df_unsat.mp); color=df_unsat.saturation, marker=:circle, alpha, From 3b1a4aea313e9eb404a6890b7c993c58db0d604d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 21 Apr 2025 10:43:54 -0700 Subject: [PATCH 027/147] add linear probe submission and analysis scripts --- electrolyte_fm/models/linear_probe.py | 39 +-------------- electrolyte_fm/utils/cli.py | 68 +++++++++++++++++++++++++++ opt/FeatureMiner/run_explore.sh | 12 +++++ opt/FeatureMiner/submit_explore.sh | 22 +++------ submit/linear_probe.jsonnet | 29 ++++++++++++ 5 files changed, 117 insertions(+), 53 deletions(-) create mode 100644 electrolyte_fm/utils/cli.py create mode 100644 opt/FeatureMiner/run_explore.sh create mode 100644 submit/linear_probe.jsonnet diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 8bc745b0..6277a34c 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -182,14 +182,9 @@ def configure_optimizers(self): import smirk import json from jsonargparse import lazy_instance - from lightning.pytorch.cli import ( - LightningCLI, - LightningArgumentParser, - _InstantiatorFn, - _get_module_type, - ) from lightning.pytorch.loggers import WandbLogger from lightning.pytorch.callbacks import ModelCheckpoint + from electrolyte_fm.utils.cli import MistLightningCLI logging.basicConfig(level=logging.INFO) @@ -200,37 +195,7 @@ def mlm_from_pretrained(name_or_path: str) -> nn.Module: name_or_path, trust_remote_code=True ) - class MyLightningCLI(LightningCLI): - def add_arguments_to_parser(self, parser: LightningArgumentParser): - parser.add_argument( - "--tags", - type=list, - help="Tags for WandB logger", - default=[], - ) - parser.link_arguments("tags", "trainer.logger.init_args.tags") - - def _add_instantiators(self) -> None: - self.config_dump = json.loads( - self.parser.dump( - self.config, skip_link_targets=False, skip_none=False, format="json" - ) - ) - if "subcommand" in self.config: - self.config_dump = self.config_dump[self.config.subcommand] - - self.parser.add_instantiator( - _InstantiatorFn(cli=self, key="model"), - _get_module_type(self._model_class), - subclasses=self.subclass_mode_model, - ) - self.parser.add_instantiator( - _InstantiatorFn(cli=self, key="data"), - _get_module_type(self._datamodule_class), - subclasses=self.subclass_mode_data, - ) - - cli = MyLightningCLI( + cli = MistLightningCLI( LightningProbe, save_config_callback=None, seed_everything_default=42, diff --git a/electrolyte_fm/utils/cli.py b/electrolyte_fm/utils/cli.py new file mode 100644 index 00000000..af724831 --- /dev/null +++ b/electrolyte_fm/utils/cli.py @@ -0,0 +1,68 @@ +import json +import torch +from lightning.pytorch.cli import ( + LightningCLI, + LightningArgumentParser, + _InstantiatorFn, + _get_module_type, +) + + +def automodel_from_pretrained( + name_or_path: str, trust_remote_code=False, add_pooling_layer=None +) -> torch.nn.Module: + from transformers import AutoModel + + kwargs = {} + if add_pooling_layer is not None: + kwargs["add_pooling_layer"] = add_pooling_layer + + return AutoModel.from_pretrained( + name_or_path, trust_remote_code=trust_remote_code, **kwargs + ) + + +def mlm_from_pretrained( + name_or_path: str, trust_remote_code: bool = True +) -> torch.nn.Module: + from transformers import AutoModelForMaskedLM + + return AutoModelForMaskedLM.from_pretrained( + name_or_path, + trust_remote_code=trust_remote_code, + ) + + +class MistLightningCLI(LightningCLI): + def __init__(self, *args, **kwargs): + kwargs.setdefault("parser_kwargs", {"parser_mode": "jsonnet"}) + super().__init__(*args, **kwargs) + + def add_arguments_to_parser(self, parser: LightningArgumentParser): + parser.add_argument( + "--tags", + type=list, + help="Tags for WandB logger", + default=[], + ) + parser.link_arguments("tags", "trainer.logger.init_args.tags") + + def _add_instantiators(self) -> None: + self.config_dump = json.loads( + self.parser.dump( + self.config, skip_link_targets=False, skip_none=False, format="json" + ) + ) + if "subcommand" in self.config: + self.config_dump = self.config_dump[self.config.subcommand] + + self.parser.add_instantiator( + _InstantiatorFn(cli=self, key="model"), + _get_module_type(self._model_class), + subclasses=self.subclass_mode_model, + ) + self.parser.add_instantiator( + _InstantiatorFn(cli=self, key="data"), + _get_module_type(self._datamodule_class), + subclasses=self.subclass_mode_data, + ) diff --git a/opt/FeatureMiner/run_explore.sh b/opt/FeatureMiner/run_explore.sh new file mode 100644 index 00000000..401fcdac --- /dev/null +++ b/opt/FeatureMiner/run_explore.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH --job-name=explore +#SBATCH --partition cpuq +#SBATCH -c 4 +#SBATCH --mem-per-cpu 4000M +#SBATCH --time 2:0:0 + +GIT_ROOT=$(git rev-parse --show-toplevel) +MODEL_DIR=../../linear-probes/ +source "${GIT_ROOT}/activate" +julia --project --startup-file=no -e 'using Pkg; Pkg.instantiate()' +./explore_probes.jl $@ diff --git a/opt/FeatureMiner/submit_explore.sh b/opt/FeatureMiner/submit_explore.sh index dbbd7aa2..b5f95f68 100644 --- a/opt/FeatureMiner/submit_explore.sh +++ b/opt/FeatureMiner/submit_explore.sh @@ -1,18 +1,8 @@ #!/bin/bash -#SBATCH --job-name=explore -#SBATCH -p cpuq -#SBATCH -N 1 -#SBATCH -n 16 -#SBATCH -c 4 -#SBATCH --mem-per-cpu 1800M +for ckpt in $(find ../../linear-probes/ -maxdepth 1 -mindepth 1 -type d); do + if [ ! -f "${ckpt}/linear_probes.jld2" ]; then + echo "Submitting ${ckpt}" + sbatch ./run_explore.sh "${ckpt}" + fi +done -GIT_ROOT=$(git rev-parse --show-toplevel) -MODEL_DIR=../../linear-probes/ -source "${GIT_ROOT}/activate" -# julia --project --startup-file=no -e 'using Pkg; Pkg.instantiate()' -find $MODEL_DIR -maxdepth 1 -mindepth 1 -print0 | \ - xargs -0 -P ${SLURM_NTASKS} -i ./explore_probes.jl {} - -# Archive results -find $MODEL_DIR -name '*.jld2' -printf '%P\n' | \ - tar -caf linear_probes.tar.xz -C $MODEL_DIR --files-from=- diff --git a/submit/linear_probe.jsonnet b/submit/linear_probe.jsonnet new file mode 100644 index 00000000..1964b5bc --- /dev/null +++ b/submit/linear_probe.jsonnet @@ -0,0 +1,29 @@ +{ + model: { + model: { + class_path: 'electrolyte_fm.models.prod_finetune.MISTFinetuned.from_pretrained', + init_args: { + name_or_path: 'models/mist-ti624ev1-moleculenet/tmQM', + }, + }, + probes: { + class_path: 'electrolyte_fm.models.linear_probe.per_layer_probe', + init_args: { + hidden_size: 512, + features: 5, + location: 'output', + n_layers: 8, + }, + }, + }, + data: { + class_path: 'electrolyte_fm.data_modules.lipinski_dataset.LipinskiDataModule', + init_args: { + name_or_path: 'tox21', + tokenizer: 'smirk', + encoding: 'smiles-kekule', + num_workers: 16, + batch_size: 16, + }, + }, +} From 952f53c3466cdd4d308c3697e92303f7ac721e28 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 22 Apr 2025 14:08:29 -0700 Subject: [PATCH 028/147] feat: construct dataset for lipinski linear probes --- .../data_modules/lipinski_dataset.py | 256 ++++++++++++++++-- electrolyte_fm/data_modules/molnet_dataset.py | 2 + electrolyte_fm/data_modules/utils.py | 12 +- electrolyte_fm/models/linear_probe.py | 2 + opt/FeatureMiner/src/lipinski.jl | 6 +- 5 files changed, 246 insertions(+), 32 deletions(-) diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index f007d518..115c3e6e 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -1,15 +1,19 @@ +import asyncio from pathlib import Path import torch -from datasets import load_dataset -from rdkit.Chem import Lipinski, MolFromSmiles +import pandas as pd +import numpy as np +from sklearn.utils import resample +from sklearn.model_selection import StratifiedShuffleSplit +from datasets import DatasetDict, Dataset, load_dataset, concatenate_datasets +from rdkit.Chem import Lipinski, MolFromSmiles, MolToInchiKey from rdkit.Chem.Crippen import MolLogP from rdkit.Chem.Descriptors import ExactMolWt -from .molnet_dataset import _URLS +from .molnet_dataset import MolNetDataModule from .property_prediction_dataset import PropertyPredictionDataModule from .utils import AbstractDataset, MolEncoding, filter_invalid_smi -from .molnet_dataset import train_val_test_split class LipinskiDataModule(PropertyPredictionDataModule): @@ -32,26 +36,24 @@ def __init__(self, name_or_path: str, **kwargs): super().__init__(**kwargs) assert self.encoding != MolEncoding.SELFIES - def _get_dataset(self) -> AbstractDataset: + def _get_dataset(cls, name_or_path) -> AbstractDataset: # Load the dataset ds: AbstractDataset = load_dataset( "csv", - name=self.name, - data_files=[self.name_or_path], + name=name, + data_files=[name_or_path], split="train", keep_in_memory=False, save_infos=False, ) # type: ignore - - ds = ds.select_columns(self.smi_column) - ds = filter_invalid_smi(ds, self.smi_column) - + ds = ds.select_columns(smi_column) + ds = filter_invalid_smi(self.smi_column) ds = ds.map( lipinki_rule_of_five, batched=False, fn_kwargs={"smi_column": self.smi_column}, ) - return train_val_test_split(ds) + return ds def collate_fn(self, batch): output = super().collate_fn(batch) @@ -63,7 +65,221 @@ def collate_fn(self, batch): return output -def lipinki_rule_of_five(x: dict, smi_column: str) -> dict: +def get_molnet_dataset(name: str): + ds = MolNetDataModule(name=name, split="all") + return ds._get_dataset().rename_column(ds.smi_column, "smi").select_columns("smi") + + +def build_probe_dataset(): + sem = asyncio.Semaphore(64) + + ds = [ + get_molnet_dataset(name) + for name in [ + "hiv", + "toxcast", + "tox21", + "clintox", + "bbbp", + "qm9", + "qm8", + "freesolv", + "lipo", + "muv", + "sider", + "esol", + ] + ] + + # # Pull molecules from Zinc + # zinc_path = "/lustre/fs0/shared/zinc_v1" + # ds_zinc = load_dataset( + # "text", + # name="zinc_train", + # data_files=str(Path(zinc_path).joinpath("data/train/*.txt")), + # split="train", + # keep_in_memory=False, + # save_infos=False, + # ) + # ds_zinc = ds_zinc.take(1_000_000) + # + # def zinc_smi(text: str): + # return {"smi": text.split(" ")[0]} + # + # ds_zinc = ds_zinc.map(zinc_smi, batched=False, input_columns="text") + # ds.append(ds_zinc) + + ds = concatenate_datasets(ds) + ds = filter_invalid_smi(ds, "smi") + + async def async_lipinski_rule_of_five(x): + async with sem: + return lipinki_rule_of_five(x, "smi") + + ds = ds.map(async_lipinski_rule_of_five, batched=False) + + # Label with inchi key for de-duplication + async def inchi_key(smi: str): + async with sem: + return {"inchi_key": MolToInchiKey(MolFromSmiles(smi))} + + ds = ds.map(inchi_key, batched=False, input_columns="smi") + + # Resample to balanced classes + df = ds.to_pandas() + df.drop_duplicates(subset="inchi_key", inplace=True) + lip_cols = [ + "lipinki_h_donor", + "lipinki_h_acceptor", + "lipinki_mwt", + "lipinki_log_p", + ] + + # Rebalance and report stats + print("Group Counts:\n", get_group_sizes(df, lip_cols)) + print("Dataset size:", len(df)) + print("Class Odds:\n", df[lip_cols].mean()) + df = downsample_ipf_binary(df, lip_cols, n_samples=10_000) + print("Group Counts:\n", get_group_sizes(df, lip_cols)) + print("Dataset size:", len(df)) + print("Class Odds:\n", df[lip_cols].mean()) + + # Split preserving the frequency of each subgroup + spliter = StratifiedShuffleSplit(train_size=0.80, random_state=721153) + train_idx, test_idx = next(spliter.split(df["smi"], df["lipinki"])) + + # Save to disk + ds = DatasetDict( + { + "train": Dataset.from_pandas(df.iloc[train_idx], preserve_index=False), + "validation": Dataset.from_pandas(df.iloc[test_idx], preserve_index=False), + } + ) + Path("lipinski").mkdir(exist_ok=True) + ds.save_to_disk("lipinski/data", num_shards={"train": 8, "validation": 8}) + + +def get_group_sizes(df: pd.DataFrame, class_columns) -> pd.DataFrame: + """ + Get the count of each unique combination of values in the given class_columns. + + Parameters: + - df: pandas DataFrame. + - class_columns: A string or list of columns to group by. + + Returns: + - A DataFrame showing each group and its count, sorted descending. + """ + if isinstance(class_columns, str): + class_columns = [class_columns] + + group_counts = df.groupby(class_columns).size().reset_index(name="count") + return group_counts.sort_values("count", ascending=False).reset_index(drop=True) + + +def downsample_ipf_binary( + df: pd.DataFrame, + class_columns: list[str], + n_samples: int | None = None, + max_iter: int = 100, + tol: float = 1e-6, + random_state: int = 42, +) -> pd.DataFrame: + """ + Downsample a DataFrame with K binary columns so that each column’s marginal is 50/50, + using iterative proportional fitting over the joint 2^K table. + + Steps: + 1. Build original cell counts for every combination of the K binaries. + 2. Initialize target cell counts = original counts. + 3. For each column, rescale all cell counts in each level (0 and 1) so that + sum_over_cells(level=1) == total/2 and sum_over_cells(level=0) == total/2. + 4. Iterate until all K marginals are within tol of 0.5. + 5. Scale target cell counts to sum to n_samples (or len(df) if n_samples is None). + 6. For each cell c, weight per row in c = target_count[c] / original_count[c]. + 7. Draw without replacement using these per‑row probabilities. + + Returns: + A new DataFrame of size n_samples with approximately perfect 50/50 marginals. + """ + rng = np.random.default_rng(random_state) + df = df.reset_index(drop=True) + N = len(df) + + if n_samples is None: + min_count_factor = 4 + min_true_counts = [df[col].sum() for col in class_columns] + min_false_counts = [len(df) - x for x in min_true_counts] + max_true_size = int(min(min_true_counts) * min_count_factor) + max_false_size = int(min(min_false_counts) * min_count_factor) + n_samples = min(len(df), max_true_size, max_false_size) + + # 1) compute original contingency table + # use tuple of column values as key + keys = list(df[class_columns].itertuples(index=False, name=None)) + uniq, inv = np.unique(keys, axis=0, return_inverse=True) + orig_counts = pd.Series(np.bincount(inv), index=range(len(uniq)), dtype=float) + + # initialize target = original + target = orig_counts.copy() + + # precompute for each column which cells have bit=1 + # uniq is array of shape (n_cells, K) + uniq_arr = np.array(uniq, dtype=int) + is_one = {col: uniq_arr[:, i] == 1 for i, col in enumerate(class_columns)} + + total = target.sum() + half = total / 2.0 + + # 2) IPF loop + for _ in range(max_iter): + max_diff = 0.0 + + for i, col in enumerate(class_columns): + mask1 = is_one[col] + mask0 = ~mask1 + + # current marginal for this column + cur1 = target[mask1].sum() + cur0 = target[mask0].sum() + + # scale factor to push cur1 -> half and cur0 -> half + if cur1 > 0: + target[mask1] *= half / cur1 + if cur0 > 0: + target[mask0] *= half / cur0 + + # track worst marginal error + max_diff = max(max_diff, abs(cur1 / total - 0.5), abs(cur0 / total - 0.5)) + + if max_diff < tol: + break + else: + # warn if not converged + print( + f"IPF did not converge in {max_iter} iters; max marginal error {max_diff:.2e}" + ) + + # 3) scale target total to n_samples + scale = n_samples / total + target *= scale + + # 4) per‐row weights: target_count[c] / orig_count[c] + # inv maps each row to its cell index + cell_weight = target.to_numpy() / orig_counts.to_numpy() + row_weights = cell_weight[inv] + row_weights = np.clip(row_weights, 0, None) + row_weights = row_weights / row_weights.sum() + + # 5) sample + chosen = rng.choice(N, size=n_samples, replace=False, p=row_weights) + return df.iloc[chosen].reset_index(drop=True) + + +SEM_LIPINSKI = asyncio.Semaphore(20) + + +def lipinki_rule_of_five(x: dict, smi_column: str = "smi") -> dict: smi = x[smi_column] mol = MolFromSmiles(smi) assert mol is not None, "invalid smi: %s" % smi @@ -76,20 +292,8 @@ def lipinki_rule_of_five(x: dict, smi_column: str) -> dict: x["log_p"] = MolLogP(mol) x["lipinki_log_p"] = x["log_p"] <= 5 x["lipinki"] = all(v for k, v in x.items() if k.startswith("lipinki")) - x["probe_target"] = [v for k, v in x.items() if k.startswith("lipinki")] return x if __name__ == "__main__": - ds = LipinskiDataModule(name_or_path="hiv") - ds = ds.dataset - df = ds.to_pandas() - df.to_csv("lipo.csv") - cols = [ - "lipinki", - "lipinki_h_donor", - "lipinki_h_acceptor", - "lipinki_mwt", - "lipinki_log_p", - ] - print({k: df[k].value_counts() for k in cols}) + build_probe_dataset() diff --git a/electrolyte_fm/data_modules/molnet_dataset.py b/electrolyte_fm/data_modules/molnet_dataset.py index f60d029e..7f898a66 100644 --- a/electrolyte_fm/data_modules/molnet_dataset.py +++ b/electrolyte_fm/data_modules/molnet_dataset.py @@ -83,6 +83,8 @@ def _get_dataset(self): return scaffold_split(ds, self.smi_column) elif self.split == "random": return train_val_test_split(ds) + elif self.split == "all": + return ds else: raise ValueError(f"Unknown split {self.split}") diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py index 411fff3e..68f2ca98 100644 --- a/electrolyte_fm/data_modules/utils.py +++ b/electrolyte_fm/data_modules/utils.py @@ -1,3 +1,4 @@ +import asyncio from enum import Enum import random from typing import Optional, TypeVar @@ -82,11 +83,14 @@ def random(self, smi: str): def filter_invalid_smi( - ds: AbstractDataset, input_column: str, **kwargs + ds: AbstractDataset, input_column: str, max_procs: int = 20, **kwargs ) -> AbstractDataset: - def is_valid(x: dict): - mol = Chem.MolFromSmiles(x[input_column]) - return mol is not None + sem = asyncio.Semaphore(max_procs) + + async def is_valid(x: dict): + async with sem: + mol = Chem.MolFromSmiles(x[input_column]) + return mol is not None return ds.filter(is_valid, batched=False, **kwargs) diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 6277a34c..e989bde2 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -59,6 +59,8 @@ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: hook.remove() state_dict.pop("_hooks_installed", None) state_dict["_prob_points"] = self._probe_points + state_dict["hookpoints"] = self.hookpoints + checkpoint["state_dict"] = state_dict # Don't save activations state_dict.pop("_activations", None) diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 4914fc28..186da82b 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -7,16 +7,18 @@ function load_fitted_probes(ckpt_dir) _, ckpt_meta = load_linear_probes(ckpt) continue end - m = match(r"layer-(\d+)-(\w+).*?--auroc-([\d\.]+)\.ckpt", basename(ckpt)) + m = match(r"layer-(\d+)-([\w\-]+?)--.*?--loss-([\d\.]+)--auroc-([\d\.]+)\.ckpt$", basename(ckpt)) layer = parse(Int, m[1]) + 1 location = m[2] - auroc = parse(Float64, m[3]) + val_loss = parse(Float64, m[3]) + auroc = parse(Float64, m[4]) try ckpt_probes, ckpt_meta = load_linear_probes(ckpt) push!(probes, (; ckpt_probes[layer]..., location, auroc, + val_loss )) catch e e isa InterruptException && rethrow() From a6ba97d3c350d015833124a40e726c5889142b4b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 22 Apr 2025 19:53:11 -0400 Subject: [PATCH 029/147] update linear probe sweep --- .../data_modules/lipinski_dataset.py | 62 +++++-------- .../property_prediction_dataset.py | 4 +- electrolyte_fm/models/linear_probe.py | 53 ++++++++--- opt/FeatureMiner/submit_linear_probes.py | 90 +++++++++---------- 4 files changed, 107 insertions(+), 102 deletions(-) diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index 115c3e6e..763e7453 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -1,10 +1,8 @@ import asyncio from pathlib import Path -import torch import pandas as pd import numpy as np -from sklearn.utils import resample from sklearn.model_selection import StratifiedShuffleSplit from datasets import DatasetDict, Dataset, load_dataset, concatenate_datasets from rdkit.Chem import Lipinski, MolFromSmiles, MolToInchiKey @@ -17,52 +15,34 @@ class LipinskiDataModule(PropertyPredictionDataModule): - def __init__(self, name_or_path: str, **kwargs): - self.name_or_path = name_or_path - if not Path(name_or_path).exists(): - # Set default smi_column - assert name_or_path in _URLS.keys() - self.name_or_path = _URLS[name_or_path] - kwargs["smi_column"] = ( - kwargs.get("smi_column", None) or "smiles" - if name_or_path != "bace" - else "mol" - ) - assert isinstance(kwargs["smi_column"], str) - kwargs["additonal_columns"] = [ - "probe_target", - *(kwargs.get("additonal_columns", None) or []), + def __init__(self, path: str, **kwargs): + self.path = Path(path) + assert self.path.exists() + + kwargs["target_columns"] = [ + "lipinki_h_donor", + "lipinki_h_acceptor", + "lipinki_mwt", + "lipinki_log_p", + "lipinki", ] + kwargs["smi_column"] = kwargs.get("smi_column", "smi") super().__init__(**kwargs) assert self.encoding != MolEncoding.SELFIES - def _get_dataset(cls, name_or_path) -> AbstractDataset: - # Load the dataset - ds: AbstractDataset = load_dataset( - "csv", - name=name, - data_files=[name_or_path], - split="train", + def _get_dataset(self) -> AbstractDataset: + return load_dataset( + "arrow", + name=str(self.path.name), + data_files={ + "train": str(self.path.joinpath("data/train/*.arrow")), + "validation": str(self.path.joinpath("data/validation/*.arrow")), + "test": str(self.path.joinpath("data/test/*.arrow")), + }, keep_in_memory=False, + streaming=True, save_infos=False, ) # type: ignore - ds = ds.select_columns(smi_column) - ds = filter_invalid_smi(self.smi_column) - ds = ds.map( - lipinki_rule_of_five, - batched=False, - fn_kwargs={"smi_column": self.smi_column}, - ) - return ds - - def collate_fn(self, batch): - output = super().collate_fn(batch) - output = self.token_collator(batch) - output["probe_target"] = torch.stack( - [torch.tensor(x["probe_target"]) for x in batch] - ) - - return output def get_molnet_dataset(name: str): diff --git a/electrolyte_fm/data_modules/property_prediction_dataset.py b/electrolyte_fm/data_modules/property_prediction_dataset.py index 24ef3556..d713afab 100644 --- a/electrolyte_fm/data_modules/property_prediction_dataset.py +++ b/electrolyte_fm/data_modules/property_prediction_dataset.py @@ -71,6 +71,7 @@ def setup(self, stage: str) -> None: ds = self.dataset ds = maybe_shard_dataset(self.trainer, ds) ds = encode_molecules(ds, self.smi_column, encoding=self.encoding) + print(ds["train"]) # Remove extraneous columns and tokenize smiles if targets := self.target_columns: @@ -137,6 +138,7 @@ def train_dataloader(self): ) def val_dataloader(self): + print("has validation dataloader") return DataLoader( self.val_dataset, collate_fn=self.collate_fn, @@ -173,4 +175,4 @@ def collate_target(x, target_columns): target.append(torch.tensor(v)) mask.append(torch.tensor(False)) - return {"target": torch.stack(target), "target_mask": torch.stack(mask)} + return {"target": target, "target_mask": mask} diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index e989bde2..313ea8cb 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -1,5 +1,5 @@ import logging -from typing import Iterable, Dict, Any, Callable +from typing import Iterable, Dict, Any, Callable, Optional from collections import defaultdict from jsonargparse import lazy_instance import torch @@ -15,13 +15,31 @@ def per_layer_probe( hidden_size: int, features: int, n_layers: int, location: str = "output" ) -> dict[str, nn.Module]: probes = {} - template: str = "*.encoder.layer.{layer}.{location}" + template: str = "*encoder.layer.{layer}.{location}" for layer in range(n_layers): hook_name = template.format(layer=layer, location=location) probes[hook_name] = nn.Linear(hidden_size, features) return probes +def probe_everything( + hidden_size: int, + features: int, + n_layers: int, + intermediate_size: Optional[int] = None, +): + probes = {} + intermediate_size = intermediate_size or hidden_size + for location, size in [ + ("output", hidden_size), + ("intermediate", intermediate_size), + ("attention", hidden_size), + ("output.dense", hidden_size), + ]: + probes.update(per_layer_probe(size, features, n_layers, location)) + return probes + + ProbeConfigCallable = Callable[Any, dict[str, nn.Module]] @@ -35,11 +53,12 @@ def __init__( ): super().__init__() - self.model = model + self.model = model.requires_grad_(False) self.optimizer = optimizer self.lr_schedule = lr_schedule self.save_hyperparameters() self.hookpoints = self._identify_hookpoints(probes.keys()) + assert len(self.hookpoints) > 0 # Setup probes self._probes = nn.ModuleList(probes.values()) @@ -51,6 +70,14 @@ def __init__( self.val_metrics = nn.ModuleList( AUROC(task="binary", thresholds=100) for probe in self._probe_points ) + print(self.val_metrics) + + # Don't error due to missing model weights + self.strict_loading = False + + def state_dict(self): + # Don't save the model, it is not being trained + return {k: v for k, v in super().state_dict().items() if "model" not in k} def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: state_dict = checkpoint["state_dict"] @@ -93,6 +120,8 @@ def _install_hooks(self): @staticmethod def _create_act_hook(name: str, results: dict): def hook(module: nn.Module, input, output: torch.Tensor): + if isinstance(output, tuple): + output = output[0] assert isinstance(output, torch.Tensor) results[name] = output[:, 0, :].detach() return None @@ -116,8 +145,8 @@ def forward(self, *args, **kwargs): def forward_fit(self, batch: dict): self.model.eval() activations = self._install_hooks() - target = batch.pop("probe_target") - self.model(**batch) + target = batch.pop("target") + self.model(batch["input_ids"], attention_mask=batch["attention_mask"]) loss = [] out = {} for name, probe in self.named_probes(): @@ -144,8 +173,8 @@ def training_step(self, batch): return out["loss"] def validation_step(self, batch): - target = batch.pop("probe_target") - out = self.forward(**batch) + target = batch.pop("target") + out = self.forward(batch["input_ids"], attention_mask=batch["attention_mask"]) metrics = {} loss = [] for probe, probe_metrics in zip(self._probe_points, self.val_metrics): @@ -190,12 +219,12 @@ def configure_optimizers(self): logging.basicConfig(level=logging.INFO) - def mlm_from_pretrained(name_or_path: str) -> nn.Module: - from transformers import AutoModelForMaskedLM + def encoder_from_finetuned(name_or_path: str) -> nn.Module: + from .prod_finetune import MISTFinetuned - return AutoModelForMaskedLM.from_pretrained( - name_or_path, trust_remote_code=True - ) + model = MISTFinetuned.from_pretrained(name_or_path) + print(model.encoder) + return model.encoder cli = MistLightningCLI( LightningProbe, diff --git a/opt/FeatureMiner/submit_linear_probes.py b/opt/FeatureMiner/submit_linear_probes.py index 21129791..a6e2dba5 100644 --- a/opt/FeatureMiner/submit_linear_probes.py +++ b/opt/FeatureMiner/submit_linear_probes.py @@ -12,26 +12,27 @@ function( encoder_class, encoder_path, - location, - dataset, tokenizer="smirk", encoding="smiles-kekule", batch_size, hidden_size, + intermediate_size, n_layers, ) { nodes: 1, gpus_per_node: 1, - container: '/lustre/fs0/awadell/sqsh-files/mist+pytorch+25.01+v2.sif', + container: '/nfs/turbo/coe-venkvis/mist/mist+pytorch+25.01+v4.sif', + walltime: "1:0:0", env: { JOBID: '$SLURM_JOB_ID', - PMIX_MCA_gds: 'hash', - NCCL_TOPO_FILE: '/cm/shared/etc/ndv4-topo.xml', - MELLANOC_VISIBLE_DEVICES: 'all', + TORCH_EXTENSIONS_DIR: '${PWD}/.cache/torch_extensions', + HF_HOME: '${PWD}/.cache/huggingface', + TOKENIZERS_PARALLELISM: true, }, program: "-m electrolyte_fm.models.linear_probe", + stage: null, train: { trainer: { max_epochs: 1000, @@ -44,11 +45,11 @@ }, }, probes: { - class_path: 'electrolyte_fm.models.linear_probe.per_layer_probe', + class_path: 'electrolyte_fm.models.linear_probe.probe_everything', init_args: { hidden_size: std.parseInt(hidden_size), + intermediate_size: std.parseInt(intermediate_size), features: 5, - location: location, n_layers: std.parseInt(n_layers), }, }, @@ -56,50 +57,49 @@ data: { class_path: 'electrolyte_fm.data_modules.lipinski_dataset.LipinskiDataModule', init_args: { - name_or_path: dataset, - tokenizer: tokenizer, - encoding: encoding, - num_workers: 16, - batch_size: std.parseInt(batch_size), + path: "./lipinski", + tokenizer: tokenizer, + encoding: encoding, + num_workers: 16, + batch_size: std.parseInt(batch_size), + randomize: true, }, }, }, } """ -template = "submit/dgx.j2" +template = "submit/artemis.j2" def submit(config: dict): script = render(template, config) print(script) + exit() subprocess.run("sbatch", input=script, text=True) # Pretrained Models -datasets = ["tox21", "toxcast", "hiv"] -locations = ["output", "intermediate", "output.dense"] models = [ - { - "encoder_path": "ibm/MoLFormer-XL-both-10pct", - "encoding": "smiles-canonical", - "tokenizer": "ibm/MoLFormer-XL-both-10pct", - }, { "encoder_path": "./models/mist-ti624ev1-moleculenet/pretrained", - }, - { - "encoder_path": "./models/mist-1.8B-dh61satti", - "batch_size": 16, - }, + } ] models.extend( [ { - "encoder_class": "electrolyte_fm.models.prod_finetune.MISTFinetuned.from_pretrained", + "encoder_class": "__main__.encoder_from_finetuned", "encoder_path": f"./models/mist-ti624ev1-moleculenet/{dataset}", } - for dataset in ["bace", "qm9", "bbbp", "muv", "qm8", "tmQM"] + for dataset in [ + "bace", + "qm9", + "bbbp", + "muv", + "qm8", + "tmQM", + "clintox", + ] ] ) @@ -114,9 +114,10 @@ def get_mist_finetune_config(path): runs = [] for model in models: run = deepcopy(model) - run.setdefault("encoder_class", "__main__.mlm_from_pretrained") - run.setdefault("batch_size", 64) - if run["encoder_class"] == "__main__.mlm_from_pretrained": + default_encoder = "electrolyte_fm.utils.cli.mlm_from_pretrained" + run.setdefault("encoder_class", default_encoder) + run.setdefault("batch_size", 128) + if run["encoder_class"] == default_encoder: model_config = AutoConfig.from_pretrained( run["encoder_path"], trust_remote_code=True, @@ -124,20 +125,13 @@ def get_mist_finetune_config(path): else: model_config = get_mist_finetune_config(run["encoder_path"]) - run.setdefault("n_layers", model_config.num_hidden_layers) - - for c in dict_product({"location": locations, "dataset": datasets}): - run.update(c) - if run["location"] == "intermediate": - hidden_size = model_config.intermediate_size - else: - hidden_size = model_config.hidden_size - - run["hidden_size"] = hidden_size - run_config = jsonnet.evaluate_snippet( - "snippet", - config, - tla_vars={k: str(v) for k, v in run.items()}, - ) - run_config = json.loads(run_config) - submit(run_config) + run["n_layers"] = model_config.num_hidden_layers + run["hidden_size"] = model_config.hidden_size + run["intermediate_size"] = model_config.intermediate_size + run_config = jsonnet.evaluate_snippet( + "snippet", + config, + tla_vars={k: str(v) for k, v in run.items()}, + ) + run_config = json.loads(run_config) + submit(run_config) From e314832d455deb73e555d9ff72116cf14f5640ab Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 23 Apr 2025 10:57:43 -0400 Subject: [PATCH 030/147] add BoxPlot config to style --- opt/MISTStyle/src/MISTStyle.jl | 3 +++ 1 file changed, 3 insertions(+) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 8b295213..58d87e41 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -169,6 +169,9 @@ function theme() markersize=5pt, marker=:x, ), + BoxPlot=(; + markersize=4pt, + ), ErrorLines=(; whiskerwidth=3, ), From fba59652ccebba69aac6149254e5efc094782d7f Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 23 Apr 2025 10:58:19 -0400 Subject: [PATCH 031/147] don't track jld2 files --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 35fb76de..dab9e7af 100644 --- a/.gitignore +++ b/.gitignore @@ -42,3 +42,4 @@ spack.lock *.tar.gz *.tar *.jsonl +*.jld2 From 8e62b976c3c1779f1129520d99efc00dee7f83f6 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 23 Apr 2025 13:18:18 -0400 Subject: [PATCH 032/147] update lipinski plots --- opt/FeatureMiner/.gitignore | 2 + opt/FeatureMiner/plots/Project.toml | 15 +++ opt/FeatureMiner/plots/lipinski_probes.jl | 131 ++++++++++++++++++++++ opt/FeatureMiner/src/lipinski.jl | 4 +- opt/MISTStyle/src/MISTStyle.jl | 2 +- 5 files changed, 151 insertions(+), 3 deletions(-) create mode 100644 opt/FeatureMiner/.gitignore create mode 100644 opt/FeatureMiner/plots/Project.toml create mode 100644 opt/FeatureMiner/plots/lipinski_probes.jl diff --git a/opt/FeatureMiner/.gitignore b/opt/FeatureMiner/.gitignore new file mode 100644 index 00000000..eee10db7 --- /dev/null +++ b/opt/FeatureMiner/.gitignore @@ -0,0 +1,2 @@ +*.tex +fig/ diff --git a/opt/FeatureMiner/plots/Project.toml b/opt/FeatureMiner/plots/Project.toml new file mode 100644 index 00000000..864529d6 --- /dev/null +++ b/opt/FeatureMiner/plots/Project.toml @@ -0,0 +1,15 @@ +[deps] +CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +FeatureMiner = "83091abd-1720-4da7-94ca-5ff8667e187f" +GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" + +[sources] +FeatureMiner = {path = "../"} +MISTStyle = {path = "../../MISTStyle"} diff --git a/opt/FeatureMiner/plots/lipinski_probes.jl b/opt/FeatureMiner/plots/lipinski_probes.jl new file mode 100644 index 00000000..78f077bc --- /dev/null +++ b/opt/FeatureMiner/plots/lipinski_probes.jl @@ -0,0 +1,131 @@ +using Makie +using DataFrames +using GLM +using StatsBase +using JLD2: jldopen +using CategoricalArrays: categorical, levelcode +using LinearAlgebra: norm, I +using RegressionTables: regtable, LatexTable + +using FeatureMiner +using MISTStyle + +function load_probes() + df = DataFrame(jldopen("lipinski_probes_ti624ev1.jld2")["probes"]) + df.encoder_id = categorical(df.encoder_id) + ordered = ["pretrained", "tmQM"] + dataset_order = setdiff(unique(df.encoder_dataset), ordered) + df.encoder_dataset = categorical(df.encoder_dataset; + levels=vcat(ordered, dataset_order), + ordered=true, + ) + df.location = categorical(df.location) + sort!(df, :encoder_dataset) + return df +end + +function figure_probe_similarity(df::DataFrame, model="dh61satti", dataset="toxcast", location="output") + df = subset(df, + :encoder => ByRow(==(model)), + :dataset => ByRow(==(dataset)), + :location => ByRow(==(location)), + skipmissing=true + ) + dropmissing!(df) + f = Figure() + + for (fdx, feat) in enumerate(unique(df.feature_id)) + @show df_f = subset(df, :feature_id => ByRow(==(feat))) + df_f = first(eachrow(df_f)) + ax = Axis(f[fdx, 1]; title=df_f.id) + heatmap!(ax, df_f.probe_similarity) + end + return f +end + +function figure_lipinski_probes(df::DataFrame) + f = Figure(size=(2inch, 1inch)) + + df = dropmissing(df) + subset!(df, + :encoder_id => ByRow(==("ti624ev1")), + :location => ByRow(in(["output", "intermediate", "attention"])), + ) + + # Mean AUROC + df_auroc = combine(groupby(df, :encoder_dataset)) do gdf + unstack(gdf[:, [:layer, :auroc]], :layer, :auroc; combine=minimum) + end + auroc = Matrix(df_auroc[:, 2:end]) + @info extrema(auroc) + + # Feature alignment + gl = GridLayout(f[1,1]) + ax = Axis(gl[1, 1]; + xticks=categorical_ticks(df.encoder_dataset), + yticks=2:2:8, + # limits=(nothing, ), + xticklabelsvisible=false, + xticksvisible=false, + ) + + h = heatmap!(ax, auroc) + Colorbar(gl[1, 2], h; label="AUROC") + + # Additive Features + df.additive_features = FeatureMiner.additive_features.(df.weight) + df_af = combine(groupby(subset!(df, :location => ByRow(==("output"))), :encoder_dataset)) do gdf + unstack(gdf[:, [:layer, :additive_features]], :layer, :additive_features) + end + af = Matrix(df_af[:, 2:end]) + ax_add = Axis(gl[2, 1]; + xticks=categorical_ticks(df.encoder_dataset), + yticks=ax.yticks, + ) + h = heatmap!(ax_add, af; colorrange=(0, 1)) + Colorbar(gl[2, 2], h; label="Additivity") + @info extrema(af) + + # Add single Y-axis label + Label(gl[:, 0], text = "Encoder Layer", rotation = pi/2) + + return f +end + +function lipinski_fixed_effect(df) + contrasts = Dict( + :encoder_dataset => EffectsCoding(; base="pretrained"), + :location => EffectsCoding(; base="output"), + ) + m_dataset = glm( + @formula( auroc ~ 1 + encoder_dataset), df, Normal(), LogitLink(); + contrasts, + ) + m_layer = glm( + @formula( auroc ~ 1 + layer), df, Normal(), LogitLink(); + contrasts, + ) + m_location = glm( + @formula( auroc ~ 1 + location), df, Normal(), LogitLink(); + contrasts, + ) + m_all = glm( + @formula( auroc ~ 1 + encoder_dataset + location + layer), df, Normal(), LogitLink(); + contrasts, + ) + display(m) + open("lipinski.tex", "w") do fid + write(fid, regtable(m_all, m_layer, m_dataset, m_location; + regression_statistics = [ + Int∘nobs => "N", + bic => "BIC", + Int∘dof_residual => "Resid. DoF", + (m -> adjr2(m, :devianceratio)) => "Adj. R2", + (m -> rmsd(response(m), predict(m))) => "RMSD", + ], + render=LatexTable(), + + ) |> string) + end + return m +end diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 186da82b..7683ed63 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -32,8 +32,8 @@ end cosine_similarity(a::Vector, b::Vector) = dot(a, b) / (norm(a) * norm(b)) function additive_features(w::Matrix) - f_overall = w[end, :] - f_componets = vec(sum(w[1:end-1, :]; dims=1)) + f_overall = w[1, :] + f_componets = vec(sum(w[2:end, :]; dims=1)) return cosine_similarity(f_overall, f_componets) end diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 58d87e41..fde29a45 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -8,7 +8,7 @@ using CairoMakie: CairoMakie const pt = 3 / 4 const inch = 96 -export pt, inch +export pt, inch, categorical_ticks """ Save duplicate figures for publication and web """ function savefig(name::String, f::Figure; dpi=300, fig_dir="fig") From 1dedb9d2b2b07894078dc668b9e3c6ddfb957631 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 13:36:35 -0400 Subject: [PATCH 033/147] update probe plots --- opt/FeatureMiner/plots/lipinski_probes.jl | 51 +++++++++++++++++++---- 1 file changed, 43 insertions(+), 8 deletions(-) diff --git a/opt/FeatureMiner/plots/lipinski_probes.jl b/opt/FeatureMiner/plots/lipinski_probes.jl index 78f077bc..84376e11 100644 --- a/opt/FeatureMiner/plots/lipinski_probes.jl +++ b/opt/FeatureMiner/plots/lipinski_probes.jl @@ -24,21 +24,45 @@ function load_probes() return df end -function figure_probe_similarity(df::DataFrame, model="dh61satti", dataset="toxcast", location="output") +function figure_probe_similarity(df::DataFrame; model="dh61satti", dataset="pretrained", location="output") df = subset(df, - :encoder => ByRow(==(model)), - :dataset => ByRow(==(dataset)), + :encoder_id => ByRow(==(model)), :location => ByRow(==(location)), skipmissing=true ) dropmissing!(df) f = Figure() - for (fdx, feat) in enumerate(unique(df.feature_id)) - @show df_f = subset(df, :feature_id => ByRow(==(feat))) - df_f = first(eachrow(df_f)) - ax = Axis(f[fdx, 1]; title=df_f.id) - heatmap!(ax, df_f.probe_similarity) + + feature_name = [ + "Lipinski", + "H-Donor", + "H-Acceptor", + "MWT", + "LogP", + ] + + cb = Colorbar(f[1:5, 1+length(levels(df.encoder_dataset))]; + label="Cosine Similarity", + colorrange=(0, 1), + ) + + for (ddx, dataset) in enumerate(unique(df.encoder_dataset)) + dfd = subset(df, :encoder_dataset => ByRow(==(dataset))) + for fdx in 1:5 + ax = Axis(f[fdx, ddx]) + if fdx == 1 + ax.title = dataset + end + hidedecorations!(ax) + if ddx == 1 + ax.ylabel = feature_name[fdx] + ax.ylabelvisible = true + end + + probe_similarity = FeatureMiner.layerwise_similarity(dfd.weight, fdx) + heatmap!(ax, probe_similarity; MISTStyle.cb_attrs(cb, Heatmap)...) + end end return f end @@ -129,3 +153,14 @@ function lipinski_fixed_effect(df) end return m end + +function all_plots() + df = load_probes() + figure_lipinski_probes(df) |> MISTStyle.savefig("lipinski_linear_probes") + for location in unique(df.location) + for encoder in unique(df.encoder_id) + fig = figure_probe_similarity(df; model=encoder, location=location) + MISTStyle.savefig("lipinski-probe-similarity-$encoder-$location", fig) + end + end +end From 574fa9f8947fde22f298140f350f9661acb443b0 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 14:11:01 -0400 Subject: [PATCH 034/147] add pandas and numpy to deps --- pyproject.toml | 4 +++- uv.lock | 4 ++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 16bdc8bf..af1f7af1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,9 @@ dependencies = [ "rdkit>=2024.3.5", "smirk @ git+ssh://git@github.com/BattModels/smirk.git@dfdd9dc27c066f956b69d1f55c2d4717beb7838d", "safetensors>=0.5.1", - "pytorch-lightning~=2.5" + "pytorch-lightning~=2.5", + "pandas>=2.2.3", + "numpy>=2.2.4", ] [optional-dependencies] diff --git a/uv.lock b/uv.lock index 9a59b0c9..f8d85550 100644 --- a/uv.lock +++ b/uv.lock @@ -415,6 +415,8 @@ dependencies = [ { name = "jsonargparse", extra = ["jsonnet", "signatures"] }, { name = "jsonnet" }, { name = "lightning" }, + { name = "numpy" }, + { name = "pandas" }, { name = "pydoe2" }, { name = "pytorch-lightning" }, { name = "rdkit" }, @@ -450,6 +452,8 @@ requires-dist = [ { name = "jsonargparse", extras = ["signatures", "jsonnet"], specifier = ">=4.27" }, { name = "jsonnet", specifier = ">=0.20.0" }, { name = "lightning", specifier = "~=2.5" }, + { name = "numpy", specifier = ">=2.2.4" }, + { name = "pandas", specifier = ">=2.2.3" }, { name = "pydoe2", specifier = ">=1.3" }, { name = "pytorch-lightning", specifier = "~=2.5" }, { name = "rdkit", specifier = ">=2024.3.5" }, From 50dc9a63eb896ea5a9490fd10743590731c68c34 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 17:46:57 -0400 Subject: [PATCH 035/147] fix formatting --- electrolyte_fm/data_modules/__init__.py | 1 - electrolyte_fm/models/linear_probe.py | 4 +- electrolyte_fm/models/sae.py | 4 +- electrolyte_fm/utils/featurizer.py | 7 +- electrolyte_fm/utils/metrics.py | 1 - opt/FeatureMiner/run_explore.sh | 0 opt/FeatureMiner/src/FeatureMiner.jl | 1 - opt/FeatureMiner/src/lipinski.jl | 1 - opt/FeatureMiner/src/stats.jl | 2 - opt/FeatureMiner/submit_explore.sh | 1 - opt/FeatureMiner/submit_linear_probes.py | 3 +- opt/MISTStyle/src/powerlaw.jl | 1 - opt/MISTStyle/src/quadrant.jl | 1 - opt/design/.gitignore | 1 - opt/design/electrolytes.csv | 186 +++++++++++------------ opt/design/src/inference.jl | 1 - opt/design/src/plot_utils.jl | 1 - opt/design/src/uq.jl | 1 - opt/synth_access/Project.toml | 1 - submit/sae_features.libsonnet | 2 +- test/test_featurizer.py | 3 +- test/test_sae.py | 1 - 22 files changed, 99 insertions(+), 125 deletions(-) mode change 100644 => 100755 opt/FeatureMiner/run_explore.sh mode change 100644 => 100755 opt/FeatureMiner/submit_explore.sh diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index 223309d5..a5af553f 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -2,5 +2,4 @@ from .property_prediction_dataset import PropertyPredictionDataModule from .roberta_dataset import RobertaDataSet from .tmqm_dataset import tmQMDataModule -from .roberta_dataset import RobertaDataSet from .mixture_dataset import ComponentDataModule diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 313ea8cb..f9d8db14 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -113,7 +113,7 @@ def _install_hooks(self): self._hooks_installed[name] = self.model.get_submodule( name ).register_forward_hook(hook) - logging.debug(f"Installed hook for %s", name) + logging.debug("Installed hook for %s", name) return self._activations @@ -210,8 +210,6 @@ def configure_optimizers(self): if __name__ == "__main__": - import smirk - import json from jsonargparse import lazy_instance from lightning.pytorch.loggers import WandbLogger from lightning.pytorch.callbacks import ModelCheckpoint diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 6554d930..30006e95 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -8,11 +8,9 @@ from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from torch import nn from torch.nn import functional as F -from torchmetrics import MetricCollection -from transformers import PreTrainedModel, AutoModel +from transformers import PreTrainedModel from .model_utils import load_encoder -from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity def init_bias(bias, w): diff --git a/electrolyte_fm/utils/featurizer.py b/electrolyte_fm/utils/featurizer.py index bb38ec4b..c47c828b 100644 --- a/electrolyte_fm/utils/featurizer.py +++ b/electrolyte_fm/utils/featurizer.py @@ -1,24 +1,19 @@ import json -import logging from math import floor from pathlib import Path from typing import Optional import torch from datasets import load_dataset -from rdkit import Chem from torch import nn from torch.utils.data import DataLoader from transformers import DataCollatorWithPadding -from torchmetrics import MetricCollection -from ..data_modules.sae_dataset import extract_hidden_state from ..data_modules.feature_tagger import FeatureCollection from ..data_modules.utils import MolEncoding, encode_molecules from ..models.model_utils import load_encoder from ..models.sae import SAE from .tokenizer import load_tokenizer -from .metrics import FeatureCorrelation class FeatureExtractor(nn.Module): @@ -122,7 +117,7 @@ def setup(self): streaming=True, save_infos=True, ) - ds = encode_molecules(ds, "text", encoding=MolEncoding.KEUKLE_SMILES) + ds = encode_molecules(ds, "text", encoding=MolEncoding.KEKULE) ds = ds.map(self.miner.tokenize, batched=True, input_columns="text") ds = ds.map(self.features, batched=False, input_columns="text") self.dataset = ds diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index f3daeabd..8b04f8bf 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -1,4 +1,3 @@ - from dataclasses import dataclass from typing import Any, Dict, Literal, Optional, Union diff --git a/opt/FeatureMiner/run_explore.sh b/opt/FeatureMiner/run_explore.sh old mode 100644 new mode 100755 diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl index f92d9823..7f802702 100644 --- a/opt/FeatureMiner/src/FeatureMiner.jl +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -64,4 +64,3 @@ include("stats.jl") include("lipinski.jl") end - diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 7683ed63..9b7106fd 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -45,4 +45,3 @@ function layerwise_similarity(weights::Vector{W}, feature::Int) where {W<:Matrix end return s end - diff --git a/opt/FeatureMiner/src/stats.jl b/opt/FeatureMiner/src/stats.jl index db4e7829..4b20125b 100644 --- a/opt/FeatureMiner/src/stats.jl +++ b/opt/FeatureMiner/src/stats.jl @@ -41,5 +41,3 @@ end StatsBase.var(o::ElementwiseVariance) = OnlineStatsBase.value(o) StatsBase.mean(o::ElementwiseVariance) = o.μ StatsBase.nobs(o::ElementwiseVariance) = o.n[] - - diff --git a/opt/FeatureMiner/submit_explore.sh b/opt/FeatureMiner/submit_explore.sh old mode 100644 new mode 100755 index b5f95f68..15165b32 --- a/opt/FeatureMiner/submit_explore.sh +++ b/opt/FeatureMiner/submit_explore.sh @@ -5,4 +5,3 @@ for ckpt in $(find ../../linear-probes/ -maxdepth 1 -mindepth 1 -type d); do sbatch ./run_explore.sh "${ckpt}" fi done - diff --git a/opt/FeatureMiner/submit_linear_probes.py b/opt/FeatureMiner/submit_linear_probes.py index a6e2dba5..d57f3b4c 100644 --- a/opt/FeatureMiner/submit_linear_probes.py +++ b/opt/FeatureMiner/submit_linear_probes.py @@ -4,7 +4,6 @@ import _jsonnet as jsonnet import subprocess from submit.submit import render -from submit.utils import dict_product from transformers import AutoConfig # jsonnet @@ -35,7 +34,7 @@ stage: null, train: { trainer: { - max_epochs: 1000, + max_epochs: 1000, }, model: { model: { diff --git a/opt/MISTStyle/src/powerlaw.jl b/opt/MISTStyle/src/powerlaw.jl index 60850ece..3b3144f9 100644 --- a/opt/MISTStyle/src/powerlaw.jl +++ b/opt/MISTStyle/src/powerlaw.jl @@ -35,4 +35,3 @@ function Makie.plot!(plt::Powerlaw) lines!(plt, points; Makie.shared_attributes(plt, Lines)...) return plt end - diff --git a/opt/MISTStyle/src/quadrant.jl b/opt/MISTStyle/src/quadrant.jl index 4b652e51..4f0e923d 100644 --- a/opt/MISTStyle/src/quadrant.jl +++ b/opt/MISTStyle/src/quadrant.jl @@ -46,4 +46,3 @@ function corner(quad::Symbol, hr) error("Quadrant $quad not recognized") end end - diff --git a/opt/design/.gitignore b/opt/design/.gitignore index 8159e7f2..3a1b5507 100644 --- a/opt/design/.gitignore +++ b/opt/design/.gitignore @@ -1,2 +1 @@ fig/ - diff --git a/opt/design/electrolytes.csv b/opt/design/electrolytes.csv index f1789d21..99e93d68 100644 --- a/opt/design/electrolytes.csv +++ b/opt/design/electrolytes.csv @@ -1,93 +1,93 @@ -name,class,subsitute,smi -"1,1,2,2-Tetrachloroethane",Alkane,Chloro,C(C(Cl)Cl)(Cl)Cl -"1,2-Dichloroethane",Alkane,Chloro,C(CCl)Cl -"1,3-Dichloropropane",Alkane,Chloro,C(CCl)CCl -1-Fluoropentane,Alkane,Fluoro,CCCCCF -Chloroform-d,Alkane,Chloro,[2H]C(Cl)(Cl)Cl -Dichloromethane,Alkane,Chloro,C(Cl)Cl -EMS,Alkane,Sulfone,CCS(=O)(=O)C -EiBS,Alkane,Sulfone,CCS(=O)(=O)CC(C)C -EiPS,Alkane,Sulfone,CCS(=O)(=O)C(C)C -iPiBS,Alkane,Sulfone,CC(C)CS(=O)(=O)C(C)C -iPsBS,Alkane,Sulfone,CCC(C)S(=O)(=O)C(C)C -EVS,Alkene,Sulfone,CCS(=O)(=O)C=C -"1,2,3-Trifluorobenzene (123-FB)",Arene,Fluoro,C1=CC(=C(C(=C1)F)F)F -"1,2,4-Trifluorobenzene",Arene,Fluoro,C1=CC(=C(C=C1F)F)F -"1,2-Difluorobenzene",Arene,Fluoro,C1=CC=C(C(=C1)F)F -"1,3-Difluorobenzene",Arene,Fluoro,C1=CC(=CC(=C1)F)F -"1,4-Difluorobenzene",Arene,Fluoro,C1=CC(=CC=C1F)F -FS,Arene,"Fluoro, Sulfone",CS(=O)(=O)C1=CC=CC=C1F -Fluorobenzene,Arene,Fluoro,C1=CC=C(C=C1)F -Methyl phenyl sulfone,Arene,Sulfone,CS(=O)(=O)C1=CC=CC=C1 -Diethyl carbonate,Carbonate ester,,CCOC(=O)OCC -Dimethyl carbonate,Carbonate ester,,COC(=O)OC -"2,2,2-trifluoroethyl methyl carbonate",Carbonate ester,Fluoro,COC(=O)OCC(F)(F)F -"2,2,2-trifluoroethyl trifluoromethyl carbonate",Carbonate ester,Fluoro,C(C(F)(F)F)OC(=O)OC(F)(F)F -"2,2,2-trifluoromethyl ethyl carbonate",Carbonate ester,Fluoro,CCOC(=O)OC(F)(F)F -"bis(2,2,2-trifluoroethyl) carbonate",Carbonate ester,Fluoro,C(C(F)(F)F)OC(=O)OCC(F)(F)F -"ethyl 2,2,2-trifluoroethyl carbonate",Carbonate ester,Fluoro,CCOC(=O)OCC(F)(F)F -Bis(trifluoromethyl) ester,Carbonate ester,Fluoro,C(=O)(OC(F)(F)F)OC(F)(F)F -DEC,Carbonate ester,,O=C(OCC)OCC -DMC,Carbonate ester,,COC(=O)OC -EMC,Carbonate ester,,CCOC(=O)OC -Ethyl methyl carbonate,Carbonate ester,,CCOC(=O)OC -methyl trifluoromethyl carbonate,Carbonate ester,Fluoro,COC(=O)OC(F)(F)F -"1,1,2,2,3,3,4-Heptafluorocyclopentane",Cycloalkane,Fluoro,C1C(C(C(C1(F)F)(F)F)(F)F)F -Fluorocyclohexane,Cycloalkane,Fluoro,C1CCC(CC1)F -TMS,Cycloalkane,Sulfone,C1CCS(=O)(=O)C1 -HHTP,Cyclophosphazene,,CCCOP1(=NP(=NP(=N1)(OCCC)OCCC)(OCCC)OCCC)OCCC -HMP,Cyclophosphazene,,COP1(=NP(=NP(=N1)(OC)OC)(OC)OC)OC -HTP,Cyclophosphazene,,CCOP1(=NP(=NP(=N1)(OCC)OCC)(OCC)OCC)OCC -DMDC,Dicarbonate,,O=C(OC(=O)OC)OC -Dimethyl dicarbonate,Dicarbonate,,O=C(OC(=O)OC)OC -DEMEMP,Ester,Phosphate,CCOP(=O)(COCCOC)OCC -DMMEMP,Ester,Phosphate,COCCOCP(=O)(OC)OC -DMMP,Ester,Phosphate,COP(=O)(C)OC -EMES,Ester,Sulfone,CCS(=O)(=O)CCOC -ESEME,Ester,Sulfone,CCS(=O)(=O)CCOCCOC -F1F2,Ether,Fluoro,C(COCC(F)F)OCCF -"1,1,2,2-Tetrafluoroethyl-2,2,3,3-tetrafluoropropylether",Ether,Fluoro,C(C(C(F)F)(F)F)OC(C(F)F)(F)F -"1,2-bis(trimethylsilyloxy)ethane",Ether,Silyl,C[Si](C)(C)OCCO[Si](C)(C)C -"1,3-bis(trimethylsilyloxy)propane",Ether,Silyl,C[Si](C)(C)OCCCO[Si](C)(C)C -"DEE (1,2-diethoxyethane)",Ether,,CCOCCOCC -F1F0 (1-Ethoxy-2-(2-fluoroethoxy)ethane),Ether,Fluoro,CCOCCOCCF -"F1F1 (Ethane, 1,2-bis(2-fluoroethoxy)-)",Ether,Fluoro,C(COCCF)OCCF -"F3DEE (1-(2,2,2-Trifluoroethoxy)-2-ethoxyethane)",Ether,Fluoro,CCOCCOCC(F)(F)F -"F4DEE (1,2-Bis(2,2-difluoroethoxy)ethane)",Ether,Fluoro,C(COCC(F)F)OCC(F)F -F5DEE (1-Difluoroethoxy-2-Trifluoroethoxyethane),Ether,Fluoro,C(COCC(F)(F)F)OCC(F)F -"F6DEE (1,1,1-Trifluoro-2-[2-(2,2,2-trifluoroethoxy)ethoxy]ethane)",Ether,Fluoro,C(COCC(F)(F)F)OCC(F)(F)F -"FDMB (2,2,3,3-Tetrafluoro-1,4-dimethoxybutane)",Ether,Fluoro,COCC(C(COC)(F)F)(F)F -DME,Ether,,COCCOC -MEEEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCOCCOCCO[Si](C)(C)C -MEEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCOCCO[Si](C)(C)C -MEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCO[Si](C)(C)C -MEEEETMS,Ether,Silyl,COCCOCCOCCOCCO[Si](C)(C)C -MEEETMS,Ether,Silyl,COCCOCCOCCO[Si](C)(C)C -MEETMS,Ether,Silyl,COCCOCCO[Si](C)(C)C -MEMS,Ether,Sulfone,COCCS(=O)(=O)C -METMS,Ether,Silyl,COCCO[Si](C)(C)C -Ethylene carbonate,Ethylene carbonate,,C1COC(=O)O1 -cis-difluoroethylene carbonate,Ethylene carbonate,Fluoro,O=C1O[C@@H](F)[C@@H](F)O1 -difluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(C(OC(=O)O1)F)F -Ethylene carbonate,Ethylene carbonate,,C1COC(=O)O1 -fluoroethylene carbonate,Ethylene carbonate,Fluoro,C1C(OC(=O)O1)F -tetrafluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(=O)OC(C(O1)(F)F)(F)F -"trans-difluoroethylene carbonate, R",Ethylene carbonate,Fluoro,O=C1O[C@H](F)[C@@H](F)O1 -"trans-difluoroethylene carbonate, S",Ethylene carbonate,Fluoro,O=C1O[C@@H](F)[C@H](F)O1 -trifluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(C(OC(=O)O1)(F)F)F -ADN,Alkane,Nitrile,C(CCC#N)CC#N -AN,Alkane,Nitrile,CC#N -AZN,Alkane,Nitrile,C(CCCC#N)CCCC#N -EPN,Ether,Nitrile,CCOCCC#N -GLN,Alkane,Nitrile,C(CC#N)CC#N -MAN,Alkane,Nitrile,C(C#N)C#N -MPN,Ether,Nitrile,COCCC#N -PMN,Alkane,Nitrile,C(CCC#N)CCC#N -SCN,Alkane,Nitrile,C(CC#N)C#N -SEN,Alkane,Nitrile,C(CCCCC#N)CCCC#N -SUN,Alkane,Nitrile,C(CCCC#N)CCC#N -FEPN,Ether,"Fluoro, Nitrile",C(COCC(F)(F)F)C#N -TMP,Ether,Phosphate,COP(=O)(OC)OC -BS,Alkane,Sulfone,CCCCS(=O)(=O)CCCC -BiBS,Alkane,Sulfone,CCCCS(=O)(=O)CC(C)C -MiPS,Alkane,Sulfone,CC(C)S(=O)(=O)C +name,class,subsitute,smi +"1,1,2,2-Tetrachloroethane",Alkane,Chloro,C(C(Cl)Cl)(Cl)Cl +"1,2-Dichloroethane",Alkane,Chloro,C(CCl)Cl +"1,3-Dichloropropane",Alkane,Chloro,C(CCl)CCl +1-Fluoropentane,Alkane,Fluoro,CCCCCF +Chloroform-d,Alkane,Chloro,[2H]C(Cl)(Cl)Cl +Dichloromethane,Alkane,Chloro,C(Cl)Cl +EMS,Alkane,Sulfone,CCS(=O)(=O)C +EiBS,Alkane,Sulfone,CCS(=O)(=O)CC(C)C +EiPS,Alkane,Sulfone,CCS(=O)(=O)C(C)C +iPiBS,Alkane,Sulfone,CC(C)CS(=O)(=O)C(C)C +iPsBS,Alkane,Sulfone,CCC(C)S(=O)(=O)C(C)C +EVS,Alkene,Sulfone,CCS(=O)(=O)C=C +"1,2,3-Trifluorobenzene (123-FB)",Arene,Fluoro,C1=CC(=C(C(=C1)F)F)F +"1,2,4-Trifluorobenzene",Arene,Fluoro,C1=CC(=C(C=C1F)F)F +"1,2-Difluorobenzene",Arene,Fluoro,C1=CC=C(C(=C1)F)F +"1,3-Difluorobenzene",Arene,Fluoro,C1=CC(=CC(=C1)F)F +"1,4-Difluorobenzene",Arene,Fluoro,C1=CC(=CC=C1F)F +FS,Arene,"Fluoro, Sulfone",CS(=O)(=O)C1=CC=CC=C1F +Fluorobenzene,Arene,Fluoro,C1=CC=C(C=C1)F +Methyl phenyl sulfone,Arene,Sulfone,CS(=O)(=O)C1=CC=CC=C1 +Diethyl carbonate,Carbonate ester,,CCOC(=O)OCC +Dimethyl carbonate,Carbonate ester,,COC(=O)OC +"2,2,2-trifluoroethyl methyl carbonate",Carbonate ester,Fluoro,COC(=O)OCC(F)(F)F +"2,2,2-trifluoroethyl trifluoromethyl carbonate",Carbonate ester,Fluoro,C(C(F)(F)F)OC(=O)OC(F)(F)F +"2,2,2-trifluoromethyl ethyl carbonate",Carbonate ester,Fluoro,CCOC(=O)OC(F)(F)F +"bis(2,2,2-trifluoroethyl) carbonate",Carbonate ester,Fluoro,C(C(F)(F)F)OC(=O)OCC(F)(F)F +"ethyl 2,2,2-trifluoroethyl carbonate",Carbonate ester,Fluoro,CCOC(=O)OCC(F)(F)F +Bis(trifluoromethyl) ester,Carbonate ester,Fluoro,C(=O)(OC(F)(F)F)OC(F)(F)F +DEC,Carbonate ester,,O=C(OCC)OCC +DMC,Carbonate ester,,COC(=O)OC +EMC,Carbonate ester,,CCOC(=O)OC +Ethyl methyl carbonate,Carbonate ester,,CCOC(=O)OC +methyl trifluoromethyl carbonate,Carbonate ester,Fluoro,COC(=O)OC(F)(F)F +"1,1,2,2,3,3,4-Heptafluorocyclopentane",Cycloalkane,Fluoro,C1C(C(C(C1(F)F)(F)F)(F)F)F +Fluorocyclohexane,Cycloalkane,Fluoro,C1CCC(CC1)F +TMS,Cycloalkane,Sulfone,C1CCS(=O)(=O)C1 +HHTP,Cyclophosphazene,,CCCOP1(=NP(=NP(=N1)(OCCC)OCCC)(OCCC)OCCC)OCCC +HMP,Cyclophosphazene,,COP1(=NP(=NP(=N1)(OC)OC)(OC)OC)OC +HTP,Cyclophosphazene,,CCOP1(=NP(=NP(=N1)(OCC)OCC)(OCC)OCC)OCC +DMDC,Dicarbonate,,O=C(OC(=O)OC)OC +Dimethyl dicarbonate,Dicarbonate,,O=C(OC(=O)OC)OC +DEMEMP,Ester,Phosphate,CCOP(=O)(COCCOC)OCC +DMMEMP,Ester,Phosphate,COCCOCP(=O)(OC)OC +DMMP,Ester,Phosphate,COP(=O)(C)OC +EMES,Ester,Sulfone,CCS(=O)(=O)CCOC +ESEME,Ester,Sulfone,CCS(=O)(=O)CCOCCOC +F1F2,Ether,Fluoro,C(COCC(F)F)OCCF +"1,1,2,2-Tetrafluoroethyl-2,2,3,3-tetrafluoropropylether",Ether,Fluoro,C(C(C(F)F)(F)F)OC(C(F)F)(F)F +"1,2-bis(trimethylsilyloxy)ethane",Ether,Silyl,C[Si](C)(C)OCCO[Si](C)(C)C +"1,3-bis(trimethylsilyloxy)propane",Ether,Silyl,C[Si](C)(C)OCCCO[Si](C)(C)C +"DEE (1,2-diethoxyethane)",Ether,,CCOCCOCC +F1F0 (1-Ethoxy-2-(2-fluoroethoxy)ethane),Ether,Fluoro,CCOCCOCCF +"F1F1 (Ethane, 1,2-bis(2-fluoroethoxy)-)",Ether,Fluoro,C(COCCF)OCCF +"F3DEE (1-(2,2,2-Trifluoroethoxy)-2-ethoxyethane)",Ether,Fluoro,CCOCCOCC(F)(F)F +"F4DEE (1,2-Bis(2,2-difluoroethoxy)ethane)",Ether,Fluoro,C(COCC(F)F)OCC(F)F +F5DEE (1-Difluoroethoxy-2-Trifluoroethoxyethane),Ether,Fluoro,C(COCC(F)(F)F)OCC(F)F +"F6DEE (1,1,1-Trifluoro-2-[2-(2,2,2-trifluoroethoxy)ethoxy]ethane)",Ether,Fluoro,C(COCC(F)(F)F)OCC(F)(F)F +"FDMB (2,2,3,3-Tetrafluoro-1,4-dimethoxybutane)",Ether,Fluoro,COCC(C(COC)(F)F)(F)F +DME,Ether,,COCCOC +MEEEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEEETMS,Ether,Silyl,COCCOCCOCCOCCOCCO[Si](C)(C)C +MEEEETMS,Ether,Silyl,COCCOCCOCCOCCO[Si](C)(C)C +MEEETMS,Ether,Silyl,COCCOCCOCCO[Si](C)(C)C +MEETMS,Ether,Silyl,COCCOCCO[Si](C)(C)C +MEMS,Ether,Sulfone,COCCS(=O)(=O)C +METMS,Ether,Silyl,COCCO[Si](C)(C)C +Ethylene carbonate,Ethylene carbonate,,C1COC(=O)O1 +cis-difluoroethylene carbonate,Ethylene carbonate,Fluoro,O=C1O[C@@H](F)[C@@H](F)O1 +difluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(C(OC(=O)O1)F)F +Ethylene carbonate,Ethylene carbonate,,C1COC(=O)O1 +fluoroethylene carbonate,Ethylene carbonate,Fluoro,C1C(OC(=O)O1)F +tetrafluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(=O)OC(C(O1)(F)F)(F)F +"trans-difluoroethylene carbonate, R",Ethylene carbonate,Fluoro,O=C1O[C@H](F)[C@@H](F)O1 +"trans-difluoroethylene carbonate, S",Ethylene carbonate,Fluoro,O=C1O[C@@H](F)[C@H](F)O1 +trifluoroethylene carbonate,Ethylene carbonate,Fluoro,C1(C(OC(=O)O1)(F)F)F +ADN,Alkane,Nitrile,C(CCC#N)CC#N +AN,Alkane,Nitrile,CC#N +AZN,Alkane,Nitrile,C(CCCC#N)CCCC#N +EPN,Ether,Nitrile,CCOCCC#N +GLN,Alkane,Nitrile,C(CC#N)CC#N +MAN,Alkane,Nitrile,C(C#N)C#N +MPN,Ether,Nitrile,COCCC#N +PMN,Alkane,Nitrile,C(CCC#N)CCC#N +SCN,Alkane,Nitrile,C(CC#N)C#N +SEN,Alkane,Nitrile,C(CCCCC#N)CCCC#N +SUN,Alkane,Nitrile,C(CCCC#N)CCC#N +FEPN,Ether,"Fluoro, Nitrile",C(COCC(F)(F)F)C#N +TMP,Ether,Phosphate,COP(=O)(OC)OC +BS,Alkane,Sulfone,CCCCS(=O)(=O)CCCC +BiBS,Alkane,Sulfone,CCCCS(=O)(=O)CC(C)C +MiPS,Alkane,Sulfone,CC(C)S(=O)(=O)C diff --git a/opt/design/src/inference.jl b/opt/design/src/inference.jl index f5ba1743..963ea726 100644 --- a/opt/design/src/inference.jl +++ b/opt/design/src/inference.jl @@ -97,4 +97,3 @@ function predict_all(df::DataFrame, models...; smi_column=:smi, n=1) end return df end - diff --git a/opt/design/src/plot_utils.jl b/opt/design/src/plot_utils.jl index b454dd57..c72503a3 100644 --- a/opt/design/src/plot_utils.jl +++ b/opt/design/src/plot_utils.jl @@ -18,4 +18,3 @@ function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Rea end Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) - diff --git a/opt/design/src/uq.jl b/opt/design/src/uq.jl index 7bdc6db7..5b1f7587 100644 --- a/opt/design/src/uq.jl +++ b/opt/design/src/uq.jl @@ -20,4 +20,3 @@ end Base.:*(x::UQReal, y::Real) = UQReal(x.mean * y, x.std * y, x.n) Base.:*(x::Real, y::UQReal) = y * x - diff --git a/opt/synth_access/Project.toml b/opt/synth_access/Project.toml index 9a5f81bb..32dd50e5 100644 --- a/opt/synth_access/Project.toml +++ b/opt/synth_access/Project.toml @@ -12,4 +12,3 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [sources] MISTStyle = {path = "../MISTStyle"} - diff --git a/submit/sae_features.libsonnet b/submit/sae_features.libsonnet index 9c81f8f4..f52ef5fa 100644 --- a/submit/sae_features.libsonnet +++ b/submit/sae_features.libsonnet @@ -1,3 +1,3 @@ { - aromatic_elements: "[a-z]{1,2}" + aromatic_elements: '[a-z]{1,2}', } diff --git a/test/test_featurizer.py b/test/test_featurizer.py index e3d06db7..e5b1eae9 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -1,6 +1,5 @@ import re from random import choice -from itertools import chain import pytest import torch @@ -107,7 +106,7 @@ def test_regex_negatives(feature: str, negatives: list[str]): "feature,positives", ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), ) -def test_regex_negatives(feature: str, positives: list[str]): +def test_regex_positives(feature: str, positives: list[str]): f = re.compile(REGEX_FEATURES[feature]) for pos in positives: assert f.match(pos) is not None, f"{feature} should match {pos}" diff --git a/test/test_sae.py b/test/test_sae.py index ba525df0..993a7f7f 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -2,7 +2,6 @@ import torch import torch.nn.functional as F from transformers import ( - AutoModel, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, From 98a44c07c73c0f913ada881fa0c557ef461ae14b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 16:18:28 -0700 Subject: [PATCH 036/147] fix spelling --- .../data_modules/lipinski_dataset.py | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index 763e7453..68258518 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -20,11 +20,11 @@ def __init__(self, path: str, **kwargs): assert self.path.exists() kwargs["target_columns"] = [ - "lipinki_h_donor", - "lipinki_h_acceptor", - "lipinki_mwt", - "lipinki_log_p", - "lipinki", + "lipinski_h_donor", + "lipinski_h_acceptor", + "lipinski_mwt", + "lipinski_log_p", + "lipinski", ] kwargs["smi_column"] = kwargs.get("smi_column", "smi") super().__init__(**kwargs) @@ -94,7 +94,7 @@ def build_probe_dataset(): async def async_lipinski_rule_of_five(x): async with sem: - return lipinki_rule_of_five(x, "smi") + return lipinski_rule_of_five(x, "smi") ds = ds.map(async_lipinski_rule_of_five, batched=False) @@ -109,10 +109,10 @@ async def inchi_key(smi: str): df = ds.to_pandas() df.drop_duplicates(subset="inchi_key", inplace=True) lip_cols = [ - "lipinki_h_donor", - "lipinki_h_acceptor", - "lipinki_mwt", - "lipinki_log_p", + "lipinski_h_donor", + "lipinski_h_acceptor", + "lipinski_mwt", + "lipinski_log_p", ] # Rebalance and report stats @@ -126,7 +126,7 @@ async def inchi_key(smi: str): # Split preserving the frequency of each subgroup spliter = StratifiedShuffleSplit(train_size=0.80, random_state=721153) - train_idx, test_idx = next(spliter.split(df["smi"], df["lipinki"])) + train_idx, test_idx = next(spliter.split(df["smi"], df["lipinski"])) # Save to disk ds = DatasetDict( @@ -259,19 +259,19 @@ def downsample_ipf_binary( SEM_LIPINSKI = asyncio.Semaphore(20) -def lipinki_rule_of_five(x: dict, smi_column: str = "smi") -> dict: +def lipinski_rule_of_five(x: dict, smi_column: str = "smi") -> dict: smi = x[smi_column] mol = MolFromSmiles(smi) assert mol is not None, "invalid smi: %s" % smi x["num_h_bond_donors"] = Lipinski.NumHDonors(mol) - x["lipinki_h_donor"] = x["num_h_bond_donors"] <= 5 + x["lipinski_h_donor"] = x["num_h_bond_donors"] <= 5 x["num_h_bond_acceptors"] = Lipinski.NumHAcceptors(mol) - x["lipinki_h_acceptor"] = x["num_h_bond_acceptors"] <= 10 + x["lipinski_h_acceptor"] = x["num_h_bond_acceptors"] <= 10 x["molecular_weight"] = ExactMolWt(mol) - x["lipinki_mwt"] = x["molecular_weight"] <= 500 + x["lipinski_mwt"] = x["molecular_weight"] <= 500 x["log_p"] = MolLogP(mol) - x["lipinki_log_p"] = x["log_p"] <= 5 - x["lipinki"] = all(v for k, v in x.items() if k.startswith("lipinki")) + x["lipinski_log_p"] = x["log_p"] <= 5 + x["lipinski"] = all(v for k, v in x.items() if k.startswith("lipinski")) return x From 9a7964bdf234797924a3281c76d81ac43ca11790 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 13 Jun 2025 14:01:03 -0400 Subject: [PATCH 037/147] feat update screening plots --- opt/MISTStyle/src/MISTStyle.jl | 10 +- opt/screening/plots.jl | 167 ---------------------- opt/screening/plots/plots.jl | 22 ++- opt/screening/plots/src/ScreeningPlots.jl | 1 + opt/screening/plots/src/pareto.jl | 162 ++++++++++++++++++--- 5 files changed, 166 insertions(+), 196 deletions(-) delete mode 100644 opt/screening/plots.jl diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 537a9816..c8a20caf 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -7,7 +7,7 @@ using CairoMakie: CairoMakie const pt = 3 / 4 const inch = 96 -export pt, inch +export pt, inch, sublabel! """ Save duplicate figures for publication and web """ function savefig(name::String, f::Figure; dpi=300, fig_dir="fig") @@ -92,8 +92,8 @@ const CONTINUOUS_COLORS = :lipari function theme() Theme( - rowgap=2, - colgap=2, + rowgap=3pt, + colgap=3pt, fonts=(; regular="Times New Roman Regular", bold="Times New Roman Bold", @@ -115,6 +115,10 @@ function theme() Lines=(; cycle=Cycle([:color, :linestyle], covary=true), ), + GridLayout=(; + default_rowgap=3pt, + default_colgap=3pt, + ), Axis=(; spinewidth=0.5, xlabelsize=8pt, diff --git a/opt/screening/plots.jl b/opt/screening/plots.jl deleted file mode 100644 index 13dde3c1..00000000 --- a/opt/screening/plots.jl +++ /dev/null @@ -1,167 +0,0 @@ -using MISTStyle: UM_COLORS -using Makie -using MISTStyle -using Metaheuristics -using DataFrames -using SQLite: SQLite -using CSV: CSV -using JSON: JSON - -const HARTREE_TO_EV = 27.211_386_245_981 - -function load_data(path) - file = !endswith(path, ".sqlite") ? joinpath(path, "merged.sqlite") : path - db = SQLite.DB(file) - df = nothing - try - df = SQLite.DBInterface.execute(db, "SELECT * from molecules") |> DataFrame - finally - close(db) - end - cols = keys(JSON.parse(df[1, :props])) - transform!(df, :props => ByRow(JSON.parse) => Symbol.(cols)) - select!(df, Not(:props)) - return df -end - -function load_all() - cases = Dict{String,DataFrame}() - for case in readdir(joinpath(@__DIR__, "out"); join=true) - isfile(joinpath(case, "merged.sqlite")) || continue - cases[basename(case)] = load_data(case) - end - return cases -end - -function get_pareto_front(df, x, y; quad=:lt) - @assert quad == :lt "not implemented" - x = df[:, x] - y = -df[:, y] # flip sign to maximize - front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, y)) - front = map(front) do p - p[2] *= -1 - p - end - return Point2.(sort(front; by=first, rev=true)) -end - -function plot_generation(cases) - f = Figure() - ax = Axis(f[1, 1]; - xlabel="Duplicate Count", - ylabel="Probability Density", - limits=((0, nothing), (0, nothing)), - ) - for (label, df) in pairs(cases) - density!(ax, df.duplicate_count; label, bandwidth=0.75) - end - axislegend(ax) - return f -end - -function plot_pareto_front(case, ref) - mp_limits = extrema(vcat(case.mp, [0])) - bp_limits = extrema(vcat(case.bp, [75])) - pareto_kwargs = (; - marker=:star5, - markersize=8pt, - ) - - # Net non-dominated - bp = -case[!, :bp] - gap = -case[!, :gap] .* HARTREE_TO_EV - homo = case[!, :homo] .* HARTREE_TO_EV - mp = case[!, :mp] - canidates = map(vcat, homo, gap, mp, bp) - front = Metaheuristics.get_non_dominated_solutions(canidates) - nidx = findall(in(front), canidates) - case = deepcopy(case) - case.dominated .= true - case.dominated[nidx] .= false - sort!(case, :dominated; rev=true) - @info "non-dominated" sort(case[nidx, :], :inchi) - front = map(front) do p - p[1] *= -1 - p[2] *= -1 - p - end - @info "non-dominated" length(front) nrow(case) length(front) / nrow(case) - - f = Figure(; size=(2inch, 1inch), figure_padding=(1, 3, 1, 2)) - ax = Axis(f[1, 1]; - limits=(mp_limits, bp_limits), - xlabel=L"Melting Point [$\degree C$]", - ylabel=L"Boiling Point [$\degree C$]", - ) - scatter!(ax, case.mp, case.bp; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : UM_COLORS.blue, case.dominated), - ) - scatterlines!(ax, get_pareto_front(ref, :mp, :bp); - color=MISTStyle.UM_COLORS.maize, - pareto_kwargs... - ) - scatterlines!(ax, get_pareto_front(case, :mp, :bp); - color=MISTStyle.UM_COLORS.blue, - pareto_kwargs... - ) - - ax = Axis(f[1, 2]; - limits=((-10, -7), (5, 12)), - xlabel=L"HOMO [eV]$$", - ylabel=L"Gap [eV]$$", - ) - scatter!(ax, case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : UM_COLORS.blue, case.dominated), - label="Generated", - ) - scatterlines!(ax, get_pareto_front(ref, :homo, :gap) .* HARTREE_TO_EV; - color=MISTStyle.UM_COLORS.maize, - label="Ref. Pareto Front", - pareto_kwargs... - ) - scatterlines!(ax, get_pareto_front(case, :homo, :gap) .* HARTREE_TO_EV; - color=MISTStyle.UM_COLORS.blue, - label="Generated Pareto Front", - pareto_kwargs... - ) - Legend(f[2, :], ax; - tellheight=true, tellwidth=false, - orientation=:horizontal, - ) - - return f -end - -function gen_plots() - dfs = load_all() - df_ref = DataFrame(CSV.File(joinpath(@__DIR__, "..", "design", "electrolytes_predictons.csv"))) - - # MP < 0degC, BP > 75 degC, HOMO < -7eV, GAP > 5eV - case_names = Dict( - "1 gpus" => "4e64e005-d988-48d4-8879-1f7a7d5f2be9", - "4 gpus" => "dcd68d1a-8190-4145-b77c-6486ac1a880b", - "16 gpus" => "09b342fa-95ad-4727-ac0f-fee0b8c4b84b", - "32 gpus" => "597f3b8a-9390-41b2-adc6-a974afd04c71", - "40 gpus" => "f1c03e9a-ee30-4671-873b-b4e2f9be17a1", - ) - - for (name, id) in pairs(case_names) - df = dfs[id] - @info "$name - $id" nrow(df) sum(df.duplicate_count) - end - - with_theme(MISTStyle.theme()) do - plot_generation( - Dict(name => dfs[id] for (name, id) in case_names) - ) |> MISTStyle.savefig("generation") - for (name, id) in pairs(case_names) - plot_pareto_front(dfs[id], df_ref) |> MISTStyle.savefig(joinpath("pareto", name * "-" * id)) - end - end -end - -function (@main)(::Any) - gen_plots() -end diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index db615c6f..3ac86981 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -54,15 +54,29 @@ regtable( ) # Plot generated molecules -production_run = first(sort!(df, :n_passing; rev=true)).path -df_mol = ScreeningPlots.load_generated_molecules(production_run) -prod_config = JSON.parsefile(joinpath(production_run, "config.json")) +production_run = (; pairs(first(sort!(df, :n_passing; rev=true)))...) +df_mol = ScreeningPlots.load_generated_molecules(production_run.path) +prod_config = JSON.parsefile(joinpath(production_run.path, "config.json")) # Reference Molecules -df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes.csv"))) +df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes_predictions.csv"))) df_mol.inchi_key = ScreeningPlots.inchi_key.(df_mol.smiles) df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi) df_novel = subset(df_mol, :inchi_key => ByRow(∉(df_ref.inchi_key))) df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df_mol.inchi_key))) @info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel) / nrow(df_mol) nrow(df_unfound) / nrow(df_ref) + +# Generate Plots +prod_id = basename(production_run.path) +trace = DataFrame(production_run[:trace]) +with_theme(MISTStyle.theme()) do + f = ScreeningPlots.plot_pareto_front(df_mol, df_ref) + MISTStyle.savefig(joinpath("pareto", "production" * "-" * prod_id), f) + + f = ScreeningPlots.plot_gen_trace(trace) + MISTStyle.savefig(joinpath("gen-trace" * "-" * prod_id), f) + + f = ScreeningPlots.figure_screening(trace, df_mol, df_ref, df) + MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) +end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index d7947b1b..cef4a17d 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -6,6 +6,7 @@ using Metaheuristics: Metaheuristics using JSON: JSON using SQLite: SQLite using PythonCall: Py, pyimport, pyconvert +using GLM: @formula, lm, glm, Normal, LogLink, coef using MISTStyle diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl index 5bc959df..d40225af 100644 --- a/opt/screening/plots/src/pareto.jl +++ b/opt/screening/plots/src/pareto.jl @@ -1,13 +1,28 @@ -function get_pareto_front(df, x, y; quad=:lt) +function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing) @assert quad == :lt "not implemented" - x = df[:, x] - y = -df[:, y] # flip sign to maximize - front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, y)) + front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, -1 .* y)) front = map(front) do p p[2] *= -1 p end - return Point2.(sort(front; by=first, rev=true)) + frontier = Point2.(sort(front; by=first, rev=true)) + if ax !== nothing + limits = lift(ax.finallimits) do hr + lx, ly = hr.origin + ux, uy = hr.origin .+ hr.widths + lx, ux = extrema([lx, ux]) + ly, uy = extrema([ly, uy]) + return (; lx, ly, ux, uy) + end + frontier = lift(limits) do limits + fs = Point2(limits.ux, frontier[1][2]) + fe = Point2(frontier[end][1], limits.ly) + vcat([fs], frontier, [fe]) + end + @info frontier + end + @info "frontier" frontier + return frontier end function plot_generation(cases) @@ -24,12 +39,107 @@ function plot_generation(cases) return f end +function plot_gen_trace(args...; kwargs...) + f = Figure(; size=(2inch, 1inch)) + plot_gen_trace!(f, args...; kwargs...) +end + +function fit_exp_decay(x, y) + df = DataFrame(x=x, y=float.(y[end] .- y)) + subset!(df, :y => ByRow(>(0))) + m = glm(@formula(y ~ x), df, Normal(), LogLink()) + A = exp.(coef(m)[1]) + τ = coef(m)[2] + y_hat = @. A * (1 - exp(x * τ)) + return (; m, y_hat, A, τ) +end + +function figure_screening(trace, case, ref, df_speed) + f = Figure(; + size=(3.42inch, 2inch), + figure_padding=(2, 2, 2, 5) + ) + gl_perf = GridLayout(f[1, 1]) + plot_pareto_front!(GridLayout(f[2, 1]), case, ref) + plot_gen_trace!(GridLayout(gl_perf[1, 1]), trace) + + gl = GridLayout(gl_perf[1, 2]) + ax = Axis(gl[1, 1]; + limits=((nothing, 9), (0, nothing)), + xlabel="GPUs", + ylabel="Evaluated/GPU-sec", + xscale=log2, + ) + # x = df_speed.gpus .+ 0.1 * randn(nrow(df_speed)) + x = df_speed.gpus .* (1 .+ 0.02 .* randn(nrow(df_speed))) + h = scatter!(ax, + x, df_speed.global_unique_throughput ./ df_speed.gpus; + color=df_speed.batch_size, + marker=:circle, + colormap=:roma, + alpha=0.8, + ) + Colorbar(gl[1, 2], h; + label="Batch Size" + ) + colgap!(gl_perf, 4pt) + + sublabel!(gl_perf[1, 1, TopLeft()], "a"; left=27pt) + sublabel!(gl_perf[1, 2, TopLeft()], "b"; left=25pt) + sublabel!(f[2, 1][1, 1, TopLeft()], "c"; left=15pt) + sublabel!(f[2, 1][1, 2, TopLeft()], "d"; left=5pt) + + resize_to_layout!(f) + + return f +end + +function plot_gen_trace!(f, trace) + ax = Axis(f[1, 1]; + xlabel="Wall Time [s]", + ylabel="Evaluated", + limits=((0, nothing), (0, 100e6)), + xlabelvisible=false, + xticksvisible=false, + xticklabelsvisible=false, + yticks=WilkinsonTicks(3), + yminorticks=IntervalsBetween(5), + yminorticksvisible=true, + ) + + m_uniq = fit_exp_decay(trace.time, trace.unique_molecules) + m_pass = fit_exp_decay(trace.time, trace.n_passing) + uniq_max_init_rate = -m_uniq.A * m_uniq.τ + pass_max_init_rate = -m_pass.A * m_pass.τ + @info "Initial Rates" uniq_max_init_rate pass_max_init_rate m_uniq.m m_pass.m + + lines!(ax, trace.time, trace.unique_molecules) + ax2 = Axis(f[2, 1]; + xlabel="Wall Time [s]", + ylabel="Passing", + limits=((0, nothing), (0, 2100)), + xminorticksvisible=true, + yminorticksvisible=true, + xminorticks=IntervalsBetween(10), + yminorticks=IntervalsBetween(5), + yticks=[0, 1000, 2000], + ) + lines!(ax2, trace.time, trace.n_passing) + linkxaxes!(ax, ax2) + + return f +end + function plot_pareto_front(case, ref) + f = Figure(; size=(2inch, 1inch), figure_padding=(1, 3, 1, 2)) + plot_pareto_front!(f, case, ref) +end +function plot_pareto_front!(f, case, ref) mp_limits = extrema(vcat(case.mp, [0])) bp_limits = extrema(vcat(case.bp, [75])) pareto_kwargs = (; - marker=:star5, - markersize=8pt, + linewidth=1.5pt, + alpha=0.7, ) # Net non-dominated @@ -52,41 +162,36 @@ function plot_pareto_front(case, ref) end @info "non-dominated" length(front) nrow(case) length(front) / nrow(case) - f = Figure(; size=(2inch, 1inch), figure_padding=(1, 3, 1, 2)) ax = Axis(f[1, 1]; limits=(mp_limits, bp_limits), xlabel=L"Melting Point [$\degree C$]", ylabel=L"Boiling Point [$\degree C$]", ) - scatter!(ax, case.mp, case.bp; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : MISTStyle.UM_COLORS.blue, case.dominated), - ) - scatterlines!(ax, get_pareto_front(ref, :mp, :bp); + scatter_samples!(ax, case.mp, case.bp, case.dominated) + stairs!(ax, get_pareto_front(ref.mp, ref.bp; ax); color=MISTStyle.UM_COLORS.maize, pareto_kwargs... ) - scatterlines!(ax, get_pareto_front(case, :mp, :bp); + stairs!(ax, get_pareto_front(case.mp, case.bp; ax); color=MISTStyle.UM_COLORS.blue, pareto_kwargs... ) ax = Axis(f[1, 2]; - limits=((-10, -7), (5, 12)), + limits=((-10.5, -7), (5, 13)), xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$", + xticks=WilkinsonTicks(5; k_max=7), + yticks=WilkinsonTicks(5; k_max=7), ) - scatter!(ax, case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : MISTStyle.UM_COLORS.blue, case.dominated), - label="Generated", - ) - scatterlines!(ax, get_pareto_front(ref, :homo, :gap) .* HARTREE_TO_EV; + h, _ = scatter_samples!(ax, case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV, case.dominated) + h.label = "Generated" + lines!(ax, get_pareto_front(ref.homo .* HARTREE_TO_EV, ref.gap .* HARTREE_TO_EV; ax); color=MISTStyle.UM_COLORS.maize, label="Ref. Pareto Front", pareto_kwargs... ) - scatterlines!(ax, get_pareto_front(case, :homo, :gap) .* HARTREE_TO_EV; + stairs!(ax, get_pareto_front(case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV; ax)[]; color=MISTStyle.UM_COLORS.blue, label="Generated Pareto Front", pareto_kwargs... @@ -98,3 +203,16 @@ function plot_pareto_front(case, ref) return f end + +function scatter_samples!(ax, x, y, dominated) + h1 = scatter!(ax, x[dominated], y[dominated]; + marker=:circle, + color=MISTStyle.CAT_COLORS[1], + alpha=0.4, + ) + h2 = scatter!(ax, x[.!dominated], y[.!dominated]; + marker=:star5, + color=MISTStyle.UM_COLORS.blue, + ) + return h1, h2 +end From 9727d585e30b73bba9c4ef3477bfec8b7e4a174a Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 14 Jun 2025 17:49:39 -0400 Subject: [PATCH 038/147] add verification plots --- opt/MISTStyle/src/MISTStyle.jl | 6 ++++++ opt/screening/plots/Project.toml | 1 + opt/screening/plots/plots.jl | 9 +++++++++ opt/screening/plots/src/ScreeningPlots.jl | 3 +++ 4 files changed, 19 insertions(+) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index c8a20caf..0342429b 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -48,6 +48,12 @@ function cb_attrs(cb::Colorbar, plt) return Base.structdiff(attrs, NamedTuple{(invalid...,)}) end +function parity_limits(x::AbstractVector, y::AbstractVector; inflate=0.05) + l, u = extrema(Iterators.flatten((x, y))) + limits = (l - inflate * (u - l), u + inflate * (u - l)) + return (limits, limits) +end + function sublabel!(f, letter; left=0, kwargs...) label_kwargs = (; diff --git a/opt/screening/plots/Project.toml b/opt/screening/plots/Project.toml index 5b8c6df9..7cfc3530 100644 --- a/opt/screening/plots/Project.toml +++ b/opt/screening/plots/Project.toml @@ -5,6 +5,7 @@ version = "0.1.0" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 3ac86981..b8de9069 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -79,4 +79,13 @@ with_theme(MISTStyle.theme()) do f = ScreeningPlots.figure_screening(trace, df_mol, df_ref, df) MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) + + # Verify qmist can reproduce QM9 calculations + qmist = realpath(joinpath(pkgdir(ScreeningPlots), "..", "..", "qmist")) + df_qm9 = ScreeningPlots.load_jsonl(joinpath(qmist, "qm9.jsonl")) + for version in [joinpath(qmist, "veri_v1")] + df_qmist = ScreeningPlots.load_qmist_results(version) + df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) + ScreeningPlots.figure_parity(df, cols) |> MISTStyle.savefig(basename(version) * "_parity") + end end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index cef4a17d..218f1054 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -7,6 +7,8 @@ using JSON: JSON using SQLite: SQLite using PythonCall: Py, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef +using Format: format +using StatsBase: cor, mad using MISTStyle @@ -18,6 +20,7 @@ function __init__() __rdkit_chem[] = pyimport("rdkit.Chem") end +include("qmist.jl") include("collate.jl") include("sqlite.jl") include("pareto.jl") From 6319b1102eb4f2947fcb3139f142cc3c581c88fb Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 17 Jun 2025 11:34:31 -0400 Subject: [PATCH 039/147] move mol_surprise into electrolyte_fm --- electrolyte_fm/models/mol_surprise.py | 71 +++++++++++++++++++++++++ opt/synth_access/main.py | 76 ++------------------------- 2 files changed, 75 insertions(+), 72 deletions(-) create mode 100644 electrolyte_fm/models/mol_surprise.py diff --git a/electrolyte_fm/models/mol_surprise.py b/electrolyte_fm/models/mol_surprise.py new file mode 100644 index 00000000..62b34e8d --- /dev/null +++ b/electrolyte_fm/models/mol_surprise.py @@ -0,0 +1,71 @@ +import torch +from torch.nn import functional as F +from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding +from ..utils.tokenizer import load_tokenizer + + +class MolSurpriseFM(torch.nn.Module): + def __init__(self, encoder, tokenizer): + super().__init__() + self.encoder = encoder + self.tokenizer = tokenizer + self.collate_fn = DataCollatorWithPadding(self.tokenizer) + + def forward( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + special_tokens_mask: torch.Tensor, + per_token: bool = False, + ): + logits = self.encoder(input_ids, attention_mask).logits + B = input_ids.shape[0] + V = logits.shape[-1] + + labels = input_ids.detach().masked_fill(special_tokens_mask.bool(), -100) + + score = ( + F.cross_entropy(logits.view(-1, V), labels.view(-1), reduction="none") + .reshape(B, -1) + .sum(-1) + ) + if per_token: + score = score / attention_mask.sum(-1) + return score + + def score(self, smiles: list[str], per_token: bool = False) -> list[float]: + batch = self.tokenizer(smiles, return_special_tokens_mask=True) + batch = self.collate_fn(batch).to(self.encoder.device) + with torch.inference_mode(): + return self.forward( + batch["input_ids"], + batch["attention_mask"], + batch["special_tokens_mask"], + per_token, + ).to("cpu") + + @classmethod + def from_checkpoint(cls, ckpt: str, **kwargs): + from ..utils.ckpt import DeepSpeedMixin + + encoder = DeepSpeedMixin.load(ckpt).model + tokenizer = load_tokenizer(ckpt) + return cls(encoder, tokenizer, **kwargs) + + @classmethod + def from_pretrained(cls, name_or_path: str, dtype=None, **kwargs): + encoder = AutoModelForMaskedLM.from_pretrained( + name_or_path, + trust_remote_code=True, + # device_map="auto", + # torch_dtype="auto", + ) + tokenizer = load_tokenizer(name_or_path) + return cls(encoder, tokenizer, **kwargs) + + @classmethod + def from_untrained(cls, name_or_path: str, dtype=None, **kwargs): + config = AutoConfig.from_pretrained(name_or_path, trust_remote_code=True) + encoder = AutoModelForMaskedLM.from_config(config) + tokenizer = load_tokenizer(name_or_path) + return cls(encoder, tokenizer, **kwargs) diff --git a/opt/synth_access/main.py b/opt/synth_access/main.py index 74f48cf1..543f9090 100644 --- a/opt/synth_access/main.py +++ b/opt/synth_access/main.py @@ -20,14 +20,11 @@ from sklearn.metrics import roc_auc_score from syba.syba import SybaClassifier from smirk import SmirkTokenizerFast -from torch.nn import functional as F -from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding from vendor.scscore.scscore import SCScorer +from electrolyte_fm.models.mol_surprise import MolSurpriseFM from electrolyte_fm.data_modules.utils import MolEncoding, encode_molecules -from electrolyte_fm.models.model_utils import DeepSpeedMixin from electrolyte_fm.utils.cache import cached_download, extract_file -from electrolyte_fm.utils.tokenizer import load_tokenizer # Suppress DeprecationWarnings for MorganGenerator rdBase.DisableLog("rdApp.warning") @@ -94,71 +91,6 @@ def syba_scorer(): return syba -class SynthAccessFM(torch.nn.Module): - def __init__(self, encoder, tokenizer): - super().__init__() - self.encoder = encoder - self.tokenizer = tokenizer - self.collate_fn = DataCollatorWithPadding(self.tokenizer) - - def forward( - self, - input_ids: torch.Tensor, - attention_mask: torch.Tensor, - special_tokens_mask: torch.Tensor, - per_token: bool = False, - ): - logits = self.encoder(input_ids, attention_mask).logits - B = input_ids.shape[0] - V = logits.shape[-1] - - labels = input_ids.detach().masked_fill(special_tokens_mask.bool(), -100) - - score = ( - F.cross_entropy(logits.view(-1, V), labels.view(-1), reduction="none") - .reshape(B, -1) - .sum(-1) - ) - if per_token: - score = score / attention_mask.sum(-1) - return score - - def score(self, smiles: List[str], per_token: bool = False) -> List[float]: - batch = self.tokenizer(smiles, return_special_tokens_mask=True) - batch = self.collate_fn(batch).to(self.encoder.device) - with torch.inference_mode(): - return self.forward( - batch["input_ids"], - batch["attention_mask"], - batch["special_tokens_mask"], - per_token, - ).to("cpu") - - @classmethod - def from_checkpoint(cls, ckpt: str, **kwargs): - encoder = DeepSpeedMixin.load(ckpt).model - tokenizer = load_tokenizer(ckpt) - return cls(encoder, tokenizer, **kwargs) - - @classmethod - def from_pretrained(cls, name_or_path: str, dtype=None, **kwargs): - encoder = AutoModelForMaskedLM.from_pretrained( - name_or_path, - trust_remote_code=True, - device_map="auto", - torch_dtype="auto", - ) - tokenizer = load_tokenizer(name_or_path) - return cls(encoder, tokenizer, **kwargs) - - @classmethod - def from_untrained(cls, name_or_path: str, dtype=None, **kwargs): - config = AutoConfig.from_pretrained(name_or_path, trust_remote_code=True) - encoder = AutoModelForMaskedLM.from_config(config) - tokenizer = load_tokenizer(name_or_path) - return cls(encoder, tokenizer, **kwargs) - - @timeout(30) # molecular_assembly (v0.2.0) timeout flag doesn't timeout def molecular_assembly_timeout(smi: str) -> Optional[int]: mol = Chem.MolFromSmiles(smi) @@ -203,7 +135,7 @@ def map_batchsize_finder(ds, f, batch_size: int = 64, **kwargs): def eval_fm_model( - metric_name: str, model: SynthAccessFM, ds: Dataset, target: str | None = None + metric_name: str, model: MolSurpriseFM, ds: Dataset, target: str | None = None ): model = model.to("cuda") model = model.eval() @@ -269,8 +201,8 @@ def evaluate_dataset( for name, metric in metrics.items(): if isinstance(metric, str): for suffix, init_model in [ - ("", SynthAccessFM.from_pretrained), - ("-untrained", SynthAccessFM.from_untrained), + ("", MolSurpriseFM.from_pretrained), + ("-untrained", MolSurpriseFM.from_untrained), ]: ds, metric_runtime, metric_auroc = eval_fm_model( metric + suffix, From fa2de7836599c2136601e0ef237eddefbf97b664 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 17 Jun 2025 11:35:49 -0400 Subject: [PATCH 040/147] Update figures for screening plots --- electrolyte_fm/models/prod_finetune.py | 4 +- opt/screening/.gitignore | 10 +- opt/screening/activate | 16 +- opt/screening/plots/plots.jl | 90 +++++++-- opt/screening/plots/src/ScreeningPlots.jl | 14 +- opt/screening/plots/src/collate.jl | 41 ++-- opt/screening/plots/src/creativity.jl | 6 + opt/screening/plots/src/pareto.jl | 36 ++-- opt/screening/plots/src/qmist.jl | 219 ++++++++++++++++++++++ opt/screening/plots/src/sqlite.jl | 1 + opt/screening/pyproject.toml | 1 + opt/screening/uv.lock | 21 ++- 12 files changed, 398 insertions(+), 61 deletions(-) create mode 100644 opt/screening/plots/src/creativity.jl create mode 100644 opt/screening/plots/src/qmist.jl diff --git a/electrolyte_fm/models/prod_finetune.py b/electrolyte_fm/models/prod_finetune.py index 91c58086..22ae83b6 100644 --- a/electrolyte_fm/models/prod_finetune.py +++ b/electrolyte_fm/models/prod_finetune.py @@ -103,7 +103,7 @@ def embed(self, smi: list[str]): return hs.to("cpu") - def predict(self, smi: list[str]): + def predict(self, smi: list[str], return_dict=True): batch = self.tokenizer(smi) collate_fn = DataCollatorWithPadding(self.tokenizer) batch = collate_fn(batch) @@ -114,7 +114,7 @@ def predict(self, smi: list[str]): with torch.inference_mode(): out = self(**batch).cpu() - if self.channels is None: + if self.channels is None or not return_dict: return out return annotate_prediction(out, self.channels) diff --git a/opt/screening/.gitignore b/opt/screening/.gitignore index 9a33bd18..af2ec7e4 100644 --- a/opt/screening/.gitignore +++ b/opt/screening/.gitignore @@ -1,8 +1,6 @@ vendor/ *.smi *.frag -out/ -out fig/ *.csv *.sqlite @@ -11,3 +9,11 @@ models/ models fig/ *.csv + +# Output folders +out/ +out +runs/ +runs +initial-sweep/ +initial-sweep diff --git a/opt/screening/activate b/opt/screening/activate index aa006cf0..9603d5e6 100755 --- a/opt/screening/activate +++ b/opt/screening/activate @@ -1,14 +1,16 @@ #!/bin/bash # Source this to activate the environment -DIR="$(git rev-parse --show-toplevel)/opt/screening" + +# Locate Project Root +GIT_ROOT=$(git rev-parse --show-toplevel) +ROOT=${GIT_ROOT}/opt/screening # Activate virtual environment -source $DIR/.venv/bin/activate +source ${ROOT}/.venv/bin/activate -# Configure HF and Tokenizers -export HF_HOME="$(git rev-parse --show-toplevel)/.cache/huggingface" -export TOKENIZERS_PARALLELISM=false +# HuggingFace +export HF_HOME="$GIT_ROOT/.cache/huggingface" -# Configure julia +# Julia export JULIA_CONDAPKG_BACKEND=Null -export JULIA_PYTHONCALL_EXE="$DIR/.venv/bin/python" +export JULIA_PYTHONCALL_EXE="${ROOT}/.venv/bin/python" diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index b8de9069..6f6877c1 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -4,15 +4,32 @@ using MISTStyle using StatsBase using DataFrames using GLM -using RegressionTables: LatexTable, regtable +using RegressionTables: RegressionTables, LatexTable, regtable using JSON: JSON using CSV: CSV +using Format: format -ROOTDIR = joinpath(pkgdir(ScreeningPlots), "..") +using ScreeningPlots: searchfirst + +ROOTDIR = realpath(joinpath(pkgdir(ScreeningPlots), "..")) +GIT_ROOT = realpath(joinpath(ROOTDIR, "..", "..")) fig_dir = joinpath(ROOTDIR, "fig") isdir(fig_dir) || mkdir(fig_dir) -df = ScreeningPlots.collate_performance_stats("initial-sweep") +# Filter to screening runs +df = ScreeningPlots.collate_performance_stats(joinpath(ROOTDIR, "runs")) +df = filter(df) do row + config = row.config + get(config, "limit_walltime", nothing) == 300 || return false + models = Set([c["model_path"] for c in config["critics"]]) + expected_models = [ + "models/mist-26.9M-b302p09x-bp", + "models/mist-26.9M-y3ge5pf9-mp", + "models/mist-x4i8qzuq-qm9", + ] + models == Set(expected_models) || return false + return true +end df.limit_ref_fragments .= something.(df.limit_ref_fragments, 122) df.limit_db_fragments .= something.(df.limit_db_fragments, 88_800_000) df.limit_ref_fragments ./= 122 @@ -30,7 +47,7 @@ df.generation_efficiency = df.global_unique_throughput ./ df.global_throughput # Linear Models to guide scaling m_eff = lm( @formula(generation_efficiency ~ log(gpus) + epoch_size + epoch_size^2 + limit_ref_fragments + limit_db_fragments), - df + subset(df, :duration => ByRow(<(400))), ) display(m_eff) coef_m = Dict(zip(coefnames(m_eff), coef(m_eff))) @@ -40,7 +57,7 @@ ideal_rel_epoch = -coef_m["epoch_size"] / (2 * coef_m["epoch_size ^ 2"]) m_speed = lm( @formula(log(global_throughput) ~ log(gpus) + batch_size + +batch_size^2 + n_fragments + epoch_size), - df + subset(df, :duration => ByRow(<(400))), ) display(m_speed) coef_speed = Dict(zip(coefnames(m_speed), coef(m_speed))) @@ -51,41 +68,86 @@ regtable( m_speed, m_eff; file=joinpath(fig_dir, "screening_lm.tex"), render=LatexTable(), + regression_statistics=[ + RegressionTables.Nobs, + RegressionTables.DOF, + RegressionTables.R2, + (m -> mad(residuals(m))) => "MAE", + (m -> rmsd(predict(m), response(m))) => "RMSE", + ] ) # Plot generated molecules -production_run = (; pairs(first(sort!(df, :n_passing; rev=true)))...) -df_mol = ScreeningPlots.load_generated_molecules(production_run.path) -prod_config = JSON.parsefile(joinpath(production_run.path, "config.json")) +prod_id = "b7c6ceb2-2114-4ba5-bd2e-41b9bfa2d5df" +production_run_path = joinpath(ROOTDIR, "runs", prod_id) +df_mol = ScreeningPlots.load_generated_molecules(production_run_path) +prod_config = JSON.parsefile(joinpath(production_run_path, "config.json")) # Reference Molecules -df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes_predictions.csv"))) +df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes_predictons.csv"))) df_mol.inchi_key = ScreeningPlots.inchi_key.(df_mol.smiles) df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi) df_novel = subset(df_mol, :inchi_key => ByRow(∉(df_ref.inchi_key))) df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df_mol.inchi_key))) @info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel) / nrow(df_mol) nrow(df_unfound) / nrow(df_ref) +@info "Prod. Perf" throughput=production_run.global_throughput / production_run.gpus uniq_throughput = production_run.global_unique_throughput / production_run.gpus # Generate Plots -prod_id = basename(production_run.path) -trace = DataFrame(production_run[:trace]) +trace, _ = ScreeningPlots.performance_trace(joinpath(production_run_path, "screen.jsonl")) +trace = DataFrame(trace) with_theme(MISTStyle.theme()) do f = ScreeningPlots.plot_pareto_front(df_mol, df_ref) - MISTStyle.savefig(joinpath("pareto", "production" * "-" * prod_id), f) + MISTStyle.savefig(joinpath("production" * "-" * prod_id), f) f = ScreeningPlots.plot_gen_trace(trace) MISTStyle.savefig(joinpath("gen-trace" * "-" * prod_id), f) + f = ScreeningPlots.weak_scaling(subset(df, :duration => ByRow(<(400)))) + MISTStyle.savefig(joinpath("scaling" * "-" * prod_id), f) + f = ScreeningPlots.figure_screening(trace, df_mol, df_ref, df) MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) # Verify qmist can reproduce QM9 calculations qmist = realpath(joinpath(pkgdir(ScreeningPlots), "..", "..", "qmist")) df_qm9 = ScreeningPlots.load_jsonl(joinpath(qmist, "qm9.jsonl")) - for version in [joinpath(qmist, "veri_v1")] + label = "QM9 (Ramakrishnan et al.)" => "Ours" + for version in [joinpath(qmist, "veri_v1"), joinpath(qmist, "veri_v2"), joinpath(qmist, "veri_v3")] df_qmist = ScreeningPlots.load_qmist_results(version) df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) - ScreeningPlots.figure_parity(df, cols) |> MISTStyle.savefig(basename(version) * "_parity") + μ, σ = mean_and_std(df_qmist.walltime) + walltime_p95 = quantile(df_qmist.walltime, 0.95) + @info basename(version) nrow(df) walltime=format("\\({:.0f} \\pm {:.0f}\\)", μ, σ) walltime_p95 + ScreeningPlots.figure_parity(df, cols; label) |> MISTStyle.savefig(basename(version) * "_parity") end + + # Parity Plots vs. QM9 Calculations + for (dir_name, label) in ["qm9" => "rdkit", "qm9_obabel" => "openbabel", "qm9_conf" => "conformer"] + f = ScreeningPlots.compare_qmist( + production_run_path, + joinpath(production_run_path, dir_name); + label="B3LYP/6-31G(2df,p)" => "MIST", + ) + MISTStyle.savefig(joinpath("parity-$(label)-$(prod_id)"), f) + end + + # Load the QM9 Model used for screening + qm9_model_name = searchfirst( + c -> occursin("qm9", c), + [ basename(c["model_path"]) for c in prod_config["critics"] ] + ) + mist_qm9 = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", qm9_model_name)) + mist_qm9 = mist_qm9.to("mps") + # + # Parity Plots with Chembl data + f = ScreeningPlots.compare_qmist( + production_run_path, + joinpath(production_run_path, "qm9_conf"), + joinpath(ROOTDIR, "veri_chembl"), + mist_qm9; + label="B3LYP/6-31G(2df,p)" => "MIST", + ) + MISTStyle.savefig(joinpath("parity-chembl-$(prod_id)"), f) + end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index 218f1054..8bc7d924 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -5,7 +5,7 @@ using DataFrames using Metaheuristics: Metaheuristics using JSON: JSON using SQLite: SQLite -using PythonCall: Py, pyimport, pyconvert +using PythonCall: Py, PyList, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef using Format: format using StatsBase: cor, mad @@ -15,12 +15,19 @@ using MISTStyle const HARTREE_TO_EV = 27.211_386_245_981 const __rdkit_chem = Ref{Py}() +const __mol_surprise = Ref{Py}() +const __prod_finetune = Ref{Py}() +const __data_utils = Ref{Py}() function __init__() __rdkit_chem[] = pyimport("rdkit.Chem") + __mol_surprise[] = pyimport("electrolyte_fm.models.mol_surprise") + __prod_finetune[] = pyimport("electrolyte_fm.models.prod_finetune") + __data_utils[] = pyimport("electrolyte_fm.data_modules.utils") end include("qmist.jl") +include("creativity.jl") include("collate.jl") include("sqlite.jl") include("pareto.jl") @@ -37,4 +44,9 @@ function inchi_key(smi::String) return pyconvert(String, __rdkit_chem[].MolToInchiKey(mol)) end +function searchfirst(f, x) + idx = findfirst(f, x) + return isnothing(idx) ? nothing : x[idx] +end + end diff --git a/opt/screening/plots/src/collate.jl b/opt/screening/plots/src/collate.jl index 69cf3bad..07bba668 100644 --- a/opt/screening/plots/src/collate.jl +++ b/opt/screening/plots/src/collate.jl @@ -6,27 +6,12 @@ function collate_performance_stats(sweep::String) (isfile(config_file) && isfile(logfile)) || continue config = JSON.parsefile(config_file) - trace = [] - rank_throughput = Dict{Int,Float64}() - for line in eachline(logfile) - msg = JSON.parse(line) - if haskey(msg, "n_passing_world") && get(msg, "global_rank", -1) == 0 - # Track global generation stats - push!(trace, (; - time=msg["elapsed_perf"], - n_passing=msg["n_passing_world"], - unique_molecules=msg["unique_molecules_world"], - )) - elseif haskey(msg, "passing_rank") - # Record the final throughput of each rank - rank = msg["global_rank"] - rank_throughput[rank] = msg["eval_throughput_rank"] - end - end + trace, rank_throughput = performance_trace(logfile) isempty(trace) && continue push!(rows, (; id=basename(run), path=joinpath(sweep, run), + config, batch_size=config["generation"]["batch_size"], limit_db_fragments=config["generation"]["limit_db_fragments"], limit_ref_fragments=config["generation"]["limit_ref_fragments"], @@ -42,3 +27,25 @@ function collate_performance_stats(sweep::String) end return DataFrame(rows) end + +function performance_trace(logfile::String) + trace = [] + rank_throughput = Dict{Int,Float64}() + for line in eachline(logfile) + msg = JSON.parse(line) + if haskey(msg, "n_passing_world") && get(msg, "global_rank", -1) == 0 + # Track global generation stats + push!(trace, (; + time=msg["elapsed_perf"], + n_passing=msg["n_passing_world"], + unique_molecules=msg["unique_molecules_world"], + )) + elseif haskey(msg, "passing_rank") + # Record the final throughput of each rank + rank = msg["global_rank"] + rank_throughput[rank] = msg["eval_throughput_rank"] + end + end + return trace, rank_throughput +end + diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl new file mode 100644 index 00000000..9aaa0359 --- /dev/null +++ b/opt/screening/plots/src/creativity.jl @@ -0,0 +1,6 @@ +function batch_score(model::Py, smiles::Vector{String}; batch_size=32) + _eval(batch) = pyconvert(Vector{Float64}, model.score(PyList(batch))) + ds = Iterators.partition(smiles, batch_size) + out = Iterators.map(_eval, ds) + return collect(Iterators.flatten(out)) +end diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl index d40225af..74070059 100644 --- a/opt/screening/plots/src/pareto.jl +++ b/opt/screening/plots/src/pareto.jl @@ -19,9 +19,7 @@ function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing) fe = Point2(frontier[end][1], limits.ly) vcat([fs], frontier, [fe]) end - @info frontier end - @info "frontier" frontier return frontier end @@ -54,17 +52,9 @@ function fit_exp_decay(x, y) return (; m, y_hat, A, τ) end -function figure_screening(trace, case, ref, df_speed) - f = Figure(; - size=(3.42inch, 2inch), - figure_padding=(2, 2, 2, 5) - ) - gl_perf = GridLayout(f[1, 1]) - plot_pareto_front!(GridLayout(f[2, 1]), case, ref) - plot_gen_trace!(GridLayout(gl_perf[1, 1]), trace) - - gl = GridLayout(gl_perf[1, 2]) - ax = Axis(gl[1, 1]; +weak_scaling(df_speed) = weak_scaling!(Figure(), df_speed) +function weak_scaling!(f, df_speed) + ax = Axis(f[1, 1]; limits=((nothing, 9), (0, nothing)), xlabel="GPUs", ylabel="Evaluated/GPU-sec", @@ -73,15 +63,27 @@ function figure_screening(trace, case, ref, df_speed) # x = df_speed.gpus .+ 0.1 * randn(nrow(df_speed)) x = df_speed.gpus .* (1 .+ 0.02 .* randn(nrow(df_speed))) h = scatter!(ax, - x, df_speed.global_unique_throughput ./ df_speed.gpus; + x, df_speed.global_throughput ./ df_speed.gpus; color=df_speed.batch_size, marker=:circle, colormap=:roma, alpha=0.8, ) - Colorbar(gl[1, 2], h; - label="Batch Size" + Colorbar(f[1, 2], h; label="Batch Size") + return f +end + +function figure_screening(trace, case, ref, df_speed) + f = Figure(; + size=(3.42inch, 2inch), + figure_padding=(2, 2, 2, 5) ) + gl_perf = GridLayout(f[1, 1]) + plot_pareto_front!(GridLayout(f[2, 1]), case, ref) + plot_gen_trace!(GridLayout(gl_perf[1, 1]), trace) + + gl = GridLayout(gl_perf[1, 2]) + weak_scaling!(gl, df_speed) colgap!(gl_perf, 4pt) sublabel!(gl_perf[1, 1, TopLeft()], "a"; left=27pt) @@ -154,7 +156,7 @@ function plot_pareto_front!(f, case, ref) case.dominated .= true case.dominated[nidx] .= false sort!(case, :dominated; rev=true) - @info "non-dominated" sort(case[nidx, :], :inchi) + @info "non-dominated" sort(case[nidx, :], :inchi_key) front = map(front) do p p[1] *= -1 p[2] *= -1 diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl new file mode 100644 index 00000000..e862e8d3 --- /dev/null +++ b/opt/screening/plots/src/qmist.jl @@ -0,0 +1,219 @@ +function load_qmist_results(folder::String) + rows = [] + for file in readdir(folder; join=true) + endswith(file, ".json") || continue + push!(rows, JSON.parsefile(file)) + end + df = DataFrame(rows) + rename!(df, "InChIKey" => "inchi_key") + return df +end + +load_mist_pretrained(folder::String) = __prod_finetune[].MISTFinetuned.from_pretrained(folder) + +function predict_mist(model::Py, smi::Vector{String}; batch_size=32) + model = model.eval() + transcode = __data_utils[].MolEncoding("smiles-kekule") + smi = map(x -> pyconvert(String, transcode(x)), smi) + ds = Iterators.partition(smi, batch_size) + channels = pyconvert(Vector{String}, [chn["name"] for chn in model.channels]) + out = Iterators.map(ds) do batch + y = model.predict(PyList(batch); return_dict=false) + yj = pyconvert(Matrix{Float64}, y) + return eachrow(yj) + end + data = stack(collect(Iterators.flatten(out)))' + df = DataFrame(data, channels) + df.smiles = smi + return df +end + +function load_jsonl(file::String) + rows = [] + open(file, "r") do io + for line in eachline(io) + push!(rows, JSON.parse(line)) + end + end + return DataFrame(rows) +end + +function merge_qmist_results(qmist::DataFrame, ref::DataFrame) + cols = filter(!=("smiles"), names(ref)) + df = innerjoin(qmist, ref; on=:smiles, renamecols = "_qmist" => "_qm9") + return df, cols +end + +figure_parity(args...; kwargs...) = figure_parity!(Figure(; size=(3.42inch, 2inch)), args...; kwargs...) +function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", other="_qmist", label::Union{Pair{String,String},Nothing}=nothing) + nrow = floor(Int, sqrt(length(cols))) + ncol = ceil(Int, length(cols) / nrow) + + gl = GridLayout(f[1, 1]) + for (idx, col) in enumerate(cols) + i, j = divrem(idx - 1, ncol) + i += 1 + x = df[!, col * ref] + y = df[!, col * other] + xlim = extrema(x) + ylim = extrema(y) + ax = Axis(gl[i, j]; + title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mad(x .- y)), + limits=MISTStyle.parity_limits(x, y), + xticks=WilkinsonTicks(2), + yticks=WilkinsonTicks(2), + ) + if "color$(ref)" in names(df) + sargs = (; alpha= 0.3, color=df[!, "color$(ref)"], colormap=MISTStyle.CAT_COLORS, colorrange=(1, 10)) + else + sargs = (; alpha=0.3) + end + scatter!(ax, x, y; marker=:circle, alpha=0.3, sargs...) + ablines!(ax, 0, 1; color=:black, linestyle=:dash) + end + + if !isnothing(label) + xlabel, ylabel = label + Label(f[:, 0], ylabel, rotation=pi/2, tellwidth=true, tellheight=false) + Label(f[end+1,:], xlabel,; tellwidth=false, tellheight=true) + end + + if "color$(ref)" in names(df) + @info "Group Sizes" combine(groupby(df, "color$(ref)"), DataFrames.nrow => :n) + ng = count(==(1), df[!, "color$(ref)"]) + ni = count(==(2), df[!, "color$(ref)"]) + elements = map(enumerate(["Generated (n=$ng)", "Inventory (n=$ni)"])) do (idx, label) + MarkerElement(; label, marker=:circle, color=MISTStyle.CAT_COLORS[idx]) + end + Legend(gl[begin, end], elements, MISTStyle.label.(elements); + fontsize=6pt, + margin=(2pt, 2pt, 2pt, 2pt), + padding=2pt, + valign=:bottom, + halign=:right, + ) + end + + resize_to_layout!(f) + return f +end + +# Compare MIST vs. qmist +function compare_qmist( + run::String, + qmist_dir::String, + cols::Vector{String} = ["gap", "lumo", "homo"]; + kwargs... +) + # Load Screening & QMist results + df_qmist = load_qmist_results(qmist_dir) + df_mist = load_generated_molecules(run) + select!(df_mist, ["inchi_key", cols...]) + select!(df_qmist, ["inchi_key", cols...]) + return compare_qmist(df_qmist, df_mist, cols; on="inchi_key", kwargs...) +end + +# Compare MIST vs. qmist +function compare_qmist( + run::String, + qmist_dir::String, + chembl_dir::String, + mist_qm9::Py; + cols::Vector{String} = ["gap", "lumo", "homo"], + kwargs... +) + # Load Screening & QMist results + df_qmist = load_qmist_results(qmist_dir) + df_mist = load_generated_molecules(run) + select!(df_mist, ["inchi_key", cols...]) + select!(df_qmist, ["inchi_key", cols...]) + df_qmist.color .= 1 + df_mist.color .= 1 + + # Load Chembl results + df_chembl = load_qmist_results(chembl_dir) + df_mist_chembl = predict_mist(mist_qm9, df_chembl.smiles) + df_mist_chembl.inchi_key = df_chembl.inchi_key + select!(df_mist_chembl, ["inchi_key", cols...]) + select!(df_chembl, ["inchi_key", cols...]) + df_chembl.color .= 2 + df_mist_chembl.color .= 2 + + df_qmist = vcat(df_qmist, df_chembl) + df_mist = vcat(df_mist, df_mist_chembl) + + return compare_qmist(df_qmist, df_mist, cols; on="inchi_key", kwargs...) +end + +function compare_qmist(df_qmist::DataFrame, df_mist::DataFrame, cols::Vector{String}; on="InChIKey" => "inchi", kwargs...) + df = innerjoin(df_qmist, df_mist; on, renamecols="_qmist" => "_mist") + disallowmissing!(df) + + f = Figure(; size=(3.42inch, 1inch)) + figure_parity!(f ,df, cols; ref="_qmist", other="_mist", kwargs...) + return f +end + +function hit_rate!(df::DataFrame, limits) + df_cols = names(df) + select!(df, filter(!endswith("_hit"), names(df))) + chns = String[] + models = String[] + for (col, limit) in limits + for dc in filter(x -> occursin(col, x), df_cols) + push!(chns, col) + push!(models, split(dc, "_")[2]) + df[!, Symbol(dc * "_hit")] = map(x -> inbounds(x, limit), df[!, dc]) + end + end + chns = unique(chns) + models = unique(models) + + # Mark overall hit rate + for model in models + transform!(df, + map(col -> "$(col)_$(model)_hit", chns) => ByRow((x...) -> all(x)) => "$(model)_hit" + ) + end + + return chns, models +end + +function retrival_quality(df::DataFrame, chns, models::Pair{String}) + hitcols = filter(endswith("_hit"), names(df)) + df_hit = select(df, hitcols) + ref_model, other_model = models + cf = Dict{String, NamedTuple}() + for chn in chns + x = df_hit[!, "$(chn)_$(ref_model)_hit"] + x_hat = df_hit[!, "$(chn)_$(other_model)_hit"] + cf[chn] = confmatrix(x_hat, x) + end + cf["all"] = confmatrix(df_hit[!, other_model * "_hit"], df_hit[!, ref_model * "_hit"]) + return retrival_quality(cf) +end + +retrival_quality(cfs::Dict) = Dict(k => retrival_quality(v) for (k, v) in cfs) + +function retrival_quality(cf::NamedTuple) + return (; + cf, + precision = cf.tp / (cf.tp + cf.fp), + ) +end + +function confmatrix(x_hat::Vector{Bool}, x::Vector{Bool}) + return (; + tp = sum(x_hat .& x), + tn = sum(.!x_hat .& .!x), + fp = sum(x_hat .& .!x), + fn = sum(.!x_hat .& x), + ) +end + +inbounds(x::Real, lim::Tuple) = inbounds(x, first(lim), last(lim)) +inbounds(x::Real, lb::Real, ub::Real) = x >= lb && x <= ub +inbounds(x::Real, lb::Nothing, ub::Real) = x <= ub +inbounds(x::Real, lb::Real, ub::Nothing) = x >= lb +inbounds(x::Real, lb::Nothing, ub::Nothing) = true + diff --git a/opt/screening/plots/src/sqlite.jl b/opt/screening/plots/src/sqlite.jl index fe53e0f8..379a56c8 100644 --- a/opt/screening/plots/src/sqlite.jl +++ b/opt/screening/plots/src/sqlite.jl @@ -17,6 +17,7 @@ function load_generated_molecules(path) cols = keys(JSON.parse(df[1, :props])) transform!(df, :props => ByRow(JSON.parse) => Symbol.(cols)) select!(df, Not(:props)) + rename!(df, "inchi" => "inchi_key") return df end diff --git a/opt/screening/pyproject.toml b/opt/screening/pyproject.toml index 89049c51..a19e5246 100644 --- a/opt/screening/pyproject.toml +++ b/opt/screening/pyproject.toml @@ -5,6 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.10, <3.14" dependencies = [ + "accelerate>=1.7.0", "electrolyte_fm", "lightning>=2.5.1", "pandas>=2.2.3", diff --git a/opt/screening/uv.lock b/opt/screening/uv.lock index 9d5ce456..3d51c261 100644 --- a/opt/screening/uv.lock +++ b/opt/screening/uv.lock @@ -1,5 +1,4 @@ version = 1 -revision = 1 requires-python = ">=3.10, <3.14" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'linux'", @@ -12,6 +11,24 @@ resolution-markers = [ "python_full_version < '3.11' and sys_platform != 'linux'", ] +[[package]] +name = "accelerate" +version = "1.7.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyyaml" }, + { name = "safetensors" }, + { name = "torch" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/97/33/47bbd507e3a851d33d19ce7b2141c5ea3689bfae91ba168044d7db24b0e9/accelerate-1.7.0.tar.gz", hash = "sha256:e8a2a5503d6237b9eee73cc8d36cf543f9c2d8dd2c6713450b322f5e6d53a610", size = 376026 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/bb/be8146c196ad6e4dec78385d91e92591f8a433576c4e04c342a636fcd811/accelerate-1.7.0-py3-none-any.whl", hash = "sha256:cf57165cca28769c6cf2650812371c81b18e05743dfa3c748524b1bb4f2b272f", size = 362095 }, +] + [[package]] name = "aiohappyeyeballs" version = "2.6.1" @@ -1887,6 +1904,7 @@ name = "screening" version = "0.1.0" source = { virtual = "." } dependencies = [ + { name = "accelerate" }, { name = "electrolyte-fm" }, { name = "lightning" }, { name = "pandas" }, @@ -1902,6 +1920,7 @@ dev = [ [package.metadata] requires-dist = [ + { name = "accelerate", specifier = ">=1.7.0" }, { name = "electrolyte-fm", editable = "../../" }, { name = "lightning", specifier = ">=2.5.1" }, { name = "pandas", specifier = ">=2.2.3" }, From 562c31e1c5f5cce2f53be0d162c0028332d9a701 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 18 Jun 2025 14:55:46 -0400 Subject: [PATCH 041/147] Update and expand screening plots --- electrolyte_fm/models/mol_surprise.py | 13 ++ opt/MISTStyle/Project.toml | 7 +- opt/MISTStyle/src/MISTStyle.jl | 2 + opt/MISTStyle/src/nbins.jl | 17 ++ opt/screening/plots/Project.toml | 5 +- opt/screening/plots/plots.jl | 44 ++++- opt/screening/plots/src/ScreeningPlots.jl | 18 +- opt/screening/plots/src/creativity.jl | 206 +++++++++++++++++++++- opt/screening/plots/src/pareto.jl | 7 +- opt/screening/plots/src/qmist.jl | 3 +- opt/synth_access/plot.jl | 2 +- 11 files changed, 305 insertions(+), 19 deletions(-) create mode 100644 opt/MISTStyle/src/nbins.jl diff --git a/electrolyte_fm/models/mol_surprise.py b/electrolyte_fm/models/mol_surprise.py index 62b34e8d..907a4561 100644 --- a/electrolyte_fm/models/mol_surprise.py +++ b/electrolyte_fm/models/mol_surprise.py @@ -1,4 +1,5 @@ import torch +from torch.fx import has_side_effect from torch.nn import functional as F from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding from ..utils.tokenizer import load_tokenizer @@ -44,6 +45,18 @@ def score(self, smiles: list[str], per_token: bool = False) -> list[float]: per_token, ).to("cpu") + def embed(self, smiles: list[str]) -> torch.Tensor: + batch = self.tokenizer(smiles, return_special_tokens_mask=True) + batch = self.collate_fn(batch).to(self.encoder.device) + input_ids = batch["input_ids"] + attention_mask = batch["attention_mask"] + if hasattr(self.encoder, "roberta_prelayernorm"): + encoder = self.encoder.roberta_prelayernorm + else: + encoder = self.encoder.encoder + with torch.inference_mode(): + return encoder(input_ids, attention_mask=attention_mask).last_hidden_state + @classmethod def from_checkpoint(cls, ckpt: str, **kwargs): from ..utils.ckpt import DeepSpeedMixin diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index 7a3c5976..50137742 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -5,7 +5,10 @@ uuid = "0a43f4a4-3db7-4939-b247-26273f54b603" CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [compat] -CairoMakie = "0.13.2" -CategoricalArrays = "0.10.8" +Makie = "0.22,0.23" +CairoMakie = "0.13,0.14" +CategoricalArrays = "0.10" +StatsBase = "0.33, 0.34" diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 0342429b..eaa394c7 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -3,6 +3,7 @@ module MISTStyle using Makie using CategoricalArrays: levels using CairoMakie: CairoMakie +using StatsBase: StatsBase, AbstractWeights const pt = 3 / 4 const inch = 96 @@ -71,6 +72,7 @@ include("errorcross.jl") include("powerlaw.jl") include("tantext.jl") include("quadrant.jl") +include("nbins.jl") const CAT_COLORS = cgrad( map(x -> RGBf(x ./ 255...), [ diff --git a/opt/MISTStyle/src/nbins.jl b/opt/MISTStyle/src/nbins.jl new file mode 100644 index 00000000..fa53bc33 --- /dev/null +++ b/opt/MISTStyle/src/nbins.jl @@ -0,0 +1,17 @@ +# Estimate Number of Histogram Bins from data +hist_nbins(x::AbstractVector) = hist_nbins(:scott, x) +hist_nbins(x::AbstractVector, w::AbstractWeights) = hist_nbins(:scott, x) +hist_nbins(method::Symbol, x::AbstractVector) = hist_nbins(method, x, StatsBase.UnitWeights{Int}(length(x))) + +hist_nbins(method, x, w) = hist_nbins(Val(Symbol(method)), x, w) +hist_nbins(method::Symbol, x::AbstractVector, w::AbstractWeights) = hist_nbins(Val(method), x, w) +function hist_nbins(::Val{:scott}, x, w) + σ = StatsBase.std(x, w) + h = 3.5 * σ / cbrt(length(x)) + n = (maximum(x) - minimum(x)) / h + return ceil(Int, n) +end +hist_nbins(::Val{:sqrt}, x, w) = ceil(Int, sqrt(length(x))) +hist_nbins(::Val{M}, args...) where {M} = MethodError(hist_nbins, M, args...) +hist_nbins(::Val{:sturges}, x, w) = ceil(Int, log2(length(x)) + 1) +hist_nbins(::Val{:sturges}, x, w::StatsBase.FrequencyWeights) = ceil(Int, log2(sum(w)) + 1) diff --git a/opt/screening/plots/Project.toml b/opt/screening/plots/Project.toml index 7cfc3530..33870f90 100644 --- a/opt/screening/plots/Project.toml +++ b/opt/screening/plots/Project.toml @@ -7,14 +7,17 @@ CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" Metaheuristics = "bcdb8e00-2c21-11e9-3065-2b553b22f898" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9" -StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [sources] MISTStyle = {path = "../../MISTStyle"} diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 6f6877c1..21615de7 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -1,7 +1,7 @@ using ScreeningPlots using Makie using MISTStyle -using StatsBase +using Statistics using DataFrames using GLM using RegressionTables: RegressionTables, LatexTable, regtable @@ -72,8 +72,8 @@ regtable( RegressionTables.Nobs, RegressionTables.DOF, RegressionTables.R2, - (m -> mad(residuals(m))) => "MAE", - (m -> rmsd(predict(m), response(m))) => "RMSE", + (m -> ScreeningPlots.mae(residuals(m))) => "MAE", + (m -> ScreeningPlots.rmsd(predict(m), response(m))) => "RMSE", ] ) @@ -91,7 +91,7 @@ df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi) df_novel = subset(df_mol, :inchi_key => ByRow(∉(df_ref.inchi_key))) df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df_mol.inchi_key))) @info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel) / nrow(df_mol) nrow(df_unfound) / nrow(df_ref) -@info "Prod. Perf" throughput=production_run.global_throughput / production_run.gpus uniq_throughput = production_run.global_unique_throughput / production_run.gpus +# @info "Prod. Perf" throughput=production_run.global_throughput / production_run.gpus uniq_throughput = production_run.global_unique_throughput / production_run.gpus # Generate Plots trace, _ = ScreeningPlots.performance_trace(joinpath(production_run_path, "screen.jsonl")) @@ -116,7 +116,8 @@ with_theme(MISTStyle.theme()) do for version in [joinpath(qmist, "veri_v1"), joinpath(qmist, "veri_v2"), joinpath(qmist, "veri_v3")] df_qmist = ScreeningPlots.load_qmist_results(version) df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) - μ, σ = mean_and_std(df_qmist.walltime) + μ = mean(df_qmist.walltime) + σ = std(df_qmist.walltime) walltime_p95 = quantile(df_qmist.walltime, 0.95) @info basename(version) nrow(df) walltime=format("\\({:.0f} \\pm {:.0f}\\)", μ, σ) walltime_p95 ScreeningPlots.figure_parity(df, cols; label) |> MISTStyle.savefig(basename(version) * "_parity") @@ -151,3 +152,36 @@ with_theme(MISTStyle.theme()) do MISTStyle.savefig(joinpath("parity-chembl-$(prod_id)"), f) end + +mol_surprise = ScreeningPlots.load_mol_surprise(joinpath(GIT_ROOT, "models", "mist-ti624ev1")) +mist_mp = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist-26.9M-y3ge5pf9-mp")) +mist_bp = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist-26.9M-b302p09x-bp")) + +f, df_surprise = ScreeningPlots.compare_creativity( + production_run_path, + joinpath(ROOTDIR, "veri_chembl"), + df_ref; + mol_surprise, + mist_mp, + mist_bp +) +MISTStyle.savefig("surprise_vs_utility", f) + +# Compute Renyi Entropy Metrics +df_surprise.embed_mean = eachrow(ScreeningPlots.mist_embedding(mol_surprise, df_surprise.smiles; pooling=ScreeningPlots.mean_pooling)) +dist_metrics = [ + "eculidean" => ScreeningPlots.eculidean_distance, + "cosine" => ScreeningPlots.cosine_distance, + "angular" => ScreeningPlots.angular_distance, +] +df_s = combine(groupby(df_surprise, :group)) do gdf + out = [] + n = nrow(gdf) + for (pool, emb) in ["first" => gdf.embed, "mean" => gdf.embed_mean] + for (metric, distance) in dist_metrics + o = ScreeningPlots.renyi_entropy_estimate(emb; distance) + push!(out, (; n, pool, metric, o...)) + end + end + return DataFrame(out) +end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index 8bc7d924..e9e886f9 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -2,13 +2,17 @@ module ScreeningPlots using Makie using DataFrames +using Graphs: complete_graph, boruvka_mst using Metaheuristics: Metaheuristics using JSON: JSON using SQLite: SQLite using PythonCall: Py, PyList, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef using Format: format -using StatsBase: cor, mad +using Statistics: cor, mean +using CategoricalArrays: categorical, levelcode +using LinearAlgebra: norm, dot +using ManifoldLearning: ManifoldLearning, DiffMap, fit, predict, transform using MISTStyle @@ -26,6 +30,18 @@ function __init__() __data_utils[] = pyimport("electrolyte_fm.data_modules.utils") end +mae(x, y) = mean(x -> abs(-(x...)), zip(x, y)) +mae(x) = mean(abs, x) +rmsd(x, y) = sqrt(mean(x -> -(x...)^2, zip(x, y))) + +default_device() = Sys.isbsd() ? "mps" : "cuda" +function load_mist_pretrained(folder::String; device=default_device()) + return __prod_finetune[].MISTFinetuned.from_pretrained(folder).to(device).eval() +end +function load_mol_surprise(path::String; device=default_device()) + return __mol_surprise[].MolSurpriseFM.from_pretrained(path).to(device).eval() +end + include("qmist.jl") include("creativity.jl") include("collate.jl") diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index 9aaa0359..ec6b7758 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -1,6 +1,208 @@ -function batch_score(model::Py, smiles::Vector{String}; batch_size=32) +function molecular_surprise(model::Py, smiles::Vector{String}; batch_size=32) _eval(batch) = pyconvert(Vector{Float64}, model.score(PyList(batch))) + batch_transcode(smiles) do batch + pyconvert(Vector{Float64}, model.score(PyList(batch))) + end +end + +first_token(E::AbstractArray{<:Real, 3}) = eachrow(reshape(E[:, 1, :], size(E, 1), :)) +first_token(E::AbstractMatrix) = vec(E[1, :]) +mean_pooling(E::AbstractArray{<:Real, 3}) = eachrow(reshape(mean(E; dims=2), size(E, 1), :)) +mean_pooling(E::AbstractMatrix) = vec(mean(E; dims=1)) +no_pooling(E) = eachslice(E; dims=1) + +function mist_embedding( + model::Py, + smiles::Vector{String}; + batch_size=32, + pooling=first_token, + collect=x -> stack(x; dims=1), +) + batch_transcode(smiles; collect) do batch + E = pyconvert(Array{Float64}, model.embed(PyList(batch)).to("cpu")) + return pooling(E) + end +end +function batch_transcode(f, smiles; batch_size=32, collect=Base.collect) + transcode = __data_utils[].MolEncoding("smiles-kekule") + smiles = map(x -> pyconvert(String, transcode(x)), smiles) ds = Iterators.partition(smiles, batch_size) - out = Iterators.map(_eval, ds) + out = Iterators.map(f, ds) return collect(Iterators.flatten(out)) end + +function compare_creativity( + run::String, + chembl_dir::String, + df_ref::DataFrame; + mol_surprise::Py, + mist_mp::Py, + mist_bp::Py, + kwargs... +) + + + df_mol = load_generated_molecules(run) + df_chembl = load_qmist_results(chembl_dir) + df = vcat( + _select_creativity(df_mol, mol_surprise, "Generated"), + _select_creativity(df_chembl, mol_surprise, "ChEMBL"), + _select_creativity(df_ref, mol_surprise, "Electrolytes"), + ) + + mp_missing = ismissing.(df.mp) + df.mp[mp_missing] .= predict_mist(mist_mp, df.smiles[mp_missing]).mp + bp_missing = ismissing.(df.bp) + df.bp[bp_missing] .= predict_mist(mist_bp, df.smiles[bp_missing]).bp + + + return compare_creativity(df), df +end + +function compare_creativity(df::DataFrame, M = missing) + f = Figure(; size=(3.42inch, 1inch)) + df.group = categorical(df.group; levels=["Generated", "ChEMBL", "Electrolytes"]) + ax1 = Axis(f[1, 1]; + ylabel="Molecular Surprise", + xticks=(1:3, unique(df.group)), + limits=((0, nothing), nothing), + ygridvisible=true, + yminorticksvisible=true, + xticklabelrotation=0.3, + ) + rainclouds!(ax1, levelcode.(df.group), df.surprise; + gap=0.2, + clouds=hist, + jitter_width=0.1, + boxplot_width=0.15, + side_nudge=0.25, + strokewidth=0.5, + whiskerwidth=1.0, + color=map(g -> MISTStyle.CAT_COLORS[levelcode(g)], df.group), + ) + + df = combine(groupby(df, :group)) do gdf + pos = map(vcat, -gdf.gap, gdf.homo, gdf.mp, -gdf.bp) + idx = Metaheuristics.get_non_dominated_solutions_perm(pos) + @info first(gdf.group) mean(gdf.surprise) mean(gdf.surprise[idx]) + gdf.group_frontier .= false + gdf[idx, :group_frontier] .= true + return gdf + end + + + ax2 = Axis(f[1, 2]; + xlabel=L"$$HOMO [eV]", + ylabel=L"$$Molecular Surprise", + xticks=WilkinsonTicks(3), + ) + color = map(df.group, df.group_frontier) do g, gf + alpha = gf ? 1.0 : 0.1 + return (MISTStyle.CAT_COLORS[levelcode(g)], alpha) + end + marker = :circle + markersize = 3 + sargs = (; marker, color, markersize) + sort!(df, :group_frontier; rev=true) + scatter!(ax2, df.homo .* HARTREE_TO_EV, df.surprise; sargs...) + + ax3 = Axis(f[1, 3]; + xlabel=L"$$Gap [eV]", + ylabel=L"$$Molecular Surprise", + xticks=WilkinsonTicks(3), + yticks=ax1.yticks, + ygridvisible=ax1.ygridvisible, + ) + scatter!(ax3, df.gap .* HARTREE_TO_EV, df.surprise; sargs...) + + + ax4 = Axis(f[1, 4]; + xlabel=L"$$Melt [$\degree C$ ]", + xticks=WilkinsonTicks(3), + ylabel=L"$$Molecular Surprise", + yticks=ax1.yticks, + ygridvisible=ax1.ygridvisible, + ) + scatter!(ax4, df.mp, df.surprise; sargs...) + + ax5 = Axis(f[1, 5]; + xlabel=L"$$Boil [$\degree C$ ]", + xticks=WilkinsonTicks(3), + ylabel=L"$$Molecular Surprise", + yticks=ax1.yticks, + ygridvisible=ax1.ygridvisible, + ) + scatter!(ax5, df.bp, df.surprise; sargs...) + + hideydecorations!(ax2; grid=false) + hideydecorations!(ax3; grid=false) + hideydecorations!(ax4; grid=false) + hideydecorations!(ax5; grid=false) + linkyaxes!(ax1, ax2, ax3) + + return f +end + +function _select_creativity(df::DataFrame, model::Py, group::String) + df = _select_creativity(df, model) + df.group .= group + return df +end +function _select_creativity(df::DataFrame, model::Py) + if "smi" in names(df) + df = rename(df, "smi" => "smiles") + end + cols = ["inchi_key", "smiles", "homo", "gap", "mp", "bp"] + df = select(df, intersect(cols, names(df))) + df.surprise = molecular_surprise(model, df.smiles) + df.embed = eachrow(mist_embedding(model, df.smiles)) + for col in cols + if !(col in names(df)) + df[!, col] .= missing + end + end + return df +end + +bounding_diameter(embeddings::Matrix) = norm(map(x -> -(extrema(x)...), eachcol(e))) +function embedding_momemt(embeddings::Matrix, n=1) + center = vec(mean(embeddings; dims=1)) + @assert size(center) == (size(embeddings, 2),) == (512,) + return mean(eachrow(embeddings)) do emb + return norm(emb .- center)^n + end +end +function embedding_spread(embeddings::Matrix) + center = vec(mean(embeddings; dims=1)) + return map(e -> norm(e .- center), eachrow(embeddings)) +end + +eculidean_distance(a, b) = norm(a .- b) +cosine_similarity(a, b) = dot(a, b) / (norm(a) * norm(b)) +cosine_distance(a, b) = sqrt(2 * max(1 - cosine_similarity(a, b), 0)) +angular_distance(a, b) = acos(min(max(cosine_similarity(a, b), 0), 1)) + +function embedding_mst_distance(embeddings::Matrix; distance=eculidean_distance) + n = size(embeddings, 1) + dd = Matrix{Float64}(undef, n, n) + for I in eachindex(IndexCartesian(), dd) + x = embeddings[I[1], :] + y = embeddings[I[2], :] + @assert length(x) == length(y) + dd[I] = distance(x, y) + end + g = complete_graph(n) + o = boruvka_mst(g, dd) + return o.weight +end + +function renyi_entropy_estimate(embeddings; kwargs...) + E = stack(embeddings; dims=1) + @assert E isa AbstractMatrix + mst = embedding_mst_distance(E; kwargs...) + n, d = size(E) + γ = (d - 1) / d + renyi = (1 / (1-γ)) * (log(mst) - γ * log(n)) + return (; renyi, mst) +end + diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl index 74070059..5a3eccff 100644 --- a/opt/screening/plots/src/pareto.jl +++ b/opt/screening/plots/src/pareto.jl @@ -1,10 +1,7 @@ function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing) @assert quad == :lt "not implemented" - front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, -1 .* y)) - front = map(front) do p - p[2] *= -1 - p - end + idx = Metaheuristics.get_non_dominated_solutions_perm(map(vcat, x, -1 .* y)) + front = Point2.(x[idx], y[idx]) frontier = Point2.(sort(front; by=first, rev=true)) if ax !== nothing limits = lift(ax.finallimits) do hr diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl index e862e8d3..eb9b4186 100644 --- a/opt/screening/plots/src/qmist.jl +++ b/opt/screening/plots/src/qmist.jl @@ -9,7 +9,6 @@ function load_qmist_results(folder::String) return df end -load_mist_pretrained(folder::String) = __prod_finetune[].MISTFinetuned.from_pretrained(folder) function predict_mist(model::Py, smi::Vector{String}; batch_size=32) model = model.eval() @@ -58,7 +57,7 @@ function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", othe xlim = extrema(x) ylim = extrema(y) ax = Axis(gl[i, j]; - title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mad(x .- y)), + title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mae(x .- y)), limits=MISTStyle.parity_limits(x, y), xticks=WilkinsonTicks(2), yticks=WilkinsonTicks(2), diff --git a/opt/synth_access/plot.jl b/opt/synth_access/plot.jl index 1d6ac5fe..63978af6 100644 --- a/opt/synth_access/plot.jl +++ b/opt/synth_access/plot.jl @@ -349,7 +349,7 @@ function create_figures() RegressionTables.Nobs, RegressionTables.DOF, RegressionTables.R2, - (m -> mad(residuals(m))) => "MAE", + (m -> mae(residuals(m))) => "MAE", (m -> rmsd(predict(m), response(m))) => "RMSE", ] ) From 1f8d14cf546aae9e6fad448179cdd2c38c0a26ad Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 24 Jun 2025 11:18:34 -0400 Subject: [PATCH 042/147] add updates to screening --- opt/config_tf_oud_wood.yaml | 70 ++++++++++++ opt/screening/.gitignore | 5 +- opt/screening/electrolyte_sweep.py | 0 opt/screening/launch.sh | 4 +- opt/screening/src/generate.py | 174 ++++++++++++++++++++++++----- opt/screening/test/test_critic.py | 2 - 6 files changed, 218 insertions(+), 37 deletions(-) create mode 100644 opt/config_tf_oud_wood.yaml mode change 100755 => 100644 opt/screening/electrolyte_sweep.py diff --git a/opt/config_tf_oud_wood.yaml b/opt/config_tf_oud_wood.yaml new file mode 100644 index 00000000..16784f56 --- /dev/null +++ b/opt/config_tf_oud_wood.yaml @@ -0,0 +1,70 @@ +generation: + db_path: zinc_fragment/fragments.sqlite + n_fragments: 1000 + reference_fragments: electrolyte.smi.frag + epoch_size: 1000 + limit_db_fragments: null + limit_ref_fragments: null + batch_size: 512 +critics: + - model_path: models/mist-26.9M-b302p09x-bp + limits: + bp: + - 75 + - null + model_cls: MISTFinetuned + - model_path: models/mist-26.9M-y3ge5pf9-mp + model_cls: MISTFinetuned + limits: + mp: + - null + - 0 + - model_path: models/mist-26.9M-48kpooqf-odour + model_cls: MISTFinetuned + limits: + woody: + - 0 + - null + amber: + - 0 + - null + - model_path: models/mist-ti624ev1-moleculenet/tox21 + model_cls: MISTFinetuned + limits: + NR-AR: + - null + - 0 + NR-AR-LBD: + - null + - 0 + NR-AhR: + - null + - 0 + NR-Aromatase: + - null + - 0 + NR-ER: + - null + - 0 + NR-ER-LBD: + - null + - 0 + NR-PPAR-gamma: + - null + - 0 + SR-ARE: + - null + - 0 + SR-ATAD5: + - null + - 0 + SR-HSE: + - null + - 0 + SR-MMP: + - null + - 0 + SR-p53: + - null + - 0 + model_cls: MISTFinetuned diff --git a/opt/screening/.gitignore b/opt/screening/.gitignore index af2ec7e4..c0ec279c 100644 --- a/opt/screening/.gitignore +++ b/opt/screening/.gitignore @@ -1,16 +1,15 @@ vendor/ *.smi *.frag -fig/ *.csv *.sqlite *.bin_cache models/ models -fig/ -*.csv # Output folders +fig/ +fig out/ out runs/ diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100755 new mode 100644 diff --git a/opt/screening/launch.sh b/opt/screening/launch.sh index eb494dfe..41eeb4a1 100755 --- a/opt/screening/launch.sh +++ b/opt/screening/launch.sh @@ -27,9 +27,9 @@ $(which apptainer) run \ --env-file $ENV_FILE \ /lustre/fs0/shared/sqsh-files/mist+pytorch+25.01+v4.sif \ ../../submit/set_node_rank \ -python screen.py --gpus-per-node ${SLURM_GPUS_PER_NODE:-1} --num-nodes ${SLURM_NNODES:-1} $@ # nsys profile \ -# --output="nsys_multinode_%q{JOBID}_%q{NODE_RANK}" \ +# --output="nsys_multinode_%q{SLURM_JOB_ID}_%q{NODE_RANK}" \ # --trace=cuda,cudnn,cublas,nvtx \ # --cuda-memory-usage=true \ # --duration 120 \ +python screen.py --gpus-per-node ${SLURM_GPUS_PER_NODE:-1} --num-nodes ${SLURM_NNODES:-1} $@ diff --git a/opt/screening/src/generate.py b/opt/screening/src/generate.py index 7a985a08..f3a7c934 100644 --- a/opt/screening/src/generate.py +++ b/opt/screening/src/generate.py @@ -1,16 +1,44 @@ import logging import time +from abc import ABC, abstractmethod import torch from lightning.fabric import Fabric -from torch import nn +from torch import Tensor, nn -from electrolyte_fm.models.prod_finetune import MISTFinetuned, MISTMultiTask -from src.hyperloglog import HyperLogLogSet +from .hyperloglog import HyperLogLogSet +from .prod_finetune import MISTFinetuned, MISTMultiTask + + +class Critic(nn.Module, ABC): + """ + Abstract base class for any “Critic.” Subclasses must implement + `active_channels`, which returns a 1‐D boolean Tensor of length = C, + where C = number of channels. True means “that channel is active.” + + We override __repr__ (and __str__) so that printing any Critic object + will show its class name + the active‐channels mask. + """ + + @property + @abstractmethod + def active_channels(self) -> Tensor: + """ + Return a 1‐D boolean Tensor (length = num_channels) indicating + which channels are active. Subclasses must override this. + """ + ... + + @abstractmethod + def forward(self, y: Tensor) -> Tensor: + """ + Given a tensor (..., C), return a tensor (...) indicating which entries are passing + """ + ... class OracleCritic(nn.Module): - def __init__(self, oracle: nn.Module, critic): + def __init__(self, oracle: nn.Module, critic: Critic): super().__init__() self.oracle = oracle self.critic = critic @@ -25,43 +53,129 @@ def forward( @classmethod def from_pretrained( cls, - save_directory: str, - limits: dict, + model_path: str, + limits: dict | None = None, + all_passing: bool | None = None, + any_passing: bool | None = None, model_cls: str = "MISTFinetuned", + **kwargs, ): - model_cls = MISTFinetuned if model_cls == "MISTFinetuned" else MISTMultiTask - oracle = model_cls.from_pretrained(save_directory) - critic = QuadrantCritic( - limits, channels=[chn["name"] for chn in oracle.channels] - ) + oracle_cls = { + "MISTFinetuned": MISTFinetuned, + "MISTMultiTask": MISTMultiTask, + }.get(model_cls) + oracle = oracle_cls.from_pretrained(model_path) + channels = [chn["name"] for chn in oracle.channels] + if limits is not None: + critic = QuadrantCritic.from_limits(limits, channels) + elif all_passing is not None: + critic = QuadrantCritic.from_all_passing(channels, **kwargs) + elif any_passing is not None: + critic = AnyCritic.from_any_passing(channels, **kwargs) + else: + raise RuntimeError("Unknown critic type") + return cls(oracle, critic) -class QuadrantCritic(nn.Module): - def __init__(self, limits: dict[str, tuple[float, float]], channels: list[str]): +def logit_limits( + channels: list[str], + pass_positive: bool = True, + flip_channels: dict[str, bool] | None = None, +): + limits = dict() + flip_channels = flip_channels or {} + for chn in channels: + flip_channels[chn] = flip_channels.get(chn, False) + if (pass_positive and not flip_channels[chn]) or ( + not pass_positive and flip_channels[chn] + ): + limits[chn] = (0, None) + else: + limits[chn] = (None, 0) + return limits + + +def limits_to_bounds(limits: dict[str, tuple[float, float]], channels: list[str]): + lower = [] + upper = [] + assert limits.keys() <= set(channels), ( + f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" + ) + for chn in channels: + if chn in limits: + lb, ub = limits[chn] + else: + lb, ub = None, None + lower.append(-torch.inf if lb is None else lb) + upper.append(torch.inf if ub is None else ub) + return lower, upper + + +class QuadrantCritic(Critic): + def __init__(self, lower: Tensor, upper: Tensor): super().__init__() - lower = [] - upper = [] - assert limits.keys() <= set( - channels - ), f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" - for chn in channels: - if chn in limits: - lb, ub = limits[chn] - else: - lb, ub = None, None - lower.append(-torch.inf if lb is None else lb) - upper.append(torch.inf if ub is None else ub) + self.register_buffer("lower", lower.view(1, -1)) + self.register_buffer("upper", upper.view(1, -1)) - self.register_buffer("lower", torch.tensor(lower).view(1, -1)) - self.register_buffer("upper", torch.tensor(upper).view(1, -1)) + @classmethod + def from_limits(cls, limits: dict[str, tuple[float, float]], channels: list[str]): + lower, upper = limits_to_bounds(limits, channels) + return cls(torch.tensor(lower), torch.tensor(upper)) + + @classmethod + def from_all_passing( + cls, + channels: list[str], + pass_positive: bool = True, + flip_channels: dict[str, bool] | None = None, + ): + limits = logit_limits(channels, pass_positive, flip_channels) + return cls.from_limits(limits, channels) @property def active_channels(self): return ~(self.lower.isinf() & self.upper.isinf()).view(-1) - def __call__(self, y: torch.Tensor): - return (self.lower < y) & (y < self.upper) + def forward(self, y: torch.Tensor): + y = torch.atleast_2d(y) + return ((self.lower < y) & (y < self.upper)).all(-1) + + +class AnyCritic(Critic): + def __init__(self, lower: Tensor, upper: Tensor, mask: Tensor): + super().__init__() + self.register_buffer("lower", lower.view(1, -1)) + self.register_buffer("upper", upper.view(1, -1)) + self.register_buffer("mask", mask.to(dtype=bool).view(1, -1)) + + @classmethod + def from_any_passing( + cls, + channels: list[str], + pass_positive: bool = True, + flip_channels: dict[str, bool] | None = None, + subset: list[str] | None = None, + ): + limits = logit_limits(channels, pass_positive, flip_channels) + lower, upper = limits_to_bounds(limits, channels) + subset_mask = [] + for chn in channels: + if subset is None: + subset_mask.append(True) + else: + subset_mask.append(chn in subset) + return cls(torch.tensor(lower), torch.tensor(upper), torch.tensor(subset_mask)) + + @property + def active_channels(self): + active_limits = ~(self.lower.isinf() & self.upper.isinf()) + return (active_limits & self.mask).view(-1) + + def forward(self, y: torch.Tensor): + """Return True if any channel is active for the molecule""" + y = torch.atleast_2d(y) + return ((self.lower < y) & (y < self.upper) & (self.mask)).any(-1) class CriticPanel(nn.Module): @@ -82,7 +196,7 @@ def forward( y = [] for critic in self.critics: yc, score = critic(input_ids, attention_mask=attention_mask) - net_score &= score.all(-1) + net_score &= score y.append(yc) return torch.cat(y, dim=-1), net_score diff --git a/opt/screening/test/test_critic.py b/opt/screening/test/test_critic.py index 9985af01..a4f8df47 100644 --- a/opt/screening/test/test_critic.py +++ b/opt/screening/test/test_critic.py @@ -1,6 +1,4 @@ import torch - -# Adjust this import if your file structure is different: from src.generate import ( AnyCritic, CriticPanel, From 4b430f6511d45117d9f358ab14e3cbd6cd99c24c Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 28 Jun 2025 14:29:41 -0400 Subject: [PATCH 043/147] Updated creativity figures --- opt/design/plots.jl | 9 ++++ opt/design/src/trends.jl | 41 ++++++++++----- opt/screening/plots/Project.toml | 3 ++ opt/screening/plots/plots.jl | 12 +++++ opt/screening/plots/src/ScreeningPlots.jl | 4 +- opt/screening/plots/src/creativity.jl | 64 +++++++++++++++++------ 6 files changed, 102 insertions(+), 31 deletions(-) diff --git a/opt/design/plots.jl b/opt/design/plots.jl index d52d055d..070fda20 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -91,3 +91,12 @@ with_theme(MISTStyle.theme()) do name_df_order=("Baseline" => df_order, "Random" => df_order_rand) ) end |> MISTStyle.savefig("permutations") + + +with_theme(MISTStyle.theme()) do + DesignRules.figure_double_bond_loc( + "Baseline" => df_perm_ref, "Random" => df_perm_rand; + name_df_order=("Baseline" => df_order, "Augmented" => df_order_rand) + ) +end |> MISTStyle.savefig("double_bond_loc") + diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 6be562c8..9747398e 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -325,20 +325,36 @@ function figure_permutations(name_df::Pair...; name_df_order) colsize!(gl_trends, 1, Relative(3 / 4)) colgap!(gl_trends, 1, 2) + figure_double_bond_loc!(gl_order, name_df...; name_df_order) + + + sublabel!(gl_order[2, 1, TopLeft()], "a"; left=15) + sublabel!(gl_order[3, 1, TopLeft()], "b"; left=15) + sublabel!(gl_trends[1, 1, TopLeft()], "c"; left=13) + + resize_to_layout!(f) + + + return f +end + +function figure_double_bond_loc(name_df::Pair...; name_df_order) + f = Figure(; size=(113, 136), figure_padding=(2,4,2,2)) + return figure_double_bond_loc!(f, name_df...; name_df_order) +end +function figure_double_bond_loc!(f, name_df::Pair...; name_df_order) # Order Sensitivity n_carbon_range = extrema(last(first(name_df_order)).n_carbon) - cb = Colorbar(gl_order[1, 1]; + cb = Colorbar(f[1, 1]; label="Number of Carbons", colorrange=n_carbon_range, vertical=false, tellwidth=false, - # flipaxis=false, ) axes = Axis[] - for (idx, (_, df)) in enumerate(name_df_order) + for (idx, (name, df)) in enumerate(name_df_order) is_last = idx == length(name_df_order) - ax = Axis(gl_order[1+idx, 1]; + ax = Axis(f[1+idx, 1]; xlabel=L"Double Bond Location$$", - ylabel=L"HOMO [eV]$$", limits=((0, 1), nothing), xtickformat="{:.0%}", xlabelvisible=is_last, @@ -346,9 +362,13 @@ function figure_permutations(name_df::Pair...; name_df_order) xticklabelsvisible=is_last, yticks=WilkinsonTicks(5), ) + text!(0.99, 0.0; + text=name, + align=(:right, :bottom), + space=:relative, + ) push!(axes, ax) df = subset(df, :n_carbon => ByRow(>(4))) - # df = subset(df, :n_carbon => ByRow(n -> n % 2 == 0)) foreach(groupby(df, :n_carbon)) do gdf n_carbon = gdf.n_carbon[1] homo = gdf.homo .* HARTREE_TO_EV @@ -365,15 +385,10 @@ function figure_permutations(name_df::Pair...; name_df_order) ) end end - linkyaxes!(axes...) - colgap!(f.layout, 1, 3) - colsize!(f.layout, 2, Relative(3 / 4)) + Label(f[:, 0]; text=L"HOMO [eV]$$", rotation=pi/2) - sublabel!(gl_order[2, 1, TopLeft()], "a"; left=15) - sublabel!(gl_order[3, 1, TopLeft()], "b"; left=15) - sublabel!(gl_trends[1, 1, TopLeft()], "c"; left=13) - + linkyaxes!(axes...) resize_to_layout!(f) diff --git a/opt/screening/plots/Project.toml b/opt/screening/plots/Project.toml index 33870f90..468a5858 100644 --- a/opt/screening/plots/Project.toml +++ b/opt/screening/plots/Project.toml @@ -4,6 +4,7 @@ version = "0.1.0" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" @@ -15,9 +16,11 @@ Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" Metaheuristics = "bcdb8e00-2c21-11e9-3065-2b553b22f898" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [sources] MISTStyle = {path = "../../MISTStyle"} diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 21615de7..3d237bc4 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -185,3 +185,15 @@ df_s = combine(groupby(df_surprise, :group)) do gdf end return DataFrame(out) end + +# Bar chart of top odor in generated molecules +odor_model = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist-26.9M-48kpooqf-odour")) +df_odor = select(df_surprise, :smiles, :group) +df_odor = innerjoin(df_odor, ScreeningPlots.predict_mist(odor_model, df_odor.smiles); on=:smiles) +f = ScreeningPlots.figure_odor_counts(subset(df_odor, :group => ByRow(!=("ChEMBL"))), odor_model) +MISTStyle.savefig("screening_odors", f) + +# Screen for odorless +df_so = innerjoin(df_surprise, df_odor; on=["smiles", "group"]) +f = ScreeningPlots.plot_pareto_front_scent(df_so, "odorless") +MISTStyle.savefig("electrolyte_odorless", f) diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index e9e886f9..0740d274 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -2,6 +2,7 @@ module ScreeningPlots using Makie using DataFrames +using Random: randperm using Graphs: complete_graph, boruvka_mst using Metaheuristics: Metaheuristics using JSON: JSON @@ -9,10 +10,11 @@ using SQLite: SQLite using PythonCall: Py, PyList, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef using Format: format -using Statistics: cor, mean +using Statistics: cor, mean, std using CategoricalArrays: categorical, levelcode using LinearAlgebra: norm, dot using ManifoldLearning: ManifoldLearning, DiffMap, fit, predict, transform +using Clustering: hclust using MISTStyle diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index ec6b7758..7767d670 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -61,10 +61,13 @@ end function compare_creativity(df::DataFrame, M = missing) f = Figure(; size=(3.42inch, 1inch)) - df.group = categorical(df.group; levels=["Generated", "ChEMBL", "Electrolytes"]) + df = subset(df, :group => ByRow(in(["Generated", "Electrolytes"]))) + df.group = categorical(df.group; levels=["Generated", "Electrolytes"]) + df.gap .*= HARTREE_TO_EV + df.homo .*= HARTREE_TO_EV ax1 = Axis(f[1, 1]; ylabel="Molecular Surprise", - xticks=(1:3, unique(df.group)), + xticks=(1:2, unique(df.group)), limits=((0, nothing), nothing), ygridvisible=true, yminorticksvisible=true, @@ -84,27 +87,26 @@ function compare_creativity(df::DataFrame, M = missing) df = combine(groupby(df, :group)) do gdf pos = map(vcat, -gdf.gap, gdf.homo, gdf.mp, -gdf.bp) idx = Metaheuristics.get_non_dominated_solutions_perm(pos) - @info first(gdf.group) mean(gdf.surprise) mean(gdf.surprise[idx]) + h_pareto = mean(gdf.surprise[idx]) + h_std_pareto = std(gdf.surprise[idx]) + h_dominated = mean(gdf.surprise[Not(idx)]) + h_std_dominated = std(gdf.surprise[Not(idx)]) + h_delta = h_pareto - h_dominated + h_std_delta = hypot(h_std_pareto, h_std_dominated) + @info first(gdf.group) mean(gdf.surprise) std(gdf.surprise) mean(gdf.surprise[idx]) h_delta h_std_delta gdf.group_frontier .= false gdf[idx, :group_frontier] .= true return gdf end - ax2 = Axis(f[1, 2]; xlabel=L"$$HOMO [eV]", ylabel=L"$$Molecular Surprise", xticks=WilkinsonTicks(3), ) - color = map(df.group, df.group_frontier) do g, gf - alpha = gf ? 1.0 : 0.1 - return (MISTStyle.CAT_COLORS[levelcode(g)], alpha) - end - marker = :circle - markersize = 3 - sargs = (; marker, color, markersize) + sargs = (; markersize = 3) sort!(df, :group_frontier; rev=true) - scatter!(ax2, df.homo .* HARTREE_TO_EV, df.surprise; sargs...) + _mark_creative!(ax2, df, :homo, :surprise, df.group_frontier; sargs...) ax3 = Axis(f[1, 3]; xlabel=L"$$Gap [eV]", @@ -113,8 +115,7 @@ function compare_creativity(df::DataFrame, M = missing) yticks=ax1.yticks, ygridvisible=ax1.ygridvisible, ) - scatter!(ax3, df.gap .* HARTREE_TO_EV, df.surprise; sargs...) - + _mark_creative!(ax3, df, :gap, :surprise, df.group_frontier; sargs...) ax4 = Axis(f[1, 4]; xlabel=L"$$Melt [$\degree C$ ]", @@ -123,7 +124,7 @@ function compare_creativity(df::DataFrame, M = missing) yticks=ax1.yticks, ygridvisible=ax1.ygridvisible, ) - scatter!(ax4, df.mp, df.surprise; sargs...) + _mark_creative!(ax4, df, :mp, :surprise, df.group_frontier; sargs...) ax5 = Axis(f[1, 5]; xlabel=L"$$Boil [$\degree C$ ]", @@ -132,7 +133,7 @@ function compare_creativity(df::DataFrame, M = missing) yticks=ax1.yticks, ygridvisible=ax1.ygridvisible, ) - scatter!(ax5, df.bp, df.surprise; sargs...) + _mark_creative!(ax5, df, :bp, :surprise, df.group_frontier; sargs...) hideydecorations!(ax2; grid=false) hideydecorations!(ax3; grid=false) @@ -143,6 +144,35 @@ function compare_creativity(df::DataFrame, M = missing) return f end +function _annotate_creative!(ax, smiles, df, x, y; label_pos=nothing, kwargs...) + key = inchi_key(smiles) + @show row = first(df[df.inchi_key .== key, :]) + if label_pos !== nothing + lx, ly = (row[x], row[y]) .+ label_pos + return annotation!(ax, label_pos..., row[x], row[y]; kwargs...) + else + return annotation!(ax, row[x], row[y]; kwargs...) + end +end + +function _mark_creative!(ax, df, x, y, pareto; kwargs...) + idx = findall(pareto) + df_other = df[Not(idx), :] + scatter!(ax, df_other[!, x], df_other[!, y]; + marker=:circle, + color=MISTStyle.CAT_COLORS[levelcode.(df_other.group)], + alpha=0.1, + kwargs... + ) + df_pareto = df[idx, :] + scatter!(ax, df_pareto[!, x], df_pareto[!, y]; + marker=:star5, + strokewidth=0.1, + color=MISTStyle.CAT_COLORS[levelcode.(df_pareto.group)], + kwargs... + ) +end + function _select_creativity(df::DataFrame, model::Py, group::String) df = _select_creativity(df, model) df.group .= group @@ -184,7 +214,7 @@ angular_distance(a, b) = acos(min(max(cosine_similarity(a, b), 0), 1)) function embedding_mst_distance(embeddings::Matrix; distance=eculidean_distance) n = size(embeddings, 1) - dd = Matrix{Float64}(undef, n, n) + dd = Matrix{}(undef, n, n) for I in eachindex(IndexCartesian(), dd) x = embeddings[I[1], :] y = embeddings[I[2], :] From c31fa9104e101485c090cc5218c6be72dc425c0c Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 2 Jul 2025 11:51:25 -0400 Subject: [PATCH 044/147] Update READMEs and probe plots --- README.md | 37 +++++--------- opt/FeatureMiner/plots/lipinski_probes.jl | 61 ++++++++++++++++------- opt/interp_embeddings/README.md | 13 +++++ 3 files changed, 69 insertions(+), 42 deletions(-) create mode 100644 opt/interp_embeddings/README.md diff --git a/README.md b/README.md index 4badf71a..8cc5b438 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,9 @@ Benchmarking RoBERTa model pre-training on molecular datasets. # Installation +The following provides installation instructions for the top-level package (`electrolyte_fm`), optional add-ons for our +various additional analysis and downstream applications (See `opt/`) may require additional configuration. + ## Polaris 1. Install [rust](https://www.rust-lang.org/tools/install) and [uv](https://docs.astral.sh/uv/getting-started/installation/) @@ -21,11 +24,18 @@ uv sync ``` ## Artemis - Same as above except: 1. Skip loading conda (just use uv) 2. Ensure a module for CUDA@12.2 exists, may need to install with spack (make sure `buildable: True`) +## Apptainer + +0. Install or load from a module [Apptainer](https://apptainer.org/) +1. Build the image `bash container/build.sh`, once build relocate the image `mv /tmp/mist.sif ./mist.sif` +2. Run training within the image `apptainer run --nv mist.sif python train.py ...` + +> See `submit/dgx.j2` or `submit/delta.j2` for a more complete example of using the container + # Submitting Jobs ```shell @@ -35,27 +45,6 @@ source ./activate # Activate Environment See `submit/submit.py --help` for more info -## Building Apptainer Image - -```shell -apptainer build --fakeroot \ - --build-arg SSH_AUTH_SOCK=$SSH_AUTH_SOCK \ - mist.sif mist.def -``` - -## Hackathon - -Create a file `hack.yaml` and include it as an overlay to `submit.py` (i.e. `./submit/submit.py ... --data hack.yaml ...`). -Put the following in `hack.yaml`: -```yaml -queue: debug -account: GPU_Hack -nodes: 2 -walltime: 1:0:0 -train: - data.path: /grand/gpu_hack/FoundEnergy/realspace_v3_dev -``` - # Development ## Pre-commit @@ -63,5 +52,5 @@ train: We use [pre-commit](https://pre-commit.com) to preform various linting checks on the code. To enable: 1. Install poetry (See above) -2. Run pre-commit: `pre-commit` -3. Run before committing: `pre-commit install --allow-missing-config` +2. Run pre-commit: `uv run pre-commit` +3. Run before committing: `uv run pre-commit install --allow-missing-config` diff --git a/opt/FeatureMiner/plots/lipinski_probes.jl b/opt/FeatureMiner/plots/lipinski_probes.jl index 84376e11..20e2f9ef 100644 --- a/opt/FeatureMiner/plots/lipinski_probes.jl +++ b/opt/FeatureMiner/plots/lipinski_probes.jl @@ -68,7 +68,13 @@ function figure_probe_similarity(df::DataFrame; model="dh61satti", dataset="pret end function figure_lipinski_probes(df::DataFrame) - f = Figure(size=(2inch, 1inch)) + f = Figure(; size=(95, 122), figure_padding=(2, 4, 2, 2)) + + # Select the best probe per location + df = combine(groupby(df, [:encoder_dataset, :location, :layer])) do gdf + sort!(gdf, :val_loss; rev=true) + return gdf[1, :] + end df = dropmissing(df) subset!(df, @@ -85,33 +91,52 @@ function figure_lipinski_probes(df::DataFrame) # Feature alignment gl = GridLayout(f[1,1]) - ax = Axis(gl[1, 1]; - xticks=categorical_ticks(df.encoder_dataset), - yticks=2:2:8, - # limits=(nothing, ), - xticklabelsvisible=false, - xticksvisible=false, + ax = Axis(gl[2, 1]; + yticks=MISTStyle.categorical_ticks(df.encoder_dataset), + ylabel="MIST-28M Variant Probed", + xticks=2:2:8, ) - h = heatmap!(ax, auroc) - Colorbar(gl[1, 2], h; label="AUROC") + h = heatmap!(ax, auroc'; + colorscale=Makie.logit, + colorrange=(0.9, 0.995), + ) + cb = Colorbar(gl[1, 1], h; + label="AUROC", + size=6pt, + vertical=false, + # flipaxis=false, + ticks=[0.9, 0.99], + tickformat="{:.0%}", + minorticks=IntervalsBetween(5), + minorticksvisible=true, + labelsize=7pt, + ) # Additive Features df.additive_features = FeatureMiner.additive_features.(df.weight) df_af = combine(groupby(subset!(df, :location => ByRow(==("output"))), :encoder_dataset)) do gdf - unstack(gdf[:, [:layer, :additive_features]], :layer, :additive_features) + gdf = select(gdf, [:layer, :additive_features]) + unstack(gdf, :layer, :additive_features) end af = Matrix(df_af[:, 2:end]) - ax_add = Axis(gl[2, 1]; - xticks=categorical_ticks(df.encoder_dataset), - yticks=ax.yticks, + ax_add = Axis(gl[2, 2]; + yticks=MISTStyle.categorical_ticks(df.encoder_dataset), + xticks=ax.xticks, + yticklabelsvisible=false, + yticksvisible=false, + ) + h = heatmap!(ax_add, af') + Colorbar(gl[1, 2], h; + ticks=WilkinsonTicks(3), + minorticks=IntervalsBetween(5), + minorticksvisible=true, + size=cb.size, + label="Additivity", + vertical=false, ) - h = heatmap!(ax_add, af; colorrange=(0, 1)) - Colorbar(gl[2, 2], h; label="Additivity") @info extrema(af) - - # Add single Y-axis label - Label(gl[:, 0], text = "Encoder Layer", rotation = pi/2) + Label(gl[end+1, :], "Encoder Layer") return f end diff --git a/opt/interp_embeddings/README.md b/opt/interp_embeddings/README.md new file mode 100644 index 00000000..f96722c0 --- /dev/null +++ b/opt/interp_embeddings/README.md @@ -0,0 +1,13 @@ +# Scripts for Exploring MIST's token Embeddings + +## Installation + +1. Install [julia] and the base environment (See [Project's README](../../README.md)) +2. Instantiate the environment: `julia --project -e 'using Pkg; Pkg.instantiate()` + +[julia]: https://julialang.org/downloads/ + +## Generating Plots + +See `plots.jl` for the code used to generate plots from the paper. To run you will need +to acquire the pretrained & finetuned MIST models and place at the indicated path (see script). From f3ce150c0d2fb8aab47372e6b2b9fca97e12628f Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 2 Jul 2025 11:53:08 -0400 Subject: [PATCH 045/147] Don't add SAE to model __init__.py Slowly breaking the import everything pattern --- electrolyte_fm/models/__init__.py | 1 - submit/sae.jsonnet | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index 75377848..5d344169 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -7,4 +7,3 @@ from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer -from .sae import LightningSAE diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index 5b59b723..3ecb43b3 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -3,7 +3,7 @@ train: { tags: ['sae', 'debug'], model: { - class_path: 'electrolyte_fm.models.LightningSAE', + class_path: 'electrolyte_fm.models.sae.LightningSAE', init_args: { name_or_path: 'ibm/MoLFormer-XL-both-10pct', sae: { From 41709ebdb068d14ebd7f846461cb57aa81d0b419 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 10 Jul 2025 14:45:30 -0400 Subject: [PATCH 046/147] Add shebang to screening plots --- opt/screening/plots/plots.jl | 1 + 1 file changed, 1 insertion(+) mode change 100644 => 100755 opt/screening/plots/plots.jl diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl old mode 100644 new mode 100755 index 3d237bc4..736317b6 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -1,3 +1,4 @@ +#!/usr/bin/env -S julia +release --color=auto --startup-file=no --project=@script using ScreeningPlots using Makie using MISTStyle From 156ed148a6315bd3aa0702679eddcdfb48d6b0f9 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 10 Jul 2025 14:48:18 -0400 Subject: [PATCH 047/147] rm unused code --- electrolyte_fm/data_modules/feature_tagger.py | 392 ------------------ test/test_featurizer.py | 179 -------- 2 files changed, 571 deletions(-) delete mode 100644 electrolyte_fm/data_modules/feature_tagger.py delete mode 100644 test/test_featurizer.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py deleted file mode 100644 index 9d4cede3..00000000 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ /dev/null @@ -1,392 +0,0 @@ -import re -from abc import abstractmethod, ABC -from typing import Iterable, Optional -import itertools -from rdkit import Chem - -import smirk -import torch - - -def flatten(*iterables) -> list: - return list(itertools.chain(*iterables)) - - -# fmt: off -ELEMENT_SYMBOLS = [ - "H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", - "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", - "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", - "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", - "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", - "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", - "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", - "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", - "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", - "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", - "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds", - "Rg", "Cn", "Nh", "Fl", "Mc", "Lv", "Ts", "Og", -] -# fmt: on - -BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] - -ELEMENT_GROUPS = [ - ["Li", "Na", "K", "Rb", "Cs", "Fr"], # Alkali Metals (Group 1) - ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) - ["Sc", "Y", "Lu", "Lr"], # Scandium Group (Group 3) - ["Ti", "Zr", "Hf", "Rf"], # Titanium Group (Group 4) - ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) - ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) - ["Mn", "Tc", "Re", "Bh"], # Manganese Group (Group 7) - ["Fe", "Ru", "Os", "Hs"], # Iron Group (Group 8) - ["Co", "Rh", "Ir", "Mt"], # Cobalt Group (Group 9) - ["Ni", "Pd", "Pt", "Ds"], # Nickel Group (Group 10) - ["Cu", "Ag", "Au", "Rg"], # Copper Group (Group 11) - ["Zn", "Cd", "Hg", "Cn"], # Zinc Group (Group 12) - ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) - ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) - ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) - ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) - ["F", "Cl", "Br", "I", "At", "Ts"], # Halogens (Group 17) - ["Ne", "Ar", "Kr", "Xe", "Rn", "Rg"], # Noble Gases (Group 18) -] - -F_BLOCK = [ - [ - "La", - "Ce", - "Pr", - "Nd", - "Pm", - "Sm", - "Eu", - "Gd", - "Tb", - "Dy", - "Ho", - "Er", - "Tm", - "Yb", - "Lu", - ], - [ - "Ac", - "Th", - "Pa", - "U", - "Np", - "Pu", - "Am", - "Cm", - "Bk", - "Cf", - "Es", - "Fm", - "Md", - "No", - "Lr", - ], -] - -REGEX_FEATURES = { - "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", - "bracked_atom": r"\[[^]]+]", - "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", - "chiral_center": r"\[[^\]]+?@[^\]]*?]", - "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", -} - -ELEMENT_FEATURES = { - "alkali_metals": ELEMENT_GROUPS[0], - "alkaline_earth_metals": ELEMENT_GROUPS[1], - "nobel_gases": ["He"] + ELEMENT_GROUPS[16], - "s_block": flatten(["H"], *ELEMENT_GROUPS[0:1]), - "f_block": flatten(*F_BLOCK), - "d_block": flatten(*ELEMENT_GROUPS[2:11]), - "p_block": flatten(*ELEMENT_GROUPS[12:15]), - "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki - "liquid_metals": ["Ga", "Hg", "Rb", "Cs", "Fr"], - "semi_metalalic": ["As", "Sb", "Bi", "Sn"], - "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 - "radioactive": flatten( # Elements that have no stable isotopes - ["Tc", "Po", "At", "Rn", "Pm"], - [group[-1] for group in ELEMENT_GROUPS], - F_BLOCK[1], - ), - "opensmiles_aromatic": [ - "b", - "c", - "n", - "o", - "p", - "s", - "se", - "as", - ], -} - -# Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html -# rdkit.Chem.Lipinski from: https://github.com/rdkit/rdkit/blob/master/rdkit/Chem/Lipinski.py -SMARTS_FEATURES = { - "ketone": "[#6][CX3](=O)[#6]", # Daylight - "aldehyde": "[$([CX3H2](=O)),$([CX3H1](=O)[#6])]", # Daylight, plus branch for Formaldehyde - "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight - "amid": "[$([NX3][CX3](=[OX1])[#6]),$(NC=O)]", # Daylight, plus branch for Formamide - "hydroxyl": "[OX2H]", # Daylight - "phenol": "[OH]c1ccccc1", - "rotatable_bond": "[!$(*#*)&!D1]-&!@[!$(*#*)&!D1]", # rdkit.Chem.Lipinski - "carboxyl_group": "[CX3]=[OX1]", # Daylight - "h_donor": "[$([N;!H0;v3]),$([N;!H0;+1;v4]),$([O,S;H1;+0]),$([n;H1;+0])]", # rdkit.Chem.Lipinski - "h_acceptor": "[$([O,S;H1;v2]-[!$(*=[O,N,P,S])]),$([O,S;H0;v2]),$([O,S;-]),$([N;v3;!$(N-*=!@[O,N,P,S])]),$([nH0,o,s;+0])]", # rdkit.Chem.Lipinski - "NH_or_OH_lipinski": "[#8H1,#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski - "NO_lipinski": "[#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski -} - - -class Feature(ABC): - def __init__(self, name: str, tokenizer: Optional[str] = None): - self.name = name - self.tokenzier = tokenizer or smirk.SmirkTokenizerFast() - self.smirk_tokenizer = ( - smirk.SmirkTokenizerFast() if tokenizer is not None else self.tokenzier - ) - - def featurize(self, smi: str, encoding: Optional[dict] = None) -> torch.BoolTensor: - encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) - assert "offset_mapping" in encoding - kwargs = self.preprocess(smi) - return self._featurize(smi, encoding, **kwargs) - - @abstractmethod - def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: - """Identify tokens in the input SMILES encoding expressing the feature""" - - def preprocess(self, smi: str) -> dict: - """Shared preprocessing steps for all features to be provided to `self._featurize` - Will be called once per feature class - """ - return {} - - @classmethod - @abstractmethod - def from_named(cls, name: str, **kwargs) -> "Feature": - """Create a feature from a named feature""" - - def align_tokens(self, encoding: dict, span: tuple[int, int]) -> Iterable[int]: - """Identify tokens overlapping span""" - token_offsets = encoding["offset_mapping"] - start, end = span - if end < start: - return # Non-matching span - - for i, t in enumerate(token_offsets): - if t[1] <= t[0]: # Token is empty - continue - - # Spans don't include the end index - elif not (end <= t[0] or t[1] <= start): - yield i - - def align_embeddings( - self, active: torch.BoolTensor, embedding: dict, other: dict - ) -> torch.BoolTensor: - if embedding == other: - return active - return active - raise NotImplementedError() - - def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: - """Convert a list of indices to a one-hot encoding""" - active = torch.zeros(n, dtype=torch.bool) - if len(indices) > 0: - active[indices] = True - return active - - -class FeatureCollection(Feature): - def __init__(self, features: dict[str, Feature], **kwargs): - super().__init__("FeatureCollection", **kwargs) - self.features = features - preprocess_steps = {} - for f in features.values(): - preprocess_steps[f.__class__] = f - self.preprocess = list(preprocess_steps.values()) - - @property - def names(self): - return list(self.features.keys()) - - def __call__(self, smi: str) -> dict: - return {"proxy_activations": self.featurize(smi)} - - def featurize( - self, smi: str, encoding: Optional[dict] = None, return_dict: bool = False - ) -> torch.BoolTensor: - encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) - kwargs = {} - for f in self.preprocess: - kwargs.update(f.preprocess(smi)) - - act = [] - for idx, f in enumerate(self.features.values()): - act.append(f._featurize(smi, encoding, **kwargs)) - - if return_dict: - return {name: act for name, act in zip(self.features.keys(), act)} - - return torch.stack(act, dim=0) - - def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: - raise RuntimeError(f"{self} should be called using featurize") - - @classmethod - def from_named(cls, feature_names: str | list[str] = "all", **kwargs): - if isinstance(feature_names, str): - feature_names = [feature_names] - - features = [] - for name in feature_names: - if name == "all": - for k in ELEMENT_FEATURES.keys(): - features.append(ElementFeature.from_named(k, **kwargs)) - for k in REGEX_FEATURES.keys(): - features.append(RegexFeature.from_named(k, **kwargs)) - for k in SMARTS_FEATURES.keys(): - features.append(SMARTSFeature.from_named(k, **kwargs)) - - elif name in ELEMENT_FEATURES.keys(): - features.append(ElementFeature.from_named(name, **kwargs)) - - elif name in REGEX_FEATURES.keys(): - features.append(RegexFeature.from_named(name, **kwargs)) - - elif name in SMARTS_FEATURES.keys(): - features.append(SMARTSFeature.from_named(name, **kwargs)) - - else: - raise ValueError(f"Unknown named feature: {name}") - - feature_map = {} - for f in features: - if f.name in feature_map.keys(): - raise ValueError(f"Duplicate feature name: {f.name}") - feature_map[f.name] = f - - return cls(feature_map, **kwargs) - - -class RegexFeature(Feature): - def __init__(self, name: str, regex: [str, re.Pattern], **kwargs): - super().__init__(name, **kwargs) - self.regex = re.compile(regex) - - @classmethod - def from_named(cls, name: str, **kwargs): - return cls(name, REGEX_FEATURES[name], **kwargs) - - def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: - n_groups = self.regex.groups - active = [] - for m in self.regex.finditer(smi): - if n_groups == 0: - active.extend(self.align_tokens(encoding, m.span())) - else: - for i in range(1, n_groups + 1): - active.extend(self.align_tokens(encoding, m.span(i))) - - return self.onehot(active, len(encoding["input_ids"])) - - -class ElementFeature(Feature): - def __init__(self, name: str, elements: list[str], **kwargs): - super().__init__(name, **kwargs) - self.elements = list(set(elements)) - self.element_ids = torch.tensor( - [ - self.smirk_tokenizer.encode(f"[{e}]", add_special_tokens=False)[1] - for e in self.elements - ] - ).reshape(-1, 1) - - @classmethod - def from_named(cls, name: str, **kwargs) -> "ElementFeature": - return cls(name, ELEMENT_FEATURES[name], **kwargs) - - def preprocess(self, smi: str) -> dict: - return { - "smirk_encoding": self.smirk_tokenizer(smi, return_offsets_mapping=True) - } - - def _featurize( - self, smi: str, encoding: dict, smirk_encoding: dict, **kwargs - ) -> torch.BoolTensor: - enc = torch.tensor(smirk_encoding["input_ids"]) - active = enc.eq(self.element_ids).any(dim=0) - return self.align_embeddings(active, encoding, smirk_encoding) - - -class RdkitFeature(Feature): - atomwise = re.compile(r"\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p") - - def preprocess(self, smi: str) -> dict: - mol = Chem.MolFromSmiles(smi, sanitize=False) - s_flags = Chem.SanitizeFlags.SANITIZE_NONE - s_flags |= Chem.SanitizeFlags.SANITIZE_SETAROMATICITY - s_flags |= Chem.SanitizeFlags.SANITIZE_SETHYBRIDIZATION - s_flags |= Chem.SanitizeFlags.SANITIZE_SETCONJUGATION - s_flags |= Chem.SANITIZE_PROPERTIES - Chem.SanitizeMol(mol, s_flags) - atom_spans = [m.span() for m in self.atomwise.finditer(smi)] - - # Validate rdkit -> smi mapping - for idx, atom in enumerate(mol.GetAtoms()): - smi_atom = smi[atom_spans[idx][0] : atom_spans[idx][1]] - smi_atom_mol = Chem.MolFromSmiles(smi_atom, sanitize=False) - Chem.SanitizeMol(smi_atom_mol, s_flags) - - assert atom.GetSymbol() == smi_atom_mol.GetAtomWithIdx(0).GetSymbol() - - # atom_smi = atom.GetSmarts() - # smi_atom_rdkit = Chem.MolToSmiles(smi_atom_mol) - # assert ( - # atom_smi == Chem.MolToSmiles(smi_atom_mol) - # ), f"Expected {atom_smi} and {smi_atom_rdkit} to match. Input atom: {smi_atom}" - - return {"rdkit_molecule": mol, "atom_spans": atom_spans} - - def align_atoms( - self, - atom_idx: int, - encoding: dict, - atom_spans: list[tuple[int, int]], - ) -> Iterable[int]: - """Map atom indices to token indices""" - span = atom_spans[atom_idx] - return self.align_tokens(encoding, span) - - -class SMARTSFeature(RdkitFeature): - def __init__(self, name: str, smarts: str, **kwargs): - super().__init__(name, **kwargs) - self.smarts = Chem.MolFromSmarts(smarts) - - @classmethod - def from_named(cls, name: str, **kwargs): - return cls(name, SMARTS_FEATURES[name], **kwargs) - - def _featurize( - self, - smi: str, - encoding: dict, - atom_spans: list[tuple[int, int]], - rdkit_molecule: Chem.Mol, - **kwargs, - ) -> torch.BoolTensor: - # TODO: Handle Bonds - matches = rdkit_molecule.GetSubstructMatches(self.smarts) - atom_indices = set(flatten(*matches)) - active: list[int] = [] - for idx in atom_indices: - active.extend(self.align_atoms(idx, encoding, atom_spans)) - return self.onehot(active, len(encoding["input_ids"])) diff --git a/test/test_featurizer.py b/test/test_featurizer.py deleted file mode 100644 index e5b1eae9..00000000 --- a/test/test_featurizer.py +++ /dev/null @@ -1,179 +0,0 @@ -import re -from random import choice - -import pytest -import torch - -from electrolyte_fm.data_modules.feature_tagger import ( - ELEMENT_FEATURES, - ELEMENT_SYMBOLS, - REGEX_FEATURES, - RegexFeature, - ElementFeature, - SMARTSFeature, -) - -REGEX_TESTS = [ - { - "feature": "chiral_tags", - "positive": ["@SP1", "@OH32", "@TB2", "@", "@@"], - "negative": ["C", "O", "c", "[Rb]"], - }, - { - "feature": "bracked_atom", - "positive": ["[C]", "[C-]", "[C+]", "[C@]", "[C@@]", "[C@H]", "[C@H-]"], - "negative": ["C", "O", "c", "F"], - }, - { - "feature": "charged_atom", - "positive": ["[C+]", "[C-]", "[C++]", "[Rb@OH2+3]"], - "negative": ["C", "O", "c", "[Rb@OH2]"], - }, - { - "feature": "chiral_center", - "positive": ["[C@]", "[C@@]", "[C@H]", "[C@H-]"], - "negative": ["C", "O", "c", "[Rb]"], - }, - { - "feature": "aromatic_bracket_atom", - "positive": ["[te+2]", "[b]", "[c@@]"], - "negative": ["C", "O", "[Rb]", "[Sn]", "[Cn]"], - }, -] - -SMARTS_TESTS = [ - { - "feature": "ketone", - "positive": ["CC(=O)C", "C[C@@H]1CCCCCCCCCCCCC(=O)C1"], - "negative": ["C=O", "OCC"], - }, - { - "feature": "aldehyde", - "positive": ["C=O", "O=CC", "CC=O"], - "negative": ["CC(=O)C", "OCC(O)CO"], - }, - { - "feature": "carboxylic_acid", - "positive": ["O=CO", "CC(=O)O", "C(=O)O", "O=C(O)C"], - "negative": ["CC(=O)C", "OCC"], - }, - { - "feature": "amid", - "positive": ["O=CN", "CC(=O)N", "C(=O)NC", "O=C(N)C"], - "negative": ["CC(=O)C", "OCC"], - }, - { - "feature": "hydroxyl", - "positive": ["CO", "CCO", "C(O)C"], - "negative": ["CC(=O)C", "C=O"], - }, - { - "feature": "phenol", - "positive": [ - "c1ccc(cc1)O", - "Oc1ccccc1", - "Oc0ccccc0Cc0cc(C1)c(O)c(c0)Cc0c(O)ccc(c0)Cc0ccc(O)c(c0)Cc0c(O)ccc(c0)Cc0c(O)ccc(c0)Cc0c(O)c(C2)cc(c0)Cc0c(O)ccc(c0)Cc(c0O)cc2cc0Cc0cc(Cc2ccc(O)cc2)c(O)c(c0)Cc0c(O)ccc(c0)C1", - ], - "negative": [ - "CC(=O)C", - "C=O", - "c1ccccc1-c2ccccc2", - "c1ccc(cc1)C[C@@H](C(=O)O)N", - ], - }, - { - "feature": "rotatable_bond", - "positive": [ - "CC-CC", - "c1ccccc1-c2ccccc2", - ], - "negative": ["CC(=O)C", "C=O", "c1ccccc1"], - }, -] - - -@pytest.mark.parametrize( - "feature,negatives", - ((x["feature"], x["negative"]) for x in REGEX_TESTS if "negative" in x), -) -def test_regex_negatives(feature: str, negatives: list[str]): - f = re.compile(REGEX_FEATURES[feature]) - for neg in negatives: - assert f.match(neg) is None, f"{feature} should not match {neg}" - - -@pytest.mark.parametrize( - "feature,positives", - ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), -) -def test_regex_positives(feature: str, positives: list[str]): - f = re.compile(REGEX_FEATURES[feature]) - for pos in positives: - assert f.match(pos) is not None, f"{feature} should match {pos}" - - -def generate_examples(): - for x in REGEX_TESTS: - yield RegexFeature, x["feature"], x["positive"], x["negative"] - - elements = set(ELEMENT_SYMBOLS) - for name, positive in ELEMENT_FEATURES.items(): - pos_examples = [f"[{e}]" for e in positive] - neg_examples = [f"[{e}]" for e in elements - set(positive)] - yield ElementFeature, name, pos_examples, neg_examples - - for x in SMARTS_TESTS: - yield SMARTSFeature, x["feature"], x["positive"], x["negative"] - - -@pytest.mark.parametrize( - "cls,feature,positive,negative", - generate_examples(), -) -def test_positive_feature(cls, feature, positive, negative): - f = cls.from_named(feature) - for pos in positive: - active = f.featurize(pos) - assert active.any(), "{} should match {}: {}".format(feature, pos, active) - - -@pytest.mark.parametrize( - "cls,feature,positive,negative", - generate_examples(), -) -def test_negative_feature(cls, feature, positive, negative): - f = cls.from_named(feature) - for neg in negative: - active = f.featurize(neg) - assert not active.any(), "{} should not match {}: {}".format( - feature, neg, active - ) - - -@pytest.mark.parametrize( - "cls,feature,positive,negative", - [e for e in generate_examples() if e[0] != SMARTSFeature], -) -def test_alignment(cls, feature, positive, negative): - f = cls.from_named(feature) - pos = choice(positive) - neg = choice(negative) - active = f.featurize(pos) - inactive = f.featurize(neg) - assert active.any() and not inactive.any() - check_active(pos + neg, torch.cat([active, inactive]), f.featurize(pos + neg)) - check_active(pos + pos, torch.cat([active, active]), f.featurize(pos + pos)) - check_active( - neg + pos + neg, - torch.cat([inactive, active, inactive]), - f.featurize(neg + pos + neg), - ) - - -def check_active(smi, expected, actual): - print(f"smi: {smi}") - print(f"expected: {expected}") - print(f"actual: {actual}") - assert isinstance(actual, torch.BoolTensor) - assert isinstance(expected, torch.BoolTensor) - assert all(actual == expected) From dd1ec06308d23de351888a00099db43afed9bddd Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sun, 13 Jul 2025 13:03:59 -0400 Subject: [PATCH 048/147] remove unused SAE codes --- electrolyte_fm/models/sae.py | 504 ----------------------------------- test/test_sae.py | 287 -------------------- 2 files changed, 791 deletions(-) delete mode 100644 electrolyte_fm/models/sae.py delete mode 100644 test/test_sae.py diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py deleted file mode 100644 index 30006e95..00000000 --- a/electrolyte_fm/models/sae.py +++ /dev/null @@ -1,504 +0,0 @@ -from math import sqrt, floor -from typing import Callable, Optional, Literal -from contextlib import contextmanager - -import lightning.pytorch as pl -from numpy import minimum -import torch -from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable -from torch import nn -from torch.nn import functional as F -from transformers import PreTrainedModel - -from .model_utils import load_encoder - - -def init_bias(bias, w): - fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(w) - bound = 1 / sqrt(fan_in) - nn.init.uniform_(bias, -bound, bound) - - -def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: - """Count the average number of active features for tensor (*, F), where F is are the feature activations""" - return x.detach().count_nonzero() / x.shape[:-1].numel() - - -def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): - """F.cross_entropy but for logits of `(B, T, C)` and target of `(B, T)`""" - if isinstance(logits, tuple): - logits = logits[0] - elif not isinstance(logits, torch.Tensor): - logits = logits.last_hidden_state - - return F.cross_entropy( - logits.view(-1, logits.shape[-1]), - target.view(-1), - ignore_index=-100, - ) - - -class AbstractSAE(nn.Module): - def __init__(self, hidden_size: int, expansion: int) -> None: - super().__init__() - self.hidden_size = hidden_size - self.num_features = hidden_size * expansion - self.num_batches_not_active = torch.zeros(self.num_features) - - def update_inactive_features(self, f: torch.Tensor): - f_act = f.sum(dim=list(range(f.ndim)[:-1])) - self.num_batches_not_active += f_act == 0 - self.num_batches_not_active[f_act > 0] = 0 - - def forward(self, x: torch.Tensor): - return self.decode(self.encode(x)) - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Encode x into a feature representation""" - raise NotImplementedError() - - def decode(self, features: torch.Tensor) -> torch.Tensor: - """Encode features into a reconstruction of x""" - raise NotImplementedError() - - def forward_with_loss(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: - """Forward pass with loss calculation -> `(self.forward(x), loss)`""" - return self.forward(x), torch.tensor(0.0) - - -InjectedCoderState = Literal["null", "dense", "sparse"] - - -class InjectedCoder(nn.Module): - def __init__(self, dense_model: nn.Module, coder: AbstractSAE): - super().__init__() - self.dense_model = dense_model - self.coder = coder - self.loss = torch.tensor(0.0) - self.state: InjectedCoderState = "sparse" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.dense_model(x) - if self.state == "dense": - return x - - elif self.state == "null": - return torch.zeros_like(x) - - if self.training: - x_hat, loss = self.coder.forward_with_loss(x) - self.loss = loss - - else: - x_hat = self.coder.forward(x) - - return x_hat - - -class SparsifiedModel(nn.Module): - def __init__(self, model: nn.Module, coders: list[InjectedCoder]): - super().__init__() - self.model = model - self.coders = coders - - @classmethod - def from_huggingface( - cls, model: PreTrainedModel, coder: AbstractSAE, layer: float | int = 0.5 - ): - layers = model.base_model.encoder.layer - if isinstance(layer, float): - n_layers = len(layers) - layer = floor(layer * n_layers) - - coders = [cls.inject_sparse_coder(layers, coder, layer)] - return cls(model, coders) - - @staticmethod - def inject_sparse_coder(layers: nn.ModuleList, coder: AbstractSAE, layer: int): - m = InjectedCoder(layers[layer].output.dense, coder) - layers[layer].output.dense = m - return m - - def set_sparsity(self, enable: bool = True): - """Enable or disable sparse coders""" - assert isinstance(enable, bool) - for coder in self.coders: - coder.state = "sparse" if enable else "dense" - - @contextmanager - def sparse(self, enable: bool = True): - sparsity = [] - for coder in self.coders: - sparsity.append(coder.state) - coder.state = "sparse" if enable else "dense" - - try: - yield self - finally: - for coder in self.coders: - coder.state = sparsity.pop() - - @contextmanager - def nullcoders(self): - state = [] - for coder in self.coders: - state.append(coder.state) - coder.state = "null" - - try: - yield self - finally: - for coder in self.coders: - coder.state = state.pop() - - def forward(self, *args, **kwargs): - return self.model.forward(*args, **kwargs) - - def forward_with_loss(self, *args, **kwargs): - y = self.model.forward(*args, **kwargs) - device = self.coders[0].loss.device - loss = torch.tensor(0.0, device=device) - for coder in self.coders: - loss += coder.loss - - return y, loss - - def sparse_parameters(self): - """Return an iterator over the parameters of the sparse autoencoders""" - for coder in self.coders: - yield from coder.coder.parameters() - - def sparse_named_parameters(self): - for coder in self.coders: - yield from coder.coder.named_parameters() - - @torch.no_grad() - def loss_recovered( - self, - target: torch.Tensor, - *args, - lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, - sparse_output: Optional[torch.Tensor] = None, - **kwargs, - ): - """Compute the fraction of the loss recovered by the SAE relative to an null-embedding""" - training = self.training - self.eval() - if sparse_output is None: - with self.sparse(True) as self: - sparse_output = self(*args, **kwargs) - assert sparse_output is not None - loss_sparse = lossfn(sparse_output, target) - - with self.sparse(False) as self: - y_dense = self(*args, **kwargs) - loss_dense = lossfn(y_dense, target) - - with self.nullcoders() as self: - y_null = self(*args, **kwargs) - loss_null = lossfn(y_null, target) - - self.train(training) - - print( - { - "loss_sparse": loss_sparse, - "loss_dense": loss_dense, - "loss_null": loss_null, - } - ) - return 1 - ((loss_sparse - loss_dense) / (loss_null - loss_dense)) - - -class GatedSAE(AbstractSAE): - def __init__( - self, - hidden_size: int, - expansion: int = 4, - l1_coef: float = 0.01, - device=None, - dtype=None, - ): - super().__init__(hidden_size, expansion) - factory_kwargs = {"device": device, "dtype": dtype} - n_features = hidden_size * expansion - self.w_gate = nn.Parameter( - torch.empty((n_features, hidden_size), **factory_kwargs) - ) - self.b_gate = nn.Parameter(torch.empty(n_features, **factory_kwargs)) - self.w_dec = nn.Parameter( - torch.empty((hidden_size, n_features), **factory_kwargs) - ) - self.b_dec = nn.Parameter(torch.empty(hidden_size, **factory_kwargs)) - self.r_mag = nn.Parameter(torch.empty(n_features, **factory_kwargs)) - self.b_enc = nn.Parameter(torch.empty(n_features, **factory_kwargs)) - self.l1_coef = torch.tensor(l1_coef, requires_grad=False, **factory_kwargs) - - # Loss parameters - self.loss = torch.tensor(0.0, **factory_kwargs) - - self.reset_parameters() - - def reset_parameters(self): - for w in [self.w_gate, self.w_dec]: - nn.init.kaiming_uniform_(w, nonlinearity="relu") - - init_bias(self.b_gate, self.w_gate) - init_bias(self.b_dec, self.w_dec) - # kaiming init for r_mag - nn.init.normal_(self.r_mag, mean=0.0, std=sqrt(2 / self.r_mag.shape[0])) - init_bias(self.b_enc, self.w_gate) - - def forward(self, x: torch.Tensor): - return self.decode(self.encode(x)) - - def encode(self, x: torch.Tensor): - x_centered = x - self.b_dec - x_enc = x_centered.matmul(self.w_gate.T) - gate = (x_enc + self.b_gate) > 0 - x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) - return x_mag * gate - - def decode(self, features: torch.Tensor): - return F.linear(features, self.w_dec, self.b_dec) - - def forward_with_loss(self, x: torch.Tensor): - x_centered = x - self.b_dec - x_enc = x_centered.matmul(self.w_gate.T) - pi_gate = x_enc + self.b_gate - pi_rect = F.relu(pi_gate) - loss_sparsity = self.l1_coef * pi_rect.abs().sum() - - x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) - gate = pi_gate > 0 - features = x_mag * gate - x_hat = F.linear(features, self.w_dec, self.b_dec) - loss_recon = F.mse_loss(x_hat, x) - - x_hat_detach = F.linear(pi_rect, self.w_dec.detach(), self.b_dec.detach()) - loss_aux = F.mse_loss(x_hat_detach, x) - - loss = loss_sparsity + loss_aux + loss_recon - return x_hat, loss - - -def init_column_fixed_l2(tensor: torch.Tensor, l2: float = 0.1): - """init a column of a tensor to a fixed l2""" - col_l2 = l2 / tensor.norm(2, dim=0) - with torch.no_grad(): - tensor.copy_(tensor * col_l2) - - -class VanillaSAE(AbstractSAE): - def __init__( - self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01 - ) -> None: - super().__init__(hidden_size, expansion) - self.encoder = nn.Linear(hidden_size, hidden_size * expansion) - self.decoder = nn.Linear(hidden_size * expansion, hidden_size) - self.l1_coef_coef = torch.tensor(l1_coef) - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - init_column_fixed_l2(self.decoder.weight) - self.decoder.bias.zero_() - self.encoder.weight.copy_(self.decoder.weight.T) - self.encoder.bias.zero_() - - def encode(self, x: torch.Tensor): - return F.relu(self.encoder(x)) - - def decode(self, features: torch.Tensor) -> torch.Tensor: - return self.decoder(features) - - def forward_with_loss(self, x: torch.Tensor): - f = self.encode(x) - x_hat = self.decode(f) - loss = ( - F.mse_loss(x_hat, x) - + self.l1_coef_coef * (self.decoder.weight.norm(2, dim=0) * f).sum() - ) - return x_hat, loss - - -class TiedBiasSAE(AbstractSAE): - def __init__(self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01): - super().__init__(hidden_size, expansion) - n_features = hidden_size * expansion - self.encoder = nn.Linear(hidden_size, n_features) - self.decoder = nn.Linear(n_features, hidden_size) - self.l1_coef_coef = torch.tensor(l1_coef) - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - self.encoder.weight.copy_(self.decoder.weight.T) - self.decoder.bias.zero_() - self.encoder.bias.zero_() - - def encode(self, x: torch.Tensor): - return F.relu(self.encoder(x - self.decoder.bias)) - - def decode(self, features): - return self.decoder(features) - - def forward_with_loss(self, x: torch.Tensor): - f = F.relu(self.encoder(x - self.decoder.bias)) - x_hat = self.decoder(f) - f_act = f * self.decoder.weight.norm(p=2, dim=0) - loss_reconstruction = F.mse_loss(x_hat, x) - loss_sparsity = self.l1_coef_coef * f_act.abs().sum() - loss = loss_reconstruction + loss_sparsity - return x_hat, loss - - -def topk(x: torch.Tensor, k: int, dim: int = -1): - k = minimum(x.shape[dim], k) - vi = torch.topk(x, k, dim=dim, sorted=False, largest=True) - return torch.zeros_like(x).scatter(dim, vi.indices, vi.values) - - -class TopKSAE(AbstractSAE): - def __init__( - self, - hidden_size: int, - expansion: int = 4, - k: int = 10, - alpha: float = 1 / 32, - dead_threshold=1_000_000, - ) -> None: - super().__init__(hidden_size, expansion) - self.encoder = nn.Linear(hidden_size, hidden_size * expansion) - self.decoder = nn.Linear(hidden_size * expansion, hidden_size) - self.k = k - self.alpha = alpha - self.dead_threshold = dead_threshold - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - init_column_fixed_l2(self.decoder.weight) - self.decoder.bias.zero_() - self.encoder.weight.copy_(self.decoder.weight.T) - self.encoder.bias.zero_() - - def encode(self, x: torch.Tensor): - return topk(self.encoder(x), self.k, dim=-1) - - def decode(self, features: torch.Tensor) -> torch.Tensor: - return self.decoder(features) - - def encode_dead_features(self, x: torch.Tensor): - f = self.encoder(x) - dead = self.num_batches_not_active > self.dead_threshold - f = f * dead - return topk(f, self.k, dim=-1) - - def forward_with_loss(self, x: torch.Tensor): - f = self.encode(x) - self.update_inactive_features(f) - x_hat = self.decode(f) - x_dead = self.decode(self.encode_dead_features(x)) - loss = F.mse_loss(x_hat, x) + self.alpha * F.mse_loss(x_dead, x) - return x_hat, loss - - -class LightningSAE(pl.LightningModule): - def __init__( - self, - name_or_path: str, - sae: AbstractSAE, - layer: int | float = 0.5, - lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, - optimizer: OptimizerCallable = torch.optim.AdamW, - lr_schedule: LRSchedulerCallable | None = None, - ): - super().__init__() - - encoder = load_encoder(name_or_path) - self.sparse_model = SparsifiedModel.from_huggingface(encoder, sae, layer) - self.optimizer = optimizer - self.lr_schedule = lr_schedule - self.lossfn = lossfn - self.save_hyperparameters(ignore=["sparse_model"]) - - def setup(self, stage: str): - if isinstance(self.logger, pl.loggers.WandbLogger): - - def define_metric(*args, **kwargs): - self.logger.experiment.define_metric(*args, **kwargs) - - # Add summary metrics for loss - for stage in ["train", "val", "test"]: - for s in ["step", "epoch"]: - define_metric( - f"{stage}/loss_{s}", summary="best,min,last", goal="minimize" - ) - - define_metric(f"{stage}/alive_features", summary="max,last") - define_metric(f"{stage}/max_feature_density", summary="min,max,last") - - def stage_step(self, stage: str, batch, recovered_loss: bool = False): - input_ids = batch["input_ids"] - attention_mask = batch["attention_mask"] - y, loss = self.sparse_model.forward_with_loss(input_ids, attention_mask) - logdict = {f"{stage}/loss": loss} - - if self.lossfn: - target = batch["target"] if "target" in batch else batch["labels"] - y_loss = self.lossfn(y, target) - logdict[f"{stage}/target_loss"] = y_loss - - if recovered_loss: - logdict[f"{stage}/recovered_loss"] = self.sparse_model.loss_recovered( - target, - input_ids, - attention_mask=attention_mask, - lossfn=self.lossfn, - sparse_output=y, - ) - - return loss, logdict - - def training_step(self, batch): - loss, logdict = self.stage_step("train", batch) - self.log_dict(logdict, on_epoch=True, on_step=True) - return loss - - def validation_step(self, batch): - loss, logdict = self.stage_step("val", batch, recovered_loss=True) - logdict = {k + "_epoch": v for k, v in logdict.items()} - self.log_dict(logdict, on_epoch=True, on_step=False) - return loss - - def test_step(self, batch): - loss, logdict = self.stage_step("test", batch, recovered_loss=True) - logdict = {k + "_epoch": v for k, v in logdict.items()} - self.log_dict(logdict, on_epoch=True, on_step=False) - return loss - - def _log_feature_metrics(self, metric, stage: str) -> None: - m = metric.compute() - feature_density = m.pop(stage + "/feature_density", None) - self.log_dict(m, on_epoch=True, sync_dist=True) - if ( - isinstance(self.logger, pl.loggers.WandbLogger) - and feature_density is not None - ): - self.logger.log_table( - stage + "/feature_density", - data=list(zip(feature_density.bin_centers, feature_density.density)), - columns=["bin_center", "density"], - ) - - metric.reset() - - def configure_optimizers(self): - optimizer = self.optimizer(self.sparse_model.sparse_parameters()) - if schedule := self.lr_schedule: - return { - "optimizer": optimizer, - "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, - } - return optimizer diff --git a/test/test_sae.py b/test/test_sae.py deleted file mode 100644 index 993a7f7f..00000000 --- a/test/test_sae.py +++ /dev/null @@ -1,287 +0,0 @@ -import pytest -import torch -import torch.nn.functional as F -from transformers import ( - AutoModelForMaskedLM, - AutoTokenizer, - DataCollatorForLanguageModeling, - DataCollatorWithPadding, - RobertaPreLayerNormConfig, - RobertaPreLayerNormForMaskedLM, - RobertaPreLayerNormModel, -) - -from electrolyte_fm.models.sae import ( - AbstractSAE, - GatedSAE, - InjectedCoder, - SparsifiedModel, - TiedBiasSAE, - TopKSAE, - VanillaSAE, - topk, - hf_cross_entropy, -) -from electrolyte_fm.utils.tokenizer import load_tokenizer - - -def get_default_device(): - if torch.cuda.is_available(): - return torch.device("cuda") - elif torch.backends.mps.is_available(): - return torch.device("mps") - else: - return torch.device("cpu") - - -DEVICE = get_default_device() - -SAE_CLASSES = [GatedSAE, TiedBiasSAE, VanillaSAE, TopKSAE] - - -@pytest.fixture() -@pytest.mark.cuda -def roberta_tokenzier(): - tokenizer = load_tokenizer("smirk") - config = RobertaPreLayerNormConfig( - vocab_size=len(tokenizer), - hidden_size=256, - num_hidden_layers=4, - num_attention_heads=4, - intermediate_size=512, - ) - return RobertaPreLayerNormForMaskedLM(config).to(DEVICE), tokenizer - - -def test_topk(): - x = torch.rand(8, 4, 20) - assert ((x > 0).sum(-1) > 5).all() - x_hat = topk(x, 5) - assert ((x_hat > 0).sum(-1) == 5).all() - - -@pytest.mark.gpu -def test_hf_crossentropy(): - name = "ibm/MoLFormer-XL-both-10pct" - model = AutoModelForMaskedLM.from_pretrained(name, trust_remote_code=True).to( - DEVICE - ) - tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True) - batch = [ - tok(smi) - for smi in [ - "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", - "CN3[C@H]1CC[C@@H]3C[C@@H](C1)OC(=O)C(CO)c2cc", - ] - ] - collate = DataCollatorForLanguageModeling(tok, mlm=False) - # collate = DataCollatorWithPadding(tok) - batch = collate(batch) - batch = {k: v.to(DEVICE) for k, v in batch.items()} - model.eval() - out = model( - batch["input_ids"], - attention_mask=batch["attention_mask"], - return_dict=True, - ) - logits = out.logits - vocab_size = model.config.vocab_size - loss_ref = F.cross_entropy(logits.view(-1, vocab_size), batch["labels"].view(-1)) - loss = hf_cross_entropy(logits, batch["labels"]) - assert loss == loss_ref - - -@pytest.mark.parametrize("sae_cls", SAE_CLASSES) -class TestSAE: - B = 3 - H = 4 - T = 8 - E = 2 - - @classmethod - def setup_class(cls): - torch.manual_seed(0) - - @property - def feature_shape(self): - return (self.B, self.T, self.H * self.E) - - def input(self): - return torch.rand(self.B, self.T, self.H) - - def test_init(self, sae_cls): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - for p in sae.parameters(): - assert p.isfinite().all() - assert not p.isnan().any() - - def test_encode(self, sae_cls): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - x = self.input() - f = sae.encode(x) - assert f.shape == self.feature_shape - x_hat = sae.decode(f) - assert x_hat.shape == x.shape - assert sae.forward(x).equal(x_hat) - - def test_forward(self, sae_cls: AbstractSAE): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - assert isinstance(sae, AbstractSAE) - x = self.input() - features = sae.forward(x) - assert features.shape == x.shape - assert features.isfinite().all() - assert not features.isnan().any() - - def test_loss(self, sae_cls): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - assert isinstance(sae, AbstractSAE) - x = self.input() - y, loss = sae.forward_with_loss(x) - assert loss.isfinite() and loss.shape == () - assert y.shape == x.shape - assert y.isfinite().all() - - -@pytest.mark.parametrize("sae_cls", SAE_CLASSES) -def test_injected_coder(sae_cls): - hidden_size = 64 - model = torch.nn.Linear(hidden_size, hidden_size) - sae = sae_cls(hidden_size=hidden_size, expansion=2) - injected = InjectedCoder(model, sae).to(DEVICE) - injected.eval() - assert isinstance(injected, InjectedCoder) - dense_model = injected.dense_model - assert not injected.training and not dense_model.training - x = torch.rand(2, 5, hidden_size, device=DEVICE) - - # Check dense - y_ref = dense_model(x) - injected.state = "dense" - y_dense = injected(x) - assert y_dense.shape == y_ref.shape - assert y_dense.equal(y_ref) - - # Check null - injected.state = "null" - y_null = injected(x) - assert y_null.shape == y_ref.shape - assert y_null.equal(torch.zeros_like(y_dense)) - - # Check sparse - injected.state = "sparse" - y_sparse = injected(x) - assert y_sparse.shape == y_dense.shape - - -def test_instrumented(): - model = AutoModelForMaskedLM.from_pretrained( - "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True - ) - tokenizer = AutoTokenizer.from_pretrained( - "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True - ) - hidden_size = model.config.hidden_size - sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) - sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=0).to(DEVICE) - model = model.to(DEVICE) - sparse_model.eval() - assert not model.training - assert not sae.training - assert not model.training - - batch = [ - tokenizer(smi) - for smi in ["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"] - ] - collate = DataCollatorForLanguageModeling(tokenizer, mlm=False) - batch = collate(batch) - batch = { - k: v.to(DEVICE) - for k, v in batch.items() - if k in ["input_ids", "attention_mask", "labels"] - } - batch["return_dict"] = True - y = model(**batch).logits - - assert all([coder.state == "sparse" for coder in sparse_model.coders]) - with sparse_model.nullcoders() as sparse_model: - assert all([coder.state == "null" for coder in sparse_model.coders]) - y_null = sparse_model(**batch).logits - assert y_null.shape == y.shape - assert y_null.device == y.device - assert y_null.dtype == y.dtype - - assert all([not coder.dense.training for coder in sparse_model.coders]) - with sparse_model.sparse(False) as sparse_model: - assert all([coder.state == "dense" for coder in sparse_model.coders]) - y_dense = sparse_model(**batch).logits - assert y_dense.shape == y.shape - assert y_dense.device == y.device - assert y_dense.dtype == y.dtype - assert y_dense.equal(y) - - rc = sparse_model.loss_recovered(target=batch["input_ids"], **batch) - assert isinstance(rc, torch.Tensor) and rc.shape == () - assert rc.isfinite() and not rc.isnan() - assert rc <= 1 - - -def test_sparse_model(roberta_tokenzier): - roberta, tokenizer = roberta_tokenzier - sae = TiedBiasSAE(hidden_size=roberta.config.hidden_size, expansion=2) - sparse_model = SparsifiedModel.from_huggingface(roberta, sae, layer=2).to(DEVICE) - robert = sparse_model.model.base_model - assert isinstance(robert, RobertaPreLayerNormModel) - assert isinstance(robert.encoder.layer[2].output.dense, InjectedCoder) - assert robert.encoder.layer[2].output.dense is sparse_model.coders[0] - - collate = DataCollatorWithPadding(tokenizer) - batch = collate([tokenizer("CNCCC")]) - batch = { - "input_ids": batch["input_ids"].to(DEVICE), - "attention_mask": batch["attention_mask"].to(DEVICE), - "return_dict": True, - } - y_sparse = sparse_model(**batch).logits - robert.eval() - y_dense = roberta(**batch).logits - assert y_sparse.shape == y_dense.shape - assert y_sparse.shape == (1, 5, len(tokenizer)) - - # Check null features - with sparse_model.nullcoders() as model: - y_null = model(**batch).logits - assert y_null.shape == y_sparse.shape - - # Check dense features - with sparse_model.sparse(False) as model: - # Run model in eval model to be deterministic - model.eval() - robert.eval() - y_dense = roberta(**batch).logits - y_dense_context = model(**batch).logits - robert.train() - model.train() - assert y_dense_context.shape == y_dense.shape - e = (y_dense_context.detach() - y_dense.detach()).abs() - assert (e < 1e-6).all() - - # Check non-encoder parameters don't get gradients - assert len(list(sparse_model.sparse_parameters())) > 0 - assert len(dict(sparse_model.sparse_named_parameters())) > 0 - - # Check_gradient - out, loss = sparse_model.forward_with_loss(**batch) - # assert out.logits.equal(y_sparse) - loss.backward() - for _, v in sparse_model.sparse_named_parameters(): - assert v.grad is not None - assert (v.grad != 0).any() - - # Check Recovered Loss - rc = sparse_model.loss_recovered( - **batch, target=batch["input_ids"], sparse_output=y_sparse - ) - assert isinstance(rc, torch.Tensor) and rc.shape == () - assert rc.isfinite() and not rc.isnan() From 15346097c8b12282ae9900ccb805c3b86353933e Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sun, 13 Jul 2025 13:06:53 -0400 Subject: [PATCH 049/147] fix pre-commit --- opt/MISTStyle/Project.toml | 2 +- opt/config_tf_oud_wood.yaml | 70 ------------------------------ opt/screening/electrolyte_sweep.py | 0 3 files changed, 1 insertion(+), 71 deletions(-) delete mode 100644 opt/config_tf_oud_wood.yaml mode change 100644 => 100755 opt/screening/electrolyte_sweep.py diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index a8557b85..50137742 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -11,4 +11,4 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" Makie = "0.22,0.23" CairoMakie = "0.13,0.14" CategoricalArrays = "0.10" -StatsBase = "0.33, 0.34" \ No newline at end of file +StatsBase = "0.33, 0.34" diff --git a/opt/config_tf_oud_wood.yaml b/opt/config_tf_oud_wood.yaml deleted file mode 100644 index 16784f56..00000000 --- a/opt/config_tf_oud_wood.yaml +++ /dev/null @@ -1,70 +0,0 @@ -generation: - db_path: zinc_fragment/fragments.sqlite - n_fragments: 1000 - reference_fragments: electrolyte.smi.frag - epoch_size: 1000 - limit_db_fragments: null - limit_ref_fragments: null - batch_size: 512 -critics: - - model_path: models/mist-26.9M-b302p09x-bp - limits: - bp: - - 75 - - null - model_cls: MISTFinetuned - - model_path: models/mist-26.9M-y3ge5pf9-mp - model_cls: MISTFinetuned - limits: - mp: - - null - - 0 - - model_path: models/mist-26.9M-48kpooqf-odour - model_cls: MISTFinetuned - limits: - woody: - - 0 - - null - amber: - - 0 - - null - - model_path: models/mist-ti624ev1-moleculenet/tox21 - model_cls: MISTFinetuned - limits: - NR-AR: - - null - - 0 - NR-AR-LBD: - - null - - 0 - NR-AhR: - - null - - 0 - NR-Aromatase: - - null - - 0 - NR-ER: - - null - - 0 - NR-ER-LBD: - - null - - 0 - NR-PPAR-gamma: - - null - - 0 - SR-ARE: - - null - - 0 - SR-ATAD5: - - null - - 0 - SR-HSE: - - null - - 0 - SR-MMP: - - null - - 0 - SR-p53: - - null - - 0 - model_cls: MISTFinetuned diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100644 new mode 100755 From 1bb45158804eb17c4250e441706d11fde31ddc3f Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 11 Aug 2025 17:04:01 -0400 Subject: [PATCH 050/147] tweaks to screening figures --- opt/screening/electrolyte_sweep.py | 0 opt/screening/plots/src/creativity.jl | 1 - opt/screening/plots/src/odor.jl | 375 ++++++++++++++++++++++++++ opt/screening/plots/src/qmist.jl | 1 - opt/screening/src/generate.py | 6 +- opt/synth_access/main.py | 2 +- 6 files changed, 379 insertions(+), 6 deletions(-) mode change 100755 => 100644 opt/screening/electrolyte_sweep.py create mode 100644 opt/screening/plots/src/odor.jl diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100755 new mode 100644 diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index 7767d670..47e7e2f4 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -235,4 +235,3 @@ function renyi_entropy_estimate(embeddings; kwargs...) renyi = (1 / (1-γ)) * (log(mst) - γ * log(n)) return (; renyi, mst) end - diff --git a/opt/screening/plots/src/odor.jl b/opt/screening/plots/src/odor.jl new file mode 100644 index 00000000..45f8c934 --- /dev/null +++ b/opt/screening/plots/src/odor.jl @@ -0,0 +1,375 @@ +function evaluate_odor(model::Py, smiles::Vector{String}; batch_size=32, filter_non_active=true) + df = predict_mist(model, smiles; batch_size) + + # Summarize odor activations + odor_summary = describe(df[!, Not("smiles")], :max, Base.Fix1(count, >(0)) => :nactive) + filter_non_active && subset!(odor_summary, :max => ByRow(>(0))) + sort!(odor_summary, :max; rev=true) + + df.max_odor = map(eachrow(df)) do row + maximum(Vector(row[Not("smiles")])) + end + select!(df, "smiles", "max_odor", odor_summary.variable...) + sort!(df, :max_odor; rev=true) + + @info "Top Odors" odor_summary + return df +end + +""" Return the names of the channels in the MIST model""" +channel_names(model::Py) = pyconvert(Vector{String}, [chn["name"] for chn in model.channels]) + +filter_non_active(df::DataFrame, odor_model::Py; kwargs...) = filter_non_active(df, channel_names(odor_model); kwargs...) + +function filter_non_active(df::DataFrame, odor_columns=Not("smiles"); limit=nothing) + odor_columns = intersect(odor_columns, names(df)) + odor_summary = describe(df[!, odor_columns], :max, Base.Fix1(count, >(0)) => :nactive) + non_odor = names(df[!, Not(odor_columns)]) + subset!(odor_summary, :max => ByRow(>(0))) + sort!(odor_summary, :max; rev=true) + if !isnothing(limit) + @show odor_summary = odor_summary[1:limit, :] + end + return select(df, non_odor..., odor_summary.variable...) +end + +function count_active(df, odor_model::Py) + scents = pyconvert(Vector{String}, [chn["name"] for chn in odor_model.channels]) + n = Dict() + for scent in scents + n[scent] = count(df[!, scent] .> 0) + end + s = collect(keys(n)) + counts = collect(values(n)) + return DataFrame(scent=s, active_count=counts) +end + +logistic(x) = inv(1 + exp(-x)) + +function correlation_matrix(df::DataFrame, columns; correlation=cor) + D = Matrix{Float64}(undef, length(columns), length(columns)) + for I in eachindex(IndexCartesian(), D) + if I[1] == I[2] + D[I] = 1 + elseif I[1] > I[2] + D[I] = correlation(df[!, columns[I.I[1]]], df[!, columns[I.I[2]]]) + D[I[2], I[1]] = D[I] + end + end + return D, columns +end + +function figure_hclust(dist::Matrix, labels::Vector; kwargs...) + c = hclust(dist; linkage=:single, branchorder=:barjoseph) + dist = dist[c.order, c.order] + labels = labels[c.order] + + f = Figure() + cb = Colorbar(f[1, 2]; + colorrange=(-1, 1), + colormap=:vik10, + tellheight=true, + tellwidth=true, + halign=:left, + valign=:top, + ) + ticks = (eachindex(labels), labels) + ax = Axis(f[1, 1]; + xticks=ticks, yticks=ticks, + xticklabelrotation=0.55, + xticklabelsvisible=false, + xticksvisible=false, + aspect=DataAspect(), + ) + heatmap!(ax, dist; MISTStyle.cb_attrs(cb, Heatmap)..., kwargs...) + return f +end + +function linear_correlation(df, cols, target; correlation=cor) + S = Matrix(df[!, cols]) + c = S \ df[!, target] + P = sortperm(abs.(c)) + rho = Vector{Float64}(undef, length(cols)) + for i in range(1, length(cols)) + Sp = @view S[:, P[1:i]] + c = Sp \ df[!, target] + rho[i] = correlation(Sp * c, df[!, target]) + end + return rho +end + +function higher_order_odors(df) + groups = Dict( + "fruity & Sweet" => ["fruity", "tropical", "apple", "banana", "pear", "pineapple", "sweet"], + "Floral" => ["floral", "rose", "green", "herbal", "ethereal"], + "Pungent" => ["sulfurous", "onion", "garlic", "cheesy", "phenolic", "vegetable"], + ) + out = Dict() + for (label, odors) in pairs(groups) + out[label] = vec(maximum(Matrix(df[!, odors]); dims=2)) + end + dfo = DataFrame(out) + dfo.smiles = df.smiles + return dfo +end + +function pick_odor(df, scent) + df = deepcopy(df) + df.group = categorical(df.group) + dfi = subset(df, scent => ByRow(>(0))) + dfo = subset(df, scent => ByRow(<(0))) + f = Figure(; size=(105, 187)) + ax = Axis(f[1, 1]; xlabel="Homo", ylabel="Gap", limits=((-0.4, -0.25), nothing)) + kwargs = (; colormap=:tab10, colorrange=(1, 10), marker=:circle) + scatter!(ax, dfo.homo, dfo.gap; color=levelcode.(dfo.group), alpha = 0.1, markersize=3pt, kwargs...) + scatter!(ax, dfi.homo, dfi.gap; color=levelcode.(dfi.group), kwargs...) + ax = Axis(f[2, 1]; xlabel="Melt", ylabel="Boil", limits=((-100, 0), (0, nothing))) + scatter!(ax, dfo.mp, dfo.bp; color=levelcode.(dfo.group), alpha = 0.1, markersize=3pt, kwargs...) + scatter!(ax, dfi.mp, dfi.bp; color=levelcode.(dfi.group), kwargs...) + return f +end + + +function plot_odor_act(df) + f = Figure(; size=(8inch, 8inch)) + odors = setdiff(names(df), ["smiles", "name", "class", "max_odor"]) + mols = map((n,c) -> "$n ($c)", df.name, df.class) + ax = Axis(f[1, 1]; + xticks=(1:length(odors), odors), + yticks=(1:length(mols), mols), + xticklabelrotation=pi/4, + ) + act = logistic.(Matrix(df[!, odors])) + heatmap!(ax, act'; colormap=:lipari) + return f +end + +function plot_odor_tsne(df::DataFrame, odor_columns=Not("smiles")) + act = Matrix(df[!, odor_columns]) + M = fit(ManifoldLearning.LLE, act') + f = plot_odor_tsne(M, df, act, odor_columns) + return f, M +end +plot_odor_tsne(M, df::DataFrame, model::Py; kwargs...) = plot_odor_tsne(M, df, channel_names(model); kwargs...) +function plot_odor_tsne(M, df::DataFrame, act, odor_columns=Not("smiles")) + act = Matrix(df[!, odor_columns]) + R = predict(M) + f = Figure(; size=(2inch, 2inch)) + ax = Axis(f[1, 1]) + hidedecorations!(ax) + dominate = map(argmax, eachrow(act)) + h = scatter!(ax, eachrow(R)...; + marker=:circle, + color=dominate, + markersize=2pt, + colormap=:tab10, + colorrange=(1, 10), + + ) + + # elements = map(enumerate(odor_columns)) do (i, label) + # PolyElement(; color=i, label, colormap=h.colormap, colorrange=h.colorrange) + # end + # Legend(f[1, 1], elements, MISTStyle.label.(elements); + # tellheight=false, tellwidth=false, + # # orientation=:horizontal, + # halign=:left, + # valign=:top, + # nbanks=2, + # ) + # ann = annotation!(ax, Point2.(eachcol(R)); text=df.name) + return f +end + +function figure_odor_tsne(df::DataFrame, odor_model::Py; limit=10) + scents = pyconvert(Vector{String}, [chn["name"] for chn in odor_model.channels]) + df_odor = filter_non_active(df, scents; limit) + active_scents = intersect(names(df_odor), scents) + df_odor.dominate = map(eachrow(df_odor)) do row + return argmax(row[active_scents]) + end + df_odor.embed = eachrow(mist_embedding(odor_model, df_odor.smiles; pooling=no_pooling)) + return df_odor +end + +function figure_odor_tsne(df::DataFrame) + E = stack(df.embed; dims=2) + M = fit(ManifoldLearning.Isomap, E) + # M = fit(ManifoldLearning.TSNE, E) + return M +end + +function figure_odor_tsne(df::DataFrame, M) + f = Figure(; size=(2inch, 2inch)) + R = predict(M) + ax = Axis(f[1, 1]) + df.dominate = categorical(string.(df.dominate)) + df = df[randperm(nrow(df)), :] + scatter!(ax, eachrow(R)...; + marker=:circle, + markersize=2pt, + color=levelcode.(df.dominate), + colormap=:tab10, + colorrange=(1, 10), + ) + return f +end + +function figure_odor_counts(df::DataFrame, odor_model::Py) + df_sum = combine(groupby(df, :group)) do gdf + nactive = ScreeningPlots.count_active(gdf, odor_model) + nactive.active_count ./= nrow(gdf) + return nactive + end + @info df_sum + groups = unique(df_sum.group) + df_sum = unstack(df_sum, :group, :active_count) + transform!(df_sum, groups => ByRow((x...) -> sum(x)) => :any_active) + subset!(df_sum, :any_active => ByRow(>(0))) + sort!(df_sum, :any_active; rev=true) + scents = df_sum.scent + @info df_sum + + df_sum = stack(df_sum, groups; variable_name="group") + df_sum.group = categorical(df_sum.group; levels=groups) + df_sum.scent = categorical(df_sum.scent; levels=scents) + + f = Figure(; size=(122, 200)) + ax = Axis(f[1, 1]; + limits=((2e-4, 1), nothing), + yticks=(1:length(scents), scents), + xlabel="Molecules with Odor", + xscale=log10, + xticks=([1e-3, 1e-2, 1e-1], ["0.1%", "1%", "10%"]), + xticklabelsize=7pt, + yticklabelsize=7pt, + ) + h = barplot!(ax, levelcode.(df_sum.scent), df_sum.value; + color=levelcode.(df_sum.group), + dodge=levelcode.(df_sum.group), + colormap=MISTStyle.CAT_COLORS, + colorrange=(1, length(MISTStyle.CAT_COLORS)), + direction=:x, + fillto=1e-5, + ) + + elements = map(enumerate(unique(df_sum.group))) do (i, label) + PolyElement(; color=i, label, colormap=h.colormap, colorrange=h.colorrange) + end + Legend(f[1, 1], elements, MISTStyle.label.(elements); + tellheight=false, tellwidth=false, + halign=:right, + valign=:top, + ) + resize_to_layout!(f) + + return f +end + +function plot_pareto_front_scent(df, scent) + df = subset(df, :group => ByRow(in(["Generated", "Electrolytes"]))) + df.group = categorical(df.group; levels=["Generated", "Electrolytes"]) + df = combine(first, groupby(df, [:inchi_key, :group])) + pareto_kwargs = (; + linewidth=1.5pt, + alpha=0.7, + ) + + # Get non-dominated solutions + df = combine(groupby(df, :group)) do gdf + pos = map(vcat, -gdf.gap, gdf.homo, gdf.mp, -gdf.bp) + idx = Metaheuristics.get_non_dominated_solutions_perm(pos) + h_pareto = mean(gdf.surprise[idx]) + h_std_pareto = std(gdf.surprise[idx]) + h_dominated = mean(gdf.surprise[Not(idx)]) + h_std_dominated = std(gdf.surprise[Not(idx)]) + h_delta = h_pareto - h_dominated + h_std_delta = hypot(h_std_pareto, h_std_dominated) + gdf.group_frontier .= false + gdf[idx, :group_frontier] .= true + return gdf + end + + + # Convert Units + df.gap .*= HARTREE_TO_EV + df.homo .*= HARTREE_TO_EV + + skwargs = (; + marker=:circle, + colormap=MISTStyle.CAT_COLORS, + colorrange=(1, length(MISTStyle.CAT_COLORS)), + ) + + f = Figure(; size=(100, 203)) + ax = Axis(f[1, 1]; + limits=((-10.5, -7), (5, 13)), + xlabel=L"HOMO [eV]$$", + ylabel=L"Gap [eV]$$", + xticks=WilkinsonTicks(5; k_max=7), + yticks=WilkinsonTicks(5; k_max=7), + ) + gen = subset(df, :group => ByRow(==("Generated"))) + w_scent = subset(df, scent => ByRow(>(0))) + stairs!(ax, get_pareto_front(gen.homo, gen.gap; ax); + color=MISTStyle.UM_COLORS.blue, + pareto_kwargs... + ) + scatter!(ax, df.homo, df.gap; color=levelcode.(df.group), alpha=0.2, markersize=3pt, skwargs...) + scatter!(ax, w_scent.homo, w_scent.gap; + color=levelcode.(w_scent.group), + markersize=4pt, + strokewidth=0.5, + skwargs... + ) + + ax = Axis(f[2, 1]; + limits=((minimum(df.mp), 0), (50, maximum(df.bp))), + xlabel=L"Melt [$\degree C$]", + ylabel=L"Boil [$\degree C$]", + ) + h_pareto = stairs!(ax, get_pareto_front(gen.mp, gen.bp; ax); + color=MISTStyle.UM_COLORS.blue, + pareto_kwargs... + ) + w_scent = subset(df, scent => ByRow(>(0))) + h_other = scatter!(ax, df.mp, df.bp; color=levelcode.(df.group), alpha=0.2, markersize=3pt, skwargs...) + h = scatter!(ax, w_scent.mp, w_scent.bp; + color=levelcode.(w_scent.group), + markersize=4pt, + strokewidth=0.5, + skwargs... + ) + + elements = map(enumerate(unique(df.group))) do (i, label) + PolyElement(; color=i, label, colormap=h.colormap, colorrange=h.colorrange) + end |> Vector{Any} + push!(elements, LineElement(; + color=h_pareto.color, + linestyle=h_pareto.linestyle, + linewidth=h_pareto.linewidth, + label="Pareto Front", + )) + push!(elements, MarkerElement(; + label="Odorless", + color=:gray, + marker=h.marker, + markersize=h.markersize, + strokewidth=h.strokewidth, + )) + # push!(elements, MarkerElement(; + # label="Other Scents", + # color=:gray, + # marker=h_other.marker, + # markersize=h_other.markersize, + # strokewidth=h_other.strokewidth, + # )) + Legend(f[2, 1], elements, MISTStyle.label.(elements); + tellheight=false, tellwidth=false, + halign=:left, + valign=:top, + ) + resize_to_layout!(f) + + return f +end diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl index eb9b4186..ddde005b 100644 --- a/opt/screening/plots/src/qmist.jl +++ b/opt/screening/plots/src/qmist.jl @@ -215,4 +215,3 @@ inbounds(x::Real, lb::Real, ub::Real) = x >= lb && x <= ub inbounds(x::Real, lb::Nothing, ub::Real) = x <= ub inbounds(x::Real, lb::Real, ub::Nothing) = x >= lb inbounds(x::Real, lb::Nothing, ub::Nothing) = true - diff --git a/opt/screening/src/generate.py b/opt/screening/src/generate.py index f3a7c934..b6107404 100644 --- a/opt/screening/src/generate.py +++ b/opt/screening/src/generate.py @@ -99,9 +99,9 @@ def logit_limits( def limits_to_bounds(limits: dict[str, tuple[float, float]], channels: list[str]): lower = [] upper = [] - assert limits.keys() <= set(channels), ( - f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" - ) + assert limits.keys() <= set( + channels + ), f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" for chn in channels: if chn in limits: lb, ub = limits[chn] diff --git a/opt/synth_access/main.py b/opt/synth_access/main.py index 543f9090..4d0646e9 100644 --- a/opt/synth_access/main.py +++ b/opt/synth_access/main.py @@ -7,7 +7,7 @@ from os import environ from pathlib import Path from time import perf_counter -from typing import Callable, List, Optional +from typing import Callable, Optional import torch import accelerate # noqa: F401 From 6e830ae2e9456a922328f6db03fa299e9cd153ad Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 30 Aug 2025 17:42:08 -0400 Subject: [PATCH 051/147] Linearly calibrate models --- opt/screening/plots/plots.jl | 27 +++++++++++------ opt/screening/plots/src/creativity.jl | 10 +++---- opt/screening/plots/src/qmist.jl | 42 +++++++++++++++++++++++---- 3 files changed, 60 insertions(+), 19 deletions(-) diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 736317b6..12f88605 100755 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -1,4 +1,5 @@ #!/usr/bin/env -S julia +release --color=auto --startup-file=no --project=@script +# Script to generate screening plots and analysis using ScreeningPlots using Makie using MISTStyle @@ -12,13 +13,18 @@ using Format: format using ScreeningPlots: searchfirst +# Input Files and Path +# ROOTDIR: opt/screening folder +# GIT_ROOT: MIST Project Root +# DATA_DIR: Path to the screening files (to be released in data drop) ROOTDIR = realpath(joinpath(pkgdir(ScreeningPlots), "..")) GIT_ROOT = realpath(joinpath(ROOTDIR, "..", "..")) +DATA_DIR = joinpath(GIT_ROOT, "data", "screening") fig_dir = joinpath(ROOTDIR, "fig") isdir(fig_dir) || mkdir(fig_dir) # Filter to screening runs -df = ScreeningPlots.collate_performance_stats(joinpath(ROOTDIR, "runs")) +df = ScreeningPlots.collate_performance_stats(joinpath(DATA_DIR, "runs")) df = filter(df) do row config = row.config get(config, "limit_walltime", nothing) == 300 || return false @@ -80,7 +86,7 @@ regtable( # Plot generated molecules prod_id = "b7c6ceb2-2114-4ba5-bd2e-41b9bfa2d5df" -production_run_path = joinpath(ROOTDIR, "runs", prod_id) +production_run_path = joinpath(DATA_DIR, "runs", prod_id) df_mol = ScreeningPlots.load_generated_molecules(production_run_path) prod_config = JSON.parsefile(joinpath(production_run_path, "config.json")) @@ -111,16 +117,19 @@ with_theme(MISTStyle.theme()) do MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) # Verify qmist can reproduce QM9 calculations - qmist = realpath(joinpath(pkgdir(ScreeningPlots), "..", "..", "qmist")) - df_qm9 = ScreeningPlots.load_jsonl(joinpath(qmist, "qm9.jsonl")) - label = "QM9 (Ramakrishnan et al.)" => "Ours" - for version in [joinpath(qmist, "veri_v1"), joinpath(qmist, "veri_v2"), joinpath(qmist, "veri_v3")] + df_qm9 = ScreeningPlots.load_jsonl(joinpath(DATA_DIR, "qm9.jsonl")) + for (version, vlabel) in [ + (joinpath(DATA_DIR, "veri_v1"), "v1"), + (joinpath(DATA_DIR, "veri_v2"), "v2"), + (joinpath(DATA_DIR, "veri_v3"), "v3"), + ] df_qmist = ScreeningPlots.load_qmist_results(version) df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) μ = mean(df_qmist.walltime) σ = std(df_qmist.walltime) walltime_p95 = quantile(df_qmist.walltime, 0.95) @info basename(version) nrow(df) walltime=format("\\({:.0f} \\pm {:.0f}\\)", μ, σ) walltime_p95 + label = "QM9 (Ramakrishnan et al.)" => "Ours ($vlabel)" ScreeningPlots.figure_parity(df, cols; label) |> MISTStyle.savefig(basename(version) * "_parity") end @@ -129,7 +138,7 @@ with_theme(MISTStyle.theme()) do f = ScreeningPlots.compare_qmist( production_run_path, joinpath(production_run_path, dir_name); - label="B3LYP/6-31G(2df,p)" => "MIST", + label="B3LYP/6-31G(2df,p) - Ours (v3)" => "MIST", ) MISTStyle.savefig(joinpath("parity-$(label)-$(prod_id)"), f) end @@ -146,7 +155,7 @@ with_theme(MISTStyle.theme()) do f = ScreeningPlots.compare_qmist( production_run_path, joinpath(production_run_path, "qm9_conf"), - joinpath(ROOTDIR, "veri_chembl"), + joinpath(DATA_DIR, "veri_chembl"), mist_qm9; label="B3LYP/6-31G(2df,p)" => "MIST", ) @@ -160,7 +169,7 @@ mist_bp = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist f, df_surprise = ScreeningPlots.compare_creativity( production_run_path, - joinpath(ROOTDIR, "veri_chembl"), + joinpath(DATA_DIR, "veri_chembl"), df_ref; mol_surprise, mist_mp, diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index 47e7e2f4..53fcb3bc 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -100,16 +100,16 @@ function compare_creativity(df::DataFrame, M = missing) end ax2 = Axis(f[1, 2]; - xlabel=L"$$HOMO [eV]", + xlabel=L"$$HOMO (eV)", ylabel=L"$$Molecular Surprise", xticks=WilkinsonTicks(3), ) - sargs = (; markersize = 3) + sargs = (; markersize = 4) sort!(df, :group_frontier; rev=true) _mark_creative!(ax2, df, :homo, :surprise, df.group_frontier; sargs...) ax3 = Axis(f[1, 3]; - xlabel=L"$$Gap [eV]", + xlabel=L"$$Gap (eV)", ylabel=L"$$Molecular Surprise", xticks=WilkinsonTicks(3), yticks=ax1.yticks, @@ -118,7 +118,7 @@ function compare_creativity(df::DataFrame, M = missing) _mark_creative!(ax3, df, :gap, :surprise, df.group_frontier; sargs...) ax4 = Axis(f[1, 4]; - xlabel=L"$$Melt [$\degree C$ ]", + xlabel=L"$$Melt ($\degree C$)", xticks=WilkinsonTicks(3), ylabel=L"$$Molecular Surprise", yticks=ax1.yticks, @@ -127,7 +127,7 @@ function compare_creativity(df::DataFrame, M = missing) _mark_creative!(ax4, df, :mp, :surprise, df.group_frontier; sargs...) ax5 = Axis(f[1, 5]; - xlabel=L"$$Boil [$\degree C$ ]", + xlabel=L"$$Boil ($\degree C$)", xticks=WilkinsonTicks(3), ylabel=L"$$Molecular Surprise", yticks=ax1.yticks, diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl index ddde005b..3e832fae 100644 --- a/opt/screening/plots/src/qmist.jl +++ b/opt/screening/plots/src/qmist.jl @@ -43,7 +43,7 @@ function merge_qmist_results(qmist::DataFrame, ref::DataFrame) return df, cols end -figure_parity(args...; kwargs...) = figure_parity!(Figure(; size=(3.42inch, 2inch)), args...; kwargs...) +figure_parity(args...; kwargs...) = figure_parity!(Figure(; size=(3.42inch, 3inch)), args...; kwargs...) function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", other="_qmist", label::Union{Pair{String,String},Nothing}=nothing) nrow = floor(Int, sqrt(length(cols))) ncol = ceil(Int, length(cols) / nrow) @@ -57,7 +57,7 @@ function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", othe xlim = extrema(x) ylim = extrema(y) ax = Axis(gl[i, j]; - title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mae(x .- y)), + title=format("{}\nρ: {:.2f}\nRMSD: {:.3f}\nMAE: {:.3f}", col, cor(x,y), rmsd(x, y), mae(x .- y)), limits=MISTStyle.parity_limits(x, y), xticks=WilkinsonTicks(2), yticks=WilkinsonTicks(2), @@ -84,12 +84,15 @@ function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", othe elements = map(enumerate(["Generated (n=$ng)", "Inventory (n=$ni)"])) do (idx, label) MarkerElement(; label, marker=:circle, color=MISTStyle.CAT_COLORS[idx]) end - Legend(gl[begin, end], elements, MISTStyle.label.(elements); + Legend(f[end+1, :], elements, MISTStyle.label.(elements); + orientation=:horizontal, fontsize=6pt, margin=(2pt, 2pt, 2pt, 2pt), padding=2pt, valign=:bottom, - halign=:right, + halign=:center, + tellwidth=false, + tellheight=true, ) end @@ -144,11 +147,40 @@ function compare_qmist( return compare_qmist(df_qmist, df_mist, cols; on="inchi_key", kwargs...) end +function calibrate_qmist(df_qm9::DataFrame, df_qmist::DataFrame...) + """ Linearly Calibrate Qmist replication attempts to QM9 Ground truth """ + cols = filter(!in(["smiles", "inchi_key", "InChI", "walltime"]), names(df_qm9)) + smiles = df_qm9.smiles + for df in df_qmist + smiles = intersect(smiles, df.smiles) + end + df_qm9 = subset(df_qm9, :smiles => ByRow(∈(smiles))) + dropmissing!(df_qm9) + + Y = Matrix(df_qm9[!, cols]) + dfo = select(df_qm9, :smiles) + transform!(dfo, :smiles => ByRow(inchi_key) => :inchi_key) + kcols = vcat(["inchi_key"], cols) + for df in df_qmist + leftjoin!(dfo, select(df, kcols); on=:inchi_key, makeunique=true) + end + dropmissing!(dfo) + X = Matrix(select(dfo, Not([:smiles, :inchi_key]))) + X = hcat(X, ones(eltype(X), size(X, 1), 1)) + + # Fit General Linear Model + B = X \ Y + df = DataFrame(eachcol(X * B), cols) + df[!, :smiles] .= dfo.smiles + transform!(df, :smiles => ByRow(inchi_key) => :InChIKey) + return B, df +end + function compare_qmist(df_qmist::DataFrame, df_mist::DataFrame, cols::Vector{String}; on="InChIKey" => "inchi", kwargs...) df = innerjoin(df_qmist, df_mist; on, renamecols="_qmist" => "_mist") disallowmissing!(df) - f = Figure(; size=(3.42inch, 1inch)) + f = Figure(; size=(3.42inch, 1.4inch)) figure_parity!(f ,df, cols; ref="_qmist", other="_mist", kwargs...) return f end From 13846c8d3ade50718d7d394880ee84b9d231fee0 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 15:30:56 -0400 Subject: [PATCH 052/147] Add script for ingesting sterochemistry dataset --- opt/sterochemistry/clc_db.py | 429 ++++++++++++++++++++++++ opt/sterochemistry/omol25.py | 526 ++++++++++++++++++++++++++++++ opt/sterochemistry/pyproject.toml | 16 + 3 files changed, 971 insertions(+) create mode 100644 opt/sterochemistry/clc_db.py create mode 100644 opt/sterochemistry/omol25.py create mode 100644 opt/sterochemistry/pyproject.toml diff --git a/opt/sterochemistry/clc_db.py b/opt/sterochemistry/clc_db.py new file mode 100644 index 00000000..76c90aba --- /dev/null +++ b/opt/sterochemistry/clc_db.py @@ -0,0 +1,429 @@ +#!/usr/bin/env -S uv run python +""" +CLC-DB bulk downloader + +Downloads all molecule SDF files and merges molecule properties into a single CSV. + +Strategy: +- Page the public search API used by the site ("/search/molecules") to list molecules. +- For each molecule, fetch its SDF from the static path used by the web UI. +- Optionally iterate by category if the categories endpoint is available; otherwise, fetch all. + +Outputs (under --out-dir): +- sdf/ (all .sdf files; one per CAS ID) +- molecules.csv (merged properties in the same schema as the website's CSV) + +Note: The site generates ZIPs client-side in the browser. This script mirrors that behavior +without trying to click buttons, using the same public endpoints visible in the JS. +""" + +import argparse +import concurrent.futures as futures +import csv +import logging +import os +from collections.abc import Iterable +from dataclasses import dataclass +from typing import Any + +import pandas as pd +import requests +from requests.adapters import HTTPAdapter +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) +from tqdm import tqdm + +# Public base URLs (axios base is not exposed directly; try a few sensible options) +API_BASE_CANDIDATES = [ + # Observed public API base + "https://compbio.sjtu.edu.cn/api", + # Fallback guesses based on site paths + "https://compbio.sjtu.edu.cn/services/clc-db/api", + "https://compbio.sjtu.edu.cn/services/clc-db", + "https://compbio.sjtu.edu.cn/services/clc-db/api/v1", +] + +# Static path for SDFs as used by the client JS +SDF_BASE = "https://compbio.sjtu.edu.cn/services/clc-db/static/all_sdfs" + + +@dataclass +class Molecule: + data: dict[str, Any] + + @property + def cas_id(self) -> str: + return str(self.data.get("cas_id", "")).strip() + + @property + def name(self) -> str: + return str(self.data.get("name", "")).strip() + + @property + def categories(self) -> list[str]: + cats = self.data.get("category") or [] + if isinstance(cats, list): + return [str(c.get("name", "")).strip() for c in cats if isinstance(c, dict)] + return [] + + def csv_row(self) -> dict[str, Any]: + """Return a row dict with the same columns the website exports. + + Column order mirrors the client code seen in the website bundle. + """ + d = self.data + + # Join helper + def _join(items: list[dict[str, Any]] | None) -> str: + if not items: + return "" + return ", ".join( + str(it.get("name", "")).strip() for it in items if isinstance(it, dict) + ) + + def _fmt(x: Any, ndigits: int) -> str: + try: + return f"{float(x):.{ndigits}f}" + except Exception: + return "" + + def _int(x: Any) -> str: + try: + return str(int(x)) + except Exception: + return "" + + return { + "Name": d.get("name", ""), + "CAS ID": d.get("cas_id", ""), + "PubChem CID": d.get("pubchem_cid", ""), + "Category": _join(d.get("category")), + "URL": d.get("url", ""), + "PubChem URL": d.get("pubchem_url", ""), + "SMILES": d.get("smiles", ""), + "Chirality": _join(d.get("chirality")), + "Description": d.get("description", ""), + "SMILES IUPAC": d.get("smiles_iupac", ""), + "Molecule Formula": d.get("molecule_formula", ""), + "Molecular Weight": _fmt(d.get("molecular_weight"), 3), + "Heavy Atom Count": _int(d.get("heavy_atom_count")), + "Ring Count": _int(d.get("ring_count")), + "Hydrogen Bond Acceptor Count": _int(d.get("hydrogen_bond_acceptor_count")), + "Hydrogen Bond Donor Count": _int(d.get("hydrogen_bond_donor_count")), + "Rotatable Bond Count": _int(d.get("rotatable_bond_count")), + "Zero-point correction": _fmt(d.get("zero_point_correction"), 6), + "Thermal correction to Energy": _fmt(d.get("thermal_correction_energy"), 6), + "Thermal correction to Enthalpy": _fmt( + d.get("thermal_correction_enthalpy"), 6 + ), + "Thermal correction to Gibbs Free Energy": _fmt( + d.get("thermal_correction_gibbs"), 6 + ), + "Sum of electronic and zero-point Energies": _fmt( + d.get("sum_electronic_zero_point"), 6 + ), + "Sum of electronic and thermal Energies": _fmt( + d.get("sum_electronic_thermal_energy"), 6 + ), + "Sum of electronic and thermal Enthalpies": _fmt( + d.get("sum_electronic_thermal_enthalpy"), 6 + ), + "Sum of electronic and thermal Free Energies": _fmt( + d.get("sum_electronic_thermal_free_energy"), 6 + ), + "HOMO Energy (eV)": _fmt(d.get("homo_energy"), 6), + "LUMO Energy (eV)": _fmt(d.get("lumo_energy"), 6), + "HOMO-LUMO Gap (eV)": _fmt(d.get("homo_lumo_gap"), 6), + } + + +def _new_session(timeout: int = 30) -> requests.Session: + s = requests.Session() + adapter = HTTPAdapter(max_retries=3, pool_connections=16, pool_maxsize=16) + s.mount("http://", adapter) + s.mount("https://", adapter) + s.headers.update( + { + "User-Agent": "clc-db-downloader/1.0 (+https://compbio.sjtu.edu.cn/services/clc-db)", + "Accept": "application/json, text/plain, */*", + "Accept-Language": "en-US,en;q=0.9", + } + ) + # store a default timeout on the session for convenience + s.request = _timeout_wrapper(s.request, timeout) + return s + + +def _timeout_wrapper(request_fn, timeout_default: int): + def wrapped(method, url, **kwargs): + if "timeout" not in kwargs: + kwargs["timeout"] = timeout_default + return request_fn(method, url, **kwargs) + + return wrapped + + +def _try_api_base(session: requests.Session) -> str | None: + for base in API_BASE_CANDIDATES: + logging.debug("Probing API base: %s", base) + try: + r = session.get(f"{base}/categories/") + if r.ok and r.headers.get("content-type", "").startswith( + "application/json" + ): + _ = r.json() + logging.info("Discovered API base: %s", base) + return base + except Exception: + logging.debug("Probe failed for %s", base, exc_info=True) + continue + return None + + +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=0.5, min=0.5, max=8), + reraise=True, + retry=retry_if_exception_type((requests.RequestException,)), +) +def _get_json( + session: requests.Session, url: str, params: dict[str, Any] | None = None +) -> dict[str, Any]: + r = session.get(url, params=params) + r.raise_for_status() + return r.json() + + +def fetch_categories(session: requests.Session, api_base: str | None) -> list[str]: + if not api_base: + return [] + try: + data = _get_json(session, f"{api_base}/categories/") + except Exception: + logging.warning("Fetching categories failed", exc_info=True) + return [] + # Expecting list of {id, name} + names = [] + if isinstance(data, list): + names = [str(x.get("name", "")).strip() for x in data if isinstance(x, dict)] + elif isinstance(data, dict) and "results" in data: + names = [ + str(x.get("name", "")).strip() + for x in data.get("results", []) + if isinstance(x, dict) + ] + names = [n for n in names if n] + logging.info("Fetched %d categories", len(names)) + return names + + +def iter_molecules( + session: requests.Session, + api_base: str | None, + category: str | None = None, + page_size: int = 30, +) -> Iterable[Molecule]: + if not api_base: + raise RuntimeError( + "API base not discovered; cannot enumerate molecules reliably." + ) + + page = 1 + while True: + params = {"page": page, "page_size": page_size} + if category: + params["category"] = category + url = f"{api_base}/search/molecules" + data = _get_json(session, url, params=params) + results = data.get("results", []) if isinstance(data, dict) else [] + logging.info( + "Fetched page %s (category=%s): %d results", + page, + category if category else "ALL", + len(results), + ) + for item in results: + if isinstance(item, dict): + yield Molecule(item) + next_url = data.get("next") if isinstance(data, dict) else None + if not next_url: + logging.info("No next page; finished pagination for %s", category or "ALL") + break + page += 1 + + +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=0.5, min=0.5, max=8), + reraise=True, + retry=retry_if_exception_type((requests.RequestException,)), +) +def download_sdf( + session: requests.Session, cas_id: str, out_path: str +) -> tuple[str, bool, str | None]: + url = f"{SDF_BASE}/{cas_id}.sdf" + r = session.get(url) + if r.status_code == 404: + logging.warning("SDF not found (404) for CAS %s", cas_id) + return (cas_id, False, "404") + r.raise_for_status() + with open(out_path, "wb") as f: + f.write(r.content) + logging.debug("Saved SDF %s -> %s", cas_id, out_path) + return (cas_id, True, None) + + +def ensure_dir(path: str) -> None: + os.makedirs(path, exist_ok=True) + + +def write_csv(rows: list[dict[str, Any]], out_csv: str) -> None: + if not rows: + # write header-only CSV + cols = Molecule({}).csv_row().keys() + with open(out_csv, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=list(cols)) + writer.writeheader() + return + + # Use pandas for convenience and de-duplication on CAS ID + df = pd.DataFrame(rows) + # Drop duplicates by CAS ID keeping first occurrence + if "CAS ID" in df.columns: + df = df.drop_duplicates(subset=["CAS ID"], keep="first") + df.to_csv(out_csv, index=False) + + +def main(argv: list[str] | None = None) -> int: + p = argparse.ArgumentParser( + description="Download all molecules from CLC-DB and merge outputs." + ) + p.add_argument( + "--out-dir", + default=os.path.join("opt", "sterochemistry", "data", "clc_db"), + help="Output directory for SDFs and CSV (default: opt/sterochemistry/data/clc_db)", + ) + p.add_argument( + "--by-category", + action="store_true", + help="Iterate through categories explicitly", + ) + p.add_argument( + "--workers", type=int, default=8, help="Concurrent download workers for SDFs" + ) + p.add_argument("--page-size", type=int, default=30, help="API page size to use") + p.add_argument( + "--log-level", + default="INFO", + choices=["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"], + help="Logging level (default: INFO)", + ) + args = p.parse_args(argv) + + logging.basicConfig( + level=getattr(logging, args.log_level), + format="%(asctime)s %(levelname)s %(message)s", + ) + + out_dir = os.path.abspath(args.out_dir) + sdf_dir = os.path.join(out_dir, "sdf") + ensure_dir(sdf_dir) + logging.info("Output directory: %s", out_dir) + logging.info("SDF directory: %s", sdf_dir) + + session = _new_session() + api_base = _try_api_base(session) + if not api_base: + logging.error("Could not discover API base endpoint; aborting.") + return 2 + + categories: list[str] = [] + if args.by_category: + categories = fetch_categories(session, api_base) + if not categories: + logging.warning( + "Categories endpoint not available; falling back to all molecules." + ) + + rows: list[dict[str, Any]] = [] + seen_cas: set[str] = set() + + def process_molecule(mol: Molecule) -> tuple[str, bool, str | None] | None: + cas = mol.cas_id + if not cas: + return None + out_path = os.path.join(sdf_dir, f"{cas}.sdf") + if os.path.exists(out_path) and os.path.getsize(out_path) > 0: + return (cas, True, None) + try: + return download_sdf(session, cas, out_path) + except Exception as e: + return (cas, False, str(e)) + + # Iterate molecules (all or by category) + if categories: + iterables: list[tuple[str, Iterable[Molecule]]] = [] + for cat in categories: + iterables.append( + ( + cat, + iter_molecules( + session, api_base, category=cat, page_size=args.page_size + ), + ) + ) + else: + iterables = [ + ( + "ALL", + iter_molecules( + session, api_base, category=None, page_size=args.page_size + ), + ) + ] + + for label, it in iterables: + logging.info("Collecting molecules for: %s", label) + batch: list[Molecule] = list(it) + logging.info("Found %d molecules for %s", len(batch), label) + # merge rows + for mol in batch: + if mol.cas_id and mol.cas_id not in seen_cas: + rows.append(mol.csv_row()) + seen_cas.add(mol.cas_id) + + # download SDFs concurrently + results: list[tuple[str, bool, str | None]] = [] + with futures.ThreadPoolExecutor(max_workers=args.workers) as ex: + for res in tqdm( + ex.map(process_molecule, batch), total=len(batch), desc=f"SDF {label}" + ): + if res is not None: + results.append(res) + total = len(results) + ok = sum(1 for _, success, _ in results if success) + missing = [cas for cas, ok_, _ in results if not ok_] + logging.info( + "Downloads finished for %s: %d ok, %d missing (of %d)", + label, + ok, + len(missing), + total, + ) + if missing: + logging.warning("Missing first few SDFs for %s: %s", label, missing[:5]) + + # Write merged CSV + out_csv = os.path.join(out_dir, "molecules.csv") + write_csv(rows, out_csv) + logging.info("Saved CSV: %s", out_csv) + logging.info("SDF folder: %s", sdf_dir) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/opt/sterochemistry/omol25.py b/opt/sterochemistry/omol25.py new file mode 100644 index 00000000..0290d7c7 --- /dev/null +++ b/opt/sterochemistry/omol25.py @@ -0,0 +1,526 @@ +#!/usr/bin/env -S uv run python +""" +Build a HuggingFace dataset from one or more ASE LMDB (".aselmdb") files. + +- Input: a directory containing ``*.aselmdb`` files (single split) OR a + directory with subdirectories, each containing ``*.aselmdb`` (multi-split). + You may also pass a single ``.aselmdb`` file. +- Processing: entries are read, normalized with RDKit (bonding, identifiers), + and curated fields are produced. +- Output: an Arrow dataset written via ``datasets`` (``save_to_disk``). +- Multiprocessing: heavy per-record processing runs with ``map(num_proc=...)``. + +Outputs per record: +- unique_id, composition, SMILES, InChI, InChIKey +- total-energy, homo, lumo, gap, nl_energy, charge, spin +- atomic-numbers, atomic-coordinates (flattened), atomic-isotope (or None) +- bond-connections (u,v pairs flattened) and bond-orders +- mulliken_charges, lowdin_charges, nbo_charges (if present) + +Bonding and identifiers are computed with RDKit from geometry, using a +sanitization pipeline modeled on opt/tmQM/build.py. +""" + +import argparse +import json +import os +from pathlib import Path +import zlib +from collections.abc import Generator +from typing import Any + +import lmdb +import numpy as np +from rdkit import Chem +from rdkit.Chem import inchi as rd_inchi +import rdkit.Chem.AllChem as AllChem # noqa: F401 +from rdkit.Chem import rdDetermineBonds, rdmolfiles, rdmolops +from rdkit.Chem import rdMolDescriptors +import logging +from datasets import Dataset, DatasetDict + + +def iter_aselmdb(path: str) -> Generator[dict[str, Any], None, None]: + env = lmdb.open(path, readonly=True, lock=False, max_readers=2048, subdir=False) + with env.begin() as txn: + cur = txn.cursor() + for _, v in cur: + try: + j = json.loads(zlib.decompress(v)) + if isinstance(j, dict): + yield j + except Exception: + logging.debug("Failed to decode LMDB value as JSON", exc_info=True) + continue + + +def _ndarray_from_json(obj: Any) -> np.ndarray | None: + if isinstance(obj, dict) and "__ndarray__" in obj: + arr = obj["__ndarray__"] + if not isinstance(arr, list) or len(arr) < 3: + return None + shape, dtype, data = arr[0], arr[1], arr[2] + try: + a = np.array(data, dtype=dtype) + if isinstance(shape, list): + a = a.reshape(shape) + return a + except Exception: + logging.debug("Failed to parse __ndarray__ payload", exc_info=True) + return None + return None + + +def _get(entry: dict[str, Any], key: str) -> Any: + if key in entry: + return entry[key] + d = entry.get("data") + if isinstance(d, dict) and key in d: + return d[key] + return None + + +def _as_float(val: Any) -> float | None: + if val is None: + return None + if isinstance(val, int | float): + return float(val) + a = _ndarray_from_json(val) + if a is not None: + try: + return float(np.ravel(a)[0]) + except Exception: + return None + try: + return float(val) + except Exception: + return None + + +def _as_list_float(val: Any) -> list[float] | None: + if val is None: + return None + if isinstance(val, list): + return [float(x) for x in val] + a = _ndarray_from_json(val) + if a is not None: + return [float(x) for x in np.ravel(a).tolist()] + return None + + +def build_rdkit_mol( + numbers: list[int], positions: np.ndarray, charge: int = 0 +) -> Chem.Mol | None: + # Build XYZ block + pt = Chem.GetPeriodicTable() + lines = [str(len(numbers)), "generated by aselmdb_to_jsonl"] + for z, (x, y, zc) in zip(numbers, positions): + sym = pt.GetElementSymbol(int(z)) + lines.append(f"{sym} {x:.8f} {y:.8f} {zc:.8f}") + xyz = "\n".join(lines) + + def _attempt(use_hueckel: bool) -> Chem.Mol: + m = Chem.MolFromXYZBlock(xyz) + if m is None: + raise ValueError("MolFromXYZBlock returned None") + rdDetermineBonds.DetermineBonds(m, charge=int(charge), useHueckel=use_hueckel) + # Sanitize following opt/tmQM/build.py + rdmolops.AssignStereochemistryFrom3D(m) + m = rdmolops.HapticBondsToDative(m) + rdmolops.SanitizeMol(m, catchErrors=True) + m = rdmolops.RemoveHs(m, sanitize=False) + smi_tmp = Chem.MolToSmiles(m) + m = Chem.MolFromSmiles(smi_tmp, sanitize=False) + rdmolops.SanitizeMol(m, catchErrors=True) + if m is None: + raise ValueError("Failed to round-trip molecule through SMILES") + return m + + # Try Hückel method first, then fallback to default + try: + logging.debug("DetermineBonds(useHueckel=True), charge=%s", charge) + mol = _attempt(True) + logging.info("Bonding determined using Hückel method") + return mol + except Exception: + logging.warning( + "Hückel bond perception failed; trying default method", exc_info=True + ) + try: + logging.debug("DetermineBonds(useHueckel=False), charge=%s", charge) + mol = _attempt(False) + logging.info("Bonding determined using default method") + return mol + except Exception: + logging.error( + "Bond perception failed with both Hückel and default methods", + exc_info=True, + ) + raise ValueError("failed to determine bonds with RDKit") + + +def smiles_inchi_from_mol(mol: Chem.Mol) -> tuple[str, str, str]: + p = rdmolfiles.SmilesWriteParams() + # p.isomericSmiles = True + p.canonical = True + p.allHsExplicit = False + p.allBondsExplicit = False + p.doKekule = False + p.includeDativeBonds = False + smi = Chem.MolToSmiles(mol, p) + + # Validate and sanitize strictly + m2 = Chem.MolFromSmiles(smi) + if m2 is None: + raise ValueError("invalid SMILES from mol") + Chem.SanitizeMol(m2) + + inchi = rd_inchi.MolToInchi(m2) + inchikey = rd_inchi.MolToInchiKey(m2) + return smi, inchi, inchikey + + +def record_from_entry(entry: dict[str, Any]) -> dict[str, Any] | None: + # Extract base fields + uid = entry.get("unique_id") + composition = _get(entry, "composition") + energy = _as_float(_get(entry, "energy")) + homo = _as_float(_get(entry, "homo_energy")) + lumo = _as_float(_get(entry, "lumo_energy")) + gap = _as_float(_get(entry, "homo_lumo_gap")) + nl_energy = _as_float(_get(entry, "nl_energy")) + charge = int(_get(entry, "charge") or 0) + spin = int(_get(entry, "spin") or 0) + + if homo is not None and lumo is None and gap is not None: + lumo = homo + gap + if gap is None and homo is not None and lumo is not None: + gap = lumo - homo + + nums_any = _get(entry, "numbers") + pos_any = _get(entry, "positions") + numbers = _as_list_float(nums_any) + numbers = [int(round(x)) for x in numbers] if numbers is not None else None + pos = _ndarray_from_json(pos_any) + if pos is None: + # Some datasets store as nested lists + if isinstance(pos_any, list) and pos_any and isinstance(pos_any[0], list): + pos = np.array(pos_any, dtype=float) + if numbers is None or pos is None: + raise ValueError("missing atomic numbers or positions in entry") + + # Build RDKit mol (sanitized) and extract bonds + identifiers + mol = build_rdkit_mol(numbers, pos, charge=charge) + + # Geometry from sanitized mol (without Hs) + conf = mol.GetConformer() if mol.GetNumConformers() else None + if conf is None: + # If no conformer retained (unlikely), fallback to original + atom_numbers = [int(z) for z in numbers] + coords_flat = [float(x) for x in np.ravel(pos).tolist()] + isotopes = [None for _ in atom_numbers] + else: + atom_numbers = [int(a.GetAtomicNum()) for a in mol.GetAtoms()] + isotopes = [ + int(a.GetIsotope()) if a.GetIsotope() != 0 else None for a in mol.GetAtoms() + ] + coords_flat = [] + for i in range(mol.GetNumAtoms()): + p = conf.GetAtomPosition(i) + coords_flat.extend([float(p.x), float(p.y), float(p.z)]) + + # Bonds + bond_conns: list[int] = [] + bond_orders: list[int] = [] + bt_map = { + Chem.BondType.SINGLE: 1, + Chem.BondType.DOUBLE: 2, + Chem.BondType.TRIPLE: 3, + Chem.BondType.AROMATIC: 1, + Chem.BondType.DATIVE: 1, + } + for b in mol.GetBonds(): + bond_conns.extend([int(b.GetBeginAtomIdx()), int(b.GetEndAtomIdx())]) + bond_orders.append(int(bt_map.get(b.GetBondType(), 1))) + + # Identifiers + try: + smiles, inchi, inchikey = smiles_inchi_from_mol(mol) + except Exception: + logging.warning("Failed to compute SMILES/InChI identifiers", exc_info=True) + smiles = inchi = inchikey = None + # If SMILES is not available, skip this entry + if not smiles: + logging.warning("Skipping entry due to missing SMILES encoding") + return None + + # Validate SMILES consistency against original geometry (no new heavy atoms) + try: + m2 = Chem.MolFromSmiles(smiles) + if m2 is None: + raise ValueError("MolFromSmiles returned None for generated SMILES") + Chem.SanitizeMol(m2) + from collections import Counter + + orig_heavy = [int(z) for z in numbers if int(z) != 1] + smi_heavy = [a.GetAtomicNum() for a in m2.GetAtoms() if a.GetAtomicNum() != 1] + if Counter(orig_heavy) != Counter(smi_heavy): + logging.warning( + "Skipping entry due to heavy-atom composition mismatch: orig=%s, smiles=%s", + Counter(orig_heavy), + Counter(smi_heavy), + ) + return None + # Optional: check total formal charge + try: + smiles_charge = rdmolops.GetFormalCharge(m2) + if smiles_charge != charge: + logging.warning( + "Skipping entry due to formal charge mismatch: dataset=%s, smiles=%s", + charge, + smiles_charge, + ) + return None + except Exception: + logging.debug("Could not compute formal charge from SMILES", exc_info=True) + except Exception: + logging.warning( + "Skipping entry due to SMILES validation failure", exc_info=True + ) + return None + + # Compute composition if missing + if not composition: + try: + composition = rdMolDescriptors.CalcMolFormula(mol) + except Exception: + logging.debug( + "Failed to compute molecular formula from RDKit mol", exc_info=True + ) + + # Charges per atom + mulliken = _as_list_float(_get(entry, "mulliken_charges")) + lowdin = _as_list_float(_get(entry, "lowdin_charges")) + nbo = _as_list_float(_get(entry, "nbo_charges")) + + rec = { + "unique_id": uid, + "composition": composition, + "SMILES": smiles, + "InChI": inchi, + "InChIKey": inchikey, + "total-energy": energy, + "homo": homo, + "lumo": lumo, + "gap": gap, + "charge": charge, + "spin": spin, + "nl_energy": nl_energy, + "atomic-numbers": atom_numbers, + "atomic-coordinates": coords_flat, + "atomic-isotope": isotopes, + "bond-connections": bond_conns, + "bond-orders": bond_orders, + "mulliken_charges": mulliken, + "lowdin_charges": lowdin, + "nbo_charges": nbo, + } + return rec + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser( + description="Build Arrow dataset from ASE LMDB (.aselmdb) files using datasets" + ) + ap.add_argument( + "input", + help=( + "Path to a .aselmdb file, a folder of *.aselmdb (single split), or a folder" + " of subfolders each containing *.aselmdb (multi-split)" + ), + ) + ap.add_argument( + "--output", + "-o", + required=True, + help="Output dataset directory (will be created, contains Arrow files)", + ) + ap.add_argument( + "--num-proc", + type=int, + default=os.cpu_count() or 1, + help="Number of processes for per-record processing (datasets.map)", + ) + ap.add_argument( + "--max-shard-size", + default="1GB", + help="Maximum shard size passed to save_to_disk (e.g., 1GB, 500MB)", + ) + ap.add_argument( + "--log-level", + default="INFO", + choices=["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"], + help="Logging level (default: INFO)", + ) + args = ap.parse_args(argv) + + inp = Path(args.input) + out_dir = Path(args.output) + + logging.basicConfig( + level=getattr(logging, args.log_level), + format="%(asctime)s %(levelname)s %(message)s", + ) + + # Discover splits + splits = _discover_splits(inp) + if not splits: + raise SystemExit(f"No .aselmdb files found under: {inp}") + + logging.info("Discovered splits: %s", {k: len(v) for k, v in splits.items()}) + + # Build dataset(s) + datasets = {} + for split, files in splits.items(): + logging.info("Building split '%s' from %d file(s)", split, len(files)) + ds = _build_split_dataset(files, num_proc=args.num_proc) + datasets[split] = ds + + # Save to disk (always as DatasetDict for predictable structure) + logging.info("Saving DatasetDict with %d split(s) to %s", len(datasets), out_dir) + ds_dict = DatasetDict(datasets) + ds_dict.save_to_disk(out_dir, max_shard_size=args.max_shard_size) + + logging.info("Done. Saved dataset(s) to %s", out_dir) + print(f"Saved dataset(s) to {out_dir}") + return 0 + + +# ------------------------ +# Dataset building helpers +# ------------------------ + + +def _discover_splits(root: Path) -> dict[str, list[str]]: + """Find input .aselmdb files and group them into splits. + + Rules: + - If ``root`` is a file ending with .aselmdb -> one split named 'train'. + - If ``root`` is a directory with ``*.aselmdb`` -> one split named after the + directory (commonly a split name like 'neural_val'). If the directory name + is 'train'/'validation'/'test', keep it, otherwise default to 'train'. + - If ``root`` contains subdirectories, each subdirectory with ``*.aselmdb`` + becomes a split named by the subdirectory. + """ + if root.is_file() and root.suffix == ".aselmdb": + return {root.stem: [str(root)]} + + if root.is_dir(): + # Case 1: directory has *.aselmdb directly + files_here = sorted(str(p) for p in root.glob("*.aselmdb")) + if files_here: + return {root.name: files_here} + + # Case 2: subdirectories are splits + splits: dict[str, list[str]] = {} + for sub in sorted([p for p in root.iterdir() if p.is_dir()]): + files = sorted(str(p) for p in sub.glob("*.aselmdb")) + if files: + splits[sub.name] = files + return splits + + return {} + + +def _entries_from_files(files: list[str]): + for f in files: + for entry in iter_aselmdb(f): + yield {"raw": json.dumps(entry)} + + +OUTPUT_COLUMNS = [ + "unique_id", + "composition", + "SMILES", + "InChI", + "InChIKey", + "total-energy", + "homo", + "lumo", + "gap", + "charge", + "spin", + "nl_energy", + "atomic-numbers", + "atomic-coordinates", + "atomic-isotope", + "bond-connections", + "bond-orders", + "mulliken_charges", + "lowdin_charges", + "nbo_charges", +] + + +def _process_batch(batch: dict) -> dict: + raws: list[str] = batch["raw"] + out: dict[str, list] = {k: [] for k in OUTPUT_COLUMNS} + keep: list[bool] = [] + for s in raws: + try: + rec = record_from_entry(json.loads(s)) + except Exception: + rec = None + if rec is None: + # Fill placeholders; we'll filter later + for k in OUTPUT_COLUMNS: + # Scalars -> None, lists -> empty list + if k in { + "atomic-numbers", + "atomic-coordinates", + "atomic-isotope", + "bond-connections", + "bond-orders", + "mulliken_charges", + "lowdin_charges", + "nbo_charges", + }: + out[k].append([]) + else: + out[k].append(None) + keep.append(False) + else: + for k in OUTPUT_COLUMNS: + out[k].append(rec.get(k)) + keep.append(True) + + out["__keep__"] = keep + return out + + +def _build_split_dataset(files: list[str], num_proc: int) -> Dataset: + # Stage 1: build a lightweight dataset of raw JSON strings + ds = Dataset.from_generator( + _entries_from_files, + gen_kwargs={"files": files}, + keep_in_memory=False, + ) + + # Stage 2: heavy processing in parallel + ds = ds.map( + _process_batch, + batched=True, + remove_columns=["raw"], + num_proc=max(1, int(num_proc)), + desc="Processing records with RDKit", + ) + + # Stage 3: drop failed rows + ds = ds.filter(lambda keep: keep, input_columns=["__keep__"]).remove_columns( + ["__keep__"] + ) + return ds + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/opt/sterochemistry/pyproject.toml b/opt/sterochemistry/pyproject.toml new file mode 100644 index 00000000..a40fc6c2 --- /dev/null +++ b/opt/sterochemistry/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "sterochemistry" +version = "0.1.0" +description = "Ingest datasets training" +readme = "README.md" +requires-python = ">=3.10, <3.14" +dependencies = [ + "datasets>=4.0.0", + "lmdb>=1.7.3", + "numpy", + "pandas>=2.2.3", + "rdkit", + "requests>=2.32.3", + "tenacity>=9.1.2", + "tqdm>=4.67.1", +] From 6fd17c0f00a1933933fe00397cc0c507873f0b6a Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 15:43:30 -0400 Subject: [PATCH 053/147] feat: support processing *.tar.gz files directly --- opt/sterochemistry/omol25.py | 162 ++++++++++++++++++++++++++++++----- 1 file changed, 142 insertions(+), 20 deletions(-) diff --git a/opt/sterochemistry/omol25.py b/opt/sterochemistry/omol25.py index 0290d7c7..c4149aee 100644 --- a/opt/sterochemistry/omol25.py +++ b/opt/sterochemistry/omol25.py @@ -3,8 +3,10 @@ Build a HuggingFace dataset from one or more ASE LMDB (".aselmdb") files. - Input: a directory containing ``*.aselmdb`` files (single split) OR a - directory with subdirectories, each containing ``*.aselmdb`` (multi-split). - You may also pass a single ``.aselmdb`` file. + directory with subdirectories, each containing ``*.aselmdb`` (multi-split), + or a directory containing ``*.tar.gz``/``*.tgz`` archives where each archive + represents a split and contains ``*.aselmdb`` inside. You may also pass a + single ``.aselmdb`` or ``.tar.gz`` file. - Processing: entries are read, normalized with RDKit (bonding, identifiers), and curated fields are produced. - Output: an Arrow dataset written via ``datasets`` (``save_to_disk``). @@ -25,6 +27,10 @@ import json import os from pathlib import Path +import tarfile +import tempfile +import threading +from queue import Queue import zlib from collections.abc import Generator from typing import Any @@ -351,6 +357,12 @@ def main(argv: list[str] | None = None) -> int: default=os.cpu_count() or 1, help="Number of processes for per-record processing (datasets.map)", ) + ap.add_argument( + "--read-workers", + type=int, + default=4, + help="Number of threads to stream LMDB entries from files/archives", + ) ap.add_argument( "--max-shard-size", default="1GB", @@ -381,9 +393,9 @@ def main(argv: list[str] | None = None) -> int: # Build dataset(s) datasets = {} - for split, files in splits.items(): - logging.info("Building split '%s' from %d file(s)", split, len(files)) - ds = _build_split_dataset(files, num_proc=args.num_proc) + for split, sources in splits.items(): + logging.info("Building split '%s' from %d source(s)", split, len(sources)) + ds = _build_split_dataset(sources, num_proc=args.num_proc, read_workers=args.read_workers) datasets[split] = ds # Save to disk (always as DatasetDict for predictable structure) @@ -401,41 +413,151 @@ def main(argv: list[str] | None = None) -> int: # ------------------------ -def _discover_splits(root: Path) -> dict[str, list[str]]: +def _discover_splits(root: Path) -> dict[str, list[tuple[str, ...]]]: """Find input .aselmdb files and group them into splits. Rules: - - If ``root`` is a file ending with .aselmdb -> one split named 'train'. + - If ``root`` is a file ending with .aselmdb -> one split named by stem. + - If ``root`` is a .tar.gz/.tgz -> one split named by archive basename; all + .aselmdb members are included. - If ``root`` is a directory with ``*.aselmdb`` -> one split named after the - directory (commonly a split name like 'neural_val'). If the directory name - is 'train'/'validation'/'test', keep it, otherwise default to 'train'. + directory (e.g., 'neural_val'). - If ``root`` contains subdirectories, each subdirectory with ``*.aselmdb`` becomes a split named by the subdirectory. + - If ``root`` contains ``*.tar.gz``/``*.tgz`` archives, each archive becomes + a split named by its basename (without the archive suffix). """ if root.is_file() and root.suffix == ".aselmdb": - return {root.stem: [str(root)]} + return {root.stem: [("file", str(root))]} + if root.is_file() and (str(root).endswith(".tar.gz") or str(root).endswith(".tgz")): + split = _basename_without_targz(root.name) + members = _list_aselmdb_in_tar(str(root)) + return {split: [("tar", str(root), m) for m in members]} if root.is_dir(): # Case 1: directory has *.aselmdb directly files_here = sorted(str(p) for p in root.glob("*.aselmdb")) if files_here: - return {root.name: files_here} + return {root.name: [("file", f) for f in files_here]} # Case 2: subdirectories are splits - splits: dict[str, list[str]] = {} + splits: dict[str, list[tuple[str, ...]]] = {} for sub in sorted([p for p in root.iterdir() if p.is_dir()]): files = sorted(str(p) for p in sub.glob("*.aselmdb")) if files: - splits[sub.name] = files + splits[sub.name] = [("file", f) for f in files] + # Tarball splits at root + for tb in sorted( + [ + p + for p in root.iterdir() + if p.is_file() and (str(p).endswith(".tar.gz") or str(p).endswith(".tgz")) + ] + ): + split = _basename_without_targz(tb.name) + members = _list_aselmdb_in_tar(str(tb)) + if members: + splits[split] = [("tar", str(tb), m) for m in members] return splits return {} +def _basename_without_targz(name: str) -> str: + for suf in (".tar.gz", ".tgz"): + if name.endswith(suf): + return name[: -len(suf)] + # fallback + return Path(name).stem + + +def _list_aselmdb_in_tar(tar_path: str) -> list[str]: + with tarfile.open(tar_path, "r:gz") as tf: + return [m.name for m in tf.getmembers() if m.isfile() and m.name.endswith(".aselmdb")] + + +def _iter_aselmdb_from_tar(tar_path: str, member_name: str): + """Iterate entries from a single .aselmdb member inside a tar.gz archive. + + Streams the member to a temporary file without extracting the entire archive, + then uses the existing LMDB reader to iterate entries. + """ + with tarfile.open(tar_path, "r:gz") as tf: + try: + m = tf.getmember(member_name) + except KeyError: + logging.warning("Missing member %s in %s", member_name, tar_path) + return + fobj = tf.extractfile(m) + if fobj is None: + logging.warning("Could not open member %s from %s", member_name, tar_path) + return + with tempfile.NamedTemporaryFile(suffix=".aselmdb", delete=False) as tmp: + tmp_path = tmp.name + while True: + chunk = fobj.read(1024 * 1024) + if not chunk: + break + tmp.write(chunk) + try: + for entry in iter_aselmdb(tmp_path): + yield entry + finally: + try: + os.remove(tmp_path) + except Exception: + logging.debug("Failed to remove temp file %s", tmp_path, exc_info=True) + + +def _entries_from_sources(sources: list[tuple[str, ...]], read_workers: int): + """Concurrent producer that streams entries from file and tar sources.""" + q: "Queue[object]" = Queue(maxsize=2048) + sentinel = object() + src_iter = iter(sources) + lock = threading.Lock() + finished = 0 + n_workers = max(1, int(read_workers)) + + def next_src(): + nonlocal src_iter + with lock: + try: + return next(src_iter) + except StopIteration: + return None + + def worker(): + nonlocal finished + while True: + src = next_src() + if src is None: + break + try: + if src[0] == "file": + _, path = src + it = iter_aselmdb(path) + elif src[0] == "tar": + _, tar_path, member = src + it = _iter_aselmdb_from_tar(tar_path, member) + else: + it = iter(()) + for entry in it: + q.put({"raw": json.dumps(entry)}, block=True) + except Exception: + logging.exception("Reader failed for source: %s", src) + with lock: + finished += 1 + if finished == n_workers: + q.put(sentinel) + + threads = [threading.Thread(target=worker, daemon=True) for _ in range(n_workers)] + for t in threads: + t.start() -def _entries_from_files(files: list[str]): - for f in files: - for entry in iter_aselmdb(f): - yield {"raw": json.dumps(entry)} + while True: + item = q.get() + if item is sentinel: + break + yield item OUTPUT_COLUMNS = [ @@ -498,11 +620,11 @@ def _process_batch(batch: dict) -> dict: return out -def _build_split_dataset(files: list[str], num_proc: int) -> Dataset: +def _build_split_dataset(sources: list[tuple[str, ...]], num_proc: int, read_workers: int) -> Dataset: # Stage 1: build a lightweight dataset of raw JSON strings ds = Dataset.from_generator( - _entries_from_files, - gen_kwargs={"files": files}, + _entries_from_sources, + gen_kwargs={"sources": sources, "read_workers": read_workers}, keep_in_memory=False, ) From 98a7f9e89ee9477a749f0e2b6a04f90a20538f5d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 16:25:20 -0400 Subject: [PATCH 054/147] feat: add process_clc_db --- opt/sterochemistry/.gitignore | 2 + .../{clc_db.py => export_clc_db.py} | 0 opt/sterochemistry/process_clc_db.py | 269 ++++++++++++++++++ opt/sterochemistry/pyproject.toml | 1 + pyproject.toml | 2 +- uv.lock | 120 ++++++-- 6 files changed, 370 insertions(+), 24 deletions(-) create mode 100644 opt/sterochemistry/.gitignore rename opt/sterochemistry/{clc_db.py => export_clc_db.py} (100%) create mode 100644 opt/sterochemistry/process_clc_db.py diff --git a/opt/sterochemistry/.gitignore b/opt/sterochemistry/.gitignore new file mode 100644 index 00000000..1e4519d8 --- /dev/null +++ b/opt/sterochemistry/.gitignore @@ -0,0 +1,2 @@ +data/ +uv.lock diff --git a/opt/sterochemistry/clc_db.py b/opt/sterochemistry/export_clc_db.py similarity index 100% rename from opt/sterochemistry/clc_db.py rename to opt/sterochemistry/export_clc_db.py diff --git a/opt/sterochemistry/process_clc_db.py b/opt/sterochemistry/process_clc_db.py new file mode 100644 index 00000000..0da9994c --- /dev/null +++ b/opt/sterochemistry/process_clc_db.py @@ -0,0 +1,269 @@ +#!/usr/bin/env -S uv run python +""" +Process CLC-DB molecules.csv into a HuggingFace datasets Arrow dataset. + +Steps: +- Read the CSV produced by export_clc_db.py +- Validate SMILES (RDKit); drop invalid/empty +- Compute InChI and InChIKey (RDKit) +- Convert "Chirality" to multi-label dict with keys: point, planar, axial +- Split into train/val/test (80/10/10) using StratifiedShuffleSplit, stratify on chirality +- Save as a DatasetDict via datasets.save_to_disk + +Usage: + uv run opt/sterochemistry/process_clc_db.py \ + --csv opt/sterochemistry/data/clc_db/molecules.csv \ + --out opt/sterochemistry/data/clc_db/hf +""" + +import argparse +import json +import math +import os +from typing import Any + +import numpy as np +import pandas as pd +from datasets import Dataset, DatasetDict, Features, Value +from rdkit import Chem +from rdkit.Chem.inchi import MolToInchi, MolToInchiKey +from sklearn.model_selection import StratifiedShuffleSplit + + +# Column names from the website/export script +COL_SMILES = "SMILES" +COL_CHIRALITY = "Chirality" +COL_MW = "Molecular Weight" +COL_ZP = "Zero-point correction" +COL_TE = "Thermal correction to Energy" +COL_TH = "Thermal correction to Enthalpy" +COL_TG = "Thermal correction to Gibbs Free Energy" +COL_HOMO = "HOMO Energy (eV)" +COL_LUMO = "LUMO Energy (eV)" +COL_GAP = "HOMO-LUMO Gap (eV)" + + +def _to_float(x: Any) -> float | None: + if x is None: + return None + if isinstance(x, float): + if math.isfinite(x): + return float(x) + return None + try: + s = str(x).strip() + if s == "" or s.lower() in {"na", "n/a", "nan", "none"}: + return None + return float(s) + except Exception: + return None + + +def parse_chirality(raw: str | None) -> dict[str, bool]: + """Parse the website's chirality string into multi-label booleans. + + - Recognizes: point, planar, axial (case-insensitive, substring match) + - Treats N/A or empty as all False + - If multiple classes are present, sets both True + """ + flags = {"point": False, "planar": False, "axial": False} + if not raw: + return flags + s = str(raw).strip() + if s == "" or s.lower() in {"na", "n/a", "none"}: + return flags + s_low = s.lower() + if "point" in s_low: + flags["point"] = True + if "planar" in s_low: + flags["planar"] = True + if "axial" in s_low: + flags["axial"] = True + return flags + + +def smiles_to_mol(smiles: str | None): + if not smiles: + return None + s = smiles.strip() + if s == "": + return None + try: + m = Chem.MolFromSmiles(s) + return m + except Exception: + return None + + +def compute_inchi(mol) -> tuple[str | None, str | None]: + if mol is None: + return None, None + try: + inchi = MolToInchi(mol) + except Exception: + inchi = None + try: + ikey = MolToInchiKey(mol) + except Exception: + ikey = None + return inchi, ikey + + +def load_rows(csv_path: str) -> list[dict[str, Any]]: + # Read all columns as strings to avoid Pandas NA coercion, then convert + df = pd.read_csv(csv_path, dtype=str, keep_default_na=False) + + rows: list[dict[str, Any]] = [] + for _, r in df.iterrows(): + smiles = r.get(COL_SMILES, "").strip() + mol = smiles_to_mol(smiles) + if mol is None: + continue # drop invalid/empty SMILES + inchi, inchi_key = compute_inchi(mol) + + ch = parse_chirality(r.get(COL_CHIRALITY, "")) + + row: dict[str, Any] = { + "smiles": smiles, + "InChI": inchi if inchi is not None else "", + "InChIKey": inchi_key if inchi_key is not None else "", + "chirality": ch, + "molecular_weight": _to_float(r.get(COL_MW)), + "zero_point_correction": _to_float(r.get(COL_ZP)), + "thermal_correction_energy": _to_float(r.get(COL_TE)), + "thermal_correction_enthalpy": _to_float(r.get(COL_TH)), + "thermal_correction_gibbs": _to_float(r.get(COL_TG)), + "homo": _to_float(r.get(COL_HOMO)), + "lumo": _to_float(r.get(COL_LUMO)), + "gap": _to_float(r.get(COL_GAP)), + } + rows.append(row) + return rows + + +def build_multilabel_targets(rows: list[dict[str, Any]]) -> np.ndarray: + y = np.zeros((len(rows), 3), dtype=int) + for i, r in enumerate(rows): + ch = r.get("chirality", {}) or {} + y[i, 0] = 1 if ch.get("point", False) else 0 + y[i, 1] = 1 if ch.get("planar", False) else 0 + y[i, 2] = 1 if ch.get("axial", False) else 0 + return y + + +def _stratify_indices( + n: int, y: np.ndarray, seed: int = 17 +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Split indices into train/val/test (80/10/10) using stratification on y. + + Primary path: pass multi-label indicator matrix directly to StratifiedShuffleSplit + as requested. If the environment's sklearn does not support this, fall back to + stratifying on label-combinations as a single multiclass target. + """ + rng = seed + + def _try_split(y_input) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + idx_all = np.arange(n) + # first split off test 10% + sss1 = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=rng) + train_val_idx, test_idx = next(sss1.split(idx_all, y_input)) + # then split train/val with val proportion 1/9 (~0.111... of remaining) + sss2 = StratifiedShuffleSplit(n_splits=1, test_size=1 / 9, random_state=rng) + train_idx, val_idx = next(sss2.split(train_val_idx, y_input[train_val_idx])) + return train_val_idx[train_idx], train_val_idx[val_idx], test_idx + + # Attempt multi-label directly + try: + return _try_split(y) + except Exception: + # Fall back to combination labels as strings + combos = np.array([f"{a}{b}{c}" for a, b, c in y], dtype=object) + return _try_split(combos) + + +def to_hf_datasets( + rows: list[dict[str, Any]], indices: dict[str, np.ndarray] +) -> DatasetDict: + features = Features( + { + "smiles": Value("string"), + "InChI": Value("string"), + "InChIKey": Value("string"), + "chirality": { + "point": Value("bool"), + "planar": Value("bool"), + "axial": Value("bool"), + }, + "molecular_weight": Value("float64"), + "zero_point_correction": Value("float64"), + "thermal_correction_energy": Value("float64"), + "thermal_correction_enthalpy": Value("float64"), + "thermal_correction_gibbs": Value("float64"), + "homo": Value("float64"), + "lumo": Value("float64"), + "gap": Value("float64"), + } + ) + + def _subset(split_idx: np.ndarray) -> Dataset: + data = [rows[i] for i in split_idx] + return Dataset.from_list(data, features=features) + + dsd = DatasetDict( + { + "train": _subset(indices["train"]), + "validation": _subset(indices["validation"]), + "test": _subset(indices["test"]), + } + ) + return dsd + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument( + "--csv", + default=os.path.join("data", "clc_db", "molecules.csv"), + help="Input molecules.csv path", + ) + ap.add_argument( + "--out", + default=os.path.join("data", "clc_db", "hf"), + help="Output directory for the saved DatasetDict", + ) + ap.add_argument("--seed", type=int, default=42, help="Random seed for splits") + args = ap.parse_args(argv) + + rows = load_rows(args.csv) + if not rows: + print("No valid molecules found after SMILES validation.") + return 1 + + y = build_multilabel_targets(rows) + train_idx, val_idx, test_idx = _stratify_indices(len(rows), y, seed=args.seed) + indices = {"train": train_idx, "validation": val_idx, "test": test_idx} + + dsd = to_hf_datasets(rows, indices) + os.makedirs(args.out, exist_ok=True) + dsd.save_to_disk(args.out, num_shards={k: 4 for k in indices.keys()}) + + # Brief summary + print( + json.dumps( + { + "counts": { + "train": len(train_idx), + "validation": len(val_idx), + "test": len(test_idx), + "total": len(rows), + }, + "out": os.path.abspath(args.out), + }, + indent=2, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/opt/sterochemistry/pyproject.toml b/opt/sterochemistry/pyproject.toml index a40fc6c2..ada6b762 100644 --- a/opt/sterochemistry/pyproject.toml +++ b/opt/sterochemistry/pyproject.toml @@ -11,6 +11,7 @@ dependencies = [ "pandas>=2.2.3", "rdkit", "requests>=2.32.3", + "scikit-learn>=1.7.1", "tenacity>=9.1.2", "tqdm>=4.67.1", ] diff --git a/pyproject.toml b/pyproject.toml index 093bf427..20a7f8fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "scikit-learn>=1.5.1", "selfies>=2.1.0", "rdkit>=2024.3.5", - "smirk @ git+ssh://git@github.com/BattModels/smirk.git@dfdd9dc27c066f956b69d1f55c2d4717beb7838d", + "smirk==0.1.0", "safetensors>=0.5.1", "pytorch-lightning~=2.5" ] diff --git a/uv.lock b/uv.lock index 9a59b0c9..eb66dad8 100644 --- a/uv.lock +++ b/uv.lock @@ -457,7 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size = 9465369 }, ] [[package]] From 411d13fdce4a1a8bab53ab92e1748b22fd98c9da Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 16:40:29 -0400 Subject: [PATCH 055/147] fix pre-commit issues --- electrolyte_fm/models/mol_surprise.py | 1 - opt/design/plots.jl | 1 - opt/screening/electrolyte_sweep.py | 0 opt/screening/plots/src/collate.jl | 1 - 4 files changed, 3 deletions(-) mode change 100644 => 100755 opt/screening/electrolyte_sweep.py diff --git a/electrolyte_fm/models/mol_surprise.py b/electrolyte_fm/models/mol_surprise.py index 907a4561..98dee914 100644 --- a/electrolyte_fm/models/mol_surprise.py +++ b/electrolyte_fm/models/mol_surprise.py @@ -1,5 +1,4 @@ import torch -from torch.fx import has_side_effect from torch.nn import functional as F from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding from ..utils.tokenizer import load_tokenizer diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 070fda20..dc984d38 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -99,4 +99,3 @@ with_theme(MISTStyle.theme()) do name_df_order=("Baseline" => df_order, "Augmented" => df_order_rand) ) end |> MISTStyle.savefig("double_bond_loc") - diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100644 new mode 100755 diff --git a/opt/screening/plots/src/collate.jl b/opt/screening/plots/src/collate.jl index 07bba668..1d99c9a4 100644 --- a/opt/screening/plots/src/collate.jl +++ b/opt/screening/plots/src/collate.jl @@ -48,4 +48,3 @@ function performance_trace(logfile::String) end return trace, rank_throughput end - From 2fcc8ce590b00c2264df2dc4142e2591e9b843fb Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 16:52:38 -0400 Subject: [PATCH 056/147] Use latest smirk, update CI to reflect --- .github/workflows/CI.yaml | 6 +-- electrolyte_fm/data_modules/pubchem_qc.py | 26 ++++++++---- opt/sterochemistry/export_clc_db.py | 0 opt/sterochemistry/omol25.py | 18 ++++++-- opt/sterochemistry/process_clc_db.py | 0 pyproject.toml | 5 ++- uv.lock | 51 ++--------------------- 7 files changed, 39 insertions(+), 67 deletions(-) mode change 100644 => 100755 opt/sterochemistry/export_clc_db.py mode change 100644 => 100755 opt/sterochemistry/omol25.py mode change 100644 => 100755 opt/sterochemistry/process_clc_db.py diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index 673b716c..d785997d 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -14,11 +14,7 @@ jobs: enable-cache: true - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - - name: Install deploy key for BattModels/smirk - uses: webfactory/ssh-agent@dc588b651fe13675774614f8e6a936a468676387 #v0.9.0 - with: - ssh-private-key: ${{ secrets.SMIRK_PRIVATE_KEY }} - name: install project run: uv sync --all-extras --dev - name: pytest - run: uv run pytest + run: uv run pytest --durations=0 diff --git a/electrolyte_fm/data_modules/pubchem_qc.py b/electrolyte_fm/data_modules/pubchem_qc.py index 18b6c809..17203567 100644 --- a/electrolyte_fm/data_modules/pubchem_qc.py +++ b/electrolyte_fm/data_modules/pubchem_qc.py @@ -597,20 +597,28 @@ def annotated_tokens( @torch.no_grad def sparse_topo_distance(mol: Chem.Mol, atom_indices: list[int]): + """Return sparse pairwise topology features for selected atom indices. + + Produces a stacked sparse tensor with the last dimension containing: + - Adjacency (0/1 hop connectivity) + - Topological distance (shortest-path hop count) + + The tensor shape is (A, A, 2) where A is 1 + max(atom_indices), matching + the behavior expected by tests that include out-of-range indices for padding. + """ adx = torch.tensor(atom_indices) rdx, cdx = torch.meshgrid(adx, adx, indexing="ij") idx = torch.stack((rdx.flatten(), cdx.flatten())) - S = [] - # Number of Hops - for d in [ - # Chem.rdmolops.GetDistanceMatrix(mol, force=True), - Chem.rdmolops.GetAdjacencyMatrix(mol, force=True), - # Chem.rdmolops.GetDistanceMatrix(mol, useBO=True, force=True), - ]: - S.append(torch.sparse_coo_tensor(idx, d.flatten())) + # Collect dense matrices from RDKit + adj = Chem.rdmolops.GetAdjacencyMatrix(mol, force=True) + topo = Chem.rdmolops.GetDistanceMatrix(mol) # shortest-path (hop) distances + + # Build sparse tensors aligned to the provided indices; infer size from idx + S_adj = torch.sparse_coo_tensor(idx, adj.flatten()) + S_topo = torch.sparse_coo_tensor(idx, topo.flatten()) - return torch.stack(S, dim=-1) + return torch.stack([S_adj, S_topo], dim=-1) @torch.no_grad diff --git a/opt/sterochemistry/export_clc_db.py b/opt/sterochemistry/export_clc_db.py old mode 100644 new mode 100755 diff --git a/opt/sterochemistry/omol25.py b/opt/sterochemistry/omol25.py old mode 100644 new mode 100755 index c4149aee..00461a45 --- a/opt/sterochemistry/omol25.py +++ b/opt/sterochemistry/omol25.py @@ -395,7 +395,9 @@ def main(argv: list[str] | None = None) -> int: datasets = {} for split, sources in splits.items(): logging.info("Building split '%s' from %d source(s)", split, len(sources)) - ds = _build_split_dataset(sources, num_proc=args.num_proc, read_workers=args.read_workers) + ds = _build_split_dataset( + sources, num_proc=args.num_proc, read_workers=args.read_workers + ) datasets[split] = ds # Save to disk (always as DatasetDict for predictable structure) @@ -451,7 +453,8 @@ def _discover_splits(root: Path) -> dict[str, list[tuple[str, ...]]]: [ p for p in root.iterdir() - if p.is_file() and (str(p).endswith(".tar.gz") or str(p).endswith(".tgz")) + if p.is_file() + and (str(p).endswith(".tar.gz") or str(p).endswith(".tgz")) ] ): split = _basename_without_targz(tb.name) @@ -462,6 +465,7 @@ def _discover_splits(root: Path) -> dict[str, list[tuple[str, ...]]]: return {} + def _basename_without_targz(name: str) -> str: for suf in (".tar.gz", ".tgz"): if name.endswith(suf): @@ -472,7 +476,11 @@ def _basename_without_targz(name: str) -> str: def _list_aselmdb_in_tar(tar_path: str) -> list[str]: with tarfile.open(tar_path, "r:gz") as tf: - return [m.name for m in tf.getmembers() if m.isfile() and m.name.endswith(".aselmdb")] + return [ + m.name + for m in tf.getmembers() + if m.isfile() and m.name.endswith(".aselmdb") + ] def _iter_aselmdb_from_tar(tar_path: str, member_name: str): @@ -620,7 +628,9 @@ def _process_batch(batch: dict) -> dict: return out -def _build_split_dataset(sources: list[tuple[str, ...]], num_proc: int, read_workers: int) -> Dataset: +def _build_split_dataset( + sources: list[tuple[str, ...]], num_proc: int, read_workers: int +) -> Dataset: # Stage 1: build a lightweight dataset of raw JSON strings ds = Dataset.from_generator( _entries_from_sources, diff --git a/opt/sterochemistry/process_clc_db.py b/opt/sterochemistry/process_clc_db.py old mode 100644 new mode 100755 diff --git a/pyproject.toml b/pyproject.toml index 6c3ba5f2..836555d4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "scikit-learn>=1.5.1", 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b/test/test_pubchemqc.py @@ -520,6 +520,7 @@ def rot(θ=1): @pytest.mark.parametrize("N", [32, 1, 2]) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_mds_svd(N): coords = torch.rand(N, 3) D = torch.cdist(coords, coords) @@ -532,6 +533,7 @@ def test_mds_svd(N): @pytest.mark.parametrize("B,N", [(8, 32), (1, 1), (1, 8), (8, 1)]) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_masked_mds_svd(B, N): mask = torch.rand(B, N) > 0.8 mask_pw = mask.unsqueeze(2) & mask.unsqueeze(1) From 733f4c7f66b42f16d21b9dd2e1704c1bee866305 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 18:02:06 -0400 Subject: [PATCH 058/147] fix mds test errors --- electrolyte_fm/data_modules/pubchem_qc.py | 137 +++++++++++++++------- test/test_pubchemqc.py | 11 +- 2 files changed, 100 insertions(+), 48 deletions(-) diff --git a/electrolyte_fm/data_modules/pubchem_qc.py b/electrolyte_fm/data_modules/pubchem_qc.py index 17203567..2da6be75 100644 --- a/electrolyte_fm/data_modules/pubchem_qc.py +++ b/electrolyte_fm/data_modules/pubchem_qc.py @@ -686,63 +686,110 @@ def mol_from_prediction( @torch.cuda.nvtx.range("mds_svd") -def mds_svd(D: torch.Tensor, dim=3): - n = D.size(0) - factory_kwargs = {"device": D.device, "dtype": D.dtype} +def mds_svd(D: torch.Tensor, dim: int = 3) -> torch.Tensor: + """ + Classical (unmasked) MDS via double-centering and eigendecomposition. - if n == 1: - return torch.zeros(*D.shape[:-1], dim, **factory_kwargs) - elif n == 2: - p1 = torch.zeros(*D.shape[1:-1], dim, **factory_kwargs) - p2 = torch.zeros(*D.shape[1:-2], dim, **factory_kwargs) - p2[..., -1] += D[..., 0, 1] - return torch.stack([p1, p2]) - elif n < dim: - raise RuntimeError( - "Insufficient points to compute coordinates (Dim reduction not implimented)" - ) + Args: + D: (N, N) distance matrix (unbatched). + dim: target embedding dimension. - # Compute the Gram matrix using double centering - B = D.pow(2) - B -= B.mean(-1, keepdim=True) - B -= B.mean(-2, keepdim=True) - B *= 0.5 + Returns: + X: (N, dim) coordinates (zero-padded if N < dim). + """ + assert D.dim() == 2 and D.shape[0] == D.shape[1], "D must be (N, N)" - # Run SVD in at least float32 precision - dtype = torch.promote_types(D.dtype, torch.float32) - u, s, _ = torch.linalg.svd(B.to(dtype=dtype)) + N = D.size(0) + factory_kwargs = {"device": D.device, "dtype": D.dtype} - # Select the top 'dim' components, clamping to avoid numerical issues - u = u[..., :dim] - s = s[:dim].clamp(min=0) - s = torch.diag_embed(s.sqrt()) + # Trivial cases + if N == 1: + return torch.zeros(N, dim, **factory_kwargs) + if N == 2: + # Put the two points along one axis separated by D[0,1] + X = torch.zeros(N, dim, **factory_kwargs) + X[1, -1] = D[0, 1] + return X + + # Work in at least float32 for stability + work_dtype = torch.promote_types(D.dtype, torch.float32) + D2 = (D.to(dtype=work_dtype)) ** 2 + + # Double-centering: B = -0.5 * (D^2 - row_mean - col_mean + grand_mean) + row_mean = D2.mean(dim=1, keepdim=True) # (N, 1) + col_mean = D2.mean(dim=0, keepdim=True) # (1, N) + grand_mean = D2.mean() # scalar + B = -0.5 * (D2 - row_mean - col_mean + grand_mean) + + # Enforce symmetry to reduce numerical noise + B = 0.5 * (B + B.T) + + # Eigendecomposition of symmetric Gram matrix + # (ascending eigenvalues) + evals, evecs = torch.linalg.eigh(B) + + # Keep top-k components with nonnegative eigenvalues + k = min(dim, N) + evals_k = evals[-k:].clamp_min(0).sqrt() # (k,) + evecs_k = evecs[:, -k:] # (N, k) + + # Coordinates: X = V * sqrt(Lambda) + Xk = evecs_k * evals_k.unsqueeze(0) # (N, k) + + # Pad to (N, dim) if N < dim + if k < dim: + X = F.pad(Xk, (0, dim - k)) + else: + X = Xk - # Compute the coordinates: X = U * sqrt(S) - return u @ s + return X.to(dtype=D.dtype) @torch.cuda.nvtx.range("batched_mds_svd") -def masked_mds_svd(D: torch.Tensor, mask: torch.Tensor, dim=3): - # Zero Mask - mask_pw = mask.unsqueeze(-1) & mask.unsqueeze(-2) +def masked_mds_svd(D: torch.Tensor, mask: torch.Tensor, dim: int = 3) -> torch.Tensor: + """ + Classical MDS with masking. Returns coordinates of shape (..., N, dim). + """ + # Pairwise mask (True where D_ij is observed) + mask_pw = (mask.unsqueeze(-1) & mask.unsqueeze(-2)).to(D.dtype) assert mask_pw.shape == D.shape - # Gram matrix from distance matrix - B = D.pow(2) - B -= B.sum(-1, keepdim=True) / mask_pw.sum(-1, keepdim=True).clamp(min=1) - B -= B.sum(-2, keepdim=True) / mask_pw.sum(-2, keepdim=True).clamp(min=1) - B *= -0.5 - B[~mask_pw] = 0 - - # Run SVD in at least float32 precision + # Work in at least float32 dtype = torch.promote_types(D.dtype, torch.float32) - u, s, _ = torch.linalg.svd(B.to(dtype=dtype)) + D2 = (D.to(dtype=dtype)) ** 2 + + # Masked means for double-centering: row, col, grand + eps1 = mask_pw.sum(-1, keepdim=True).clamp_min(1.0) # (..., N, 1) + eps2 = mask_pw.sum(-2, keepdim=True).clamp_min(1.0) # (..., 1, N) + epsg = mask_pw.sum(dim=(-1, -2), keepdim=True).clamp_min(1.0) # (..., 1, 1) + + row_mean = (D2 * mask_pw).sum(-1, keepdim=True) / eps1 # (..., N, 1) + col_mean = (D2 * mask_pw).sum(-2, keepdim=True) / eps2 # (..., 1, N) + grand_mean = (D2 * mask_pw).sum(dim=(-1, -2), keepdim=True) / epsg # (..., 1, 1) + + # Double-centered Gram matrix with masking + B = -0.5 * (D2 - row_mean - col_mean + grand_mean) + + # Zero out unobserved entries (optional but keeps SVD clean) and re-symmetrize + B = B * mask_pw + B = 0.5 * (B + B.transpose(-1, -2)) + + # SVD + u, s, _ = torch.linalg.svd(B) + k = min(dim, B.shape[-1]) + + # Take top-k components, sqrt the eigenvalues + u_k = u[..., :k] + s_k = s[..., :k].clamp_min(0).sqrt() + coords_k = u_k * s_k.unsqueeze(-2) # (..., N, k) + + # Pad to requested dim if needed + if k < dim: + coords = F.pad(coords_k, (0, dim - k)) # pad last dimension + else: + coords = coords_k - u = u[..., :dim] - s = s[..., :dim].clamp(min=0) - s = torch.diag_embed(s.sqrt()) - coords_raw = u @ s - return coords_raw + return coords.to(D.dtype) def mol_from_pairwise( diff --git a/test/test_pubchemqc.py b/test/test_pubchemqc.py index 2e76224e..af5af6f4 100644 --- a/test/test_pubchemqc.py +++ b/test/test_pubchemqc.py @@ -27,6 +27,11 @@ from electrolyte_fm.models.token_level import distance_matrix_loss +@pytest.fixture(autouse=True) +def set_random_seed(): + random.seed(42) # Set a fixed seed for reproducibility + + def pubchem_qc_dataset_path(): dir = Path(__file__).parent.parent.joinpath( "opt", "pubchem-qc", "pubchemqc_jcim2017-split" @@ -324,9 +329,9 @@ def test_smi_token_type(smi: str, token_types: list[SmiTokenType]): zip(pubchem_qc.smi_token_type(tok, tokens), token_types) ): token = tok.convert_ids_to_tokens(tokens[idx]) - assert ( - token_type == ref - ), f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" + assert token_type == ref, ( + f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" + ) @pytest.mark.skipif( From 5cf7e9a61812e9ff46f9dca317d8b88449d2bd2e Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 18:02:16 -0400 Subject: [PATCH 059/147] testing: cut back on molnet tests --- electrolyte_fm/data_modules/pubchem_qc.py | 1 + test/test_molnet.py | 10 +++++++--- test/test_pubchemqc.py | 6 +++--- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/electrolyte_fm/data_modules/pubchem_qc.py b/electrolyte_fm/data_modules/pubchem_qc.py index 2da6be75..57f662c3 100644 --- a/electrolyte_fm/data_modules/pubchem_qc.py +++ b/electrolyte_fm/data_modules/pubchem_qc.py @@ -10,6 +10,7 @@ from enum import IntEnum, auto import torch +from torch.nn import functional as F from torch.utils.data import DataLoader from torch.nn.utils.rnn import pad_sequence from transformers import DataCollatorWithPadding diff --git a/test/test_molnet.py b/test/test_molnet.py index b382c4a6..a8c28889 100644 --- a/test/test_molnet.py +++ b/test/test_molnet.py @@ -15,6 +15,10 @@ "submit", "moleculenet_tasks.libsonnet" ) +# Only test a subset of dataset +MOLNET_DATASETS = ["hiv", "sider"] +# MOLNET_DATASETS = MOLNET_URLS.keys() # Uncomment to test all + @pytest.mark.xfail(strict=False, reason="downloads are flaky", raises=FileNotFoundError) def check_datamodule(dm: LightningDataModule, stage="fit", limit_batches=100): @@ -33,7 +37,7 @@ def check_dataloader(dl, limit_batches, keys=["input_ids", "attention_mask"]): break -@pytest.mark.parametrize("name", MOLNET_URLS.keys()) +@pytest.mark.parametrize("name", MOLNET_DATASETS) def test_datamodule(name): task_config = json.loads(jsonnet.evaluate_file(str(MOLNET_CONFIG)))[name] dm = MolNetDataModule( @@ -44,7 +48,7 @@ def test_datamodule(name): check_datamodule(dm) -@pytest.mark.parametrize("name", MOLNET_URLS.keys()) +@pytest.mark.parametrize("name", MOLNET_DATASETS) @pytest.mark.xfail(strict=False, reason="downloads are flaky", raises=FileNotFoundError) def test_prepare(name): task_config = json.loads(jsonnet.evaluate_file(str(MOLNET_CONFIG)))[name] @@ -88,7 +92,7 @@ def test_validate_molnet_config(): @pytest.mark.parametrize( - "dataset,split", product(DATASET_SIZE.keys(), ["scaffold", "random"]) + "dataset,split", product(MOLNET_DATASETS, ["scaffold", "random"]) ) @pytest.mark.xfail(strict=False, reason="downloads are flaky", raises=FileNotFoundError) def test_splits(dataset, split): diff --git a/test/test_pubchemqc.py b/test/test_pubchemqc.py index af5af6f4..16dcd4a2 100644 --- a/test/test_pubchemqc.py +++ b/test/test_pubchemqc.py @@ -329,9 +329,9 @@ def test_smi_token_type(smi: str, token_types: list[SmiTokenType]): zip(pubchem_qc.smi_token_type(tok, tokens), token_types) ): token = tok.convert_ids_to_tokens(tokens[idx]) - assert token_type == ref, ( - f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" - ) + assert ( + token_type == ref + ), f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" @pytest.mark.skipif( From f8657ce2f3229660d88aedd02f0b1489c4372479 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Tue, 22 Jul 2025 18:00:50 -0400 Subject: [PATCH 060/147] fix: variance calculation --- electrolyte_fm/models/normalize.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/electrolyte_fm/models/normalize.py b/electrolyte_fm/models/normalize.py index ed1faec6..62399854 100644 --- a/electrolyte_fm/models/normalize.py +++ b/electrolyte_fm/models/normalize.py @@ -136,9 +136,9 @@ def fit(self, ds) -> dict: n += mask.view(-1, num_outputs).sum(0) xs = x.view(-1, num_outputs).sum(0) delta = xs - mean - delta2 = xs - mean # Only update masked values mean += (delta / n).get_data().masked_fill(~delta.get_mask(), 0) + delta2 = xs - mean m2 += (delta * delta2).get_data().masked_fill(~delta.get_mask(), 0) self.mean = mean.to(self.mean) From 63580e6b768da72df9b69e47864e60f7fe316fa2 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 1 Sep 2025 18:55:08 -0400 Subject: [PATCH 061/147] fix: progressive thawing works with configure model --- electrolyte_fm/utils/progressive_thawing.py | 86 +++++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 electrolyte_fm/utils/progressive_thawing.py diff --git a/electrolyte_fm/utils/progressive_thawing.py b/electrolyte_fm/utils/progressive_thawing.py new file mode 100644 index 00000000..b74bb4d6 --- /dev/null +++ b/electrolyte_fm/utils/progressive_thawing.py @@ -0,0 +1,86 @@ +import logging +from fnmatch import fnmatch +from lightning.pytorch.callbacks import BaseFinetuning + + +class ProgressiveThawing(BaseFinetuning): + def __init__( + self, + initial: list[str], + stages: list[list[str]], + stage_duration: int = 1, + lr_initial: float = 10, + lr_decay: float = 0.99, + ): + super().__init__() + self.initial = initial + self.stages = stages + self.stage_duration = stage_duration + self.current_stage = 0 + self.lr_scale = [0] + self.lr_initial = float(lr_initial) + self.lr_decay = float(lr_decay) + + def setup(self, trainer, pl_module, stage) -> None: + if not hasattr(pl_module, "encoder"): + pl_module.configure_model() + self.freeze_before_training(pl_module) + + @classmethod + def matching_modules(cls, pl_module, patterns): + for pattern in patterns: + pattern_matched = False + for name, module in pl_module.named_modules(): + if name == "": + continue + logging.debug( + "Matching %s against %s: %d", name, pattern, fnmatch(name, pattern) + ) + if fnmatch(name, pattern): + pattern_matched = True + yield name, module + + if not pattern_matched: + names = [name for name, _ in pl_module.named_modules()] + raise ValueError(f"Pattern {pattern} not found in module: {names}") + + def unfreeze_and_add_param_group(self, modules, optimizer): + self.make_trainable(modules) + params = self.filter_params(modules) + params = self.filter_on_optimizer(optimizer, params) + base_lr = optimizer.param_groups[0]["lr"] + if params: + self.lr_scale.append(self.lr_initial) + lr_scale = self.lr_scale[-1] + 1 + optimizer.add_param_group({"params": params, "lr": base_lr / lr_scale}) + + def on_train_batch_start(self, trainer, *args, **kwargs): + # Decay lr offset + for idx in range(len(self.lr_scale)): + self.lr_scale[idx] *= self.lr_decay + + for opt in trainer.optimizers: + base_lr = opt.param_groups[0]["lr"] + for idx, param_group in enumerate(opt.param_groups): + param_group["lr"] = base_lr / (self.lr_scale[idx] + 1) + + def lr_scheduler_step(self, scheduler, metric) -> None: + print("LR Scheduler step", scheduler, metric) + + def freeze_before_training(self, pl_module): + for name, module in self.matching_modules(pl_module, self.initial): + logging.info("Freezing %s", name) + self.freeze_module(module) + + def finetune_function(self, pl_module, epoch: int, optimizer) -> None: + if self.current_stage == len(self.stages): + return + + if epoch == self.stage_duration * (self.current_stage + 1): + thaw = self.stages[self.current_stage] + self.current_stage += 1 + to_thaw = [] + for name, module in self.matching_modules(pl_module, thaw): + logging.info("Thawing %s", name) + to_thaw.append(module) + self.unfreeze_and_add_param_group(to_thaw, optimizer) From d25a73147b91d5963e14cb3930ef8eb6a8944755 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 1 Sep 2025 18:56:14 -0400 Subject: [PATCH 062/147] feat: isotopes dataloader and config --- .../data_modules/isotope_dataset.py | 28 +++++++ submit/isotopes.jsonnet | 77 +++++++++++++++++++ 2 files changed, 105 insertions(+) create mode 100644 electrolyte_fm/data_modules/isotope_dataset.py create mode 100644 submit/isotopes.jsonnet diff --git a/electrolyte_fm/data_modules/isotope_dataset.py b/electrolyte_fm/data_modules/isotope_dataset.py new file mode 100644 index 00000000..05022e6e --- /dev/null +++ b/electrolyte_fm/data_modules/isotope_dataset.py @@ -0,0 +1,28 @@ +from .property_prediction_dataset import PropertyPredictionDataModule +from .molnet_dataset import train_val_test_split +from datasets import Dataset, load_dataset +from pathlib import Path + + +class IsotopeDataModule(PropertyPredictionDataModule): + def __init__(self, path: str, **kwargs): + # Set default smi_column + self.path = path + assert Path(self.path).is_file() + super().__init__(**kwargs) + + def prepare_data(self): + # Fetch data from the head node + self.dataset + + def _get_dataset(self): + # Load the dataset + ds: Dataset = load_dataset( + "csv", + data_files=[self.path], + split="train", + keep_in_memory=False, + save_infos=False, + ) # type: ignore + + return train_val_test_split(ds) diff --git a/submit/isotopes.jsonnet b/submit/isotopes.jsonnet new file mode 100644 index 00000000..e7039677 --- /dev/null +++ b/submit/isotopes.jsonnet @@ -0,0 +1,77 @@ +{ + walltime: '1:0:0', + nodes: 1, // Multi-node is not currently supported. Config is only on leader node + env: { + TOKENIZERS_PARALLELISM: true, + }, + train: { + tags: ['finetuning', 'isotopes', 'thaw'], + data: { + class_path: 'electrolyte_fm.data_modules.IsotopeDataModule', + init_args: { + path: '/nfs/turbo/coe-venkvis/abhutani/electrolyte-fm/opt/isotope_half_lives.csv', + batch_size: 16, + val_batch_size: 2 * self.batch_size, + tokenizer: $.train.model.init_args.encoder_ckpt, + target_columns: ['half_life', 'log_time'], + num_workers: 4, + prefetch_factor: 8, + }, + }, + model: { + class_path: 'electrolyte_fm.models.LMFinetuning', + init_args: { + encoder_ckpt: '/nfs/turbo/coe-venkvis/mist/ti624ev1/pretrained/checkpoints/last.ckpt', + task: 'regression', + metrics: ['mae', 'mae-channel', 'r2-channel'], + freeze_encoder: false, + transform: ['standardize', 'standardize'], + output_size: std.length($.train.data.init_args.target_columns), + target_columns: $.train.data.init_args.target_columns, + + // Duplicate pre-training optimizer config + optimizer: { + class_path: 'torch.optim.AdamW', + init_args: { + lr: 1.6e-4, + weight_decay: 0.01, + }, + }, + + lr_schedule: { + class_path: 'electrolyte_fm.utils.lr_schedule.RelativeCosineWarmup', + init_args: { + num_training_steps: $.train.trainer.max_steps, + num_warmup_steps: 'beta2', + rel_decay: 0.1, + }, + }, + }, + }, + trainer: { + max_steps: 5000, + precision: 'bf16-true', + enable_progress_bar: false, + strategy: 'auto', + callbacks: [ + { + class_path: 'electrolyte_fm.utils.progressive_thawing.ProgressiveThawing', + init_args: { + initial: ['encoder'], + stages: [ + ['encoder.embeddings'], + ['encoder.encoder.layer.7'], + ['encoder.encoder.layer.6'], + ['encoder.encoder.layer.5'], + ['encoder.encoder.layer.4'], + ['encoder.encoder.layer.3'], + ['encoder.encoder.layer.2'], + ['encoder.encoder.layer.1'], + ], + stage_duration: 3, + }, + }, + ], + }, + }, +} From 8f7e04d7338a45a3cedff4697322f819d7b8b9aa Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 19:34:03 -0400 Subject: [PATCH 063/147] tweak: normalize clc_db processing --- opt/sterochemistry/export_clc_db.py | 80 ++++++++++++++-------------- opt/sterochemistry/process_clc_db.py | 46 ++++++++++------ 2 files changed, 70 insertions(+), 56 deletions(-) diff --git a/opt/sterochemistry/export_clc_db.py b/opt/sterochemistry/export_clc_db.py index 76c90aba..175bcc60 100755 --- a/opt/sterochemistry/export_clc_db.py +++ b/opt/sterochemistry/export_clc_db.py @@ -71,9 +71,10 @@ def categories(self) -> list[str]: return [] def csv_row(self) -> dict[str, Any]: - """Return a row dict with the same columns the website exports. + """Return a row dict using snake_case field names. - Column order mirrors the client code seen in the website bundle. + The website exports Title Case columns; we normalize to snake_case so + downstream processing can rely on consistent names. """ d = self.data @@ -98,46 +99,47 @@ def _int(x: Any) -> str: return "" return { - "Name": d.get("name", ""), - "CAS ID": d.get("cas_id", ""), - "PubChem CID": d.get("pubchem_cid", ""), - "Category": _join(d.get("category")), - "URL": d.get("url", ""), - "PubChem URL": d.get("pubchem_url", ""), - "SMILES": d.get("smiles", ""), - "Chirality": _join(d.get("chirality")), - "Description": d.get("description", ""), - "SMILES IUPAC": d.get("smiles_iupac", ""), - "Molecule Formula": d.get("molecule_formula", ""), - "Molecular Weight": _fmt(d.get("molecular_weight"), 3), - "Heavy Atom Count": _int(d.get("heavy_atom_count")), - "Ring Count": _int(d.get("ring_count")), - "Hydrogen Bond Acceptor Count": _int(d.get("hydrogen_bond_acceptor_count")), - "Hydrogen Bond Donor Count": _int(d.get("hydrogen_bond_donor_count")), - "Rotatable Bond Count": _int(d.get("rotatable_bond_count")), - "Zero-point correction": _fmt(d.get("zero_point_correction"), 6), - "Thermal correction to Energy": _fmt(d.get("thermal_correction_energy"), 6), - "Thermal correction to Enthalpy": _fmt( + # identifiers and core metadata + "name": d.get("name", ""), + "cas_id": d.get("cas_id", ""), + "pubchem_cid": d.get("pubchem_cid", ""), + "category": _join(d.get("category")), + "url": d.get("url", ""), + "pubchem_url": d.get("pubchem_url", ""), + # chemistry strings + "smiles": d.get("smiles", ""), + "chirality": _join(d.get("chirality")), + "description": d.get("description", ""), + "smiles_iupac": d.get("smiles_iupac", ""), + "molecule_formula": d.get("molecule_formula", ""), + # basic properties + "molecular_weight": _fmt(d.get("molecular_weight"), 3), + "heavy_atom_count": _int(d.get("heavy_atom_count")), + "ring_count": _int(d.get("ring_count")), + "hydrogen_bond_acceptor_count": _int(d.get("hydrogen_bond_acceptor_count")), + "hydrogen_bond_donor_count": _int(d.get("hydrogen_bond_donor_count")), + "rotatable_bond_count": _int(d.get("rotatable_bond_count")), + # thermochemistry (snake_case normalized names) + "zero_point_correction": _fmt(d.get("zero_point_correction"), 6), + "thermal_correction_energy": _fmt(d.get("thermal_correction_energy"), 6), + "thermal_correction_enthalpy": _fmt( d.get("thermal_correction_enthalpy"), 6 ), - "Thermal correction to Gibbs Free Energy": _fmt( - d.get("thermal_correction_gibbs"), 6 - ), - "Sum of electronic and zero-point Energies": _fmt( - d.get("sum_electronic_zero_point"), 6 - ), - "Sum of electronic and thermal Energies": _fmt( + "thermal_correction_gibbs": _fmt(d.get("thermal_correction_gibbs"), 6), + "sum_electronic_zero_point": _fmt(d.get("sum_electronic_zero_point"), 6), + "sum_electronic_thermal_energy": _fmt( d.get("sum_electronic_thermal_energy"), 6 ), - "Sum of electronic and thermal Enthalpies": _fmt( + "sum_electronic_thermal_enthalpy": _fmt( d.get("sum_electronic_thermal_enthalpy"), 6 ), - "Sum of electronic and thermal Free Energies": _fmt( + "sum_electronic_thermal_free_energy": _fmt( d.get("sum_electronic_thermal_free_energy"), 6 ), - "HOMO Energy (eV)": _fmt(d.get("homo_energy"), 6), - "LUMO Energy (eV)": _fmt(d.get("lumo_energy"), 6), - "HOMO-LUMO Gap (eV)": _fmt(d.get("homo_lumo_gap"), 6), + # frontier orbitals + "homo_energy": _fmt(d.get("homo_energy"), 6), + "lumo_energy": _fmt(d.get("lumo_energy"), 6), + "homo_lumo_gap": _fmt(d.get("homo_lumo_gap"), 6), } @@ -292,9 +294,9 @@ def write_csv(rows: list[dict[str, Any]], out_csv: str) -> None: # Use pandas for convenience and de-duplication on CAS ID df = pd.DataFrame(rows) - # Drop duplicates by CAS ID keeping first occurrence - if "CAS ID" in df.columns: - df = df.drop_duplicates(subset=["CAS ID"], keep="first") + # Drop duplicates by cas_id keeping first occurrence + if "cas_id" in df.columns: + df = df.drop_duplicates(subset=["cas_id"], keep="first") df.to_csv(out_csv, index=False) @@ -304,8 +306,8 @@ def main(argv: list[str] | None = None) -> int: ) p.add_argument( "--out-dir", - default=os.path.join("opt", "sterochemistry", "data", "clc_db"), - help="Output directory for SDFs and CSV (default: opt/sterochemistry/data/clc_db)", + default=os.path.join("data", "clc_db"), + help="Output directory for SDFs and CSV (default: data/clc_db)", ) p.add_argument( "--by-category", diff --git a/opt/sterochemistry/process_clc_db.py b/opt/sterochemistry/process_clc_db.py index 0da9994c..371eef5d 100755 --- a/opt/sterochemistry/process_clc_db.py +++ b/opt/sterochemistry/process_clc_db.py @@ -30,17 +30,21 @@ from sklearn.model_selection import StratifiedShuffleSplit -# Column names from the website/export script -COL_SMILES = "SMILES" -COL_CHIRALITY = "Chirality" -COL_MW = "Molecular Weight" -COL_ZP = "Zero-point correction" -COL_TE = "Thermal correction to Energy" -COL_TH = "Thermal correction to Enthalpy" -COL_TG = "Thermal correction to Gibbs Free Energy" -COL_HOMO = "HOMO Energy (eV)" -COL_LUMO = "LUMO Energy (eV)" -COL_GAP = "HOMO-LUMO Gap (eV)" +# Column names from the normalized snake_case export +COL_SMILES = "smiles" +COL_CHIRALITY = "chirality" +COL_MW = "molecular_weight" +COL_ZP = "zero_point_correction" +COL_TE = "thermal_correction_energy" +COL_TH = "thermal_correction_enthalpy" +COL_TG = "thermal_correction_gibbs" +COL_SUM_ZP = "sum_electronic_zero_point" +COL_SUM_TE = "sum_electronic_thermal_energy" +COL_SUM_TH = "sum_electronic_thermal_enthalpy" +COL_SUM_TG = "sum_electronic_thermal_free_energy" +COL_HOMO = "homo_energy" +COL_LUMO = "lumo_energy" +COL_GAP = "homo_lumo_gap" def _to_float(x: Any) -> float | None: @@ -133,9 +137,13 @@ def load_rows(csv_path: str) -> list[dict[str, Any]]: "thermal_correction_energy": _to_float(r.get(COL_TE)), "thermal_correction_enthalpy": _to_float(r.get(COL_TH)), "thermal_correction_gibbs": _to_float(r.get(COL_TG)), - "homo": _to_float(r.get(COL_HOMO)), - "lumo": _to_float(r.get(COL_LUMO)), - "gap": _to_float(r.get(COL_GAP)), + "sum_electronic_zero_point": _to_float(r.get(COL_SUM_ZP)), + "sum_electronic_thermal_energy": _to_float(r.get(COL_SUM_TE)), + "sum_electronic_thermal_enthalpy": _to_float(r.get(COL_SUM_TH)), + "sum_electronic_thermal_free_energy": _to_float(r.get(COL_SUM_TG)), + "homo_energy": _to_float(r.get(COL_HOMO)), + "lumo_energy": _to_float(r.get(COL_LUMO)), + "homo_lumo_gap": _to_float(r.get(COL_GAP)), } rows.append(row) return rows @@ -199,9 +207,13 @@ def to_hf_datasets( "thermal_correction_energy": Value("float64"), "thermal_correction_enthalpy": Value("float64"), "thermal_correction_gibbs": Value("float64"), - "homo": Value("float64"), - "lumo": Value("float64"), - "gap": Value("float64"), + "sum_electronic_zero_point": Value("float64"), + "sum_electronic_thermal_energy": Value("float64"), + "sum_electronic_thermal_enthalpy": Value("float64"), + "sum_electronic_thermal_free_energy": Value("float64"), + "homo_energy": Value("float64"), + "lumo_energy": Value("float64"), + "homo_lumo_gap": Value("float64"), } ) From 373f3f6804b4a3fb999f6c4de9bd8db9bee7d29c Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Wed, 3 Sep 2025 10:03:24 -0400 Subject: [PATCH 064/147] embedding coloured by decay type figure --- opt/interp_embeddings/embedding_figure.jl | 112 ++++++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/opt/interp_embeddings/embedding_figure.jl b/opt/interp_embeddings/embedding_figure.jl index e8efa52f..1c2ccf44 100644 --- a/opt/interp_embeddings/embedding_figure.jl +++ b/opt/interp_embeddings/embedding_figure.jl @@ -1,5 +1,9 @@ using Makie using DataFrames +using Random +using Statistics +using UMAP +using Distances using CairoMakie: CairoMakie using CSV: CSV using CategoricalArrays: categorical, levelcode @@ -169,3 +173,111 @@ function figure_olfactory() return f end + + +function classify_decay_from_NZ(N::Real, Z::Real; + tol_offset::Real=0.8, tol_scale::Real=0.02) + + # Beta-stability valley (from SEMF): Z_beta(A) ≈ A / (2 + 0.015 * A^(2/3)) + predict_Z_beta(A::Real) = A / (2 + 0.015 * A^(2/3)) + + A = N + Z + # very heavy nuclei + if Z ≥ 92 && A ≥ 240 + return "Spontaneous \nFission" + elseif Z ≥ 84 && A ≥ 210 + return L"$\alpha$" + end + zβ = predict_Z_beta(A) + δ = Z - zβ + tol = tol_offset + tol_scale * Z # widen tolerance for heavier Z + if abs(δ) ≤ tol + return "Stable" + elseif δ < 0 + return L"$\beta^-$" # too many neutrons -> beta- decay + else + return L"$\beta^+$" # too many protons -> beta+/EC + end +end + +function figure_isotopes_umap(n_neighbors::Int=10, min_dist::Real=2, metric::Symbol=:manhattan, seed::Int=42) + + csv_path = "isotope_embeddings.csv" + df = DataFrame(CSV.File(csv_path)) + + # Labels from N/Z + N = Float64.(coalesce.(df.neutrons, NaN)) + Z = Float64.(coalesce.(df.protons, NaN)) + labels_raw = [classify_decay_from_NZ(N[i], Z[i]) for i in eachindex(N)] + + # Embedding matrix: numeric columns excluding smiles/N/Z + exclude = Set([:smiles, :neutrons, :protons]) + _is_numeric_col(col) = (eltype(col) <: Real) || (eltype(col) <: Union{Missing,Real}) + embed_cols = [nm for nm in names(df) if nm ∉ exclude && _is_numeric_col(df[!, nm])] + isempty(embed_cols) && error("No numeric embedding columns found (after excluding smiles/N/Z).") + + X = Matrix{Float64}(coalesce.(df[!, embed_cols], 0.0)) + + # Standardize columns + for j in axes(X, 2) + μ, σ = mean(X[:, j]), std(X[:, j]) + if σ > 0 && isfinite(σ) + X[:, j] .= (X[:, j] .- μ) ./ σ + else + X[:, j] .= 0.0 + end + end + + # UMAP → 2D + Random.seed!(seed) + metric_obj = if metric == :cosine + CosineDist() + elseif metric == :manhattan + Cityblock() + else + Euclidean() + end + Y = umap(Matrix(X'), 2; n_neighbors=n_neighbors, min_dist=min_dist, metric=metric_obj) + Y = Matrix(Y') + + canonical = [L"$\beta^+$", L"$\beta^-$", L"$\alpha$", "Stable", "Spontaneous Fission"] + present = unique(labels_raw) + levels = [c for c in canonical if c in present] + append!(levels, [x for x in present if x ∉ Set(canonical)]) + decay_cat = categorical(labels_raw; levels=levels, ordered=true) + n = length(levels) + + f = Figure(;size = (1.5inch, 1.5inch)) + ax = Axis( + f[1, 1]; + limits = (nothing, (minimum(Y[:, 2]) - 7, nothing)), + ) + hidedecorations!(ax) + + palette = MISTStyle.CAT_COLORS[1:n] + + h = scatter!(ax, Y[:, 1], Y[:, 2]; + color = levelcode.(decay_cat), + colormap = palette, + colorrange = (1, n), + marker = :circle, + ) + + # Create legend elements + elements = [MarkerElement(; + marker = :circle, + color = palette[i], + ) for i in 1:length(levels)] + + Legend(f[1, 1], elements, levels; + tellheight = false, + tellwidth = false, + orientation = :horizontal, + halign = :right, + valign = :bottom, + padding=(1, 1, 1, 1), + margin=(1, 1, 1, 1), + ) + + return f +end From a0723d0d197dafa99a5ee17254c362bb2e26789d Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Wed, 1 Oct 2025 12:34:47 -0400 Subject: [PATCH 065/147] correct task head name --- .../models/ionic_conductivity_model.py | 8 +- electrolyte_fm/models/physics_task_heads.py | 78 ------------------- 2 files changed, 3 insertions(+), 83 deletions(-) diff --git a/electrolyte_fm/models/ionic_conductivity_model.py b/electrolyte_fm/models/ionic_conductivity_model.py index 7313bf1b..3ed09f6f 100644 --- a/electrolyte_fm/models/ionic_conductivity_model.py +++ b/electrolyte_fm/models/ionic_conductivity_model.py @@ -10,7 +10,7 @@ from ..utils.metrics import get_metrics, masked_metric_update from ..utils.tokenizer import load_tokenizer from .model_utils import DeepSpeedMixin, LoggingMixin -from .physics_task_heads import VFTTaskHeadWithDecay +from .physics_task_heads import VFTTaskHead class IonicConductivityModel(LightningModule, DeepSpeedMixin, LoggingMixin): @@ -58,9 +58,7 @@ def __init__( assert ( self.encoder.config.vocab_size == vocab_size ), f"Expected vocab size to match. got {self.encoder.config.vocab_size} and {vocab_size}" - self.task_network = VFTTaskHeadWithDecay( - embed_dim=self.encoder.config.hidden_size - ) + self.task_network = VFTTaskHead(embed_dim=self.encoder.config.hidden_size) self.lossfn = torch.nn.MSELoss(reduction="mean") metrics = get_metrics( @@ -81,7 +79,7 @@ def forward(self, batch, transform=True, **kwargs): # type: ignore[override] attention_mask=batch[f"attention_mask_{i}"], return_dict=True, output_hidden_states=True, - ).last_hidden_state.mean(axis=1) + ).last_hidden_state[:, 0, :] embedding = torch.stack( [ diff --git a/electrolyte_fm/models/physics_task_heads.py b/electrolyte_fm/models/physics_task_heads.py index d184a2fb..71e482a7 100644 --- a/electrolyte_fm/models/physics_task_heads.py +++ b/electrolyte_fm/models/physics_task_heads.py @@ -1,85 +1,7 @@ -import torch from torch import nn -class ArrheniusTaskHead(nn.Module): - def __init__(self, embed_dim: int) -> None: - super().__init__() - self.desc_skip_connection = True - - self.fc1 = nn.Linear(embed_dim, embed_dim) - self.relu1 = nn.GELU() - self.fc2 = nn.Linear(embed_dim, embed_dim) - self.relu2 = nn.GELU() - self.fc3 = nn.Linear(embed_dim, int(0.5 * embed_dim)) - self.relu3 = nn.GELU() - self.final = nn.Linear(int(0.5 * embed_dim), 2) - - def forward(self, emb, temperature): - x_out = self.fc1(emb) - x_out = self.relu1(x_out) - - if self.desc_skip_connection is True: - x_out = x_out + emb - - z = self.fc2(x_out) - z = self.relu2(z) - z = self.fc3(z) - z = self.relu3(z) - z = self.final(z) - - logA = z[:, 0] - Ea = z[:, 1] - R = 8.63e-5 - - e = torch.exp(torch.tensor([1], device=logA.device)) - C = torch.log10(e) / R - - cond = logA - C * Ea / temperature - - return cond - - class VFTTaskHead(nn.Module): - def __init__(self, embed_dim: int) -> None: - super().__init__() - self.desc_skip_connection = True - - self.fc1 = nn.Linear(embed_dim, embed_dim) - self.relu1 = nn.GELU() - self.fc2 = nn.Linear(embed_dim, embed_dim) - self.relu2 = nn.GELU() - self.fc3 = nn.Linear(embed_dim, int(0.5 * embed_dim)) - self.relu3 = nn.GELU() - self.final = nn.Linear(int(0.5 * embed_dim), 3) - - def forward(self, emb, temperature): - x_out = self.fc1(emb) - x_out = self.relu1(x_out) - - if self.desc_skip_connection is True: - x_out = x_out + emb - - z = self.fc2(x_out) - z = self.relu2(z) - z = self.fc3(z) - z = self.relu3(z) - z = self.final(z) - - logA = z[:, 0] - Ea = z[:, 1] - T_g = z[:, 2] - R = 8.63e-5 - - e = torch.exp(torch.tensor([1], device=logA.device)) - C = torch.log10(e) / R - - cond = logA - C * Ea / (temperature - T_g) - - return cond - - -class VFTTaskHeadWithDecay(nn.Module): def __init__(self, embed_dim: int) -> None: super().__init__() self.desc_skip_connection = True From 12922cdf7e1df61f1b5df36bdfcf397970d8e0d9 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 16:23:43 -0400 Subject: [PATCH 066/147] feat: return intermidiate params --- .../data_modules/mixture_dataset.py | 3 + .../models/ionic_conductivity_model.py | 45 ++++------ electrolyte_fm/models/physics_task_heads.py | 90 ++++++++++++++++++- 3 files changed, 108 insertions(+), 30 deletions(-) diff --git a/electrolyte_fm/data_modules/mixture_dataset.py b/electrolyte_fm/data_modules/mixture_dataset.py index e6556d82..4b1731e1 100644 --- a/electrolyte_fm/data_modules/mixture_dataset.py +++ b/electrolyte_fm/data_modules/mixture_dataset.py @@ -97,6 +97,7 @@ def __init__( self.val_batch_size = val_batch_size or batch_size self.num_workers = num_workers self.prefetch_factor = prefetch_factor + self.hparams["vocab_size"] = self.vocab_size self.save_hyperparameters(logger=False) self.data_collator = DataCollatorWithPadding( self.tokenizer, max_length=max_length, padding="max_length" @@ -171,6 +172,8 @@ def setup(self, stage: str) -> None: def collator(self, batch): output = {} + for i in range(self.n_components): + output[f"smi{i+1}"] = [x[f"smi{i+1}"] for x in batch] for i in range(self.n_components): output[f"input_ids_{i}"] = torch.stack( diff --git a/electrolyte_fm/models/ionic_conductivity_model.py b/electrolyte_fm/models/ionic_conductivity_model.py index 3ed09f6f..da3e0c5d 100644 --- a/electrolyte_fm/models/ionic_conductivity_model.py +++ b/electrolyte_fm/models/ionic_conductivity_model.py @@ -7,10 +7,10 @@ from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from lightning.pytorch.loggers import WandbLogger -from ..utils.metrics import get_metrics, masked_metric_update +from ..utils.metrics import get_metrics from ..utils.tokenizer import load_tokenizer from .model_utils import DeepSpeedMixin, LoggingMixin -from .physics_task_heads import VFTTaskHead +from .physics_task_heads import VFTDecayTaskHead class IonicConductivityModel(LightningModule, DeepSpeedMixin, LoggingMixin): @@ -58,7 +58,7 @@ def __init__( assert ( self.encoder.config.vocab_size == vocab_size ), f"Expected vocab size to match. got {self.encoder.config.vocab_size} and {vocab_size}" - self.task_network = VFTTaskHead(embed_dim=self.encoder.config.hidden_size) + self.task_network = VFTDecayTaskHead(embed_dim=self.encoder.config.hidden_size) self.lossfn = torch.nn.MSELoss(reduction="mean") metrics = get_metrics( @@ -71,7 +71,7 @@ def __init__( self.val_metrics = metrics.clone(prefix="val/") self.test_metrics = metrics.clone(prefix="test/") - def forward(self, batch, transform=True, **kwargs): # type: ignore[override] + def forward(self, batch, return_all=False, **kwargs): # type: ignore[override] mix_embedding = None for i in range(self.n_components): embedding = self.encoder( @@ -92,9 +92,12 @@ def forward(self, batch, transform=True, **kwargs): # type: ignore[override] else: mix_embedding += embedding - pred_unscaled, alpha, beta, lmbda = self.task_network( - mix_embedding, batch["temperature"] - ) + params = self.task_network(mix_embedding, batch["temperature"]) + + pred_unscaled = params["conductivity"] + alpha = params["alpha"] + beta = params["beta"] + lmbda = params["beta"] exponent = torch.div(-1 * alpha + batch["composition_4"], lmbda) pred_decay = torch.mul((1 - beta), torch.exp(exponent)) + beta @@ -104,7 +107,9 @@ def forward(self, batch, transform=True, **kwargs): # type: ignore[override] # else predicted conductivity pred = torch.where(batch["composition_4"] > alpha, pred, pred_unscaled) - return pred.view(-1, 1), alpha, beta + if return_all: + return pred.view(-1, 1), params + return pred.view(-1, 1), alpha def setup(self, stage: str) -> None: if isinstance(self.logger, WandbLogger): @@ -114,7 +119,7 @@ def setup(self, stage: str) -> None: def _scaled_pred_loss(self, batch): """Compute loss before transforming the model's predictions""" - preds, alpha, beta = self.forward(batch, transform=False) + preds, alpha = self.forward(batch, return_all=False) target = batch["target"] loss = self.lossfn(preds, target) + alpha.abs().mean() return preds, loss @@ -130,12 +135,7 @@ def training_step(self, batch, batch_idx: int) -> torch.FloatTensor: sync_dist=True, ) - masked_metric_update( - self.train_metrics, - preds, - batch["target"], - batch["target_mask"], - ) + self.train_metrics.update(preds, batch["target"]) return loss def on_train_epoch_end(self): @@ -155,13 +155,7 @@ def validation_step(self, batch, batch_idx: int) -> torch.FloatTensor: on_epoch=True, sync_dist=True, ) - - masked_metric_update( - self.val_metrics, - preds, - batch["target"], - batch["target_mask"], - ) + self.val_metrics.update(preds, batch["target"]) return loss def on_validation_epoch_end(self): @@ -181,12 +175,7 @@ def test_step(self, batch, batch_idx: int) -> torch.FloatTensor: on_epoch=True, sync_dist=True, ) - masked_metric_update( - self.test_metrics, - preds.to(dtype=torch.float32), - batch["target"].to(dtype=torch.float32), - batch["target_mask"], - ) + self.test_metrics.update(preds, batch["target"]) return loss def on_test_epoch_end(self): diff --git a/electrolyte_fm/models/physics_task_heads.py b/electrolyte_fm/models/physics_task_heads.py index 71e482a7..54a62b4a 100644 --- a/electrolyte_fm/models/physics_task_heads.py +++ b/electrolyte_fm/models/physics_task_heads.py @@ -1,7 +1,85 @@ +import torch from torch import nn +class ArrheniusTaskHead(nn.Module): + def __init__(self, embed_dim: int) -> None: + super().__init__() + self.desc_skip_connection = True + + self.fc1 = nn.Linear(embed_dim, embed_dim) + self.relu1 = nn.GELU() + self.fc2 = nn.Linear(embed_dim, embed_dim) + self.relu2 = nn.GELU() + self.fc3 = nn.Linear(embed_dim, int(0.5 * embed_dim)) + self.relu3 = nn.GELU() + self.final = nn.Linear(int(0.5 * embed_dim), 2) + + def forward(self, emb, temperature): + x_out = self.fc1(emb) + x_out = self.relu1(x_out) + + if self.desc_skip_connection is True: + x_out = x_out + emb + + z = self.fc2(x_out) + z = self.relu2(z) + z = self.fc3(z) + z = self.relu3(z) + z = self.final(z) + + logA = z[:, 0] + Ea = z[:, 1] + R = 8.63e-5 + + e = torch.exp(torch.tensor([1], device=logA.device)) + C = torch.log10(e) / R + + cond = logA - C * Ea / temperature + + return cond + + class VFTTaskHead(nn.Module): + def __init__(self, embed_dim: int) -> None: + super().__init__() + self.desc_skip_connection = True + + self.fc1 = nn.Linear(embed_dim, embed_dim) + self.relu1 = nn.GELU() + self.fc2 = nn.Linear(embed_dim, embed_dim) + self.relu2 = nn.GELU() + self.fc3 = nn.Linear(embed_dim, int(0.5 * embed_dim)) + self.relu3 = nn.GELU() + self.final = nn.Linear(int(0.5 * embed_dim), 3) + + def forward(self, emb, temperature): + x_out = self.fc1(emb) + x_out = self.relu1(x_out) + + if self.desc_skip_connection is True: + x_out = x_out + emb + + z = self.fc2(x_out) + z = self.relu2(z) + z = self.fc3(z) + z = self.relu3(z) + z = self.final(z) + + logA = z[:, 0] + Ea = z[:, 1] + T_g = z[:, 2] + R = 8.63e-5 + + e = torch.exp(torch.tensor([1], device=logA.device)) + C = torch.log10(e) / R + + cond = logA - C * Ea / (temperature - T_g) + + return cond + + +class VFTDecayTaskHead(nn.Module): def __init__(self, embed_dim: int) -> None: super().__init__() self.desc_skip_connection = True @@ -34,5 +112,13 @@ def forward(self, emb, temperature): alpha = self.sigmoid(z[:, 3]) beta = z[:, 4] lmbda = self.sigmoid(z[:, 5]) - - return ln_A - Ea / (temperature - Tg), alpha, beta, lmbda + params = { + "conductivity": ln_A - Ea / (temperature - Tg), + "ln_A": ln_A, + "Ea": Ea, + "Tg": Tg, + "alpha": alpha, + "beta": beta, + "lmbda": lmbda, + } + return params From 47aa9919b2807ad2bfde16b8b4708b2f37b8a479 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 16:48:52 -0400 Subject: [PATCH 067/147] add command for exporting mixture models --- opt/package/__main__.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 02cbd79d..1dd4f64f 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -15,6 +15,7 @@ sys.path.append(str(Path(__file__).parent.parent.parent)) from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer +from electrolyte_fm.models.model_utils import DeepSpeedMixin from electrolyte_fm.utils.tokenizer import load_tokenizer import utils @@ -84,6 +85,26 @@ def finetuned(ckpt: Path, name: Optional[str] = None, safe: bool = True): utils.create_tar_gz(save_dir) +@cli.command() +def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True): + """Export a mixture model""" + if Path(ckpt).joinpath("config.json").is_file(): + ckpt = get_best_ckpt(ckpt) + + model = DeepSpeedMixin.load(ckpt) + + name = utils.name_model( + model, + template=name or "mist-{model_size}-{ckpt}", + ckpt=ckpt_id(ckpt), + ) + save_dir = create_save_directory(name, ckpt) + utils.export_code(save_dir, model, model.transform, model.task_network) + utils.save_model(model, save_dir, safe) + logging.info("Saved model to %s", save_dir) + utils.create_tar_gz(save_dir) + + def export_multitask( encoder_ckpt: Path, task_ckpt: list[Path], From 8a1d0f89e6606a4619297b244fc5f96bd82f83eb Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 17:30:31 -0400 Subject: [PATCH 068/147] export for mixture models --- opt/package/__main__.py | 20 ++++++++++++++++---- opt/package/utils.py | 2 -- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 1dd4f64f..70087d1f 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -16,10 +16,11 @@ sys.path.append(str(Path(__file__).parent.parent.parent)) from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer from electrolyte_fm.models.model_utils import DeepSpeedMixin +from electrolyte_fm.models.prod_finetune import save_model from electrolyte_fm.utils.tokenizer import load_tokenizer import utils -from utils import get_best_ckpt, create_save_directory, ckpt_id +from utils import get_best_ckpt, create_save_directory, ckpt_id, save_tokenizer cli = typer.Typer() @@ -92,15 +93,26 @@ def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True): ckpt = get_best_ckpt(ckpt) model = DeepSpeedMixin.load(ckpt) - name = utils.name_model( model, template=name or "mist-{model_size}-{ckpt}", ckpt=ckpt_id(ckpt), ) save_dir = create_save_directory(name, ckpt) - utils.export_code(save_dir, model, model.transform, model.task_network) - utils.save_model(model, save_dir, safe) + + # Some mixture model classes don't have a transform + if hasattr(model, "transform"): + utils.export_code(save_dir, model, model.transform, model.task_network) + else: + utils.export_code(save_dir, model, model.task_network) + + config = json.loads(Path(ckpt.parent.parent, "config.json").read_text()) + + if hasattr(model, "tokenizer"): + save_tokenizer(save_dir, model.tokenizer) + + Path(save_dir, "config.json").write_text(json.dumps(config, indent=4)) + save_model(model, save_dir, safe_serialization=True) logging.info("Saved model to %s", save_dir) utils.create_tar_gz(save_dir) diff --git a/opt/package/utils.py b/opt/package/utils.py index 4f1b3fac..cb3c9969 100644 --- a/opt/package/utils.py +++ b/opt/package/utils.py @@ -25,8 +25,6 @@ def get_best_ckpt(ckpt_dir: Path) -> Path: best = None CKPT_REGEX = re.compile(r".*step=(\d+?)-val_loss=([\d\.]+?)\.ckpt") for ckpt in Path(ckpt_dir, "checkpoints").iterdir(): - if not ckpt.is_dir(): - continue if m := CKPT_REGEX.match(ckpt.name): step = int(m.group(1)) loss = float(m.group(2)) From 3c412f7828a762a031fe074634a6c3966844b520 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 19:25:27 -0400 Subject: [PATCH 069/147] fix: mixture export --- electrolyte_fm/models/__init__.py | 1 + electrolyte_fm/models/prod_mixture.py | 555 ++++++++++++++++++++++++++ opt/package/__main__.py | 44 +- 3 files changed, 582 insertions(+), 18 deletions(-) create mode 100644 electrolyte_fm/models/prod_mixture.py diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index ad3e55e7..8f313b33 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -3,6 +3,7 @@ from .lm_finetuning import LMFinetuning from .mixture_model import MixtureModel from .prod_finetune import MISTFinetuned, MISTMultiTask +from .prod_mixture import MISTIonicConductivity, MISTExcessPhysics from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer diff --git a/electrolyte_fm/models/prod_mixture.py b/electrolyte_fm/models/prod_mixture.py new file mode 100644 index 00000000..d94fb19e --- /dev/null +++ b/electrolyte_fm/models/prod_mixture.py @@ -0,0 +1,555 @@ +import json +import torch +from pathlib import Path +from transformers import AutoConfig, AutoModel, AutoTokenizer +from transformers.data.data_collator import DataCollatorWithPadding +from .prod_finetune import load_model + + +class MISTIonicConductivity(torch.nn.Module): + def __init__(self, encoder, task_network, tokenizer, n_components=38): + super().__init__() + self.encoder = encoder + self.task_network = task_network + self.tokenizer = tokenizer + self.n_components = n_components + + def forward(self, batch, return_all=False): + """ + Forward pass for mixture ionic conductivity prediction. + + Args: + batch: Dictionary containing input_ids, attention_mask, composition, + and temperature for each component + return_all: If True, return all parameters along with prediction + + Returns: + Predicted conductivity and alpha (or all params if return_all=True) + """ + mix_embedding = None + for i in range(self.n_components): + embedding = self.encoder( + batch[f"input_ids_{i}"], + attention_mask=batch[f"attention_mask_{i}"], + return_dict=True, + output_hidden_states=True, + ).last_hidden_state[:, 0, :] + + embedding = torch.stack( + [ + torch.mul(embedding[j, :], batch[f"composition_{i}"][j]) + for j in range(embedding.shape[0]) + ] + ) + if mix_embedding is None: + mix_embedding = embedding + else: + mix_embedding += embedding + + params = self.task_network(mix_embedding, batch["temperature"]) + + pred_unscaled = params["conductivity"] + alpha = params["alpha"] + beta = params["beta"] + lmbda = params["beta"] + + exponent = torch.div(-1 * alpha + batch["composition_4"], lmbda) + pred_decay = torch.mul((1 - beta), torch.exp(exponent)) + beta + pred = torch.mul(pred_unscaled, pred_decay) + + # predicted conductivity*decay if salt molarity > alpha + # else predicted conductivity + pred = torch.where(batch["composition_4"] > alpha, pred, pred_unscaled) + + if return_all: + return pred.view(-1, 1), params + return pred.view(-1, 1), alpha + + def save_pretrained(self, save_directory, safe_serialization=False): + """Save model configuration and weights.""" + config = { + "architectures": [ + self.__class__.__name__, + ], + "tokenizer_class": self.tokenizer.__class__.__name__, + "encoder": self.encoder.config.to_diff_dict(), + "task_network": { + "embed_dim": self.encoder.config.hidden_size, + }, + "n_components": self.n_components, + } + + Path(save_directory).mkdir(parents=True, exist_ok=True) + Path(save_directory, "config.json").write_text(json.dumps(config, indent=4)) + + # Save model state dict + if safe_serialization: + from safetensors.torch import save_file + + save_file(self.state_dict(), Path(save_directory, "model.safetensors")) + else: + torch.save(self.state_dict(), Path(save_directory, "pytorch_model.bin")) + + # Save tokenizer + self.tokenizer.save_pretrained(save_directory) + + def embed_mixture( + self, smiles_list: list[list[str]], compositions: list[list[float]] + ): + """ + Generate embeddings for mixture components. + + Args: + smiles_list: List of SMILES lists, where each inner list contains + SMILES for all components in a mixture + compositions: List of composition lists corresponding to each mixture + + Returns: + Mixture embeddings tensor + """ + batch_size = len(smiles_list) + mix_embeddings = [] + + with torch.inference_mode(): + for batch_idx in range(batch_size): + mix_embedding = None + for comp_idx in range( + min(len(smiles_list[batch_idx]), self.n_components) + ): + smi = smiles_list[batch_idx][comp_idx] + comp = compositions[batch_idx][comp_idx] + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids = tokens["input_ids"].to(self.encoder.device) + attention_mask = tokens["attention_mask"].to(self.encoder.device) + + embedding = self.encoder( + input_ids, + attention_mask=attention_mask, + return_dict=True, + output_hidden_states=True, + ).last_hidden_state[:, 0, :] + + embedding = embedding * comp + + if mix_embedding is None: + mix_embedding = embedding + else: + mix_embedding += embedding + + mix_embeddings.append(mix_embedding) + + return torch.cat(mix_embeddings, dim=0).cpu() + + def predict( + self, + smiles_list: list[list[str]], + compositions: list[list[float]], + temperatures: list[float], + salt_molarities: list[float], + return_dict=True, + ): + """ + Predict ionic conductivity for mixtures. + + Args: + smiles_list: List of SMILES lists for mixture components + compositions: List of composition arrays for each mixture + temperatures: List of temperatures + salt_molarities: List of salt molarities (composition_4) + return_dict: If True, return dictionary with detailed predictions + + Returns: + Predictions (tensor or dict depending on return_dict) + """ + batch_size = len(smiles_list) + batch = { + "temperature": torch.tensor(temperatures).float().to(self.encoder.device), + "composition_4": torch.tensor(salt_molarities) + .float() + .to(self.encoder.device), + } + + # Tokenize and prepare batch for each component + for i in range(self.n_components): + input_ids_list = [] + attention_mask_list = [] + comp_list = [] + + for batch_idx in range(batch_size): + if i < len(smiles_list[batch_idx]): + smi = smiles_list[batch_idx][i] + comp = compositions[batch_idx][i] + else: + smi = "[H]" # Dummy molecule + comp = 0.0 + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids_list.append(tokens["input_ids"].squeeze(0)) + attention_mask_list.append(tokens["attention_mask"].squeeze(0)) + comp_list.append(comp) + + # Collate with padding + collator = DataCollatorWithPadding(self.tokenizer) + collated = collator( + [ + {"input_ids": ids, "attention_mask": mask} + for ids, mask in zip(input_ids_list, attention_mask_list) + ] + ) + + batch[f"input_ids_{i}"] = collated["input_ids"].to(self.encoder.device) + batch[f"attention_mask_{i}"] = collated["attention_mask"].to( + self.encoder.device + ) + batch[f"composition_{i}"] = ( + torch.tensor(comp_list).float().to(self.encoder.device) + ) + + with torch.inference_mode(): + pred, params = self(**batch, return_all=return_dict) + + if not return_dict: + return pred.cpu() + + result = { + "conductivity": pred.cpu(), + "alpha": params["alpha"].cpu(), + "beta": params["beta"].cpu(), + "conductivity_unscaled": params["conductivity"].cpu(), + } + return result + + @classmethod + def from_pretrained(cls, save_directory: str): + """Load model from saved directory.""" + config = json.loads(Path(save_directory, "config.json").read_text()) + + encoder_config = AutoConfig.for_model( + config["encoder"]["model_type"] + ).from_dict(config["encoder"]) + encoder = AutoModel.from_config(encoder_config, add_pooling_layer=False) + tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) + + from .physics_task_heads import VFTDecayTaskHead + + task_network = VFTDecayTaskHead(embed_dim=config["task_network"]["embed_dim"]) + + tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) + n_components = config.get("n_components", 5) + + model = cls(encoder, task_network, tokenizer, n_components) + load_model(model, save_directory) + return model + + +class MISTExcessPhysics(torch.nn.Module): + def __init__( + self, + encoder, + task_network, + transform, + tokenizer, + n_components=2, + temperature_normalization=(273, 400), + ): + super().__init__() + self.encoder = encoder + self.task_network = task_network + self.transform = transform + self.tokenizer = tokenizer + self.n_components = n_components + self.temperature_normalization = temperature_normalization + + def forward(self, batch, transform=True): + """ + Forward pass for mixture property prediction. + + Args: + batch: Dictionary containing input_ids, attention_mask for each component, + temperature, and composition data + transform: If True, apply normalization transform to predictions + + Returns: + Predicted property values + """ + mn, mx = self.temperature_normalization + # Normalize temperature once per mixture + temperature = (batch["temperature"] - mn) / (mx - mn) # (B,) + batch["temperature"] = temperature + + for i in range(self.n_components): + enc_out = self.encoder( + input_ids=batch[f"input_ids_{i}"], + attention_mask=batch[f"attention_mask_{i}"], + return_dict=True, + output_hidden_states=False, + ) + + token_seq = enc_out.last_hidden_state # (B, L_i, d) + padmask = batch[f"attention_mask_{i}"] == 0 # (B, L_i) bool + + # Save for cross-attention fusion + batch[f"tokens_{i}"] = token_seq.float() + batch[f"padmask_{i}"] = padmask + + # Mean-pool tokens_i: single-molecule embedding + pooled = token_seq.masked_fill(padmask.unsqueeze(-1), 0).mean(dim=1) + batch[f"embedding_{i}"] = pooled + + # Property prediction + pred = self.task_network(batch) # (B, 1) + + if transform: + pred = self.transform.forward(pred) # Rescale to original units + return pred + + def save_pretrained(self, save_directory, safe_serialization=False): + """Save model configuration and weights.""" + config = { + "architectures": [ + self.__class__.__name__, + ], + "tokenizer_class": self.tokenizer.__class__.__name__, + "encoder": self.encoder.config.to_diff_dict(), + "task_network": { + "embed_dim": self.encoder.config.hidden_size, + "polynomial_order": getattr(self.task_network, "polynomial_order", 4), + "n_components": self.n_components, + "num_heads": getattr(self.task_network, "num_heads", 4), + "include_linear_mixing": getattr( + self.task_network, "include_linear_mixing", True + ), + "fusion": getattr(self.task_network, "fusion", "attention"), + "basis": self.task_network.__class__.__name__, + }, + "transform": self.transform.to_config(), + "n_components": self.n_components, + "temperature_normalization": self.temperature_normalization, + } + + Path(save_directory).mkdir(parents=True, exist_ok=True) + Path(save_directory, "config.json").write_text(json.dumps(config, indent=4)) + + # Save model state dict + if safe_serialization: + from safetensors.torch import save_file + + save_file(self.state_dict(), Path(save_directory, "model.safetensors")) + else: + torch.save(self.state_dict(), Path(save_directory, "pytorch_model.bin")) + + # Save tokenizer + self.tokenizer.save_pretrained(save_directory) + + def embed_components( + self, smiles_list: list[list[str]], compositions: list[list[float]] + ): + """ + Generate embeddings for mixture components. + + Args: + smiles_list: List of SMILES lists, where each inner list contains + SMILES for all components in a mixture + compositions: List of composition lists corresponding to each mixture + + Returns: + Dictionary with embeddings for each component + """ + batch_size = len(smiles_list) + component_embeddings = {i: [] for i in range(self.n_components)} + + with torch.inference_mode(): + for batch_idx in range(batch_size): + for comp_idx in range(self.n_components): + if comp_idx < len(smiles_list[batch_idx]): + smi = smiles_list[batch_idx][comp_idx] + else: + smi = "[H]" # Dummy molecule + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids = tokens["input_ids"].to(self.encoder.device) + attention_mask = tokens["attention_mask"].to(self.encoder.device) + + enc_out = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + output_hidden_states=False, + ) + + token_seq = enc_out.last_hidden_state + padmask = attention_mask == 0 + pooled = token_seq.masked_fill(padmask.unsqueeze(-1), 0).mean(dim=1) + + component_embeddings[comp_idx].append(pooled) + + # Concatenate all embeddings per component + result = {} + for comp_idx in range(self.n_components): + result[f"component_{comp_idx}"] = torch.cat( + component_embeddings[comp_idx], dim=0 + ).cpu() + + return result + + def predict( + self, + smiles_list: list[list[str]], + compositions: list[list[float]], + temperatures: list[float], + return_dict=False, + ): + """ + Predict mixture properties. + + Args: + smiles_list: List of SMILES lists for mixture components + e.g., [["CCO", "CC"], ["CCCO", "CCC"]] for 2 binary mixtures + compositions: List of composition arrays for each mixture + e.g., [[0.5, 0.5], [0.3, 0.7]] + temperatures: List of temperatures (in Kelvin) + return_dict: If True, return dictionary with detailed information + + Returns: + Predictions (tensor or dict depending on return_dict) + """ + batch_size = len(smiles_list) + + # Validate inputs + assert len(compositions) == batch_size, "Mismatch in batch sizes" + assert len(temperatures) == batch_size, "Mismatch in batch sizes" + + for i, (smiles, comps) in enumerate(zip(smiles_list, compositions)): + assert len(smiles) == len( + comps + ), f"Mixture {i}: SMILES and composition lengths don't match" + assert ( + len(smiles) <= self.n_components + ), f"Mixture {i}: Too many components (max {self.n_components})" + + batch = { + "temperature": torch.tensor(temperatures, dtype=torch.float32).to( + self.encoder.device + ) + } + + # Tokenize and prepare batch for each component + for i in range(self.n_components): + input_ids_list = [] + attention_mask_list = [] + comp_list = [] + + for batch_idx in range(batch_size): + if i < len(smiles_list[batch_idx]): + smi = smiles_list[batch_idx][i] + comp = compositions[batch_idx][i] + else: + smi = "[H]" # Dummy molecule for padding + comp = 0.0 + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids_list.append(tokens["input_ids"].squeeze(0)) + attention_mask_list.append(tokens["attention_mask"].squeeze(0)) + comp_list.append(comp) + + # Collate with padding + collator = DataCollatorWithPadding(self.tokenizer) + collated = collator( + [ + {"input_ids": ids, "attention_mask": mask} + for ids, mask in zip(input_ids_list, attention_mask_list) + ] + ) + + batch[f"input_ids_{i}"] = collated["input_ids"].to(self.encoder.device) + batch[f"attention_mask_{i}"] = collated["attention_mask"].to( + self.encoder.device + ) + batch[f"composition_{i}"] = torch.tensor(comp_list, dtype=torch.float32).to( + self.encoder.device + ) + + with torch.inference_mode(): + pred = self(batch, transform=True) + + if not return_dict: + return pred.cpu() + + result = { + "prediction": pred.cpu(), + "smiles": smiles_list, + "compositions": compositions, + "temperatures": temperatures, + } + return result + + def predict_single( + self, smiles: list[str], composition: list[float], temperature: float + ): + """ + Convenience method to predict for a single mixture. + + Args: + smiles: List of SMILES for the mixture components + composition: List of mole fractions (should sum to 1.0) + temperature: Temperature in Kelvin + + Returns: + Predicted property value (scalar tensor) + """ + pred = self.predict([smiles], [composition], [temperature]) + return pred.squeeze() + + @classmethod + def from_pretrained(cls, save_directory: str): + """Load model from saved directory.""" + config = json.loads(Path(save_directory, "config.json").read_text()) + + encoder_config = AutoConfig.for_model( + config["encoder"]["model_type"] + ).from_dict(config["encoder"]) + encoder = AutoModel.from_config(encoder_config, add_pooling_layer=False) + + from .polynomial_task_head import PolynomialHead + + basis_name = config["task_network"]["basis"] + task_network_config = { + "embed_dim": config["task_network"]["embed_dim"], + "polynomial_order": config["task_network"]["polynomial_order"], + "n_components": config["task_network"]["n_components"], + "num_heads": config["task_network"]["num_heads"], + "include_linear_mixing": config["task_network"]["include_linear_mixing"], + "fusion": config["task_network"]["fusion"], + } + + # Instantiate the appropriate polynomial head + # This assumes you have a way to get the class from the basis name + # Adjust based on your actual implementation + task_network = PolynomialHead.get_class(basis_name)(**task_network_config) + + # Load transform + from your_module.normalize import AbstractNormalizer # Update this import + + transform = AbstractNormalizer.get( + config["transform"]["class"], config["transform"]["num_outputs"] + ) + + tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) + n_components = config.get("n_components", 2) + temperature_normalization = tuple( + config.get("temperature_normalization", (273, 400)) + ) + + model = cls( + encoder, + task_network, + transform, + tokenizer, + n_components, + temperature_normalization, + ) + + model = cls(encoder, task_network, tokenizer, n_components) + load_model(model, save_directory) + return model diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 70087d1f..c4b43f71 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -15,8 +15,6 @@ sys.path.append(str(Path(__file__).parent.parent.parent)) from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer -from electrolyte_fm.models.model_utils import DeepSpeedMixin -from electrolyte_fm.models.prod_finetune import save_model from electrolyte_fm.utils.tokenizer import load_tokenizer import utils @@ -86,33 +84,43 @@ def finetuned(ckpt: Path, name: Optional[str] = None, safe: bool = True): utils.create_tar_gz(save_dir) +def export_conductivity(ckpt: Path): + from electrolyte_fm.models import MISTIonicConductivity + + model = SaveConfigWithCkpts.load(ckpt) + model_config = json.loads(ckpt.parent.parent.joinpath("config.json").read_text()) + tokenizer_name = model_config["data"]["init_args"]["tokenizer"] + tokenizer = load_tokenizer(tokenizer_name) + return MISTIonicConductivity( + model.encoder, + model.task_network, + tokenizer=tokenizer, + n_components=model_config["model"]["init_args"]["n_components"], + ) + + @cli.command() -def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True): - """Export a mixture model""" +def conductivity(ckpt: Path, name: Optional[str] = None, safe: bool = True): + """Export a mixture conductivity model""" + if Path(ckpt).joinpath("config.json").is_file(): ckpt = get_best_ckpt(ckpt) + model = export_conductivity(ckpt) - model = DeepSpeedMixin.load(ckpt) name = utils.name_model( model, - template=name or "mist-{model_size}-{ckpt}", + template=name or "mist-conductivity-{model_size}-{ckpt}", ckpt=ckpt_id(ckpt), ) - save_dir = create_save_directory(name, ckpt) - - # Some mixture model classes don't have a transform - if hasattr(model, "transform"): - utils.export_code(save_dir, model, model.transform, model.task_network) - else: - utils.export_code(save_dir, model, model.task_network) - - config = json.loads(Path(ckpt.parent.parent, "config.json").read_text()) + save_dir = create_save_directory(name, ckpt) + utils.export_code(save_dir, model, model.task_network) if hasattr(model, "tokenizer"): save_tokenizer(save_dir, model.tokenizer) - - Path(save_dir, "config.json").write_text(json.dumps(config, indent=4)) - save_model(model, save_dir, safe_serialization=True) + model.save_pretrained(save_dir, safe_serialization=safe) + # Validate + model.__class__.from_pretrained(save_dir) + shutil.move(Path(save_dir, "prod_mixture.py"), Path(save_dir, "model.py")) logging.info("Saved model to %s", save_dir) utils.create_tar_gz(save_dir) From 7f414112d0dd17fe7bbe70e2cc4b474ec636c27d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 15:30:56 -0400 Subject: [PATCH 070/147] Add script for ingesting sterochemistry dataset --- opt/sterochemistry/clc_db.py | 429 ++++++++++++++++++++++++ opt/sterochemistry/omol25.py | 526 ++++++++++++++++++++++++++++++ opt/sterochemistry/pyproject.toml | 16 + 3 files changed, 971 insertions(+) create mode 100644 opt/sterochemistry/clc_db.py create mode 100644 opt/sterochemistry/omol25.py create mode 100644 opt/sterochemistry/pyproject.toml diff --git a/opt/sterochemistry/clc_db.py b/opt/sterochemistry/clc_db.py new file mode 100644 index 00000000..76c90aba --- /dev/null +++ b/opt/sterochemistry/clc_db.py @@ -0,0 +1,429 @@ +#!/usr/bin/env -S uv run python +""" +CLC-DB bulk downloader + +Downloads all molecule SDF files and merges molecule properties into a single CSV. + +Strategy: +- Page the public search API used by the site ("/search/molecules") to list molecules. +- For each molecule, fetch its SDF from the static path used by the web UI. +- Optionally iterate by category if the categories endpoint is available; otherwise, fetch all. + +Outputs (under --out-dir): +- sdf/ (all .sdf files; one per CAS ID) +- molecules.csv (merged properties in the same schema as the website's CSV) + +Note: The site generates ZIPs client-side in the browser. This script mirrors that behavior +without trying to click buttons, using the same public endpoints visible in the JS. +""" + +import argparse +import concurrent.futures as futures +import csv +import logging +import os +from collections.abc import Iterable +from dataclasses import dataclass +from typing import Any + +import pandas as pd +import requests +from requests.adapters import HTTPAdapter +from tenacity import ( + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) +from tqdm import tqdm + +# Public base URLs (axios base is not exposed directly; try a few sensible options) +API_BASE_CANDIDATES = [ + # Observed public API base + "https://compbio.sjtu.edu.cn/api", + # Fallback guesses based on site paths + "https://compbio.sjtu.edu.cn/services/clc-db/api", + "https://compbio.sjtu.edu.cn/services/clc-db", + "https://compbio.sjtu.edu.cn/services/clc-db/api/v1", +] + +# Static path for SDFs as used by the client JS +SDF_BASE = "https://compbio.sjtu.edu.cn/services/clc-db/static/all_sdfs" + + +@dataclass +class Molecule: + data: dict[str, Any] + + @property + def cas_id(self) -> str: + return str(self.data.get("cas_id", "")).strip() + + @property + def name(self) -> str: + return str(self.data.get("name", "")).strip() + + @property + def categories(self) -> list[str]: + cats = self.data.get("category") or [] + if isinstance(cats, list): + return [str(c.get("name", "")).strip() for c in cats if isinstance(c, dict)] + return [] + + def csv_row(self) -> dict[str, Any]: + """Return a row dict with the same columns the website exports. + + Column order mirrors the client code seen in the website bundle. + """ + d = self.data + + # Join helper + def _join(items: list[dict[str, Any]] | None) -> str: + if not items: + return "" + return ", ".join( + str(it.get("name", "")).strip() for it in items if isinstance(it, dict) + ) + + def _fmt(x: Any, ndigits: int) -> str: + try: + return f"{float(x):.{ndigits}f}" + except Exception: + return "" + + def _int(x: Any) -> str: + try: + return str(int(x)) + except Exception: + return "" + + return { + "Name": d.get("name", ""), + "CAS ID": d.get("cas_id", ""), + "PubChem CID": d.get("pubchem_cid", ""), + "Category": _join(d.get("category")), + "URL": d.get("url", ""), + "PubChem URL": d.get("pubchem_url", ""), + "SMILES": d.get("smiles", ""), + "Chirality": _join(d.get("chirality")), + "Description": d.get("description", ""), + "SMILES IUPAC": d.get("smiles_iupac", ""), + "Molecule Formula": d.get("molecule_formula", ""), + "Molecular Weight": _fmt(d.get("molecular_weight"), 3), + "Heavy Atom Count": _int(d.get("heavy_atom_count")), + "Ring Count": _int(d.get("ring_count")), + "Hydrogen Bond Acceptor Count": _int(d.get("hydrogen_bond_acceptor_count")), + "Hydrogen Bond Donor Count": _int(d.get("hydrogen_bond_donor_count")), + "Rotatable Bond Count": _int(d.get("rotatable_bond_count")), + "Zero-point correction": _fmt(d.get("zero_point_correction"), 6), + "Thermal correction to Energy": _fmt(d.get("thermal_correction_energy"), 6), + "Thermal correction to Enthalpy": _fmt( + d.get("thermal_correction_enthalpy"), 6 + ), + "Thermal correction to Gibbs Free Energy": _fmt( + d.get("thermal_correction_gibbs"), 6 + ), + "Sum of electronic and zero-point Energies": _fmt( + d.get("sum_electronic_zero_point"), 6 + ), + "Sum of electronic and thermal Energies": _fmt( + d.get("sum_electronic_thermal_energy"), 6 + ), + "Sum of electronic and thermal Enthalpies": _fmt( + d.get("sum_electronic_thermal_enthalpy"), 6 + ), + "Sum of electronic and thermal Free Energies": _fmt( + d.get("sum_electronic_thermal_free_energy"), 6 + ), + "HOMO Energy (eV)": _fmt(d.get("homo_energy"), 6), + "LUMO Energy (eV)": _fmt(d.get("lumo_energy"), 6), + "HOMO-LUMO Gap (eV)": _fmt(d.get("homo_lumo_gap"), 6), + } + + +def _new_session(timeout: int = 30) -> requests.Session: + s = requests.Session() + adapter = HTTPAdapter(max_retries=3, pool_connections=16, pool_maxsize=16) + s.mount("http://", adapter) + s.mount("https://", adapter) + s.headers.update( + { + "User-Agent": "clc-db-downloader/1.0 (+https://compbio.sjtu.edu.cn/services/clc-db)", + "Accept": "application/json, text/plain, */*", + "Accept-Language": "en-US,en;q=0.9", + } + ) + # store a default timeout on the session for convenience + s.request = _timeout_wrapper(s.request, timeout) + return s + + +def _timeout_wrapper(request_fn, timeout_default: int): + def wrapped(method, url, **kwargs): + if "timeout" not in kwargs: + kwargs["timeout"] = timeout_default + return request_fn(method, url, **kwargs) + + return wrapped + + +def _try_api_base(session: requests.Session) -> str | None: + for base in API_BASE_CANDIDATES: + logging.debug("Probing API base: %s", base) + try: + r = session.get(f"{base}/categories/") + if r.ok and r.headers.get("content-type", "").startswith( + "application/json" + ): + _ = r.json() + logging.info("Discovered API base: %s", base) + return base + except Exception: + logging.debug("Probe failed for %s", base, exc_info=True) + continue + return None + + +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=0.5, min=0.5, max=8), + reraise=True, + retry=retry_if_exception_type((requests.RequestException,)), +) +def _get_json( + session: requests.Session, url: str, params: dict[str, Any] | None = None +) -> dict[str, Any]: + r = session.get(url, params=params) + r.raise_for_status() + return r.json() + + +def fetch_categories(session: requests.Session, api_base: str | None) -> list[str]: + if not api_base: + return [] + try: + data = _get_json(session, f"{api_base}/categories/") + except Exception: + logging.warning("Fetching categories failed", exc_info=True) + return [] + # Expecting list of {id, name} + names = [] + if isinstance(data, list): + names = [str(x.get("name", "")).strip() for x in data if isinstance(x, dict)] + elif isinstance(data, dict) and "results" in data: + names = [ + str(x.get("name", "")).strip() + for x in data.get("results", []) + if isinstance(x, dict) + ] + names = [n for n in names if n] + logging.info("Fetched %d categories", len(names)) + return names + + +def iter_molecules( + session: requests.Session, + api_base: str | None, + category: str | None = None, + page_size: int = 30, +) -> Iterable[Molecule]: + if not api_base: + raise RuntimeError( + "API base not discovered; cannot enumerate molecules reliably." + ) + + page = 1 + while True: + params = {"page": page, "page_size": page_size} + if category: + params["category"] = category + url = f"{api_base}/search/molecules" + data = _get_json(session, url, params=params) + results = data.get("results", []) if isinstance(data, dict) else [] + logging.info( + "Fetched page %s (category=%s): %d results", + page, + category if category else "ALL", + len(results), + ) + for item in results: + if isinstance(item, dict): + yield Molecule(item) + next_url = data.get("next") if isinstance(data, dict) else None + if not next_url: + logging.info("No next page; finished pagination for %s", category or "ALL") + break + page += 1 + + +@retry( + stop=stop_after_attempt(5), + wait=wait_exponential(multiplier=0.5, min=0.5, max=8), + reraise=True, + retry=retry_if_exception_type((requests.RequestException,)), +) +def download_sdf( + session: requests.Session, cas_id: str, out_path: str +) -> tuple[str, bool, str | None]: + url = f"{SDF_BASE}/{cas_id}.sdf" + r = session.get(url) + if r.status_code == 404: + logging.warning("SDF not found (404) for CAS %s", cas_id) + return (cas_id, False, "404") + r.raise_for_status() + with open(out_path, "wb") as f: + f.write(r.content) + logging.debug("Saved SDF %s -> %s", cas_id, out_path) + return (cas_id, True, None) + + +def ensure_dir(path: str) -> None: + os.makedirs(path, exist_ok=True) + + +def write_csv(rows: list[dict[str, Any]], out_csv: str) -> None: + if not rows: + # write header-only CSV + cols = Molecule({}).csv_row().keys() + with open(out_csv, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=list(cols)) + writer.writeheader() + return + + # Use pandas for convenience and de-duplication on CAS ID + df = pd.DataFrame(rows) + # Drop duplicates by CAS ID keeping first occurrence + if "CAS ID" in df.columns: + df = df.drop_duplicates(subset=["CAS ID"], keep="first") + df.to_csv(out_csv, index=False) + + +def main(argv: list[str] | None = None) -> int: + p = argparse.ArgumentParser( + description="Download all molecules from CLC-DB and merge outputs." + ) + p.add_argument( + "--out-dir", + default=os.path.join("opt", "sterochemistry", "data", "clc_db"), + help="Output directory for SDFs and CSV (default: opt/sterochemistry/data/clc_db)", + ) + p.add_argument( + "--by-category", + action="store_true", + help="Iterate through categories explicitly", + ) + p.add_argument( + "--workers", type=int, default=8, help="Concurrent download workers for SDFs" + ) + p.add_argument("--page-size", type=int, default=30, help="API page size to use") + p.add_argument( + "--log-level", + default="INFO", + choices=["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"], + help="Logging level (default: INFO)", + ) + args = p.parse_args(argv) + + logging.basicConfig( + level=getattr(logging, args.log_level), + format="%(asctime)s %(levelname)s %(message)s", + ) + + out_dir = os.path.abspath(args.out_dir) + sdf_dir = os.path.join(out_dir, "sdf") + ensure_dir(sdf_dir) + logging.info("Output directory: %s", out_dir) + logging.info("SDF directory: %s", sdf_dir) + + session = _new_session() + api_base = _try_api_base(session) + if not api_base: + logging.error("Could not discover API base endpoint; aborting.") + return 2 + + categories: list[str] = [] + if args.by_category: + categories = fetch_categories(session, api_base) + if not categories: + logging.warning( + "Categories endpoint not available; falling back to all molecules." + ) + + rows: list[dict[str, Any]] = [] + seen_cas: set[str] = set() + + def process_molecule(mol: Molecule) -> tuple[str, bool, str | None] | None: + cas = mol.cas_id + if not cas: + return None + out_path = os.path.join(sdf_dir, f"{cas}.sdf") + if os.path.exists(out_path) and os.path.getsize(out_path) > 0: + return (cas, True, None) + try: + return download_sdf(session, cas, out_path) + except Exception as e: + return (cas, False, str(e)) + + # Iterate molecules (all or by category) + if categories: + iterables: list[tuple[str, Iterable[Molecule]]] = [] + for cat in categories: + iterables.append( + ( + cat, + iter_molecules( + session, api_base, category=cat, page_size=args.page_size + ), + ) + ) + else: + iterables = [ + ( + "ALL", + iter_molecules( + session, api_base, category=None, page_size=args.page_size + ), + ) + ] + + for label, it in iterables: + logging.info("Collecting molecules for: %s", label) + batch: list[Molecule] = list(it) + logging.info("Found %d molecules for %s", len(batch), label) + # merge rows + for mol in batch: + if mol.cas_id and mol.cas_id not in seen_cas: + rows.append(mol.csv_row()) + seen_cas.add(mol.cas_id) + + # download SDFs concurrently + results: list[tuple[str, bool, str | None]] = [] + with futures.ThreadPoolExecutor(max_workers=args.workers) as ex: + for res in tqdm( + ex.map(process_molecule, batch), total=len(batch), desc=f"SDF {label}" + ): + if res is not None: + results.append(res) + total = len(results) + ok = sum(1 for _, success, _ in results if success) + missing = [cas for cas, ok_, _ in results if not ok_] + logging.info( + "Downloads finished for %s: %d ok, %d missing (of %d)", + label, + ok, + len(missing), + total, + ) + if missing: + logging.warning("Missing first few SDFs for %s: %s", label, missing[:5]) + + # Write merged CSV + out_csv = os.path.join(out_dir, "molecules.csv") + write_csv(rows, out_csv) + logging.info("Saved CSV: %s", out_csv) + logging.info("SDF folder: %s", sdf_dir) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/opt/sterochemistry/omol25.py b/opt/sterochemistry/omol25.py new file mode 100644 index 00000000..0290d7c7 --- /dev/null +++ b/opt/sterochemistry/omol25.py @@ -0,0 +1,526 @@ +#!/usr/bin/env -S uv run python +""" +Build a HuggingFace dataset from one or more ASE LMDB (".aselmdb") files. + +- Input: a directory containing ``*.aselmdb`` files (single split) OR a + directory with subdirectories, each containing ``*.aselmdb`` (multi-split). + You may also pass a single ``.aselmdb`` file. +- Processing: entries are read, normalized with RDKit (bonding, identifiers), + and curated fields are produced. +- Output: an Arrow dataset written via ``datasets`` (``save_to_disk``). +- Multiprocessing: heavy per-record processing runs with ``map(num_proc=...)``. + +Outputs per record: +- unique_id, composition, SMILES, InChI, InChIKey +- total-energy, homo, lumo, gap, nl_energy, charge, spin +- atomic-numbers, atomic-coordinates (flattened), atomic-isotope (or None) +- bond-connections (u,v pairs flattened) and bond-orders +- mulliken_charges, lowdin_charges, nbo_charges (if present) + +Bonding and identifiers are computed with RDKit from geometry, using a +sanitization pipeline modeled on opt/tmQM/build.py. +""" + +import argparse +import json +import os +from pathlib import Path +import zlib +from collections.abc import Generator +from typing import Any + +import lmdb +import numpy as np +from rdkit import Chem +from rdkit.Chem import inchi as rd_inchi +import rdkit.Chem.AllChem as AllChem # noqa: F401 +from rdkit.Chem import rdDetermineBonds, rdmolfiles, rdmolops +from rdkit.Chem import rdMolDescriptors +import logging +from datasets import Dataset, DatasetDict + + +def iter_aselmdb(path: str) -> Generator[dict[str, Any], None, None]: + env = lmdb.open(path, readonly=True, lock=False, max_readers=2048, subdir=False) + with env.begin() as txn: + cur = txn.cursor() + for _, v in cur: + try: + j = json.loads(zlib.decompress(v)) + if isinstance(j, dict): + yield j + except Exception: + logging.debug("Failed to decode LMDB value as JSON", exc_info=True) + continue + + +def _ndarray_from_json(obj: Any) -> np.ndarray | None: + if isinstance(obj, dict) and "__ndarray__" in obj: + arr = obj["__ndarray__"] + if not isinstance(arr, list) or len(arr) < 3: + return None + shape, dtype, data = arr[0], arr[1], arr[2] + try: + a = np.array(data, dtype=dtype) + if isinstance(shape, list): + a = a.reshape(shape) + return a + except Exception: + logging.debug("Failed to parse __ndarray__ payload", exc_info=True) + return None + return None + + +def _get(entry: dict[str, Any], key: str) -> Any: + if key in entry: + return entry[key] + d = entry.get("data") + if isinstance(d, dict) and key in d: + return d[key] + return None + + +def _as_float(val: Any) -> float | None: + if val is None: + return None + if isinstance(val, int | float): + return float(val) + a = _ndarray_from_json(val) + if a is not None: + try: + return float(np.ravel(a)[0]) + except Exception: + return None + try: + return float(val) + except Exception: + return None + + +def _as_list_float(val: Any) -> list[float] | None: + if val is None: + return None + if isinstance(val, list): + return [float(x) for x in val] + a = _ndarray_from_json(val) + if a is not None: + return [float(x) for x in np.ravel(a).tolist()] + return None + + +def build_rdkit_mol( + numbers: list[int], positions: np.ndarray, charge: int = 0 +) -> Chem.Mol | None: + # Build XYZ block + pt = Chem.GetPeriodicTable() + lines = [str(len(numbers)), "generated by aselmdb_to_jsonl"] + for z, (x, y, zc) in zip(numbers, positions): + sym = pt.GetElementSymbol(int(z)) + lines.append(f"{sym} {x:.8f} {y:.8f} {zc:.8f}") + xyz = "\n".join(lines) + + def _attempt(use_hueckel: bool) -> Chem.Mol: + m = Chem.MolFromXYZBlock(xyz) + if m is None: + raise ValueError("MolFromXYZBlock returned None") + rdDetermineBonds.DetermineBonds(m, charge=int(charge), useHueckel=use_hueckel) + # Sanitize following opt/tmQM/build.py + rdmolops.AssignStereochemistryFrom3D(m) + m = rdmolops.HapticBondsToDative(m) + rdmolops.SanitizeMol(m, catchErrors=True) + m = rdmolops.RemoveHs(m, sanitize=False) + smi_tmp = Chem.MolToSmiles(m) + m = Chem.MolFromSmiles(smi_tmp, sanitize=False) + rdmolops.SanitizeMol(m, catchErrors=True) + if m is None: + raise ValueError("Failed to round-trip molecule through SMILES") + return m + + # Try Hückel method first, then fallback to default + try: + logging.debug("DetermineBonds(useHueckel=True), charge=%s", charge) + mol = _attempt(True) + logging.info("Bonding determined using Hückel method") + return mol + except Exception: + logging.warning( + "Hückel bond perception failed; trying default method", exc_info=True + ) + try: + logging.debug("DetermineBonds(useHueckel=False), charge=%s", charge) + mol = _attempt(False) + logging.info("Bonding determined using default method") + return mol + except Exception: + logging.error( + "Bond perception failed with both Hückel and default methods", + exc_info=True, + ) + raise ValueError("failed to determine bonds with RDKit") + + +def smiles_inchi_from_mol(mol: Chem.Mol) -> tuple[str, str, str]: + p = rdmolfiles.SmilesWriteParams() + # p.isomericSmiles = True + p.canonical = True + p.allHsExplicit = False + p.allBondsExplicit = False + p.doKekule = False + p.includeDativeBonds = False + smi = Chem.MolToSmiles(mol, p) + + # Validate and sanitize strictly + m2 = Chem.MolFromSmiles(smi) + if m2 is None: + raise ValueError("invalid SMILES from mol") + Chem.SanitizeMol(m2) + + inchi = rd_inchi.MolToInchi(m2) + inchikey = rd_inchi.MolToInchiKey(m2) + return smi, inchi, inchikey + + +def record_from_entry(entry: dict[str, Any]) -> dict[str, Any] | None: + # Extract base fields + uid = entry.get("unique_id") + composition = _get(entry, "composition") + energy = _as_float(_get(entry, "energy")) + homo = _as_float(_get(entry, "homo_energy")) + lumo = _as_float(_get(entry, "lumo_energy")) + gap = _as_float(_get(entry, "homo_lumo_gap")) + nl_energy = _as_float(_get(entry, "nl_energy")) + charge = int(_get(entry, "charge") or 0) + spin = int(_get(entry, "spin") or 0) + + if homo is not None and lumo is None and gap is not None: + lumo = homo + gap + if gap is None and homo is not None and lumo is not None: + gap = lumo - homo + + nums_any = _get(entry, "numbers") + pos_any = _get(entry, "positions") + numbers = _as_list_float(nums_any) + numbers = [int(round(x)) for x in numbers] if numbers is not None else None + pos = _ndarray_from_json(pos_any) + if pos is None: + # Some datasets store as nested lists + if isinstance(pos_any, list) and pos_any and isinstance(pos_any[0], list): + pos = np.array(pos_any, dtype=float) + if numbers is None or pos is None: + raise ValueError("missing atomic numbers or positions in entry") + + # Build RDKit mol (sanitized) and extract bonds + identifiers + mol = build_rdkit_mol(numbers, pos, charge=charge) + + # Geometry from sanitized mol (without Hs) + conf = mol.GetConformer() if mol.GetNumConformers() else None + if conf is None: + # If no conformer retained (unlikely), fallback to original + atom_numbers = [int(z) for z in numbers] + coords_flat = [float(x) for x in np.ravel(pos).tolist()] + isotopes = [None for _ in atom_numbers] + else: + atom_numbers = [int(a.GetAtomicNum()) for a in mol.GetAtoms()] + isotopes = [ + int(a.GetIsotope()) if a.GetIsotope() != 0 else None for a in mol.GetAtoms() + ] + coords_flat = [] + for i in range(mol.GetNumAtoms()): + p = conf.GetAtomPosition(i) + coords_flat.extend([float(p.x), float(p.y), float(p.z)]) + + # Bonds + bond_conns: list[int] = [] + bond_orders: list[int] = [] + bt_map = { + Chem.BondType.SINGLE: 1, + Chem.BondType.DOUBLE: 2, + Chem.BondType.TRIPLE: 3, + Chem.BondType.AROMATIC: 1, + Chem.BondType.DATIVE: 1, + } + for b in mol.GetBonds(): + bond_conns.extend([int(b.GetBeginAtomIdx()), int(b.GetEndAtomIdx())]) + bond_orders.append(int(bt_map.get(b.GetBondType(), 1))) + + # Identifiers + try: + smiles, inchi, inchikey = smiles_inchi_from_mol(mol) + except Exception: + logging.warning("Failed to compute SMILES/InChI identifiers", exc_info=True) + smiles = inchi = inchikey = None + # If SMILES is not available, skip this entry + if not smiles: + logging.warning("Skipping entry due to missing SMILES encoding") + return None + + # Validate SMILES consistency against original geometry (no new heavy atoms) + try: + m2 = Chem.MolFromSmiles(smiles) + if m2 is None: + raise ValueError("MolFromSmiles returned None for generated SMILES") + Chem.SanitizeMol(m2) + from collections import Counter + + orig_heavy = [int(z) for z in numbers if int(z) != 1] + smi_heavy = [a.GetAtomicNum() for a in m2.GetAtoms() if a.GetAtomicNum() != 1] + if Counter(orig_heavy) != Counter(smi_heavy): + logging.warning( + "Skipping entry due to heavy-atom composition mismatch: orig=%s, smiles=%s", + Counter(orig_heavy), + Counter(smi_heavy), + ) + return None + # Optional: check total formal charge + try: + smiles_charge = rdmolops.GetFormalCharge(m2) + if smiles_charge != charge: + logging.warning( + "Skipping entry due to formal charge mismatch: dataset=%s, smiles=%s", + charge, + smiles_charge, + ) + return None + except Exception: + logging.debug("Could not compute formal charge from SMILES", exc_info=True) + except Exception: + logging.warning( + "Skipping entry due to SMILES validation failure", exc_info=True + ) + return None + + # Compute composition if missing + if not composition: + try: + composition = rdMolDescriptors.CalcMolFormula(mol) + except Exception: + logging.debug( + "Failed to compute molecular formula from RDKit mol", exc_info=True + ) + + # Charges per atom + mulliken = _as_list_float(_get(entry, "mulliken_charges")) + lowdin = _as_list_float(_get(entry, "lowdin_charges")) + nbo = _as_list_float(_get(entry, "nbo_charges")) + + rec = { + "unique_id": uid, + "composition": composition, + "SMILES": smiles, + "InChI": inchi, + "InChIKey": inchikey, + "total-energy": energy, + "homo": homo, + "lumo": lumo, + "gap": gap, + "charge": charge, + "spin": spin, + "nl_energy": nl_energy, + "atomic-numbers": atom_numbers, + "atomic-coordinates": coords_flat, + "atomic-isotope": isotopes, + "bond-connections": bond_conns, + "bond-orders": bond_orders, + "mulliken_charges": mulliken, + "lowdin_charges": lowdin, + "nbo_charges": nbo, + } + return rec + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser( + description="Build Arrow dataset from ASE LMDB (.aselmdb) files using datasets" + ) + ap.add_argument( + "input", + help=( + "Path to a .aselmdb file, a folder of *.aselmdb (single split), or a folder" + " of subfolders each containing *.aselmdb (multi-split)" + ), + ) + ap.add_argument( + "--output", + "-o", + required=True, + help="Output dataset directory (will be created, contains Arrow files)", + ) + ap.add_argument( + "--num-proc", + type=int, + default=os.cpu_count() or 1, + help="Number of processes for per-record processing (datasets.map)", + ) + ap.add_argument( + "--max-shard-size", + default="1GB", + help="Maximum shard size passed to save_to_disk (e.g., 1GB, 500MB)", + ) + ap.add_argument( + "--log-level", + default="INFO", + choices=["CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG"], + help="Logging level (default: INFO)", + ) + args = ap.parse_args(argv) + + inp = Path(args.input) + out_dir = Path(args.output) + + logging.basicConfig( + level=getattr(logging, args.log_level), + format="%(asctime)s %(levelname)s %(message)s", + ) + + # Discover splits + splits = _discover_splits(inp) + if not splits: + raise SystemExit(f"No .aselmdb files found under: {inp}") + + logging.info("Discovered splits: %s", {k: len(v) for k, v in splits.items()}) + + # Build dataset(s) + datasets = {} + for split, files in splits.items(): + logging.info("Building split '%s' from %d file(s)", split, len(files)) + ds = _build_split_dataset(files, num_proc=args.num_proc) + datasets[split] = ds + + # Save to disk (always as DatasetDict for predictable structure) + logging.info("Saving DatasetDict with %d split(s) to %s", len(datasets), out_dir) + ds_dict = DatasetDict(datasets) + ds_dict.save_to_disk(out_dir, max_shard_size=args.max_shard_size) + + logging.info("Done. Saved dataset(s) to %s", out_dir) + print(f"Saved dataset(s) to {out_dir}") + return 0 + + +# ------------------------ +# Dataset building helpers +# ------------------------ + + +def _discover_splits(root: Path) -> dict[str, list[str]]: + """Find input .aselmdb files and group them into splits. + + Rules: + - If ``root`` is a file ending with .aselmdb -> one split named 'train'. + - If ``root`` is a directory with ``*.aselmdb`` -> one split named after the + directory (commonly a split name like 'neural_val'). If the directory name + is 'train'/'validation'/'test', keep it, otherwise default to 'train'. + - If ``root`` contains subdirectories, each subdirectory with ``*.aselmdb`` + becomes a split named by the subdirectory. + """ + if root.is_file() and root.suffix == ".aselmdb": + return {root.stem: [str(root)]} + + if root.is_dir(): + # Case 1: directory has *.aselmdb directly + files_here = sorted(str(p) for p in root.glob("*.aselmdb")) + if files_here: + return {root.name: files_here} + + # Case 2: subdirectories are splits + splits: dict[str, list[str]] = {} + for sub in sorted([p for p in root.iterdir() if p.is_dir()]): + files = sorted(str(p) for p in sub.glob("*.aselmdb")) + if files: + splits[sub.name] = files + return splits + + return {} + + +def _entries_from_files(files: list[str]): + for f in files: + for entry in iter_aselmdb(f): + yield {"raw": json.dumps(entry)} + + +OUTPUT_COLUMNS = [ + "unique_id", + "composition", + "SMILES", + "InChI", + "InChIKey", + "total-energy", + "homo", + "lumo", + "gap", + "charge", + "spin", + "nl_energy", + "atomic-numbers", + "atomic-coordinates", + "atomic-isotope", + "bond-connections", + "bond-orders", + "mulliken_charges", + "lowdin_charges", + "nbo_charges", +] + + +def _process_batch(batch: dict) -> dict: + raws: list[str] = batch["raw"] + out: dict[str, list] = {k: [] for k in OUTPUT_COLUMNS} + keep: list[bool] = [] + for s in raws: + try: + rec = record_from_entry(json.loads(s)) + except Exception: + rec = None + if rec is None: + # Fill placeholders; we'll filter later + for k in OUTPUT_COLUMNS: + # Scalars -> None, lists -> empty list + if k in { + "atomic-numbers", + "atomic-coordinates", + "atomic-isotope", + "bond-connections", + "bond-orders", + "mulliken_charges", + "lowdin_charges", + "nbo_charges", + }: + out[k].append([]) + else: + out[k].append(None) + keep.append(False) + else: + for k in OUTPUT_COLUMNS: + out[k].append(rec.get(k)) + keep.append(True) + + out["__keep__"] = keep + return out + + +def _build_split_dataset(files: list[str], num_proc: int) -> Dataset: + # Stage 1: build a lightweight dataset of raw JSON strings + ds = Dataset.from_generator( + _entries_from_files, + gen_kwargs={"files": files}, + keep_in_memory=False, + ) + + # Stage 2: heavy processing in parallel + ds = ds.map( + _process_batch, + batched=True, + remove_columns=["raw"], + num_proc=max(1, int(num_proc)), + desc="Processing records with RDKit", + ) + + # Stage 3: drop failed rows + ds = ds.filter(lambda keep: keep, input_columns=["__keep__"]).remove_columns( + ["__keep__"] + ) + return ds + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/opt/sterochemistry/pyproject.toml b/opt/sterochemistry/pyproject.toml new file mode 100644 index 00000000..a40fc6c2 --- /dev/null +++ b/opt/sterochemistry/pyproject.toml @@ -0,0 +1,16 @@ +[project] +name = "sterochemistry" +version = "0.1.0" +description = "Ingest datasets training" +readme = "README.md" +requires-python = ">=3.10, <3.14" +dependencies = [ + "datasets>=4.0.0", + "lmdb>=1.7.3", + "numpy", + "pandas>=2.2.3", + "rdkit", + "requests>=2.32.3", + "tenacity>=9.1.2", + "tqdm>=4.67.1", +] From bbda2c321f0f3745e8849753edacc41288b1ff67 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 15:43:30 -0400 Subject: [PATCH 071/147] feat: support processing *.tar.gz files directly --- opt/sterochemistry/omol25.py | 162 ++++++++++++++++++++++++++++++----- 1 file changed, 142 insertions(+), 20 deletions(-) diff --git a/opt/sterochemistry/omol25.py b/opt/sterochemistry/omol25.py index 0290d7c7..c4149aee 100644 --- a/opt/sterochemistry/omol25.py +++ b/opt/sterochemistry/omol25.py @@ -3,8 +3,10 @@ Build a HuggingFace dataset from one or more ASE LMDB (".aselmdb") files. - Input: a directory containing ``*.aselmdb`` files (single split) OR a - directory with subdirectories, each containing ``*.aselmdb`` (multi-split). - You may also pass a single ``.aselmdb`` file. + directory with subdirectories, each containing ``*.aselmdb`` (multi-split), + or a directory containing ``*.tar.gz``/``*.tgz`` archives where each archive + represents a split and contains ``*.aselmdb`` inside. You may also pass a + single ``.aselmdb`` or ``.tar.gz`` file. - Processing: entries are read, normalized with RDKit (bonding, identifiers), and curated fields are produced. - Output: an Arrow dataset written via ``datasets`` (``save_to_disk``). @@ -25,6 +27,10 @@ import json import os from pathlib import Path +import tarfile +import tempfile +import threading +from queue import Queue import zlib from collections.abc import Generator from typing import Any @@ -351,6 +357,12 @@ def main(argv: list[str] | None = None) -> int: default=os.cpu_count() or 1, help="Number of processes for per-record processing (datasets.map)", ) + ap.add_argument( + "--read-workers", + type=int, + default=4, + help="Number of threads to stream LMDB entries from files/archives", + ) ap.add_argument( "--max-shard-size", default="1GB", @@ -381,9 +393,9 @@ def main(argv: list[str] | None = None) -> int: # Build dataset(s) datasets = {} - for split, files in splits.items(): - logging.info("Building split '%s' from %d file(s)", split, len(files)) - ds = _build_split_dataset(files, num_proc=args.num_proc) + for split, sources in splits.items(): + logging.info("Building split '%s' from %d source(s)", split, len(sources)) + ds = _build_split_dataset(sources, num_proc=args.num_proc, read_workers=args.read_workers) datasets[split] = ds # Save to disk (always as DatasetDict for predictable structure) @@ -401,41 +413,151 @@ def main(argv: list[str] | None = None) -> int: # ------------------------ -def _discover_splits(root: Path) -> dict[str, list[str]]: +def _discover_splits(root: Path) -> dict[str, list[tuple[str, ...]]]: """Find input .aselmdb files and group them into splits. Rules: - - If ``root`` is a file ending with .aselmdb -> one split named 'train'. + - If ``root`` is a file ending with .aselmdb -> one split named by stem. + - If ``root`` is a .tar.gz/.tgz -> one split named by archive basename; all + .aselmdb members are included. - If ``root`` is a directory with ``*.aselmdb`` -> one split named after the - directory (commonly a split name like 'neural_val'). If the directory name - is 'train'/'validation'/'test', keep it, otherwise default to 'train'. + directory (e.g., 'neural_val'). - If ``root`` contains subdirectories, each subdirectory with ``*.aselmdb`` becomes a split named by the subdirectory. + - If ``root`` contains ``*.tar.gz``/``*.tgz`` archives, each archive becomes + a split named by its basename (without the archive suffix). """ if root.is_file() and root.suffix == ".aselmdb": - return {root.stem: [str(root)]} + return {root.stem: [("file", str(root))]} + if root.is_file() and (str(root).endswith(".tar.gz") or str(root).endswith(".tgz")): + split = _basename_without_targz(root.name) + members = _list_aselmdb_in_tar(str(root)) + return {split: [("tar", str(root), m) for m in members]} if root.is_dir(): # Case 1: directory has *.aselmdb directly files_here = sorted(str(p) for p in root.glob("*.aselmdb")) if files_here: - return {root.name: files_here} + return {root.name: [("file", f) for f in files_here]} # Case 2: subdirectories are splits - splits: dict[str, list[str]] = {} + splits: dict[str, list[tuple[str, ...]]] = {} for sub in sorted([p for p in root.iterdir() if p.is_dir()]): files = sorted(str(p) for p in sub.glob("*.aselmdb")) if files: - splits[sub.name] = files + splits[sub.name] = [("file", f) for f in files] + # Tarball splits at root + for tb in sorted( + [ + p + for p in root.iterdir() + if p.is_file() and (str(p).endswith(".tar.gz") or str(p).endswith(".tgz")) + ] + ): + split = _basename_without_targz(tb.name) + members = _list_aselmdb_in_tar(str(tb)) + if members: + splits[split] = [("tar", str(tb), m) for m in members] return splits return {} +def _basename_without_targz(name: str) -> str: + for suf in (".tar.gz", ".tgz"): + if name.endswith(suf): + return name[: -len(suf)] + # fallback + return Path(name).stem + + +def _list_aselmdb_in_tar(tar_path: str) -> list[str]: + with tarfile.open(tar_path, "r:gz") as tf: + return [m.name for m in tf.getmembers() if m.isfile() and m.name.endswith(".aselmdb")] + + +def _iter_aselmdb_from_tar(tar_path: str, member_name: str): + """Iterate entries from a single .aselmdb member inside a tar.gz archive. + + Streams the member to a temporary file without extracting the entire archive, + then uses the existing LMDB reader to iterate entries. + """ + with tarfile.open(tar_path, "r:gz") as tf: + try: + m = tf.getmember(member_name) + except KeyError: + logging.warning("Missing member %s in %s", member_name, tar_path) + return + fobj = tf.extractfile(m) + if fobj is None: + logging.warning("Could not open member %s from %s", member_name, tar_path) + return + with tempfile.NamedTemporaryFile(suffix=".aselmdb", delete=False) as tmp: + tmp_path = tmp.name + while True: + chunk = fobj.read(1024 * 1024) + if not chunk: + break + tmp.write(chunk) + try: + for entry in iter_aselmdb(tmp_path): + yield entry + finally: + try: + os.remove(tmp_path) + except Exception: + logging.debug("Failed to remove temp file %s", tmp_path, exc_info=True) + + +def _entries_from_sources(sources: list[tuple[str, ...]], read_workers: int): + """Concurrent producer that streams entries from file and tar sources.""" + q: "Queue[object]" = Queue(maxsize=2048) + sentinel = object() + src_iter = iter(sources) + lock = threading.Lock() + finished = 0 + n_workers = max(1, int(read_workers)) + + def next_src(): + nonlocal src_iter + with lock: + try: + return next(src_iter) + except StopIteration: + return None + + def worker(): + nonlocal finished + while True: + src = next_src() + if src is None: + break + try: + if src[0] == "file": + _, path = src + it = iter_aselmdb(path) + elif src[0] == "tar": + _, tar_path, member = src + it = _iter_aselmdb_from_tar(tar_path, member) + else: + it = iter(()) + for entry in it: + q.put({"raw": json.dumps(entry)}, block=True) + except Exception: + logging.exception("Reader failed for source: %s", src) + with lock: + finished += 1 + if finished == n_workers: + q.put(sentinel) + + threads = [threading.Thread(target=worker, daemon=True) for _ in range(n_workers)] + for t in threads: + t.start() -def _entries_from_files(files: list[str]): - for f in files: - for entry in iter_aselmdb(f): - yield {"raw": json.dumps(entry)} + while True: + item = q.get() + if item is sentinel: + break + yield item OUTPUT_COLUMNS = [ @@ -498,11 +620,11 @@ def _process_batch(batch: dict) -> dict: return out -def _build_split_dataset(files: list[str], num_proc: int) -> Dataset: +def _build_split_dataset(sources: list[tuple[str, ...]], num_proc: int, read_workers: int) -> Dataset: # Stage 1: build a lightweight dataset of raw JSON strings ds = Dataset.from_generator( - _entries_from_files, - gen_kwargs={"files": files}, + _entries_from_sources, + gen_kwargs={"sources": sources, "read_workers": read_workers}, keep_in_memory=False, ) From 886e141f1074a2c1e6862cc47819628b2e7d0684 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 16:25:20 -0400 Subject: [PATCH 072/147] feat: add process_clc_db --- opt/sterochemistry/.gitignore | 2 + .../{clc_db.py => export_clc_db.py} | 0 opt/sterochemistry/process_clc_db.py | 269 ++++++++++++++++++ opt/sterochemistry/pyproject.toml | 1 + pyproject.toml | 2 +- 5 files changed, 273 insertions(+), 1 deletion(-) create mode 100644 opt/sterochemistry/.gitignore rename opt/sterochemistry/{clc_db.py => export_clc_db.py} (100%) create mode 100644 opt/sterochemistry/process_clc_db.py diff --git a/opt/sterochemistry/.gitignore b/opt/sterochemistry/.gitignore new file mode 100644 index 00000000..1e4519d8 --- /dev/null +++ b/opt/sterochemistry/.gitignore @@ -0,0 +1,2 @@ +data/ +uv.lock diff --git a/opt/sterochemistry/clc_db.py b/opt/sterochemistry/export_clc_db.py similarity index 100% rename from opt/sterochemistry/clc_db.py rename to opt/sterochemistry/export_clc_db.py diff --git a/opt/sterochemistry/process_clc_db.py b/opt/sterochemistry/process_clc_db.py new file mode 100644 index 00000000..0da9994c --- /dev/null +++ b/opt/sterochemistry/process_clc_db.py @@ -0,0 +1,269 @@ +#!/usr/bin/env -S uv run python +""" +Process CLC-DB molecules.csv into a HuggingFace datasets Arrow dataset. + +Steps: +- Read the CSV produced by export_clc_db.py +- Validate SMILES (RDKit); drop invalid/empty +- Compute InChI and InChIKey (RDKit) +- Convert "Chirality" to multi-label dict with keys: point, planar, axial +- Split into train/val/test (80/10/10) using StratifiedShuffleSplit, stratify on chirality +- Save as a DatasetDict via datasets.save_to_disk + +Usage: + uv run opt/sterochemistry/process_clc_db.py \ + --csv opt/sterochemistry/data/clc_db/molecules.csv \ + --out opt/sterochemistry/data/clc_db/hf +""" + +import argparse +import json +import math +import os +from typing import Any + +import numpy as np +import pandas as pd +from datasets import Dataset, DatasetDict, Features, Value +from rdkit import Chem +from rdkit.Chem.inchi import MolToInchi, MolToInchiKey +from sklearn.model_selection import StratifiedShuffleSplit + + +# Column names from the website/export script +COL_SMILES = "SMILES" +COL_CHIRALITY = "Chirality" +COL_MW = "Molecular Weight" +COL_ZP = "Zero-point correction" +COL_TE = "Thermal correction to Energy" +COL_TH = "Thermal correction to Enthalpy" +COL_TG = "Thermal correction to Gibbs Free Energy" +COL_HOMO = "HOMO Energy (eV)" +COL_LUMO = "LUMO Energy (eV)" +COL_GAP = "HOMO-LUMO Gap (eV)" + + +def _to_float(x: Any) -> float | None: + if x is None: + return None + if isinstance(x, float): + if math.isfinite(x): + return float(x) + return None + try: + s = str(x).strip() + if s == "" or s.lower() in {"na", "n/a", "nan", "none"}: + return None + return float(s) + except Exception: + return None + + +def parse_chirality(raw: str | None) -> dict[str, bool]: + """Parse the website's chirality string into multi-label booleans. + + - Recognizes: point, planar, axial (case-insensitive, substring match) + - Treats N/A or empty as all False + - If multiple classes are present, sets both True + """ + flags = {"point": False, "planar": False, "axial": False} + if not raw: + return flags + s = str(raw).strip() + if s == "" or s.lower() in {"na", "n/a", "none"}: + return flags + s_low = s.lower() + if "point" in s_low: + flags["point"] = True + if "planar" in s_low: + flags["planar"] = True + if "axial" in s_low: + flags["axial"] = True + return flags + + +def smiles_to_mol(smiles: str | None): + if not smiles: + return None + s = smiles.strip() + if s == "": + return None + try: + m = Chem.MolFromSmiles(s) + return m + except Exception: + return None + + +def compute_inchi(mol) -> tuple[str | None, str | None]: + if mol is None: + return None, None + try: + inchi = MolToInchi(mol) + except Exception: + inchi = None + try: + ikey = MolToInchiKey(mol) + except Exception: + ikey = None + return inchi, ikey + + +def load_rows(csv_path: str) -> list[dict[str, Any]]: + # Read all columns as strings to avoid Pandas NA coercion, then convert + df = pd.read_csv(csv_path, dtype=str, keep_default_na=False) + + rows: list[dict[str, Any]] = [] + for _, r in df.iterrows(): + smiles = r.get(COL_SMILES, "").strip() + mol = smiles_to_mol(smiles) + if mol is None: + continue # drop invalid/empty SMILES + inchi, inchi_key = compute_inchi(mol) + + ch = parse_chirality(r.get(COL_CHIRALITY, "")) + + row: dict[str, Any] = { + "smiles": smiles, + "InChI": inchi if inchi is not None else "", + "InChIKey": inchi_key if inchi_key is not None else "", + "chirality": ch, + "molecular_weight": _to_float(r.get(COL_MW)), + "zero_point_correction": _to_float(r.get(COL_ZP)), + "thermal_correction_energy": _to_float(r.get(COL_TE)), + "thermal_correction_enthalpy": _to_float(r.get(COL_TH)), + "thermal_correction_gibbs": _to_float(r.get(COL_TG)), + "homo": _to_float(r.get(COL_HOMO)), + "lumo": _to_float(r.get(COL_LUMO)), + "gap": _to_float(r.get(COL_GAP)), + } + rows.append(row) + return rows + + +def build_multilabel_targets(rows: list[dict[str, Any]]) -> np.ndarray: + y = np.zeros((len(rows), 3), dtype=int) + for i, r in enumerate(rows): + ch = r.get("chirality", {}) or {} + y[i, 0] = 1 if ch.get("point", False) else 0 + y[i, 1] = 1 if ch.get("planar", False) else 0 + y[i, 2] = 1 if ch.get("axial", False) else 0 + return y + + +def _stratify_indices( + n: int, y: np.ndarray, seed: int = 17 +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Split indices into train/val/test (80/10/10) using stratification on y. + + Primary path: pass multi-label indicator matrix directly to StratifiedShuffleSplit + as requested. If the environment's sklearn does not support this, fall back to + stratifying on label-combinations as a single multiclass target. + """ + rng = seed + + def _try_split(y_input) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + idx_all = np.arange(n) + # first split off test 10% + sss1 = StratifiedShuffleSplit(n_splits=1, test_size=0.1, random_state=rng) + train_val_idx, test_idx = next(sss1.split(idx_all, y_input)) + # then split train/val with val proportion 1/9 (~0.111... of remaining) + sss2 = StratifiedShuffleSplit(n_splits=1, test_size=1 / 9, random_state=rng) + train_idx, val_idx = next(sss2.split(train_val_idx, y_input[train_val_idx])) + return train_val_idx[train_idx], train_val_idx[val_idx], test_idx + + # Attempt multi-label directly + try: + return _try_split(y) + except Exception: + # Fall back to combination labels as strings + combos = np.array([f"{a}{b}{c}" for a, b, c in y], dtype=object) + return _try_split(combos) + + +def to_hf_datasets( + rows: list[dict[str, Any]], indices: dict[str, np.ndarray] +) -> DatasetDict: + features = Features( + { + "smiles": Value("string"), + "InChI": Value("string"), + "InChIKey": Value("string"), + "chirality": { + "point": Value("bool"), + "planar": Value("bool"), + "axial": Value("bool"), + }, + "molecular_weight": Value("float64"), + "zero_point_correction": Value("float64"), + "thermal_correction_energy": Value("float64"), + "thermal_correction_enthalpy": Value("float64"), + "thermal_correction_gibbs": Value("float64"), + "homo": Value("float64"), + "lumo": Value("float64"), + "gap": Value("float64"), + } + ) + + def _subset(split_idx: np.ndarray) -> Dataset: + data = [rows[i] for i in split_idx] + return Dataset.from_list(data, features=features) + + dsd = DatasetDict( + { + "train": _subset(indices["train"]), + "validation": _subset(indices["validation"]), + "test": _subset(indices["test"]), + } + ) + return dsd + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description=__doc__) + ap.add_argument( + "--csv", + default=os.path.join("data", "clc_db", "molecules.csv"), + help="Input molecules.csv path", + ) + ap.add_argument( + "--out", + default=os.path.join("data", "clc_db", "hf"), + help="Output directory for the saved DatasetDict", + ) + ap.add_argument("--seed", type=int, default=42, help="Random seed for splits") + args = ap.parse_args(argv) + + rows = load_rows(args.csv) + if not rows: + print("No valid molecules found after SMILES validation.") + return 1 + + y = build_multilabel_targets(rows) + train_idx, val_idx, test_idx = _stratify_indices(len(rows), y, seed=args.seed) + indices = {"train": train_idx, "validation": val_idx, "test": test_idx} + + dsd = to_hf_datasets(rows, indices) + os.makedirs(args.out, exist_ok=True) + dsd.save_to_disk(args.out, num_shards={k: 4 for k in indices.keys()}) + + # Brief summary + print( + json.dumps( + { + "counts": { + "train": len(train_idx), + "validation": len(val_idx), + "test": len(test_idx), + "total": len(rows), + }, + "out": os.path.abspath(args.out), + }, + indent=2, + ) + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/opt/sterochemistry/pyproject.toml b/opt/sterochemistry/pyproject.toml index a40fc6c2..ada6b762 100644 --- a/opt/sterochemistry/pyproject.toml +++ b/opt/sterochemistry/pyproject.toml @@ -11,6 +11,7 @@ dependencies = [ "pandas>=2.2.3", "rdkit", "requests>=2.32.3", + "scikit-learn>=1.7.1", "tenacity>=9.1.2", "tqdm>=4.67.1", ] diff --git a/pyproject.toml b/pyproject.toml index 45d58c6e..4b817d25 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "scikit-learn>=1.5.1", "selfies>=2.1.0", "rdkit>=2024.3.5", - "smirk @ git+ssh://git@github.com/BattModels/smirk.git@dfdd9dc27c066f956b69d1f55c2d4717beb7838d", + "smirk==0.1.0", "safetensors>=0.5.1", "pytorch-lightning~=2.5" ] From 470a801a7479f48a1a8f9c9260e640f53e9cadf1 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 17 Oct 2024 18:38:02 -0700 Subject: [PATCH 073/147] Initial sae code --- electrolyte_fm/data_modules/__init__.py | 2 + electrolyte_fm/data_modules/sae_dataset.py | 245 +++++++++++++++++++++ electrolyte_fm/models/__init__.py | 3 +- electrolyte_fm/models/model_utils.py | 3 +- electrolyte_fm/models/sae.py | 231 +++++++++++++++++++ electrolyte_fm/utils/ckpt.py | 15 ++ electrolyte_fm/utils/metrics.py | 5 +- submit/default.jsonnet | 1 + submit/sae.jsonnet | 49 +++++ 9 files changed, 550 insertions(+), 4 deletions(-) create mode 100644 electrolyte_fm/data_modules/sae_dataset.py create mode 100644 electrolyte_fm/models/sae.py create mode 100644 submit/sae.jsonnet diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index c0dcb7ce..26d226a4 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -1,5 +1,7 @@ from .molnet_dataset import MolNetDataModule from .property_prediction_dataset import PropertyPredictionDataModule +from .roberta_dataset import RobertaDataSet +from .sae_dataset import HiddenStateDataModule from .tmqm_dataset import tmQMDataModule from .roberta_dataset import RobertaDataSet from .mixture_dataset import ComponentDataModule diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py new file mode 100644 index 00000000..094277db --- /dev/null +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -0,0 +1,245 @@ +import re +from math import floor +from pathlib import Path +from typing import Optional, Union + +import pytorch_lightning as pl +import torch +from datasets import Dataset, load_dataset +from torch.utils.data import DataLoader +from transformers import DataCollatorWithPadding, PreTrainedModel + +from ..models.model_utils import load_encoder +from ..utils.tokenizer import load_tokenizer +from .roberta_dataset import maybe_shard_dataset + + +def extract_hidden_state( + input_ids, + attention_mask, + encoder: PreTrainedModel = None, + collate: DataCollatorWithPadding = None, + layer: Union[int, float] = 0.5, + device="cpu", +): + batch = collate({"input_ids": input_ids, "attention_mask": attention_mask}) + attention_mask = batch["attention_mask"] + batch = batch.to(encoder.device) + + # Disable gradients + with torch.inference_mode(): + enc = encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) + + if isinstance(layer, float): + layer = floor(len(enc["hidden_states"]) * layer) + + # Flatten hidden states + hs = enc["hidden_states"][layer].to(device) + hidden_state = [] + d_model = hs.shape[-1] + assert attention_mask.shape[0] == hs.shape[0], "batch size mismatch" + assert attention_mask.shape[1] == hs.shape[1], "seq. length mismatch" + for bdx in range(hs.shape[0]): + hs_molecule = hs[bdx][attention_mask[bdx] > 0] + assert hs_molecule.shape == (len(input_ids[bdx]), d_model) + hidden_state.append(hs_molecule) + + return {"hidden_state": hidden_state} + + + + +class HiddenStateDataModule(pl.LightningDataModule): + def __init__( + self, + name_or_path: str, + path: str, + layer: Union[int, float] = 0.5, + tokenizer: Optional[str] = None, + batch_size: int = 64, + val_batch_size: Optional[int] = None, + encoder_batch_size: Optional[int] = None, + encoder_device: str = "cuda", + return_molecule: bool = False, + canonical=False, + ): + super().__init__() + + # Locate Tokeniser and dataset + self.name_or_path = name_or_path + self.layer = layer + self.tokenizer = load_tokenizer(tokenizer or name_or_path) + self.encoder_device = torch.device(encoder_device) + self.vocab_size = len(self.tokenizer) + self.path: Path = Path(path) + self.return_molecule = return_molecule + assert self.path.is_dir() or self.path.is_file() + + self.batch_size = batch_size + self.val_batch_size = val_batch_size or batch_size + self.encoder_batch_size = encoder_batch_size or batch_size + self.hparams["tokenizer"] = tokenizer + self.save_hyperparameters(logger=False, ignore=["encoder_device"]) + self.data_collator = DataCollatorWithPadding(self.tokenizer, "longest") + + + def prepare_data(self): + self.dataset + + @property + def dataset(self): + if hasattr(self, "_dataset"): + return self._dataset + self._dataset = load_dataset( + "text", + name=str(self.path.name), + data_files={ + "train": str(self.path.joinpath("data/train/*.txt")), + "validation": str(self.path.joinpath("data/val/*.txt")), + "test": str(self.path.joinpath("data/test/*.txt")), + }, + keep_in_memory=False, + streaming=True, + save_infos=True, + ) + return self._dataset + + def setup(self, stage: str) -> None: + self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) + ds = maybe_shard_dataset(self.trainer, self.dataset) + ds = ds.map( + self.tokenizer, + batched=True, + input_columns="text", + remove_columns="text", + ) + tok_columns = ["input_ids", "attention_mask", ] + ds = ds.select_columns(tok_columns) + + # Extract per molecule hidden states + ds = ds.map( + extract_hidden_state, + batched=True, + batch_size=self.encoder_batch_size, + fn_kwargs={ + "encoder": self.encoder, + "layer": self.layer, + "collate": self.data_collator, + }, + input_columns=tok_columns, + ) + ds = ds.map( + lambda x: {"hidden_state": torch.cat(x, dim=0)}, + batched=True, + input_columns=["hidden_state"], + remove_columns=tok_columns, + ) + + self.train_dataset: Dataset = ds["train"].shuffle( + buffer_size=10 * self.encoder_batch_size + ) + self.val_dataset: Dataset = ds["validation"] + self.test_dataset: Dataset = ds["test"] + + def train_dataloader(self): + return DataLoader( + self.train_dataset, + collate_fn=self.collate_fn, + batch_size=self.batch_size, + pin_memory=True, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + collate_fn=self.collate_fn, + batch_size=self.val_batch_size, + pin_memory=True, + ) + + def test_dataloader(self): + return DataLoader( + self.test_dataset, + collate_fn=self.collate_fn, + batch_size=self.val_batch_size, + ) + + @classmethod + def collate_fn(cls, batch): + return torch.stack([x["hidden_state"] for x in batch]).detach() + +class FeatureTaggerDataModule(HiddenStateDataModule): + def __init__(self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs): + kwargs["return_molecule"] = True + self.features = features + super().__init__(name_or_path, path, **kwargs) + + def setup(self, stage: str) -> None: + self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) + ds = maybe_shard_dataset(self.trainer, self.dataset) + ds = ds.map( + self.tokenizer, + batched=True, + input_columns="text", + fn_kwargs={"return_offsets_mapping": True}, + ) + tok_columns = ["input_ids", "attention_mask", "offsets_mapping"] + ds = ds.select_columns(tok_columns) + + # Extract per molecule hidden states + ds = ds.map( + extract_hidden_state, + batched=True, + batch_size=self.encoder_batch_size, + fn_kwargs={ + "encoder": self.encoder, + "layer": self.layer, + "collate": self.data_collator, + }, + input_columns=tok_columns, + ) + + ds = ds.map( + tag_features, + batched=False, + input_columns=tok_columns, + fn_kwargs={ + "features": self.features, + }, + ) + + # Tag features + self.train_dataset: Dataset = ds["train"].shuffle( + buffer_size=10 * self.encoder_batch_size + ) + self.val_dataset: Dataset = ds["validation"] + self.test_dataset: Dataset = ds["test"] + super().setup(stage) + self.train_dataset + + +def tag_features(obs:dict, features:dict): + token_overlap = torch.zeros(len(features), len(obs["input_ids"]), dtype=torch.bool) + offsets_mapping = obs["offsets_mapping"] + for fdx, (name, pattern) in features.items(): + m = pattern.search(obs["text"]) + if m is None: + continue + for idx, (start, end) in enumerate(offsets_mapping): + if start <= m.span[0] and end >= m.span[1]: + token_overlap[idx, fdx] = True + + return {"features": token_overlap.T.detach()} + + + + + + + + diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index d2e34cd5..b26e0ccd 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -1,7 +1,6 @@ from .ionic_conductivity_model import IonicConductivityModel from .lm_finetuning import LMFinetuning -from .mixture_model import MixtureModel -from .prod_finetune import MISTFinetuned, MISTMultiTask from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer +from .prod_finetune import MISTFinetuned, MISTMultiTask diff --git a/electrolyte_fm/models/model_utils.py b/electrolyte_fm/models/model_utils.py index b031dd66..bfa59bb3 100644 --- a/electrolyte_fm/models/model_utils.py +++ b/electrolyte_fm/models/model_utils.py @@ -1,7 +1,8 @@ from pathlib import Path import torch -from pytorch_lightning.loggers import WandbLogger +from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint +from lightning.pytorch.loggers import WandbLogger from torchmetrics import MetricCollection from ..utils.ckpt import SaveConfigWithCkpts diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py new file mode 100644 index 00000000..49145746 --- /dev/null +++ b/electrolyte_fm/models/sae.py @@ -0,0 +1,231 @@ +from math import sqrt +from typing import Optional, Union + +import pytorch_lightning as pl +import torch +from pytorch_lightning.cli import LRSchedulerCallable, OptimizerCallable +from torch import nn +from torch.nn import functional as F +from torchmetrics import MetricCollection + +from ..utils.ckpt import get_hidden_size +from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity +from .model_utils import load_encoder + + +def init_bias(bias, w): + fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(w) + bound = 1 / sqrt(fan_in) + nn.init.uniform_(bias, -bound, bound) + + +def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: + """Count the average number of active features for tensor (*, F), where F is are the feature activations""" + return x.detach().count_nonzero() / x.shape[:-1].numel() + + +class GatedSAE(nn.Module): + def __init__(self, hidden_size: int, expansion: int = 4, device=None, dtype=None): + super().__init__() + factory_kwargs = {"device": device, "dtype": dtype} + n_features = hidden_size * expansion + self.w_gate = nn.Parameter( + torch.empty((n_features, hidden_size), **factory_kwargs) + ) + self.b_gate = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.w_dec = nn.Parameter( + torch.empty((hidden_size, n_features), **factory_kwargs) + ) + self.b_dec = nn.Parameter(torch.empty(hidden_size, **factory_kwargs)) + self.r_mag = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.b_enc = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.reset_parameters() + + def reset_parameters(self): + for w in [self.w_gate, self.w_dec]: + nn.init.kaiming_uniform_(w, nonlinearity="relu") + + init_bias(self.b_gate, self.w_gate) + init_bias(self.b_dec, self.w_dec) + # kaiming init for r_mag + nn.init.normal_(self.r_mag, mean=0.0, std=sqrt(2 / self.r_mag.shape[0])) + init_bias(self.b_enc, self.w_gate) + + def forward(self, x): + x_centered = x - self.b_dec + x_enc = x_centered.matmul(self.w_gate.T) + gate = (x_enc + self.b_gate) > 0 + x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) + return x_mag * gate + + def reconstruct(self, features): + return F.linear(features, self.w_dec, self.b_dec) + + @torch.compile + def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + x_centered = x - self.b_dec + x_enc = x_centered.matmul(self.w_gate.T) + pi_gate = x_enc + self.b_gate + pi_rect = F.relu(pi_gate) + loss_sparsity = l1_coef * pi_rect.abs().sum() + + x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) + gate = pi_gate > 0 + features = x_mag * gate + x_hat = F.linear(features, self.w_dec, self.b_dec) + loss_recon = F.mse_loss(x_hat, x) + + x_hat_detach = F.linear(pi_rect, self.w_dec.detach(), self.b_dec.detach()) + loss_aux = F.mse_loss(x_hat_detach, x) + + return { + "loss": loss_sparsity + loss_aux + loss_recon, + "features": features, + } + + +class TiedBiasSAE(nn.Module): + def __init__(self, hidden_size: int, expansion: int = 4): + super().__init__() + n_features = hidden_size * expansion + self.encoder = nn.Linear(hidden_size, n_features) + self.decoder = nn.Linear(n_features, hidden_size) + + def forward(self, x): + return F.relu(self.encoder(x - self.decoder.bias)) + + def reconstruct(self, features): + return self.decoder(features) + + @torch.compile + def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + f = F.relu(self.encoder(x - self.decoder.bias)) + x_hat = self.decoder(f) + f_act = f * self.decoder.weight.norm(p=2, dim=0) + loss = F.mse_loss(x_hat, x) + l1_coef * f_act.abs().sum() + return {"loss": loss, "features": f} + + +class SAE(pl.LightningModule): + def __init__( + self, + hidden_size: Union[int, str], + sae: str = "gated", + expansion: int = 4, + l1_coef: float = 0.01, + optimizer: OptimizerCallable = torch.optim.AdamW, + lr_schedule: LRSchedulerCallable | None = None, + ): + super().__init__() + if isinstance(hidden_size, str): + hidden_size = get_hidden_size(hidden_size) + + if sae == "gated": + self.sae = GatedSAE(hidden_size, expansion) + elif sae == "tied_bias": + self.sae = TiedBiasSAE(hidden_size, expansion) + else: + raise ValueError(f"Unknown sae type: {sae}") + + self.l1_coef = l1_coef + self.optimizer = optimizer + self.lr_schedule = lr_schedule + self.hidden_size = hidden_size + self.num_features = hidden_size * expansion + self.save_hyperparameters(ignore=["sae"]) + + metrics = MetricCollection( + { + "alive_features": AliveFeatures(self.num_features), + "max_feature_density": MaxFeatureDensity(self.num_features), + "feature_density": FeatureDensity(self.num_features), + } + ) + print(metrics) + self.train_metrics = metrics.clone(prefix="train/") + self.val_metrics = metrics.clone(prefix="val/") + self.test_metrics = metrics.clone(prefix="test/") + + def setup(self, stage: str): + if isinstance(self.logger, pl.loggers.WandbLogger): + + def define_metric(*args, **kwargs): + self.logger.experiment.define_metric(*args, **kwargs) + + # Add summary metrics for loss + for stage in ["train", "val", "test"]: + for s in ["step", "epoch"]: + define_metric( + f"{stage}/loss_{s}", summary="best,min,last", goal="minimize" + ) + + define_metric(f"{stage}/alive_features", summary="max,last") + define_metric(f"{stage}/max_feature_density", summary="min,max,last") + + def stage_step(self, stage: str, batch): + out = self.sae.loss(batch, self.l1_coef) + self.log_dict( + { + f"{stage}/loss": out["loss"], + f"{stage}/avg_l0_loss": avg_l0_norm(out["features"]), + }, + sync_dist=True, + on_step=True, + on_epoch=True, + ) + return out + + def backward(self, loss, *args, **kwargs) -> None: + # Retain graph during backprop + loss.backward(retain_graph=True) + + def training_step(self, batch): + out = self.stage_step("train", batch) + self.train_metrics.update(out["features"]) + return out["loss"] + + def validation_step(self, batch): + out = self.stage_step("val", batch) + self.val_metrics.update(out["features"]) + return out["loss"] + + def test_step(self, batch): + out = self.stage_step("test", batch) + self.test_metrics.update(out["features"]) + return out["loss"] + + def _log_feature_metrics(self, metric, stage: str) -> None: + m = metric.compute() + feature_density = m.pop(stage + "/feature_density", None) + self.log_dict(m, on_epoch=True, sync_dist=True) + if ( + isinstance(self.logger, pl.loggers.WandbLogger) + and feature_density is not None + ): + self.logger.log_table( + stage + "/feature_density", + data=list( + zip(feature_density.bin_centers, feature_density.density) + ), + columns=["bin_center", "density"], + ) + + metric.reset() + + def on_train_epoch_end(self): + self._log_feature_metrics(self.train_metrics, "train") + + def on_validation_epoch_end(self): + self._log_feature_metrics(self.val_metrics, "val") + + def on_test_epoch_end(self): + self._log_feature_metrics(self.test_metrics, "test") + + def configure_optimizers(self): + optimizer = self.optimizer(self.parameters()) + if schedule := self.lr_schedule: + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, + } + return optimizer diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index 14391afb..bfb5250e 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -240,6 +240,21 @@ def get_ckpt_tokenizer(path: str | Path) -> str: return config["data"]["init_args"]["tokenizer"] +def get_hidden_size(name_or_path: str) -> int: + config_path = Path(name_or_path).parent.parent.joinpath("config.json") + if Path(config_path).is_file(): + with open(config_path, "r") as fid: + config = json.load(fid) + return config["model"]["init_args"]["hidden_size"] + + # Special Case models + elif name_or_path.startswith("ibm/MoLFormer-XL-both-10pct"): + return 768 + + else: + raise ValueError(f"Could not find hidden size for {name_or_path}") + + def norm_class_config(config: dict, class_path: Optional[str] = None) -> (str, dict): """Parse a dictionary of hparams for a class name and init args""" init_args = dict() diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index f2d468fd..7437429c 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -1,7 +1,8 @@ from typing import Any, Dict, Literal, Optional, Union import torch -from torchmetrics import Metric +from numpy import geomspace +from torchmetrics import Metric, MetricCollection from torchmetrics import MetricCollection as TmMetricCollection from torchmetrics.classification import ( AUROC, @@ -16,6 +17,8 @@ R2Score, ) from torchmetrics.wrappers import BootStrapper +from torchmetrics.wrappers.abstract import WrapperMetric +from torchmetrics.wrappers.classwise import ClasswiseWrapper from torchmetrics.wrappers.classwise import ClasswiseWrapper as TmClasswiseWrapper """ Target Value to indicate missing data """ diff --git a/submit/default.jsonnet b/submit/default.jsonnet index b4d43733..896d3c76 100644 --- a/submit/default.jsonnet +++ b/submit/default.jsonnet @@ -3,5 +3,6 @@ // Set cache directories to the working directory TORCH_EXTENSIONS_DIR: '${PWD}/.cache/torch_extensions', HF_HOME: '${PWD}/.cache/huggingface', + TOKENIZERS_PARALLELISM: true, }, } diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet new file mode 100644 index 00000000..c3b57b79 --- /dev/null +++ b/submit/sae.jsonnet @@ -0,0 +1,49 @@ +{ + train: { + tags: ['sae', 'debug'], + model: { + class_path: 'electrolyte_fm.models.SAE', + init_args: { + sae: 'gated', + hidden_size: $.train.data.init_args.name_or_path, + expansion: 16, + l1_coef: 1e-5, + optimizer: { + class_path: 'torch.optim.AdamW', + init_args: { + lr: 1e-3, + betas: [0.0, 0.999], + }, + }, + lr_schedule: { + class_path: 'electrolyte_fm.utils.lr_schedule.RelativeCosineWarmup', + init_args: { + num_training_steps: 10000, + num_warmup_steps: 'beta2', + }, + }, + + }, + }, + data: { + class_path: 'electrolyte_fm.data_modules.HiddenStateDataModule', + init_args: { + name_or_path: 'ibm/MoLFormer-XL-both-10pct', + batch_size: 1024, + encoder_batch_size: 256, + val_batch_size: 4 * self.batch_size, + }, + }, + trainer: { + max_steps: $.train.model.init_args.lr_schedule.init_args.num_training_steps, + val_check_interval: 100, + limit_val_batches: 50, + precision: 'bf16-true', + enable_progress_bar: false, + strategy: "ddp", + }, + }, + env: { + TOKENIZER_PARALLELISM: 'true', + }, +} From 4954665f42f98d9e1355e7e7ebcf6db98ceb17e9 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sun, 20 Oct 2024 16:43:12 -0700 Subject: [PATCH 074/147] add nvtx --- pyproject.toml | 45 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4b817d25..cfe6ce6a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "pytorch-lightning~=2.5" ] +<<<<<<< HEAD [optional-dependencies] tokenizers = [ "rdkit", @@ -49,11 +50,53 @@ tokenizers = [ [tool.setuptools] packages = ["electrolyte_fm"] +======= +[tool.poetry.dependencies] +python = ">=3.10,<3.14" +torch = {version = "~2.3"} +torchmetrics = "^1.4.0" +lightning = "~2.3" +jsonargparse = {extras = ["signatures", "jsonnet"], version = "^4.27"} +transformers = "^4.40" +deepspeed = { git = "https://github.com/microsoft/DeepSpeed.git", rev = "5f631abc2f930ecece38fae05dc9bd3923c555dd"} +wandb = "^0.16" +typer = "^0.9" +datasets = "^2.20" +pyspark = "^3.5.0" +rich = "^13" +pydoe2 = "^1.3" +jsonnet = "^0.20.0" +scikit-learn= "^1.5.1" +selfies= "^2.1.0" +rdkit = "^2024.3.5" +smirk = {git = "git@github.com:BattModels/smirk.git", rev = "8061286a219420571d606d98083dd900aaa21d02" } +nvtx = "^0.2.10" + +# Dependencies to support additional tokenizers +smilespe = { version = "==0.0.3", optional = true } +gensim = {version = "^4.3.3", optional = true } # Needed to avoid 4.3.2 (https://github.com/piskvorky/gensim/issues/3541) +apetokenizer = { git = "https://github.com/mikemayuare/apetokenizer.git", rev = "8b070d6278503bcf8ad2eedc1756ac34f4bbd981", optional = true } + +[tool.poetry.extras] +tokenizers = ["rdkit", "smilespe", "gensim", "apetokenizer"] + +[tool.poetry.group.dev.dependencies] +pytest = "^8" +pre-commit = "^3.7" +ruff = "^0.5.4" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" +>>>>>>> 093f4f5 (add nvtx) [tool.pytest.ini_options] testpaths = ["test"] pythonpath = [".", "test"] addopts = "--color yes" +[tool.ruff.lint] +ignore = ["F401"] + [tool.ruff.lint.extend-per-file-ignores] -"**/__init__.py" = ["F401"] +"test_*.py" = ["F811"] From dce27ec22c04e5e9800d631a6e08feef0a43a8f8 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 21 Oct 2024 15:05:38 -0700 Subject: [PATCH 075/147] perf: add multi-processing to HiddenStateDataModule --- electrolyte_fm/data_modules/sae_dataset.py | 73 ++++++++++++++-------- submit/sae.jsonnet | 1 + 2 files changed, 48 insertions(+), 26 deletions(-) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 094277db..5f3a1da7 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -3,6 +3,8 @@ from pathlib import Path from typing import Optional, Union +import nvtx +import torch import pytorch_lightning as pl import torch from datasets import Dataset, load_dataset @@ -27,31 +29,43 @@ def extract_hidden_state( batch = batch.to(encoder.device) # Disable gradients - with torch.inference_mode(): - enc = encoder( - batch["input_ids"], - attention_mask=batch["attention_mask"], - return_dict=True, - output_hidden_states=True, - ) + with nvtx.annotate("encoder"): + with torch.inference_mode(): + enc = encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) if isinstance(layer, float): layer = floor(len(enc["hidden_states"]) * layer) # Flatten hidden states - hs = enc["hidden_states"][layer].to(device) + hidden_state = flatten_hidden_states( + enc["hidden_states"][layer], + attention_mask, + device=device, + ) + return {"hidden_state": hidden_state} + + +@nvtx.annotate() +def flatten_hidden_states(hs, attention_mask, device="cpu"): + hs = hs.to(device) hidden_state = [] - d_model = hs.shape[-1] assert attention_mask.shape[0] == hs.shape[0], "batch size mismatch" assert attention_mask.shape[1] == hs.shape[1], "seq. length mismatch" for bdx in range(hs.shape[0]): hs_molecule = hs[bdx][attention_mask[bdx] > 0] - assert hs_molecule.shape == (len(input_ids[bdx]), d_model) hidden_state.append(hs_molecule) - return {"hidden_state": hidden_state} + return hidden_state +@nvtx.annotate() +def collate_hidden_states(hidden_states): + return {"hidden_state": torch.cat(hidden_states, dim=0)} class HiddenStateDataModule(pl.LightningDataModule): @@ -63,6 +77,8 @@ def __init__( tokenizer: Optional[str] = None, batch_size: int = 64, val_batch_size: Optional[int] = None, + num_workers: int = 1, + prefetch_factor: int = 4, encoder_batch_size: Optional[int] = None, encoder_device: str = "cuda", return_molecule: bool = False, @@ -87,7 +103,6 @@ def __init__( self.save_hyperparameters(logger=False, ignore=["encoder_device"]) self.data_collator = DataCollatorWithPadding(self.tokenizer, "longest") - def prepare_data(self): self.dataset @@ -118,7 +133,10 @@ def setup(self, stage: str) -> None: input_columns="text", remove_columns="text", ) - tok_columns = ["input_ids", "attention_mask", ] + tok_columns = [ + "input_ids", + "attention_mask", + ] ds = ds.select_columns(tok_columns) # Extract per molecule hidden states @@ -134,14 +152,14 @@ def setup(self, stage: str) -> None: input_columns=tok_columns, ) ds = ds.map( - lambda x: {"hidden_state": torch.cat(x, dim=0)}, + collate_hidden_states, batched=True, input_columns=["hidden_state"], remove_columns=tok_columns, ) self.train_dataset: Dataset = ds["train"].shuffle( - buffer_size=10 * self.encoder_batch_size + buffer_size=10 * self.batch_size ) self.val_dataset: Dataset = ds["validation"] self.test_dataset: Dataset = ds["test"] @@ -149,6 +167,9 @@ def setup(self, stage: str) -> None: def train_dataloader(self): return DataLoader( self.train_dataset, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, + multiprocessing_context="spawn", collate_fn=self.collate_fn, batch_size=self.batch_size, pin_memory=True, @@ -157,6 +178,8 @@ def train_dataloader(self): def val_dataloader(self): return DataLoader( self.val_dataset, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, collate_fn=self.collate_fn, batch_size=self.val_batch_size, pin_memory=True, @@ -165,16 +188,22 @@ def val_dataloader(self): def test_dataloader(self): return DataLoader( self.test_dataset, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, collate_fn=self.collate_fn, batch_size=self.val_batch_size, ) @classmethod + @nvtx.annotate() def collate_fn(cls, batch): return torch.stack([x["hidden_state"] for x in batch]).detach() + class FeatureTaggerDataModule(HiddenStateDataModule): - def __init__(self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs): + def __init__( + self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs + ): kwargs["return_molecule"] = True self.features = features super().__init__(name_or_path, path, **kwargs) @@ -215,7 +244,7 @@ def setup(self, stage: str) -> None: # Tag features self.train_dataset: Dataset = ds["train"].shuffle( - buffer_size=10 * self.encoder_batch_size + buffer_size=2 * self.encoder_batch_size ) self.val_dataset: Dataset = ds["validation"] self.test_dataset: Dataset = ds["test"] @@ -223,7 +252,7 @@ def setup(self, stage: str) -> None: self.train_dataset -def tag_features(obs:dict, features:dict): +def tag_features(obs: dict, features: dict): token_overlap = torch.zeros(len(features), len(obs["input_ids"]), dtype=torch.bool) offsets_mapping = obs["offsets_mapping"] for fdx, (name, pattern) in features.items(): @@ -235,11 +264,3 @@ def tag_features(obs:dict, features:dict): token_overlap[idx, fdx] = True return {"features": token_overlap.T.detach()} - - - - - - - - diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index c3b57b79..fffb6f68 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -1,4 +1,5 @@ { + container: '/lustre/fs0/awadell/sqsh-files/0535844560745234+mist+08e9e89.sqsh', train: { tags: ['sae', 'debug'], model: { From 0878687854b5df99121231d7411db909b924bf64 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 29 Oct 2024 15:56:12 -0700 Subject: [PATCH 076/147] wip: start enumerating possible features --- electrolyte_fm/data_modules/feature_tagger.py | 80 +++++++++++++++++++ test/test_featurizer.py | 48 +++++++++++ 2 files changed, 128 insertions(+) create mode 100644 electrolyte_fm/data_modules/feature_tagger.py create mode 100644 test/test_featurizer.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py new file mode 100644 index 00000000..1d50ab40 --- /dev/null +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -0,0 +1,80 @@ +import re +import torch +import smirk +from rdkit import Chem + +# fmt: off +ELEMENT_SYMBOLS = [ + "H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", + "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", + "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", + "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", + "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", + "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", + "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", + "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", + "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", + "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", + "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds", + "Rg", "Cn", "Nh", "Fl", "Mc", "Lv", "Ts", "Og", +] +# fmt: on + +BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] + +REGEX_FEATURES = { + "chiral_tags": re.compile(r"@{1,2}(?:[A-Z]{2}\d{1,2})?"), + "bracked_atom": re.compile(r"\[[^]]+]"), + "charged_atom": re.compile(r"\[[^\]]+?[+-]{1,2}\d{0,2}]"), + "chiral_center": re.compile(r"\[[^\]]+?@[^\]]*?]"), + "aromatic_atom": re.compile(r"(?:b|c|o|p|se?|as)|(?:\[[a-z]{1,2}[^\]]+?])"), +} + +ELEMENT_GROUPS = [ + ["Li", "Na", "K", "Rb", "Cs", "Fr"], # Alkali Metals (Group 1) + ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) + ["Sc", "Y", "Lu", "Lr"], # Scandium Group (Group 3) + ["Ti", "Zr", "Hf", "Rf"], # Titanium Group (Group 4) + ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) + ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) + ["Mn", "Tc", "Re", "Bh"], # Manganese Group (Group 7) + ["Fe", "Ru", "Os", "Hs"], # Iron Group (Group 8) + ["Co", "Rh", "Ir", "Mt"], # Cobalt Group (Group 9) + ["Ni", "Pd", "Pt", "Ds"], # Nickel Group (Group 10) + ["Cu", "Ag", "Au", "Rg"], # Copper Group (Group 11) + ["Zn", "Cd", "Hg", "Cn"], # Zinc Group (Group 12) + ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) + ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) + ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) + ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) + ["F", "Cl", "Br", "I", "At", "Ts"] # Halogens (Group 17) + # Lanthanides (f-block) + ["La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu"], + # Actinides (f-block) + ["Ac", "Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", "Md", "No", "Lr"] +] + +ELEMENT_SETS = { + "alkali_metals": ELEMENT_GROUPS[0], + "alkaline_earth_metals": ELEMENT_GROUPS[1], + "nobel_gases": ["He"] + ELEMENT_GROUPS[17], + "s_block": ["H"] + ELEMENT_GROUPS[1] + ELEMENT_GROUPS[1], + "f_block": [*ELEMENT_GROUPS[17:18]], + "d_block": [*ELEMENT_GROUPS[2:11]], + "p_block": [*ELEMENT_GROUPS[12:16]], + "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki + "liquid_metals": ["Ga", "Hg", "Rb", "Cs", "Fr"], + "semi_metalalic": ["As", "Sb", "Bi", "Sn"], + "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 +} + +# Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html +SMARTS_FEATURES = { + "ketone": "[#6][CX3](=O)[#6]", # Daylight + "aldehyde": "[CX3H1](=O)[#6]", # Daylight + "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight + "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight + "hydroxyl": "[OX2H]", # Daylight + "phenol": "[OX2H][cX3]:[c]", # Daylight + "rotable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight +} diff --git a/test/test_featurizer.py b/test/test_featurizer.py new file mode 100644 index 00000000..0d230e4d --- /dev/null +++ b/test/test_featurizer.py @@ -0,0 +1,48 @@ +import pytest +from electrolyte_fm.data_modules.feature_tagger import REGEX_FEATURES + + +REGEX_TESTS = [ + { + "feature": "chiral_tags", + "positive": ["@SP1", "@OH32", "@TB2", "@", "@@"], + "negative": ["C", "O", "c", "[Rb]"], + }, + { + "feature": "bracked_atom", + "positive": ["[C]", "[C-]", "[C+]", "[C@]", "[C@@]", "[C@H]", "[C@H-]"], + "negative": ["C", "O", "c", "F"], + }, + { + "feature": "charged_atom", + "positive": ["[C+]", "[C-]", "[C++]", "[Rb@OH2+3]"], + "negative": ["C", "O", "c", "[Rb@OH2]"], + }, + { + "feature": "chiral_center", + "positive": ["[C@]", "[C@@]", "[C@H]", "[C@H-]"], + "negative": ["C", "O", "c", "[Rb]"], + }, + {"feature": "aromatic_atom", + "positive": ["[te+2]", "b", "c", "o", "p", "se", "as"], + "negative": ["C", "O", "[Rb]"], + }, +] + + +@pytest.mark.parametrize( + "feature,negatives", + ((x["feature"], x["negative"]) for x in REGEX_TESTS if "negative" in x), +) +def test_regex_negatives(feature: str, negatives: list[str]): + for neg in negatives: + assert REGEX_FEATURES[feature].match(neg) is None, f"{feature} should not match {neg}" + + +@pytest.mark.parametrize( + "feature,positives", + ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), +) +def test_regex_negatives(feature: str, positives: list[str]): + for pos in positives: + assert REGEX_FEATURES[feature].match(pos) is not None, f"{feature} should match {pos}" From 5d1add418813a9f366e0248b7b34806a7b70b601 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 30 Oct 2024 12:43:04 -0700 Subject: [PATCH 077/147] Element and Regex features are working --- electrolyte_fm/data_modules/feature_tagger.py | 233 +++++++++++++++--- electrolyte_fm/data_modules/sae_dataset.py | 66 ----- test/test_featurizer.py | 85 ++++++- 3 files changed, 279 insertions(+), 105 deletions(-) diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index 1d50ab40..a179e104 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -1,7 +1,15 @@ import re -import torch +from abc import abstractmethod +from typing import Iterable, Optional +import itertools + import smirk -from rdkit import Chem +import torch + + +def flatten(*iterables) -> list: + return list(itertools.chain(*iterables)) + # fmt: off ELEMENT_SYMBOLS = [ @@ -23,11 +31,11 @@ BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] REGEX_FEATURES = { - "chiral_tags": re.compile(r"@{1,2}(?:[A-Z]{2}\d{1,2})?"), - "bracked_atom": re.compile(r"\[[^]]+]"), - "charged_atom": re.compile(r"\[[^\]]+?[+-]{1,2}\d{0,2}]"), - "chiral_center": re.compile(r"\[[^\]]+?@[^\]]*?]"), - "aromatic_atom": re.compile(r"(?:b|c|o|p|se?|as)|(?:\[[a-z]{1,2}[^\]]+?])"), + "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", + "bracked_atom": r"\[[^]]+]", + "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", + "chiral_center": r"\[[^\]]+?@[^\]]*?]", + "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", } ELEMENT_GROUPS = [ @@ -35,46 +43,205 @@ ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) ["Sc", "Y", "Lu", "Lr"], # Scandium Group (Group 3) ["Ti", "Zr", "Hf", "Rf"], # Titanium Group (Group 4) - ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) - ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) + ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) + ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) ["Mn", "Tc", "Re", "Bh"], # Manganese Group (Group 7) ["Fe", "Ru", "Os", "Hs"], # Iron Group (Group 8) ["Co", "Rh", "Ir", "Mt"], # Cobalt Group (Group 9) ["Ni", "Pd", "Pt", "Ds"], # Nickel Group (Group 10) ["Cu", "Ag", "Au", "Rg"], # Copper Group (Group 11) ["Zn", "Cd", "Hg", "Cn"], # Zinc Group (Group 12) - ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) - ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) - ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) - ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) - ["F", "Cl", "Br", "I", "At", "Ts"] # Halogens (Group 17) - # Lanthanides (f-block) - ["La", "Ce", "Pr", "Nd", "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", "Lu"], - # Actinides (f-block) - ["Ac", "Th", "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", "Md", "No", "Lr"] + ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) + ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) + ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) + ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) + ["F", "Cl", "Br", "I", "At", "Ts"], # Halogens (Group 17) + ["Ne", "Ar", "Kr", "Xe", "Rn", "Rg"], # Noble Gases (Group 18) +] + +F_BLOCK = [ + [ + "La", + "Ce", + "Pr", + "Nd", + "Pm", + "Sm", + "Eu", + "Gd", + "Tb", + "Dy", + "Ho", + "Er", + "Tm", + "Yb", + "Lu", + ], + [ + "Ac", + "Th", + "Pa", + "U", + "Np", + "Pu", + "Am", + "Cm", + "Bk", + "Cf", + "Es", + "Fm", + "Md", + "No", + "Lr", + ], ] -ELEMENT_SETS = { +ELEMENT_FEATURES = { "alkali_metals": ELEMENT_GROUPS[0], "alkaline_earth_metals": ELEMENT_GROUPS[1], - "nobel_gases": ["He"] + ELEMENT_GROUPS[17], - "s_block": ["H"] + ELEMENT_GROUPS[1] + ELEMENT_GROUPS[1], - "f_block": [*ELEMENT_GROUPS[17:18]], - "d_block": [*ELEMENT_GROUPS[2:11]], - "p_block": [*ELEMENT_GROUPS[12:16]], - "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki + "nobel_gases": ["He"] + ELEMENT_GROUPS[16], + "s_block": flatten(["H"], *ELEMENT_GROUPS[0:1]), + "f_block": flatten(*F_BLOCK), + "d_block": flatten(*ELEMENT_GROUPS[2:11]), + "p_block": flatten(*ELEMENT_GROUPS[12:15]), + "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki "liquid_metals": ["Ga", "Hg", "Rb", "Cs", "Fr"], "semi_metalalic": ["As", "Sb", "Bi", "Sn"], - "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 + "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 + "radioactive": flatten( # Elements that have no stable isotopes + ["Tc", "Po", "At", "Rn", "Pm"], + [group[-1] for group in ELEMENT_GROUPS], + F_BLOCK[1], + ), + "opensmiles_aromatic": [ + "b", + "c", + "n", + "o", + "p", + "s" "se", + "as", + ], } # Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html SMARTS_FEATURES = { - "ketone": "[#6][CX3](=O)[#6]", # Daylight - "aldehyde": "[CX3H1](=O)[#6]", # Daylight - "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight - "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight - "hydroxyl": "[OX2H]", # Daylight - "phenol": "[OX2H][cX3]:[c]", # Daylight - "rotable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight + "ketone": "[#6][CX3](=O)[#6]", # Daylight + "aldehyde": "[CX3H1](=O)[#6]", # Daylight + "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight + "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight + "hydroxyl": "[OX2H]", # Daylight + "phenol": "[OX2H][cX3]:[c]", # Daylight + "rotatable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight } + + +class Feature: + requires_smirk = False + + def __init__(self, name: str, tokenizer: Optional[str] = None): + self.name = name + self.tokenzier = tokenizer or smirk.SmirkTokenizerFast() + self.smirk_tokenizer = ( + smirk.SmirkTokenizerFast() if tokenizer is not None else self.tokenzier + ) + + def featurize(self, smi: str, encoding: Optional[dict] = None) -> torch.BoolTensor: + encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) + assert "offset_mapping" in encoding + + kwargs = {} + if self.requires_smirk: + smirk_encoding = self.smirk_tokenizer( + smi, + return_offsets_mapping=True, + add_special_tokens=False, + ) + kwargs["smirk_encoding"] = smirk_encoding + + return self._featurize(smi, encoding, **kwargs) + + @abstractmethod + def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: + """Identify tokens in the input SMILES encoding expressing the feature""" + + @classmethod + @abstractmethod + def from_named(cls, name: str, **kwargs) -> "Feature": + """Create a feature from a named feature""" + + def align_tokens(self, encoding: dict, span: tuple[int, int]) -> Iterable[int]: + """Identify tokens overlapping span""" + token_offsets = encoding["offset_mapping"] + start, end = span + if end < start: + return # Non-matching span + + for i, t in enumerate(token_offsets): + if t[1] <= t[0]: # Token is empty + continue + + # Spans don't include the end index + elif not (end <= t[0] or t[1] <= start): + print(f"token: {t}, span: {start}, {end}") + yield i + + def align_embeddings( + self, active: torch.BoolTensor, embedding: dict, other: dict + ) -> torch.BoolTensor: + if embedding == other: + return active + raise NotImplementedError() + + def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: + """Convert a list of indices to a one-hot encoding""" + active = torch.zeros(n, dtype=torch.bool) + active[indices] = True + return active + + +class RegexFeature(Feature): + def __init__(self, name: str, regex: [str, re.Pattern], **kwargs): + super().__init__(name, **kwargs) + self.regex = re.compile(regex) + + @classmethod + def from_named(cls, name: str, **kwargs): + return cls(name, REGEX_FEATURES[name], **kwargs) + + def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: + n_groups = self.regex.groups + active = [] + for m in self.regex.finditer(smi): + if n_groups == 0: + active.extend(self.align_tokens(encoding, m.span())) + else: + for i in range(1, n_groups + 1): + active.extend(self.align_tokens(encoding, m.span(i))) + + return self.onehot(active, len(encoding["input_ids"])) + + +class ElementFeature(Feature): + requires_smirk = True + + def __init__(self, name: str, elements: list[str], **kwargs): + super().__init__(name, **kwargs) + self.elements = list(set(elements)) + self.element_ids = torch.tensor( + [ + self.smirk_tokenizer.encode(f"[{e}]", add_special_tokens=False)[1] + for e in self.elements + ] + ).reshape(-1, 1) + + @classmethod + def from_named(cls, name: str, **kwargs) -> "ElementFeature": + return cls(name, ELEMENT_FEATURES[name], **kwargs) + + def _featurize( + self, smi: str, encoding: dict, smirk_encoding: dict + ) -> torch.BoolTensor: + enc = torch.tensor(smirk_encoding["input_ids"]) + active = enc.eq(self.element_ids).any(dim=0) + return self.align_embeddings(active, encoding, smirk_encoding) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 5f3a1da7..6d651d6b 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -198,69 +198,3 @@ def test_dataloader(self): @nvtx.annotate() def collate_fn(cls, batch): return torch.stack([x["hidden_state"] for x in batch]).detach() - - -class FeatureTaggerDataModule(HiddenStateDataModule): - def __init__( - self, name_or_path: str, path: str, features: dict[str, re.Pattern], **kwargs - ): - kwargs["return_molecule"] = True - self.features = features - super().__init__(name_or_path, path, **kwargs) - - def setup(self, stage: str) -> None: - self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) - ds = maybe_shard_dataset(self.trainer, self.dataset) - ds = ds.map( - self.tokenizer, - batched=True, - input_columns="text", - fn_kwargs={"return_offsets_mapping": True}, - ) - tok_columns = ["input_ids", "attention_mask", "offsets_mapping"] - ds = ds.select_columns(tok_columns) - - # Extract per molecule hidden states - ds = ds.map( - extract_hidden_state, - batched=True, - batch_size=self.encoder_batch_size, - fn_kwargs={ - "encoder": self.encoder, - "layer": self.layer, - "collate": self.data_collator, - }, - input_columns=tok_columns, - ) - - ds = ds.map( - tag_features, - batched=False, - input_columns=tok_columns, - fn_kwargs={ - "features": self.features, - }, - ) - - # Tag features - self.train_dataset: Dataset = ds["train"].shuffle( - buffer_size=2 * self.encoder_batch_size - ) - self.val_dataset: Dataset = ds["validation"] - self.test_dataset: Dataset = ds["test"] - super().setup(stage) - self.train_dataset - - -def tag_features(obs: dict, features: dict): - token_overlap = torch.zeros(len(features), len(obs["input_ids"]), dtype=torch.bool) - offsets_mapping = obs["offsets_mapping"] - for fdx, (name, pattern) in features.items(): - m = pattern.search(obs["text"]) - if m is None: - continue - for idx, (start, end) in enumerate(offsets_mapping): - if start <= m.span[0] and end >= m.span[1]: - token_overlap[idx, fdx] = True - - return {"features": token_overlap.T.detach()} diff --git a/test/test_featurizer.py b/test/test_featurizer.py index 0d230e4d..c82a699c 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -1,6 +1,17 @@ +import re +from random import choice +from itertools import chain + import pytest -from electrolyte_fm.data_modules.feature_tagger import REGEX_FEATURES +import torch +from electrolyte_fm.data_modules.feature_tagger import ( + ELEMENT_FEATURES, + ELEMENT_SYMBOLS, + REGEX_FEATURES, + RegexFeature, + ElementFeature, +) REGEX_TESTS = [ { @@ -23,9 +34,10 @@ "positive": ["[C@]", "[C@@]", "[C@H]", "[C@H-]"], "negative": ["C", "O", "c", "[Rb]"], }, - {"feature": "aromatic_atom", - "positive": ["[te+2]", "b", "c", "o", "p", "se", "as"], - "negative": ["C", "O", "[Rb]"], + { + "feature": "aromatic_bracket_atom", + "positive": ["[te+2]", "[b]", "[c@@]"], + "negative": ["C", "O", "[Rb]", "[Sn]", "[Cn]"], }, ] @@ -35,8 +47,9 @@ ((x["feature"], x["negative"]) for x in REGEX_TESTS if "negative" in x), ) def test_regex_negatives(feature: str, negatives: list[str]): + f = re.compile(REGEX_FEATURES[feature]) for neg in negatives: - assert REGEX_FEATURES[feature].match(neg) is None, f"{feature} should not match {neg}" + assert f.match(neg) is None, f"{feature} should not match {neg}" @pytest.mark.parametrize( @@ -44,5 +57,65 @@ def test_regex_negatives(feature: str, negatives: list[str]): ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), ) def test_regex_negatives(feature: str, positives: list[str]): + f = re.compile(REGEX_FEATURES[feature]) for pos in positives: - assert REGEX_FEATURES[feature].match(pos) is not None, f"{feature} should match {pos}" + assert f.match(pos) is not None, f"{feature} should match {pos}" + + +def generate_examples(): + for x in REGEX_TESTS: + yield RegexFeature, x["feature"], x["positive"], x["negative"] + + elements = set(ELEMENT_SYMBOLS) + for name, positive in ELEMENT_FEATURES.items(): + pos_examples = [f"[{e}]" for e in positive] + neg_examples = [f"[{e}]" for e in elements - set(positive)] + yield ElementFeature, name, pos_examples, neg_examples + + +@pytest.mark.parametrize( + "cls,feature,positive,negative", + generate_examples(), +) +def test_positive_feature(cls, feature, positive, negative): + f = cls.from_named(feature) + for pos in positive: + active = f.featurize(pos) + assert active.any(), "{} should match {}".format(feature, pos) + + +@pytest.mark.parametrize( + "cls,feature,positive,negative", + generate_examples(), +) +def test_negative_feature(cls, feature, positive, negative): + f = cls.from_named(feature) + for neg in negative: + active = f.featurize(neg) + assert not active.any(), "{} should not match {}".format(feature, neg) + + +@pytest.mark.parametrize("cls,feature,positive,negative", generate_examples()) +def test_alignment(cls, feature, positive, negative): + f = cls.from_named(feature) + pos = choice(positive) + neg = choice(negative) + active = f.featurize(pos) + inactive = f.featurize(neg) + assert active.any() and not inactive.any() + check_active(pos + neg, torch.cat([active, inactive]), f.featurize(pos + neg)) + check_active(pos + pos, torch.cat([active, active]), f.featurize(pos + pos)) + check_active( + neg + pos + neg, + torch.cat([inactive, active, inactive]), + f.featurize(neg + pos + neg), + ) + + +def check_active(smi, expected, actual): + print(f"smi: {smi}") + print(f"expected: {expected}") + print(f"actual: {actual}") + assert isinstance(actual, torch.BoolTensor) + assert isinstance(expected, torch.BoolTensor) + assert all(actual == expected) From 15a54a26b662f06d42efa269c407e2b2d3c6b599 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 30 Oct 2024 17:28:05 -0700 Subject: [PATCH 078/147] first pass at using SMARTS to featurize Bonds are not properly handled (Can tag atoms but not the bonds) --- electrolyte_fm/data_modules/feature_tagger.py | 112 +++++++++++++++--- test/test_featurizer.py | 62 +++++++++- 2 files changed, 152 insertions(+), 22 deletions(-) diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index a179e104..ca951d82 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -1,7 +1,8 @@ import re -from abc import abstractmethod +from abc import abstractmethod, ABC from typing import Iterable, Optional import itertools +from rdkit import Chem import smirk import torch @@ -125,18 +126,24 @@ def flatten(*iterables) -> list: } # Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html +# rdkit.Chem.Lipinski from: https://github.com/rdkit/rdkit/blob/master/rdkit/Chem/Lipinski.py SMARTS_FEATURES = { "ketone": "[#6][CX3](=O)[#6]", # Daylight - "aldehyde": "[CX3H1](=O)[#6]", # Daylight + "aldehyde": "[$([CX3H2](=O)),$([CX3H1](=O)[#6])]", # Daylight, plus branch for Formaldehyde "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight - "amid": "[NX3][CX3](=[OX1])[#6]", # Daylight + "amid": "[$([NX3][CX3](=[OX1])[#6]),$(NC=O)]", # Daylight, plus branch for Formamide "hydroxyl": "[OX2H]", # Daylight - "phenol": "[OX2H][cX3]:[c]", # Daylight - "rotatable_bond": "[!$(*#*)&!D1]-!@[!$(*#*)&!D1]", # Daylight + "phenol": "[OH]c1ccccc1", + "rotatable_bond": "[!$(*#*)&!D1]-&!@[!$(*#*)&!D1]", # rdkit.Chem.Lipinski + "carboxyl_group": "[CX3]=[OX1]", # Daylight + "h_donor": "[$([N;!H0;v3]),$([N;!H0;+1;v4]),$([O,S;H1;+0]),$([n;H1;+0])]", # rdkit.Chem.Lipinski + "h_acceptor": "[$([O,S;H1;v2]-[!$(*=[O,N,P,S])]),$([O,S;H0;v2]),$([O,S;-]),$([N;v3;!$(N-*=!@[O,N,P,S])]),$([nH0,o,s;+0])]", # rdkit.Chem.Lipinski + "NH_or_OH_lipinski": "[#8H1,#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski + "NO_lipinski": "[#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski } -class Feature: +class Feature(ABC): requires_smirk = False def __init__(self, name: str, tokenizer: Optional[str] = None): @@ -149,22 +156,19 @@ def __init__(self, name: str, tokenizer: Optional[str] = None): def featurize(self, smi: str, encoding: Optional[dict] = None) -> torch.BoolTensor: encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) assert "offset_mapping" in encoding - - kwargs = {} - if self.requires_smirk: - smirk_encoding = self.smirk_tokenizer( - smi, - return_offsets_mapping=True, - add_special_tokens=False, - ) - kwargs["smirk_encoding"] = smirk_encoding - + kwargs = self.preprocess(smi) return self._featurize(smi, encoding, **kwargs) @abstractmethod def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: """Identify tokens in the input SMILES encoding expressing the feature""" + def preprocess(self, smi: str) -> dict: + """Shared preprocessing steps for all features to be provided to `self._featurize` + Will be called once per feature class + """ + return {} + @classmethod @abstractmethod def from_named(cls, name: str, **kwargs) -> "Feature": @@ -196,7 +200,9 @@ def align_embeddings( def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: """Convert a list of indices to a one-hot encoding""" active = torch.zeros(n, dtype=torch.bool) - active[indices] = True + if len(indices) > 0: + print(indices) + active[indices] = True return active @@ -223,8 +229,6 @@ def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: class ElementFeature(Feature): - requires_smirk = True - def __init__(self, name: str, elements: list[str], **kwargs): super().__init__(name, **kwargs) self.elements = list(set(elements)) @@ -239,9 +243,79 @@ def __init__(self, name: str, elements: list[str], **kwargs): def from_named(cls, name: str, **kwargs) -> "ElementFeature": return cls(name, ELEMENT_FEATURES[name], **kwargs) + def preprocess(self, smi: str) -> dict: + return { + "smirk_encoding": self.smirk_tokenizer(smi, return_offsets_mapping=True) + } + def _featurize( self, smi: str, encoding: dict, smirk_encoding: dict ) -> torch.BoolTensor: enc = torch.tensor(smirk_encoding["input_ids"]) active = enc.eq(self.element_ids).any(dim=0) return self.align_embeddings(active, encoding, smirk_encoding) + + +class RdkitFeature(Feature): + atomwise = re.compile(r"\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p") + + def preprocess(self, smi: str) -> dict: + mol = Chem.MolFromSmiles(smi, sanitize=False) + s_flags = Chem.SanitizeFlags.SANITIZE_NONE + s_flags |= Chem.SanitizeFlags.SANITIZE_SETAROMATICITY + s_flags |= Chem.SanitizeFlags.SANITIZE_SETHYBRIDIZATION + s_flags |= Chem.SanitizeFlags.SANITIZE_SETCONJUGATION + s_flags |= Chem.SANITIZE_PROPERTIES + Chem.SanitizeMol(mol, s_flags) + atom_spans = [m.span() for m in self.atomwise.finditer(smi)] + + # Validate rdkit -> smi mapping + for idx, atom in enumerate(mol.GetAtoms()): + smi_atom = smi[atom_spans[idx][0] : atom_spans[idx][1]] + smi_atom_mol = Chem.MolFromSmiles(smi_atom, sanitize=False) + Chem.SanitizeMol(smi_atom_mol, s_flags) + + assert atom.GetSymbol() == smi_atom_mol.GetAtomWithIdx(0).GetSymbol() + + # atom_smi = atom.GetSmarts() + # smi_atom_rdkit = Chem.MolToSmiles(smi_atom_mol) + # assert ( + # atom_smi == Chem.MolToSmiles(smi_atom_mol) + # ), f"Expected {atom_smi} and {smi_atom_rdkit} to match. Input atom: {smi_atom}" + + return {"rdkit_molecule": mol, "atom_spans": atom_spans} + + def align_atoms( + self, + atom_idx: int, + encoding: dict, + atom_spans: list[tuple[int, int]], + ) -> Iterable[int]: + """Map atom indices to token indices""" + span = atom_spans[atom_idx] + return self.align_tokens(encoding, span) + + +class SMARTSFeature(RdkitFeature): + def __init__(self, name: str, smarts: str, **kwargs): + super().__init__(name, **kwargs) + self.smarts = Chem.MolFromSmarts(smarts) + + @classmethod + def from_named(cls, name: str, **kwargs): + return cls(name, SMARTS_FEATURES[name], **kwargs) + + def _featurize( + self, + smi: str, + encoding: dict, + atom_spans: list[tuple[int, int]], + rdkit_molecule: Chem.Mol, + ) -> torch.BoolTensor: + # TODO: Handle Bonds + matches = rdkit_molecule.GetSubstructMatches(self.smarts) + atom_indices = set(flatten(*matches)) + active: list[int] = [] + for idx in atom_indices: + active.extend(self.align_atoms(idx, encoding, atom_spans)) + return self.onehot(active, len(encoding["input_ids"])) diff --git a/test/test_featurizer.py b/test/test_featurizer.py index c82a699c..be723ec6 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -11,6 +11,7 @@ REGEX_FEATURES, RegexFeature, ElementFeature, + SMARTSFeature, ) REGEX_TESTS = [ @@ -41,6 +42,53 @@ }, ] +SMARTS_TESTS = [ + { + "feature": "ketone", + "positive": ["CC(=O)C", "C[C@@H]1CCCCCCCCCCCCC(=O)C1"], + "negative": ["C=O", "OCC"], + }, + { + "feature": "aldehyde", + "positive": ["C=O", "O=CC", "CC=O"], + "negative": ["CC(=O)C", "OCC(O)CO"], + }, + { + "feature": "carboxylic_acid", + "positive": ["O=CO", "CC(=O)O", "C(=O)O", "O=C(O)C"], + "negative": ["CC(=O)C", "OCC"], + }, + { + "feature": "amid", + "positive": ["O=CN", "CC(=O)N", "C(=O)NC", "O=C(N)C"], + "negative": ["CC(=O)C", "OCC"], + }, + { + "feature": "hydroxyl", + "positive": ["CO", "CCO", "C(O)C"], + "negative": ["CC(=O)C", "C=O"], + }, + { + "feature": "phenol", + "positive": [ + "c1ccc(cc1)O", + "Oc1ccccc1", + "Oc0ccccc0Cc0cc(C1)c(O)c(c0)Cc0c(O)ccc(c0)Cc0ccc(O)c(c0)Cc0c(O)ccc(c0)Cc0c(O)ccc(c0)Cc0c(O)c(C2)cc(c0)Cc0c(O)ccc(c0)Cc(c0O)cc2cc0Cc0cc(Cc2ccc(O)cc2)c(O)c(c0)Cc0c(O)ccc(c0)C1", + ], + "negative": [ + "CC(=O)C", + "C=O", + "c1ccccc1-c2ccccc2", + "c1ccc(cc1)C[C@@H](C(=O)O)N", + ], + }, + { + "feature": "rotatable_bond", + "positive": ["CC-CC", "c1ccccc1-c2ccccc2"], + "negative": ["CC(=O)C", "C=O", "c1ccccc1"], + }, +] + @pytest.mark.parametrize( "feature,negatives", @@ -72,6 +120,9 @@ def generate_examples(): neg_examples = [f"[{e}]" for e in elements - set(positive)] yield ElementFeature, name, pos_examples, neg_examples + for x in SMARTS_TESTS: + yield SMARTSFeature, x["feature"], x["positive"], x["negative"] + @pytest.mark.parametrize( "cls,feature,positive,negative", @@ -81,7 +132,7 @@ def test_positive_feature(cls, feature, positive, negative): f = cls.from_named(feature) for pos in positive: active = f.featurize(pos) - assert active.any(), "{} should match {}".format(feature, pos) + assert active.any(), "{} should match {}: {}".format(feature, pos, active) @pytest.mark.parametrize( @@ -92,10 +143,15 @@ def test_negative_feature(cls, feature, positive, negative): f = cls.from_named(feature) for neg in negative: active = f.featurize(neg) - assert not active.any(), "{} should not match {}".format(feature, neg) + assert not active.any(), "{} should not match {}: {}".format( + feature, neg, active + ) -@pytest.mark.parametrize("cls,feature,positive,negative", generate_examples()) +@pytest.mark.parametrize( + "cls,feature,positive,negative", + [e for e in generate_examples() if e[0] != SMARTSFeature], +) def test_alignment(cls, feature, positive, negative): f = cls.from_named(feature) pos = choice(positive) From 599fafefb6677e892db98886c085a03a7a451210 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 19 Nov 2024 14:54:05 -0800 Subject: [PATCH 079/147] wip: more sae pipeline --- electrolyte_fm/data_modules/feature_tagger.py | 96 ++++++++++-- electrolyte_fm/data_modules/sae_dataset.py | 7 +- electrolyte_fm/models/sae.py | 20 ++- electrolyte_fm/utils/ckpt.py | 3 +- electrolyte_fm/utils/featurizer.py | 144 ++++++++++++++++++ electrolyte_fm/utils/metrics.py | 15 +- opt/FeatureMiner/Project.toml | 11 ++ opt/FeatureMiner/src/FeatureMiner.jl | 29 ++++ opt/FeatureMiner/src/identification.jl | 74 +++++++++ opt/FeatureMiner/src/stats.jl | 45 ++++++ pyproject.toml | 40 ----- submit/nsys.yaml | 1 + submit/sae_features.libsonnet | 3 + test/test_featurizer.py | 5 +- test/test_sae.py | 99 ++++++++++++ 15 files changed, 525 insertions(+), 67 deletions(-) create mode 100644 electrolyte_fm/utils/featurizer.py create mode 100644 opt/FeatureMiner/Project.toml create mode 100644 opt/FeatureMiner/src/FeatureMiner.jl create mode 100644 opt/FeatureMiner/src/identification.jl create mode 100644 opt/FeatureMiner/src/stats.jl create mode 100644 submit/sae_features.libsonnet create mode 100644 test/test_sae.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index ca951d82..fe08864b 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -31,14 +31,6 @@ def flatten(*iterables) -> list: BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] -REGEX_FEATURES = { - "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", - "bracked_atom": r"\[[^]]+]", - "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", - "chiral_center": r"\[[^\]]+?@[^\]]*?]", - "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", -} - ELEMENT_GROUPS = [ ["Li", "Na", "K", "Rb", "Cs", "Fr"], # Alkali Metals (Group 1) ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) @@ -97,6 +89,14 @@ def flatten(*iterables) -> list: ], ] +REGEX_FEATURES = { + "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", + "bracked_atom": r"\[[^]]+]", + "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", + "chiral_center": r"\[[^\]]+?@[^\]]*?]", + "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", +} + ELEMENT_FEATURES = { "alkali_metals": ELEMENT_GROUPS[0], "alkaline_earth_metals": ELEMENT_GROUPS[1], @@ -144,8 +144,6 @@ def flatten(*iterables) -> list: class Feature(ABC): - requires_smirk = False - def __init__(self, name: str, tokenizer: Optional[str] = None): self.name = name self.tokenzier = tokenizer or smirk.SmirkTokenizerFast() @@ -187,7 +185,6 @@ def align_tokens(self, encoding: dict, span: tuple[int, int]) -> Iterable[int]: # Spans don't include the end index elif not (end <= t[0] or t[1] <= start): - print(f"token: {t}, span: {start}, {end}") yield i def align_embeddings( @@ -195,17 +192,89 @@ def align_embeddings( ) -> torch.BoolTensor: if embedding == other: return active + return active raise NotImplementedError() def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: """Convert a list of indices to a one-hot encoding""" active = torch.zeros(n, dtype=torch.bool) if len(indices) > 0: - print(indices) active[indices] = True return active +class FeatureCollection(Feature): + def __init__(self, features: dict[str, Feature], **kwargs): + super().__init__("FeatureCollection", **kwargs) + self.features = features + preprocess_steps = {} + for f in features.values(): + preprocess_steps[f.__class__] = f + self.preprocess = list(preprocess_steps.values()) + + @property + def names(self): + return list(self.features.keys()) + + def __call__(self, smi: str) -> dict: + return {"proxy_activations": self.featurize(smi)} + + def featurize( + self, smi: str, encoding: Optional[dict] = None, return_dict: bool = False + ) -> torch.BoolTensor: + encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) + kwargs = {} + for f in self.preprocess: + kwargs.update(f.preprocess(smi)) + + act = [] + for idx, f in enumerate(self.features.values()): + act.append(f._featurize(smi, encoding, **kwargs)) + + if return_dict: + return {name: act for name, act in zip(self.features.keys(), act)} + + return torch.stack(act, dim=0) + + def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: + raise RuntimeError(f"{self} should be called using featurize") + + @classmethod + def from_named(cls, feature_names: str | list[str] = "all", **kwargs): + if isinstance(feature_names, str): + feature_names = [feature_names] + + features = [] + for name in feature_names: + if name == "all": + for k in ELEMENT_FEATURES.keys(): + features.append(ElementFeature.from_named(k, **kwargs)) + for k in REGEX_FEATURES.keys(): + features.append(RegexFeature.from_named(k, **kwargs)) + for k in SMARTS_FEATURES.keys(): + features.append(SMARTSFeature.from_named(k, **kwargs)) + + elif name in ELEMENT_FEATURES.keys(): + features.append(ElementFeature.from_named(name, **kwargs)) + + elif name in REGEX_FEATURES.keys(): + features.append(RegexFeature.from_named(name, **kwargs)) + + elif name in SMARTS_FEATURES.keys(): + features.append(SMARTSFeature.from_named(name, **kwargs)) + + else: + raise ValueError(f"Unknown named feature: {name}") + + feature_map = {} + for f in features: + if f.name in feature_map.keys(): + raise ValueError(f"Duplicate feature name: {f.name}") + feature_map[f.name] = f + + return cls(feature_map, **kwargs) + + class RegexFeature(Feature): def __init__(self, name: str, regex: [str, re.Pattern], **kwargs): super().__init__(name, **kwargs) @@ -249,7 +318,7 @@ def preprocess(self, smi: str) -> dict: } def _featurize( - self, smi: str, encoding: dict, smirk_encoding: dict + self, smi: str, encoding: dict, smirk_encoding: dict, **kwargs ) -> torch.BoolTensor: enc = torch.tensor(smirk_encoding["input_ids"]) active = enc.eq(self.element_ids).any(dim=0) @@ -311,6 +380,7 @@ def _featurize( encoding: dict, atom_spans: list[tuple[int, int]], rdkit_molecule: Chem.Mol, + **kwargs, ) -> torch.BoolTensor: # TODO: Handle Bonds matches = rdkit_molecule.GetSubstructMatches(self.smarts) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 6d651d6b..99c22078 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -14,6 +14,7 @@ from ..models.model_utils import load_encoder from ..utils.tokenizer import load_tokenizer from .roberta_dataset import maybe_shard_dataset +from .utils import MolEncoding, encode_molecules def extract_hidden_state( @@ -82,7 +83,7 @@ def __init__( encoder_batch_size: Optional[int] = None, encoder_device: str = "cuda", return_molecule: bool = False, - canonical=False, + encoding: Optional[str | MolEncoding] = "smiles", ): super().__init__() @@ -94,11 +95,14 @@ def __init__( self.vocab_size = len(self.tokenizer) self.path: Path = Path(path) self.return_molecule = return_molecule + self.encoding = MolEncoding(encoding) assert self.path.is_dir() or self.path.is_file() self.batch_size = batch_size self.val_batch_size = val_batch_size or batch_size self.encoder_batch_size = encoder_batch_size or batch_size + self.num_workers = num_workers + self.prefetch_factor = prefetch_factor self.hparams["tokenizer"] = tokenizer self.save_hyperparameters(logger=False, ignore=["encoder_device"]) self.data_collator = DataCollatorWithPadding(self.tokenizer, "longest") @@ -127,6 +131,7 @@ def dataset(self): def setup(self, stage: str) -> None: self.encoder = load_encoder(self.name_or_path).to(self.encoder_device) ds = maybe_shard_dataset(self.trainer, self.dataset) + ds = encode_molecules(ds, "text", encoding=self.encoding) ds = ds.map( self.tokenizer, batched=True, diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 49145746..de5831c9 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -80,6 +80,7 @@ def loss(self, x, l1_coef: torch.FloatTensor = 0.01): return { "loss": loss_sparsity + loss_aux + loss_recon, + "loss_reconstruction": loss_recon, "features": features, } @@ -102,8 +103,13 @@ def loss(self, x, l1_coef: torch.FloatTensor = 0.01): f = F.relu(self.encoder(x - self.decoder.bias)) x_hat = self.decoder(f) f_act = f * self.decoder.weight.norm(p=2, dim=0) - loss = F.mse_loss(x_hat, x) + l1_coef * f_act.abs().sum() - return {"loss": loss, "features": f} + loss_reconstruction = F.mse_loss(x_hat, x) + loss_sparsity = l1_coef * f_act.abs().sum() + return { + "loss": loss_reconstruction + loss_sparsity, + "loss_reconstruction": loss_reconstruction, + "features": f, + } class SAE(pl.LightningModule): @@ -137,11 +143,14 @@ def __init__( metrics = MetricCollection( { "alive_features": AliveFeatures(self.num_features), + "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), + "alive_features_10k": AliveFeatures( + self.num_features, threshold=10_000 + ), "max_feature_density": MaxFeatureDensity(self.num_features), "feature_density": FeatureDensity(self.num_features), } ) - print(metrics) self.train_metrics = metrics.clone(prefix="train/") self.val_metrics = metrics.clone(prefix="val/") self.test_metrics = metrics.clone(prefix="test/") @@ -167,6 +176,7 @@ def stage_step(self, stage: str, batch): self.log_dict( { f"{stage}/loss": out["loss"], + f"{stage}/loss_reconstruction": out["loss_reconstruction"], f"{stage}/avg_l0_loss": avg_l0_norm(out["features"]), }, sync_dist=True, @@ -204,9 +214,7 @@ def _log_feature_metrics(self, metric, stage: str) -> None: ): self.logger.log_table( stage + "/feature_density", - data=list( - zip(feature_density.bin_centers, feature_density.density) - ), + data=list(zip(feature_density.bin_centers, feature_density.density)), columns=["bin_center", "density"], ) diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index bfb5250e..b2708dcf 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -114,7 +114,8 @@ def instantiate( config["lightning_module"], class_path=config.get("class_path", None), ) - model_config["vocab_size"] = config["datamodule"]["vocab_size"] + if "vocab_size" in config["datamodule"]: + model_config["vocab_size"] = config["datamodule"]["vocab_size"] else: cls_name, model_config = norm_class_config(config) diff --git a/electrolyte_fm/utils/featurizer.py b/electrolyte_fm/utils/featurizer.py new file mode 100644 index 00000000..bb38ec4b --- /dev/null +++ b/electrolyte_fm/utils/featurizer.py @@ -0,0 +1,144 @@ +import json +import logging +from math import floor +from pathlib import Path +from typing import Optional + +import torch +from datasets import load_dataset +from rdkit import Chem +from torch import nn +from torch.utils.data import DataLoader +from transformers import DataCollatorWithPadding +from torchmetrics import MetricCollection + +from ..data_modules.sae_dataset import extract_hidden_state +from ..data_modules.feature_tagger import FeatureCollection +from ..data_modules.utils import MolEncoding, encode_molecules +from ..models.model_utils import load_encoder +from ..models.sae import SAE +from .tokenizer import load_tokenizer +from .metrics import FeatureCorrelation + + +class FeatureExtractor(nn.Module): + def __init__(self, encoder, tokenizer, sae, layer: int): + super().__init__() + self.encoder = encoder + self.tokenizer = tokenizer + self.sae = sae + self.layer = layer + + def forward(self, batch): + if isinstance(batch, list) and isinstance(batch[0], str): + batch = self.tokenize(batch) + + # Extract hidden states + with torch.inference_mode(): + enc = self.encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) + if isinstance(self.layer, float): + layer = floor(len(enc["hidden_states"]) * self.layer) + else: + layer = self.layer + + hidden_state = enc["hidden_states"][layer] + + # Compute features activations + B, S, D = hidden_state.shape + feature_act = self.sae.forward(hidden_state.reshape(B * S, D)).reshape(B, S, -1) + feature_act *= batch["attention_mask"].unsqueeze(-1) + + return feature_act + + def tokenize(self, batch): + return self.tokenizer( + batch, + return_offsets_mapping=True, + truncation=True, + max_length=self.encoder.config.max_position_embeddings, + ) + + @classmethod + def from_checkpoint(cls, sae_checkpoint: str): + sae_checkpoint = Path(sae_checkpoint) + config_path = sae_checkpoint.parent.parent.joinpath("config.json") + sae_config = json.loads(config_path.read_text()) + + # Load components + encoder_args = sae_config["data"]["init_args"] + encoder = load_encoder(encoder_args["name_or_path"]) + tokenizer = encoder_args["tokenizer"] or encoder_args["name_or_path"] + tokenizer = load_tokenizer(tokenizer) + + # Load just the Sparse Autoencoder + sae = SAE.load_from_checkpoint( + sae_checkpoint, **sae_config["model"]["init_args"] + ).sae + layer = sae_config["data"]["init_args"]["layer"] + + return cls(encoder, tokenizer, sae, layer) + + +def collate_fn(batch, encoder_collate): + encoder_input = {k: batch[k] for k in ["input_ids", "attention_mask"]} + batch.update(encoder_collate(encoder_input)) + return batch + + +class FeaturePipeline: + def __init__( + self, sae_ckpt: str, dataset_path: str, features: Optional[list[str]] = None + ): + self.sae_ckpt = sae_ckpt + self.dataset_path = dataset_path + self.features = FeatureCollection.from_named(features or ["all"]) + + @property + def feature_names(self): + return self.features.names + + def collate_fn(self, batch): + tokens = [{k: x[k] for k in ["input_ids", "attention_mask"]} for x in batch] + out = self.token_collator(tokens) + for k in out.keys(): + if k not in ["input_ids", "attention_mask"]: + out[k] = batch[k] + return out + + def setup(self): + self.miner = FeatureExtractor.from_checkpoint(self.sae_ckpt) + + # Load dataset + dataset_path = Path(self.dataset_path) + ds = load_dataset( + "text", + data_files={"val": str(dataset_path.joinpath("data/val/*.txt"))}, + keep_in_memory=False, + streaming=True, + save_infos=True, + ) + ds = encode_molecules(ds, "text", encoding=MolEncoding.KEUKLE_SMILES) + ds = ds.map(self.miner.tokenize, batched=True, input_columns="text") + ds = ds.map(self.features, batched=False, input_columns="text") + self.dataset = ds + self.token_collator = DataCollatorWithPadding(self.miner.tokenizer, "longest") + + def iter(self, split: str = "val", **kwargs): + dl = DataLoader(self.dataset[split], collate_fn=self.collate_fn, **kwargs) + miner = self.miner.to("cuda") + for batch in dl: + x = { + "input_ids": batch["input_ids"].to("cuda"), + "attention_mask": batch["attention_mask"].to("cuda"), + } + f_act = miner(x) + yield { + "feature_activations": f_act.to("cpu"), + "attention_mask": batch["attention_mask"], + "proxy_activations": x["proxy_activations"], + } diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index 7437429c..72af7bbb 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -2,7 +2,7 @@ import torch from numpy import geomspace -from torchmetrics import Metric, MetricCollection +from torchmetrics import Metric from torchmetrics import MetricCollection as TmMetricCollection from torchmetrics.classification import ( AUROC, @@ -16,9 +16,10 @@ PearsonCorrCoef, R2Score, ) +from torchmetrics.regression.pearson import ( + _final_aggregation as final_pearson_aggregation, +) from torchmetrics.wrappers import BootStrapper -from torchmetrics.wrappers.abstract import WrapperMetric -from torchmetrics.wrappers.classwise import ClasswiseWrapper from torchmetrics.wrappers.classwise import ClasswiseWrapper as TmClasswiseWrapper """ Target Value to indicate missing data """ @@ -441,8 +442,12 @@ def __init__(self, reduction="mean", **kwargs): self.add_state("distance", default=torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") + def __init__(self, *args, threshold: Optional[int] = None, **kwargs): + super().__init__(*args, **kwargs) + self.threshold = threshold + def compute(self): - return self.distance / self.total + return self.distance / self.total def update(self, preds: torch.Tensor, targets: torch.Tensor): dists = self.procrustes_disparity(preds, targets) @@ -487,4 +492,4 @@ def procrustes_alignment(pc1: torch.Tensor, pc2: torch.Tensor) -> torch.Tensor: s = s.to(u) R = u.matmul(s).matmul(v).mT.to(pc1) - return R + return R \ No newline at end of file diff --git a/opt/FeatureMiner/Project.toml b/opt/FeatureMiner/Project.toml new file mode 100644 index 00000000..58a56987 --- /dev/null +++ b/opt/FeatureMiner/Project.toml @@ -0,0 +1,11 @@ +name = "FeatureMiner" +uuid = "83091abd-1720-4da7-94ca-5ff8667e187f" +version = "0.1.0" + +[deps] +ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63" +DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" +OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" +OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338" +PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl new file mode 100644 index 00000000..520ef207 --- /dev/null +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -0,0 +1,29 @@ +module FeatureMiner + +using ArgParse +using PythonCall: Py, pyimport +using OnlineStats: OnlineStats, KHist, Variance, Series, fit! +using OnlineStatsBase: OnlineStatsBase, OnlineStat, EqualWeight, smooth, bessel, nobs +using StatsBase: StatsBase + + +function FeatureExtractor(ckpt_path::String) + cls = pyimport("electrolyte_fm.models.sae.FeatureExtractor") + return cls.from_checkpoint(ckpt_path) +end + +function FeaturizedSmiles(path::String, miner::Py) + cls = pyimport("electrolyte_fm.data_modules.sae_dataset.FeaturizedSmiles") + return cls(path, miner) +end + +function split_dataset_by_node(dataset::Py, rank::Int, size::Int) + m = pyimport("datasets.distributed") + return m.split_dataset_by_node(dataset, rank, size) +end + +include("identification.jl") +include("stats.jl") + +end + diff --git a/opt/FeatureMiner/src/identification.jl b/opt/FeatureMiner/src/identification.jl new file mode 100644 index 00000000..e138fa45 --- /dev/null +++ b/opt/FeatureMiner/src/identification.jl @@ -0,0 +1,74 @@ +struct FeatureStats{TD,M,I} + feature_activations::Vector{TD} + positive_proxy::Vector{M} + negative_proxy::Vector{M} + proxy_count::Vector{I} + n::Ref{I} +end + +function FeatureStats(n_feature::Integer, n_proxy::Integer) + f_act = map(_ -> Series(; hist=KHist(100), var=Variance()), 1:n_feature) + pos_proxy = map(_ -> ElementwiseVariance(Float32, n_feature), 1:n_proxy) + proxy_count = zeros(UInt64, n_proxy) + neg_proxy = map(_ -> ElementwiseVariance(Float32, n_feature), 1:n_proxy) + count = zero(UInt64) + TD = eltype(f_act) + M = eltype(pos_proxy) + I = eltype(proxy_count) + FeatureStats{TD,M,I}(f_act, pos_proxy, neg_proxy, proxy_count, count) +end + + +""" + fit!(stats, f_act, mask, proxy_act) + +Update feature statistics `stats` with the provided activations for a single observation. + +`f_act`: activations of the features (seq_len, n_features) +`mask`: mask of the observations (seq_len,) +`proxy_act`: activations of the proxies (seq_len, n_proxies) + +""" +function OnlineStatsBase.fit!(stats::FeatureStats, f_act::AbstractMatrix, proxy_act::AbstractMatrix, mask::AbstractVector,) + stats.n[] += 1 + + # Update f activation statistics + for i in axes(f_act, 2) + f_act_masked = f_act[mask, i] + OnlineStats.fit!(stats.feature_activations[i], Float64.(f_act_masked)) + end + + # Update proxy activation statistics + for fdx in axes(proxy_act, 2) + for tdx in axes(proxy_act, 1) + mask[tdx] || continue # Skip if token is masked + fa = @view f_act[tdx, :] + proxy_active = false + # Maintain separate statistics for positive and negative proxies + pa = proxy_act[tdx, fdx] + if pa + proxy_active = true + OnlineStats.fit!(stats.positive_proxy[fdx], fa) + else + OnlineStats.fit!(stats.negative_proxy[fdx], fa) + end + stats.proxy_count[fdx] += proxy_active + end + end + + return nothing +end + +function tabulate_features(pipeline::Py; batch_size::Integer=64) + stats = FeatureStats(512, 32) + for batch in pipeline.iter("val"; batch_size) + f_act = pyconvert(Array, batch["feature_activations"]) + mask = pyconvert(Matrix, batch["attention_mask"].T) + proxy_act = pyconvert.(Matrix{Bool}, batch["proxy_activations"]) + for idx in axes(f_act, 1) + # Update activation statistics + fit!(stats, selectdim(f_act, 1, idx), selectdim(mask, 2, idx), proxy_act[idx, :]) + end + end + return stats +end diff --git a/opt/FeatureMiner/src/stats.jl b/opt/FeatureMiner/src/stats.jl new file mode 100644 index 00000000..db4e7829 --- /dev/null +++ b/opt/FeatureMiner/src/stats.jl @@ -0,0 +1,45 @@ +struct ElementwiseVariance{T,S,W} <: OnlineStat{T} + σ2::S + μ::T + weight::W + n::Ref{Int} +end + +function ElementwiseVariance(T::Type{<:Number}, n::Integer; weight=EqualWeight()) + s = Vector{T}(undef, n) + m = Vector{T}(undef, n) + s .= zero(T)^2 / one(T) + m .= zero(T) / one(T) + ElementwiseVariance{Vector{T},typeof(s),typeof(weight)}(s, m, weight, 0) +end + +OnlineStatsBase.fit!(o::ElementwiseVariance, x::AbstractVector{T}) where {T} = (OnlineStatsBase._fit!(o, x); return o) +function OnlineStatsBase._fit!(o::ElementwiseVariance, x) + μ = o.μ + γ = o.weight(o.n[] += 1) + @. o.μ = smooth(o.μ, x, γ) + @. o.σ2 = smooth(o.σ2, (x - o.μ) * (x - μ), γ) + return o +end + +function OnlineStatsBase._merge!(o::ElementwiseVariance, o2::ElementwiseVariance) + γ = o2.n / (o.n += o2.n) + @. δ = o2.μ - o.μ + @. o.σ2 = smooth(o.σ2, o2.σ2, γ) + δ^2 * γ * (1.0 - γ) + @. o.μ = smooth(o.μ, o2.μ, γ) + return o +end + +function OnlineStatsBase.value(o::ElementwiseVariance) + if nobs(o) > 0 + return @. o.σ2 * bessel(o) + else + return NaN + end +end + +StatsBase.var(o::ElementwiseVariance) = OnlineStatsBase.value(o) +StatsBase.mean(o::ElementwiseVariance) = o.μ +StatsBase.nobs(o::ElementwiseVariance) = o.n[] + + diff --git a/pyproject.toml b/pyproject.toml index cfe6ce6a..43285a38 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,6 @@ dependencies = [ "pytorch-lightning~=2.5" ] -<<<<<<< HEAD [optional-dependencies] tokenizers = [ "rdkit", @@ -50,45 +49,6 @@ tokenizers = [ [tool.setuptools] packages = ["electrolyte_fm"] -======= -[tool.poetry.dependencies] -python = ">=3.10,<3.14" -torch = {version = "~2.3"} -torchmetrics = "^1.4.0" -lightning = "~2.3" -jsonargparse = {extras = ["signatures", "jsonnet"], version = "^4.27"} -transformers = "^4.40" -deepspeed = { git = "https://github.com/microsoft/DeepSpeed.git", rev = "5f631abc2f930ecece38fae05dc9bd3923c555dd"} -wandb = "^0.16" -typer = "^0.9" -datasets = "^2.20" -pyspark = "^3.5.0" -rich = "^13" -pydoe2 = "^1.3" -jsonnet = "^0.20.0" -scikit-learn= "^1.5.1" -selfies= "^2.1.0" -rdkit = "^2024.3.5" -smirk = {git = "git@github.com:BattModels/smirk.git", rev = "8061286a219420571d606d98083dd900aaa21d02" } -nvtx = "^0.2.10" - -# Dependencies to support additional tokenizers -smilespe = { version = "==0.0.3", optional = true } -gensim = {version = "^4.3.3", optional = true } # Needed to avoid 4.3.2 (https://github.com/piskvorky/gensim/issues/3541) -apetokenizer = { git = "https://github.com/mikemayuare/apetokenizer.git", rev = "8b070d6278503bcf8ad2eedc1756ac34f4bbd981", optional = true } - -[tool.poetry.extras] -tokenizers = ["rdkit", "smilespe", "gensim", "apetokenizer"] - -[tool.poetry.group.dev.dependencies] -pytest = "^8" -pre-commit = "^3.7" -ruff = "^0.5.4" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" ->>>>>>> 093f4f5 (add nvtx) [tool.pytest.ini_options] testpaths = ["test"] diff --git a/submit/nsys.yaml b/submit/nsys.yaml index b12c85f7..df2c4971 100644 --- a/submit/nsys.yaml +++ b/submit/nsys.yaml @@ -2,6 +2,7 @@ nsys: trace: cuda,cudnn,cublas,nvtx o: "nsys_multinode_%q{JOBID}_%q{NODE_RANK}" sampling-period: 16000000 + duration: 200 # sample: none # Disable CPU Profiling # Uncomment to profile GPU memory usage diff --git a/submit/sae_features.libsonnet b/submit/sae_features.libsonnet new file mode 100644 index 00000000..9c81f8f4 --- /dev/null +++ b/submit/sae_features.libsonnet @@ -0,0 +1,3 @@ +{ + aromatic_elements: "[a-z]{1,2}" +} diff --git a/test/test_featurizer.py b/test/test_featurizer.py index be723ec6..e3d06db7 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -84,7 +84,10 @@ }, { "feature": "rotatable_bond", - "positive": ["CC-CC", "c1ccccc1-c2ccccc2"], + "positive": [ + "CC-CC", + "c1ccccc1-c2ccccc2", + ], "negative": ["CC(=O)C", "C=O", "c1ccccc1"], }, ] diff --git a/test/test_sae.py b/test/test_sae.py new file mode 100644 index 00000000..24efa9f0 --- /dev/null +++ b/test/test_sae.py @@ -0,0 +1,99 @@ +import pytest +import torch +from transformers import DataCollatorWithPadding + +from electrolyte_fm.data_modules.sae_dataset import ( + HiddenStateDataModule, + extract_hidden_state, +) +from electrolyte_fm.models.model_utils import load_encoder +from electrolyte_fm.models.sae import GatedSAE, TiedBiasSAE, avg_l0_norm +from electrolyte_fm.utils.tokenizer import load_tokenizer + + +@pytest.mark.parametrize("sae_cls", [GatedSAE, TiedBiasSAE]) +class TestSAE: + B = 3 + H = 4 + E = 2 + + @classmethod + def setup_class(cls): + torch.manual_seed(0) + + @property + def feature_shape(self): + return (self.B, self.H * self.E) + + def input(self): + return torch.rand(self.B, self.H) + + def test_init(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + for p in sae.parameters(): + assert p.isfinite().all() + assert not p.isnan().any() + + def test_forward(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + features = sae(self.input()) + assert features.shape == self.feature_shape + assert features.isfinite().all() + assert not features.isnan().any() + + def test_loss(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + x = self.input() + out = sae.loss(x) + for v in out.values(): + assert v.isfinite().all() + assert not v.isnan().any() + + # Check output + assert out["loss"].shape == () + assert out["features"].shape == self.feature_shape + assert out["features"].isclose(sae(x)).all() + # assert out["x_hat"].shape == x.shape + # assert out["x_hat"].isclose(sae.reconstruct(sae(x))).all() + + +def test_avg_l0_norm(): + x = torch.tensor([[1, 0, 0], [0, 5, 0]]) + assert avg_l0_norm(x) == 1 + assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) + + +def test_dataloader(): + ckpt_path = "ibm/MoLFormer-XL-both-10pct" + path = "/lustre/fs0/awadell/realspace" + dm = HiddenStateDataModule(ckpt_path, path) + dm.prepare_data() + dm.setup("fit") + for batch in dm.train_dataloader(): + assert isinstance(batch, torch.Tensor) + assert batch.shape == (dm.batch_size, 768) + assert not batch.requires_grad + break + + +def test_extract_hidden_state(): + ckpt_path = "ibm/MoLFormer-XL-both-10pct" + encoder = load_encoder(ckpt_path) + d_model = 768 + tok = load_tokenizer(ckpt_path) + smiles = [ + "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", + "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", + ] + tokens = [tok(smi) for smi in smiles] + input_ids = [x["input_ids"] for x in tokens] + attention_mask = [x["attention_mask"] for x in tokens] + batch = extract_hidden_state( + input_ids, + attention_mask, + encoder=encoder, + collate=DataCollatorWithPadding(tok), + layer=0.5, + ) + assert "hidden_state" in batch + assert batch["hidden_state"].shape == (len(smiles), d_model) From c950191caf45e008280648009d6e1fed924e207b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 3 Mar 2025 12:38:28 -0800 Subject: [PATCH 080/147] wip: rework sae to inject coders into the model --- .github/workflows/CI.yaml | 9 + electrolyte_fm/data_modules/__init__.py | 1 - electrolyte_fm/models/__init__.py | 4 + electrolyte_fm/models/sae.py | 370 ++++++++++++++++++------ pyproject.toml | 3 + submit/sae.jsonnet | 26 +- test/test_sae.py | 284 ++++++++++++++---- 7 files changed, 526 insertions(+), 171 deletions(-) diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index 673b716c..e81c7776 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -11,14 +11,23 @@ jobs: uses: astral-sh/setup-uv@v5 with: python-version: 3.11.8 +<<<<<<< HEAD enable-cache: true - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 +======= + cache: poetry +>>>>>>> 5b359dc (wip: rework sae to inject coders into the model) - name: Install deploy key for BattModels/smirk uses: webfactory/ssh-agent@dc588b651fe13675774614f8e6a936a468676387 #v0.9.0 with: ssh-private-key: ${{ secrets.SMIRK_PRIVATE_KEY }} +<<<<<<< HEAD - name: install project run: uv sync --all-extras --dev - name: pytest run: uv run pytest +======= + - run: poetry install + - run: source activate && pytest -m "not gpu" +>>>>>>> 5b359dc (wip: rework sae to inject coders into the model) diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index 26d226a4..223309d5 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -1,7 +1,6 @@ from .molnet_dataset import MolNetDataModule from .property_prediction_dataset import PropertyPredictionDataModule from .roberta_dataset import RobertaDataSet -from .sae_dataset import HiddenStateDataModule from .tmqm_dataset import tmQMDataModule from .roberta_dataset import RobertaDataSet from .mixture_dataset import ComponentDataModule diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index b26e0ccd..2a641e6d 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -3,4 +3,8 @@ from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer +<<<<<<< HEAD from .prod_finetune import MISTFinetuned, MISTMultiTask +======= +from .sae import LightningSAE +>>>>>>> 5b359dc (wip: rework sae to inject coders into the model) diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index de5831c9..a27a9799 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -1,16 +1,17 @@ -from math import sqrt -from typing import Optional, Union +from math import sqrt, floor +from typing import Callable, Optional, Literal +from contextlib import contextmanager -import pytorch_lightning as pl +import lightning.pytorch as pl import torch -from pytorch_lightning.cli import LRSchedulerCallable, OptimizerCallable +from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from torch import nn from torch.nn import functional as F from torchmetrics import MetricCollection +from transformers import PreTrainedModel, AutoModel -from ..utils.ckpt import get_hidden_size -from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity from .model_utils import load_encoder +from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity def init_bias(bias, w): @@ -24,8 +25,177 @@ def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: return x.detach().count_nonzero() / x.shape[:-1].numel() -class GatedSAE(nn.Module): - def __init__(self, hidden_size: int, expansion: int = 4, device=None, dtype=None): +def hf_cross_entropy(logits: torch.FloatTensor, target: torch.IntTensor): + """F.cross_entropy but for logits of `(B, T, C)` and target of `(B, T)`""" + return F.cross_entropy(logits.view(-1, logits.shape[-1]), target.view(-1)) + + +class AbstractSAE(nn.Module): + def forward(self, x: torch.Tensor): + return self.decode(self.encode(x)) + + def encode(self, x: torch.Tensor) -> torch.Tensor: + """Encode x into a feature representation""" + raise NotImplementedError() + + def decode(self, features: torch.Tensor) -> torch.Tensor: + """Encode features into a reconstruction of x""" + raise NotImplementedError() + + def forward_with_loss(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Forward pass with loss calculation -> `(self.forward(x), loss)`""" + return self.forward(x), torch.tensor(0.0) + + +InjectedCoderState = Literal["null", "dense", "sparse"] + + +class InjectedCoder(nn.Module): + def __init__(self, dense_model: nn.Module, coder: AbstractSAE): + super().__init__() + self.dense_model = dense_model + self.coder = coder + self.loss = torch.tensor(0.0) + self.state: InjectedCoderState = "sparse" + + def forward(self, *args, **kwargs): + if self.state == "null": + return (*args,) + + output = self.dense_model(*args, **kwargs) + if self.state == "dense": + return output + + assert self.state == "sparse" + hs = output[0] + if self.training: + hs, loss = self.coder.forward_with_loss(hs) + self.loss = loss + + else: + hs = self.coder.forward(hs) + + return (hs, *output[1:]) + + +class SparsifiedModel(nn.Module): + def __init__(self, model: nn.Module, coders: list[InjectedCoder]): + super().__init__() + self.model = model + self.coders = coders + + @classmethod + def from_huggingface( + cls, model: PreTrainedModel, coder: AbstractSAE, layer: float | int = 0.5 + ): + layers = model.base_model.encoder.layer + if isinstance(layer, float): + n_layers = len(layers) + layer = floor(layer * n_layers) + + coders = [cls.inject_sparse_coder(layers, coder, layer)] + return cls(model, coders) + + @staticmethod + def inject_sparse_coder(layers: nn.ModuleList, coder: AbstractSAE, layer: int): + m = InjectedCoder(layers[layer], coder) + layers[layer] = m + return m + + def set_sparsity(self, enable: bool = True): + """Enable or disable sparse coders""" + assert isinstance(enable, bool) + for coder in self.coders: + coder.state = "sparse" if enable else "dense" + + @contextmanager + def sparse(self, enable: bool = True): + sparsity = [] + for coder in self.coders: + sparsity.append(coder.state) + coder.state = "sparse" if enable else "dense" + + try: + yield self + finally: + for coder in self.coders: + coder.state = sparsity.pop() + + @contextmanager + def nullcoders(self): + state = [] + for coder in self.coders: + state.append(coder.state) + coder.state = "null" + + try: + yield self + finally: + for coder in self.coders: + coder.state = state.pop() + + def forward( + self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None + ) -> torch.Tensor: + return self.model.forward(input_ids, attention_mask)[0] + + def forward_with_loss(self, *args, **kwargs): + y = self.model.forward(*args, **kwargs)[0] + device = self.coders[0].loss.device + loss = torch.tensor(0.0, device=device) + for coder in self.coders: + loss += coder.loss + + return y, loss + + def sparse_parameters(self): + """Return an iterator over the parameters of the sparse autoencoders""" + for coder in self.coders: + yield from coder.coder.parameters() + + def sparse_named_parameters(self): + for coder in self.coders: + yield from coder.coder.named_parameters() + + @torch.no_grad() + def loss_recovered( + self, + target: torch.Tensor, + input_ids: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, + sparse_output: Optional[torch.Tensor] = None, + ): + """Compute the fraction of the loss recovered by the SAE relative to an null-embedding""" + training = self.training + self.eval() + if sparse_output is None: + with self.sparse(True) as self: + sparse_output = self.forward(input_ids, attention_mask=attention_mask) + loss_sparse = lossfn(sparse_output, target) + + with self.sparse(False) as self: + y_dense = self.forward(input_ids, attention_mask=attention_mask) + loss_dense = lossfn(y_dense, target) + + with self.nullcoders() as self: + y_null = self.forward(input_ids, attention_mask=attention_mask) + loss_null = lossfn(y_null, target) + + self.train(training) + + return 1 - (loss_sparse - loss_dense) / (loss_null - loss_dense) + + +class GatedSAE(AbstractSAE): + def __init__( + self, + hidden_size: int, + expansion: int = 4, + l1_coef: float = 0.01, + device=None, + dtype=None, + ): super().__init__() factory_kwargs = {"device": device, "dtype": dtype} n_features = hidden_size * expansion @@ -39,6 +209,11 @@ def __init__(self, hidden_size: int, expansion: int = 4, device=None, dtype=None self.b_dec = nn.Parameter(torch.empty(hidden_size, **factory_kwargs)) self.r_mag = nn.Parameter(torch.empty(n_features, **factory_kwargs)) self.b_enc = nn.Parameter(torch.empty(n_features, **factory_kwargs)) + self.l1_coef = torch.tensor(l1_coef, requires_grad=False, **factory_kwargs) + + # Loss parameters + self.loss = torch.tensor(0.0, **factory_kwargs) + self.reset_parameters() def reset_parameters(self): @@ -51,23 +226,25 @@ def reset_parameters(self): nn.init.normal_(self.r_mag, mean=0.0, std=sqrt(2 / self.r_mag.shape[0])) init_bias(self.b_enc, self.w_gate) - def forward(self, x): + def forward(self, x: torch.Tensor): + return self.decode(self.encode(x)) + + def encode(self, x: torch.Tensor): x_centered = x - self.b_dec x_enc = x_centered.matmul(self.w_gate.T) gate = (x_enc + self.b_gate) > 0 x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) return x_mag * gate - def reconstruct(self, features): + def decode(self, features: torch.Tensor): return F.linear(features, self.w_dec, self.b_dec) - @torch.compile - def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + def forward_with_loss(self, x: torch.Tensor): x_centered = x - self.b_dec x_enc = x_centered.matmul(self.w_gate.T) pi_gate = x_enc + self.b_gate pi_rect = F.relu(pi_gate) - loss_sparsity = l1_coef * pi_rect.abs().sum() + loss_sparsity = self.l1_coef * pi_rect.abs().sum() x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) gate = pi_gate > 0 @@ -78,82 +255,86 @@ def loss(self, x, l1_coef: torch.FloatTensor = 0.01): x_hat_detach = F.linear(pi_rect, self.w_dec.detach(), self.b_dec.detach()) loss_aux = F.mse_loss(x_hat_detach, x) - return { - "loss": loss_sparsity + loss_aux + loss_recon, - "loss_reconstruction": loss_recon, - "features": features, - } + loss = loss_sparsity + loss_aux + loss_recon + return x_hat, loss -class TiedBiasSAE(nn.Module): - def __init__(self, hidden_size: int, expansion: int = 4): +class TiedBiasSAE(AbstractSAE): + def __init__(self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01): super().__init__() n_features = hidden_size * expansion self.encoder = nn.Linear(hidden_size, n_features) self.decoder = nn.Linear(n_features, hidden_size) + self.l1_coef_coef = torch.tensor(l1_coef) - def forward(self, x): - return F.relu(self.encoder(x - self.decoder.bias)) + def encode(self, x: torch.Tensor): + return self.encoder(x - self.decoder.bias) - def reconstruct(self, features): + def decode(self, features): return self.decoder(features) - @torch.compile - def loss(self, x, l1_coef: torch.FloatTensor = 0.01): + def forward_with_loss(self, x: torch.Tensor): f = F.relu(self.encoder(x - self.decoder.bias)) x_hat = self.decoder(f) f_act = f * self.decoder.weight.norm(p=2, dim=0) loss_reconstruction = F.mse_loss(x_hat, x) - loss_sparsity = l1_coef * f_act.abs().sum() - return { - "loss": loss_reconstruction + loss_sparsity, - "loss_reconstruction": loss_reconstruction, - "features": f, - } + loss_sparsity = self.l1_coef_coef * f_act.abs().sum() + loss = loss_reconstruction + loss_sparsity + return x_hat, loss -class SAE(pl.LightningModule): +def pretrained_sae( + name_or_path: str, sae: str = "gated", layer: int | float = 0.5, expansion: int = 2 +): + model = AutoModel.from_pretrained(name_or_path, trust_remote_code=True) + hidden_size = model.config.hidden_size + if sae == "gated": + sae = GatedSAE(hidden_size=hidden_size, expansion=expansion) + + +class LightningSAE(pl.LightningModule): def __init__( self, - hidden_size: Union[int, str], - sae: str = "gated", - expansion: int = 4, + name_or_path: str, + sae_type: str = "gated", + layer: int | float = 0.5, l1_coef: float = 0.01, + expansion: int = 2, + lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, optimizer: OptimizerCallable = torch.optim.AdamW, lr_schedule: LRSchedulerCallable | None = None, ): super().__init__() - if isinstance(hidden_size, str): - hidden_size = get_hidden_size(hidden_size) - if sae == "gated": - self.sae = GatedSAE(hidden_size, expansion) - elif sae == "tied_bias": - self.sae = TiedBiasSAE(hidden_size, expansion) + encoder = load_encoder(name_or_path) + hidden_size = encoder.config.hidden_size + if sae_type == "gated": + sae = GatedSAE( + hidden_size=hidden_size, expansion=expansion, l1_coef=l1_coef + ) else: - raise ValueError(f"Unknown sae type: {sae}") + raise ValueError(f"Unknown sae type: {sae_type}") - self.l1_coef = l1_coef + self.sparse_model = SparsifiedModel.from_huggingface(encoder, sae, layer) self.optimizer = optimizer self.lr_schedule = lr_schedule - self.hidden_size = hidden_size - self.num_features = hidden_size * expansion - self.save_hyperparameters(ignore=["sae"]) - - metrics = MetricCollection( - { - "alive_features": AliveFeatures(self.num_features), - "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), - "alive_features_10k": AliveFeatures( - self.num_features, threshold=10_000 - ), - "max_feature_density": MaxFeatureDensity(self.num_features), - "feature_density": FeatureDensity(self.num_features), - } - ) - self.train_metrics = metrics.clone(prefix="train/") - self.val_metrics = metrics.clone(prefix="val/") - self.test_metrics = metrics.clone(prefix="test/") + self.lossfn = lossfn + self.save_hyperparameters(ignore=["sparse_model"]) + + # metrics = MetricCollection( + # { + # "alive_features": AliveFeatures(self.num_features), + # "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), + # "alive_features_10k": AliveFeatures( + # self.num_features, threshold=10_000 + # ), + # "max_feature_density": MaxFeatureDensity(self.num_features), + # "feature_density": FeatureDensity(self.num_features), + # } + # ) + # self.train_metrics = metrics.clone(prefix="train/") + # self.val_metrics = metrics.clone(prefix="val/") + # self.test_metrics = metrics.clone(prefix="test/") def setup(self, stage: str): if isinstance(self.logger, pl.loggers.WandbLogger): @@ -171,38 +352,44 @@ def define_metric(*args, **kwargs): define_metric(f"{stage}/alive_features", summary="max,last") define_metric(f"{stage}/max_feature_density", summary="min,max,last") - def stage_step(self, stage: str, batch): - out = self.sae.loss(batch, self.l1_coef) - self.log_dict( - { - f"{stage}/loss": out["loss"], - f"{stage}/loss_reconstruction": out["loss_reconstruction"], - f"{stage}/avg_l0_loss": avg_l0_norm(out["features"]), - }, - sync_dist=True, - on_step=True, - on_epoch=True, - ) - return out + def stage_step(self, stage: str, batch, recovered_loss: bool = False): + input_ids = batch["input_ids"] + attention_mask = batch["attention_mask"] + y, loss = self.sparse_model.forward_with_loss(input_ids, attention_mask) + logdict = {f"{stage}/loss": loss} + + if self.lossfn: + target = batch["target"] if "target" in batch else batch["labels"] + y_loss = self.lossfn(y, target) + logdict[f"{stage}/target_loss"] = y_loss + + if recovered_loss: + logdict[f"{stage}/recovered_loss"] = self.sparse_model.loss_recovered( + target, + input_ids, + attention_mask, + lossfn=self.lossfn, + sparse_output=y, + ) - def backward(self, loss, *args, **kwargs) -> None: - # Retain graph during backprop - loss.backward(retain_graph=True) + return loss, logdict def training_step(self, batch): - out = self.stage_step("train", batch) - self.train_metrics.update(out["features"]) - return out["loss"] + loss, logdict = self.stage_step("train", batch) + self.log_dict(logdict, on_epoch=True, on_step=True) + return loss def validation_step(self, batch): - out = self.stage_step("val", batch) - self.val_metrics.update(out["features"]) - return out["loss"] + loss, logdict = self.stage_step("val", batch, recovered_loss=True) + logdict = {k + "_epoch": v for k, v in logdict.items()} + self.log_dict(logdict, on_epoch=True, on_step=False) + return loss def test_step(self, batch): - out = self.stage_step("test", batch) - self.test_metrics.update(out["features"]) - return out["loss"] + loss, logdict = self.stage_step("test", batch, recovered_loss=True) + logdict = {k + "_epoch": v for k, v in logdict.items()} + self.log_dict(logdict, on_epoch=True, on_step=False) + return loss def _log_feature_metrics(self, metric, stage: str) -> None: m = metric.compute() @@ -220,17 +407,8 @@ def _log_feature_metrics(self, metric, stage: str) -> None: metric.reset() - def on_train_epoch_end(self): - self._log_feature_metrics(self.train_metrics, "train") - - def on_validation_epoch_end(self): - self._log_feature_metrics(self.val_metrics, "val") - - def on_test_epoch_end(self): - self._log_feature_metrics(self.test_metrics, "test") - def configure_optimizers(self): - optimizer = self.optimizer(self.parameters()) + optimizer = self.optimizer(self.sparse_model.sparse_parameters()) if schedule := self.lr_schedule: return { "optimizer": optimizer, diff --git a/pyproject.toml b/pyproject.toml index 43285a38..bf7b13fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,6 +55,9 @@ testpaths = ["test"] pythonpath = [".", "test"] addopts = "--color yes" +[tool.pytest.ini_options.markers] +gpu = "tests that require a GPU to run" + [tool.ruff.lint] ignore = ["F401"] diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index fffb6f68..73f97820 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -1,19 +1,18 @@ { - container: '/lustre/fs0/awadell/sqsh-files/0535844560745234+mist+08e9e89.sqsh', + container: '/lustre/fs0/shared/sqsh-files/mist+pytorch+25.01+v2.sif', train: { tags: ['sae', 'debug'], model: { - class_path: 'electrolyte_fm.models.SAE', + class_path: 'electrolyte_fm.models.LightningSAE', init_args: { - sae: 'gated', - hidden_size: $.train.data.init_args.name_or_path, - expansion: 16, + name_or_path: 'ibm/MoLFormer-XL-both-10pct', + sae_type: 'gated', + expansion: 4, l1_coef: 1e-5, optimizer: { class_path: 'torch.optim.AdamW', init_args: { - lr: 1e-3, - betas: [0.0, 0.999], + lr: 5e-4, }, }, lr_schedule: { @@ -27,21 +26,22 @@ }, }, data: { - class_path: 'electrolyte_fm.data_modules.HiddenStateDataModule', + class_path: 'electrolyte_fm.data_modules.RobertaDataSet', init_args: { - name_or_path: 'ibm/MoLFormer-XL-both-10pct', - batch_size: 1024, - encoder_batch_size: 256, + path: '/lustre/fs0/awadell/realspace', + batch_size: 128, val_batch_size: 4 * self.batch_size, + num_workers: 8, + prefetch_factor: 8, }, }, trainer: { max_steps: $.train.model.init_args.lr_schedule.init_args.num_training_steps, val_check_interval: 100, limit_val_batches: 50, - precision: 'bf16-true', + precision: '32', enable_progress_bar: false, - strategy: "ddp", + strategy: 'auto', }, }, env: { diff --git a/test/test_sae.py b/test/test_sae.py index 24efa9f0..9facd59a 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -1,16 +1,56 @@ +from datasets import features +from numpy import zeros_like import pytest import torch -from transformers import DataCollatorWithPadding - -from electrolyte_fm.data_modules.sae_dataset import ( - HiddenStateDataModule, - extract_hidden_state, +from transformers import ( + AutoModel, + AutoTokenizer, + DataCollatorWithPadding, + RobertaPreLayerNormConfig, + RobertaPreLayerNormForMaskedLM, + RobertaPreLayerNormModel, ) + +# from electrolyte_fm.data_modules.sae_dataset import ( +# HiddenStateDataModule, +# extract_hidden_state, +# ) from electrolyte_fm.models.model_utils import load_encoder -from electrolyte_fm.models.sae import GatedSAE, TiedBiasSAE, avg_l0_norm +from electrolyte_fm.models.sae import ( + AbstractSAE, + GatedSAE, + InjectedCoder, + SparsifiedModel, + TiedBiasSAE, + avg_l0_norm, +) from electrolyte_fm.utils.tokenizer import load_tokenizer +def get_default_device(): + if torch.cuda.is_available(): + return torch.device("cuda") + else: + return torch.device("cpu") + + +DEVICE = get_default_device() + + +@pytest.fixture() +@pytest.mark.cuda +def roberta_tokenzier(): + tokenizer = load_tokenizer("smirk") + config = RobertaPreLayerNormConfig( + vocab_size=len(tokenizer), + hidden_size=256, + num_hidden_layers=4, + num_attention_heads=4, + intermediate_size=512, + ) + return RobertaPreLayerNormForMaskedLM(config).to(DEVICE), tokenizer + + @pytest.mark.parametrize("sae_cls", [GatedSAE, TiedBiasSAE]) class TestSAE: B = 3 @@ -34,66 +74,188 @@ def test_init(self, sae_cls): assert p.isfinite().all() assert not p.isnan().any() - def test_forward(self, sae_cls): + def test_forward(self, sae_cls: AbstractSAE): sae = sae_cls(hidden_size=self.H, expansion=self.E) - features = sae(self.input()) - assert features.shape == self.feature_shape + assert isinstance(sae, AbstractSAE) + x = self.input() + features = sae.forward(x) + assert features.shape == x.shape assert features.isfinite().all() assert not features.isnan().any() def test_loss(self, sae_cls): sae = sae_cls(hidden_size=self.H, expansion=self.E) + assert isinstance(sae, AbstractSAE) x = self.input() - out = sae.loss(x) - for v in out.values(): - assert v.isfinite().all() - assert not v.isnan().any() - - # Check output - assert out["loss"].shape == () - assert out["features"].shape == self.feature_shape - assert out["features"].isclose(sae(x)).all() - # assert out["x_hat"].shape == x.shape - # assert out["x_hat"].isclose(sae.reconstruct(sae(x))).all() - - -def test_avg_l0_norm(): - x = torch.tensor([[1, 0, 0], [0, 5, 0]]) - assert avg_l0_norm(x) == 1 - assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) - - -def test_dataloader(): - ckpt_path = "ibm/MoLFormer-XL-both-10pct" - path = "/lustre/fs0/awadell/realspace" - dm = HiddenStateDataModule(ckpt_path, path) - dm.prepare_data() - dm.setup("fit") - for batch in dm.train_dataloader(): - assert isinstance(batch, torch.Tensor) - assert batch.shape == (dm.batch_size, 768) - assert not batch.requires_grad - break - - -def test_extract_hidden_state(): - ckpt_path = "ibm/MoLFormer-XL-both-10pct" - encoder = load_encoder(ckpt_path) - d_model = 768 - tok = load_tokenizer(ckpt_path) - smiles = [ - "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", - "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", - ] - tokens = [tok(smi) for smi in smiles] - input_ids = [x["input_ids"] for x in tokens] - attention_mask = [x["attention_mask"] for x in tokens] - batch = extract_hidden_state( - input_ids, - attention_mask, - encoder=encoder, - collate=DataCollatorWithPadding(tok), - layer=0.5, + y, loss = sae.forward_with_loss(x) + assert loss.isfinite() and loss.shape == () + assert y.shape == x.shape + assert y.isfinite().all() + + +# def test_avg_l0_norm(): +# x = torch.tensor([[1, 0, 0], [0, 5, 0]]) +# assert avg_l0_norm(x) == 1 +# assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) + + +# def test_dataloader(): +# ckpt_path = "ibm/MoLFormer-XL-both-10pct" +# path = "/lustre/fs0/awadell/realspace" +# dm = HiddenStateDataModule(ckpt_path, path) +# dm.prepare_data() +# dm.setup("fit") +# for batch in dm.train_dataloader(): +# assert isinstance(batch, torch.Tensor) +# assert batch.shape == (dm.batch_size, 768) +# assert not batch.requires_grad +# break + + +def test_injected_coder(roberta_tokenzier): + roberta, _ = roberta_tokenzier + hidden_size = roberta.config.hidden_size + sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) + injected = InjectedCoder(roberta.base_model.encoder.layer[2], sae) + injected.eval() + assert isinstance(injected, InjectedCoder) + dense_model = injected.dense_model + assert not injected.training and not dense_model.training + x = torch.rand(1, 5, hidden_size, device=DEVICE) + + # Check dense + y_ref = dense_model(x)[0] + injected.state = "dense" + y_dense = injected(x)[0] + print(y_ref, y_dense) + assert y_dense.equal(y_ref) + + # Check null + injected.state = "null" + y_null = injected(x)[0] + assert y_null.equal(x) + + # Check sparse + injected.state = "sparse" + y_sparse = injected(x)[0] + assert y_sparse.shape == y_dense.shape + + +def test_instrumented(roberta_tokenzier): + model = AutoModel.from_pretrained( + "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True + ) + tokenizer = AutoTokenizer.from_pretrained( + "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True + ) + model, tokenizer = roberta_tokenzier + hidden_size = model.config.hidden_size + sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) + sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=2).to(DEVICE) + model = model.to(DEVICE) + sparse_model.eval() + model.eval() + + batch = tokenizer("CNCCC") + input_ids = torch.tensor(batch["input_ids"]).to(DEVICE) + y = model(input_ids)[0] + assert y.equal(model(input_ids)[0]) + print(y) + + with sparse_model.nullcoders() as sparse_model: + y_null = sparse_model(input_ids)[0] + print(y_null) + + with sparse_model.sparse(False) as sparse_model: + y_dense = sparse_model(input_ids)[0] + print(y_dense) + assert y_dense.equal(y) + + assert False + + +def test_sparse_model(roberta_tokenzier): + roberta, tokenizer = roberta_tokenzier + sae = GatedSAE(hidden_size=roberta.config.hidden_size, expansion=2) + sparse_model = SparsifiedModel.from_huggingface(roberta, sae, layer=2).to(DEVICE) + robert = sparse_model.model.base_model + assert isinstance(robert, RobertaPreLayerNormModel) + assert isinstance(robert.encoder.layer[2], InjectedCoder) + assert robert.encoder.layer[2] is sparse_model.coders[0] + + collate = DataCollatorWithPadding(tokenizer) + batch = collate([tokenizer("CNCCC")]) + input_ids = batch["input_ids"].to(DEVICE) + attention_mask = batch["attention_mask"].to(DEVICE) + y_sparse = sparse_model.forward(input_ids, attention_mask) + robert.eval() + y_dense = roberta(input_ids, attention_mask)[0] + assert y_sparse.shape == y_dense.shape + assert y_sparse.shape == (1, 5, len(tokenizer)) + + # Check null features + with sparse_model.nullcoders() as model: + y_null = model.forward(input_ids, attention_mask) + assert y_null.shape == y_sparse.shape + + # Check dense features + with sparse_model.sparse(False) as model: + # Run model in eval model to be deterministic + model.eval() + robert.eval() + y_dense = roberta(input_ids, attention_mask)[0] + y_dense_context = model.forward(input_ids, attention_mask) + robert.train() + model.train() + assert y_dense_context.shape == y_dense.shape + e = (y_dense_context.detach() - y_dense.detach()).abs() + assert (e < 1e-6).all() + + # Check non-encoder parameters don't get gradients + assert len(list(sparse_model.sparse_parameters())) > 0 + assert len(dict(sparse_model.sparse_named_parameters())) > 0 + + # Check_gradient + y, loss = sparse_model.forward_with_loss(input_ids, attention_mask) + loss.backward() + for k, v in sparse_model.sparse_named_parameters(): + assert v.grad is not None + assert (v.grad != 0).any() + + # Check Recovered Loss + rc = sparse_model.loss_recovered( + input_ids, input_ids, attention_mask, sparse_output=y_sparse ) - assert "hidden_state" in batch - assert batch["hidden_state"].shape == (len(smiles), d_model) + print(rc) + assert isinstance(rc, torch.Tensor) and rc.shape == () + assert rc.isfinite() and not rc.isnan() + assert False + + +# def test_extract_hidden_state(): +# ckpt_path = "ibm/MoLFormer-XL-both-10pct" +# encoder = load_encoder(ckpt_path) +# d_model = 768 +# tok = load_tokenizer(ckpt_path) +# smiles = [ +# "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", +# "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", +# ] +# tokens = [tok(smi) for smi in smiles] +# input_ids = [x["input_ids"] for x in tokens] +# attention_mask = [x["attention_mask"] for x in tokens] +# batch = extract_hidden_state( +# input_ids, +# attention_mask, +# encoder=encoder, +# collate=DataCollatorWithPadding(tok), +# layer=0.5, +# ) +# assert "hidden_state" in batch +# assert batch["hidden_state"].shape == (len(smiles), d_model) + + +# def test_wrapped_sae(): +# encoder = load_encoder("ibm/MoLFormer-XL-both-10pct") +# coder = GatedSAE(hidden_size=768, expansion=4) +# model = WrappedSAE(encoder, coder, 2) From 131d315ab42a7a109e7100b7e1ec8e36e0fb4dd3 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 7 Mar 2025 14:21:41 -0500 Subject: [PATCH 081/147] add more sae types --- electrolyte_fm/data_modules/sae_dataset.py | 21 +-- electrolyte_fm/models/sae.py | 209 +++++++++++++++------ pyproject.toml | 22 +++ submit/sae.jsonnet | 17 +- test/test_sae.py | 194 +++++++++---------- 5 files changed, 280 insertions(+), 183 deletions(-) diff --git a/electrolyte_fm/data_modules/sae_dataset.py b/electrolyte_fm/data_modules/sae_dataset.py index 99c22078..c8b1e7b6 100644 --- a/electrolyte_fm/data_modules/sae_dataset.py +++ b/electrolyte_fm/data_modules/sae_dataset.py @@ -1,10 +1,7 @@ -import re from math import floor from pathlib import Path from typing import Optional, Union -import nvtx -import torch import pytorch_lightning as pl import torch from datasets import Dataset, load_dataset @@ -30,14 +27,13 @@ def extract_hidden_state( batch = batch.to(encoder.device) # Disable gradients - with nvtx.annotate("encoder"): - with torch.inference_mode(): - enc = encoder( - batch["input_ids"], - attention_mask=batch["attention_mask"], - return_dict=True, - output_hidden_states=True, - ) + with torch.inference_mode(): + enc = encoder( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + output_hidden_states=True, + ) if isinstance(layer, float): layer = floor(len(enc["hidden_states"]) * layer) @@ -51,7 +47,6 @@ def extract_hidden_state( return {"hidden_state": hidden_state} -@nvtx.annotate() def flatten_hidden_states(hs, attention_mask, device="cpu"): hs = hs.to(device) hidden_state = [] @@ -64,7 +59,6 @@ def flatten_hidden_states(hs, attention_mask, device="cpu"): return hidden_state -@nvtx.annotate() def collate_hidden_states(hidden_states): return {"hidden_state": torch.cat(hidden_states, dim=0)} @@ -200,6 +194,5 @@ def test_dataloader(self): ) @classmethod - @nvtx.annotate() def collate_fn(cls, batch): return torch.stack([x["hidden_state"] for x in batch]).detach() diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index a27a9799..41925c30 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -1,8 +1,9 @@ from math import sqrt, floor -from typing import Callable, Optional, Literal +from typing import Callable, Optional, Literal, override from contextlib import contextmanager import lightning.pytorch as pl +from numpy import minimum import torch from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from torch import nn @@ -25,12 +26,28 @@ def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: return x.detach().count_nonzero() / x.shape[:-1].numel() -def hf_cross_entropy(logits: torch.FloatTensor, target: torch.IntTensor): +def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): """F.cross_entropy but for logits of `(B, T, C)` and target of `(B, T)`""" + if isinstance(logits, tuple): + logits = logits[0] + elif not isinstance(logits, torch.Tensor): + logits = logits.logits + return F.cross_entropy(logits.view(-1, logits.shape[-1]), target.view(-1)) class AbstractSAE(nn.Module): + def __init__(self, hidden_size: int, expansion: int) -> None: + super().__init__() + self.hidden_size = hidden_size + self.num_features = hidden_size * expansion + self.num_batches_not_active = torch.zeros(self.num_features) + + def update_inactive_features(self, f: torch.Tensor): + f_act = f.sum(dim=list(range(f.ndim)[:-1])) + self.num_batches_not_active += f_act == 0 + self.num_batches_not_active[f_act > 0] = 0 + def forward(self, x: torch.Tensor): return self.decode(self.encode(x)) @@ -58,24 +75,22 @@ def __init__(self, dense_model: nn.Module, coder: AbstractSAE): self.loss = torch.tensor(0.0) self.state: InjectedCoderState = "sparse" - def forward(self, *args, **kwargs): - if self.state == "null": - return (*args,) - - output = self.dense_model(*args, **kwargs) + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.dense_model(x) if self.state == "dense": - return output + return x + + elif self.state == "null": + return torch.zeros_like(x) - assert self.state == "sparse" - hs = output[0] if self.training: - hs, loss = self.coder.forward_with_loss(hs) + x_hat, loss = self.coder.forward_with_loss(x) self.loss = loss else: - hs = self.coder.forward(hs) + x_hat = self.coder.forward(x) - return (hs, *output[1:]) + return x_hat class SparsifiedModel(nn.Module): @@ -98,8 +113,8 @@ def from_huggingface( @staticmethod def inject_sparse_coder(layers: nn.ModuleList, coder: AbstractSAE, layer: int): - m = InjectedCoder(layers[layer], coder) - layers[layer] = m + m = InjectedCoder(layers[layer].output.dense, coder) + layers[layer].output.dense = m return m def set_sparsity(self, enable: bool = True): @@ -134,13 +149,11 @@ def nullcoders(self): for coder in self.coders: coder.state = state.pop() - def forward( - self, input_ids: torch.Tensor, attention_mask: Optional[torch.Tensor] = None - ) -> torch.Tensor: - return self.model.forward(input_ids, attention_mask)[0] + def forward(self, *args, **kwargs): + return self.model.forward(*args, **kwargs) def forward_with_loss(self, *args, **kwargs): - y = self.model.forward(*args, **kwargs)[0] + y = self.model.forward(*args, **kwargs) device = self.coders[0].loss.device loss = torch.tensor(0.0, device=device) for coder in self.coders: @@ -161,30 +174,38 @@ def sparse_named_parameters(self): def loss_recovered( self, target: torch.Tensor, - input_ids: torch.Tensor, - attention_mask: Optional[torch.Tensor] = None, + *args, lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, sparse_output: Optional[torch.Tensor] = None, + **kwargs, ): """Compute the fraction of the loss recovered by the SAE relative to an null-embedding""" training = self.training self.eval() if sparse_output is None: with self.sparse(True) as self: - sparse_output = self.forward(input_ids, attention_mask=attention_mask) + sparse_output = self(*args, **kwargs) + assert sparse_output is not None loss_sparse = lossfn(sparse_output, target) with self.sparse(False) as self: - y_dense = self.forward(input_ids, attention_mask=attention_mask) + y_dense = self(*args, **kwargs) loss_dense = lossfn(y_dense, target) with self.nullcoders() as self: - y_null = self.forward(input_ids, attention_mask=attention_mask) + y_null = self(*args, **kwargs) loss_null = lossfn(y_null, target) self.train(training) - return 1 - (loss_sparse - loss_dense) / (loss_null - loss_dense) + print( + { + "loss_sparse": loss_sparse, + "loss_dense": loss_dense, + "loss_null": loss_null, + } + ) + return 1 - ((loss_sparse - loss_dense) / (loss_null - loss_dense)) class GatedSAE(AbstractSAE): @@ -196,7 +217,7 @@ def __init__( device=None, dtype=None, ): - super().__init__() + super().__init__(hidden_size, expansion) factory_kwargs = {"device": device, "dtype": dtype} n_features = hidden_size * expansion self.w_gate = nn.Parameter( @@ -259,16 +280,63 @@ def forward_with_loss(self, x: torch.Tensor): return x_hat, loss +def init_column_fixed_l2(tensor: torch.Tensor, l2: float = 0.1): + """init a column of a tensor to a fixed l2""" + col_l2 = l2 / tensor.norm(2, dim=0) + with torch.no_grad(): + tensor.copy_(tensor * col_l2) + + +class VanillaSAE(AbstractSAE): + def __init__( + self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01 + ) -> None: + super().__init__(hidden_size, expansion) + self.encoder = nn.Linear(hidden_size, hidden_size * expansion) + self.decoder = nn.Linear(hidden_size * expansion, hidden_size) + self.l1_coef_coef = torch.tensor(l1_coef) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + init_column_fixed_l2(self.decoder.weight) + self.decoder.bias.zero_() + self.encoder.weight.copy_(self.decoder.weight.T) + self.encoder.bias.zero_() + + def encode(self, x: torch.Tensor): + return F.relu(self.encoder(x)) + + def decode(self, features: torch.Tensor) -> torch.Tensor: + return self.decoder(features) + + def forward_with_loss(self, x: torch.Tensor): + f = self.encode(x) + x_hat = self.decode(f) + loss = ( + F.mse_loss(x_hat, x) + + self.l1_coef_coef * (self.decoder.weight.norm(2, dim=0) * f).sum() + ) + return x_hat, loss + + class TiedBiasSAE(AbstractSAE): def __init__(self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01): - super().__init__() + super().__init__(hidden_size, expansion) n_features = hidden_size * expansion self.encoder = nn.Linear(hidden_size, n_features) self.decoder = nn.Linear(n_features, hidden_size) self.l1_coef_coef = torch.tensor(l1_coef) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + self.encoder.weight.copy_(self.decoder.weight.T) + self.decoder.bias.zero_() + self.encoder.bias.zero_() def encode(self, x: torch.Tensor): - return self.encoder(x - self.decoder.bias) + return F.relu(self.encoder(x - self.decoder.bias)) def decode(self, features): return self.decoder(features) @@ -283,23 +351,63 @@ def forward_with_loss(self, x: torch.Tensor): return x_hat, loss -def pretrained_sae( - name_or_path: str, sae: str = "gated", layer: int | float = 0.5, expansion: int = 2 -): - model = AutoModel.from_pretrained(name_or_path, trust_remote_code=True) - hidden_size = model.config.hidden_size - if sae == "gated": - sae = GatedSAE(hidden_size=hidden_size, expansion=expansion) +def topk(x: torch.Tensor, k: int, dim: int = -1): + k = minimum(x.shape[dim], k) + vi = torch.topk(x, k, dim=dim, sorted=False, largest=True) + return torch.zeros_like(x).scatter(dim, vi.indices, vi.values) + + +class TopKSAE(AbstractSAE): + def __init__( + self, + hidden_size: int, + expansion: int = 4, + k: int = 10, + alpha: float = 1 / 32, + dead_threshold=1_000_000, + ) -> None: + super().__init__(hidden_size, expansion) + self.encoder = nn.Linear(hidden_size, hidden_size * expansion) + self.decoder = nn.Linear(hidden_size * expansion, hidden_size) + self.k = k + self.alpha = alpha + self.dead_threshold = dead_threshold + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + init_column_fixed_l2(self.decoder.weight) + self.decoder.bias.zero_() + self.encoder.weight.copy_(self.decoder.weight.T) + self.encoder.bias.zero_() + + def encode(self, x: torch.Tensor): + return topk(self.encoder(x), self.k, dim=-1) + + def decode(self, features: torch.Tensor) -> torch.Tensor: + return self.decoder(features) + + def encode_dead_features(self, x: torch.Tensor): + f = self.encoder(x) + dead = self.num_batches_not_active > self.dead_threshold + f = f * dead + return topk(f, self.k, dim=-1) + + def forward_with_loss(self, x: torch.Tensor): + f = self.encode(x) + self.update_inactive_features(f) + x_hat = self.decode(f) + x_dead = self.decode(self.encode_dead_features(x)) + loss = F.mse_loss(x_hat, x) + self.alpha * F.mse_loss(x_dead, x) + return x_hat, loss class LightningSAE(pl.LightningModule): def __init__( self, name_or_path: str, - sae_type: str = "gated", + sae: AbstractSAE, layer: int | float = 0.5, - l1_coef: float = 0.01, - expansion: int = 2, lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, optimizer: OptimizerCallable = torch.optim.AdamW, lr_schedule: LRSchedulerCallable | None = None, @@ -307,35 +415,12 @@ def __init__( super().__init__() encoder = load_encoder(name_or_path) - hidden_size = encoder.config.hidden_size - if sae_type == "gated": - sae = GatedSAE( - hidden_size=hidden_size, expansion=expansion, l1_coef=l1_coef - ) - else: - raise ValueError(f"Unknown sae type: {sae_type}") - self.sparse_model = SparsifiedModel.from_huggingface(encoder, sae, layer) self.optimizer = optimizer self.lr_schedule = lr_schedule self.lossfn = lossfn self.save_hyperparameters(ignore=["sparse_model"]) - # metrics = MetricCollection( - # { - # "alive_features": AliveFeatures(self.num_features), - # "alive_features_1k": AliveFeatures(self.num_features, threshold=1_000), - # "alive_features_10k": AliveFeatures( - # self.num_features, threshold=10_000 - # ), - # "max_feature_density": MaxFeatureDensity(self.num_features), - # "feature_density": FeatureDensity(self.num_features), - # } - # ) - # self.train_metrics = metrics.clone(prefix="train/") - # self.val_metrics = metrics.clone(prefix="val/") - # self.test_metrics = metrics.clone(prefix="test/") - def setup(self, stage: str): if isinstance(self.logger, pl.loggers.WandbLogger): diff --git a/pyproject.toml b/pyproject.toml index bf7b13fd..ae2487e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "pytorch-lightning~=2.5" ] +<<<<<<< HEAD [optional-dependencies] tokenizers = [ "rdkit", @@ -33,6 +34,27 @@ tokenizers = [ "apetokenizer @ git+https://github.com/mikemayuare/apetokenizer.git@8b070d6278503bcf8ad2eedc1756ac34f4bbd981", "pyspark>=3.5.0", ] +======= +[tool.poetry.dependencies] +python = ">=3.10,<3.14" +torch = {version = "~2.3"} +torchmetrics = "^1.4.0" +lightning = "~2.3" +jsonargparse = {extras = ["signatures", "jsonnet"], version = "^4.27"} +transformers = "^4.40" +deepspeed = { git = "https://github.com/microsoft/DeepSpeed.git", rev = "5f631abc2f930ecece38fae05dc9bd3923c555dd"} +wandb = "^0.16" +typer = "^0.9" +datasets = "^2.20" +pyspark = "^3.5.0" +rich = "^13" +pydoe2 = "^1.3" +jsonnet = "^0.20.0" +scikit-learn= "^1.5.1" +selfies= "^2.1.0" +rdkit = "^2024.3.5" +smirk = {git = "git@github.com:BattModels/smirk.git", rev = "8061286a219420571d606d98083dd900aaa21d02" } +>>>>>>> e3ec515 (add more sae types) [dependency-groups] dev = [ diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index 73f97820..5b59b723 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -6,13 +6,20 @@ class_path: 'electrolyte_fm.models.LightningSAE', init_args: { name_or_path: 'ibm/MoLFormer-XL-both-10pct', - sae_type: 'gated', - expansion: 4, - l1_coef: 1e-5, + sae: { + class_path: 'electrolyte_fm.models.sae.VanillaSAE', + init_args: { + hidden_size: 768, + expansion: 4, + l1_coef: 1e-5, + }, + }, optimizer: { class_path: 'torch.optim.AdamW', init_args: { - lr: 5e-4, + lr: 5, + beta: [0.9, 0.999], + weight_decay: 0, }, }, lr_schedule: { @@ -42,6 +49,8 @@ precision: '32', enable_progress_bar: false, strategy: 'auto', + gradient_clip_val: 1, + gradient_clip_algorithm: 'norm', }, }, env: { diff --git a/test/test_sae.py b/test/test_sae.py index 9facd59a..5188933a 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -1,9 +1,7 @@ -from datasets import features -from numpy import zeros_like import pytest import torch from transformers import ( - AutoModel, + AutoModelForMaskedLM, AutoTokenizer, DataCollatorWithPadding, RobertaPreLayerNormConfig, @@ -11,18 +9,15 @@ RobertaPreLayerNormModel, ) -# from electrolyte_fm.data_modules.sae_dataset import ( -# HiddenStateDataModule, -# extract_hidden_state, -# ) -from electrolyte_fm.models.model_utils import load_encoder from electrolyte_fm.models.sae import ( AbstractSAE, GatedSAE, InjectedCoder, SparsifiedModel, TiedBiasSAE, - avg_l0_norm, + VanillaSAE, + TopKSAE, + topk, ) from electrolyte_fm.utils.tokenizer import load_tokenizer @@ -30,12 +25,16 @@ def get_default_device(): if torch.cuda.is_available(): return torch.device("cuda") + elif torch.backends.mps.is_available(): + return torch.device("mps") else: return torch.device("cpu") DEVICE = get_default_device() +SAE_CLASSES = [GatedSAE, TiedBiasSAE, VanillaSAE, TopKSAE] + @pytest.fixture() @pytest.mark.cuda @@ -51,10 +50,18 @@ def roberta_tokenzier(): return RobertaPreLayerNormForMaskedLM(config).to(DEVICE), tokenizer -@pytest.mark.parametrize("sae_cls", [GatedSAE, TiedBiasSAE]) +def test_topk(): + x = torch.rand(8, 4, 20) + assert ((x > 0).sum(-1) > 5).all() + x_hat = topk(x, 5) + assert ((x_hat > 0).sum(-1) == 5).all() + + +@pytest.mark.parametrize("sae_cls", SAE_CLASSES) class TestSAE: B = 3 H = 4 + T = 8 E = 2 @classmethod @@ -63,10 +70,10 @@ def setup_class(cls): @property def feature_shape(self): - return (self.B, self.H * self.E) + return (self.B, self.T, self.H * self.E) def input(self): - return torch.rand(self.B, self.H) + return torch.rand(self.B, self.T, self.H) def test_init(self, sae_cls): sae = sae_cls(hidden_size=self.H, expansion=self.E) @@ -74,6 +81,15 @@ def test_init(self, sae_cls): assert p.isfinite().all() assert not p.isnan().any() + def test_encode(self, sae_cls): + sae = sae_cls(hidden_size=self.H, expansion=self.E) + x = self.input() + f = sae.encode(x) + assert f.shape == self.feature_shape + x_hat = sae.decode(f) + assert x_hat.shape == x.shape + assert sae.forward(x).equal(x_hat) + def test_forward(self, sae_cls: AbstractSAE): sae = sae_cls(hidden_size=self.H, expansion=self.E) assert isinstance(sae, AbstractSAE) @@ -93,109 +109,111 @@ def test_loss(self, sae_cls): assert y.isfinite().all() -# def test_avg_l0_norm(): -# x = torch.tensor([[1, 0, 0], [0, 5, 0]]) -# assert avg_l0_norm(x) == 1 -# assert avg_l0_norm(x.T).isclose(torch.tensor(1 / 3)) - - -# def test_dataloader(): -# ckpt_path = "ibm/MoLFormer-XL-both-10pct" -# path = "/lustre/fs0/awadell/realspace" -# dm = HiddenStateDataModule(ckpt_path, path) -# dm.prepare_data() -# dm.setup("fit") -# for batch in dm.train_dataloader(): -# assert isinstance(batch, torch.Tensor) -# assert batch.shape == (dm.batch_size, 768) -# assert not batch.requires_grad -# break - - -def test_injected_coder(roberta_tokenzier): - roberta, _ = roberta_tokenzier - hidden_size = roberta.config.hidden_size - sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) - injected = InjectedCoder(roberta.base_model.encoder.layer[2], sae) +@pytest.mark.parametrize("sae_cls", SAE_CLASSES) +def test_injected_coder(sae_cls): + hidden_size = 64 + model = torch.nn.Linear(hidden_size, hidden_size) + sae = sae_cls(hidden_size=hidden_size, expansion=2) + injected = InjectedCoder(model, sae).to(DEVICE) injected.eval() assert isinstance(injected, InjectedCoder) dense_model = injected.dense_model assert not injected.training and not dense_model.training - x = torch.rand(1, 5, hidden_size, device=DEVICE) + x = torch.rand(2, 5, hidden_size, device=DEVICE) # Check dense - y_ref = dense_model(x)[0] + y_ref = dense_model(x) injected.state = "dense" - y_dense = injected(x)[0] - print(y_ref, y_dense) + y_dense = injected(x) + assert y_dense.shape == y_ref.shape assert y_dense.equal(y_ref) # Check null injected.state = "null" - y_null = injected(x)[0] - assert y_null.equal(x) + y_null = injected(x) + assert y_null.shape == y_ref.shape + assert y_null.equal(torch.zeros_like(y_dense)) # Check sparse injected.state = "sparse" - y_sparse = injected(x)[0] + y_sparse = injected(x) assert y_sparse.shape == y_dense.shape -def test_instrumented(roberta_tokenzier): - model = AutoModel.from_pretrained( +def test_instrumented(): + model = AutoModelForMaskedLM.from_pretrained( "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained( "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True ) - model, tokenizer = roberta_tokenzier hidden_size = model.config.hidden_size sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) - sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=2).to(DEVICE) + sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=0).to(DEVICE) model = model.to(DEVICE) sparse_model.eval() - model.eval() - - batch = tokenizer("CNCCC") - input_ids = torch.tensor(batch["input_ids"]).to(DEVICE) - y = model(input_ids)[0] - assert y.equal(model(input_ids)[0]) - print(y) + assert not model.training + assert not sae.training + assert not model.training + batch = tokenizer(["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"]) + collate = DataCollatorWithPadding(tokenizer) + batch = collate(batch) + batch = { + k: v.to(DEVICE) + for k, v in batch.items() + if k in ["input_ids", "attention_mask"] + } + batch["return_dict"] = True + y = model(**batch).logits + + assert all([coder.state == "sparse" for coder in sparse_model.coders]) with sparse_model.nullcoders() as sparse_model: - y_null = sparse_model(input_ids)[0] - print(y_null) + assert all([coder.state == "null" for coder in sparse_model.coders]) + y_null = sparse_model(**batch).logits + assert y_null.shape == y.shape + assert y_null.device == y.device + assert y_null.dtype == y.dtype with sparse_model.sparse(False) as sparse_model: - y_dense = sparse_model(input_ids)[0] - print(y_dense) + assert all([coder.state == "dense" for coder in sparse_model.coders]) + y_dense = sparse_model(**batch).logits + assert y_dense.shape == y.shape + assert y_dense.device == y.device + assert y_dense.dtype == y.dtype assert y_dense.equal(y) - assert False + rc = sparse_model.loss_recovered(target=batch["input_ids"], **batch) + assert isinstance(rc, torch.Tensor) and rc.shape == () + assert rc.isfinite() and not rc.isnan() + assert rc <= 1 def test_sparse_model(roberta_tokenzier): roberta, tokenizer = roberta_tokenzier - sae = GatedSAE(hidden_size=roberta.config.hidden_size, expansion=2) + sae = TiedBiasSAE(hidden_size=roberta.config.hidden_size, expansion=2) sparse_model = SparsifiedModel.from_huggingface(roberta, sae, layer=2).to(DEVICE) robert = sparse_model.model.base_model assert isinstance(robert, RobertaPreLayerNormModel) - assert isinstance(robert.encoder.layer[2], InjectedCoder) - assert robert.encoder.layer[2] is sparse_model.coders[0] + assert isinstance(robert.encoder.layer[2].output.dense, InjectedCoder) + assert robert.encoder.layer[2].output.dense is sparse_model.coders[0] collate = DataCollatorWithPadding(tokenizer) batch = collate([tokenizer("CNCCC")]) - input_ids = batch["input_ids"].to(DEVICE) - attention_mask = batch["attention_mask"].to(DEVICE) - y_sparse = sparse_model.forward(input_ids, attention_mask) + batch = { + "input_ids": batch["input_ids"].to(DEVICE), + "attention_mask": batch["attention_mask"].to(DEVICE), + "return_dict": True, + } + y_sparse = sparse_model(**batch).logits robert.eval() - y_dense = roberta(input_ids, attention_mask)[0] + y_dense = roberta(**batch).logits assert y_sparse.shape == y_dense.shape assert y_sparse.shape == (1, 5, len(tokenizer)) # Check null features with sparse_model.nullcoders() as model: - y_null = model.forward(input_ids, attention_mask) + y_null = model(**batch).logits assert y_null.shape == y_sparse.shape # Check dense features @@ -203,8 +221,8 @@ def test_sparse_model(roberta_tokenzier): # Run model in eval model to be deterministic model.eval() robert.eval() - y_dense = roberta(input_ids, attention_mask)[0] - y_dense_context = model.forward(input_ids, attention_mask) + y_dense = roberta(**batch).logits + y_dense_context = model(**batch).logits robert.train() model.train() assert y_dense_context.shape == y_dense.shape @@ -216,46 +234,16 @@ def test_sparse_model(roberta_tokenzier): assert len(dict(sparse_model.sparse_named_parameters())) > 0 # Check_gradient - y, loss = sparse_model.forward_with_loss(input_ids, attention_mask) + out, loss = sparse_model.forward_with_loss(**batch) + # assert out.logits.equal(y_sparse) loss.backward() - for k, v in sparse_model.sparse_named_parameters(): + for _, v in sparse_model.sparse_named_parameters(): assert v.grad is not None assert (v.grad != 0).any() # Check Recovered Loss rc = sparse_model.loss_recovered( - input_ids, input_ids, attention_mask, sparse_output=y_sparse + **batch, target=batch["input_ids"], sparse_output=y_sparse ) - print(rc) assert isinstance(rc, torch.Tensor) and rc.shape == () assert rc.isfinite() and not rc.isnan() - assert False - - -# def test_extract_hidden_state(): -# ckpt_path = "ibm/MoLFormer-XL-both-10pct" -# encoder = load_encoder(ckpt_path) -# d_model = 768 -# tok = load_tokenizer(ckpt_path) -# smiles = [ -# "CCC(=O)OC1(C(CC2C1(CC(C3(C2CC(C4=CC(=O)C=CC43C)F)F)O)C)C)C(=O)SCF", -# "CNCCC(c1ccccc1)Oc2ccc(cc2)C(F)(F)F", -# ] -# tokens = [tok(smi) for smi in smiles] -# input_ids = [x["input_ids"] for x in tokens] -# attention_mask = [x["attention_mask"] for x in tokens] -# batch = extract_hidden_state( -# input_ids, -# attention_mask, -# encoder=encoder, -# collate=DataCollatorWithPadding(tok), -# layer=0.5, -# ) -# assert "hidden_state" in batch -# assert batch["hidden_state"].shape == (len(smiles), d_model) - - -# def test_wrapped_sae(): -# encoder = load_encoder("ibm/MoLFormer-XL-both-10pct") -# coder = GatedSAE(hidden_size=768, expansion=4) -# model = WrappedSAE(encoder, coder, 2) From 5935b3997b5be18fa5bd0ecdbf24c4509d34cc6f Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 8 Mar 2025 18:52:58 -0500 Subject: [PATCH 082/147] first pass at linear probe --- .../data_modules/lipinski_dataset.py | 95 +++++++++++++ .../property_prediction_dataset.py | 8 +- electrolyte_fm/data_modules/utils.py | 10 ++ electrolyte_fm/models/linear_probe.py | 133 ++++++++++++++++++ electrolyte_fm/models/sae.py | 6 +- test/test_sae.py | 47 ++++++- 6 files changed, 292 insertions(+), 7 deletions(-) create mode 100644 electrolyte_fm/data_modules/lipinski_dataset.py create mode 100644 electrolyte_fm/models/linear_probe.py diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py new file mode 100644 index 00000000..c0e53177 --- /dev/null +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -0,0 +1,95 @@ +from pathlib import Path + +import torch +from datasets import load_dataset +from rdkit.Chem import Lipinski, MolFromSmiles +from rdkit.Chem.Crippen import MolLogP +from rdkit.Chem.Descriptors import ExactMolWt + +from .molnet_dataset import _URLS +from .property_prediction_dataset import PropertyPredictionDataModule +from .utils import AbstractDataset, MolEncoding, filter_invalid_smi +from .molnet_dataset import train_val_test_split + + +class LipinskiDataModule(PropertyPredictionDataModule): + def __init__(self, name_or_path: str, **kwargs): + self.name_or_path = name_or_path + if not Path(name_or_path).exists(): + # Set default smi_column + assert name_or_path in _URLS.keys() + self.name_or_path = _URLS[name_or_path] + kwargs["smi_column"] = ( + kwargs.get("smi_column", None) or "smiles" + if name_or_path != "bace" + else "mol" + ) + assert isinstance(kwargs["smi_column"], str) + kwargs["additonal_columns"] = [ + "probe_target", + *kwargs.get("additonal_columns", []), + ] + super().__init__(**kwargs) + assert self.encoding != MolEncoding.SELFIES + + def _get_dataset(self) -> AbstractDataset: + # Load the dataset + ds: AbstractDataset = load_dataset( + "csv", + name=self.name, + data_files=[self.name_or_path], + split="train", + keep_in_memory=False, + save_infos=False, + ) # type: ignore + + ds = ds.select_columns(self.smi_column) + ds = filter_invalid_smi(ds, self.smi_column) + + ds = ds.map( + lipinki_rule_of_five, + batched=False, + fn_kwargs={"smi_column": self.smi_column}, + ) + return train_val_test_split(ds) + + def collate_fn(self, batch): + output = super().collate_fn(batch) + output = self.token_collator(batch) + output["probe_target"] = torch.stack( + [torch.tensor(x["probe_target"]) for x in batch] + ) + + return output + + +def lipinki_rule_of_five(x: dict, smi_column: str) -> dict: + smi = x[smi_column] + mol = MolFromSmiles(smi) + assert mol is not None, "invalid smi: %s" % smi + x["num_h_bond_donors"] = Lipinski.NumHDonors(mol) + x["lipinki_h_donor"] = x["num_h_bond_donors"] <= 5 + x["num_h_bond_acceptors"] = Lipinski.NumHAcceptors(mol) + x["lipinki_h_acceptor"] = x["num_h_bond_acceptors"] <= 10 + x["molecular_weight"] = ExactMolWt(mol) + x["lipinki_mwt"] = x["molecular_weight"] <= 500 + x["log_p"] = MolLogP(mol) + x["lipinki_log_p"] = x["log_p"] <= 5 + x["lipinki"] = all(v for k, v in x.items() if k.startswith("lipinki")) + x["probe_target"] = [v for k, v in x.items() if k.startswith("lipinki")] + return x + + +if __name__ == "__main__": + ds = LipinskiDataModule(name_or_path="hiv") + ds = ds.dataset + df = ds.to_pandas() + df.to_csv("lipo.csv") + cols = [ + "lipinki", + "lipinki_h_donor", + "lipinki_h_acceptor", + "lipinki_mwt", + "lipinki_log_p", + ] + print({k: df[k].value_counts() for k in cols}) diff --git a/electrolyte_fm/data_modules/property_prediction_dataset.py b/electrolyte_fm/data_modules/property_prediction_dataset.py index 4720e236..25b9b943 100644 --- a/electrolyte_fm/data_modules/property_prediction_dataset.py +++ b/electrolyte_fm/data_modules/property_prediction_dataset.py @@ -30,6 +30,7 @@ def __init__( target_columns: Optional[List[str]] = None, val_batch_size: Optional[int] = None, encoding: str = MolEncoding.SMILES.value, + additonal_columns: Optional[List[str]] = None, include_encoding: bool = False, randomize: bool = False, truncation: bool = False, @@ -48,6 +49,7 @@ def __init__( self.smi_column = smi_column self.target_columns = target_columns + self.additonal_columns = additonal_columns or [] self.encoding = MolEncoding(encoding) self.include_encoding = include_encoding self.randomize = randomize @@ -88,9 +90,11 @@ def setup(self, stage: str) -> None: # Save training dataset for target transformations self.target_dataset = ds["train"].select_columns(["target", "target_mask"]) - ds = ds.select_columns([self.smi_column, "target", "target_mask"]) + ds = ds.select_columns( + [self.smi_column, "target", "target_mask", *self.additonal_columns] + ) else: - ds = ds.select_columns([self.smi_column]) + ds = ds.select_columns([self.smi_column, *self.additonal_columns]) # Tokenize ds = ds.map( diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py index 6bbbc008..b1738c55 100644 --- a/electrolyte_fm/data_modules/utils.py +++ b/electrolyte_fm/data_modules/utils.py @@ -82,6 +82,16 @@ def random(self, smi: str): assert False, "Not Reachable, missing Enum Branch" +def filter_invalid_smi( + ds: AbstractDataset, input_column: str, **kwargs +) -> AbstractDataset: + def is_valid(x: dict): + mol = Chem.MolFromSmiles(x[input_column]) + return mol is not None + + return ds.filter(is_valid, batched=False, **kwargs) + + def encode_molecules( ds: AbstractDataset, input_column: str, diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py new file mode 100644 index 00000000..37c31706 --- /dev/null +++ b/electrolyte_fm/models/linear_probe.py @@ -0,0 +1,133 @@ +from typing import Iterable +from collections import defaultdict +import torch +from torch import nn +from torch.nn import functional as F +import lightning.pytorch as pl +from lightning.pytorch.cli import OptimizerCallable, LRSchedulerCallable +from fnmatch import fnmatchcase + + +class LightningProbe(pl.LightningModule): + def __init__( + self, + model: nn.Module, + probes: dict[str, nn.Module], + optimizer: OptimizerCallable = torch.optim.AdamW, + lr_schedule: LRSchedulerCallable | None = None, + ): + super().__init__() + + self.model = model + self.optimizer = optimizer + self.lr_schedule = lr_schedule + self.hookpoints = self._identify_hookpoints(probes.keys()) + self._probes = nn.ModuleList(probes.values()) + self._probe_points = list(probes.keys()) + self.activations = dict() + + @property + def probes(self): + yield from zip(self._probe_points, self._probes) + + def _identify_hookpoints(self, hooks: Iterable[str]): + hooks: set[str] = set() + for name, _ in self.model.named_modules(): + if any((fnmatchcase(name, hook) for hook in hooks)): + hooks.add(name) + + hooks = list(hooks) + hooks.sort() + return hooks + + def _install_hooks(self, results: dict[str, torch.Tensor]): + for name in self.hookpoints: + + def hook(module: nn.Module, input, output: torch.Tensor): + assert isinstance(output, torch.Tensor) + results[name] = output + return None + + self.model.get_submodule(name).register_forward_hook(hook) + + def on_train_start(self): + self.model.to(self.device) + self._install_hooks(self.activations) + + def forward(self, batch: dict): + self.model.eval() + with torch.no_grad(): + self.model(batch) + out = defaultdict(dict) + for name, probe in self.probs.items(): + for hook in self.hookpoints: + if fnmatchcase(name, hook): + act = self.activations[hook] + out[name][hook] = probe(act) + + return out + + def forward_fit(self, batch: dict): + self.model.eval() + target = batch.pop("probe_target") + self.model(**batch) + loss = torch.tensor(0.0) + out = {} + for name, probe in self.probs.items(): + probe_loss = torch.tensor(0.0) + for hook in self.hookpoints: + if fnmatchcase(name, hook): + act = self.activations[hook] + y = probe(act) + probe_loss += F.binary_cross_entropy_with_logits(y, target) + + out[f"{name}-probe-loss"] = probe_loss + loss += probe_loss + + out["loss"] = loss + return out + + def training_step(self, batch): + out = self.forward_fit(batch) + self.log_dict({f"train/{k}": v for k, v in out.items()}) + return out["loss"] + + def validation_step(self, batch): + out = self.forward_fit(batch) + self.log_dict( + {f"train/{k}": v for k, v in out.items()}, on_step=False, on_epoch=True + ) + return out["loss"] + + def configure_optimizers(self): + optimizer = self.optimizer(self._probes.parameters()) + if schedule := self.lr_schedule: + return { + "optimizer": optimizer, + "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, + } + return optimizer + + +if __name__ == "__main__": + from transformers import AutoModelForMaskedLM + from ..data_modules.lipinski_dataset import LipinskiDataModule + from lightning.pytorch import Trainer + + name_or_path = "ibm/MoLFormer-XL-both-10pct" + model = AutoModelForMaskedLM.from_pretrained(name_or_path, trust_remote_code=True) + dm = LipinskiDataModule( + name_or_path="hiv", + tokenizer=name_or_path, + encoding="smiles-canonical", + num_workers=4, + ) + hidden_size = model.config.hidden_size + probes = { + f"*.encoder.layer.{layer}.output": nn.Linear(hidden_size, 5) + for layer in range(hidden_size) + } + lm = LightningProbe(model, probes) + + trainer = Trainer() + trainer.fit(lm, datamodule=dm) diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 41925c30..6fd691e1 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -33,7 +33,11 @@ def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): elif not isinstance(logits, torch.Tensor): logits = logits.logits - return F.cross_entropy(logits.view(-1, logits.shape[-1]), target.view(-1)) + return F.cross_entropy( + logits.view(-1, logits.shape[-1]), + target.view(-1), + ignore_index=-100, + ) class AbstractSAE(nn.Module): diff --git a/test/test_sae.py b/test/test_sae.py index 5188933a..ba525df0 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -1,8 +1,11 @@ import pytest import torch +import torch.nn.functional as F from transformers import ( + AutoModel, AutoModelForMaskedLM, AutoTokenizer, + DataCollatorForLanguageModeling, DataCollatorWithPadding, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM, @@ -15,9 +18,10 @@ InjectedCoder, SparsifiedModel, TiedBiasSAE, - VanillaSAE, TopKSAE, + VanillaSAE, topk, + hf_cross_entropy, ) from electrolyte_fm.utils.tokenizer import load_tokenizer @@ -57,6 +61,37 @@ def test_topk(): assert ((x_hat > 0).sum(-1) == 5).all() +@pytest.mark.gpu +def test_hf_crossentropy(): + name = "ibm/MoLFormer-XL-both-10pct" + model = AutoModelForMaskedLM.from_pretrained(name, trust_remote_code=True).to( + DEVICE + ) + tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True) + batch = [ + tok(smi) + for smi in [ + "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", + "CN3[C@H]1CC[C@@H]3C[C@@H](C1)OC(=O)C(CO)c2cc", + ] + ] + collate = DataCollatorForLanguageModeling(tok, mlm=False) + # collate = DataCollatorWithPadding(tok) + batch = collate(batch) + batch = {k: v.to(DEVICE) for k, v in batch.items()} + model.eval() + out = model( + batch["input_ids"], + attention_mask=batch["attention_mask"], + return_dict=True, + ) + logits = out.logits + vocab_size = model.config.vocab_size + loss_ref = F.cross_entropy(logits.view(-1, vocab_size), batch["labels"].view(-1)) + loss = hf_cross_entropy(logits, batch["labels"]) + assert loss == loss_ref + + @pytest.mark.parametrize("sae_cls", SAE_CLASSES) class TestSAE: B = 3 @@ -156,13 +191,16 @@ def test_instrumented(): assert not sae.training assert not model.training - batch = tokenizer(["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"]) - collate = DataCollatorWithPadding(tokenizer) + batch = [ + tokenizer(smi) + for smi in ["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"] + ] + collate = DataCollatorForLanguageModeling(tokenizer, mlm=False) batch = collate(batch) batch = { k: v.to(DEVICE) for k, v in batch.items() - if k in ["input_ids", "attention_mask"] + if k in ["input_ids", "attention_mask", "labels"] } batch["return_dict"] = True y = model(**batch).logits @@ -175,6 +213,7 @@ def test_instrumented(): assert y_null.device == y.device assert y_null.dtype == y.dtype + assert all([not coder.dense.training for coder in sparse_model.coders]) with sparse_model.sparse(False) as sparse_model: assert all([coder.state == "dense" for coder in sparse_model.coders]) y_dense = sparse_model(**batch).logits From 8046e1de8cadd8cffd6db65c70c9a5b5336e0285 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 10 Mar 2025 07:20:48 -0700 Subject: [PATCH 083/147] initial run over linear probes --- electrolyte_fm/data_modules/feature_tagger.py | 3 +- .../data_modules/lipinski_dataset.py | 2 +- .../property_prediction_dataset.py | 4 +- electrolyte_fm/models/linear_probe.py | 247 ++++++++++++++---- electrolyte_fm/models/prod_finetune.py | 24 ++ electrolyte_fm/models/sae.py | 6 +- electrolyte_fm/utils/ckpt.py | 43 +-- opt/linear-probes/submit_linear_probes.py | 143 ++++++++++ opt/package/__main__.py | 5 + submit/dgx.j2 | 41 ++- 10 files changed, 426 insertions(+), 92 deletions(-) create mode 100644 opt/linear-probes/submit_linear_probes.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py index fe08864b..9d4cede3 100644 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ b/electrolyte_fm/data_modules/feature_tagger.py @@ -120,7 +120,8 @@ def flatten(*iterables) -> list: "n", "o", "p", - "s" "se", + "s", + "se", "as", ], } diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index c0e53177..f007d518 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -27,7 +27,7 @@ def __init__(self, name_or_path: str, **kwargs): assert isinstance(kwargs["smi_column"], str) kwargs["additonal_columns"] = [ "probe_target", - *kwargs.get("additonal_columns", []), + *(kwargs.get("additonal_columns", None) or []), ] super().__init__(**kwargs) assert self.encoding != MolEncoding.SELFIES diff --git a/electrolyte_fm/data_modules/property_prediction_dataset.py b/electrolyte_fm/data_modules/property_prediction_dataset.py index 25b9b943..15fb498b 100644 --- a/electrolyte_fm/data_modules/property_prediction_dataset.py +++ b/electrolyte_fm/data_modules/property_prediction_dataset.py @@ -38,7 +38,9 @@ def __init__( ): super().__init__() - self.tokenizer = load_tokenizer(tokenizer) + self.tokenizer = ( + load_tokenizer(tokenizer) if isinstance(tokenizer, str) else tokenizer + ) self.token_collator = DataCollatorWithPadding(self.tokenizer) self.vocab_size = len(self.tokenizer) self.truncation = truncation diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 37c31706..0903e536 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -1,18 +1,35 @@ -from typing import Iterable +import logging +from typing import Iterable, Dict, Any, Callable from collections import defaultdict +from jsonargparse import lazy_instance import torch from torch import nn from torch.nn import functional as F import lightning.pytorch as pl from lightning.pytorch.cli import OptimizerCallable, LRSchedulerCallable from fnmatch import fnmatchcase +from torchmetrics import AUROC + + +def per_layer_probe( + hidden_size: int, features: int, n_layers: int, location: str = "output" +) -> dict[str, nn.Module]: + probes = {} + template: str = "*.encoder.layer.{layer}.{location}" + for layer in range(n_layers): + hook_name = template.format(layer=layer, location=location) + probes[hook_name] = nn.Linear(hidden_size, features) + return probes + + +ProbeConfigCallable = Callable[Any, dict[str, nn.Module]] class LightningProbe(pl.LightningModule): def __init__( self, model: nn.Module, - probes: dict[str, nn.Module], + probes: ProbeConfigCallable = per_layer_probe, optimizer: OptimizerCallable = torch.optim.AdamW, lr_schedule: LRSchedulerCallable | None = None, ): @@ -21,84 +38,135 @@ def __init__( self.model = model self.optimizer = optimizer self.lr_schedule = lr_schedule + self.save_hyperparameters() self.hookpoints = self._identify_hookpoints(probes.keys()) + + # Setup probes self._probes = nn.ModuleList(probes.values()) self._probe_points = list(probes.keys()) - self.activations = dict() + self._hooks_installed = dict() + self._activations = {} + + # Add metrics + self.val_metrics = nn.ModuleList( + AUROC(task="binary", thresholds=100) for probe in self._probe_points + ) + + def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: + state_dict = checkpoint["state_dict"] + # Remove hooks from model (not picklable)) + for hook in state_dict.pop("_hooks_installed", {}).values(): + hook.remove() + state_dict["_hooks_installed"] = {} - @property - def probes(self): + # Don't save activations + state_dict["_activations"] = {} + + def named_probes(self): yield from zip(self._probe_points, self._probes) - def _identify_hookpoints(self, hooks: Iterable[str]): + def _identify_hookpoints(self, probe_locs: Iterable[str]): hooks: set[str] = set() for name, _ in self.model.named_modules(): - if any((fnmatchcase(name, hook) for hook in hooks)): - hooks.add(name) + for loc in probe_locs: + if fnmatchcase(name, loc): + hooks.add(name) hooks = list(hooks) hooks.sort() return hooks - def _install_hooks(self, results: dict[str, torch.Tensor]): + def _install_hooks(self): for name in self.hookpoints: + if name not in self._hooks_installed: + hook = self._create_act_hook(name, self._activations) + self._hooks_installed[name] = self.model.get_submodule( + name + ).register_forward_hook(hook) + logging.debug(f"Installed hook for %s", name) - def hook(module: nn.Module, input, output: torch.Tensor): - assert isinstance(output, torch.Tensor) - results[name] = output - return None + return self._activations - self.model.get_submodule(name).register_forward_hook(hook) + @staticmethod + def _create_act_hook(name: str, results: dict): + def hook(module: nn.Module, input, output: torch.Tensor): + assert isinstance(output, torch.Tensor) + results[name] = output[:, 0, :].detach() + return None - def on_train_start(self): - self.model.to(self.device) - self._install_hooks(self.activations) + return hook - def forward(self, batch: dict): + def forward(self, *args, **kwargs): self.model.eval() + activations = self._install_hooks() with torch.no_grad(): - self.model(batch) + self.model(*args, **kwargs) out = defaultdict(dict) - for name, probe in self.probs.items(): + for name, probe in self.named_probes(): for hook in self.hookpoints: - if fnmatchcase(name, hook): - act = self.activations[hook] + if fnmatchcase(hook, name): + act = activations[hook] out[name][hook] = probe(act) return out def forward_fit(self, batch: dict): self.model.eval() + activations = self._install_hooks() target = batch.pop("probe_target") self.model(**batch) - loss = torch.tensor(0.0) + loss = [] out = {} - for name, probe in self.probs.items(): - probe_loss = torch.tensor(0.0) + for name, probe in self.named_probes(): + probe_loss = [] for hook in self.hookpoints: - if fnmatchcase(name, hook): - act = self.activations[hook] + if fnmatchcase(hook, name): + act = activations[hook].detach() y = probe(act) - probe_loss += F.binary_cross_entropy_with_logits(y, target) + probe_loss.append( + F.binary_cross_entropy_with_logits(y, target.to(dtype=y.dtype)) + ) - out[f"{name}-probe-loss"] = probe_loss - loss += probe_loss + out[f"{name}-probe-loss"] = sum(probe_loss) + loss.append(sum(probe_loss)) - out["loss"] = loss + out["loss"] = sum(loss) / len(loss) return out def training_step(self, batch): - out = self.forward_fit(batch) - self.log_dict({f"train/{k}": v for k, v in out.items()}) - return out["loss"] - - def validation_step(self, batch): out = self.forward_fit(batch) self.log_dict( {f"train/{k}": v for k, v in out.items()}, on_step=False, on_epoch=True ) return out["loss"] + def validation_step(self, batch): + target = batch.pop("probe_target") + out = self.forward(**batch) + metrics = {} + loss = [] + for probe, probe_metrics in zip(self._probe_points, self.val_metrics): + probe_pred = [] + for hook in self.hookpoints: + if fnmatchcase(hook, probe): + probe_pred.append(out[probe][hook]) + + probe_pred = torch.stack(probe_pred) + probe_pred = probe_pred.view(-1, probe_pred.shape[-1]) + probe_loss = F.binary_cross_entropy_with_logits( + probe_pred, + target.to(dtype=probe_pred.dtype), + ) + probe_metrics.update(probe_pred, target) + metrics[f"val/{probe}-loss"] = probe_loss + metrics[f"val/{probe}-auroc"] = probe_metrics + loss.append(probe_loss) + + metrics["val/loss"] = sum(loss) / len(loss) + self.log_dict(metrics, on_step=False, on_epoch=True) + + return metrics["val/loss"] + def configure_optimizers(self): optimizer = self.optimizer(self._probes.parameters()) if schedule := self.lr_schedule: @@ -110,24 +178,91 @@ def configure_optimizers(self): if __name__ == "__main__": - from transformers import AutoModelForMaskedLM - from ..data_modules.lipinski_dataset import LipinskiDataModule - from lightning.pytorch import Trainer - - name_or_path = "ibm/MoLFormer-XL-both-10pct" - model = AutoModelForMaskedLM.from_pretrained(name_or_path, trust_remote_code=True) - dm = LipinskiDataModule( - name_or_path="hiv", - tokenizer=name_or_path, - encoding="smiles-canonical", - num_workers=4, + import smirk + import json + from jsonargparse import lazy_instance + from lightning.pytorch.cli import ( + LightningCLI, + LightningArgumentParser, + _InstantiatorFn, + _get_module_type, ) - hidden_size = model.config.hidden_size - probes = { - f"*.encoder.layer.{layer}.output": nn.Linear(hidden_size, 5) - for layer in range(hidden_size) - } - lm = LightningProbe(model, probes) - - trainer = Trainer() - trainer.fit(lm, datamodule=dm) + from lightning.pytorch.loggers import WandbLogger + from lightning.pytorch.callbacks import ModelCheckpoint + + logging.basicConfig(level=logging.INFO) + + def mlm_from_pretrained(name_or_path: str) -> nn.Module: + from transformers import AutoModelForMaskedLM + + return AutoModelForMaskedLM.from_pretrained( + name_or_path, trust_remote_code=True + ) + + class MyLightningCLI(LightningCLI): + def add_arguments_to_parser(self, parser: LightningArgumentParser): + parser.add_argument( + "--tags", + type=list, + help="Tags for WandB logger", + default=[], + ) + parser.link_arguments("tags", "trainer.logger.init_args.tags") + + def _add_instantiators(self) -> None: + self.config_dump = json.loads( + self.parser.dump( + self.config, skip_link_targets=False, skip_none=False, format="json" + ) + ) + if "subcommand" in self.config: + self.config_dump = self.config_dump[self.config.subcommand] + + self.parser.add_instantiator( + _InstantiatorFn(cli=self, key="model"), + _get_module_type(self._model_class), + subclasses=self.subclass_mode_model, + ) + self.parser.add_instantiator( + _InstantiatorFn(cli=self, key="data"), + _get_module_type(self._datamodule_class), + subclasses=self.subclass_mode_data, + ) + + cli = MyLightningCLI( + LightningProbe, + save_config_callback=None, + seed_everything_default=42, + trainer_defaults={ + "logger": lazy_instance( + WandbLogger, project="linear-probes", save_code=True + ), + "max_epochs": 1000, + }, + parser_kwargs={"parser_mode": "jsonnet"}, + run=False, + ) + trainer: pl.Trainer = cli.trainer + model: LightningProbe = cli.model + ckpts = [] + for probe, _ in model.named_probes(): + probe_name = probe.replace(".", "-").replace("*", "star") + monitor = f"val/{probe}-loss" + auroc = f"val/{probe}-auroc" + ckpts.append( + ModelCheckpoint( + monitor=monitor, + save_top_k=1, + save_weights_only=True, + auto_insert_metric_name=False, + filename=probe_name + + "--epoch-{epoch}--loss-{" + + monitor + + ":.3f}--auroc-{" + + auroc + + ":.3f}", + ) + ) + trainer.callbacks.extend(ckpts) + + trainer.fit(model, cli.datamodule) diff --git a/electrolyte_fm/models/prod_finetune.py b/electrolyte_fm/models/prod_finetune.py index 91c58086..f4677510 100644 --- a/electrolyte_fm/models/prod_finetune.py +++ b/electrolyte_fm/models/prod_finetune.py @@ -66,8 +66,13 @@ def __init__(self, encoder, task_network, transform, tokenizer, channels=None): self.tokenizer = tokenizer self.channels = channels +<<<<<<< HEAD def forward(self, input_ids, attention_mask=None): hs = self.encoder(input_ids, attention_mask=attention_mask).last_hidden_state +======= + def forward(self, **kwargs): + hs = self.encoder(**kwargs).last_hidden_state +>>>>>>> 2c78388 (initial run over linear probes) y = self.task_network(hs) return self.transform.forward(y) @@ -90,6 +95,7 @@ def save_pretrained(self, save_directory, safe_serialization=False): Path(save_directory, "config.json").write_text(json.dumps(config, indent=4)) save_model(self, save_directory, safe_serialization) +<<<<<<< HEAD def embed(self, smi: list[str]): batch = self.tokenizer(smi) collate_fn = DataCollatorWithPadding(self.tokenizer) @@ -114,14 +120,26 @@ def predict(self, smi: list[str]): with torch.inference_mode(): out = self(**batch).cpu() +======= + def predict(self, smi: list[str], tokenizer): + batch = tokenizer(smi) + collate_fn = DataCollatorWithPadding(tokenizer) + batch = collate_fn(batch).to(self.encoder.device) + out = self(**batch) +>>>>>>> 2c78388 (initial run over linear probes) if self.channels is None: return out return annotate_prediction(out, self.channels) @classmethod +<<<<<<< HEAD def from_pretrained(cls, save_directory: str): config = json.loads(Path(save_directory, "config.json").read_text()) +======= + def from_pretrained(cls, name_or_path: str) -> "MISTFinetuned": + config = json.loads(Path(name_or_path, "config.json").read_text()) +>>>>>>> 2c78388 (initial run over linear probes) encoder_config = AutoConfig.for_model( config["encoder"]["model_type"] ).from_dict(config["encoder"]) @@ -131,11 +149,17 @@ def from_pretrained(cls, save_directory: str): config["transform"]["class"], config["transform"]["num_outputs"] ) +<<<<<<< HEAD tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) channels = list(maybe_get_annotated_channels(config["channels"])) model = cls(encoder, task_network, transform, tokenizer, channels) load_model(model, save_directory) +======= + # Instantiate model + model = cls(encoder, task_network, transform, config["channels"]) + load_model(model, name_or_path) +>>>>>>> 2c78388 (initial run over linear probes) return model diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 6fd691e1..6554d930 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -1,5 +1,5 @@ from math import sqrt, floor -from typing import Callable, Optional, Literal, override +from typing import Callable, Optional, Literal from contextlib import contextmanager import lightning.pytorch as pl @@ -31,7 +31,7 @@ def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): if isinstance(logits, tuple): logits = logits[0] elif not isinstance(logits, torch.Tensor): - logits = logits.logits + logits = logits.last_hidden_state return F.cross_entropy( logits.view(-1, logits.shape[-1]), @@ -456,7 +456,7 @@ def stage_step(self, stage: str, batch, recovered_loss: bool = False): logdict[f"{stage}/recovered_loss"] = self.sparse_model.loss_recovered( target, input_ids, - attention_mask, + attention_mask=attention_mask, lossfn=self.lossfn, sparse_output=y, ) diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index b2708dcf..faa72fbd 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -1,9 +1,10 @@ +import logging import importlib import json import logging import os -from pathlib import Path from typing import Optional +from pathlib import Path import torch from jsonargparse import Namespace @@ -114,8 +115,7 @@ def instantiate( config["lightning_module"], class_path=config.get("class_path", None), ) - if "vocab_size" in config["datamodule"]: - model_config["vocab_size"] = config["datamodule"]["vocab_size"] + model_config["vocab_size"] = config["datamodule"]["vocab_size"] else: cls_name, model_config = norm_class_config(config) @@ -151,9 +151,15 @@ def load( config_path = config_path or checkpoint_dir.parent.parent.joinpath( "model_hparams.json" ) +<<<<<<< HEAD assert ( checkpoint_dir.exists() ), f"Missing deepspeed checkpoint directory: {checkpoint_dir}" +======= + assert checkpoint_dir.exists(), ( + f"Missing deepspeed checkpoint directory: {checkpoint_dir}" + ) +>>>>>>> 2c78388 (initial run over linear probes) assert config_path.is_file(), f"Missing model config file {config_path}" model = SaveConfigWithCkpts.instantiate(config_path, max_position_embeddings) @@ -172,6 +178,14 @@ def load( model.load_state_dict(state["state_dict"], strict=True, assign=True) return model +<<<<<<< HEAD +======= + if checkpoint_dir.is_file(): + state = torch.load(checkpoint_dir) + model.load_state_dict(state["state_dict"], strict=True, assign=True) + return model + +>>>>>>> 2c78388 (initial run over linear probes) # Load model weights from the checkpoint try: from deepspeed.utils.zero_to_fp32 import ( @@ -179,9 +193,12 @@ def load( ) state = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) +<<<<<<< HEAD if max_position_embeddings is not None: state = adjust_state_position_embeddings(state, max_position_embeddings) +======= +>>>>>>> 2c78388 (initial run over linear probes) model.load_state_dict(state, strict=False, assign=True) except FileNotFoundError: logging.error( @@ -189,11 +206,16 @@ def load( checkpoint_dir, ) file = Path(checkpoint_dir, "checkpoint", "mp_rank_00_model_states.pt") +<<<<<<< HEAD state = torch.load(file, map_location=map_location) logging.info("loaded %s", file) if max_position_embeddings is not None: state = adjust_state_position_embeddings(state, max_position_embeddings) +======= + state = torch.load(file) + logging.info("loaded %s", file) +>>>>>>> 2c78388 (initial run over linear probes) model.load_state_dict(state["module"], strict=True, assign=True) return model @@ -241,21 +263,6 @@ def get_ckpt_tokenizer(path: str | Path) -> str: return config["data"]["init_args"]["tokenizer"] -def get_hidden_size(name_or_path: str) -> int: - config_path = Path(name_or_path).parent.parent.joinpath("config.json") - if Path(config_path).is_file(): - with open(config_path, "r") as fid: - config = json.load(fid) - return config["model"]["init_args"]["hidden_size"] - - # Special Case models - elif name_or_path.startswith("ibm/MoLFormer-XL-both-10pct"): - return 768 - - else: - raise ValueError(f"Could not find hidden size for {name_or_path}") - - def norm_class_config(config: dict, class_path: Optional[str] = None) -> (str, dict): """Parse a dictionary of hparams for a class name and init args""" init_args = dict() diff --git a/opt/linear-probes/submit_linear_probes.py b/opt/linear-probes/submit_linear_probes.py new file mode 100644 index 00000000..21129791 --- /dev/null +++ b/opt/linear-probes/submit_linear_probes.py @@ -0,0 +1,143 @@ +from pathlib import Path +from copy import deepcopy +import json +import _jsonnet as jsonnet +import subprocess +from submit.submit import render +from submit.utils import dict_product +from transformers import AutoConfig + +# jsonnet +config = """ +function( + encoder_class, + encoder_path, + location, + dataset, + tokenizer="smirk", + encoding="smiles-kekule", + batch_size, + hidden_size, + n_layers, +) + +{ + nodes: 1, + gpus_per_node: 1, + container: '/lustre/fs0/awadell/sqsh-files/mist+pytorch+25.01+v2.sif', + env: { + JOBID: '$SLURM_JOB_ID', + PMIX_MCA_gds: 'hash', + NCCL_TOPO_FILE: '/cm/shared/etc/ndv4-topo.xml', + MELLANOC_VISIBLE_DEVICES: 'all', + }, + program: "-m electrolyte_fm.models.linear_probe", + train: { + trainer: { + max_epochs: 1000, + }, + model: { + model: { + class_path: encoder_class, + init_args: { + name_or_path: encoder_path, + }, + }, + probes: { + class_path: 'electrolyte_fm.models.linear_probe.per_layer_probe', + init_args: { + hidden_size: std.parseInt(hidden_size), + features: 5, + location: location, + n_layers: std.parseInt(n_layers), + }, + }, + }, + data: { + class_path: 'electrolyte_fm.data_modules.lipinski_dataset.LipinskiDataModule', + init_args: { + name_or_path: dataset, + tokenizer: tokenizer, + encoding: encoding, + num_workers: 16, + batch_size: std.parseInt(batch_size), + }, + }, + }, +} +""" + +template = "submit/dgx.j2" + + +def submit(config: dict): + script = render(template, config) + print(script) + subprocess.run("sbatch", input=script, text=True) + + +# Pretrained Models +datasets = ["tox21", "toxcast", "hiv"] +locations = ["output", "intermediate", "output.dense"] +models = [ + { + "encoder_path": "ibm/MoLFormer-XL-both-10pct", + "encoding": "smiles-canonical", + "tokenizer": "ibm/MoLFormer-XL-both-10pct", + }, + { + "encoder_path": "./models/mist-ti624ev1-moleculenet/pretrained", + }, + { + "encoder_path": "./models/mist-1.8B-dh61satti", + "batch_size": 16, + }, +] +models.extend( + [ + { + "encoder_class": "electrolyte_fm.models.prod_finetune.MISTFinetuned.from_pretrained", + "encoder_path": f"./models/mist-ti624ev1-moleculenet/{dataset}", + } + for dataset in ["bace", "qm9", "bbbp", "muv", "qm8", "tmQM"] + ] +) + + +def get_mist_finetune_config(path): + config = json.loads(Path(path, "config.json").read_text()) + return AutoConfig.for_model(config["encoder"]["model_type"]).from_dict( + config["encoder"] + ) + + +runs = [] +for model in models: + run = deepcopy(model) + run.setdefault("encoder_class", "__main__.mlm_from_pretrained") + run.setdefault("batch_size", 64) + if run["encoder_class"] == "__main__.mlm_from_pretrained": + model_config = AutoConfig.from_pretrained( + run["encoder_path"], + trust_remote_code=True, + ) + else: + model_config = get_mist_finetune_config(run["encoder_path"]) + + run.setdefault("n_layers", model_config.num_hidden_layers) + + for c in dict_product({"location": locations, "dataset": datasets}): + run.update(c) + if run["location"] == "intermediate": + hidden_size = model_config.intermediate_size + else: + hidden_size = model_config.hidden_size + + run["hidden_size"] = hidden_size + run_config = jsonnet.evaluate_snippet( + "snippet", + config, + tla_vars={k: str(v) for k, v in run.items()}, + ) + run_config = json.loads(run_config) + submit(run_config) diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 02cbd79d..c5f62ff7 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -52,9 +52,14 @@ def export_finetuned(ckpt: Path): from electrolyte_fm.models import MISTFinetuned model = SaveConfigWithCkpts.load(ckpt) +<<<<<<< HEAD model_config = json.loads(ckpt.parent.parent.joinpath("config.json").read_text()) tokenizer_name = model_config["data"]["init_args"]["tokenizer"] tokenizer = load_tokenizer(tokenizer_name) +======= + config_file = Path(ckpt).parent.parent.joinpath("config.json") + model_config = json.loads(config_file.read_text()) +>>>>>>> 2c78388 (initial run over linear probes) return MISTFinetuned( model.encoder, model.task_network, diff --git a/submit/dgx.j2 b/submit/dgx.j2 index b4cd52a3..7366dd46 100644 --- a/submit/dgx.j2 +++ b/submit/dgx.j2 @@ -32,24 +32,41 @@ cd $(git rev-parse --show-toplevel) ENV_FILE="${TMPDIR}/env-${SLURM_JOB_ID}.sh" cat > $ENV_FILE< $TMPDIR/deepspeed.json< $PL_CONFIG +export JOB_CONFIG=$(cat $PL_CONFIG) +{% else %} -{{ export_job_config( __config__ ) }} +{{- export_job_config( __config__ ) }} # Write out lightning config export PL_CONFIG=$TMPDIR/lightning-${SLURM_JOB_ID}.json cat > ${PL_CONFIG}< Date: Mon, 10 Mar 2025 11:43:41 -0400 Subject: [PATCH 084/147] add analysis code for linear probes --- electrolyte_fm/models/linear_probe.py | 5 ++- opt/FeatureMiner/Project.toml | 10 +++++ opt/FeatureMiner/explore_probes.jl | 17 +++++++++ opt/FeatureMiner/src/FeatureMiner.jl | 37 ++++++++++++++++++- opt/FeatureMiner/src/lipinski.jl | 23 ++++++++++++ .../submit_linear_probes.py | 0 6 files changed, 88 insertions(+), 4 deletions(-) create mode 100644 opt/FeatureMiner/explore_probes.jl create mode 100644 opt/FeatureMiner/src/lipinski.jl rename opt/{linear-probes => FeatureMiner}/submit_linear_probes.py (100%) diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 0903e536..8bc745b0 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -57,10 +57,11 @@ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: # Remove hooks from model (not picklable)) for hook in state_dict.pop("_hooks_installed", {}).values(): hook.remove() - state_dict["_hooks_installed"] = {} + state_dict.pop("_hooks_installed", None) + state_dict["_prob_points"] = self._probe_points # Don't save activations - state_dict["_activations"] = {} + state_dict.pop("_activations", None) def named_probes(self): yield from zip(self._probe_points, self._probes) diff --git a/opt/FeatureMiner/Project.toml b/opt/FeatureMiner/Project.toml index 58a56987..863dab58 100644 --- a/opt/FeatureMiner/Project.toml +++ b/opt/FeatureMiner/Project.toml @@ -4,8 +4,18 @@ version = "0.1.0" [deps] ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63" +CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" OnlineStatsBase = "925886fa-5bf2-5e8e-b522-a9147a512338" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" + +[compat] +CSV = "0.10.15" +DataFrames = "1.7.0" +LinearAlgebra = "1.11.0" +Makie = "0.22.2" diff --git a/opt/FeatureMiner/explore_probes.jl b/opt/FeatureMiner/explore_probes.jl new file mode 100644 index 00000000..a21d8afe --- /dev/null +++ b/opt/FeatureMiner/explore_probes.jl @@ -0,0 +1,17 @@ +using Makie +using DataFrames +using CSV +using FeatureMiner: load_fitted_probes + + +df = DataFrame() +for ckpt in readdir(joinpath(@__DIR__, "..", "..", "linear-probes"); join=true) + isdir(ckpt) || continue + isdir(joinpath(ckpt, "checkpoints")) || continue + + ckpt_probes = load_fitted_probes(ckpt) + select!(ckpt_probes, Not(:weight)) + select!(ckpt_probes, Not(:Bias)) + append!(df, ckpt_probes) +end +CSV.write("linear_probes.csv", df) diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl index 520ef207..8f95ddf4 100644 --- a/opt/FeatureMiner/src/FeatureMiner.jl +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -1,11 +1,12 @@ module FeatureMiner using ArgParse -using PythonCall: Py, pyimport +using DataFrames +using PythonCall: Py, pyimport, pyconvert, @pyconst using OnlineStats: OnlineStats, KHist, Variance, Series, fit! using OnlineStatsBase: OnlineStatsBase, OnlineStat, EqualWeight, smooth, bessel, nobs using StatsBase: StatsBase - +using LinearAlgebra: norm, dot function FeatureExtractor(ckpt_path::String) cls = pyimport("electrolyte_fm.models.sae.FeatureExtractor") @@ -22,8 +23,40 @@ function split_dataset_by_node(dataset::Py, rank::Int, size::Int) return m.split_dataset_by_node(dataset, rank, size) end +function load_linear_probes(ckpt) + torch = @pyconst(pyimport("torch")) + data = torch.load(ckpt; map_location=torch.device("cpu")) + + # Extract weights + probe_weights = Dict() + for (k, v) in data["state_dict"].items() + if pyconvert(Bool, k.startswith("_probes")) + probe_weights[pyconvert(String, k)] = pyconvert(Array, v) + end + end + + # Collate probes + probes = [] + location = pyconvert(String, data["hyper_parameters"]["probes"]["init_args"]["location"]) + for idx in range(0; length=fld(length(probe_weights), 2)) + push!(probes, (; + weight=probe_weights["_probes.$idx.weight"], + bias=probe_weights["_probes.$idx.bias"], + location=location, + layer=idx, + )) + end + + meta = (; + name_or_path=pyconvert(String, data["hyper_parameters"]["model"]["init_args"]["name_or_path"]), + ) + + return probes, meta +end + include("identification.jl") include("stats.jl") +include("lipinski.jl") end diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl new file mode 100644 index 00000000..63ee15dd --- /dev/null +++ b/opt/FeatureMiner/src/lipinski.jl @@ -0,0 +1,23 @@ +function load_fitted_probes(ckpt_dir) + probes = [] + for ckpt in readdir(ckpt_dir; join=true) + startswith(basename(ckpt), "star") || continue + m = match(r"layer-(\d+)-(\w+).*?--auroc-([\d\.]+)\.ckpt", basename(ckpt)) + layer = parse(Int, m[1]) + 1 + location = m[2] + auroc = parse(Float64, m[3]) + ckpt_probes, ckpt_meta = load_linear_probes(ckpt) + push!(probes, (; + ckpt_probes[layer]..., + model=ckpt_meta.name_or_path, + auroc, + )) + end + return DataFrame(probes) +end + +function additive_features(w::Matrix) + f_overall = w[end, :] + f_componets = sum(w[1:end-1, :]; dims=1) + return dot(f_overall, f_componets) / (norm(f_overall) * norm(f_componets)) +end diff --git a/opt/linear-probes/submit_linear_probes.py b/opt/FeatureMiner/submit_linear_probes.py similarity index 100% rename from opt/linear-probes/submit_linear_probes.py rename to opt/FeatureMiner/submit_linear_probes.py From a0975df28fbdf968e4237de11cfaefde8e0b17fd Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 10 Mar 2025 12:33:17 -0700 Subject: [PATCH 085/147] add layerwise similarity and submission script --- .gitignore | 6 ++++ opt/FeatureMiner/Project.toml | 4 +++ opt/FeatureMiner/explore_probes.jl | 39 ++++++++++++++++--------- opt/FeatureMiner/src/FeatureMiner.jl | 9 ++++-- opt/FeatureMiner/src/lipinski.jl | 43 +++++++++++++++++++++------- opt/FeatureMiner/submit_explore.sh | 18 ++++++++++++ 6 files changed, 94 insertions(+), 25 deletions(-) mode change 100644 => 100755 opt/FeatureMiner/explore_probes.jl create mode 100644 opt/FeatureMiner/submit_explore.sh diff --git a/.gitignore b/.gitignore index bb1184b9..f78e7933 100644 --- a/.gitignore +++ b/.gitignore @@ -36,9 +36,15 @@ spack.lock # Output files *.json +<<<<<<< HEAD *.jsonl *.bson *.png *.pdf *.h5 *.csv +======= +*.tar.xz +*.tar.gz +*.tar +>>>>>>> 41f0134 (add layerwise similarity and submission script) diff --git a/opt/FeatureMiner/Project.toml b/opt/FeatureMiner/Project.toml index 863dab58..da3c46fd 100644 --- a/opt/FeatureMiner/Project.toml +++ b/opt/FeatureMiner/Project.toml @@ -7,6 +7,8 @@ ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" @@ -17,5 +19,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [compat] CSV = "0.10.15" DataFrames = "1.7.0" +JLD2 = "0.5.11" +JSON = "0.21.4" LinearAlgebra = "1.11.0" Makie = "0.22.2" diff --git a/opt/FeatureMiner/explore_probes.jl b/opt/FeatureMiner/explore_probes.jl old mode 100644 new mode 100755 index a21d8afe..11c4f773 --- a/opt/FeatureMiner/explore_probes.jl +++ b/opt/FeatureMiner/explore_probes.jl @@ -1,17 +1,30 @@ -using Makie +#!/usr/bin/env -S julia --project=@script --color=yes --startup-file=no using DataFrames -using CSV -using FeatureMiner: load_fitted_probes +using JLD2: JLD2 +using FeatureMiner: FeatureMiner, load_fitted_probes, additive_features - -df = DataFrame() -for ckpt in readdir(joinpath(@__DIR__, "..", "..", "linear-probes"); join=true) - isdir(ckpt) || continue - isdir(joinpath(ckpt, "checkpoints")) || continue - - ckpt_probes = load_fitted_probes(ckpt) +function process_checkpoint(ckpt::String) + ckpt_probes, ckpt_meta = load_fitted_probes(joinpath(ckpt, "checkpoints")) + transform!(ckpt_probes, + :weight => ByRow(additive_features) => :lipinski_alignment + ) + probe_similarity = map(1:5) do idx + FeatureMiner.layerwise_similarity(ckpt_probes.weight, idx) + end + probes = collect(zip(ckpt_probes.weight, ckpt_probes.bias)) select!(ckpt_probes, Not(:weight)) - select!(ckpt_probes, Not(:Bias)) - append!(df, ckpt_probes) + select!(ckpt_probes, Not(:bias)) + ckpt_meta = (; ckpt_meta..., id=basename(ckpt)) + return (; + meta=ckpt_meta, + probes, + probe_stats=ckpt_probes, + probe_similarity, + ) +end + +function (@main)(args::Vector{String}) + ckpt = args[1] + out = process_checkpoint(ckpt) + JLD2.jldsave(joinpath(ckpt, "linear_probes.jld2"); out...) end -CSV.write("linear_probes.csv", df) diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl index 8f95ddf4..f92d9823 100644 --- a/opt/FeatureMiner/src/FeatureMiner.jl +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -2,7 +2,7 @@ module FeatureMiner using ArgParse using DataFrames -using PythonCall: Py, pyimport, pyconvert, @pyconst +using PythonCall: Py, pyimport, pyconvert, @pyconst, GIL using OnlineStats: OnlineStats, KHist, Variance, Series, fit! using OnlineStatsBase: OnlineStatsBase, OnlineStat, EqualWeight, smooth, bessel, nobs using StatsBase: StatsBase @@ -38,17 +38,22 @@ function load_linear_probes(ckpt) # Collate probes probes = [] location = pyconvert(String, data["hyper_parameters"]["probes"]["init_args"]["location"]) + hidden_size = pyconvert(Int, data["hyper_parameters"]["probes"]["init_args"]["hidden_size"]) for idx in range(0; length=fld(length(probe_weights), 2)) push!(probes, (; weight=probe_weights["_probes.$idx.weight"], bias=probe_weights["_probes.$idx.bias"], - location=location, + location, + hidden_size, layer=idx, )) end meta = (; name_or_path=pyconvert(String, data["hyper_parameters"]["model"]["init_args"]["name_or_path"]), + dataset=pyconvert(String, data["datamodule_hyper_parameters"]["init_args"]["name_or_path"]), + encoding=pyconvert(String, data["datamodule_hyper_parameters"]["init_args"]["encoding"]), + tokenizer=pyconvert(String, data["datamodule_hyper_parameters"]["init_args"]["tokenizer"]), ) return probes, meta diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 63ee15dd..4914fc28 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -1,23 +1,46 @@ function load_fitted_probes(ckpt_dir) probes = [] + local ckpt_meta for ckpt in readdir(ckpt_dir; join=true) - startswith(basename(ckpt), "star") || continue + @info realpath(ckpt) + if !startswith(basename(ckpt), "star") + _, ckpt_meta = load_linear_probes(ckpt) + continue + end m = match(r"layer-(\d+)-(\w+).*?--auroc-([\d\.]+)\.ckpt", basename(ckpt)) layer = parse(Int, m[1]) + 1 location = m[2] auroc = parse(Float64, m[3]) - ckpt_probes, ckpt_meta = load_linear_probes(ckpt) - push!(probes, (; - ckpt_probes[layer]..., - model=ckpt_meta.name_or_path, - auroc, - )) + try + ckpt_probes, ckpt_meta = load_linear_probes(ckpt) + push!(probes, (; + ckpt_probes[layer]..., + location, + auroc, + )) + catch e + e isa InterruptException && rethrow() + @error "failed to load $ckpt" e catch_backtrace() + continue + end end - return DataFrame(probes) + return DataFrame(probes), ckpt_meta end +cosine_similarity(a::Vector, b::Vector) = dot(a, b) / (norm(a) * norm(b)) + function additive_features(w::Matrix) f_overall = w[end, :] - f_componets = sum(w[1:end-1, :]; dims=1) - return dot(f_overall, f_componets) / (norm(f_overall) * norm(f_componets)) + f_componets = vec(sum(w[1:end-1, :]; dims=1)) + return cosine_similarity(f_overall, f_componets) end + +function layerwise_similarity(weights::Vector{W}, feature::Int) where {W<:Matrix{T}} where {T} + N = length(weights) + s = Matrix{T}(undef, N, N) + for I in CartesianIndices(s) + s[I] = cosine_similarity(weights[I[1]][feature, :], weights[I[2]][feature, :]) + end + return s +end + diff --git a/opt/FeatureMiner/submit_explore.sh b/opt/FeatureMiner/submit_explore.sh new file mode 100644 index 00000000..dbbd7aa2 --- /dev/null +++ b/opt/FeatureMiner/submit_explore.sh @@ -0,0 +1,18 @@ +#!/bin/bash +#SBATCH --job-name=explore +#SBATCH -p cpuq +#SBATCH -N 1 +#SBATCH -n 16 +#SBATCH -c 4 +#SBATCH --mem-per-cpu 1800M + +GIT_ROOT=$(git rev-parse --show-toplevel) +MODEL_DIR=../../linear-probes/ +source "${GIT_ROOT}/activate" +# julia --project --startup-file=no -e 'using Pkg; Pkg.instantiate()' +find $MODEL_DIR -maxdepth 1 -mindepth 1 -print0 | \ + xargs -0 -P ${SLURM_NTASKS} -i ./explore_probes.jl {} + +# Archive results +find $MODEL_DIR -name '*.jld2' -printf '%P\n' | \ + tar -caf linear_probes.tar.xz -C $MODEL_DIR --files-from=- From f41d413389443da574854b231cf7ea8383705dde Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 21 Apr 2025 10:43:54 -0700 Subject: [PATCH 086/147] add linear probe submission and analysis scripts --- electrolyte_fm/models/linear_probe.py | 39 ++------------------------- electrolyte_fm/utils/cli.py | 3 +++ opt/FeatureMiner/run_explore.sh | 12 +++++++++ opt/FeatureMiner/submit_explore.sh | 22 +++++---------- submit/linear_probe.jsonnet | 29 ++++++++++++++++++++ 5 files changed, 52 insertions(+), 53 deletions(-) create mode 100644 opt/FeatureMiner/run_explore.sh create mode 100644 submit/linear_probe.jsonnet diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 8bc745b0..6277a34c 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -182,14 +182,9 @@ def configure_optimizers(self): import smirk import json from jsonargparse import lazy_instance - from lightning.pytorch.cli import ( - LightningCLI, - LightningArgumentParser, - _InstantiatorFn, - _get_module_type, - ) from lightning.pytorch.loggers import WandbLogger from lightning.pytorch.callbacks import ModelCheckpoint + from electrolyte_fm.utils.cli import MistLightningCLI logging.basicConfig(level=logging.INFO) @@ -200,37 +195,7 @@ def mlm_from_pretrained(name_or_path: str) -> nn.Module: name_or_path, trust_remote_code=True ) - class MyLightningCLI(LightningCLI): - def add_arguments_to_parser(self, parser: LightningArgumentParser): - parser.add_argument( - "--tags", - type=list, - help="Tags for WandB logger", - default=[], - ) - parser.link_arguments("tags", "trainer.logger.init_args.tags") - - def _add_instantiators(self) -> None: - self.config_dump = json.loads( - self.parser.dump( - self.config, skip_link_targets=False, skip_none=False, format="json" - ) - ) - if "subcommand" in self.config: - self.config_dump = self.config_dump[self.config.subcommand] - - self.parser.add_instantiator( - _InstantiatorFn(cli=self, key="model"), - _get_module_type(self._model_class), - subclasses=self.subclass_mode_model, - ) - self.parser.add_instantiator( - _InstantiatorFn(cli=self, key="data"), - _get_module_type(self._datamodule_class), - subclasses=self.subclass_mode_data, - ) - - cli = MyLightningCLI( + cli = MistLightningCLI( LightningProbe, save_config_callback=None, seed_everything_default=42, diff --git a/electrolyte_fm/utils/cli.py b/electrolyte_fm/utils/cli.py index 6506c357..a8aa9894 100644 --- a/electrolyte_fm/utils/cli.py +++ b/electrolyte_fm/utils/cli.py @@ -33,6 +33,7 @@ def mlm_from_pretrained( ) +<<<<<<< HEAD def recursive_update(original, updates): """ Recursively updates a dictionary with another dictionary. @@ -51,6 +52,8 @@ def recursive_update(original, updates): return original +======= +>>>>>>> 3b1a4ae (add linear probe submission and analysis scripts) class MistLightningCLI(LightningCLI): def __init__(self, *args, **kwargs): kwargs.setdefault("parser_kwargs", {"parser_mode": "jsonnet"}) diff --git a/opt/FeatureMiner/run_explore.sh b/opt/FeatureMiner/run_explore.sh new file mode 100644 index 00000000..401fcdac --- /dev/null +++ b/opt/FeatureMiner/run_explore.sh @@ -0,0 +1,12 @@ +#!/bin/bash +#SBATCH --job-name=explore +#SBATCH --partition cpuq +#SBATCH -c 4 +#SBATCH --mem-per-cpu 4000M +#SBATCH --time 2:0:0 + +GIT_ROOT=$(git rev-parse --show-toplevel) +MODEL_DIR=../../linear-probes/ +source "${GIT_ROOT}/activate" +julia --project --startup-file=no -e 'using Pkg; Pkg.instantiate()' +./explore_probes.jl $@ diff --git a/opt/FeatureMiner/submit_explore.sh b/opt/FeatureMiner/submit_explore.sh index dbbd7aa2..b5f95f68 100644 --- a/opt/FeatureMiner/submit_explore.sh +++ b/opt/FeatureMiner/submit_explore.sh @@ -1,18 +1,8 @@ #!/bin/bash -#SBATCH --job-name=explore -#SBATCH -p cpuq -#SBATCH -N 1 -#SBATCH -n 16 -#SBATCH -c 4 -#SBATCH --mem-per-cpu 1800M +for ckpt in $(find ../../linear-probes/ -maxdepth 1 -mindepth 1 -type d); do + if [ ! -f "${ckpt}/linear_probes.jld2" ]; then + echo "Submitting ${ckpt}" + sbatch ./run_explore.sh "${ckpt}" + fi +done -GIT_ROOT=$(git rev-parse --show-toplevel) -MODEL_DIR=../../linear-probes/ -source "${GIT_ROOT}/activate" -# julia --project --startup-file=no -e 'using Pkg; Pkg.instantiate()' -find $MODEL_DIR -maxdepth 1 -mindepth 1 -print0 | \ - xargs -0 -P ${SLURM_NTASKS} -i ./explore_probes.jl {} - -# Archive results -find $MODEL_DIR -name '*.jld2' -printf '%P\n' | \ - tar -caf linear_probes.tar.xz -C $MODEL_DIR --files-from=- diff --git a/submit/linear_probe.jsonnet b/submit/linear_probe.jsonnet new file mode 100644 index 00000000..1964b5bc --- /dev/null +++ b/submit/linear_probe.jsonnet @@ -0,0 +1,29 @@ +{ + model: { + model: { + class_path: 'electrolyte_fm.models.prod_finetune.MISTFinetuned.from_pretrained', + init_args: { + name_or_path: 'models/mist-ti624ev1-moleculenet/tmQM', + }, + }, + probes: { + class_path: 'electrolyte_fm.models.linear_probe.per_layer_probe', + init_args: { + hidden_size: 512, + features: 5, + location: 'output', + n_layers: 8, + }, + }, + }, + data: { + class_path: 'electrolyte_fm.data_modules.lipinski_dataset.LipinskiDataModule', + init_args: { + name_or_path: 'tox21', + tokenizer: 'smirk', + encoding: 'smiles-kekule', + num_workers: 16, + batch_size: 16, + }, + }, +} From ecaa29ea7841d21ae315788163fffe458ad727dd Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 14 Mar 2025 09:58:23 -0400 Subject: [PATCH 087/147] wip: exploring design rules with MIST --- opt/design/MIST.jl | 63 +++++++ opt/design/plots.jl | 28 +++ opt/design/reaction.jl | 344 +++++++++++++++++++++++++++++++++++ opt/design/sat_fats.csv | 24 +++ opt/design/saturated_fats.py | 31 ++++ 5 files changed, 490 insertions(+) create mode 100644 opt/design/MIST.jl create mode 100644 opt/design/reaction.jl create mode 100644 opt/design/sat_fats.csv create mode 100644 opt/design/saturated_fats.py diff --git a/opt/design/MIST.jl b/opt/design/MIST.jl new file mode 100644 index 00000000..6d90d64c --- /dev/null +++ b/opt/design/MIST.jl @@ -0,0 +1,63 @@ +module MIST + +using PythonCall +using DataFrames +using Statistics: mean, std + +function finetuned(ckpt) + MISTFinetuned = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTFinetuned + return MISTFinetuned.from_pretrained(ckpt) +end + +function multitask(ckpt) + MISTMultiTask = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTMultiTask + return MISTMultiTask.from_pretrained(ckpt) +end + +function predict(smi::Vector{String}, model::Py) + py_out = model.predict(PyList(smi)) + out = [] + for py_y in py_out + y = Dict{String,Float64}() + for (k, v) in py_y.items() + y[pyconvert(String, k)] = pyconvert(Float64, v["value"]) + end + push!(out, y) + end + df = DataFrame(out) + insertcols!(df, 1, :smi => smi) + return df +end + +function embed(smi::Vector{String}, model::Py) + pyconvert(Matrix, model.embed(PyList(smi))) +end + +function predict_monte(smi::Vector{String}, model::Py; n=10) + model = model.train() + results = Dict() + for i in 1:n + df_sample = predict(smi, model) + sample = Dict(zip(names(df_sample), eachcol(df_sample))) + pop!(sample, "smi") + for (k, v) in sample + if haskey(results, k) + results[k] = hcat(results[k], vec(v)) + else + results[k] = v + end + end + end + + # Compute statistics + summary = Dict() + for (k, v) in pairs(results) + summary["$(k)_mean"] = vec(mean(v; dims=2)) + summary["$(k)_stderr"] = vec(std(v; dims=2)) / sqrt(n) + end + df = DataFrame(summary) + insertcols!(df, 1, :smi => smi) + return df +end + +end diff --git a/opt/design/plots.jl b/opt/design/plots.jl index d52d055d..95e7a7ab 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -1,3 +1,4 @@ +<<<<<<< HEAD using PythonCall # Load the MIST models @@ -91,3 +92,30 @@ with_theme(MISTStyle.theme()) do name_df_order=("Baseline" => df_order, "Random" => df_order_rand) ) end |> MISTStyle.savefig("permutations") +======= +using Makie +using DataFrames +using CSV + +function plot_sat_fats(file) + f = Figure(size=96 .* (4.5, 6)) + df = DataFrame(CSV.File(file)) + df.n_carbon = map(x -> count(==('C'), x), df.smi) + + ax = Axis(f[1, 1], ylabel="Diploe Moment [D]", xlabel="Number of Carbons") + scatter!(ax, df.n_carbon, df.mu) + + ax = Axis(f[2, 1], ylabel="Energy [Ha]", xlabel="Number of Carbons") + scatter!(ax, df.n_carbon, df.u0; marker=:+, label="Internal 0K") + scatter!(ax, df.n_carbon, df.u298; marker=:x, label="Internal at 298.15K") + scatter!(ax, df.n_carbon, df.h298; marker=:circle, label="Enthalpy at 298.15K") + scatter!(ax, df.n_carbon, df.g298; marker=:diamond, label="Gibbs at 298.15K") + axislegend(ax; position=:rb, nbanks=2) + + ax = Axis(f[3, 1], ylabel="HOMO-LUMO Gap", xlabel="Number of Carbons") + scatter!(ax, df.n_carbon, df.gap; marker=:x, label="Direct") + scatter!(ax, df.n_carbon, df.lumo - df.homo; marker=:+, label="Calculated") + axislegend(ax; position=:rb) + return f +end +>>>>>>> 760fdfb (wip: exploring design rules with MIST) diff --git a/opt/design/reaction.jl b/opt/design/reaction.jl new file mode 100644 index 00000000..7f8e159b --- /dev/null +++ b/opt/design/reaction.jl @@ -0,0 +1,344 @@ + +# include("MIST.jl") +# using .MIST + +using PythonCall +using DataFrames +using Makie +using CategoricalArrays +using ManifoldLearning + + +steps = [ + "CC(C)(CO)C(C(=O)NCCC(=O)O)O", + "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)O)O", + "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)N[C@@H](CS)C(=O)O)O", + "O=C(NCCS)CCNC(=O)[C@H](O)C(C)(C)COP(=O)(O)O", + "CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@@H](O1)N2C=NC3=C(N=CN=C32)N)O)O)[C@H](C(=O)NCCC(=O)NCCS)O", + "O=C(NCCS)CCNC(=O)C(O)C(C)(C)COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n2cnc1c(ncnc12)N)[C@H](O)[C@@H]3OP(=O)(O)O", +] + +function predict_pathway(steps, model, tok) + o = model.predict(PyList(steps), tok) + G = pyconvert(Vector{Float64}, o["g298"]) + f = Figure() + ax = Axis(f[1, 1]; ylabel="Gibbs Free Energy @ 298K [Ha]") + stairs!(ax, G, step=:center) + f +end + +function alkene_chain_gen(n::Int) + smi = [] + for len in range(3, n) + for pos in range(1, len - 1; step=1) + push!(smi, (; + len, + pos, + smi=("C"^pos) * "=" * "C"^(len - pos), + )) + end + end + return DataFrame(smi) +end + +function plot_alkene_chain(n, model, tok) + df = alkene_chain_gen(n) + df.type .= "normal" + df2 = copy(df) + df2.smi = map(smi -> replace(smi, "=" => "/C=C/"), df2.smi) + df2.len .+= 2 + df2.type .= "cis" + df3 = copy(df) + df3.smi = map(smi -> replace(smi, "=" => "/C=C\\"), df2.smi) + df3.len .+= 2 + df3.type .= "trans" + df = vcat(df, df2, df3) + + pred = model.predict(PyList(df.smi), tok) + df.h298 = pyconvert(Vector{Float64}, pred["h298"]) + df.mu = pyconvert(Vector{Float64}, pred["mu"]) + + f = Figure() + ax = Axis(f[1, 1]; + # limits=((0, 1), nothing), + # yscale=log10, + ylabel="H298 [kcal/mol]") + cb = Colorbar(f[1, 2], limits=(3, n + 2), colormap=:viridis, label="Length") + for len in unique(df.len) + for (type, marker) in [("normal", :x), ("cis", :cross), ("trans", :diamond)] + df_len = subset(df, :len => ByRow(==(len)), :type => ByRow(==(type))) + scatter!(ax, + range(1, nrow(df_len)), + df_len.h298; + color=len, + marker, + colorrange=cb.limits, + colormap=cb.colormap, + ) + end + end + return f, df +end + +alkyne(n::Int) = "C#" * "C"^(n-1) +alkene(n::Int) = "C=" * "C"^(n-1) +alkane(n::Int) = "C" ^ n +isoalkane(n::Int) = "C(C)" * "C" ^ (n - 2) +alcohol(n::Int) = "O" * "C" ^ n +aldehyde(n::Int) = "O=" * "C"^n +nitrile(n::Int) = "N#" * "C"^n +dinitrile(n::Int) = "N#" * "C"^n * "#N" +amine(n::Int) = "N" * "C"^ n +carboxylic_acid(n::Int) = "C(=O)" * "C"^(n-1) +halide(n::Int, element::String) = element * "C"^n +halide(element::String) = Base.Fix2(halide, element) +tetra_sub_alkene(n::Int) = "$("C"^n)C(=C($("C"^n))$("C"^n))$("C"^n)" +disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" +triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" +trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" + +function carbon_chains(n::Int) + return DataFrame(vcat( + [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], + # [(; n, type="Isoalkanes", smi=isoalkane(n)) for n in 3:n], + [(; n, type="Alcohols", smi=alcohol(n)) for n in 1:n], + [(; n, type="Nitrile", smi=nitrile(n)) for n in 1:n], + # [(; n, type="Dinitriles", smi=dinitrile(n)) for n in 1:n], + [(; n, type="Amines", smi=amine(n)) for n in 1:n], + [(; n, type="Carboxylic Acids", smi=carboxylic_acid(n)) for n in 2:n], + [(; n, type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], + # [(; n, type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], + # [(; n, type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], + [(; n, type="Tetra subsitued Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], + [(; n, type="Disubsitued Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], + )) +end + +function melt_boil_trends_monte(n::Int, model) + alkanes = [alkane(n) for n in 1:n] + isoalkanes = [isoalkane(n) for n in 3:n] + + df_alkanes = MIST.predict_monte(alkanes, model; n=50) + df_alkanes.n = 1:n + df_isoalkanes = MIST.predict_monte(isoalkanes, model; n=50) + df_isoalkanes.n = 3:n + + f = Figure() + ax = Axis(f[1, 1]; ylabel="Boiling Point [°C]") + errorbars!(ax, df_alkanes.n, df_alkanes.bp_mean, df_alkanes.bp_stderr; label="Alkanes") + errorbars!(ax, df_isoalkanes.n, df_isoalkanes.bp_mean, df_isoalkanes.bp_stderr; label="Isoalkanes") + axislegend(ax; position=:rb) + + ax = Axis(f[2, 1]; ylabel="Melting Point [°C]") + errorbars!(ax, df_alkanes.n, df_alkanes.mp_mean, df_alkanes.mp_stderr; label="Alkanes") + errorbars!(ax, df_isoalkanes.n, df_isoalkanes.mp_mean, df_isoalkanes.mp_stderr; label="Isoalkanes") + + return f +end + +function predict_class(class::Dict, model) + smi = Iterators.flatten(values(class)) + df = MIST.predict(collect(smi), model) + types = map((kv) -> repeat([kv[1]], length(kv[2])), collect(pairs(class))) + df.types = collect(Iterators.flatten(types)) + return df +end + + + +function melt_boil_trends(n::Int, model) + alkanes = + isoalkanes = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] + alchols = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] + df = DataFrame(vcat( + [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], + [(; n, type="Isoalkane", smi=isoalkane(n)) for n in 3:n], + [(; n, type="Alchols", smi=alchol(n)) for n in 1:n], + )) + return df + + + + + model = model.eval() + df_alkanes = MIST.predict(alkanes, model) + df_alkanes.n = 1:n + df_isoalkanes = MIST.predict(isoalkanes, model) + df_isoalkanes.n = 3:n + + f = Figure() + ax_bp = Axis(f[1, 1]; ylabel="Boiling Point [°C]") + ax_mp = Axis(f[2, 1]; ylabel="Melting Point [°C]") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + lines!(ax_bp, df.n, df.bp; label=type) + lines!(ax_mp, df.n, df.bp; label=type) + end + axislegend(ax_bp; position=:rb) + + return f +end + +function dipole_trends(n::Int; qm, dn, kt, mpbp, samples=20) + df = carbon_chains(n) + df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) + select!(df_qm9, Not(:smi)) + df_dn = MIST.predict_monte(df.smi, dn; n=samples) + select!(df_dn, "BF3 affinity_mean" => :donor_number_mean, "BF3 affinity_stderr" => :donor_number_stderr) + df_mpbp = MIST.predict_monte(df.smi, mpbp; n=samples) + select!(df_mpbp, :mp_mean, :mp_stderr, :bp_mean, :bp_stderr) + df_kt = MIST.predict_monte(df.smi, kt; n=samples) + select!(df_kt, Not(:smi)) + rename!(df_kt, + "alpha_mean" => :alpha_kt_mean, + "alpha_stderr" => :alpha_kt_stderr, + "beta_mean" => :beta_kt_mean, + "beta_stderr" => :beta_kt_stderr, + ) + df = hcat(df, df_qm9, df_dn, df_kt, df_mpbp) + + f = Figure() + gl_trends = GridLayout(f[1,1]) + rowgap!(gl_trends, 5) + ax = Axis(gl_trends[1, 1]; ylabel=L"$\mu$ [D]") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.mu_mean; label=type) + errorbars!(ax, df_type.n, df_type.mu_mean, df_type.mu_stderr; color=h.color) + end + + ax = Axis(gl_trends[2, 1]; ylabel=L"$G^{\degree}$ [kJ/mol]") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.h298_mean; label=type) + errorbars!(ax, df_type.n, df_type.h298_mean, df_type.h298_stderr; color=h.color) + end + + ax = Axis(gl_trends[3, 1]; ylabel=L"DN", xlabel=L"$$Number of Carbons") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.donor_number_mean; label=type) + errorbars!(ax, df_type.n, df_type.donor_number_mean, df_type.donor_number_stderr; color=h.color) + end + + ax = Axis(gl_trends[4, 1]; ylabel=L"$\alpha$ [$\alpha_0^2$]", xlabel=L"$$Number of Carbons") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.alpha_mean; label=type) + errorbars!(ax, df_type.n, df_type.alpha_mean, df_type.alpha_stderr; color=h.color) + end + + ax = Axis(gl_trends[5, 1]; ylabel=L"KT $\beta$", xlabel=L"$$Number of Carbons") + hidexdecorations!(ax) + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.beta_kt_mean; label=type) + errorbars!(ax, df_type.n, df_type.beta_kt_mean, df_type.beta_kt_stderr; color=h.color) + end + + ax = Axis(gl_trends[6, 1]; ylabel=L"Melting Point [$^{°}C$]", xlabel=L"$$Number of Carbons") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + sort!(df_type, :n) + h = lines!(ax, df_type.n, df_type.mp_mean; label=type) + errorbars!(ax, df_type.n, df_type.mp_mean, df_type.mp_stderr; color=h.color) + end + + gl_corr = GridLayout(f[1,2]) + ax = Axis(gl_corr[1, 1]; ylabel=L"DN", xlabel=L"Kamlet-Taft $\beta$") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + h = errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.donor_number_stderr, label=type) + errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.beta_kt_stderr; direction=:x, color=h.color) + end + + ax = Axis(gl_corr[2, 1]; ylabel=L"$\alpha$ [$a_0^3$]", xlabel=L"$\mu$ [D]") + for type in unique(df.type) + df_type = subset(df, :type => ByRow(==(type))) + h = errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.alpha_stderr, label=type) + errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.mu_stderr; direction=:x, color=h.color) + end + + Legend(f[2, :], ax; nbanks=2, tellheight=true) + rowgap!(gl_trends, 5) + resize_to_layout!(f) + + return f +end + +labels(x) = x.label[] + +function hard_soft_acid(; qm, kt, samples=20) + df = DataFrame(vcat( + [(; smi, type="Hard Acid") for smi in [ + "FB(F)F", "O=S(=O)=O", "OB(O)O", "O(B(OC)OC)C", + map(triboroester, 1:5)..., + ]], + [(; smi, type="Borderline Acid") for smi in [ + map(aldehyde, 1:2:10)..., + ]], + [(; smi, type="Soft Acid") for smi in [ + map(aldehyde, 1:5:30)..., + ]], + [(; smi, type="Hard Base") for smi in [ + map(amine, 1:5:30)..., + map(alcohol, 1:5:30)..., + ]], + [(; smi, type="Soft Base") for smi in [ + map(alkene, 1:5:30)..., + map(tetra_sub_alkene, 1:10)..., + map(disubstituted_alkyne, 1:3:15)..., + ]], + )) + df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) + select!(df_qm9, Not(:smi)) + df_kt = MIST.predict_monte(df.smi, kt; n=samples) + select!(df_kt, :pKa_mean, :pKa_stderr) + df = hcat(df, df_qm9, df_kt) + df.type = categorical(df.type) + color = map(type -> MISTStyle.CAT_COLORS[levelcode(type)], df.type) + + + f = Figure() + ax = Axis(f[1, 1]; xlabel=L"$\alpha$ [$\alpha^2_0$]", ylabel=L"$$pKa") + xyerrorbars!(ax, df.alpha_mean, df.pKa_mean, df.alpha_stderr, df.pKa_stderr; label=df.type, color) + + ax = Axis(f[2, 1]; xlabel=L"$$LUMO [Har]", ylabel=L"$$pKa") + h = xyerrorbars!(ax, df.lumo_mean, df.pKa_mean, df.lumo_stderr, df.pKa_stderr; label=df.type, color) + + ax = Axis(f[1, 2]) + hidexdecorations!(ax) + hideydecorations!(ax) + emb = MIST.embed(df.smi, qm) + m = fit(TSNE, emb'; p=30, maxoutdim=2) + r = predict(m) + scatter!(ax, r[1, :], r[2, :]; color) + + types = map(enumerate(levels(df.type))) do (i, label) + PolyElement( + color=MISTStyle.CAT_COLORS[i], + label=label + ) + end + + Legend(f[2, 2], types, labels.(types)) + + return f + + +end + +function xyerrorbars!(ax, x, y, errorx, errory; label=nothing, kwargs...) + h = errorbars!(ax, x, y, errory; label, kwargs...) + errorbars!(ax, x, y, errorx; direction=:x, color=h.color) + return h +end diff --git a/opt/design/sat_fats.csv b/opt/design/sat_fats.csv new file mode 100644 index 00000000..14eddae8 --- /dev/null +++ b/opt/design/sat_fats.csv @@ -0,0 +1,24 @@ +smi,mu,alpha,homo,lumo,gap,r2,zpve,u0,u298,h298,g298,cv +O=C(O)CCC,4.769931,71.028,-0.26474136,-0.07180658,0.19366309,1116.128,0.067295,-455.21826,-454.96674,-454.93094,-455.2984,25.358398 +O=C(O)CCCC,4.971653,73.43377,-0.2669614,-0.073890984,0.19384971,1196.3062,0.0732354,-449.44058,-449.31204,-449.23938,-449.54324,25.694302 +O=C(O)CCCCC,5.2371836,75.44909,-0.26635858,-0.07276623,0.19430482,1281.5747,0.079891905,-443.68478,-443.6336,-443.54514,-443.796,26.355312 +O=C(O)CCCCCC,5.5895414,76.27199,-0.26350012,-0.06660598,0.19753295,1344.3341,0.08678694,-439.01654,-439.0433,-438.93744,-439.1521,27.013805 +O=C(O)CCCCCCC,5.649559,76.24556,-0.26225054,-0.060204238,0.20261885,1392.9761,0.09317882,-435.1404,-435.2054,-435.09824,-435.26633,27.856176 +O=C(O)CCCCCCCC,5.2911034,76.77113,-0.2604756,-0.05375296,0.20719764,1487.1074,0.10051406,-430.50854,-430.59537,-430.4845,-430.6233,28.716946 +O=C(O)CCCCCCCCC,5.344325,76.20799,-0.25762165,-0.04362812,0.21440068,1522.6865,0.10760877,-426.41803,-426.52133,-426.4131,-426.52682,29.321398 +O=C(O)CCCCCCCCCC,5.166279,76.36943,-0.2543188,-0.033491634,0.22118333,1555.0681,0.11445395,-422.9598,-423.07117,-422.96436,-423.07388,29.88532 +O=C(O)CCCCCCCCCCC,4.919032,76.11539,-0.2517563,-0.024300717,0.22780345,1547.0695,0.1217105,-419.9719,-420.09467,-419.9814,-420.09427,30.262314 +O=C(O)CCCCCCCCCCCC,4.680345,76.35677,-0.25301033,-0.014943976,0.23838933,1569.8961,0.1287832,-416.5352,-416.67746,-416.56747,-416.66455,30.87184 +O=C(O)CCCCCCCCCCCCC,4.3812184,76.159874,-0.25552556,-0.0074483696,0.24833615,1553.9812,0.13486302,-412.0727,-412.20636,-412.10773,-412.1919,31.143152 +O=C(O)CCCCCCCCCCCCCC,4.0451183,76.726524,-0.25839686,-0.0001775017,0.2584055,1606.5541,0.14232084,-407.17923,-407.2892,-407.22147,-407.24896,31.79684 +O=C(O)CCCCCCCCCCCCCCC,3.9297128,77.519806,-0.2595612,0.0036514928,0.2633514,1667.9335,0.14853513,-405.0917,-405.16525,-405.13257,-405.1035,32.368652 +O=C(O)CCCCCCCCCCCCCCCC,3.804593,79.58518,-0.25875157,0.007440024,0.26625136,1797.5773,0.15650843,-405.41147,-405.47327,-405.46625,-405.358,33.53738 +O=C(O)CCCCCCCCCCCCCCCCC,3.6632895,81.52695,-0.25755906,0.01096091,0.26849225,1999.3899,0.16350242,-403.53384,-403.5899,-403.5829,-403.4258,34.75062 +O=C(O)CCCCCCCCCCCCCCCCCC,3.4002395,83.31501,-0.25374886,0.014807183,0.26848534,2125.2043,0.17007431,-401.65665,-401.6828,-401.6706,-401.51105,35.88625 +O=C(O)CCCCCCCCCCCCCCCCCCC,3.1372354,84.147804,-0.25196138,0.011332356,0.26324683,2245.4724,0.17593305,-400.616,-400.64716,-400.5887,-400.49283,36.77691 +O=C(O)CCCCCCCCCCCCCCCCCCCC,2.9869483,85.180084,-0.24901211,0.0060275695,0.255123,2341.8481,0.18185005,-400.038,-400.08688,-399.95813,-399.9605,37.780804 +O=C(O)CCCCCCCCCCCCCCCCCCCCC,2.8969917,85.900406,-0.24720106,0.0011447165,0.24848019,2374.979,0.18786205,-398.368,-398.41678,-398.2661,-398.28995,38.320934 +O=C(O)CCCCCCCCCCCCCCCCCCCCCC,2.8028266,86.91344,-0.2461817,-0.0023705475,0.24399008,2413.5757,0.1948694,-395.99924,-396.05588,-395.89774,-395.90454,38.907734 +O=C(O)CCCCCCCCCCCCCCCCCCCCCCC,2.7076352,87.758156,-0.24554278,-0.0029556807,0.24283132,2438.045,0.2007028,-393.5421,-393.59503,-393.43665,-393.429,39.42135 +O=C(O)CCCCCCCCCCCCCCCCCCCCCCCC,2.6062937,88.49571,-0.24500887,-0.0012770239,0.2441615,2500.8901,0.2061011,-392.06732,-392.14346,-391.94748,-391.9792,39.92587 +O=C(O)CCCCCCCCCCCCCCCCCCCCCCCCC,2.4823546,89.42716,-0.24477701,0.0033710813,0.248929,2618.2927,0.21180302,-390.44525,-390.58524,-390.31842,-390.42496,40.447372 diff --git a/opt/design/saturated_fats.py b/opt/design/saturated_fats.py new file mode 100644 index 00000000..88b477c1 --- /dev/null +++ b/opt/design/saturated_fats.py @@ -0,0 +1,31 @@ +from argparse import ArgumentParser +import torch +import pandas as pd +from electrolyte_fm.models.prod_finetune import MISTFinetuned +from smirk import SmirkTokenizerFast + +if torch.cuda.is_available(): + device = "cuda" +elif torch.backends.mps.is_available(): + device = "mps" +else: + device = "cpu" + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--model", type=str, default=".") + parser.add_argument("--output", default="sat_fats.csv") + args = parser.parse_args() + + model = MISTFinetuned.from_pretrained(args.model).eval().to(device) + tok = SmirkTokenizerFast() + + smi: list[str] = [] + for n in range(3, 40): + # do nitrile + smi.append("N#" + "C" * n) + + pred = model.predict(smi, tok) + + df = pd.DataFrame(pred, index=smi) + df.to_csv(args.output, index_label="smi") From 3284d973a487d9a9861bdc40429b4bc65c3e3d76 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 14 Mar 2025 18:23:24 -0400 Subject: [PATCH 088/147] rework: refactor design rule plots --- .github/workflows/CI.yaml | 9 - .gitignore | 3 - electrolyte_fm/models/__init__.py | 3 - electrolyte_fm/models/prod_finetune.py | 26 -- electrolyte_fm/utils/ckpt.py | 23 -- electrolyte_fm/utils/cli.py | 3 - opt/design/MIST.jl | 93 +++++-- opt/design/figures.jl | 54 ++++ opt/design/plots.jl | 30 +-- opt/design/reaction.jl | 344 ------------------------- opt/package/__main__.py | 5 - pyproject.toml | 22 -- 12 files changed, 129 insertions(+), 486 deletions(-) create mode 100644 opt/design/figures.jl delete mode 100644 opt/design/reaction.jl diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index e81c7776..673b716c 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -11,23 +11,14 @@ jobs: uses: astral-sh/setup-uv@v5 with: python-version: 3.11.8 -<<<<<<< HEAD enable-cache: true - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 -======= - cache: poetry ->>>>>>> 5b359dc (wip: rework sae to inject coders into the model) - name: Install deploy key for BattModels/smirk uses: webfactory/ssh-agent@dc588b651fe13675774614f8e6a936a468676387 #v0.9.0 with: ssh-private-key: ${{ secrets.SMIRK_PRIVATE_KEY }} -<<<<<<< HEAD - name: install project run: uv sync --all-extras --dev - name: pytest run: uv run pytest -======= - - run: poetry install - - run: source activate && pytest -m "not gpu" ->>>>>>> 5b359dc (wip: rework sae to inject coders into the model) diff --git a/.gitignore b/.gitignore index f78e7933..54a8d647 100644 --- a/.gitignore +++ b/.gitignore @@ -36,15 +36,12 @@ spack.lock # Output files *.json -<<<<<<< HEAD *.jsonl *.bson *.png *.pdf *.h5 *.csv -======= *.tar.xz *.tar.gz *.tar ->>>>>>> 41f0134 (add layerwise similarity and submission script) diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index 2a641e6d..c4ae3702 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -3,8 +3,5 @@ from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer -<<<<<<< HEAD from .prod_finetune import MISTFinetuned, MISTMultiTask -======= from .sae import LightningSAE ->>>>>>> 5b359dc (wip: rework sae to inject coders into the model) diff --git a/electrolyte_fm/models/prod_finetune.py b/electrolyte_fm/models/prod_finetune.py index f4677510..21b122eb 100644 --- a/electrolyte_fm/models/prod_finetune.py +++ b/electrolyte_fm/models/prod_finetune.py @@ -66,13 +66,8 @@ def __init__(self, encoder, task_network, transform, tokenizer, channels=None): self.tokenizer = tokenizer self.channels = channels -<<<<<<< HEAD def forward(self, input_ids, attention_mask=None): hs = self.encoder(input_ids, attention_mask=attention_mask).last_hidden_state -======= - def forward(self, **kwargs): - hs = self.encoder(**kwargs).last_hidden_state ->>>>>>> 2c78388 (initial run over linear probes) y = self.task_network(hs) return self.transform.forward(y) @@ -95,7 +90,6 @@ def save_pretrained(self, save_directory, safe_serialization=False): Path(save_directory, "config.json").write_text(json.dumps(config, indent=4)) save_model(self, save_directory, safe_serialization) -<<<<<<< HEAD def embed(self, smi: list[str]): batch = self.tokenizer(smi) collate_fn = DataCollatorWithPadding(self.tokenizer) @@ -120,26 +114,14 @@ def predict(self, smi: list[str]): with torch.inference_mode(): out = self(**batch).cpu() -======= - def predict(self, smi: list[str], tokenizer): - batch = tokenizer(smi) - collate_fn = DataCollatorWithPadding(tokenizer) - batch = collate_fn(batch).to(self.encoder.device) - out = self(**batch) ->>>>>>> 2c78388 (initial run over linear probes) if self.channels is None: return out return annotate_prediction(out, self.channels) @classmethod -<<<<<<< HEAD - def from_pretrained(cls, save_directory: str): - config = json.loads(Path(save_directory, "config.json").read_text()) -======= def from_pretrained(cls, name_or_path: str) -> "MISTFinetuned": config = json.loads(Path(name_or_path, "config.json").read_text()) ->>>>>>> 2c78388 (initial run over linear probes) encoder_config = AutoConfig.for_model( config["encoder"]["model_type"] ).from_dict(config["encoder"]) @@ -149,17 +131,9 @@ def from_pretrained(cls, name_or_path: str) -> "MISTFinetuned": config["transform"]["class"], config["transform"]["num_outputs"] ) -<<<<<<< HEAD - tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) - channels = list(maybe_get_annotated_channels(config["channels"])) - - model = cls(encoder, task_network, transform, tokenizer, channels) - load_model(model, save_directory) -======= # Instantiate model model = cls(encoder, task_network, transform, config["channels"]) load_model(model, name_or_path) ->>>>>>> 2c78388 (initial run over linear probes) return model diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index faa72fbd..ded96d91 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -151,15 +151,9 @@ def load( config_path = config_path or checkpoint_dir.parent.parent.joinpath( "model_hparams.json" ) -<<<<<<< HEAD assert ( checkpoint_dir.exists() ), f"Missing deepspeed checkpoint directory: {checkpoint_dir}" -======= - assert checkpoint_dir.exists(), ( - f"Missing deepspeed checkpoint directory: {checkpoint_dir}" - ) ->>>>>>> 2c78388 (initial run over linear probes) assert config_path.is_file(), f"Missing model config file {config_path}" model = SaveConfigWithCkpts.instantiate(config_path, max_position_embeddings) @@ -178,14 +172,11 @@ def load( model.load_state_dict(state["state_dict"], strict=True, assign=True) return model -<<<<<<< HEAD -======= if checkpoint_dir.is_file(): state = torch.load(checkpoint_dir) model.load_state_dict(state["state_dict"], strict=True, assign=True) return model ->>>>>>> 2c78388 (initial run over linear probes) # Load model weights from the checkpoint try: from deepspeed.utils.zero_to_fp32 import ( @@ -193,12 +184,6 @@ def load( ) state = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) -<<<<<<< HEAD - if max_position_embeddings is not None: - state = adjust_state_position_embeddings(state, max_position_embeddings) - -======= ->>>>>>> 2c78388 (initial run over linear probes) model.load_state_dict(state, strict=False, assign=True) except FileNotFoundError: logging.error( @@ -206,16 +191,8 @@ def load( checkpoint_dir, ) file = Path(checkpoint_dir, "checkpoint", "mp_rank_00_model_states.pt") -<<<<<<< HEAD - state = torch.load(file, map_location=map_location) - logging.info("loaded %s", file) - if max_position_embeddings is not None: - state = adjust_state_position_embeddings(state, max_position_embeddings) - -======= state = torch.load(file) logging.info("loaded %s", file) ->>>>>>> 2c78388 (initial run over linear probes) model.load_state_dict(state["module"], strict=True, assign=True) return model diff --git a/electrolyte_fm/utils/cli.py b/electrolyte_fm/utils/cli.py index a8aa9894..6506c357 100644 --- a/electrolyte_fm/utils/cli.py +++ b/electrolyte_fm/utils/cli.py @@ -33,7 +33,6 @@ def mlm_from_pretrained( ) -<<<<<<< HEAD def recursive_update(original, updates): """ Recursively updates a dictionary with another dictionary. @@ -52,8 +51,6 @@ def recursive_update(original, updates): return original -======= ->>>>>>> 3b1a4ae (add linear probe submission and analysis scripts) class MistLightningCLI(LightningCLI): def __init__(self, *args, **kwargs): kwargs.setdefault("parser_kwargs", {"parser_mode": "jsonnet"}) diff --git a/opt/design/MIST.jl b/opt/design/MIST.jl index 6d90d64c..688d1b0a 100644 --- a/opt/design/MIST.jl +++ b/opt/design/MIST.jl @@ -2,27 +2,46 @@ module MIST using PythonCall using DataFrames -using Statistics: mean, std +# using Makie +using Statistics: Statistics, mean, std +using StatsBase: StatsBase, stderror, mean_and_std function finetuned(ckpt) - MISTFinetuned = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTFinetuned - return MISTFinetuned.from_pretrained(ckpt) + MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned + model = MISTFinetuned.from_pretrained(ckpt) + return model end function multitask(ckpt) - MISTMultiTask = @pyconst(pyimport("electrolyte_fm.models.prod_finetune")).MISTMultiTask + MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask return MISTMultiTask.from_pretrained(ckpt) end +struct UQReal{T} + mean::T + std::T + n::Int +end + +Statistics.mean(x::UQReal) = x.mean +Statistics.std(x::Real) = x.std +StatsBase.stderror(x) = x.std / sqrt(x.n) +function Base.show(io::IO, x::UQReal) + μ = mean(x) + se = stderror(x) + if get(io, :compact, false)::Bool + μ = round(μ; sigdigits=5) + se = round(se; sigdigits=5) + end + print(io, "$μ ± $se") +end + function predict(smi::Vector{String}, model::Py) + model = model.to("mps") py_out = model.predict(PyList(smi)) - out = [] - for py_y in py_out - y = Dict{String,Float64}() - for (k, v) in py_y.items() - y[pyconvert(String, k)] = pyconvert(Float64, v["value"]) - end - push!(out, y) + out = Dict{String,Vector{Float64}}() + for (k, v) in py_out.items() + out[pyconvert(String, k)] = pyconvert(valtype(out), v["value"]) end df = DataFrame(out) insertcols!(df, 1, :smi => smi) @@ -34,15 +53,17 @@ function embed(smi::Vector{String}, model::Py) end function predict_monte(smi::Vector{String}, model::Py; n=10) + training = model.training model = model.train() results = Dict() - for i in 1:n - df_sample = predict(smi, model) - sample = Dict(zip(names(df_sample), eachcol(df_sample))) - pop!(sample, "smi") - for (k, v) in sample + py_smi = PyList(smi) + for _ in 1:n + sample = model.predict(py_smi) + for (k, v) in sample.items() + k = pyconvert(String, k) + v = pyconvert(Vector{Float64}, v["value"]) if haskey(results, k) - results[k] = hcat(results[k], vec(v)) + results[k] = hcat(results[k], v) else results[k] = v end @@ -52,12 +73,46 @@ function predict_monte(smi::Vector{String}, model::Py; n=10) # Compute statistics summary = Dict() for (k, v) in pairs(results) - summary["$(k)_mean"] = vec(mean(v; dims=2)) - summary["$(k)_stderr"] = vec(std(v; dims=2)) / sqrt(n) + mu_std = map(mean_and_std, eachrow(v)) + summary[k] = map(x -> UQReal(x..., n), mu_std) end df = DataFrame(summary) insertcols!(df, 1, :smi => smi) + model.train(training) return df end +# @recipe(ErrorLines, x, y, error_y) do scene +# Attributes() +# end +# Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), stderror.(y)) +# +# function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Real},AbstractVector{<:Real}}}) +# lines!(plt, plt.x, plt.y, Makie.shared_attributes(plt, Lines)) +# if !isnothing(plt.error_y) +# errorbars!(plt, plt.x, plt.y, plt.error_y, Makie.shared_attributes(plt, Makie.Errorbars)) +# end +# return plt +# end +# +# @recipe(ErrorCross, x, y, error_x, error_y) do scene +# Attributes() +# end +# +# Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) +# +# function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) +# attrs = Makie.shared_attributes(plt, Errorbars) +# h = errorbars!(plt, plt.x, plt.y, plt.error_y; direction=:y, attrs...) +# errorbars!(plt, plt.x, plt.y, plt.error_x; +# color=h.color, +# colorscale=h.colorscale, +# colormap=h.colormap, +# colorrange=h.colorrange, +# direction=:x, +# attrs... +# ) +# return plt +# end + end diff --git a/opt/design/figures.jl b/opt/design/figures.jl new file mode 100644 index 00000000..ba3d2e5e --- /dev/null +++ b/opt/design/figures.jl @@ -0,0 +1,54 @@ +function hydrocarbon_trends(df) + f = Figure() + + # Trends with Size + df = rename(df, "BF3 affinity" => :dn) + gl_trends = GridLayout(f[1, 1]) + rowgap!(gl_trends, 5) + axes = [ + :mu => L"$\mu$ [D]", + :h298 => L"$G\degree$\n[kJ/mol]", + :alpha => L"$\alpha$\n[$\alpha_0^2$]", + :mp => L"$$Melt\n[$\degree C$]", + :dn => L"$$DN\n[kJ/mol]", + :pKa_kt => L"pKa", + :alpha_kt => L"KT $\alpha$", + :beta_kt => L"KT $\beta$", + ] + axes = map(enumerate(axes)) do (idx, (col, ylabel)) + is_last = idx == length(axes) + col => Axis(gl_trends[idx, 1]; + xlabel=L"$$Number of Carbons", ylabel, + limits=((1, 25), nothing), + xlabelvisible=is_last, + xticksvisible=is_last, + xticklabelsvisible=is_last, + ) + end |> Dict + foreach(groupby(df, :type)) do gdf + for (col, ax) in pairs(axes) + errorlines!(ax, gdf.n_carbon, gdf[:, col]; label=first(gdf.type)) + end + end + + + # Design Rules + gl_dr = GridLayout(f[1, 2]) + ax = Axis(gl_dr[1, 1]; + xlabel="HOMO [eV]", ylabel="pKa", + ) + cb = Colorbar(gl_dr[1, 2]; + label="DN", + colorrange=(0, 30), + ) + errorcross!(ax, df.homo .* 27.2114, df.pKa_kt; + # color=mean.(df.dn), + color=df.n_carbon, + cb_attrs(cb)..., + ) + + rowgap!(gl_trends, 5) + resize_to_layout!(f) + + return f +end diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 95e7a7ab..c28ec5de 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -1,4 +1,3 @@ -<<<<<<< HEAD using PythonCall # Load the MIST models @@ -91,31 +90,4 @@ with_theme(MISTStyle.theme()) do "Baseline" => df_perm_ref, "Random" => df_perm_rand; name_df_order=("Baseline" => df_order, "Random" => df_order_rand) ) -end |> MISTStyle.savefig("permutations") -======= -using Makie -using DataFrames -using CSV - -function plot_sat_fats(file) - f = Figure(size=96 .* (4.5, 6)) - df = DataFrame(CSV.File(file)) - df.n_carbon = map(x -> count(==('C'), x), df.smi) - - ax = Axis(f[1, 1], ylabel="Diploe Moment [D]", xlabel="Number of Carbons") - scatter!(ax, df.n_carbon, df.mu) - - ax = Axis(f[2, 1], ylabel="Energy [Ha]", xlabel="Number of Carbons") - scatter!(ax, df.n_carbon, df.u0; marker=:+, label="Internal 0K") - scatter!(ax, df.n_carbon, df.u298; marker=:x, label="Internal at 298.15K") - scatter!(ax, df.n_carbon, df.h298; marker=:circle, label="Enthalpy at 298.15K") - scatter!(ax, df.n_carbon, df.g298; marker=:diamond, label="Gibbs at 298.15K") - axislegend(ax; position=:rb, nbanks=2) - - ax = Axis(f[3, 1], ylabel="HOMO-LUMO Gap", xlabel="Number of Carbons") - scatter!(ax, df.n_carbon, df.gap; marker=:x, label="Direct") - scatter!(ax, df.n_carbon, df.lumo - df.homo; marker=:+, label="Calculated") - axislegend(ax; position=:rb) - return f -end ->>>>>>> 760fdfb (wip: exploring design rules with MIST) +end |> MISTStyle.savefig("permutations") \ No newline at end of file diff --git a/opt/design/reaction.jl b/opt/design/reaction.jl deleted file mode 100644 index 7f8e159b..00000000 --- a/opt/design/reaction.jl +++ /dev/null @@ -1,344 +0,0 @@ - -# include("MIST.jl") -# using .MIST - -using PythonCall -using DataFrames -using Makie -using CategoricalArrays -using ManifoldLearning - - -steps = [ - "CC(C)(CO)C(C(=O)NCCC(=O)O)O", - "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)O)O", - "CC(C)(COP(=O)(O)O)[C@H](C(=O)NCCC(=O)N[C@@H](CS)C(=O)O)O", - "O=C(NCCS)CCNC(=O)[C@H](O)C(C)(C)COP(=O)(O)O", - "CC(C)(COP(=O)(O)OP(=O)(O)OC[C@@H]1[C@H]([C@H]([C@@H](O1)N2C=NC3=C(N=CN=C32)N)O)O)[C@H](C(=O)NCCC(=O)NCCS)O", - "O=C(NCCS)CCNC(=O)C(O)C(C)(C)COP(=O)(O)OP(=O)(O)OC[C@H]3O[C@@H](n2cnc1c(ncnc12)N)[C@H](O)[C@@H]3OP(=O)(O)O", -] - -function predict_pathway(steps, model, tok) - o = model.predict(PyList(steps), tok) - G = pyconvert(Vector{Float64}, o["g298"]) - f = Figure() - ax = Axis(f[1, 1]; ylabel="Gibbs Free Energy @ 298K [Ha]") - stairs!(ax, G, step=:center) - f -end - -function alkene_chain_gen(n::Int) - smi = [] - for len in range(3, n) - for pos in range(1, len - 1; step=1) - push!(smi, (; - len, - pos, - smi=("C"^pos) * "=" * "C"^(len - pos), - )) - end - end - return DataFrame(smi) -end - -function plot_alkene_chain(n, model, tok) - df = alkene_chain_gen(n) - df.type .= "normal" - df2 = copy(df) - df2.smi = map(smi -> replace(smi, "=" => "/C=C/"), df2.smi) - df2.len .+= 2 - df2.type .= "cis" - df3 = copy(df) - df3.smi = map(smi -> replace(smi, "=" => "/C=C\\"), df2.smi) - df3.len .+= 2 - df3.type .= "trans" - df = vcat(df, df2, df3) - - pred = model.predict(PyList(df.smi), tok) - df.h298 = pyconvert(Vector{Float64}, pred["h298"]) - df.mu = pyconvert(Vector{Float64}, pred["mu"]) - - f = Figure() - ax = Axis(f[1, 1]; - # limits=((0, 1), nothing), - # yscale=log10, - ylabel="H298 [kcal/mol]") - cb = Colorbar(f[1, 2], limits=(3, n + 2), colormap=:viridis, label="Length") - for len in unique(df.len) - for (type, marker) in [("normal", :x), ("cis", :cross), ("trans", :diamond)] - df_len = subset(df, :len => ByRow(==(len)), :type => ByRow(==(type))) - scatter!(ax, - range(1, nrow(df_len)), - df_len.h298; - color=len, - marker, - colorrange=cb.limits, - colormap=cb.colormap, - ) - end - end - return f, df -end - -alkyne(n::Int) = "C#" * "C"^(n-1) -alkene(n::Int) = "C=" * "C"^(n-1) -alkane(n::Int) = "C" ^ n -isoalkane(n::Int) = "C(C)" * "C" ^ (n - 2) -alcohol(n::Int) = "O" * "C" ^ n -aldehyde(n::Int) = "O=" * "C"^n -nitrile(n::Int) = "N#" * "C"^n -dinitrile(n::Int) = "N#" * "C"^n * "#N" -amine(n::Int) = "N" * "C"^ n -carboxylic_acid(n::Int) = "C(=O)" * "C"^(n-1) -halide(n::Int, element::String) = element * "C"^n -halide(element::String) = Base.Fix2(halide, element) -tetra_sub_alkene(n::Int) = "$("C"^n)C(=C($("C"^n))$("C"^n))$("C"^n)" -disubstituted_alkyne(n::Int) = "$("C"^n)#$("C"^n)" -triboroester(n::Int) = "O(B(O$("C"^n))O$("C"^n))$("C"^n)" -trialkylborane(n::Int) = "B($("C"^n))($("C"^n))$("C"^n)" - -function carbon_chains(n::Int) - return DataFrame(vcat( - [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], - # [(; n, type="Isoalkanes", smi=isoalkane(n)) for n in 3:n], - [(; n, type="Alcohols", smi=alcohol(n)) for n in 1:n], - [(; n, type="Nitrile", smi=nitrile(n)) for n in 1:n], - # [(; n, type="Dinitriles", smi=dinitrile(n)) for n in 1:n], - [(; n, type="Amines", smi=amine(n)) for n in 1:n], - [(; n, type="Carboxylic Acids", smi=carboxylic_acid(n)) for n in 2:n], - [(; n, type="Fluoroalkanes", smi=halide(n, "F")) for n in 2:n], - # [(; n, type="Bromoalkanes", smi=halide(n, "Br")) for n in 2:n], - # [(; n, type="Chloroalkanes", smi=halide(n, "Cl")) for n in 2:n], - [(; n, type="Tetra subsitued Alkenes", smi=tetra_sub_alkene(n)) for n in 1:cld(n, 4)], - [(; n, type="Disubsitued Alkynes", smi=disubstituted_alkyne(n)) for n in 1:cld(n, 2)], - )) -end - -function melt_boil_trends_monte(n::Int, model) - alkanes = [alkane(n) for n in 1:n] - isoalkanes = [isoalkane(n) for n in 3:n] - - df_alkanes = MIST.predict_monte(alkanes, model; n=50) - df_alkanes.n = 1:n - df_isoalkanes = MIST.predict_monte(isoalkanes, model; n=50) - df_isoalkanes.n = 3:n - - f = Figure() - ax = Axis(f[1, 1]; ylabel="Boiling Point [°C]") - errorbars!(ax, df_alkanes.n, df_alkanes.bp_mean, df_alkanes.bp_stderr; label="Alkanes") - errorbars!(ax, df_isoalkanes.n, df_isoalkanes.bp_mean, df_isoalkanes.bp_stderr; label="Isoalkanes") - axislegend(ax; position=:rb) - - ax = Axis(f[2, 1]; ylabel="Melting Point [°C]") - errorbars!(ax, df_alkanes.n, df_alkanes.mp_mean, df_alkanes.mp_stderr; label="Alkanes") - errorbars!(ax, df_isoalkanes.n, df_isoalkanes.mp_mean, df_isoalkanes.mp_stderr; label="Isoalkanes") - - return f -end - -function predict_class(class::Dict, model) - smi = Iterators.flatten(values(class)) - df = MIST.predict(collect(smi), model) - types = map((kv) -> repeat([kv[1]], length(kv[2])), collect(pairs(class))) - df.types = collect(Iterators.flatten(types)) - return df -end - - - -function melt_boil_trends(n::Int, model) - alkanes = - isoalkanes = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] - alchols = [(; n, type="isoalkane", smi=isoalkane(n)) for n in 3:n] - df = DataFrame(vcat( - [(; n, type="Alkanes", smi=alkane(n)) for n in 1:n], - [(; n, type="Isoalkane", smi=isoalkane(n)) for n in 3:n], - [(; n, type="Alchols", smi=alchol(n)) for n in 1:n], - )) - return df - - - - - model = model.eval() - df_alkanes = MIST.predict(alkanes, model) - df_alkanes.n = 1:n - df_isoalkanes = MIST.predict(isoalkanes, model) - df_isoalkanes.n = 3:n - - f = Figure() - ax_bp = Axis(f[1, 1]; ylabel="Boiling Point [°C]") - ax_mp = Axis(f[2, 1]; ylabel="Melting Point [°C]") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - lines!(ax_bp, df.n, df.bp; label=type) - lines!(ax_mp, df.n, df.bp; label=type) - end - axislegend(ax_bp; position=:rb) - - return f -end - -function dipole_trends(n::Int; qm, dn, kt, mpbp, samples=20) - df = carbon_chains(n) - df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) - select!(df_qm9, Not(:smi)) - df_dn = MIST.predict_monte(df.smi, dn; n=samples) - select!(df_dn, "BF3 affinity_mean" => :donor_number_mean, "BF3 affinity_stderr" => :donor_number_stderr) - df_mpbp = MIST.predict_monte(df.smi, mpbp; n=samples) - select!(df_mpbp, :mp_mean, :mp_stderr, :bp_mean, :bp_stderr) - df_kt = MIST.predict_monte(df.smi, kt; n=samples) - select!(df_kt, Not(:smi)) - rename!(df_kt, - "alpha_mean" => :alpha_kt_mean, - "alpha_stderr" => :alpha_kt_stderr, - "beta_mean" => :beta_kt_mean, - "beta_stderr" => :beta_kt_stderr, - ) - df = hcat(df, df_qm9, df_dn, df_kt, df_mpbp) - - f = Figure() - gl_trends = GridLayout(f[1,1]) - rowgap!(gl_trends, 5) - ax = Axis(gl_trends[1, 1]; ylabel=L"$\mu$ [D]") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.mu_mean; label=type) - errorbars!(ax, df_type.n, df_type.mu_mean, df_type.mu_stderr; color=h.color) - end - - ax = Axis(gl_trends[2, 1]; ylabel=L"$G^{\degree}$ [kJ/mol]") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.h298_mean; label=type) - errorbars!(ax, df_type.n, df_type.h298_mean, df_type.h298_stderr; color=h.color) - end - - ax = Axis(gl_trends[3, 1]; ylabel=L"DN", xlabel=L"$$Number of Carbons") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.donor_number_mean; label=type) - errorbars!(ax, df_type.n, df_type.donor_number_mean, df_type.donor_number_stderr; color=h.color) - end - - ax = Axis(gl_trends[4, 1]; ylabel=L"$\alpha$ [$\alpha_0^2$]", xlabel=L"$$Number of Carbons") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.alpha_mean; label=type) - errorbars!(ax, df_type.n, df_type.alpha_mean, df_type.alpha_stderr; color=h.color) - end - - ax = Axis(gl_trends[5, 1]; ylabel=L"KT $\beta$", xlabel=L"$$Number of Carbons") - hidexdecorations!(ax) - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.beta_kt_mean; label=type) - errorbars!(ax, df_type.n, df_type.beta_kt_mean, df_type.beta_kt_stderr; color=h.color) - end - - ax = Axis(gl_trends[6, 1]; ylabel=L"Melting Point [$^{°}C$]", xlabel=L"$$Number of Carbons") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - sort!(df_type, :n) - h = lines!(ax, df_type.n, df_type.mp_mean; label=type) - errorbars!(ax, df_type.n, df_type.mp_mean, df_type.mp_stderr; color=h.color) - end - - gl_corr = GridLayout(f[1,2]) - ax = Axis(gl_corr[1, 1]; ylabel=L"DN", xlabel=L"Kamlet-Taft $\beta$") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - h = errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.donor_number_stderr, label=type) - errorbars!(ax, df_type.beta_kt_mean, df_type.donor_number_mean, df_type.beta_kt_stderr; direction=:x, color=h.color) - end - - ax = Axis(gl_corr[2, 1]; ylabel=L"$\alpha$ [$a_0^3$]", xlabel=L"$\mu$ [D]") - for type in unique(df.type) - df_type = subset(df, :type => ByRow(==(type))) - h = errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.alpha_stderr, label=type) - errorbars!(ax, df_type.mu_mean, df_type.alpha_mean, df_type.mu_stderr; direction=:x, color=h.color) - end - - Legend(f[2, :], ax; nbanks=2, tellheight=true) - rowgap!(gl_trends, 5) - resize_to_layout!(f) - - return f -end - -labels(x) = x.label[] - -function hard_soft_acid(; qm, kt, samples=20) - df = DataFrame(vcat( - [(; smi, type="Hard Acid") for smi in [ - "FB(F)F", "O=S(=O)=O", "OB(O)O", "O(B(OC)OC)C", - map(triboroester, 1:5)..., - ]], - [(; smi, type="Borderline Acid") for smi in [ - map(aldehyde, 1:2:10)..., - ]], - [(; smi, type="Soft Acid") for smi in [ - map(aldehyde, 1:5:30)..., - ]], - [(; smi, type="Hard Base") for smi in [ - map(amine, 1:5:30)..., - map(alcohol, 1:5:30)..., - ]], - [(; smi, type="Soft Base") for smi in [ - map(alkene, 1:5:30)..., - map(tetra_sub_alkene, 1:10)..., - map(disubstituted_alkyne, 1:3:15)..., - ]], - )) - df_qm9 = MIST.predict_monte(df.smi, qm; n=samples) - select!(df_qm9, Not(:smi)) - df_kt = MIST.predict_monte(df.smi, kt; n=samples) - select!(df_kt, :pKa_mean, :pKa_stderr) - df = hcat(df, df_qm9, df_kt) - df.type = categorical(df.type) - color = map(type -> MISTStyle.CAT_COLORS[levelcode(type)], df.type) - - - f = Figure() - ax = Axis(f[1, 1]; xlabel=L"$\alpha$ [$\alpha^2_0$]", ylabel=L"$$pKa") - xyerrorbars!(ax, df.alpha_mean, df.pKa_mean, df.alpha_stderr, df.pKa_stderr; label=df.type, color) - - ax = Axis(f[2, 1]; xlabel=L"$$LUMO [Har]", ylabel=L"$$pKa") - h = xyerrorbars!(ax, df.lumo_mean, df.pKa_mean, df.lumo_stderr, df.pKa_stderr; label=df.type, color) - - ax = Axis(f[1, 2]) - hidexdecorations!(ax) - hideydecorations!(ax) - emb = MIST.embed(df.smi, qm) - m = fit(TSNE, emb'; p=30, maxoutdim=2) - r = predict(m) - scatter!(ax, r[1, :], r[2, :]; color) - - types = map(enumerate(levels(df.type))) do (i, label) - PolyElement( - color=MISTStyle.CAT_COLORS[i], - label=label - ) - end - - Legend(f[2, 2], types, labels.(types)) - - return f - - -end - -function xyerrorbars!(ax, x, y, errorx, errory; label=nothing, kwargs...) - h = errorbars!(ax, x, y, errory; label, kwargs...) - errorbars!(ax, x, y, errorx; direction=:x, color=h.color) - return h -end diff --git a/opt/package/__main__.py b/opt/package/__main__.py index c5f62ff7..02cbd79d 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -52,14 +52,9 @@ def export_finetuned(ckpt: Path): from electrolyte_fm.models import MISTFinetuned model = SaveConfigWithCkpts.load(ckpt) -<<<<<<< HEAD model_config = json.loads(ckpt.parent.parent.joinpath("config.json").read_text()) tokenizer_name = model_config["data"]["init_args"]["tokenizer"] tokenizer = load_tokenizer(tokenizer_name) -======= - config_file = Path(ckpt).parent.parent.joinpath("config.json") - model_config = json.loads(config_file.read_text()) ->>>>>>> 2c78388 (initial run over linear probes) return MISTFinetuned( model.encoder, model.task_network, diff --git a/pyproject.toml b/pyproject.toml index ae2487e3..bf7b13fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,6 @@ dependencies = [ "pytorch-lightning~=2.5" ] -<<<<<<< HEAD [optional-dependencies] tokenizers = [ "rdkit", @@ -34,27 +33,6 @@ tokenizers = [ "apetokenizer @ git+https://github.com/mikemayuare/apetokenizer.git@8b070d6278503bcf8ad2eedc1756ac34f4bbd981", "pyspark>=3.5.0", ] -======= -[tool.poetry.dependencies] -python = ">=3.10,<3.14" -torch = {version = "~2.3"} -torchmetrics = "^1.4.0" -lightning = "~2.3" -jsonargparse = {extras = ["signatures", "jsonnet"], version = "^4.27"} -transformers = "^4.40" -deepspeed = { git = "https://github.com/microsoft/DeepSpeed.git", rev = "5f631abc2f930ecece38fae05dc9bd3923c555dd"} -wandb = "^0.16" -typer = "^0.9" -datasets = "^2.20" -pyspark = "^3.5.0" -rich = "^13" -pydoe2 = "^1.3" -jsonnet = "^0.20.0" -scikit-learn= "^1.5.1" -selfies= "^2.1.0" -rdkit = "^2024.3.5" -smirk = {git = "git@github.com:BattModels/smirk.git", rev = "8061286a219420571d606d98083dd900aaa21d02" } ->>>>>>> e3ec515 (add more sae types) [dependency-groups] dev = [ From 7fab304cc8ae4e6c543ace31f343b0bc3f6afb0c Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 27 Mar 2025 10:57:49 -0400 Subject: [PATCH 089/147] refactor: Move MistStyle into a proper package --- opt/MISTStyle/src/MISTStyle.jl | 56 ++++++++++++++++++++++++++++++++++ opt/design/src/DesignRules.jl | 12 ++++++++ 2 files changed, 68 insertions(+) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 23ba6600..430de6ae 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -87,6 +87,7 @@ const CAT_COLORS = cgrad( 10 ) +<<<<<<< HEAD UM_COLORS = (; blue=colorant"#00274C", maize=colorant"#FFCB05", @@ -98,6 +99,8 @@ UM_COLORS = (; ) +======= +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) const CONTINUOUS_COLORS = :lipari function theme() @@ -112,7 +115,11 @@ function theme() size=(246, 152), figure_padding=(2, 2, 2, 2), colormap=:lipari, +<<<<<<< HEAD linewidth=1pt, +======= + linewidth=0.5, +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) CairoMakie=(; pt_per_unit=2, px_per_unit=300 / inch @@ -122,17 +129,30 @@ function theme() color=CAT_COLORS, linestyle=[:solid, :dot, :dashdot], ), +<<<<<<< HEAD Axis=(; spinewidth=0.5, xlabelsize=6pt, ylabelsize=6pt, yticklabelsize=5pt, xticklabelsize=5pt, +======= + Lines=(; + cycle=Cycle([:color, :linestyle], covary=true), + ), + Axis=(; + spinewidth=0.5, + xlabelsize=8pt, + ylabelsize=8pt, + yticklabelsize=6pt, + xticklabelsize=6pt, +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) ylabelpadding=1pt, xlabelpadding=1pt, yticklabelpad=2pt, xticklabelpad=2pt, yticksize=2pt, +<<<<<<< HEAD ytickwidth=0.5pt, yminortickwidth=0.25pt, yminorticksize=1pt, @@ -140,10 +160,20 @@ function theme() xticksize=2pt, xminortickwidth=0.25pt, xminorticksize=1pt, +======= + ytickwidth=0.5, + yminortickwidth=0.5, + yminorticksize=2, + xtickwidth=0.5, + xticksize=2pt, + xminortickwidth=0.5, + xminorticksize=2, +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) xgridwidth=0.5, ygridwidth=0.5, xminorgridwidth=0.5, yminorgridwidth=0.5, +<<<<<<< HEAD titlegap=2pt, ), Legend=(; @@ -153,11 +183,22 @@ function theme() patchlabelgap=2pt, rowgap=0.5pt, colgap=1pt, +======= + ), + Legend=(; + titlegap=0, + labelsize=8pt, + patchsize=(8, 8), + patchlabelgap=3pt, + rowgap=1pt, + colgap=3pt, +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) groupgap=4pt, famevisible=true, framewidth=0.5, tellheight=false, tellwidth=false, +<<<<<<< HEAD padding=(1pt, 1pt, 1pt, 1pt), margin=(1pt, 1pt, 1pt, 1pt), ), @@ -172,6 +213,18 @@ function theme() labelpadding=1pt, ticklabelpad=0pt, size=6pt, +======= + padding=(2pt, 2pt, 2pt, 2pt), + ), + Colorbar=(; + spinewidth=0.5, + tickwidth=0.5, + ticksize=2, + labelsize=8pt, + ticklabelsize=6pt, + labelpadding=0pt, + ticklabelpad=0pt, +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) ), Scatter=(; markersize=5pt, @@ -183,12 +236,15 @@ function theme() ErrorCross=(; markersize=5pt, whiskerwidth=3, +<<<<<<< HEAD ), Quadrant=(; color=UM_COLORS.maize, linestyle=:solid, linecolor=UM_COLORS.blue, alpha=0.2, +======= +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) ) ) end diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index a9102e58..2fdc5912 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -1,11 +1,16 @@ module DesignRules +<<<<<<< HEAD using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport +======= +using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) using DataFrames using Makie using MISTStyle using MISTStyle: label, sublabel! using Statistics: Statistics, mean, std +<<<<<<< HEAD using StatsBase: StatsBase, stderror, mean_and_std, mean, range using CategoricalArrays: levelcode, categorical using JSON: JSON @@ -14,6 +19,13 @@ const HARTREE_TO_EV = 27.211_386_245_981 const JOULES_TO_CALORIES = inv(4.184) sigmoid(x) = 1 / (1 + exp(-x)) +======= +using StatsBase: StatsBase, stderror, mean_and_std, mean +using MISTStyle: MISTStyle, label, cb_attrs, ErrorCross +using CategoricalArrays: levelcode, categorical + +const HARTREE_TO_EV = 27.211_386_245_981 +>>>>>>> db6a992 (refactor: Move MistStyle into a proper package) include("uq.jl") include("inference.jl") From 4106659fae5316a0d2acbc9643bdff977ebdbf4b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 27 Mar 2025 19:27:36 -0400 Subject: [PATCH 090/147] feat: better plots for design trends --- opt/MISTStyle/src/MISTStyle.jl | 3 +- opt/design/figures.jl | 54 ---------------------------------- opt/design/src/DesignRules.jl | 9 +++++- 3 files changed, 10 insertions(+), 56 deletions(-) delete mode 100644 opt/design/figures.jl diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 430de6ae..9b45f537 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -188,7 +188,7 @@ function theme() Legend=(; titlegap=0, labelsize=8pt, - patchsize=(8, 8), + patchsize=(8pt, 8pt), patchlabelgap=3pt, rowgap=1pt, colgap=3pt, @@ -215,6 +215,7 @@ function theme() size=6pt, ======= padding=(2pt, 2pt, 2pt, 2pt), + margin=(2pt, 2pt, 2pt, 2pt), ), Colorbar=(; spinewidth=0.5, diff --git a/opt/design/figures.jl b/opt/design/figures.jl deleted file mode 100644 index ba3d2e5e..00000000 --- a/opt/design/figures.jl +++ /dev/null @@ -1,54 +0,0 @@ -function hydrocarbon_trends(df) - f = Figure() - - # Trends with Size - df = rename(df, "BF3 affinity" => :dn) - gl_trends = GridLayout(f[1, 1]) - rowgap!(gl_trends, 5) - axes = [ - :mu => L"$\mu$ [D]", - :h298 => L"$G\degree$\n[kJ/mol]", - :alpha => L"$\alpha$\n[$\alpha_0^2$]", - :mp => L"$$Melt\n[$\degree C$]", - :dn => L"$$DN\n[kJ/mol]", - :pKa_kt => L"pKa", - :alpha_kt => L"KT $\alpha$", - :beta_kt => L"KT $\beta$", - ] - axes = map(enumerate(axes)) do (idx, (col, ylabel)) - is_last = idx == length(axes) - col => Axis(gl_trends[idx, 1]; - xlabel=L"$$Number of Carbons", ylabel, - limits=((1, 25), nothing), - xlabelvisible=is_last, - xticksvisible=is_last, - xticklabelsvisible=is_last, - ) - end |> Dict - foreach(groupby(df, :type)) do gdf - for (col, ax) in pairs(axes) - errorlines!(ax, gdf.n_carbon, gdf[:, col]; label=first(gdf.type)) - end - end - - - # Design Rules - gl_dr = GridLayout(f[1, 2]) - ax = Axis(gl_dr[1, 1]; - xlabel="HOMO [eV]", ylabel="pKa", - ) - cb = Colorbar(gl_dr[1, 2]; - label="DN", - colorrange=(0, 30), - ) - errorcross!(ax, df.homo .* 27.2114, df.pKa_kt; - # color=mean.(df.dn), - color=df.n_carbon, - cb_attrs(cb)..., - ) - - rowgap!(gl_trends, 5) - resize_to_layout!(f) - - return f -end diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index 2fdc5912..4aabc1e8 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -1,5 +1,6 @@ module DesignRules +<<<<<<< HEAD <<<<<<< HEAD using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport ======= @@ -9,6 +10,13 @@ using DataFrames using Makie using MISTStyle using MISTStyle: label, sublabel! +======= +using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport +using DataFrames +using Makie +using MISTStyle +using MISTStyle: label +>>>>>>> 133d7c3 (feat: better plots for design trends) using Statistics: Statistics, mean, std <<<<<<< HEAD using StatsBase: StatsBase, stderror, mean_and_std, mean, range @@ -21,7 +29,6 @@ const JOULES_TO_CALORIES = inv(4.184) sigmoid(x) = 1 / (1 + exp(-x)) ======= using StatsBase: StatsBase, stderror, mean_and_std, mean -using MISTStyle: MISTStyle, label, cb_attrs, ErrorCross using CategoricalArrays: levelcode, categorical const HARTREE_TO_EV = 27.211_386_245_981 From 3c3bf6f037a89b2101a1ca140009fd4d7856d79c Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 15:46:59 -0400 Subject: [PATCH 091/147] add sat/unsat fats figure --- opt/MISTStyle/Project.toml | 2 ++ opt/design/sat_fats.csv | 24 ------------------------ opt/design/saturated_fats.py | 31 ------------------------------- 3 files changed, 2 insertions(+), 55 deletions(-) delete mode 100644 opt/design/sat_fats.csv delete mode 100644 opt/design/saturated_fats.py diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index 1179db05..36924d30 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -4,8 +4,10 @@ uuid = "0a43f4a4-3db7-4939-b247-26273f54b603" [deps] CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" [compat] Makie = "0.24" CategoricalArrays = "0.10.8" +GLMakie = "0.11.3" diff --git a/opt/design/sat_fats.csv b/opt/design/sat_fats.csv deleted file mode 100644 index 14eddae8..00000000 --- a/opt/design/sat_fats.csv +++ /dev/null @@ -1,24 +0,0 @@ -smi,mu,alpha,homo,lumo,gap,r2,zpve,u0,u298,h298,g298,cv -O=C(O)CCC,4.769931,71.028,-0.26474136,-0.07180658,0.19366309,1116.128,0.067295,-455.21826,-454.96674,-454.93094,-455.2984,25.358398 -O=C(O)CCCC,4.971653,73.43377,-0.2669614,-0.073890984,0.19384971,1196.3062,0.0732354,-449.44058,-449.31204,-449.23938,-449.54324,25.694302 -O=C(O)CCCCC,5.2371836,75.44909,-0.26635858,-0.07276623,0.19430482,1281.5747,0.079891905,-443.68478,-443.6336,-443.54514,-443.796,26.355312 -O=C(O)CCCCCC,5.5895414,76.27199,-0.26350012,-0.06660598,0.19753295,1344.3341,0.08678694,-439.01654,-439.0433,-438.93744,-439.1521,27.013805 -O=C(O)CCCCCCC,5.649559,76.24556,-0.26225054,-0.060204238,0.20261885,1392.9761,0.09317882,-435.1404,-435.2054,-435.09824,-435.26633,27.856176 -O=C(O)CCCCCCCC,5.2911034,76.77113,-0.2604756,-0.05375296,0.20719764,1487.1074,0.10051406,-430.50854,-430.59537,-430.4845,-430.6233,28.716946 -O=C(O)CCCCCCCCC,5.344325,76.20799,-0.25762165,-0.04362812,0.21440068,1522.6865,0.10760877,-426.41803,-426.52133,-426.4131,-426.52682,29.321398 -O=C(O)CCCCCCCCCC,5.166279,76.36943,-0.2543188,-0.033491634,0.22118333,1555.0681,0.11445395,-422.9598,-423.07117,-422.96436,-423.07388,29.88532 -O=C(O)CCCCCCCCCCC,4.919032,76.11539,-0.2517563,-0.024300717,0.22780345,1547.0695,0.1217105,-419.9719,-420.09467,-419.9814,-420.09427,30.262314 -O=C(O)CCCCCCCCCCCC,4.680345,76.35677,-0.25301033,-0.014943976,0.23838933,1569.8961,0.1287832,-416.5352,-416.67746,-416.56747,-416.66455,30.87184 -O=C(O)CCCCCCCCCCCCC,4.3812184,76.159874,-0.25552556,-0.0074483696,0.24833615,1553.9812,0.13486302,-412.0727,-412.20636,-412.10773,-412.1919,31.143152 -O=C(O)CCCCCCCCCCCCCC,4.0451183,76.726524,-0.25839686,-0.0001775017,0.2584055,1606.5541,0.14232084,-407.17923,-407.2892,-407.22147,-407.24896,31.79684 -O=C(O)CCCCCCCCCCCCCCC,3.9297128,77.519806,-0.2595612,0.0036514928,0.2633514,1667.9335,0.14853513,-405.0917,-405.16525,-405.13257,-405.1035,32.368652 -O=C(O)CCCCCCCCCCCCCCCC,3.804593,79.58518,-0.25875157,0.007440024,0.26625136,1797.5773,0.15650843,-405.41147,-405.47327,-405.46625,-405.358,33.53738 -O=C(O)CCCCCCCCCCCCCCCCC,3.6632895,81.52695,-0.25755906,0.01096091,0.26849225,1999.3899,0.16350242,-403.53384,-403.5899,-403.5829,-403.4258,34.75062 -O=C(O)CCCCCCCCCCCCCCCCCC,3.4002395,83.31501,-0.25374886,0.014807183,0.26848534,2125.2043,0.17007431,-401.65665,-401.6828,-401.6706,-401.51105,35.88625 -O=C(O)CCCCCCCCCCCCCCCCCCC,3.1372354,84.147804,-0.25196138,0.011332356,0.26324683,2245.4724,0.17593305,-400.616,-400.64716,-400.5887,-400.49283,36.77691 -O=C(O)CCCCCCCCCCCCCCCCCCCC,2.9869483,85.180084,-0.24901211,0.0060275695,0.255123,2341.8481,0.18185005,-400.038,-400.08688,-399.95813,-399.9605,37.780804 -O=C(O)CCCCCCCCCCCCCCCCCCCCC,2.8969917,85.900406,-0.24720106,0.0011447165,0.24848019,2374.979,0.18786205,-398.368,-398.41678,-398.2661,-398.28995,38.320934 -O=C(O)CCCCCCCCCCCCCCCCCCCCCC,2.8028266,86.91344,-0.2461817,-0.0023705475,0.24399008,2413.5757,0.1948694,-395.99924,-396.05588,-395.89774,-395.90454,38.907734 -O=C(O)CCCCCCCCCCCCCCCCCCCCCCC,2.7076352,87.758156,-0.24554278,-0.0029556807,0.24283132,2438.045,0.2007028,-393.5421,-393.59503,-393.43665,-393.429,39.42135 -O=C(O)CCCCCCCCCCCCCCCCCCCCCCCC,2.6062937,88.49571,-0.24500887,-0.0012770239,0.2441615,2500.8901,0.2061011,-392.06732,-392.14346,-391.94748,-391.9792,39.92587 -O=C(O)CCCCCCCCCCCCCCCCCCCCCCCCC,2.4823546,89.42716,-0.24477701,0.0033710813,0.248929,2618.2927,0.21180302,-390.44525,-390.58524,-390.31842,-390.42496,40.447372 diff --git a/opt/design/saturated_fats.py b/opt/design/saturated_fats.py deleted file mode 100644 index 88b477c1..00000000 --- a/opt/design/saturated_fats.py +++ /dev/null @@ -1,31 +0,0 @@ -from argparse import ArgumentParser -import torch -import pandas as pd -from electrolyte_fm.models.prod_finetune import MISTFinetuned -from smirk import SmirkTokenizerFast - -if torch.cuda.is_available(): - device = "cuda" -elif torch.backends.mps.is_available(): - device = "mps" -else: - device = "cpu" - -if __name__ == "__main__": - parser = ArgumentParser() - parser.add_argument("--model", type=str, default=".") - parser.add_argument("--output", default="sat_fats.csv") - args = parser.parse_args() - - model = MISTFinetuned.from_pretrained(args.model).eval().to(device) - tok = SmirkTokenizerFast() - - smi: list[str] = [] - for n in range(3, 40): - # do nitrile - smi.append("N#" + "C" * n) - - pred = model.predict(smi, tok) - - df = pd.DataFrame(pred, index=smi) - df.to_csv(args.output, index_label="smi") From bcc49305bceb77b90c428b4cef6fc859875cf255 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 15:48:47 -0400 Subject: [PATCH 092/147] rm unused code --- opt/design/MIST.jl | 118 --------------------------------------------- 1 file changed, 118 deletions(-) delete mode 100644 opt/design/MIST.jl diff --git a/opt/design/MIST.jl b/opt/design/MIST.jl deleted file mode 100644 index 688d1b0a..00000000 --- a/opt/design/MIST.jl +++ /dev/null @@ -1,118 +0,0 @@ -module MIST - -using PythonCall -using DataFrames -# using Makie -using Statistics: Statistics, mean, std -using StatsBase: StatsBase, stderror, mean_and_std - -function finetuned(ckpt) - MISTFinetuned = pyimport("electrolyte_fm.models.prod_finetune").MISTFinetuned - model = MISTFinetuned.from_pretrained(ckpt) - return model -end - -function multitask(ckpt) - MISTMultiTask = pyimport("electrolyte_fm.models.prod_finetune").MISTMultiTask - return MISTMultiTask.from_pretrained(ckpt) -end - -struct UQReal{T} - mean::T - std::T - n::Int -end - -Statistics.mean(x::UQReal) = x.mean -Statistics.std(x::Real) = x.std -StatsBase.stderror(x) = x.std / sqrt(x.n) -function Base.show(io::IO, x::UQReal) - μ = mean(x) - se = stderror(x) - if get(io, :compact, false)::Bool - μ = round(μ; sigdigits=5) - se = round(se; sigdigits=5) - end - print(io, "$μ ± $se") -end - -function predict(smi::Vector{String}, model::Py) - model = model.to("mps") - py_out = model.predict(PyList(smi)) - out = Dict{String,Vector{Float64}}() - for (k, v) in py_out.items() - out[pyconvert(String, k)] = pyconvert(valtype(out), v["value"]) - end - df = DataFrame(out) - insertcols!(df, 1, :smi => smi) - return df -end - -function embed(smi::Vector{String}, model::Py) - pyconvert(Matrix, model.embed(PyList(smi))) -end - -function predict_monte(smi::Vector{String}, model::Py; n=10) - training = model.training - model = model.train() - results = Dict() - py_smi = PyList(smi) - for _ in 1:n - sample = model.predict(py_smi) - for (k, v) in sample.items() - k = pyconvert(String, k) - v = pyconvert(Vector{Float64}, v["value"]) - if haskey(results, k) - results[k] = hcat(results[k], v) - else - results[k] = v - end - end - end - - # Compute statistics - summary = Dict() - for (k, v) in pairs(results) - mu_std = map(mean_and_std, eachrow(v)) - summary[k] = map(x -> UQReal(x..., n), mu_std) - end - df = DataFrame(summary) - insertcols!(df, 1, :smi => smi) - model.train(training) - return df -end - -# @recipe(ErrorLines, x, y, error_y) do scene -# Attributes() -# end -# Makie.convert_arguments(::Type{<:ErrorLines}, x::Any, y::AbstractVector{<:UQReal}) = (x, mean.(y), stderror.(y)) -# -# function Makie.plot!(plt::ErrorLines{<:Tuple{AbstractVector,AbstractVector{<:Real},AbstractVector{<:Real}}}) -# lines!(plt, plt.x, plt.y, Makie.shared_attributes(plt, Lines)) -# if !isnothing(plt.error_y) -# errorbars!(plt, plt.x, plt.y, plt.error_y, Makie.shared_attributes(plt, Makie.Errorbars)) -# end -# return plt -# end -# -# @recipe(ErrorCross, x, y, error_x, error_y) do scene -# Attributes() -# end -# -# Makie.convert_arguments(::Type{<:ErrorCross}, x::AbstractVector{<:UQReal}, y::AbstractVector{<:UQReal}) = (mean.(x), mean.(y), stderror.(x), stderror.(y)) -# -# function Makie.plot!(plt::ErrorCross{<:NTuple{4,AbstractVector}}) -# attrs = Makie.shared_attributes(plt, Errorbars) -# h = errorbars!(plt, plt.x, plt.y, plt.error_y; direction=:y, attrs...) -# errorbars!(plt, plt.x, plt.y, plt.error_x; -# color=h.color, -# colorscale=h.colorscale, -# colormap=h.colormap, -# colorrange=h.colorrange, -# direction=:x, -# attrs... -# ) -# return plt -# end - -end From 5d6ff257f94e7c708ac5b141803ee1599660eb8c Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 1 Apr 2025 17:29:01 -0400 Subject: [PATCH 093/147] tweak plots --- opt/MISTStyle/src/MISTStyle.jl | 59 +--------------------------------- opt/design/src/DesignRules.jl | 21 +----------- opt/design/src/trends.jl | 1 + 3 files changed, 3 insertions(+), 78 deletions(-) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 9b45f537..e9df1c57 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -87,7 +87,6 @@ const CAT_COLORS = cgrad( 10 ) -<<<<<<< HEAD UM_COLORS = (; blue=colorant"#00274C", maize=colorant"#FFCB05", @@ -99,8 +98,6 @@ UM_COLORS = (; ) -======= ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) const CONTINUOUS_COLORS = :lipari function theme() @@ -115,11 +112,7 @@ function theme() size=(246, 152), figure_padding=(2, 2, 2, 2), colormap=:lipari, -<<<<<<< HEAD linewidth=1pt, -======= - linewidth=0.5, ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) CairoMakie=(; pt_per_unit=2, px_per_unit=300 / inch @@ -129,30 +122,17 @@ function theme() color=CAT_COLORS, linestyle=[:solid, :dot, :dashdot], ), -<<<<<<< HEAD Axis=(; spinewidth=0.5, xlabelsize=6pt, ylabelsize=6pt, yticklabelsize=5pt, xticklabelsize=5pt, -======= - Lines=(; - cycle=Cycle([:color, :linestyle], covary=true), - ), - Axis=(; - spinewidth=0.5, - xlabelsize=8pt, - ylabelsize=8pt, - yticklabelsize=6pt, - xticklabelsize=6pt, ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) ylabelpadding=1pt, xlabelpadding=1pt, yticklabelpad=2pt, xticklabelpad=2pt, yticksize=2pt, -<<<<<<< HEAD ytickwidth=0.5pt, yminortickwidth=0.25pt, yminorticksize=1pt, @@ -160,20 +140,10 @@ function theme() xticksize=2pt, xminortickwidth=0.25pt, xminorticksize=1pt, -======= - ytickwidth=0.5, - yminortickwidth=0.5, - yminorticksize=2, - xtickwidth=0.5, - xticksize=2pt, - xminortickwidth=0.5, - xminorticksize=2, ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) xgridwidth=0.5, ygridwidth=0.5, xminorgridwidth=0.5, yminorgridwidth=0.5, -<<<<<<< HEAD titlegap=2pt, ), Legend=(; @@ -183,22 +153,11 @@ function theme() patchlabelgap=2pt, rowgap=0.5pt, colgap=1pt, -======= - ), - Legend=(; - titlegap=0, - labelsize=8pt, - patchsize=(8pt, 8pt), - patchlabelgap=3pt, - rowgap=1pt, - colgap=3pt, ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) groupgap=4pt, famevisible=true, framewidth=0.5, tellheight=false, tellwidth=false, -<<<<<<< HEAD padding=(1pt, 1pt, 1pt, 1pt), margin=(1pt, 1pt, 1pt, 1pt), ), @@ -213,19 +172,6 @@ function theme() labelpadding=1pt, ticklabelpad=0pt, size=6pt, -======= - padding=(2pt, 2pt, 2pt, 2pt), - margin=(2pt, 2pt, 2pt, 2pt), - ), - Colorbar=(; - spinewidth=0.5, - tickwidth=0.5, - ticksize=2, - labelsize=8pt, - ticklabelsize=6pt, - labelpadding=0pt, - ticklabelpad=0pt, ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) ), Scatter=(; markersize=5pt, @@ -237,16 +183,13 @@ function theme() ErrorCross=(; markersize=5pt, whiskerwidth=3, -<<<<<<< HEAD ), Quadrant=(; color=UM_COLORS.maize, linestyle=:solid, linecolor=UM_COLORS.blue, alpha=0.2, -======= ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) ) ) end -end +end \ No newline at end of file diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index 4aabc1e8..5100e9cf 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -1,24 +1,11 @@ module DesignRules -<<<<<<< HEAD -<<<<<<< HEAD using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport -======= -using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) using DataFrames using Makie using MISTStyle using MISTStyle: label, sublabel! -======= -using PythonCall: PythonCall, Py, PyList, pyconvert, @pyconst, pyimport -using DataFrames -using Makie -using MISTStyle -using MISTStyle: label ->>>>>>> 133d7c3 (feat: better plots for design trends) using Statistics: Statistics, mean, std -<<<<<<< HEAD using StatsBase: StatsBase, stderror, mean_and_std, mean, range using CategoricalArrays: levelcode, categorical using JSON: JSON @@ -27,12 +14,6 @@ const HARTREE_TO_EV = 27.211_386_245_981 const JOULES_TO_CALORIES = inv(4.184) sigmoid(x) = 1 / (1 + exp(-x)) -======= -using StatsBase: StatsBase, stderror, mean_and_std, mean -using CategoricalArrays: levelcode, categorical - -const HARTREE_TO_EV = 27.211_386_245_981 ->>>>>>> db6a992 (refactor: Move MistStyle into a proper package) include("uq.jl") include("inference.jl") @@ -42,4 +23,4 @@ include("hydrocarbons.jl") include("plot_utils.jl") include("trends.jl") -end +end \ No newline at end of file diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 6be562c8..dc1585d8 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -444,6 +444,7 @@ function figure_fatty_acids(df; omega=3, alpha=0.8, qm_model="") :bp => L"$$Boil\n[$\degree C$ ]", :fp => L"$$Flash\n[$\degree C$ ]", ] + x_sat = 0.3 axes = map(enumerate(axes)) do (idx, (col, ylabel)) is_last = idx == length(axes) col => Axis(gl_trends[idx, 1]; From c91bb046750c95765d6250bd2e4df530f023b5e9 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 10 Apr 2025 15:47:26 -0400 Subject: [PATCH 094/147] update plots per victor's comments --- opt/design/src/trends.jl | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index dc1585d8..2575f945 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -59,7 +59,7 @@ function hydrocarbon_trends(df; qm_model="") foreach(groupby(df, :type)) do gdf for (col, ax) in pairs(axes) y = convert_units(gdf[:, col], col) - lines!(ax, gdf.n_carbon, mean.(y); + errorlines!(ax, gdf.n_carbon, y; label=string(first(gdf.type)), color=levelcode.(gdf.type), colormap, @@ -444,7 +444,6 @@ function figure_fatty_acids(df; omega=3, alpha=0.8, qm_model="") :bp => L"$$Boil\n[$\degree C$ ]", :fp => L"$$Flash\n[$\degree C$ ]", ] - x_sat = 0.3 axes = map(enumerate(axes)) do (idx, (col, ylabel)) is_last = idx == length(axes) col => Axis(gl_trends[idx, 1]; From b88f3da6dce2b05ac676d5fce17591f9cf4cde30 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 22 Apr 2025 14:08:29 -0700 Subject: [PATCH 095/147] feat: construct dataset for lipinski linear probes --- .../data_modules/lipinski_dataset.py | 256 ++++++++++++++++-- electrolyte_fm/data_modules/molnet_dataset.py | 2 + electrolyte_fm/data_modules/utils.py | 12 +- electrolyte_fm/models/linear_probe.py | 2 + opt/FeatureMiner/src/lipinski.jl | 6 +- 5 files changed, 246 insertions(+), 32 deletions(-) diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index f007d518..115c3e6e 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -1,15 +1,19 @@ +import asyncio from pathlib import Path import torch -from datasets import load_dataset -from rdkit.Chem import Lipinski, MolFromSmiles +import pandas as pd +import numpy as np +from sklearn.utils import resample +from sklearn.model_selection import StratifiedShuffleSplit +from datasets import DatasetDict, Dataset, load_dataset, concatenate_datasets +from rdkit.Chem import Lipinski, MolFromSmiles, MolToInchiKey from rdkit.Chem.Crippen import MolLogP from rdkit.Chem.Descriptors import ExactMolWt -from .molnet_dataset import _URLS +from .molnet_dataset import MolNetDataModule from .property_prediction_dataset import PropertyPredictionDataModule from .utils import AbstractDataset, MolEncoding, filter_invalid_smi -from .molnet_dataset import train_val_test_split class LipinskiDataModule(PropertyPredictionDataModule): @@ -32,26 +36,24 @@ def __init__(self, name_or_path: str, **kwargs): super().__init__(**kwargs) assert self.encoding != MolEncoding.SELFIES - def _get_dataset(self) -> AbstractDataset: + def _get_dataset(cls, name_or_path) -> AbstractDataset: # Load the dataset ds: AbstractDataset = load_dataset( "csv", - name=self.name, - data_files=[self.name_or_path], + name=name, + data_files=[name_or_path], split="train", keep_in_memory=False, save_infos=False, ) # type: ignore - - ds = ds.select_columns(self.smi_column) - ds = filter_invalid_smi(ds, self.smi_column) - + ds = ds.select_columns(smi_column) + ds = filter_invalid_smi(self.smi_column) ds = ds.map( lipinki_rule_of_five, batched=False, fn_kwargs={"smi_column": self.smi_column}, ) - return train_val_test_split(ds) + return ds def collate_fn(self, batch): output = super().collate_fn(batch) @@ -63,7 +65,221 @@ def collate_fn(self, batch): return output -def lipinki_rule_of_five(x: dict, smi_column: str) -> dict: +def get_molnet_dataset(name: str): + ds = MolNetDataModule(name=name, split="all") + return ds._get_dataset().rename_column(ds.smi_column, "smi").select_columns("smi") + + +def build_probe_dataset(): + sem = asyncio.Semaphore(64) + + ds = [ + get_molnet_dataset(name) + for name in [ + "hiv", + "toxcast", + "tox21", + "clintox", + "bbbp", + "qm9", + "qm8", + "freesolv", + "lipo", + "muv", + "sider", + "esol", + ] + ] + + # # Pull molecules from Zinc + # zinc_path = "/lustre/fs0/shared/zinc_v1" + # ds_zinc = load_dataset( + # "text", + # name="zinc_train", + # data_files=str(Path(zinc_path).joinpath("data/train/*.txt")), + # split="train", + # keep_in_memory=False, + # save_infos=False, + # ) + # ds_zinc = ds_zinc.take(1_000_000) + # + # def zinc_smi(text: str): + # return {"smi": text.split(" ")[0]} + # + # ds_zinc = ds_zinc.map(zinc_smi, batched=False, input_columns="text") + # ds.append(ds_zinc) + + ds = concatenate_datasets(ds) + ds = filter_invalid_smi(ds, "smi") + + async def async_lipinski_rule_of_five(x): + async with sem: + return lipinki_rule_of_five(x, "smi") + + ds = ds.map(async_lipinski_rule_of_five, batched=False) + + # Label with inchi key for de-duplication + async def inchi_key(smi: str): + async with sem: + return {"inchi_key": MolToInchiKey(MolFromSmiles(smi))} + + ds = ds.map(inchi_key, batched=False, input_columns="smi") + + # Resample to balanced classes + df = ds.to_pandas() + df.drop_duplicates(subset="inchi_key", inplace=True) + lip_cols = [ + "lipinki_h_donor", + "lipinki_h_acceptor", + "lipinki_mwt", + "lipinki_log_p", + ] + + # Rebalance and report stats + print("Group Counts:\n", get_group_sizes(df, lip_cols)) + print("Dataset size:", len(df)) + print("Class Odds:\n", df[lip_cols].mean()) + df = downsample_ipf_binary(df, lip_cols, n_samples=10_000) + print("Group Counts:\n", get_group_sizes(df, lip_cols)) + print("Dataset size:", len(df)) + print("Class Odds:\n", df[lip_cols].mean()) + + # Split preserving the frequency of each subgroup + spliter = StratifiedShuffleSplit(train_size=0.80, random_state=721153) + train_idx, test_idx = next(spliter.split(df["smi"], df["lipinki"])) + + # Save to disk + ds = DatasetDict( + { + "train": Dataset.from_pandas(df.iloc[train_idx], preserve_index=False), + "validation": Dataset.from_pandas(df.iloc[test_idx], preserve_index=False), + } + ) + Path("lipinski").mkdir(exist_ok=True) + ds.save_to_disk("lipinski/data", num_shards={"train": 8, "validation": 8}) + + +def get_group_sizes(df: pd.DataFrame, class_columns) -> pd.DataFrame: + """ + Get the count of each unique combination of values in the given class_columns. + + Parameters: + - df: pandas DataFrame. + - class_columns: A string or list of columns to group by. + + Returns: + - A DataFrame showing each group and its count, sorted descending. + """ + if isinstance(class_columns, str): + class_columns = [class_columns] + + group_counts = df.groupby(class_columns).size().reset_index(name="count") + return group_counts.sort_values("count", ascending=False).reset_index(drop=True) + + +def downsample_ipf_binary( + df: pd.DataFrame, + class_columns: list[str], + n_samples: int | None = None, + max_iter: int = 100, + tol: float = 1e-6, + random_state: int = 42, +) -> pd.DataFrame: + """ + Downsample a DataFrame with K binary columns so that each column’s marginal is 50/50, + using iterative proportional fitting over the joint 2^K table. + + Steps: + 1. Build original cell counts for every combination of the K binaries. + 2. Initialize target cell counts = original counts. + 3. For each column, rescale all cell counts in each level (0 and 1) so that + sum_over_cells(level=1) == total/2 and sum_over_cells(level=0) == total/2. + 4. Iterate until all K marginals are within tol of 0.5. + 5. Scale target cell counts to sum to n_samples (or len(df) if n_samples is None). + 6. For each cell c, weight per row in c = target_count[c] / original_count[c]. + 7. Draw without replacement using these per‑row probabilities. + + Returns: + A new DataFrame of size n_samples with approximately perfect 50/50 marginals. + """ + rng = np.random.default_rng(random_state) + df = df.reset_index(drop=True) + N = len(df) + + if n_samples is None: + min_count_factor = 4 + min_true_counts = [df[col].sum() for col in class_columns] + min_false_counts = [len(df) - x for x in min_true_counts] + max_true_size = int(min(min_true_counts) * min_count_factor) + max_false_size = int(min(min_false_counts) * min_count_factor) + n_samples = min(len(df), max_true_size, max_false_size) + + # 1) compute original contingency table + # use tuple of column values as key + keys = list(df[class_columns].itertuples(index=False, name=None)) + uniq, inv = np.unique(keys, axis=0, return_inverse=True) + orig_counts = pd.Series(np.bincount(inv), index=range(len(uniq)), dtype=float) + + # initialize target = original + target = orig_counts.copy() + + # precompute for each column which cells have bit=1 + # uniq is array of shape (n_cells, K) + uniq_arr = np.array(uniq, dtype=int) + is_one = {col: uniq_arr[:, i] == 1 for i, col in enumerate(class_columns)} + + total = target.sum() + half = total / 2.0 + + # 2) IPF loop + for _ in range(max_iter): + max_diff = 0.0 + + for i, col in enumerate(class_columns): + mask1 = is_one[col] + mask0 = ~mask1 + + # current marginal for this column + cur1 = target[mask1].sum() + cur0 = target[mask0].sum() + + # scale factor to push cur1 -> half and cur0 -> half + if cur1 > 0: + target[mask1] *= half / cur1 + if cur0 > 0: + target[mask0] *= half / cur0 + + # track worst marginal error + max_diff = max(max_diff, abs(cur1 / total - 0.5), abs(cur0 / total - 0.5)) + + if max_diff < tol: + break + else: + # warn if not converged + print( + f"IPF did not converge in {max_iter} iters; max marginal error {max_diff:.2e}" + ) + + # 3) scale target total to n_samples + scale = n_samples / total + target *= scale + + # 4) per‐row weights: target_count[c] / orig_count[c] + # inv maps each row to its cell index + cell_weight = target.to_numpy() / orig_counts.to_numpy() + row_weights = cell_weight[inv] + row_weights = np.clip(row_weights, 0, None) + row_weights = row_weights / row_weights.sum() + + # 5) sample + chosen = rng.choice(N, size=n_samples, replace=False, p=row_weights) + return df.iloc[chosen].reset_index(drop=True) + + +SEM_LIPINSKI = asyncio.Semaphore(20) + + +def lipinki_rule_of_five(x: dict, smi_column: str = "smi") -> dict: smi = x[smi_column] mol = MolFromSmiles(smi) assert mol is not None, "invalid smi: %s" % smi @@ -76,20 +292,8 @@ def lipinki_rule_of_five(x: dict, smi_column: str) -> dict: x["log_p"] = MolLogP(mol) x["lipinki_log_p"] = x["log_p"] <= 5 x["lipinki"] = all(v for k, v in x.items() if k.startswith("lipinki")) - x["probe_target"] = [v for k, v in x.items() if k.startswith("lipinki")] return x if __name__ == "__main__": - ds = LipinskiDataModule(name_or_path="hiv") - ds = ds.dataset - df = ds.to_pandas() - df.to_csv("lipo.csv") - cols = [ - "lipinki", - "lipinki_h_donor", - "lipinki_h_acceptor", - "lipinki_mwt", - "lipinki_log_p", - ] - print({k: df[k].value_counts() for k in cols}) + build_probe_dataset() diff --git a/electrolyte_fm/data_modules/molnet_dataset.py b/electrolyte_fm/data_modules/molnet_dataset.py index f60d029e..7f898a66 100644 --- a/electrolyte_fm/data_modules/molnet_dataset.py +++ b/electrolyte_fm/data_modules/molnet_dataset.py @@ -83,6 +83,8 @@ def _get_dataset(self): return scaffold_split(ds, self.smi_column) elif self.split == "random": return train_val_test_split(ds) + elif self.split == "all": + return ds else: raise ValueError(f"Unknown split {self.split}") diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py index b1738c55..07c551ab 100644 --- a/electrolyte_fm/data_modules/utils.py +++ b/electrolyte_fm/data_modules/utils.py @@ -1,3 +1,4 @@ +import asyncio from enum import Enum import random from typing import TypeVar @@ -83,11 +84,14 @@ def random(self, smi: str): def filter_invalid_smi( - ds: AbstractDataset, input_column: str, **kwargs + ds: AbstractDataset, input_column: str, max_procs: int = 20, **kwargs ) -> AbstractDataset: - def is_valid(x: dict): - mol = Chem.MolFromSmiles(x[input_column]) - return mol is not None + sem = asyncio.Semaphore(max_procs) + + async def is_valid(x: dict): + async with sem: + mol = Chem.MolFromSmiles(x[input_column]) + return mol is not None return ds.filter(is_valid, batched=False, **kwargs) diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 6277a34c..e989bde2 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -59,6 +59,8 @@ def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: hook.remove() state_dict.pop("_hooks_installed", None) state_dict["_prob_points"] = self._probe_points + state_dict["hookpoints"] = self.hookpoints + checkpoint["state_dict"] = state_dict # Don't save activations state_dict.pop("_activations", None) diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 4914fc28..186da82b 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -7,16 +7,18 @@ function load_fitted_probes(ckpt_dir) _, ckpt_meta = load_linear_probes(ckpt) continue end - m = match(r"layer-(\d+)-(\w+).*?--auroc-([\d\.]+)\.ckpt", basename(ckpt)) + m = match(r"layer-(\d+)-([\w\-]+?)--.*?--loss-([\d\.]+)--auroc-([\d\.]+)\.ckpt$", basename(ckpt)) layer = parse(Int, m[1]) + 1 location = m[2] - auroc = parse(Float64, m[3]) + val_loss = parse(Float64, m[3]) + auroc = parse(Float64, m[4]) try ckpt_probes, ckpt_meta = load_linear_probes(ckpt) push!(probes, (; ckpt_probes[layer]..., location, auroc, + val_loss )) catch e e isa InterruptException && rethrow() From cba9b6f32d44c2eceef813bbe1274c272f7b4747 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 22 Apr 2025 19:53:11 -0400 Subject: [PATCH 096/147] update linear probe sweep --- .../data_modules/lipinski_dataset.py | 62 +++++-------- .../property_prediction_dataset.py | 2 + electrolyte_fm/models/linear_probe.py | 53 ++++++++--- opt/FeatureMiner/submit_linear_probes.py | 90 +++++++++---------- 4 files changed, 106 insertions(+), 101 deletions(-) diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index 115c3e6e..763e7453 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -1,10 +1,8 @@ import asyncio from pathlib import Path -import torch import pandas as pd import numpy as np -from sklearn.utils import resample from sklearn.model_selection import StratifiedShuffleSplit from datasets import DatasetDict, Dataset, load_dataset, concatenate_datasets from rdkit.Chem import Lipinski, MolFromSmiles, MolToInchiKey @@ -17,52 +15,34 @@ class LipinskiDataModule(PropertyPredictionDataModule): - def __init__(self, name_or_path: str, **kwargs): - self.name_or_path = name_or_path - if not Path(name_or_path).exists(): - # Set default smi_column - assert name_or_path in _URLS.keys() - self.name_or_path = _URLS[name_or_path] - kwargs["smi_column"] = ( - kwargs.get("smi_column", None) or "smiles" - if name_or_path != "bace" - else "mol" - ) - assert isinstance(kwargs["smi_column"], str) - kwargs["additonal_columns"] = [ - "probe_target", - *(kwargs.get("additonal_columns", None) or []), + def __init__(self, path: str, **kwargs): + self.path = Path(path) + assert self.path.exists() + + kwargs["target_columns"] = [ + "lipinki_h_donor", + "lipinki_h_acceptor", + "lipinki_mwt", + "lipinki_log_p", + "lipinki", ] + kwargs["smi_column"] = kwargs.get("smi_column", "smi") super().__init__(**kwargs) assert self.encoding != MolEncoding.SELFIES - def _get_dataset(cls, name_or_path) -> AbstractDataset: - # Load the dataset - ds: AbstractDataset = load_dataset( - "csv", - name=name, - data_files=[name_or_path], - split="train", + def _get_dataset(self) -> AbstractDataset: + return load_dataset( + "arrow", + name=str(self.path.name), + data_files={ + "train": str(self.path.joinpath("data/train/*.arrow")), + "validation": str(self.path.joinpath("data/validation/*.arrow")), + "test": str(self.path.joinpath("data/test/*.arrow")), + }, keep_in_memory=False, + streaming=True, save_infos=False, ) # type: ignore - ds = ds.select_columns(smi_column) - ds = filter_invalid_smi(self.smi_column) - ds = ds.map( - lipinki_rule_of_five, - batched=False, - fn_kwargs={"smi_column": self.smi_column}, - ) - return ds - - def collate_fn(self, batch): - output = super().collate_fn(batch) - output = self.token_collator(batch) - output["probe_target"] = torch.stack( - [torch.tensor(x["probe_target"]) for x in batch] - ) - - return output def get_molnet_dataset(name: str): diff --git a/electrolyte_fm/data_modules/property_prediction_dataset.py b/electrolyte_fm/data_modules/property_prediction_dataset.py index 15fb498b..28bba342 100644 --- a/electrolyte_fm/data_modules/property_prediction_dataset.py +++ b/electrolyte_fm/data_modules/property_prediction_dataset.py @@ -80,6 +80,7 @@ def setup(self, stage: str) -> None: ds = self.dataset ds = maybe_shard_dataset(self.trainer, ds) ds = encode_molecules(ds, self.smi_column, encoding=self.encoding) + print(ds["train"]) # Remove extraneous columns and tokenize smiles if targets := self.target_columns: @@ -146,6 +147,7 @@ def train_dataloader(self): ) def val_dataloader(self): + print("has validation dataloader") return DataLoader( self.val_dataset, collate_fn=self.collate_fn, diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index e989bde2..313ea8cb 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -1,5 +1,5 @@ import logging -from typing import Iterable, Dict, Any, Callable +from typing import Iterable, Dict, Any, Callable, Optional from collections import defaultdict from jsonargparse import lazy_instance import torch @@ -15,13 +15,31 @@ def per_layer_probe( hidden_size: int, features: int, n_layers: int, location: str = "output" ) -> dict[str, nn.Module]: probes = {} - template: str = "*.encoder.layer.{layer}.{location}" + template: str = "*encoder.layer.{layer}.{location}" for layer in range(n_layers): hook_name = template.format(layer=layer, location=location) probes[hook_name] = nn.Linear(hidden_size, features) return probes +def probe_everything( + hidden_size: int, + features: int, + n_layers: int, + intermediate_size: Optional[int] = None, +): + probes = {} + intermediate_size = intermediate_size or hidden_size + for location, size in [ + ("output", hidden_size), + ("intermediate", intermediate_size), + ("attention", hidden_size), + ("output.dense", hidden_size), + ]: + probes.update(per_layer_probe(size, features, n_layers, location)) + return probes + + ProbeConfigCallable = Callable[Any, dict[str, nn.Module]] @@ -35,11 +53,12 @@ def __init__( ): super().__init__() - self.model = model + self.model = model.requires_grad_(False) self.optimizer = optimizer self.lr_schedule = lr_schedule self.save_hyperparameters() self.hookpoints = self._identify_hookpoints(probes.keys()) + assert len(self.hookpoints) > 0 # Setup probes self._probes = nn.ModuleList(probes.values()) @@ -51,6 +70,14 @@ def __init__( self.val_metrics = nn.ModuleList( AUROC(task="binary", thresholds=100) for probe in self._probe_points ) + print(self.val_metrics) + + # Don't error due to missing model weights + self.strict_loading = False + + def state_dict(self): + # Don't save the model, it is not being trained + return {k: v for k, v in super().state_dict().items() if "model" not in k} def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: state_dict = checkpoint["state_dict"] @@ -93,6 +120,8 @@ def _install_hooks(self): @staticmethod def _create_act_hook(name: str, results: dict): def hook(module: nn.Module, input, output: torch.Tensor): + if isinstance(output, tuple): + output = output[0] assert isinstance(output, torch.Tensor) results[name] = output[:, 0, :].detach() return None @@ -116,8 +145,8 @@ def forward(self, *args, **kwargs): def forward_fit(self, batch: dict): self.model.eval() activations = self._install_hooks() - target = batch.pop("probe_target") - self.model(**batch) + target = batch.pop("target") + self.model(batch["input_ids"], attention_mask=batch["attention_mask"]) loss = [] out = {} for name, probe in self.named_probes(): @@ -144,8 +173,8 @@ def training_step(self, batch): return out["loss"] def validation_step(self, batch): - target = batch.pop("probe_target") - out = self.forward(**batch) + target = batch.pop("target") + out = self.forward(batch["input_ids"], attention_mask=batch["attention_mask"]) metrics = {} loss = [] for probe, probe_metrics in zip(self._probe_points, self.val_metrics): @@ -190,12 +219,12 @@ def configure_optimizers(self): logging.basicConfig(level=logging.INFO) - def mlm_from_pretrained(name_or_path: str) -> nn.Module: - from transformers import AutoModelForMaskedLM + def encoder_from_finetuned(name_or_path: str) -> nn.Module: + from .prod_finetune import MISTFinetuned - return AutoModelForMaskedLM.from_pretrained( - name_or_path, trust_remote_code=True - ) + model = MISTFinetuned.from_pretrained(name_or_path) + print(model.encoder) + return model.encoder cli = MistLightningCLI( LightningProbe, diff --git a/opt/FeatureMiner/submit_linear_probes.py b/opt/FeatureMiner/submit_linear_probes.py index 21129791..a6e2dba5 100644 --- a/opt/FeatureMiner/submit_linear_probes.py +++ b/opt/FeatureMiner/submit_linear_probes.py @@ -12,26 +12,27 @@ function( encoder_class, encoder_path, - location, - dataset, tokenizer="smirk", encoding="smiles-kekule", batch_size, hidden_size, + intermediate_size, n_layers, ) { nodes: 1, gpus_per_node: 1, - container: '/lustre/fs0/awadell/sqsh-files/mist+pytorch+25.01+v2.sif', + container: '/nfs/turbo/coe-venkvis/mist/mist+pytorch+25.01+v4.sif', + walltime: "1:0:0", env: { JOBID: '$SLURM_JOB_ID', - PMIX_MCA_gds: 'hash', - NCCL_TOPO_FILE: '/cm/shared/etc/ndv4-topo.xml', - MELLANOC_VISIBLE_DEVICES: 'all', + TORCH_EXTENSIONS_DIR: '${PWD}/.cache/torch_extensions', + HF_HOME: '${PWD}/.cache/huggingface', + TOKENIZERS_PARALLELISM: true, }, program: "-m electrolyte_fm.models.linear_probe", + stage: null, train: { trainer: { max_epochs: 1000, @@ -44,11 +45,11 @@ }, }, probes: { - class_path: 'electrolyte_fm.models.linear_probe.per_layer_probe', + class_path: 'electrolyte_fm.models.linear_probe.probe_everything', init_args: { hidden_size: std.parseInt(hidden_size), + intermediate_size: std.parseInt(intermediate_size), features: 5, - location: location, n_layers: std.parseInt(n_layers), }, }, @@ -56,50 +57,49 @@ data: { class_path: 'electrolyte_fm.data_modules.lipinski_dataset.LipinskiDataModule', init_args: { - name_or_path: dataset, - tokenizer: tokenizer, - encoding: encoding, - num_workers: 16, - batch_size: std.parseInt(batch_size), + path: "./lipinski", + tokenizer: tokenizer, + encoding: encoding, + num_workers: 16, + batch_size: std.parseInt(batch_size), + randomize: true, }, }, }, } """ -template = "submit/dgx.j2" +template = "submit/artemis.j2" def submit(config: dict): script = render(template, config) print(script) + exit() subprocess.run("sbatch", input=script, text=True) # Pretrained Models -datasets = ["tox21", "toxcast", "hiv"] -locations = ["output", "intermediate", "output.dense"] models = [ - { - "encoder_path": "ibm/MoLFormer-XL-both-10pct", - "encoding": "smiles-canonical", - "tokenizer": "ibm/MoLFormer-XL-both-10pct", - }, { "encoder_path": "./models/mist-ti624ev1-moleculenet/pretrained", - }, - { - "encoder_path": "./models/mist-1.8B-dh61satti", - "batch_size": 16, - }, + } ] models.extend( [ { - "encoder_class": "electrolyte_fm.models.prod_finetune.MISTFinetuned.from_pretrained", + "encoder_class": "__main__.encoder_from_finetuned", "encoder_path": f"./models/mist-ti624ev1-moleculenet/{dataset}", } - for dataset in ["bace", "qm9", "bbbp", "muv", "qm8", "tmQM"] + for dataset in [ + "bace", + "qm9", + "bbbp", + "muv", + "qm8", + "tmQM", + "clintox", + ] ] ) @@ -114,9 +114,10 @@ def get_mist_finetune_config(path): runs = [] for model in models: run = deepcopy(model) - run.setdefault("encoder_class", "__main__.mlm_from_pretrained") - run.setdefault("batch_size", 64) - if run["encoder_class"] == "__main__.mlm_from_pretrained": + default_encoder = "electrolyte_fm.utils.cli.mlm_from_pretrained" + run.setdefault("encoder_class", default_encoder) + run.setdefault("batch_size", 128) + if run["encoder_class"] == default_encoder: model_config = AutoConfig.from_pretrained( run["encoder_path"], trust_remote_code=True, @@ -124,20 +125,13 @@ def get_mist_finetune_config(path): else: model_config = get_mist_finetune_config(run["encoder_path"]) - run.setdefault("n_layers", model_config.num_hidden_layers) - - for c in dict_product({"location": locations, "dataset": datasets}): - run.update(c) - if run["location"] == "intermediate": - hidden_size = model_config.intermediate_size - else: - hidden_size = model_config.hidden_size - - run["hidden_size"] = hidden_size - run_config = jsonnet.evaluate_snippet( - "snippet", - config, - tla_vars={k: str(v) for k, v in run.items()}, - ) - run_config = json.loads(run_config) - submit(run_config) + run["n_layers"] = model_config.num_hidden_layers + run["hidden_size"] = model_config.hidden_size + run["intermediate_size"] = model_config.intermediate_size + run_config = jsonnet.evaluate_snippet( + "snippet", + config, + tla_vars={k: str(v) for k, v in run.items()}, + ) + run_config = json.loads(run_config) + submit(run_config) From ba102a28853ee6ff12d6a9493fcd2757db3762fc Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 23 Apr 2025 10:57:43 -0400 Subject: [PATCH 097/147] add BoxPlot config to style --- opt/MISTStyle/src/MISTStyle.jl | 3 +++ 1 file changed, 3 insertions(+) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index e9df1c57..50d5a4d4 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -177,6 +177,9 @@ function theme() markersize=5pt, marker=:x, ), + BoxPlot=(; + markersize=4pt, + ), ErrorLines=(; whiskerwidth=3, ), From b95fab0c8765f16d27f3d95417428255e763ec39 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 23 Apr 2025 10:58:19 -0400 Subject: [PATCH 098/147] don't track jld2 files --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 54a8d647..b78c3cbb 100644 --- a/.gitignore +++ b/.gitignore @@ -45,3 +45,5 @@ spack.lock *.tar.xz *.tar.gz *.tar +*.jsonl +*.jld2 From ebfe70998a39bf7d9bc8ed69778ef91d2f1443da Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 23 Apr 2025 13:18:18 -0400 Subject: [PATCH 099/147] update lipinski plots --- opt/FeatureMiner/.gitignore | 2 + opt/FeatureMiner/plots/Project.toml | 15 +++ opt/FeatureMiner/plots/lipinski_probes.jl | 131 ++++++++++++++++++++++ opt/FeatureMiner/src/lipinski.jl | 4 +- 4 files changed, 150 insertions(+), 2 deletions(-) create mode 100644 opt/FeatureMiner/.gitignore create mode 100644 opt/FeatureMiner/plots/Project.toml create mode 100644 opt/FeatureMiner/plots/lipinski_probes.jl diff --git a/opt/FeatureMiner/.gitignore b/opt/FeatureMiner/.gitignore new file mode 100644 index 00000000..eee10db7 --- /dev/null +++ b/opt/FeatureMiner/.gitignore @@ -0,0 +1,2 @@ +*.tex +fig/ diff --git a/opt/FeatureMiner/plots/Project.toml b/opt/FeatureMiner/plots/Project.toml new file mode 100644 index 00000000..864529d6 --- /dev/null +++ b/opt/FeatureMiner/plots/Project.toml @@ -0,0 +1,15 @@ +[deps] +CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +FeatureMiner = "83091abd-1720-4da7-94ca-5ff8667e187f" +GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" +MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" +Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" + +[sources] +FeatureMiner = {path = "../"} +MISTStyle = {path = "../../MISTStyle"} diff --git a/opt/FeatureMiner/plots/lipinski_probes.jl b/opt/FeatureMiner/plots/lipinski_probes.jl new file mode 100644 index 00000000..78f077bc --- /dev/null +++ b/opt/FeatureMiner/plots/lipinski_probes.jl @@ -0,0 +1,131 @@ +using Makie +using DataFrames +using GLM +using StatsBase +using JLD2: jldopen +using CategoricalArrays: categorical, levelcode +using LinearAlgebra: norm, I +using RegressionTables: regtable, LatexTable + +using FeatureMiner +using MISTStyle + +function load_probes() + df = DataFrame(jldopen("lipinski_probes_ti624ev1.jld2")["probes"]) + df.encoder_id = categorical(df.encoder_id) + ordered = ["pretrained", "tmQM"] + dataset_order = setdiff(unique(df.encoder_dataset), ordered) + df.encoder_dataset = categorical(df.encoder_dataset; + levels=vcat(ordered, dataset_order), + ordered=true, + ) + df.location = categorical(df.location) + sort!(df, :encoder_dataset) + return df +end + +function figure_probe_similarity(df::DataFrame, model="dh61satti", dataset="toxcast", location="output") + df = subset(df, + :encoder => ByRow(==(model)), + :dataset => ByRow(==(dataset)), + :location => ByRow(==(location)), + skipmissing=true + ) + dropmissing!(df) + f = Figure() + + for (fdx, feat) in enumerate(unique(df.feature_id)) + @show df_f = subset(df, :feature_id => ByRow(==(feat))) + df_f = first(eachrow(df_f)) + ax = Axis(f[fdx, 1]; title=df_f.id) + heatmap!(ax, df_f.probe_similarity) + end + return f +end + +function figure_lipinski_probes(df::DataFrame) + f = Figure(size=(2inch, 1inch)) + + df = dropmissing(df) + subset!(df, + :encoder_id => ByRow(==("ti624ev1")), + :location => ByRow(in(["output", "intermediate", "attention"])), + ) + + # Mean AUROC + df_auroc = combine(groupby(df, :encoder_dataset)) do gdf + unstack(gdf[:, [:layer, :auroc]], :layer, :auroc; combine=minimum) + end + auroc = Matrix(df_auroc[:, 2:end]) + @info extrema(auroc) + + # Feature alignment + gl = GridLayout(f[1,1]) + ax = Axis(gl[1, 1]; + xticks=categorical_ticks(df.encoder_dataset), + yticks=2:2:8, + # limits=(nothing, ), + xticklabelsvisible=false, + xticksvisible=false, + ) + + h = heatmap!(ax, auroc) + Colorbar(gl[1, 2], h; label="AUROC") + + # Additive Features + df.additive_features = FeatureMiner.additive_features.(df.weight) + df_af = combine(groupby(subset!(df, :location => ByRow(==("output"))), :encoder_dataset)) do gdf + unstack(gdf[:, [:layer, :additive_features]], :layer, :additive_features) + end + af = Matrix(df_af[:, 2:end]) + ax_add = Axis(gl[2, 1]; + xticks=categorical_ticks(df.encoder_dataset), + yticks=ax.yticks, + ) + h = heatmap!(ax_add, af; colorrange=(0, 1)) + Colorbar(gl[2, 2], h; label="Additivity") + @info extrema(af) + + # Add single Y-axis label + Label(gl[:, 0], text = "Encoder Layer", rotation = pi/2) + + return f +end + +function lipinski_fixed_effect(df) + contrasts = Dict( + :encoder_dataset => EffectsCoding(; base="pretrained"), + :location => EffectsCoding(; base="output"), + ) + m_dataset = glm( + @formula( auroc ~ 1 + encoder_dataset), df, Normal(), LogitLink(); + contrasts, + ) + m_layer = glm( + @formula( auroc ~ 1 + layer), df, Normal(), LogitLink(); + contrasts, + ) + m_location = glm( + @formula( auroc ~ 1 + location), df, Normal(), LogitLink(); + contrasts, + ) + m_all = glm( + @formula( auroc ~ 1 + encoder_dataset + location + layer), df, Normal(), LogitLink(); + contrasts, + ) + display(m) + open("lipinski.tex", "w") do fid + write(fid, regtable(m_all, m_layer, m_dataset, m_location; + regression_statistics = [ + Int∘nobs => "N", + bic => "BIC", + Int∘dof_residual => "Resid. DoF", + (m -> adjr2(m, :devianceratio)) => "Adj. R2", + (m -> rmsd(response(m), predict(m))) => "RMSD", + ], + render=LatexTable(), + + ) |> string) + end + return m +end diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 186da82b..7683ed63 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -32,8 +32,8 @@ end cosine_similarity(a::Vector, b::Vector) = dot(a, b) / (norm(a) * norm(b)) function additive_features(w::Matrix) - f_overall = w[end, :] - f_componets = vec(sum(w[1:end-1, :]; dims=1)) + f_overall = w[1, :] + f_componets = vec(sum(w[2:end, :]; dims=1)) return cosine_similarity(f_overall, f_componets) end From 1d6467acb37efdbf0b25c5aa9268b4979459659b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 13:36:35 -0400 Subject: [PATCH 100/147] update probe plots --- opt/FeatureMiner/plots/lipinski_probes.jl | 51 +++++++++++++++++++---- 1 file changed, 43 insertions(+), 8 deletions(-) diff --git a/opt/FeatureMiner/plots/lipinski_probes.jl b/opt/FeatureMiner/plots/lipinski_probes.jl index 78f077bc..84376e11 100644 --- a/opt/FeatureMiner/plots/lipinski_probes.jl +++ b/opt/FeatureMiner/plots/lipinski_probes.jl @@ -24,21 +24,45 @@ function load_probes() return df end -function figure_probe_similarity(df::DataFrame, model="dh61satti", dataset="toxcast", location="output") +function figure_probe_similarity(df::DataFrame; model="dh61satti", dataset="pretrained", location="output") df = subset(df, - :encoder => ByRow(==(model)), - :dataset => ByRow(==(dataset)), + :encoder_id => ByRow(==(model)), :location => ByRow(==(location)), skipmissing=true ) dropmissing!(df) f = Figure() - for (fdx, feat) in enumerate(unique(df.feature_id)) - @show df_f = subset(df, :feature_id => ByRow(==(feat))) - df_f = first(eachrow(df_f)) - ax = Axis(f[fdx, 1]; title=df_f.id) - heatmap!(ax, df_f.probe_similarity) + + feature_name = [ + "Lipinski", + "H-Donor", + "H-Acceptor", + "MWT", + "LogP", + ] + + cb = Colorbar(f[1:5, 1+length(levels(df.encoder_dataset))]; + label="Cosine Similarity", + colorrange=(0, 1), + ) + + for (ddx, dataset) in enumerate(unique(df.encoder_dataset)) + dfd = subset(df, :encoder_dataset => ByRow(==(dataset))) + for fdx in 1:5 + ax = Axis(f[fdx, ddx]) + if fdx == 1 + ax.title = dataset + end + hidedecorations!(ax) + if ddx == 1 + ax.ylabel = feature_name[fdx] + ax.ylabelvisible = true + end + + probe_similarity = FeatureMiner.layerwise_similarity(dfd.weight, fdx) + heatmap!(ax, probe_similarity; MISTStyle.cb_attrs(cb, Heatmap)...) + end end return f end @@ -129,3 +153,14 @@ function lipinski_fixed_effect(df) end return m end + +function all_plots() + df = load_probes() + figure_lipinski_probes(df) |> MISTStyle.savefig("lipinski_linear_probes") + for location in unique(df.location) + for encoder in unique(df.encoder_id) + fig = figure_probe_similarity(df; model=encoder, location=location) + MISTStyle.savefig("lipinski-probe-similarity-$encoder-$location", fig) + end + end +end From 7bed0f5a36acdf32dda743250001e345bab8f3a4 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 14:11:01 -0400 Subject: [PATCH 101/147] add pandas and numpy to deps --- pyproject.toml | 4 +++- uv.lock | 4 ++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index bf7b13fd..d6c3a2d6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,9 @@ dependencies = [ "rdkit>=2024.3.5", "smirk==0.1.0", "safetensors>=0.5.1", - "pytorch-lightning~=2.5" + "pytorch-lightning~=2.5", + "pandas>=2.2.3", + "numpy>=2.2.4", ] [optional-dependencies] diff --git a/uv.lock b/uv.lock index ff3eb2b1..b6c971c7 100644 --- a/uv.lock +++ b/uv.lock @@ -480,6 +480,8 @@ dependencies = [ { name = "jsonargparse", extra = ["jsonnet", "signatures"] }, { name = "jsonnet" }, { name = "lightning" }, + { name = "numpy" }, + { name = "pandas" }, { name = "pydoe2" }, { name = "pytorch-lightning" }, { name = "rdkit" }, @@ -515,6 +517,8 @@ requires-dist = [ { name = "jsonargparse", extras = ["signatures", "jsonnet"], specifier = ">=4.27" }, { name = "jsonnet", specifier = ">=0.21.0" }, { name = "lightning", specifier = "~=2.5" }, + { name = "numpy", specifier = ">=2.2.4" }, + { name = "pandas", specifier = ">=2.2.3" }, { name = "pydoe2", specifier = ">=1.3" }, { name = "pytorch-lightning", specifier = "~=2.5" }, { name = "rdkit", specifier = ">=2024.3.5" }, From 0ea2eae466d0178933426d8d3240e4e84b719555 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 17:46:57 -0400 Subject: [PATCH 102/147] fix formatting --- electrolyte_fm/data_modules/__init__.py | 1 - electrolyte_fm/models/linear_probe.py | 4 +--- electrolyte_fm/models/sae.py | 4 +--- electrolyte_fm/utils/featurizer.py | 7 +------ electrolyte_fm/utils/metrics.py | 1 + opt/FeatureMiner/run_explore.sh | 0 opt/FeatureMiner/src/FeatureMiner.jl | 1 - opt/FeatureMiner/src/lipinski.jl | 1 - opt/FeatureMiner/src/stats.jl | 2 -- opt/FeatureMiner/submit_explore.sh | 1 - opt/FeatureMiner/submit_linear_probes.py | 3 +-- submit/sae_features.libsonnet | 2 +- test/test_featurizer.py | 3 +-- test/test_sae.py | 1 - 14 files changed, 7 insertions(+), 24 deletions(-) mode change 100644 => 100755 opt/FeatureMiner/run_explore.sh mode change 100644 => 100755 opt/FeatureMiner/submit_explore.sh diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index 223309d5..a5af553f 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -2,5 +2,4 @@ from .property_prediction_dataset import PropertyPredictionDataModule from .roberta_dataset import RobertaDataSet from .tmqm_dataset import tmQMDataModule -from .roberta_dataset import RobertaDataSet from .mixture_dataset import ComponentDataModule diff --git a/electrolyte_fm/models/linear_probe.py b/electrolyte_fm/models/linear_probe.py index 313ea8cb..f9d8db14 100644 --- a/electrolyte_fm/models/linear_probe.py +++ b/electrolyte_fm/models/linear_probe.py @@ -113,7 +113,7 @@ def _install_hooks(self): self._hooks_installed[name] = self.model.get_submodule( name ).register_forward_hook(hook) - logging.debug(f"Installed hook for %s", name) + logging.debug("Installed hook for %s", name) return self._activations @@ -210,8 +210,6 @@ def configure_optimizers(self): if __name__ == "__main__": - import smirk - import json from jsonargparse import lazy_instance from lightning.pytorch.loggers import WandbLogger from lightning.pytorch.callbacks import ModelCheckpoint diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py index 6554d930..30006e95 100644 --- a/electrolyte_fm/models/sae.py +++ b/electrolyte_fm/models/sae.py @@ -8,11 +8,9 @@ from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from torch import nn from torch.nn import functional as F -from torchmetrics import MetricCollection -from transformers import PreTrainedModel, AutoModel +from transformers import PreTrainedModel from .model_utils import load_encoder -from ..utils.metrics import AliveFeatures, FeatureDensity, MaxFeatureDensity def init_bias(bias, w): diff --git a/electrolyte_fm/utils/featurizer.py b/electrolyte_fm/utils/featurizer.py index bb38ec4b..c47c828b 100644 --- a/electrolyte_fm/utils/featurizer.py +++ b/electrolyte_fm/utils/featurizer.py @@ -1,24 +1,19 @@ import json -import logging from math import floor from pathlib import Path from typing import Optional import torch from datasets import load_dataset -from rdkit import Chem from torch import nn from torch.utils.data import DataLoader from transformers import DataCollatorWithPadding -from torchmetrics import MetricCollection -from ..data_modules.sae_dataset import extract_hidden_state from ..data_modules.feature_tagger import FeatureCollection from ..data_modules.utils import MolEncoding, encode_molecules from ..models.model_utils import load_encoder from ..models.sae import SAE from .tokenizer import load_tokenizer -from .metrics import FeatureCorrelation class FeatureExtractor(nn.Module): @@ -122,7 +117,7 @@ def setup(self): streaming=True, save_infos=True, ) - ds = encode_molecules(ds, "text", encoding=MolEncoding.KEUKLE_SMILES) + ds = encode_molecules(ds, "text", encoding=MolEncoding.KEKULE) ds = ds.map(self.miner.tokenize, batched=True, input_columns="text") ds = ds.map(self.features, batched=False, input_columns="text") self.dataset = ds diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index 72af7bbb..927b1e4d 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -1,3 +1,4 @@ +from dataclasses import dataclass from typing import Any, Dict, Literal, Optional, Union import torch diff --git a/opt/FeatureMiner/run_explore.sh b/opt/FeatureMiner/run_explore.sh old mode 100644 new mode 100755 diff --git a/opt/FeatureMiner/src/FeatureMiner.jl b/opt/FeatureMiner/src/FeatureMiner.jl index f92d9823..7f802702 100644 --- a/opt/FeatureMiner/src/FeatureMiner.jl +++ b/opt/FeatureMiner/src/FeatureMiner.jl @@ -64,4 +64,3 @@ include("stats.jl") include("lipinski.jl") end - diff --git a/opt/FeatureMiner/src/lipinski.jl b/opt/FeatureMiner/src/lipinski.jl index 7683ed63..9b7106fd 100644 --- a/opt/FeatureMiner/src/lipinski.jl +++ b/opt/FeatureMiner/src/lipinski.jl @@ -45,4 +45,3 @@ function layerwise_similarity(weights::Vector{W}, feature::Int) where {W<:Matrix end return s end - diff --git a/opt/FeatureMiner/src/stats.jl b/opt/FeatureMiner/src/stats.jl index db4e7829..4b20125b 100644 --- a/opt/FeatureMiner/src/stats.jl +++ b/opt/FeatureMiner/src/stats.jl @@ -41,5 +41,3 @@ end StatsBase.var(o::ElementwiseVariance) = OnlineStatsBase.value(o) StatsBase.mean(o::ElementwiseVariance) = o.μ StatsBase.nobs(o::ElementwiseVariance) = o.n[] - - diff --git a/opt/FeatureMiner/submit_explore.sh b/opt/FeatureMiner/submit_explore.sh old mode 100644 new mode 100755 index b5f95f68..15165b32 --- a/opt/FeatureMiner/submit_explore.sh +++ b/opt/FeatureMiner/submit_explore.sh @@ -5,4 +5,3 @@ for ckpt in $(find ../../linear-probes/ -maxdepth 1 -mindepth 1 -type d); do sbatch ./run_explore.sh "${ckpt}" fi done - diff --git a/opt/FeatureMiner/submit_linear_probes.py b/opt/FeatureMiner/submit_linear_probes.py index a6e2dba5..d57f3b4c 100644 --- a/opt/FeatureMiner/submit_linear_probes.py +++ b/opt/FeatureMiner/submit_linear_probes.py @@ -4,7 +4,6 @@ import _jsonnet as jsonnet import subprocess from submit.submit import render -from submit.utils import dict_product from transformers import AutoConfig # jsonnet @@ -35,7 +34,7 @@ stage: null, train: { trainer: { - max_epochs: 1000, + max_epochs: 1000, }, model: { model: { diff --git a/submit/sae_features.libsonnet b/submit/sae_features.libsonnet index 9c81f8f4..f52ef5fa 100644 --- a/submit/sae_features.libsonnet +++ b/submit/sae_features.libsonnet @@ -1,3 +1,3 @@ { - aromatic_elements: "[a-z]{1,2}" + aromatic_elements: '[a-z]{1,2}', } diff --git a/test/test_featurizer.py b/test/test_featurizer.py index e3d06db7..e5b1eae9 100644 --- a/test/test_featurizer.py +++ b/test/test_featurizer.py @@ -1,6 +1,5 @@ import re from random import choice -from itertools import chain import pytest import torch @@ -107,7 +106,7 @@ def test_regex_negatives(feature: str, negatives: list[str]): "feature,positives", ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), ) -def test_regex_negatives(feature: str, positives: list[str]): +def test_regex_positives(feature: str, positives: list[str]): f = re.compile(REGEX_FEATURES[feature]) for pos in positives: assert f.match(pos) is not None, f"{feature} should match {pos}" diff --git a/test/test_sae.py b/test/test_sae.py index ba525df0..993a7f7f 100644 --- a/test/test_sae.py +++ b/test/test_sae.py @@ -2,7 +2,6 @@ import torch import torch.nn.functional as F from transformers import ( - AutoModel, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, From 06b8ef2e7257e7073aa569da22b90e60b076cba4 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 24 Apr 2025 16:18:28 -0700 Subject: [PATCH 103/147] fix spelling --- .../data_modules/lipinski_dataset.py | 34 +++++++++---------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/electrolyte_fm/data_modules/lipinski_dataset.py b/electrolyte_fm/data_modules/lipinski_dataset.py index 763e7453..68258518 100644 --- a/electrolyte_fm/data_modules/lipinski_dataset.py +++ b/electrolyte_fm/data_modules/lipinski_dataset.py @@ -20,11 +20,11 @@ def __init__(self, path: str, **kwargs): assert self.path.exists() kwargs["target_columns"] = [ - "lipinki_h_donor", - "lipinki_h_acceptor", - "lipinki_mwt", - "lipinki_log_p", - "lipinki", + "lipinski_h_donor", + "lipinski_h_acceptor", + "lipinski_mwt", + "lipinski_log_p", + "lipinski", ] kwargs["smi_column"] = kwargs.get("smi_column", "smi") super().__init__(**kwargs) @@ -94,7 +94,7 @@ def build_probe_dataset(): async def async_lipinski_rule_of_five(x): async with sem: - return lipinki_rule_of_five(x, "smi") + return lipinski_rule_of_five(x, "smi") ds = ds.map(async_lipinski_rule_of_five, batched=False) @@ -109,10 +109,10 @@ async def inchi_key(smi: str): df = ds.to_pandas() df.drop_duplicates(subset="inchi_key", inplace=True) lip_cols = [ - "lipinki_h_donor", - "lipinki_h_acceptor", - "lipinki_mwt", - "lipinki_log_p", + "lipinski_h_donor", + "lipinski_h_acceptor", + "lipinski_mwt", + "lipinski_log_p", ] # Rebalance and report stats @@ -126,7 +126,7 @@ async def inchi_key(smi: str): # Split preserving the frequency of each subgroup spliter = StratifiedShuffleSplit(train_size=0.80, random_state=721153) - train_idx, test_idx = next(spliter.split(df["smi"], df["lipinki"])) + train_idx, test_idx = next(spliter.split(df["smi"], df["lipinski"])) # Save to disk ds = DatasetDict( @@ -259,19 +259,19 @@ def downsample_ipf_binary( SEM_LIPINSKI = asyncio.Semaphore(20) -def lipinki_rule_of_five(x: dict, smi_column: str = "smi") -> dict: +def lipinski_rule_of_five(x: dict, smi_column: str = "smi") -> dict: smi = x[smi_column] mol = MolFromSmiles(smi) assert mol is not None, "invalid smi: %s" % smi x["num_h_bond_donors"] = Lipinski.NumHDonors(mol) - x["lipinki_h_donor"] = x["num_h_bond_donors"] <= 5 + x["lipinski_h_donor"] = x["num_h_bond_donors"] <= 5 x["num_h_bond_acceptors"] = Lipinski.NumHAcceptors(mol) - x["lipinki_h_acceptor"] = x["num_h_bond_acceptors"] <= 10 + x["lipinski_h_acceptor"] = x["num_h_bond_acceptors"] <= 10 x["molecular_weight"] = ExactMolWt(mol) - x["lipinki_mwt"] = x["molecular_weight"] <= 500 + x["lipinski_mwt"] = x["molecular_weight"] <= 500 x["log_p"] = MolLogP(mol) - x["lipinki_log_p"] = x["log_p"] <= 5 - x["lipinki"] = all(v for k, v in x.items() if k.startswith("lipinki")) + x["lipinski_log_p"] = x["log_p"] <= 5 + x["lipinski"] = all(v for k, v in x.items() if k.startswith("lipinski")) return x From 635657b5b34a0448080aa6b9a208dd98883b105d Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 2 Jul 2025 11:51:25 -0400 Subject: [PATCH 104/147] Update READMEs and probe plots --- README.md | 37 +++++--------- opt/FeatureMiner/plots/lipinski_probes.jl | 61 ++++++++++++++++------- opt/interp_embeddings/README.md | 13 +++++ 3 files changed, 69 insertions(+), 42 deletions(-) create mode 100644 opt/interp_embeddings/README.md diff --git a/README.md b/README.md index 4badf71a..8cc5b438 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,9 @@ Benchmarking RoBERTa model pre-training on molecular datasets. # Installation +The following provides installation instructions for the top-level package (`electrolyte_fm`), optional add-ons for our +various additional analysis and downstream applications (See `opt/`) may require additional configuration. + ## Polaris 1. Install [rust](https://www.rust-lang.org/tools/install) and [uv](https://docs.astral.sh/uv/getting-started/installation/) @@ -21,11 +24,18 @@ uv sync ``` ## Artemis - Same as above except: 1. Skip loading conda (just use uv) 2. Ensure a module for CUDA@12.2 exists, may need to install with spack (make sure `buildable: True`) +## Apptainer + +0. Install or load from a module [Apptainer](https://apptainer.org/) +1. Build the image `bash container/build.sh`, once build relocate the image `mv /tmp/mist.sif ./mist.sif` +2. Run training within the image `apptainer run --nv mist.sif python train.py ...` + +> See `submit/dgx.j2` or `submit/delta.j2` for a more complete example of using the container + # Submitting Jobs ```shell @@ -35,27 +45,6 @@ source ./activate # Activate Environment See `submit/submit.py --help` for more info -## Building Apptainer Image - -```shell -apptainer build --fakeroot \ - --build-arg SSH_AUTH_SOCK=$SSH_AUTH_SOCK \ - mist.sif mist.def -``` - -## Hackathon - -Create a file `hack.yaml` and include it as an overlay to `submit.py` (i.e. `./submit/submit.py ... --data hack.yaml ...`). -Put the following in `hack.yaml`: -```yaml -queue: debug -account: GPU_Hack -nodes: 2 -walltime: 1:0:0 -train: - data.path: /grand/gpu_hack/FoundEnergy/realspace_v3_dev -``` - # Development ## Pre-commit @@ -63,5 +52,5 @@ train: We use [pre-commit](https://pre-commit.com) to preform various linting checks on the code. To enable: 1. Install poetry (See above) -2. Run pre-commit: `pre-commit` -3. Run before committing: `pre-commit install --allow-missing-config` +2. Run pre-commit: `uv run pre-commit` +3. Run before committing: `uv run pre-commit install --allow-missing-config` diff --git a/opt/FeatureMiner/plots/lipinski_probes.jl b/opt/FeatureMiner/plots/lipinski_probes.jl index 84376e11..20e2f9ef 100644 --- a/opt/FeatureMiner/plots/lipinski_probes.jl +++ b/opt/FeatureMiner/plots/lipinski_probes.jl @@ -68,7 +68,13 @@ function figure_probe_similarity(df::DataFrame; model="dh61satti", dataset="pret end function figure_lipinski_probes(df::DataFrame) - f = Figure(size=(2inch, 1inch)) + f = Figure(; size=(95, 122), figure_padding=(2, 4, 2, 2)) + + # Select the best probe per location + df = combine(groupby(df, [:encoder_dataset, :location, :layer])) do gdf + sort!(gdf, :val_loss; rev=true) + return gdf[1, :] + end df = dropmissing(df) subset!(df, @@ -85,33 +91,52 @@ function figure_lipinski_probes(df::DataFrame) # Feature alignment gl = GridLayout(f[1,1]) - ax = Axis(gl[1, 1]; - xticks=categorical_ticks(df.encoder_dataset), - yticks=2:2:8, - # limits=(nothing, ), - xticklabelsvisible=false, - xticksvisible=false, + ax = Axis(gl[2, 1]; + yticks=MISTStyle.categorical_ticks(df.encoder_dataset), + ylabel="MIST-28M Variant Probed", + xticks=2:2:8, ) - h = heatmap!(ax, auroc) - Colorbar(gl[1, 2], h; label="AUROC") + h = heatmap!(ax, auroc'; + colorscale=Makie.logit, + colorrange=(0.9, 0.995), + ) + cb = Colorbar(gl[1, 1], h; + label="AUROC", + size=6pt, + vertical=false, + # flipaxis=false, + ticks=[0.9, 0.99], + tickformat="{:.0%}", + minorticks=IntervalsBetween(5), + minorticksvisible=true, + labelsize=7pt, + ) # Additive Features df.additive_features = FeatureMiner.additive_features.(df.weight) df_af = combine(groupby(subset!(df, :location => ByRow(==("output"))), :encoder_dataset)) do gdf - unstack(gdf[:, [:layer, :additive_features]], :layer, :additive_features) + gdf = select(gdf, [:layer, :additive_features]) + unstack(gdf, :layer, :additive_features) end af = Matrix(df_af[:, 2:end]) - ax_add = Axis(gl[2, 1]; - xticks=categorical_ticks(df.encoder_dataset), - yticks=ax.yticks, + ax_add = Axis(gl[2, 2]; + yticks=MISTStyle.categorical_ticks(df.encoder_dataset), + xticks=ax.xticks, + yticklabelsvisible=false, + yticksvisible=false, + ) + h = heatmap!(ax_add, af') + Colorbar(gl[1, 2], h; + ticks=WilkinsonTicks(3), + minorticks=IntervalsBetween(5), + minorticksvisible=true, + size=cb.size, + label="Additivity", + vertical=false, ) - h = heatmap!(ax_add, af; colorrange=(0, 1)) - Colorbar(gl[2, 2], h; label="Additivity") @info extrema(af) - - # Add single Y-axis label - Label(gl[:, 0], text = "Encoder Layer", rotation = pi/2) + Label(gl[end+1, :], "Encoder Layer") return f end diff --git a/opt/interp_embeddings/README.md b/opt/interp_embeddings/README.md new file mode 100644 index 00000000..f96722c0 --- /dev/null +++ b/opt/interp_embeddings/README.md @@ -0,0 +1,13 @@ +# Scripts for Exploring MIST's token Embeddings + +## Installation + +1. Install [julia] and the base environment (See [Project's README](../../README.md)) +2. Instantiate the environment: `julia --project -e 'using Pkg; Pkg.instantiate()` + +[julia]: https://julialang.org/downloads/ + +## Generating Plots + +See `plots.jl` for the code used to generate plots from the paper. To run you will need +to acquire the pretrained & finetuned MIST models and place at the indicated path (see script). From b8a0dbc0693634fdd4f6c20cf460bba27f0f0ee7 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 2 Jul 2025 11:53:08 -0400 Subject: [PATCH 105/147] Don't add SAE to model __init__.py Slowly breaking the import everything pattern --- electrolyte_fm/models/__init__.py | 3 +-- submit/sae.jsonnet | 2 +- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index c4ae3702..ac7ac769 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -3,5 +3,4 @@ from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer -from .prod_finetune import MISTFinetuned, MISTMultiTask -from .sae import LightningSAE +from .prod_finetune import MISTFinetuned, MISTMultiTask \ No newline at end of file diff --git a/submit/sae.jsonnet b/submit/sae.jsonnet index 5b59b723..3ecb43b3 100644 --- a/submit/sae.jsonnet +++ b/submit/sae.jsonnet @@ -3,7 +3,7 @@ train: { tags: ['sae', 'debug'], model: { - class_path: 'electrolyte_fm.models.LightningSAE', + class_path: 'electrolyte_fm.models.sae.LightningSAE', init_args: { name_or_path: 'ibm/MoLFormer-XL-both-10pct', sae: { From 946427f121d7a080696ff7c8371b22a1b1ad2fb7 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 10 Jul 2025 14:48:18 -0400 Subject: [PATCH 106/147] rm unused code --- electrolyte_fm/data_modules/feature_tagger.py | 392 ------------------ test/test_featurizer.py | 179 -------- 2 files changed, 571 deletions(-) delete mode 100644 electrolyte_fm/data_modules/feature_tagger.py delete mode 100644 test/test_featurizer.py diff --git a/electrolyte_fm/data_modules/feature_tagger.py b/electrolyte_fm/data_modules/feature_tagger.py deleted file mode 100644 index 9d4cede3..00000000 --- a/electrolyte_fm/data_modules/feature_tagger.py +++ /dev/null @@ -1,392 +0,0 @@ -import re -from abc import abstractmethod, ABC -from typing import Iterable, Optional -import itertools -from rdkit import Chem - -import smirk -import torch - - -def flatten(*iterables) -> list: - return list(itertools.chain(*iterables)) - - -# fmt: off -ELEMENT_SYMBOLS = [ - "H", "He", "Li", "Be", "B", "C", "N", "O", "F", "Ne", - "Na", "Mg", "Al", "Si", "P", "S", "Cl", "Ar", "K", "Ca", - "Sc", "Ti", "V", "Cr", "Mn", "Fe", "Co", "Ni", "Cu", "Zn", - "Ga", "Ge", "As", "Se", "Br", "Kr", "Rb", "Sr", "Y", "Zr", - "Nb", "Mo", "Tc", "Ru", "Rh", "Pd", "Ag", "Cd", "In", "Sn", - "Sb", "Te", "I", "Xe", "Cs", "Ba", "La", "Ce", "Pr", "Nd", - "Pm", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Tm", "Yb", - "Lu", "Hf", "Ta", "W", "Re", "Os", "Ir", "Pt", "Au", "Hg", - "Tl", "Pb", "Bi", "Po", "At", "Rn", "Fr", "Ra", "Ac", "Th", - "Pa", "U", "Np", "Pu", "Am", "Cm", "Bk", "Cf", "Es", "Fm", - "Md", "No", "Lr", "Rf", "Db", "Sg", "Bh", "Hs", "Mt", "Ds", - "Rg", "Cn", "Nh", "Fl", "Mc", "Lv", "Ts", "Og", -] -# fmt: on - -BOND_TYPES = ["-", "=", "#", ":", "$", "/", "\\"] - -ELEMENT_GROUPS = [ - ["Li", "Na", "K", "Rb", "Cs", "Fr"], # Alkali Metals (Group 1) - ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"], # Alkaline Earth Metals (Group 2) - ["Sc", "Y", "Lu", "Lr"], # Scandium Group (Group 3) - ["Ti", "Zr", "Hf", "Rf"], # Titanium Group (Group 4) - ["V", "Nb", "Ta", "Db"], # Vanadium Group (Group 5) - ["Cr", "Mo", "W", "Sg"], # Chromium Group (Group 6) - ["Mn", "Tc", "Re", "Bh"], # Manganese Group (Group 7) - ["Fe", "Ru", "Os", "Hs"], # Iron Group (Group 8) - ["Co", "Rh", "Ir", "Mt"], # Cobalt Group (Group 9) - ["Ni", "Pd", "Pt", "Ds"], # Nickel Group (Group 10) - ["Cu", "Ag", "Au", "Rg"], # Copper Group (Group 11) - ["Zn", "Cd", "Hg", "Cn"], # Zinc Group (Group 12) - ["B", "Al", "Ga", "In", "Tl", "Nh"], # Boron Group (Group 13) - ["C", "Si", "Ge", "Sn", "Pb", "Fl"], # Carbon Group (Group 14) - ["N", "P", "As", "Sb", "Bi", "Mc"], # Nitrogen Group (Group 15) - ["O", "S", "Se", "Te", "Po", "Lv"], # Chalcogens (Group 16) - ["F", "Cl", "Br", "I", "At", "Ts"], # Halogens (Group 17) - ["Ne", "Ar", "Kr", "Xe", "Rn", "Rg"], # Noble Gases (Group 18) -] - -F_BLOCK = [ - [ - "La", - "Ce", - "Pr", - "Nd", - "Pm", - "Sm", - "Eu", - "Gd", - "Tb", - "Dy", - "Ho", - "Er", - "Tm", - "Yb", - "Lu", - ], - [ - "Ac", - "Th", - "Pa", - "U", - "Np", - "Pu", - "Am", - "Cm", - "Bk", - "Cf", - "Es", - "Fm", - "Md", - "No", - "Lr", - ], -] - -REGEX_FEATURES = { - "chiral_tags": r"@{1,2}(?:[A-Z]{2}\d{1,2})?", - "bracked_atom": r"\[[^]]+]", - "charged_atom": r"\[[^\]]+?[+-]{1,2}\d{0,2}]", - "chiral_center": r"\[[^\]]+?@[^\]]*?]", - "aromatic_bracket_atom": r"\[[a-z]{1,2}[^\]]*?]", -} - -ELEMENT_FEATURES = { - "alkali_metals": ELEMENT_GROUPS[0], - "alkaline_earth_metals": ELEMENT_GROUPS[1], - "nobel_gases": ["He"] + ELEMENT_GROUPS[16], - "s_block": flatten(["H"], *ELEMENT_GROUPS[0:1]), - "f_block": flatten(*F_BLOCK), - "d_block": flatten(*ELEMENT_GROUPS[2:11]), - "p_block": flatten(*ELEMENT_GROUPS[12:15]), - "metalloids": ["B", "Si", "Ge", "As", "Sb", "Te"], # Commonly recognized per wiki - "liquid_metals": ["Ga", "Hg", "Rb", "Cs", "Fr"], - "semi_metalalic": ["As", "Sb", "Bi", "Sn"], - "toxic_metals": ["As", "Be", "Cd", "Cr", "Pb", "Hg", "Ni"], # Goyer & Clarkson 1996 - "radioactive": flatten( # Elements that have no stable isotopes - ["Tc", "Po", "At", "Rn", "Pm"], - [group[-1] for group in ELEMENT_GROUPS], - F_BLOCK[1], - ), - "opensmiles_aromatic": [ - "b", - "c", - "n", - "o", - "p", - "s", - "se", - "as", - ], -} - -# Daylight Examples Marked (Daylight): https://daylight.com/dayhtml_tutorials/languages/smarts/smarts_examples.html -# rdkit.Chem.Lipinski from: https://github.com/rdkit/rdkit/blob/master/rdkit/Chem/Lipinski.py -SMARTS_FEATURES = { - "ketone": "[#6][CX3](=O)[#6]", # Daylight - "aldehyde": "[$([CX3H2](=O)),$([CX3H1](=O)[#6])]", # Daylight, plus branch for Formaldehyde - "carboxylic_acid": "[CX3](=O)[OX2H1]", # Daylight - "amid": "[$([NX3][CX3](=[OX1])[#6]),$(NC=O)]", # Daylight, plus branch for Formamide - "hydroxyl": "[OX2H]", # Daylight - "phenol": "[OH]c1ccccc1", - "rotatable_bond": "[!$(*#*)&!D1]-&!@[!$(*#*)&!D1]", # rdkit.Chem.Lipinski - "carboxyl_group": "[CX3]=[OX1]", # Daylight - "h_donor": "[$([N;!H0;v3]),$([N;!H0;+1;v4]),$([O,S;H1;+0]),$([n;H1;+0])]", # rdkit.Chem.Lipinski - "h_acceptor": "[$([O,S;H1;v2]-[!$(*=[O,N,P,S])]),$([O,S;H0;v2]),$([O,S;-]),$([N;v3;!$(N-*=!@[O,N,P,S])]),$([nH0,o,s;+0])]", # rdkit.Chem.Lipinski - "NH_or_OH_lipinski": "[#8H1,#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski - "NO_lipinski": "[#7H1,#7H2,#7H3]", # rdkit.Chem.Lipinski -} - - -class Feature(ABC): - def __init__(self, name: str, tokenizer: Optional[str] = None): - self.name = name - self.tokenzier = tokenizer or smirk.SmirkTokenizerFast() - self.smirk_tokenizer = ( - smirk.SmirkTokenizerFast() if tokenizer is not None else self.tokenzier - ) - - def featurize(self, smi: str, encoding: Optional[dict] = None) -> torch.BoolTensor: - encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) - assert "offset_mapping" in encoding - kwargs = self.preprocess(smi) - return self._featurize(smi, encoding, **kwargs) - - @abstractmethod - def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: - """Identify tokens in the input SMILES encoding expressing the feature""" - - def preprocess(self, smi: str) -> dict: - """Shared preprocessing steps for all features to be provided to `self._featurize` - Will be called once per feature class - """ - return {} - - @classmethod - @abstractmethod - def from_named(cls, name: str, **kwargs) -> "Feature": - """Create a feature from a named feature""" - - def align_tokens(self, encoding: dict, span: tuple[int, int]) -> Iterable[int]: - """Identify tokens overlapping span""" - token_offsets = encoding["offset_mapping"] - start, end = span - if end < start: - return # Non-matching span - - for i, t in enumerate(token_offsets): - if t[1] <= t[0]: # Token is empty - continue - - # Spans don't include the end index - elif not (end <= t[0] or t[1] <= start): - yield i - - def align_embeddings( - self, active: torch.BoolTensor, embedding: dict, other: dict - ) -> torch.BoolTensor: - if embedding == other: - return active - return active - raise NotImplementedError() - - def onehot(self, indices: list[int], n: int) -> torch.BoolTensor: - """Convert a list of indices to a one-hot encoding""" - active = torch.zeros(n, dtype=torch.bool) - if len(indices) > 0: - active[indices] = True - return active - - -class FeatureCollection(Feature): - def __init__(self, features: dict[str, Feature], **kwargs): - super().__init__("FeatureCollection", **kwargs) - self.features = features - preprocess_steps = {} - for f in features.values(): - preprocess_steps[f.__class__] = f - self.preprocess = list(preprocess_steps.values()) - - @property - def names(self): - return list(self.features.keys()) - - def __call__(self, smi: str) -> dict: - return {"proxy_activations": self.featurize(smi)} - - def featurize( - self, smi: str, encoding: Optional[dict] = None, return_dict: bool = False - ) -> torch.BoolTensor: - encoding = encoding or self.tokenzier(smi, return_offsets_mapping=True) - kwargs = {} - for f in self.preprocess: - kwargs.update(f.preprocess(smi)) - - act = [] - for idx, f in enumerate(self.features.values()): - act.append(f._featurize(smi, encoding, **kwargs)) - - if return_dict: - return {name: act for name, act in zip(self.features.keys(), act)} - - return torch.stack(act, dim=0) - - def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: - raise RuntimeError(f"{self} should be called using featurize") - - @classmethod - def from_named(cls, feature_names: str | list[str] = "all", **kwargs): - if isinstance(feature_names, str): - feature_names = [feature_names] - - features = [] - for name in feature_names: - if name == "all": - for k in ELEMENT_FEATURES.keys(): - features.append(ElementFeature.from_named(k, **kwargs)) - for k in REGEX_FEATURES.keys(): - features.append(RegexFeature.from_named(k, **kwargs)) - for k in SMARTS_FEATURES.keys(): - features.append(SMARTSFeature.from_named(k, **kwargs)) - - elif name in ELEMENT_FEATURES.keys(): - features.append(ElementFeature.from_named(name, **kwargs)) - - elif name in REGEX_FEATURES.keys(): - features.append(RegexFeature.from_named(name, **kwargs)) - - elif name in SMARTS_FEATURES.keys(): - features.append(SMARTSFeature.from_named(name, **kwargs)) - - else: - raise ValueError(f"Unknown named feature: {name}") - - feature_map = {} - for f in features: - if f.name in feature_map.keys(): - raise ValueError(f"Duplicate feature name: {f.name}") - feature_map[f.name] = f - - return cls(feature_map, **kwargs) - - -class RegexFeature(Feature): - def __init__(self, name: str, regex: [str, re.Pattern], **kwargs): - super().__init__(name, **kwargs) - self.regex = re.compile(regex) - - @classmethod - def from_named(cls, name: str, **kwargs): - return cls(name, REGEX_FEATURES[name], **kwargs) - - def _featurize(self, smi: str, encoding: dict, **kwargs) -> torch.BoolTensor: - n_groups = self.regex.groups - active = [] - for m in self.regex.finditer(smi): - if n_groups == 0: - active.extend(self.align_tokens(encoding, m.span())) - else: - for i in range(1, n_groups + 1): - active.extend(self.align_tokens(encoding, m.span(i))) - - return self.onehot(active, len(encoding["input_ids"])) - - -class ElementFeature(Feature): - def __init__(self, name: str, elements: list[str], **kwargs): - super().__init__(name, **kwargs) - self.elements = list(set(elements)) - self.element_ids = torch.tensor( - [ - self.smirk_tokenizer.encode(f"[{e}]", add_special_tokens=False)[1] - for e in self.elements - ] - ).reshape(-1, 1) - - @classmethod - def from_named(cls, name: str, **kwargs) -> "ElementFeature": - return cls(name, ELEMENT_FEATURES[name], **kwargs) - - def preprocess(self, smi: str) -> dict: - return { - "smirk_encoding": self.smirk_tokenizer(smi, return_offsets_mapping=True) - } - - def _featurize( - self, smi: str, encoding: dict, smirk_encoding: dict, **kwargs - ) -> torch.BoolTensor: - enc = torch.tensor(smirk_encoding["input_ids"]) - active = enc.eq(self.element_ids).any(dim=0) - return self.align_embeddings(active, encoding, smirk_encoding) - - -class RdkitFeature(Feature): - atomwise = re.compile(r"\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p") - - def preprocess(self, smi: str) -> dict: - mol = Chem.MolFromSmiles(smi, sanitize=False) - s_flags = Chem.SanitizeFlags.SANITIZE_NONE - s_flags |= Chem.SanitizeFlags.SANITIZE_SETAROMATICITY - s_flags |= Chem.SanitizeFlags.SANITIZE_SETHYBRIDIZATION - s_flags |= Chem.SanitizeFlags.SANITIZE_SETCONJUGATION - s_flags |= Chem.SANITIZE_PROPERTIES - Chem.SanitizeMol(mol, s_flags) - atom_spans = [m.span() for m in self.atomwise.finditer(smi)] - - # Validate rdkit -> smi mapping - for idx, atom in enumerate(mol.GetAtoms()): - smi_atom = smi[atom_spans[idx][0] : atom_spans[idx][1]] - smi_atom_mol = Chem.MolFromSmiles(smi_atom, sanitize=False) - Chem.SanitizeMol(smi_atom_mol, s_flags) - - assert atom.GetSymbol() == smi_atom_mol.GetAtomWithIdx(0).GetSymbol() - - # atom_smi = atom.GetSmarts() - # smi_atom_rdkit = Chem.MolToSmiles(smi_atom_mol) - # assert ( - # atom_smi == Chem.MolToSmiles(smi_atom_mol) - # ), f"Expected {atom_smi} and {smi_atom_rdkit} to match. Input atom: {smi_atom}" - - return {"rdkit_molecule": mol, "atom_spans": atom_spans} - - def align_atoms( - self, - atom_idx: int, - encoding: dict, - atom_spans: list[tuple[int, int]], - ) -> Iterable[int]: - """Map atom indices to token indices""" - span = atom_spans[atom_idx] - return self.align_tokens(encoding, span) - - -class SMARTSFeature(RdkitFeature): - def __init__(self, name: str, smarts: str, **kwargs): - super().__init__(name, **kwargs) - self.smarts = Chem.MolFromSmarts(smarts) - - @classmethod - def from_named(cls, name: str, **kwargs): - return cls(name, SMARTS_FEATURES[name], **kwargs) - - def _featurize( - self, - smi: str, - encoding: dict, - atom_spans: list[tuple[int, int]], - rdkit_molecule: Chem.Mol, - **kwargs, - ) -> torch.BoolTensor: - # TODO: Handle Bonds - matches = rdkit_molecule.GetSubstructMatches(self.smarts) - atom_indices = set(flatten(*matches)) - active: list[int] = [] - for idx in atom_indices: - active.extend(self.align_atoms(idx, encoding, atom_spans)) - return self.onehot(active, len(encoding["input_ids"])) diff --git a/test/test_featurizer.py b/test/test_featurizer.py deleted file mode 100644 index e5b1eae9..00000000 --- a/test/test_featurizer.py +++ /dev/null @@ -1,179 +0,0 @@ -import re -from random import choice - -import pytest -import torch - -from electrolyte_fm.data_modules.feature_tagger import ( - ELEMENT_FEATURES, - ELEMENT_SYMBOLS, - REGEX_FEATURES, - RegexFeature, - ElementFeature, - SMARTSFeature, -) - -REGEX_TESTS = [ - { - "feature": "chiral_tags", - "positive": ["@SP1", "@OH32", "@TB2", "@", "@@"], - "negative": ["C", "O", "c", "[Rb]"], - }, - { - "feature": "bracked_atom", - "positive": ["[C]", "[C-]", "[C+]", "[C@]", "[C@@]", "[C@H]", "[C@H-]"], - "negative": ["C", "O", "c", "F"], - }, - { - "feature": "charged_atom", - "positive": ["[C+]", "[C-]", "[C++]", "[Rb@OH2+3]"], - "negative": ["C", "O", "c", "[Rb@OH2]"], - }, - { - "feature": "chiral_center", - "positive": ["[C@]", "[C@@]", "[C@H]", "[C@H-]"], - "negative": ["C", "O", "c", "[Rb]"], - }, - { - "feature": "aromatic_bracket_atom", - "positive": ["[te+2]", "[b]", "[c@@]"], - "negative": ["C", "O", "[Rb]", "[Sn]", "[Cn]"], - }, -] - -SMARTS_TESTS = [ - { - "feature": "ketone", - "positive": ["CC(=O)C", "C[C@@H]1CCCCCCCCCCCCC(=O)C1"], - "negative": ["C=O", "OCC"], - }, - { - "feature": "aldehyde", - "positive": ["C=O", "O=CC", "CC=O"], - "negative": ["CC(=O)C", "OCC(O)CO"], - }, - { - "feature": "carboxylic_acid", - "positive": ["O=CO", "CC(=O)O", "C(=O)O", "O=C(O)C"], - "negative": ["CC(=O)C", "OCC"], - }, - { - "feature": "amid", - "positive": ["O=CN", "CC(=O)N", "C(=O)NC", "O=C(N)C"], - "negative": ["CC(=O)C", "OCC"], - }, - { - "feature": "hydroxyl", - "positive": ["CO", "CCO", "C(O)C"], - "negative": ["CC(=O)C", "C=O"], - }, - { - "feature": "phenol", - "positive": [ - "c1ccc(cc1)O", - "Oc1ccccc1", - "Oc0ccccc0Cc0cc(C1)c(O)c(c0)Cc0c(O)ccc(c0)Cc0ccc(O)c(c0)Cc0c(O)ccc(c0)Cc0c(O)ccc(c0)Cc0c(O)c(C2)cc(c0)Cc0c(O)ccc(c0)Cc(c0O)cc2cc0Cc0cc(Cc2ccc(O)cc2)c(O)c(c0)Cc0c(O)ccc(c0)C1", - ], - "negative": [ - "CC(=O)C", - "C=O", - "c1ccccc1-c2ccccc2", - "c1ccc(cc1)C[C@@H](C(=O)O)N", - ], - }, - { - "feature": "rotatable_bond", - "positive": [ - "CC-CC", - "c1ccccc1-c2ccccc2", - ], - "negative": ["CC(=O)C", "C=O", "c1ccccc1"], - }, -] - - -@pytest.mark.parametrize( - "feature,negatives", - ((x["feature"], x["negative"]) for x in REGEX_TESTS if "negative" in x), -) -def test_regex_negatives(feature: str, negatives: list[str]): - f = re.compile(REGEX_FEATURES[feature]) - for neg in negatives: - assert f.match(neg) is None, f"{feature} should not match {neg}" - - -@pytest.mark.parametrize( - "feature,positives", - ((x["feature"], x["positive"]) for x in REGEX_TESTS if "positive" in x), -) -def test_regex_positives(feature: str, positives: list[str]): - f = re.compile(REGEX_FEATURES[feature]) - for pos in positives: - assert f.match(pos) is not None, f"{feature} should match {pos}" - - -def generate_examples(): - for x in REGEX_TESTS: - yield RegexFeature, x["feature"], x["positive"], x["negative"] - - elements = set(ELEMENT_SYMBOLS) - for name, positive in ELEMENT_FEATURES.items(): - pos_examples = [f"[{e}]" for e in positive] - neg_examples = [f"[{e}]" for e in elements - set(positive)] - yield ElementFeature, name, pos_examples, neg_examples - - for x in SMARTS_TESTS: - yield SMARTSFeature, x["feature"], x["positive"], x["negative"] - - -@pytest.mark.parametrize( - "cls,feature,positive,negative", - generate_examples(), -) -def test_positive_feature(cls, feature, positive, negative): - f = cls.from_named(feature) - for pos in positive: - active = f.featurize(pos) - assert active.any(), "{} should match {}: {}".format(feature, pos, active) - - -@pytest.mark.parametrize( - "cls,feature,positive,negative", - generate_examples(), -) -def test_negative_feature(cls, feature, positive, negative): - f = cls.from_named(feature) - for neg in negative: - active = f.featurize(neg) - assert not active.any(), "{} should not match {}: {}".format( - feature, neg, active - ) - - -@pytest.mark.parametrize( - "cls,feature,positive,negative", - [e for e in generate_examples() if e[0] != SMARTSFeature], -) -def test_alignment(cls, feature, positive, negative): - f = cls.from_named(feature) - pos = choice(positive) - neg = choice(negative) - active = f.featurize(pos) - inactive = f.featurize(neg) - assert active.any() and not inactive.any() - check_active(pos + neg, torch.cat([active, inactive]), f.featurize(pos + neg)) - check_active(pos + pos, torch.cat([active, active]), f.featurize(pos + pos)) - check_active( - neg + pos + neg, - torch.cat([inactive, active, inactive]), - f.featurize(neg + pos + neg), - ) - - -def check_active(smi, expected, actual): - print(f"smi: {smi}") - print(f"expected: {expected}") - print(f"actual: {actual}") - assert isinstance(actual, torch.BoolTensor) - assert isinstance(expected, torch.BoolTensor) - assert all(actual == expected) From b4ab118e9c3d64ee965cd927ac6c104afb65e88a Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sun, 13 Jul 2025 13:03:59 -0400 Subject: [PATCH 107/147] remove unused SAE codes --- electrolyte_fm/models/sae.py | 504 ----------------------------------- test/test_sae.py | 287 -------------------- 2 files changed, 791 deletions(-) delete mode 100644 electrolyte_fm/models/sae.py delete mode 100644 test/test_sae.py diff --git a/electrolyte_fm/models/sae.py b/electrolyte_fm/models/sae.py deleted file mode 100644 index 30006e95..00000000 --- a/electrolyte_fm/models/sae.py +++ /dev/null @@ -1,504 +0,0 @@ -from math import sqrt, floor -from typing import Callable, Optional, Literal -from contextlib import contextmanager - -import lightning.pytorch as pl -from numpy import minimum -import torch -from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable -from torch import nn -from torch.nn import functional as F -from transformers import PreTrainedModel - -from .model_utils import load_encoder - - -def init_bias(bias, w): - fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(w) - bound = 1 / sqrt(fan_in) - nn.init.uniform_(bias, -bound, bound) - - -def avg_l0_norm(x: torch.Tensor) -> torch.FloatTensor: - """Count the average number of active features for tensor (*, F), where F is are the feature activations""" - return x.detach().count_nonzero() / x.shape[:-1].numel() - - -def hf_cross_entropy(logits: torch.Tensor, target: torch.Tensor): - """F.cross_entropy but for logits of `(B, T, C)` and target of `(B, T)`""" - if isinstance(logits, tuple): - logits = logits[0] - elif not isinstance(logits, torch.Tensor): - logits = logits.last_hidden_state - - return F.cross_entropy( - logits.view(-1, logits.shape[-1]), - target.view(-1), - ignore_index=-100, - ) - - -class AbstractSAE(nn.Module): - def __init__(self, hidden_size: int, expansion: int) -> None: - super().__init__() - self.hidden_size = hidden_size - self.num_features = hidden_size * expansion - self.num_batches_not_active = torch.zeros(self.num_features) - - def update_inactive_features(self, f: torch.Tensor): - f_act = f.sum(dim=list(range(f.ndim)[:-1])) - self.num_batches_not_active += f_act == 0 - self.num_batches_not_active[f_act > 0] = 0 - - def forward(self, x: torch.Tensor): - return self.decode(self.encode(x)) - - def encode(self, x: torch.Tensor) -> torch.Tensor: - """Encode x into a feature representation""" - raise NotImplementedError() - - def decode(self, features: torch.Tensor) -> torch.Tensor: - """Encode features into a reconstruction of x""" - raise NotImplementedError() - - def forward_with_loss(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: - """Forward pass with loss calculation -> `(self.forward(x), loss)`""" - return self.forward(x), torch.tensor(0.0) - - -InjectedCoderState = Literal["null", "dense", "sparse"] - - -class InjectedCoder(nn.Module): - def __init__(self, dense_model: nn.Module, coder: AbstractSAE): - super().__init__() - self.dense_model = dense_model - self.coder = coder - self.loss = torch.tensor(0.0) - self.state: InjectedCoderState = "sparse" - - def forward(self, x: torch.Tensor) -> torch.Tensor: - x = self.dense_model(x) - if self.state == "dense": - return x - - elif self.state == "null": - return torch.zeros_like(x) - - if self.training: - x_hat, loss = self.coder.forward_with_loss(x) - self.loss = loss - - else: - x_hat = self.coder.forward(x) - - return x_hat - - -class SparsifiedModel(nn.Module): - def __init__(self, model: nn.Module, coders: list[InjectedCoder]): - super().__init__() - self.model = model - self.coders = coders - - @classmethod - def from_huggingface( - cls, model: PreTrainedModel, coder: AbstractSAE, layer: float | int = 0.5 - ): - layers = model.base_model.encoder.layer - if isinstance(layer, float): - n_layers = len(layers) - layer = floor(layer * n_layers) - - coders = [cls.inject_sparse_coder(layers, coder, layer)] - return cls(model, coders) - - @staticmethod - def inject_sparse_coder(layers: nn.ModuleList, coder: AbstractSAE, layer: int): - m = InjectedCoder(layers[layer].output.dense, coder) - layers[layer].output.dense = m - return m - - def set_sparsity(self, enable: bool = True): - """Enable or disable sparse coders""" - assert isinstance(enable, bool) - for coder in self.coders: - coder.state = "sparse" if enable else "dense" - - @contextmanager - def sparse(self, enable: bool = True): - sparsity = [] - for coder in self.coders: - sparsity.append(coder.state) - coder.state = "sparse" if enable else "dense" - - try: - yield self - finally: - for coder in self.coders: - coder.state = sparsity.pop() - - @contextmanager - def nullcoders(self): - state = [] - for coder in self.coders: - state.append(coder.state) - coder.state = "null" - - try: - yield self - finally: - for coder in self.coders: - coder.state = state.pop() - - def forward(self, *args, **kwargs): - return self.model.forward(*args, **kwargs) - - def forward_with_loss(self, *args, **kwargs): - y = self.model.forward(*args, **kwargs) - device = self.coders[0].loss.device - loss = torch.tensor(0.0, device=device) - for coder in self.coders: - loss += coder.loss - - return y, loss - - def sparse_parameters(self): - """Return an iterator over the parameters of the sparse autoencoders""" - for coder in self.coders: - yield from coder.coder.parameters() - - def sparse_named_parameters(self): - for coder in self.coders: - yield from coder.coder.named_parameters() - - @torch.no_grad() - def loss_recovered( - self, - target: torch.Tensor, - *args, - lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, - sparse_output: Optional[torch.Tensor] = None, - **kwargs, - ): - """Compute the fraction of the loss recovered by the SAE relative to an null-embedding""" - training = self.training - self.eval() - if sparse_output is None: - with self.sparse(True) as self: - sparse_output = self(*args, **kwargs) - assert sparse_output is not None - loss_sparse = lossfn(sparse_output, target) - - with self.sparse(False) as self: - y_dense = self(*args, **kwargs) - loss_dense = lossfn(y_dense, target) - - with self.nullcoders() as self: - y_null = self(*args, **kwargs) - loss_null = lossfn(y_null, target) - - self.train(training) - - print( - { - "loss_sparse": loss_sparse, - "loss_dense": loss_dense, - "loss_null": loss_null, - } - ) - return 1 - ((loss_sparse - loss_dense) / (loss_null - loss_dense)) - - -class GatedSAE(AbstractSAE): - def __init__( - self, - hidden_size: int, - expansion: int = 4, - l1_coef: float = 0.01, - device=None, - dtype=None, - ): - super().__init__(hidden_size, expansion) - factory_kwargs = {"device": device, "dtype": dtype} - n_features = hidden_size * expansion - self.w_gate = nn.Parameter( - torch.empty((n_features, hidden_size), **factory_kwargs) - ) - self.b_gate = nn.Parameter(torch.empty(n_features, **factory_kwargs)) - self.w_dec = nn.Parameter( - torch.empty((hidden_size, n_features), **factory_kwargs) - ) - self.b_dec = nn.Parameter(torch.empty(hidden_size, **factory_kwargs)) - self.r_mag = nn.Parameter(torch.empty(n_features, **factory_kwargs)) - self.b_enc = nn.Parameter(torch.empty(n_features, **factory_kwargs)) - self.l1_coef = torch.tensor(l1_coef, requires_grad=False, **factory_kwargs) - - # Loss parameters - self.loss = torch.tensor(0.0, **factory_kwargs) - - self.reset_parameters() - - def reset_parameters(self): - for w in [self.w_gate, self.w_dec]: - nn.init.kaiming_uniform_(w, nonlinearity="relu") - - init_bias(self.b_gate, self.w_gate) - init_bias(self.b_dec, self.w_dec) - # kaiming init for r_mag - nn.init.normal_(self.r_mag, mean=0.0, std=sqrt(2 / self.r_mag.shape[0])) - init_bias(self.b_enc, self.w_gate) - - def forward(self, x: torch.Tensor): - return self.decode(self.encode(x)) - - def encode(self, x: torch.Tensor): - x_centered = x - self.b_dec - x_enc = x_centered.matmul(self.w_gate.T) - gate = (x_enc + self.b_gate) > 0 - x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) - return x_mag * gate - - def decode(self, features: torch.Tensor): - return F.linear(features, self.w_dec, self.b_dec) - - def forward_with_loss(self, x: torch.Tensor): - x_centered = x - self.b_dec - x_enc = x_centered.matmul(self.w_gate.T) - pi_gate = x_enc + self.b_gate - pi_rect = F.relu(pi_gate) - loss_sparsity = self.l1_coef * pi_rect.abs().sum() - - x_mag = F.relu(self.r_mag.exp() * x_enc + self.b_enc) - gate = pi_gate > 0 - features = x_mag * gate - x_hat = F.linear(features, self.w_dec, self.b_dec) - loss_recon = F.mse_loss(x_hat, x) - - x_hat_detach = F.linear(pi_rect, self.w_dec.detach(), self.b_dec.detach()) - loss_aux = F.mse_loss(x_hat_detach, x) - - loss = loss_sparsity + loss_aux + loss_recon - return x_hat, loss - - -def init_column_fixed_l2(tensor: torch.Tensor, l2: float = 0.1): - """init a column of a tensor to a fixed l2""" - col_l2 = l2 / tensor.norm(2, dim=0) - with torch.no_grad(): - tensor.copy_(tensor * col_l2) - - -class VanillaSAE(AbstractSAE): - def __init__( - self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01 - ) -> None: - super().__init__(hidden_size, expansion) - self.encoder = nn.Linear(hidden_size, hidden_size * expansion) - self.decoder = nn.Linear(hidden_size * expansion, hidden_size) - self.l1_coef_coef = torch.tensor(l1_coef) - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - init_column_fixed_l2(self.decoder.weight) - self.decoder.bias.zero_() - self.encoder.weight.copy_(self.decoder.weight.T) - self.encoder.bias.zero_() - - def encode(self, x: torch.Tensor): - return F.relu(self.encoder(x)) - - def decode(self, features: torch.Tensor) -> torch.Tensor: - return self.decoder(features) - - def forward_with_loss(self, x: torch.Tensor): - f = self.encode(x) - x_hat = self.decode(f) - loss = ( - F.mse_loss(x_hat, x) - + self.l1_coef_coef * (self.decoder.weight.norm(2, dim=0) * f).sum() - ) - return x_hat, loss - - -class TiedBiasSAE(AbstractSAE): - def __init__(self, hidden_size: int, expansion: int = 4, l1_coef: float = 0.01): - super().__init__(hidden_size, expansion) - n_features = hidden_size * expansion - self.encoder = nn.Linear(hidden_size, n_features) - self.decoder = nn.Linear(n_features, hidden_size) - self.l1_coef_coef = torch.tensor(l1_coef) - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - self.encoder.weight.copy_(self.decoder.weight.T) - self.decoder.bias.zero_() - self.encoder.bias.zero_() - - def encode(self, x: torch.Tensor): - return F.relu(self.encoder(x - self.decoder.bias)) - - def decode(self, features): - return self.decoder(features) - - def forward_with_loss(self, x: torch.Tensor): - f = F.relu(self.encoder(x - self.decoder.bias)) - x_hat = self.decoder(f) - f_act = f * self.decoder.weight.norm(p=2, dim=0) - loss_reconstruction = F.mse_loss(x_hat, x) - loss_sparsity = self.l1_coef_coef * f_act.abs().sum() - loss = loss_reconstruction + loss_sparsity - return x_hat, loss - - -def topk(x: torch.Tensor, k: int, dim: int = -1): - k = minimum(x.shape[dim], k) - vi = torch.topk(x, k, dim=dim, sorted=False, largest=True) - return torch.zeros_like(x).scatter(dim, vi.indices, vi.values) - - -class TopKSAE(AbstractSAE): - def __init__( - self, - hidden_size: int, - expansion: int = 4, - k: int = 10, - alpha: float = 1 / 32, - dead_threshold=1_000_000, - ) -> None: - super().__init__(hidden_size, expansion) - self.encoder = nn.Linear(hidden_size, hidden_size * expansion) - self.decoder = nn.Linear(hidden_size * expansion, hidden_size) - self.k = k - self.alpha = alpha - self.dead_threshold = dead_threshold - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - init_column_fixed_l2(self.decoder.weight) - self.decoder.bias.zero_() - self.encoder.weight.copy_(self.decoder.weight.T) - self.encoder.bias.zero_() - - def encode(self, x: torch.Tensor): - return topk(self.encoder(x), self.k, dim=-1) - - def decode(self, features: torch.Tensor) -> torch.Tensor: - return self.decoder(features) - - def encode_dead_features(self, x: torch.Tensor): - f = self.encoder(x) - dead = self.num_batches_not_active > self.dead_threshold - f = f * dead - return topk(f, self.k, dim=-1) - - def forward_with_loss(self, x: torch.Tensor): - f = self.encode(x) - self.update_inactive_features(f) - x_hat = self.decode(f) - x_dead = self.decode(self.encode_dead_features(x)) - loss = F.mse_loss(x_hat, x) + self.alpha * F.mse_loss(x_dead, x) - return x_hat, loss - - -class LightningSAE(pl.LightningModule): - def __init__( - self, - name_or_path: str, - sae: AbstractSAE, - layer: int | float = 0.5, - lossfn: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = hf_cross_entropy, - optimizer: OptimizerCallable = torch.optim.AdamW, - lr_schedule: LRSchedulerCallable | None = None, - ): - super().__init__() - - encoder = load_encoder(name_or_path) - self.sparse_model = SparsifiedModel.from_huggingface(encoder, sae, layer) - self.optimizer = optimizer - self.lr_schedule = lr_schedule - self.lossfn = lossfn - self.save_hyperparameters(ignore=["sparse_model"]) - - def setup(self, stage: str): - if isinstance(self.logger, pl.loggers.WandbLogger): - - def define_metric(*args, **kwargs): - self.logger.experiment.define_metric(*args, **kwargs) - - # Add summary metrics for loss - for stage in ["train", "val", "test"]: - for s in ["step", "epoch"]: - define_metric( - f"{stage}/loss_{s}", summary="best,min,last", goal="minimize" - ) - - define_metric(f"{stage}/alive_features", summary="max,last") - define_metric(f"{stage}/max_feature_density", summary="min,max,last") - - def stage_step(self, stage: str, batch, recovered_loss: bool = False): - input_ids = batch["input_ids"] - attention_mask = batch["attention_mask"] - y, loss = self.sparse_model.forward_with_loss(input_ids, attention_mask) - logdict = {f"{stage}/loss": loss} - - if self.lossfn: - target = batch["target"] if "target" in batch else batch["labels"] - y_loss = self.lossfn(y, target) - logdict[f"{stage}/target_loss"] = y_loss - - if recovered_loss: - logdict[f"{stage}/recovered_loss"] = self.sparse_model.loss_recovered( - target, - input_ids, - attention_mask=attention_mask, - lossfn=self.lossfn, - sparse_output=y, - ) - - return loss, logdict - - def training_step(self, batch): - loss, logdict = self.stage_step("train", batch) - self.log_dict(logdict, on_epoch=True, on_step=True) - return loss - - def validation_step(self, batch): - loss, logdict = self.stage_step("val", batch, recovered_loss=True) - logdict = {k + "_epoch": v for k, v in logdict.items()} - self.log_dict(logdict, on_epoch=True, on_step=False) - return loss - - def test_step(self, batch): - loss, logdict = self.stage_step("test", batch, recovered_loss=True) - logdict = {k + "_epoch": v for k, v in logdict.items()} - self.log_dict(logdict, on_epoch=True, on_step=False) - return loss - - def _log_feature_metrics(self, metric, stage: str) -> None: - m = metric.compute() - feature_density = m.pop(stage + "/feature_density", None) - self.log_dict(m, on_epoch=True, sync_dist=True) - if ( - isinstance(self.logger, pl.loggers.WandbLogger) - and feature_density is not None - ): - self.logger.log_table( - stage + "/feature_density", - data=list(zip(feature_density.bin_centers, feature_density.density)), - columns=["bin_center", "density"], - ) - - metric.reset() - - def configure_optimizers(self): - optimizer = self.optimizer(self.sparse_model.sparse_parameters()) - if schedule := self.lr_schedule: - return { - "optimizer": optimizer, - "lr_scheduler": {"scheduler": schedule(optimizer), "interval": "step"}, - } - return optimizer diff --git a/test/test_sae.py b/test/test_sae.py deleted file mode 100644 index 993a7f7f..00000000 --- a/test/test_sae.py +++ /dev/null @@ -1,287 +0,0 @@ -import pytest -import torch -import torch.nn.functional as F -from transformers import ( - AutoModelForMaskedLM, - AutoTokenizer, - DataCollatorForLanguageModeling, - DataCollatorWithPadding, - RobertaPreLayerNormConfig, - RobertaPreLayerNormForMaskedLM, - RobertaPreLayerNormModel, -) - -from electrolyte_fm.models.sae import ( - AbstractSAE, - GatedSAE, - InjectedCoder, - SparsifiedModel, - TiedBiasSAE, - TopKSAE, - VanillaSAE, - topk, - hf_cross_entropy, -) -from electrolyte_fm.utils.tokenizer import load_tokenizer - - -def get_default_device(): - if torch.cuda.is_available(): - return torch.device("cuda") - elif torch.backends.mps.is_available(): - return torch.device("mps") - else: - return torch.device("cpu") - - -DEVICE = get_default_device() - -SAE_CLASSES = [GatedSAE, TiedBiasSAE, VanillaSAE, TopKSAE] - - -@pytest.fixture() -@pytest.mark.cuda -def roberta_tokenzier(): - tokenizer = load_tokenizer("smirk") - config = RobertaPreLayerNormConfig( - vocab_size=len(tokenizer), - hidden_size=256, - num_hidden_layers=4, - num_attention_heads=4, - intermediate_size=512, - ) - return RobertaPreLayerNormForMaskedLM(config).to(DEVICE), tokenizer - - -def test_topk(): - x = torch.rand(8, 4, 20) - assert ((x > 0).sum(-1) > 5).all() - x_hat = topk(x, 5) - assert ((x_hat > 0).sum(-1) == 5).all() - - -@pytest.mark.gpu -def test_hf_crossentropy(): - name = "ibm/MoLFormer-XL-both-10pct" - model = AutoModelForMaskedLM.from_pretrained(name, trust_remote_code=True).to( - DEVICE - ) - tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True) - batch = [ - tok(smi) - for smi in [ - "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", - "CN3[C@H]1CC[C@@H]3C[C@@H](C1)OC(=O)C(CO)c2cc", - ] - ] - collate = DataCollatorForLanguageModeling(tok, mlm=False) - # collate = DataCollatorWithPadding(tok) - batch = collate(batch) - batch = {k: v.to(DEVICE) for k, v in batch.items()} - model.eval() - out = model( - batch["input_ids"], - attention_mask=batch["attention_mask"], - return_dict=True, - ) - logits = out.logits - vocab_size = model.config.vocab_size - loss_ref = F.cross_entropy(logits.view(-1, vocab_size), batch["labels"].view(-1)) - loss = hf_cross_entropy(logits, batch["labels"]) - assert loss == loss_ref - - -@pytest.mark.parametrize("sae_cls", SAE_CLASSES) -class TestSAE: - B = 3 - H = 4 - T = 8 - E = 2 - - @classmethod - def setup_class(cls): - torch.manual_seed(0) - - @property - def feature_shape(self): - return (self.B, self.T, self.H * self.E) - - def input(self): - return torch.rand(self.B, self.T, self.H) - - def test_init(self, sae_cls): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - for p in sae.parameters(): - assert p.isfinite().all() - assert not p.isnan().any() - - def test_encode(self, sae_cls): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - x = self.input() - f = sae.encode(x) - assert f.shape == self.feature_shape - x_hat = sae.decode(f) - assert x_hat.shape == x.shape - assert sae.forward(x).equal(x_hat) - - def test_forward(self, sae_cls: AbstractSAE): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - assert isinstance(sae, AbstractSAE) - x = self.input() - features = sae.forward(x) - assert features.shape == x.shape - assert features.isfinite().all() - assert not features.isnan().any() - - def test_loss(self, sae_cls): - sae = sae_cls(hidden_size=self.H, expansion=self.E) - assert isinstance(sae, AbstractSAE) - x = self.input() - y, loss = sae.forward_with_loss(x) - assert loss.isfinite() and loss.shape == () - assert y.shape == x.shape - assert y.isfinite().all() - - -@pytest.mark.parametrize("sae_cls", SAE_CLASSES) -def test_injected_coder(sae_cls): - hidden_size = 64 - model = torch.nn.Linear(hidden_size, hidden_size) - sae = sae_cls(hidden_size=hidden_size, expansion=2) - injected = InjectedCoder(model, sae).to(DEVICE) - injected.eval() - assert isinstance(injected, InjectedCoder) - dense_model = injected.dense_model - assert not injected.training and not dense_model.training - x = torch.rand(2, 5, hidden_size, device=DEVICE) - - # Check dense - y_ref = dense_model(x) - injected.state = "dense" - y_dense = injected(x) - assert y_dense.shape == y_ref.shape - assert y_dense.equal(y_ref) - - # Check null - injected.state = "null" - y_null = injected(x) - assert y_null.shape == y_ref.shape - assert y_null.equal(torch.zeros_like(y_dense)) - - # Check sparse - injected.state = "sparse" - y_sparse = injected(x) - assert y_sparse.shape == y_dense.shape - - -def test_instrumented(): - model = AutoModelForMaskedLM.from_pretrained( - "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True - ) - tokenizer = AutoTokenizer.from_pretrained( - "ibm/MoLFormer-XL-both-10pct", trust_remote_code=True - ) - hidden_size = model.config.hidden_size - sae = TiedBiasSAE(hidden_size=hidden_size, expansion=2) - sparse_model = SparsifiedModel.from_huggingface(model, sae, layer=0).to(DEVICE) - model = model.to(DEVICE) - sparse_model.eval() - assert not model.training - assert not sae.training - assert not model.training - - batch = [ - tokenizer(smi) - for smi in ["CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "C1=CC2=C(C=C1O)C(=CN2)CCN"] - ] - collate = DataCollatorForLanguageModeling(tokenizer, mlm=False) - batch = collate(batch) - batch = { - k: v.to(DEVICE) - for k, v in batch.items() - if k in ["input_ids", "attention_mask", "labels"] - } - batch["return_dict"] = True - y = model(**batch).logits - - assert all([coder.state == "sparse" for coder in sparse_model.coders]) - with sparse_model.nullcoders() as sparse_model: - assert all([coder.state == "null" for coder in sparse_model.coders]) - y_null = sparse_model(**batch).logits - assert y_null.shape == y.shape - assert y_null.device == y.device - assert y_null.dtype == y.dtype - - assert all([not coder.dense.training for coder in sparse_model.coders]) - with sparse_model.sparse(False) as sparse_model: - assert all([coder.state == "dense" for coder in sparse_model.coders]) - y_dense = sparse_model(**batch).logits - assert y_dense.shape == y.shape - assert y_dense.device == y.device - assert y_dense.dtype == y.dtype - assert y_dense.equal(y) - - rc = sparse_model.loss_recovered(target=batch["input_ids"], **batch) - assert isinstance(rc, torch.Tensor) and rc.shape == () - assert rc.isfinite() and not rc.isnan() - assert rc <= 1 - - -def test_sparse_model(roberta_tokenzier): - roberta, tokenizer = roberta_tokenzier - sae = TiedBiasSAE(hidden_size=roberta.config.hidden_size, expansion=2) - sparse_model = SparsifiedModel.from_huggingface(roberta, sae, layer=2).to(DEVICE) - robert = sparse_model.model.base_model - assert isinstance(robert, RobertaPreLayerNormModel) - assert isinstance(robert.encoder.layer[2].output.dense, InjectedCoder) - assert robert.encoder.layer[2].output.dense is sparse_model.coders[0] - - collate = DataCollatorWithPadding(tokenizer) - batch = collate([tokenizer("CNCCC")]) - batch = { - "input_ids": batch["input_ids"].to(DEVICE), - "attention_mask": batch["attention_mask"].to(DEVICE), - "return_dict": True, - } - y_sparse = sparse_model(**batch).logits - robert.eval() - y_dense = roberta(**batch).logits - assert y_sparse.shape == y_dense.shape - assert y_sparse.shape == (1, 5, len(tokenizer)) - - # Check null features - with sparse_model.nullcoders() as model: - y_null = model(**batch).logits - assert y_null.shape == y_sparse.shape - - # Check dense features - with sparse_model.sparse(False) as model: - # Run model in eval model to be deterministic - model.eval() - robert.eval() - y_dense = roberta(**batch).logits - y_dense_context = model(**batch).logits - robert.train() - model.train() - assert y_dense_context.shape == y_dense.shape - e = (y_dense_context.detach() - y_dense.detach()).abs() - assert (e < 1e-6).all() - - # Check non-encoder parameters don't get gradients - assert len(list(sparse_model.sparse_parameters())) > 0 - assert len(dict(sparse_model.sparse_named_parameters())) > 0 - - # Check_gradient - out, loss = sparse_model.forward_with_loss(**batch) - # assert out.logits.equal(y_sparse) - loss.backward() - for _, v in sparse_model.sparse_named_parameters(): - assert v.grad is not None - assert (v.grad != 0).any() - - # Check Recovered Loss - rc = sparse_model.loss_recovered( - **batch, target=batch["input_ids"], sparse_output=y_sparse - ) - assert isinstance(rc, torch.Tensor) and rc.shape == () - assert rc.isfinite() and not rc.isnan() From 5196bba9347ceabc8f3b00d8ee2b35c6a3ae60ea Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 16:52:38 -0400 Subject: [PATCH 108/147] Use latest smirk, update CI to reflect --- .github/workflows/CI.yaml | 6 +----- electrolyte_fm/data_modules/pubchem_qc.py | 26 +++++++++++++++-------- opt/sterochemistry/export_clc_db.py | 0 opt/sterochemistry/omol25.py | 18 ++++++++++++---- opt/sterochemistry/process_clc_db.py | 0 pyproject.toml | 6 +++++- uv.lock | 7 +++++- 7 files changed, 43 insertions(+), 20 deletions(-) mode change 100644 => 100755 opt/sterochemistry/export_clc_db.py mode change 100644 => 100755 opt/sterochemistry/omol25.py mode change 100644 => 100755 opt/sterochemistry/process_clc_db.py diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index 673b716c..d785997d 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -14,11 +14,7 @@ jobs: enable-cache: true - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - - name: Install deploy key for BattModels/smirk - uses: webfactory/ssh-agent@dc588b651fe13675774614f8e6a936a468676387 #v0.9.0 - with: - ssh-private-key: ${{ secrets.SMIRK_PRIVATE_KEY }} - name: install project run: uv sync --all-extras --dev - name: pytest - run: uv run pytest + run: uv run pytest --durations=0 diff --git a/electrolyte_fm/data_modules/pubchem_qc.py b/electrolyte_fm/data_modules/pubchem_qc.py index 18b6c809..17203567 100644 --- a/electrolyte_fm/data_modules/pubchem_qc.py +++ b/electrolyte_fm/data_modules/pubchem_qc.py @@ -597,20 +597,28 @@ def annotated_tokens( @torch.no_grad def sparse_topo_distance(mol: Chem.Mol, atom_indices: list[int]): + """Return sparse pairwise topology features for selected atom indices. + + Produces a stacked sparse tensor with the last dimension containing: + - Adjacency (0/1 hop connectivity) + - Topological distance (shortest-path hop count) + + The tensor shape is (A, A, 2) where A is 1 + max(atom_indices), matching + the behavior expected by tests that include out-of-range indices for padding. + """ adx = torch.tensor(atom_indices) rdx, cdx = torch.meshgrid(adx, adx, indexing="ij") idx = torch.stack((rdx.flatten(), cdx.flatten())) - S = [] - # Number of Hops - for d in [ - # Chem.rdmolops.GetDistanceMatrix(mol, force=True), - Chem.rdmolops.GetAdjacencyMatrix(mol, force=True), - # Chem.rdmolops.GetDistanceMatrix(mol, useBO=True, force=True), - ]: - S.append(torch.sparse_coo_tensor(idx, d.flatten())) + # Collect dense matrices from RDKit + adj = Chem.rdmolops.GetAdjacencyMatrix(mol, force=True) + topo = Chem.rdmolops.GetDistanceMatrix(mol) # shortest-path (hop) distances + + # Build sparse tensors aligned to the provided indices; infer size from idx + S_adj = torch.sparse_coo_tensor(idx, adj.flatten()) + S_topo = torch.sparse_coo_tensor(idx, topo.flatten()) - return torch.stack(S, dim=-1) + return torch.stack([S_adj, S_topo], dim=-1) @torch.no_grad diff --git a/opt/sterochemistry/export_clc_db.py b/opt/sterochemistry/export_clc_db.py old mode 100644 new mode 100755 diff --git a/opt/sterochemistry/omol25.py b/opt/sterochemistry/omol25.py old mode 100644 new mode 100755 index c4149aee..00461a45 --- a/opt/sterochemistry/omol25.py +++ b/opt/sterochemistry/omol25.py @@ -395,7 +395,9 @@ def main(argv: list[str] | None = None) -> int: datasets = {} for split, sources in splits.items(): logging.info("Building split '%s' from %d source(s)", split, len(sources)) - ds = _build_split_dataset(sources, num_proc=args.num_proc, read_workers=args.read_workers) + ds = _build_split_dataset( + sources, num_proc=args.num_proc, read_workers=args.read_workers + ) datasets[split] = ds # Save to disk (always as DatasetDict for predictable structure) @@ -451,7 +453,8 @@ def _discover_splits(root: Path) -> dict[str, list[tuple[str, ...]]]: [ p for p in root.iterdir() - if p.is_file() and (str(p).endswith(".tar.gz") or str(p).endswith(".tgz")) + if p.is_file() + and (str(p).endswith(".tar.gz") or str(p).endswith(".tgz")) ] ): split = _basename_without_targz(tb.name) @@ -462,6 +465,7 @@ def _discover_splits(root: Path) -> dict[str, list[tuple[str, ...]]]: return {} + def _basename_without_targz(name: str) -> str: for suf in (".tar.gz", ".tgz"): if name.endswith(suf): @@ -472,7 +476,11 @@ def _basename_without_targz(name: str) -> str: def _list_aselmdb_in_tar(tar_path: str) -> list[str]: with tarfile.open(tar_path, "r:gz") as tf: - return [m.name for m in tf.getmembers() if m.isfile() and m.name.endswith(".aselmdb")] + return [ + m.name + for m in tf.getmembers() + if m.isfile() and m.name.endswith(".aselmdb") + ] def _iter_aselmdb_from_tar(tar_path: str, member_name: str): @@ -620,7 +628,9 @@ def _process_batch(batch: dict) -> dict: return out -def _build_split_dataset(sources: list[tuple[str, ...]], num_proc: int, read_workers: int) -> Dataset: +def _build_split_dataset( + sources: list[tuple[str, ...]], num_proc: int, read_workers: int +) -> Dataset: # Stage 1: build a lightweight dataset of raw JSON strings ds = Dataset.from_generator( _entries_from_sources, diff --git a/opt/sterochemistry/process_clc_db.py b/opt/sterochemistry/process_clc_db.py old mode 100644 new mode 100755 diff --git a/pyproject.toml b/pyproject.toml index d6c3a2d6..e468f226 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,7 +20,7 @@ dependencies = [ "scikit-learn>=1.5.1", "selfies>=2.1.0", "rdkit>=2024.3.5", - "smirk==0.1.0", + "smirk", "safetensors>=0.5.1", "pytorch-lightning~=2.5", "pandas>=2.2.3", @@ -65,3 +65,7 @@ ignore = ["F401"] [tool.ruff.lint.extend-per-file-ignores] "test_*.py" = ["F811"] +"**/__init__.py" = ["F401"] + +[tool.uv.sources] +smirk = { git = "https://github.com/BattModels/smirk", rev = "main" } diff --git a/uv.lock b/uv.lock index b6c971c7..8cf44e28 100644 --- a/uv.lock +++ b/uv.lock @@ -526,7 +526,7 @@ requires-dist = [ { name = "safetensors", specifier = ">=0.5.1" }, { name = "scikit-learn", specifier = ">=1.5.1" }, { name = "selfies", specifier = ">=2.1.0" }, - { name = "smirk", git = "ssh://git@github.com/BattModels/smirk.git?rev=dfdd9dc27c066f956b69d1f55c2d4717beb7838d" }, + { name = "smirk", git = "https://github.com/BattModels/smirk?rev=main" }, { name = "torch", specifier = "~=2.6" }, { name = "torchmetrics", specifier = "~=1.7" }, { name = "transformers", specifier = ">=4.40" }, @@ -2658,8 +2658,13 @@ wheels = [ [[package]] name = "smirk" +<<<<<<< HEAD version = "0.2.0" source = { git = "ssh://git@github.com/BattModels/smirk.git?rev=dfdd9dc27c066f956b69d1f55c2d4717beb7838d#dfdd9dc27c066f956b69d1f55c2d4717beb7838d" } +======= +version = "0.2.0.dev0" +source = { git = "https://github.com/BattModels/smirk?rev=main#742e81c85acc123873f3c58ab84edc6ce7e90c18" } +>>>>>>> 2fcc8ce (Use latest smirk, update CI to reflect) dependencies = [ { name = "transformers" }, ] From 4a3f8632a346b4a58d731880212c7021872edae5 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 17:17:51 -0400 Subject: [PATCH 109/147] test: skip mds tests if missing cuda --- test/test_pubchemqc.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/test_pubchemqc.py b/test/test_pubchemqc.py index 85d3d46a..2e76224e 100644 --- a/test/test_pubchemqc.py +++ b/test/test_pubchemqc.py @@ -520,6 +520,7 @@ def rot(θ=1): @pytest.mark.parametrize("N", [32, 1, 2]) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_mds_svd(N): coords = torch.rand(N, 3) D = torch.cdist(coords, coords) @@ -532,6 +533,7 @@ def test_mds_svd(N): @pytest.mark.parametrize("B,N", [(8, 32), (1, 1), (1, 8), (8, 1)]) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_masked_mds_svd(B, N): mask = torch.rand(B, N) > 0.8 mask_pw = mask.unsqueeze(2) & mask.unsqueeze(1) From b9393ff85c38452d228ededf99fa6db7bef1124e Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 18:02:06 -0400 Subject: [PATCH 110/147] fix mds test errors --- electrolyte_fm/data_modules/pubchem_qc.py | 137 +++++++++++++++------- test/test_pubchemqc.py | 11 +- 2 files changed, 100 insertions(+), 48 deletions(-) diff --git a/electrolyte_fm/data_modules/pubchem_qc.py b/electrolyte_fm/data_modules/pubchem_qc.py index 17203567..2da6be75 100644 --- a/electrolyte_fm/data_modules/pubchem_qc.py +++ b/electrolyte_fm/data_modules/pubchem_qc.py @@ -686,63 +686,110 @@ def mol_from_prediction( @torch.cuda.nvtx.range("mds_svd") -def mds_svd(D: torch.Tensor, dim=3): - n = D.size(0) - factory_kwargs = {"device": D.device, "dtype": D.dtype} +def mds_svd(D: torch.Tensor, dim: int = 3) -> torch.Tensor: + """ + Classical (unmasked) MDS via double-centering and eigendecomposition. - if n == 1: - return torch.zeros(*D.shape[:-1], dim, **factory_kwargs) - elif n == 2: - p1 = torch.zeros(*D.shape[1:-1], dim, **factory_kwargs) - p2 = torch.zeros(*D.shape[1:-2], dim, **factory_kwargs) - p2[..., -1] += D[..., 0, 1] - return torch.stack([p1, p2]) - elif n < dim: - raise RuntimeError( - "Insufficient points to compute coordinates (Dim reduction not implimented)" - ) + Args: + D: (N, N) distance matrix (unbatched). + dim: target embedding dimension. - # Compute the Gram matrix using double centering - B = D.pow(2) - B -= B.mean(-1, keepdim=True) - B -= B.mean(-2, keepdim=True) - B *= 0.5 + Returns: + X: (N, dim) coordinates (zero-padded if N < dim). + """ + assert D.dim() == 2 and D.shape[0] == D.shape[1], "D must be (N, N)" - # Run SVD in at least float32 precision - dtype = torch.promote_types(D.dtype, torch.float32) - u, s, _ = torch.linalg.svd(B.to(dtype=dtype)) + N = D.size(0) + factory_kwargs = {"device": D.device, "dtype": D.dtype} - # Select the top 'dim' components, clamping to avoid numerical issues - u = u[..., :dim] - s = s[:dim].clamp(min=0) - s = torch.diag_embed(s.sqrt()) + # Trivial cases + if N == 1: + return torch.zeros(N, dim, **factory_kwargs) + if N == 2: + # Put the two points along one axis separated by D[0,1] + X = torch.zeros(N, dim, **factory_kwargs) + X[1, -1] = D[0, 1] + return X + + # Work in at least float32 for stability + work_dtype = torch.promote_types(D.dtype, torch.float32) + D2 = (D.to(dtype=work_dtype)) ** 2 + + # Double-centering: B = -0.5 * (D^2 - row_mean - col_mean + grand_mean) + row_mean = D2.mean(dim=1, keepdim=True) # (N, 1) + col_mean = D2.mean(dim=0, keepdim=True) # (1, N) + grand_mean = D2.mean() # scalar + B = -0.5 * (D2 - row_mean - col_mean + grand_mean) + + # Enforce symmetry to reduce numerical noise + B = 0.5 * (B + B.T) + + # Eigendecomposition of symmetric Gram matrix + # (ascending eigenvalues) + evals, evecs = torch.linalg.eigh(B) + + # Keep top-k components with nonnegative eigenvalues + k = min(dim, N) + evals_k = evals[-k:].clamp_min(0).sqrt() # (k,) + evecs_k = evecs[:, -k:] # (N, k) + + # Coordinates: X = V * sqrt(Lambda) + Xk = evecs_k * evals_k.unsqueeze(0) # (N, k) + + # Pad to (N, dim) if N < dim + if k < dim: + X = F.pad(Xk, (0, dim - k)) + else: + X = Xk - # Compute the coordinates: X = U * sqrt(S) - return u @ s + return X.to(dtype=D.dtype) @torch.cuda.nvtx.range("batched_mds_svd") -def masked_mds_svd(D: torch.Tensor, mask: torch.Tensor, dim=3): - # Zero Mask - mask_pw = mask.unsqueeze(-1) & mask.unsqueeze(-2) +def masked_mds_svd(D: torch.Tensor, mask: torch.Tensor, dim: int = 3) -> torch.Tensor: + """ + Classical MDS with masking. Returns coordinates of shape (..., N, dim). + """ + # Pairwise mask (True where D_ij is observed) + mask_pw = (mask.unsqueeze(-1) & mask.unsqueeze(-2)).to(D.dtype) assert mask_pw.shape == D.shape - # Gram matrix from distance matrix - B = D.pow(2) - B -= B.sum(-1, keepdim=True) / mask_pw.sum(-1, keepdim=True).clamp(min=1) - B -= B.sum(-2, keepdim=True) / mask_pw.sum(-2, keepdim=True).clamp(min=1) - B *= -0.5 - B[~mask_pw] = 0 - - # Run SVD in at least float32 precision + # Work in at least float32 dtype = torch.promote_types(D.dtype, torch.float32) - u, s, _ = torch.linalg.svd(B.to(dtype=dtype)) + D2 = (D.to(dtype=dtype)) ** 2 + + # Masked means for double-centering: row, col, grand + eps1 = mask_pw.sum(-1, keepdim=True).clamp_min(1.0) # (..., N, 1) + eps2 = mask_pw.sum(-2, keepdim=True).clamp_min(1.0) # (..., 1, N) + epsg = mask_pw.sum(dim=(-1, -2), keepdim=True).clamp_min(1.0) # (..., 1, 1) + + row_mean = (D2 * mask_pw).sum(-1, keepdim=True) / eps1 # (..., N, 1) + col_mean = (D2 * mask_pw).sum(-2, keepdim=True) / eps2 # (..., 1, N) + grand_mean = (D2 * mask_pw).sum(dim=(-1, -2), keepdim=True) / epsg # (..., 1, 1) + + # Double-centered Gram matrix with masking + B = -0.5 * (D2 - row_mean - col_mean + grand_mean) + + # Zero out unobserved entries (optional but keeps SVD clean) and re-symmetrize + B = B * mask_pw + B = 0.5 * (B + B.transpose(-1, -2)) + + # SVD + u, s, _ = torch.linalg.svd(B) + k = min(dim, B.shape[-1]) + + # Take top-k components, sqrt the eigenvalues + u_k = u[..., :k] + s_k = s[..., :k].clamp_min(0).sqrt() + coords_k = u_k * s_k.unsqueeze(-2) # (..., N, k) + + # Pad to requested dim if needed + if k < dim: + coords = F.pad(coords_k, (0, dim - k)) # pad last dimension + else: + coords = coords_k - u = u[..., :dim] - s = s[..., :dim].clamp(min=0) - s = torch.diag_embed(s.sqrt()) - coords_raw = u @ s - return coords_raw + return coords.to(D.dtype) def mol_from_pairwise( diff --git a/test/test_pubchemqc.py b/test/test_pubchemqc.py index 2e76224e..af5af6f4 100644 --- a/test/test_pubchemqc.py +++ b/test/test_pubchemqc.py @@ -27,6 +27,11 @@ from electrolyte_fm.models.token_level import distance_matrix_loss +@pytest.fixture(autouse=True) +def set_random_seed(): + random.seed(42) # Set a fixed seed for reproducibility + + def pubchem_qc_dataset_path(): dir = Path(__file__).parent.parent.joinpath( "opt", "pubchem-qc", "pubchemqc_jcim2017-split" @@ -324,9 +329,9 @@ def test_smi_token_type(smi: str, token_types: list[SmiTokenType]): zip(pubchem_qc.smi_token_type(tok, tokens), token_types) ): token = tok.convert_ids_to_tokens(tokens[idx]) - assert ( - token_type == ref - ), f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" + assert token_type == ref, ( + f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" + ) @pytest.mark.skipif( From f6b98f52077c5f57301bdb66f3ed98e8ce0cd7c4 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 18:02:16 -0400 Subject: [PATCH 111/147] testing: cut back on molnet tests --- electrolyte_fm/data_modules/pubchem_qc.py | 1 + test/test_molnet.py | 10 +++++++--- test/test_pubchemqc.py | 6 +++--- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/electrolyte_fm/data_modules/pubchem_qc.py b/electrolyte_fm/data_modules/pubchem_qc.py index 2da6be75..57f662c3 100644 --- a/electrolyte_fm/data_modules/pubchem_qc.py +++ b/electrolyte_fm/data_modules/pubchem_qc.py @@ -10,6 +10,7 @@ from enum import IntEnum, auto import torch +from torch.nn import functional as F from torch.utils.data import DataLoader from torch.nn.utils.rnn import pad_sequence from transformers import DataCollatorWithPadding diff --git a/test/test_molnet.py b/test/test_molnet.py index b382c4a6..a8c28889 100644 --- a/test/test_molnet.py +++ b/test/test_molnet.py @@ -15,6 +15,10 @@ "submit", "moleculenet_tasks.libsonnet" ) +# Only test a subset of dataset +MOLNET_DATASETS = ["hiv", "sider"] +# MOLNET_DATASETS = MOLNET_URLS.keys() # Uncomment to test all + @pytest.mark.xfail(strict=False, reason="downloads are flaky", raises=FileNotFoundError) def check_datamodule(dm: LightningDataModule, stage="fit", limit_batches=100): @@ -33,7 +37,7 @@ def check_dataloader(dl, limit_batches, keys=["input_ids", "attention_mask"]): break -@pytest.mark.parametrize("name", MOLNET_URLS.keys()) +@pytest.mark.parametrize("name", MOLNET_DATASETS) def test_datamodule(name): task_config = json.loads(jsonnet.evaluate_file(str(MOLNET_CONFIG)))[name] dm = MolNetDataModule( @@ -44,7 +48,7 @@ def test_datamodule(name): check_datamodule(dm) -@pytest.mark.parametrize("name", MOLNET_URLS.keys()) +@pytest.mark.parametrize("name", MOLNET_DATASETS) @pytest.mark.xfail(strict=False, reason="downloads are flaky", raises=FileNotFoundError) def test_prepare(name): task_config = json.loads(jsonnet.evaluate_file(str(MOLNET_CONFIG)))[name] @@ -88,7 +92,7 @@ def test_validate_molnet_config(): @pytest.mark.parametrize( - "dataset,split", product(DATASET_SIZE.keys(), ["scaffold", "random"]) + "dataset,split", product(MOLNET_DATASETS, ["scaffold", "random"]) ) @pytest.mark.xfail(strict=False, reason="downloads are flaky", raises=FileNotFoundError) def test_splits(dataset, split): diff --git a/test/test_pubchemqc.py b/test/test_pubchemqc.py index af5af6f4..16dcd4a2 100644 --- a/test/test_pubchemqc.py +++ b/test/test_pubchemqc.py @@ -329,9 +329,9 @@ def test_smi_token_type(smi: str, token_types: list[SmiTokenType]): zip(pubchem_qc.smi_token_type(tok, tokens), token_types) ): token = tok.convert_ids_to_tokens(tokens[idx]) - assert token_type == ref, ( - f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" - ) + assert ( + token_type == ref + ), f"Wrong label for {token} at pos {idx}: {str(token_type)} != {str(ref)}" @pytest.mark.skipif( From 33238464c0d9e65700f0dcc886cdb98681e34aa4 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 19:34:03 -0400 Subject: [PATCH 112/147] tweak: normalize clc_db processing --- opt/sterochemistry/export_clc_db.py | 80 ++++++++++++++-------------- opt/sterochemistry/process_clc_db.py | 46 ++++++++++------ 2 files changed, 70 insertions(+), 56 deletions(-) diff --git a/opt/sterochemistry/export_clc_db.py b/opt/sterochemistry/export_clc_db.py index 76c90aba..175bcc60 100755 --- a/opt/sterochemistry/export_clc_db.py +++ b/opt/sterochemistry/export_clc_db.py @@ -71,9 +71,10 @@ def categories(self) -> list[str]: return [] def csv_row(self) -> dict[str, Any]: - """Return a row dict with the same columns the website exports. + """Return a row dict using snake_case field names. - Column order mirrors the client code seen in the website bundle. + The website exports Title Case columns; we normalize to snake_case so + downstream processing can rely on consistent names. """ d = self.data @@ -98,46 +99,47 @@ def _int(x: Any) -> str: return "" return { - "Name": d.get("name", ""), - "CAS ID": d.get("cas_id", ""), - "PubChem CID": d.get("pubchem_cid", ""), - "Category": _join(d.get("category")), - "URL": d.get("url", ""), - "PubChem URL": d.get("pubchem_url", ""), - "SMILES": d.get("smiles", ""), - "Chirality": _join(d.get("chirality")), - "Description": d.get("description", ""), - "SMILES IUPAC": d.get("smiles_iupac", ""), - "Molecule Formula": d.get("molecule_formula", ""), - "Molecular Weight": _fmt(d.get("molecular_weight"), 3), - "Heavy Atom Count": _int(d.get("heavy_atom_count")), - "Ring Count": _int(d.get("ring_count")), - "Hydrogen Bond Acceptor Count": _int(d.get("hydrogen_bond_acceptor_count")), - "Hydrogen Bond Donor Count": _int(d.get("hydrogen_bond_donor_count")), - "Rotatable Bond Count": _int(d.get("rotatable_bond_count")), - "Zero-point correction": _fmt(d.get("zero_point_correction"), 6), - "Thermal correction to Energy": _fmt(d.get("thermal_correction_energy"), 6), - "Thermal correction to Enthalpy": _fmt( + # identifiers and core metadata + "name": d.get("name", ""), + "cas_id": d.get("cas_id", ""), + "pubchem_cid": d.get("pubchem_cid", ""), + "category": _join(d.get("category")), + "url": d.get("url", ""), + "pubchem_url": d.get("pubchem_url", ""), + # chemistry strings + "smiles": d.get("smiles", ""), + "chirality": _join(d.get("chirality")), + "description": d.get("description", ""), + "smiles_iupac": d.get("smiles_iupac", ""), + "molecule_formula": d.get("molecule_formula", ""), + # basic properties + "molecular_weight": _fmt(d.get("molecular_weight"), 3), + "heavy_atom_count": _int(d.get("heavy_atom_count")), + "ring_count": _int(d.get("ring_count")), + "hydrogen_bond_acceptor_count": _int(d.get("hydrogen_bond_acceptor_count")), + "hydrogen_bond_donor_count": _int(d.get("hydrogen_bond_donor_count")), + "rotatable_bond_count": _int(d.get("rotatable_bond_count")), + # thermochemistry (snake_case normalized names) + "zero_point_correction": _fmt(d.get("zero_point_correction"), 6), + "thermal_correction_energy": _fmt(d.get("thermal_correction_energy"), 6), + "thermal_correction_enthalpy": _fmt( d.get("thermal_correction_enthalpy"), 6 ), - "Thermal correction to Gibbs Free Energy": _fmt( - d.get("thermal_correction_gibbs"), 6 - ), - "Sum of electronic and zero-point Energies": _fmt( - d.get("sum_electronic_zero_point"), 6 - ), - "Sum of electronic and thermal Energies": _fmt( + "thermal_correction_gibbs": _fmt(d.get("thermal_correction_gibbs"), 6), + "sum_electronic_zero_point": _fmt(d.get("sum_electronic_zero_point"), 6), + "sum_electronic_thermal_energy": _fmt( d.get("sum_electronic_thermal_energy"), 6 ), - "Sum of electronic and thermal Enthalpies": _fmt( + "sum_electronic_thermal_enthalpy": _fmt( d.get("sum_electronic_thermal_enthalpy"), 6 ), - "Sum of electronic and thermal Free Energies": _fmt( + "sum_electronic_thermal_free_energy": _fmt( d.get("sum_electronic_thermal_free_energy"), 6 ), - "HOMO Energy (eV)": _fmt(d.get("homo_energy"), 6), - "LUMO Energy (eV)": _fmt(d.get("lumo_energy"), 6), - "HOMO-LUMO Gap (eV)": _fmt(d.get("homo_lumo_gap"), 6), + # frontier orbitals + "homo_energy": _fmt(d.get("homo_energy"), 6), + "lumo_energy": _fmt(d.get("lumo_energy"), 6), + "homo_lumo_gap": _fmt(d.get("homo_lumo_gap"), 6), } @@ -292,9 +294,9 @@ def write_csv(rows: list[dict[str, Any]], out_csv: str) -> None: # Use pandas for convenience and de-duplication on CAS ID df = pd.DataFrame(rows) - # Drop duplicates by CAS ID keeping first occurrence - if "CAS ID" in df.columns: - df = df.drop_duplicates(subset=["CAS ID"], keep="first") + # Drop duplicates by cas_id keeping first occurrence + if "cas_id" in df.columns: + df = df.drop_duplicates(subset=["cas_id"], keep="first") df.to_csv(out_csv, index=False) @@ -304,8 +306,8 @@ def main(argv: list[str] | None = None) -> int: ) p.add_argument( "--out-dir", - default=os.path.join("opt", "sterochemistry", "data", "clc_db"), - help="Output directory for SDFs and CSV (default: opt/sterochemistry/data/clc_db)", + default=os.path.join("data", "clc_db"), + help="Output directory for SDFs and CSV (default: data/clc_db)", ) p.add_argument( "--by-category", diff --git a/opt/sterochemistry/process_clc_db.py b/opt/sterochemistry/process_clc_db.py index 0da9994c..371eef5d 100755 --- a/opt/sterochemistry/process_clc_db.py +++ b/opt/sterochemistry/process_clc_db.py @@ -30,17 +30,21 @@ from sklearn.model_selection import StratifiedShuffleSplit -# Column names from the website/export script -COL_SMILES = "SMILES" -COL_CHIRALITY = "Chirality" -COL_MW = "Molecular Weight" -COL_ZP = "Zero-point correction" -COL_TE = "Thermal correction to Energy" -COL_TH = "Thermal correction to Enthalpy" -COL_TG = "Thermal correction to Gibbs Free Energy" -COL_HOMO = "HOMO Energy (eV)" -COL_LUMO = "LUMO Energy (eV)" -COL_GAP = "HOMO-LUMO Gap (eV)" +# Column names from the normalized snake_case export +COL_SMILES = "smiles" +COL_CHIRALITY = "chirality" +COL_MW = "molecular_weight" +COL_ZP = "zero_point_correction" +COL_TE = "thermal_correction_energy" +COL_TH = "thermal_correction_enthalpy" +COL_TG = "thermal_correction_gibbs" +COL_SUM_ZP = "sum_electronic_zero_point" +COL_SUM_TE = "sum_electronic_thermal_energy" +COL_SUM_TH = "sum_electronic_thermal_enthalpy" +COL_SUM_TG = "sum_electronic_thermal_free_energy" +COL_HOMO = "homo_energy" +COL_LUMO = "lumo_energy" +COL_GAP = "homo_lumo_gap" def _to_float(x: Any) -> float | None: @@ -133,9 +137,13 @@ def load_rows(csv_path: str) -> list[dict[str, Any]]: "thermal_correction_energy": _to_float(r.get(COL_TE)), "thermal_correction_enthalpy": _to_float(r.get(COL_TH)), "thermal_correction_gibbs": _to_float(r.get(COL_TG)), - "homo": _to_float(r.get(COL_HOMO)), - "lumo": _to_float(r.get(COL_LUMO)), - "gap": _to_float(r.get(COL_GAP)), + "sum_electronic_zero_point": _to_float(r.get(COL_SUM_ZP)), + "sum_electronic_thermal_energy": _to_float(r.get(COL_SUM_TE)), + "sum_electronic_thermal_enthalpy": _to_float(r.get(COL_SUM_TH)), + "sum_electronic_thermal_free_energy": _to_float(r.get(COL_SUM_TG)), + "homo_energy": _to_float(r.get(COL_HOMO)), + "lumo_energy": _to_float(r.get(COL_LUMO)), + "homo_lumo_gap": _to_float(r.get(COL_GAP)), } rows.append(row) return rows @@ -199,9 +207,13 @@ def to_hf_datasets( "thermal_correction_energy": Value("float64"), "thermal_correction_enthalpy": Value("float64"), "thermal_correction_gibbs": Value("float64"), - "homo": Value("float64"), - "lumo": Value("float64"), - "gap": Value("float64"), + "sum_electronic_zero_point": Value("float64"), + "sum_electronic_thermal_energy": Value("float64"), + "sum_electronic_thermal_enthalpy": Value("float64"), + "sum_electronic_thermal_free_energy": Value("float64"), + "homo_energy": Value("float64"), + "lumo_energy": Value("float64"), + "homo_lumo_gap": Value("float64"), } ) From 1e13a7f7acf7f0321c89087cda53fb07f207ecb4 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 1 Sep 2025 18:55:08 -0400 Subject: [PATCH 113/147] fix: progressive thawing works with configure model --- electrolyte_fm/utils/progressive_thawing.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/electrolyte_fm/utils/progressive_thawing.py b/electrolyte_fm/utils/progressive_thawing.py index 7389fc31..b74bb4d6 100644 --- a/electrolyte_fm/utils/progressive_thawing.py +++ b/electrolyte_fm/utils/progressive_thawing.py @@ -7,7 +7,7 @@ class ProgressiveThawing(BaseFinetuning): def __init__( self, initial: list[str], - stages: list[str], + stages: list[list[str]], stage_duration: int = 1, lr_initial: float = 10, lr_decay: float = 0.99, @@ -21,6 +21,11 @@ def __init__( self.lr_initial = float(lr_initial) self.lr_decay = float(lr_decay) + def setup(self, trainer, pl_module, stage) -> None: + if not hasattr(pl_module, "encoder"): + pl_module.configure_model() + self.freeze_before_training(pl_module) + @classmethod def matching_modules(cls, pl_module, patterns): for pattern in patterns: @@ -29,7 +34,7 @@ def matching_modules(cls, pl_module, patterns): if name == "": continue logging.debug( - f"Matching %s against %s: %d", name, pattern, fnmatch(name, pattern) + "Matching %s against %s: %d", name, pattern, fnmatch(name, pattern) ) if fnmatch(name, pattern): pattern_matched = True @@ -60,7 +65,7 @@ def on_train_batch_start(self, trainer, *args, **kwargs): param_group["lr"] = base_lr / (self.lr_scale[idx] + 1) def lr_scheduler_step(self, scheduler, metric) -> None: - print(f"LR Scheduler step", scheduler, metric) + print("LR Scheduler step", scheduler, metric) def freeze_before_training(self, pl_module): for name, module in self.matching_modules(pl_module, self.initial): From ef561e7163ecd52f6ee8f76c2f6fa877d4342c21 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 1 Sep 2025 18:56:14 -0400 Subject: [PATCH 114/147] feat: isotopes dataloader and config --- .../data_modules/isotope_dataset.py | 28 +++++++ submit/isotopes.jsonnet | 77 +++++++++++++++++++ 2 files changed, 105 insertions(+) create mode 100644 electrolyte_fm/data_modules/isotope_dataset.py create mode 100644 submit/isotopes.jsonnet diff --git a/electrolyte_fm/data_modules/isotope_dataset.py b/electrolyte_fm/data_modules/isotope_dataset.py new file mode 100644 index 00000000..05022e6e --- /dev/null +++ b/electrolyte_fm/data_modules/isotope_dataset.py @@ -0,0 +1,28 @@ +from .property_prediction_dataset import PropertyPredictionDataModule +from .molnet_dataset import train_val_test_split +from datasets import Dataset, load_dataset +from pathlib import Path + + +class IsotopeDataModule(PropertyPredictionDataModule): + def __init__(self, path: str, **kwargs): + # Set default smi_column + self.path = path + assert Path(self.path).is_file() + super().__init__(**kwargs) + + def prepare_data(self): + # Fetch data from the head node + self.dataset + + def _get_dataset(self): + # Load the dataset + ds: Dataset = load_dataset( + "csv", + data_files=[self.path], + split="train", + keep_in_memory=False, + save_infos=False, + ) # type: ignore + + return train_val_test_split(ds) diff --git a/submit/isotopes.jsonnet b/submit/isotopes.jsonnet new file mode 100644 index 00000000..e7039677 --- /dev/null +++ b/submit/isotopes.jsonnet @@ -0,0 +1,77 @@ +{ + walltime: '1:0:0', + nodes: 1, // Multi-node is not currently supported. Config is only on leader node + env: { + TOKENIZERS_PARALLELISM: true, + }, + train: { + tags: ['finetuning', 'isotopes', 'thaw'], + data: { + class_path: 'electrolyte_fm.data_modules.IsotopeDataModule', + init_args: { + path: '/nfs/turbo/coe-venkvis/abhutani/electrolyte-fm/opt/isotope_half_lives.csv', + batch_size: 16, + val_batch_size: 2 * self.batch_size, + tokenizer: $.train.model.init_args.encoder_ckpt, + target_columns: ['half_life', 'log_time'], + num_workers: 4, + prefetch_factor: 8, + }, + }, + model: { + class_path: 'electrolyte_fm.models.LMFinetuning', + init_args: { + encoder_ckpt: '/nfs/turbo/coe-venkvis/mist/ti624ev1/pretrained/checkpoints/last.ckpt', + task: 'regression', + metrics: ['mae', 'mae-channel', 'r2-channel'], + freeze_encoder: false, + transform: ['standardize', 'standardize'], + output_size: std.length($.train.data.init_args.target_columns), + target_columns: $.train.data.init_args.target_columns, + + // Duplicate pre-training optimizer config + optimizer: { + class_path: 'torch.optim.AdamW', + init_args: { + lr: 1.6e-4, + weight_decay: 0.01, + }, + }, + + lr_schedule: { + class_path: 'electrolyte_fm.utils.lr_schedule.RelativeCosineWarmup', + init_args: { + num_training_steps: $.train.trainer.max_steps, + num_warmup_steps: 'beta2', + rel_decay: 0.1, + }, + }, + }, + }, + trainer: { + max_steps: 5000, + precision: 'bf16-true', + enable_progress_bar: false, + strategy: 'auto', + callbacks: [ + { + class_path: 'electrolyte_fm.utils.progressive_thawing.ProgressiveThawing', + init_args: { + initial: ['encoder'], + stages: [ + ['encoder.embeddings'], + ['encoder.encoder.layer.7'], + ['encoder.encoder.layer.6'], + ['encoder.encoder.layer.5'], + ['encoder.encoder.layer.4'], + ['encoder.encoder.layer.3'], + ['encoder.encoder.layer.2'], + ['encoder.encoder.layer.1'], + ], + stage_duration: 3, + }, + }, + ], + }, + }, +} From 788a48a52ccb2a8fc5a34f2e6a848967a3e7bd49 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Wed, 3 Sep 2025 10:03:24 -0400 Subject: [PATCH 115/147] embedding coloured by decay type figure --- opt/interp_embeddings/embedding_figure.jl | 112 ++++++++++++++++++++++ 1 file changed, 112 insertions(+) diff --git a/opt/interp_embeddings/embedding_figure.jl b/opt/interp_embeddings/embedding_figure.jl index e8efa52f..1c2ccf44 100644 --- a/opt/interp_embeddings/embedding_figure.jl +++ b/opt/interp_embeddings/embedding_figure.jl @@ -1,5 +1,9 @@ using Makie using DataFrames +using Random +using Statistics +using UMAP +using Distances using CairoMakie: CairoMakie using CSV: CSV using CategoricalArrays: categorical, levelcode @@ -169,3 +173,111 @@ function figure_olfactory() return f end + + +function classify_decay_from_NZ(N::Real, Z::Real; + tol_offset::Real=0.8, tol_scale::Real=0.02) + + # Beta-stability valley (from SEMF): Z_beta(A) ≈ A / (2 + 0.015 * A^(2/3)) + predict_Z_beta(A::Real) = A / (2 + 0.015 * A^(2/3)) + + A = N + Z + # very heavy nuclei + if Z ≥ 92 && A ≥ 240 + return "Spontaneous \nFission" + elseif Z ≥ 84 && A ≥ 210 + return L"$\alpha$" + end + zβ = predict_Z_beta(A) + δ = Z - zβ + tol = tol_offset + tol_scale * Z # widen tolerance for heavier Z + if abs(δ) ≤ tol + return "Stable" + elseif δ < 0 + return L"$\beta^-$" # too many neutrons -> beta- decay + else + return L"$\beta^+$" # too many protons -> beta+/EC + end +end + +function figure_isotopes_umap(n_neighbors::Int=10, min_dist::Real=2, metric::Symbol=:manhattan, seed::Int=42) + + csv_path = "isotope_embeddings.csv" + df = DataFrame(CSV.File(csv_path)) + + # Labels from N/Z + N = Float64.(coalesce.(df.neutrons, NaN)) + Z = Float64.(coalesce.(df.protons, NaN)) + labels_raw = [classify_decay_from_NZ(N[i], Z[i]) for i in eachindex(N)] + + # Embedding matrix: numeric columns excluding smiles/N/Z + exclude = Set([:smiles, :neutrons, :protons]) + _is_numeric_col(col) = (eltype(col) <: Real) || (eltype(col) <: Union{Missing,Real}) + embed_cols = [nm for nm in names(df) if nm ∉ exclude && _is_numeric_col(df[!, nm])] + isempty(embed_cols) && error("No numeric embedding columns found (after excluding smiles/N/Z).") + + X = Matrix{Float64}(coalesce.(df[!, embed_cols], 0.0)) + + # Standardize columns + for j in axes(X, 2) + μ, σ = mean(X[:, j]), std(X[:, j]) + if σ > 0 && isfinite(σ) + X[:, j] .= (X[:, j] .- μ) ./ σ + else + X[:, j] .= 0.0 + end + end + + # UMAP → 2D + Random.seed!(seed) + metric_obj = if metric == :cosine + CosineDist() + elseif metric == :manhattan + Cityblock() + else + Euclidean() + end + Y = umap(Matrix(X'), 2; n_neighbors=n_neighbors, min_dist=min_dist, metric=metric_obj) + Y = Matrix(Y') + + canonical = [L"$\beta^+$", L"$\beta^-$", L"$\alpha$", "Stable", "Spontaneous Fission"] + present = unique(labels_raw) + levels = [c for c in canonical if c in present] + append!(levels, [x for x in present if x ∉ Set(canonical)]) + decay_cat = categorical(labels_raw; levels=levels, ordered=true) + n = length(levels) + + f = Figure(;size = (1.5inch, 1.5inch)) + ax = Axis( + f[1, 1]; + limits = (nothing, (minimum(Y[:, 2]) - 7, nothing)), + ) + hidedecorations!(ax) + + palette = MISTStyle.CAT_COLORS[1:n] + + h = scatter!(ax, Y[:, 1], Y[:, 2]; + color = levelcode.(decay_cat), + colormap = palette, + colorrange = (1, n), + marker = :circle, + ) + + # Create legend elements + elements = [MarkerElement(; + marker = :circle, + color = palette[i], + ) for i in 1:length(levels)] + + Legend(f[1, 1], elements, levels; + tellheight = false, + tellwidth = false, + orientation = :horizontal, + halign = :right, + valign = :bottom, + padding=(1, 1, 1, 1), + margin=(1, 1, 1, 1), + ) + + return f +end From e412ffbbbb9e0bb91132a3f880ba0ce0c87abbb3 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Fri, 13 Jun 2025 14:01:03 -0400 Subject: [PATCH 116/147] feat update screening plots --- opt/MISTStyle/src/MISTStyle.jl | 11 +- opt/screening/plots.jl | 167 ---------------------- opt/screening/plots/plots.jl | 22 ++- opt/screening/plots/src/ScreeningPlots.jl | 1 + opt/screening/plots/src/pareto.jl | 162 ++++++++++++++++++--- 5 files changed, 168 insertions(+), 195 deletions(-) delete mode 100644 opt/screening/plots.jl diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 50d5a4d4..2f55fdc8 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -102,8 +102,8 @@ const CONTINUOUS_COLORS = :lipari function theme() Theme( - rowgap=2, - colgap=2, + rowgap=3pt, + colgap=3pt, fonts=(; regular="Times New Roman Regular", bold="Times New Roman Bold", @@ -122,6 +122,13 @@ function theme() color=CAT_COLORS, linestyle=[:solid, :dot, :dashdot], ), + Lines=(; + cycle=Cycle([:color, :linestyle], covary=true), + ), + GridLayout=(; + default_rowgap=3pt, + default_colgap=3pt, + ), Axis=(; spinewidth=0.5, xlabelsize=6pt, diff --git a/opt/screening/plots.jl b/opt/screening/plots.jl deleted file mode 100644 index 13dde3c1..00000000 --- a/opt/screening/plots.jl +++ /dev/null @@ -1,167 +0,0 @@ -using MISTStyle: UM_COLORS -using Makie -using MISTStyle -using Metaheuristics -using DataFrames -using SQLite: SQLite -using CSV: CSV -using JSON: JSON - -const HARTREE_TO_EV = 27.211_386_245_981 - -function load_data(path) - file = !endswith(path, ".sqlite") ? joinpath(path, "merged.sqlite") : path - db = SQLite.DB(file) - df = nothing - try - df = SQLite.DBInterface.execute(db, "SELECT * from molecules") |> DataFrame - finally - close(db) - end - cols = keys(JSON.parse(df[1, :props])) - transform!(df, :props => ByRow(JSON.parse) => Symbol.(cols)) - select!(df, Not(:props)) - return df -end - -function load_all() - cases = Dict{String,DataFrame}() - for case in readdir(joinpath(@__DIR__, "out"); join=true) - isfile(joinpath(case, "merged.sqlite")) || continue - cases[basename(case)] = load_data(case) - end - return cases -end - -function get_pareto_front(df, x, y; quad=:lt) - @assert quad == :lt "not implemented" - x = df[:, x] - y = -df[:, y] # flip sign to maximize - front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, y)) - front = map(front) do p - p[2] *= -1 - p - end - return Point2.(sort(front; by=first, rev=true)) -end - -function plot_generation(cases) - f = Figure() - ax = Axis(f[1, 1]; - xlabel="Duplicate Count", - ylabel="Probability Density", - limits=((0, nothing), (0, nothing)), - ) - for (label, df) in pairs(cases) - density!(ax, df.duplicate_count; label, bandwidth=0.75) - end - axislegend(ax) - return f -end - -function plot_pareto_front(case, ref) - mp_limits = extrema(vcat(case.mp, [0])) - bp_limits = extrema(vcat(case.bp, [75])) - pareto_kwargs = (; - marker=:star5, - markersize=8pt, - ) - - # Net non-dominated - bp = -case[!, :bp] - gap = -case[!, :gap] .* HARTREE_TO_EV - homo = case[!, :homo] .* HARTREE_TO_EV - mp = case[!, :mp] - canidates = map(vcat, homo, gap, mp, bp) - front = Metaheuristics.get_non_dominated_solutions(canidates) - nidx = findall(in(front), canidates) - case = deepcopy(case) - case.dominated .= true - case.dominated[nidx] .= false - sort!(case, :dominated; rev=true) - @info "non-dominated" sort(case[nidx, :], :inchi) - front = map(front) do p - p[1] *= -1 - p[2] *= -1 - p - end - @info "non-dominated" length(front) nrow(case) length(front) / nrow(case) - - f = Figure(; size=(2inch, 1inch), figure_padding=(1, 3, 1, 2)) - ax = Axis(f[1, 1]; - limits=(mp_limits, bp_limits), - xlabel=L"Melting Point [$\degree C$]", - ylabel=L"Boiling Point [$\degree C$]", - ) - scatter!(ax, case.mp, case.bp; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : UM_COLORS.blue, case.dominated), - ) - scatterlines!(ax, get_pareto_front(ref, :mp, :bp); - color=MISTStyle.UM_COLORS.maize, - pareto_kwargs... - ) - scatterlines!(ax, get_pareto_front(case, :mp, :bp); - color=MISTStyle.UM_COLORS.blue, - pareto_kwargs... - ) - - ax = Axis(f[1, 2]; - limits=((-10, -7), (5, 12)), - xlabel=L"HOMO [eV]$$", - ylabel=L"Gap [eV]$$", - ) - scatter!(ax, case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : UM_COLORS.blue, case.dominated), - label="Generated", - ) - scatterlines!(ax, get_pareto_front(ref, :homo, :gap) .* HARTREE_TO_EV; - color=MISTStyle.UM_COLORS.maize, - label="Ref. Pareto Front", - pareto_kwargs... - ) - scatterlines!(ax, get_pareto_front(case, :homo, :gap) .* HARTREE_TO_EV; - color=MISTStyle.UM_COLORS.blue, - label="Generated Pareto Front", - pareto_kwargs... - ) - Legend(f[2, :], ax; - tellheight=true, tellwidth=false, - orientation=:horizontal, - ) - - return f -end - -function gen_plots() - dfs = load_all() - df_ref = DataFrame(CSV.File(joinpath(@__DIR__, "..", "design", "electrolytes_predictons.csv"))) - - # MP < 0degC, BP > 75 degC, HOMO < -7eV, GAP > 5eV - case_names = Dict( - "1 gpus" => "4e64e005-d988-48d4-8879-1f7a7d5f2be9", - "4 gpus" => "dcd68d1a-8190-4145-b77c-6486ac1a880b", - "16 gpus" => "09b342fa-95ad-4727-ac0f-fee0b8c4b84b", - "32 gpus" => "597f3b8a-9390-41b2-adc6-a974afd04c71", - "40 gpus" => "f1c03e9a-ee30-4671-873b-b4e2f9be17a1", - ) - - for (name, id) in pairs(case_names) - df = dfs[id] - @info "$name - $id" nrow(df) sum(df.duplicate_count) - end - - with_theme(MISTStyle.theme()) do - plot_generation( - Dict(name => dfs[id] for (name, id) in case_names) - ) |> MISTStyle.savefig("generation") - for (name, id) in pairs(case_names) - plot_pareto_front(dfs[id], df_ref) |> MISTStyle.savefig(joinpath("pareto", name * "-" * id)) - end - end -end - -function (@main)(::Any) - gen_plots() -end diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index db615c6f..3ac86981 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -54,15 +54,29 @@ regtable( ) # Plot generated molecules -production_run = first(sort!(df, :n_passing; rev=true)).path -df_mol = ScreeningPlots.load_generated_molecules(production_run) -prod_config = JSON.parsefile(joinpath(production_run, "config.json")) +production_run = (; pairs(first(sort!(df, :n_passing; rev=true)))...) +df_mol = ScreeningPlots.load_generated_molecules(production_run.path) +prod_config = JSON.parsefile(joinpath(production_run.path, "config.json")) # Reference Molecules -df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes.csv"))) +df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes_predictions.csv"))) df_mol.inchi_key = ScreeningPlots.inchi_key.(df_mol.smiles) df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi) df_novel = subset(df_mol, :inchi_key => ByRow(∉(df_ref.inchi_key))) df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df_mol.inchi_key))) @info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel) / nrow(df_mol) nrow(df_unfound) / nrow(df_ref) + +# Generate Plots +prod_id = basename(production_run.path) +trace = DataFrame(production_run[:trace]) +with_theme(MISTStyle.theme()) do + f = ScreeningPlots.plot_pareto_front(df_mol, df_ref) + MISTStyle.savefig(joinpath("pareto", "production" * "-" * prod_id), f) + + f = ScreeningPlots.plot_gen_trace(trace) + MISTStyle.savefig(joinpath("gen-trace" * "-" * prod_id), f) + + f = ScreeningPlots.figure_screening(trace, df_mol, df_ref, df) + MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) +end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index d7947b1b..cef4a17d 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -6,6 +6,7 @@ using Metaheuristics: Metaheuristics using JSON: JSON using SQLite: SQLite using PythonCall: Py, pyimport, pyconvert +using GLM: @formula, lm, glm, Normal, LogLink, coef using MISTStyle diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl index 5bc959df..d40225af 100644 --- a/opt/screening/plots/src/pareto.jl +++ b/opt/screening/plots/src/pareto.jl @@ -1,13 +1,28 @@ -function get_pareto_front(df, x, y; quad=:lt) +function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing) @assert quad == :lt "not implemented" - x = df[:, x] - y = -df[:, y] # flip sign to maximize - front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, y)) + front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, -1 .* y)) front = map(front) do p p[2] *= -1 p end - return Point2.(sort(front; by=first, rev=true)) + frontier = Point2.(sort(front; by=first, rev=true)) + if ax !== nothing + limits = lift(ax.finallimits) do hr + lx, ly = hr.origin + ux, uy = hr.origin .+ hr.widths + lx, ux = extrema([lx, ux]) + ly, uy = extrema([ly, uy]) + return (; lx, ly, ux, uy) + end + frontier = lift(limits) do limits + fs = Point2(limits.ux, frontier[1][2]) + fe = Point2(frontier[end][1], limits.ly) + vcat([fs], frontier, [fe]) + end + @info frontier + end + @info "frontier" frontier + return frontier end function plot_generation(cases) @@ -24,12 +39,107 @@ function plot_generation(cases) return f end +function plot_gen_trace(args...; kwargs...) + f = Figure(; size=(2inch, 1inch)) + plot_gen_trace!(f, args...; kwargs...) +end + +function fit_exp_decay(x, y) + df = DataFrame(x=x, y=float.(y[end] .- y)) + subset!(df, :y => ByRow(>(0))) + m = glm(@formula(y ~ x), df, Normal(), LogLink()) + A = exp.(coef(m)[1]) + τ = coef(m)[2] + y_hat = @. A * (1 - exp(x * τ)) + return (; m, y_hat, A, τ) +end + +function figure_screening(trace, case, ref, df_speed) + f = Figure(; + size=(3.42inch, 2inch), + figure_padding=(2, 2, 2, 5) + ) + gl_perf = GridLayout(f[1, 1]) + plot_pareto_front!(GridLayout(f[2, 1]), case, ref) + plot_gen_trace!(GridLayout(gl_perf[1, 1]), trace) + + gl = GridLayout(gl_perf[1, 2]) + ax = Axis(gl[1, 1]; + limits=((nothing, 9), (0, nothing)), + xlabel="GPUs", + ylabel="Evaluated/GPU-sec", + xscale=log2, + ) + # x = df_speed.gpus .+ 0.1 * randn(nrow(df_speed)) + x = df_speed.gpus .* (1 .+ 0.02 .* randn(nrow(df_speed))) + h = scatter!(ax, + x, df_speed.global_unique_throughput ./ df_speed.gpus; + color=df_speed.batch_size, + marker=:circle, + colormap=:roma, + alpha=0.8, + ) + Colorbar(gl[1, 2], h; + label="Batch Size" + ) + colgap!(gl_perf, 4pt) + + sublabel!(gl_perf[1, 1, TopLeft()], "a"; left=27pt) + sublabel!(gl_perf[1, 2, TopLeft()], "b"; left=25pt) + sublabel!(f[2, 1][1, 1, TopLeft()], "c"; left=15pt) + sublabel!(f[2, 1][1, 2, TopLeft()], "d"; left=5pt) + + resize_to_layout!(f) + + return f +end + +function plot_gen_trace!(f, trace) + ax = Axis(f[1, 1]; + xlabel="Wall Time [s]", + ylabel="Evaluated", + limits=((0, nothing), (0, 100e6)), + xlabelvisible=false, + xticksvisible=false, + xticklabelsvisible=false, + yticks=WilkinsonTicks(3), + yminorticks=IntervalsBetween(5), + yminorticksvisible=true, + ) + + m_uniq = fit_exp_decay(trace.time, trace.unique_molecules) + m_pass = fit_exp_decay(trace.time, trace.n_passing) + uniq_max_init_rate = -m_uniq.A * m_uniq.τ + pass_max_init_rate = -m_pass.A * m_pass.τ + @info "Initial Rates" uniq_max_init_rate pass_max_init_rate m_uniq.m m_pass.m + + lines!(ax, trace.time, trace.unique_molecules) + ax2 = Axis(f[2, 1]; + xlabel="Wall Time [s]", + ylabel="Passing", + limits=((0, nothing), (0, 2100)), + xminorticksvisible=true, + yminorticksvisible=true, + xminorticks=IntervalsBetween(10), + yminorticks=IntervalsBetween(5), + yticks=[0, 1000, 2000], + ) + lines!(ax2, trace.time, trace.n_passing) + linkxaxes!(ax, ax2) + + return f +end + function plot_pareto_front(case, ref) + f = Figure(; size=(2inch, 1inch), figure_padding=(1, 3, 1, 2)) + plot_pareto_front!(f, case, ref) +end +function plot_pareto_front!(f, case, ref) mp_limits = extrema(vcat(case.mp, [0])) bp_limits = extrema(vcat(case.bp, [75])) pareto_kwargs = (; - marker=:star5, - markersize=8pt, + linewidth=1.5pt, + alpha=0.7, ) # Net non-dominated @@ -52,41 +162,36 @@ function plot_pareto_front(case, ref) end @info "non-dominated" length(front) nrow(case) length(front) / nrow(case) - f = Figure(; size=(2inch, 1inch), figure_padding=(1, 3, 1, 2)) ax = Axis(f[1, 1]; limits=(mp_limits, bp_limits), xlabel=L"Melting Point [$\degree C$]", ylabel=L"Boiling Point [$\degree C$]", ) - scatter!(ax, case.mp, case.bp; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : MISTStyle.UM_COLORS.blue, case.dominated), - ) - scatterlines!(ax, get_pareto_front(ref, :mp, :bp); + scatter_samples!(ax, case.mp, case.bp, case.dominated) + stairs!(ax, get_pareto_front(ref.mp, ref.bp; ax); color=MISTStyle.UM_COLORS.maize, pareto_kwargs... ) - scatterlines!(ax, get_pareto_front(case, :mp, :bp); + stairs!(ax, get_pareto_front(case.mp, case.bp; ax); color=MISTStyle.UM_COLORS.blue, pareto_kwargs... ) ax = Axis(f[1, 2]; - limits=((-10, -7), (5, 12)), + limits=((-10.5, -7), (5, 13)), xlabel=L"HOMO [eV]$$", ylabel=L"Gap [eV]$$", + xticks=WilkinsonTicks(5; k_max=7), + yticks=WilkinsonTicks(5; k_max=7), ) - scatter!(ax, case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV; - marker=map(d -> d ? :circle : :star5, case.dominated), - color=map(d -> d ? MISTStyle.CAT_COLORS[1] : MISTStyle.UM_COLORS.blue, case.dominated), - label="Generated", - ) - scatterlines!(ax, get_pareto_front(ref, :homo, :gap) .* HARTREE_TO_EV; + h, _ = scatter_samples!(ax, case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV, case.dominated) + h.label = "Generated" + lines!(ax, get_pareto_front(ref.homo .* HARTREE_TO_EV, ref.gap .* HARTREE_TO_EV; ax); color=MISTStyle.UM_COLORS.maize, label="Ref. Pareto Front", pareto_kwargs... ) - scatterlines!(ax, get_pareto_front(case, :homo, :gap) .* HARTREE_TO_EV; + stairs!(ax, get_pareto_front(case.homo .* HARTREE_TO_EV, case.gap .* HARTREE_TO_EV; ax)[]; color=MISTStyle.UM_COLORS.blue, label="Generated Pareto Front", pareto_kwargs... @@ -98,3 +203,16 @@ function plot_pareto_front(case, ref) return f end + +function scatter_samples!(ax, x, y, dominated) + h1 = scatter!(ax, x[dominated], y[dominated]; + marker=:circle, + color=MISTStyle.CAT_COLORS[1], + alpha=0.4, + ) + h2 = scatter!(ax, x[.!dominated], y[.!dominated]; + marker=:star5, + color=MISTStyle.UM_COLORS.blue, + ) + return h1, h2 +end From c58351c4225cd87c17a04d81ab016fca88777ff5 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 14 Jun 2025 17:49:39 -0400 Subject: [PATCH 117/147] add verification plots --- opt/MISTStyle/src/MISTStyle.jl | 6 ++++++ opt/screening/plots/Project.toml | 1 + opt/screening/plots/plots.jl | 9 +++++++++ opt/screening/plots/src/ScreeningPlots.jl | 3 +++ 4 files changed, 19 insertions(+) diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index 2f55fdc8..e39e1658 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -51,6 +51,12 @@ function cb_attrs(cb::Colorbar, plt) return Base.structdiff(attrs, NamedTuple{(invalid...,)}) end +function parity_limits(x::AbstractVector, y::AbstractVector; inflate=0.05) + l, u = extrema(Iterators.flatten((x, y))) + limits = (l - inflate * (u - l), u + inflate * (u - l)) + return (limits, limits) +end + function sublabel!(f, letter; left=0, kwargs...) label_kwargs = (; diff --git a/opt/screening/plots/Project.toml b/opt/screening/plots/Project.toml index 5b8c6df9..7cfc3530 100644 --- a/opt/screening/plots/Project.toml +++ b/opt/screening/plots/Project.toml @@ -5,6 +5,7 @@ version = "0.1.0" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 3ac86981..b8de9069 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -79,4 +79,13 @@ with_theme(MISTStyle.theme()) do f = ScreeningPlots.figure_screening(trace, df_mol, df_ref, df) MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) + + # Verify qmist can reproduce QM9 calculations + qmist = realpath(joinpath(pkgdir(ScreeningPlots), "..", "..", "qmist")) + df_qm9 = ScreeningPlots.load_jsonl(joinpath(qmist, "qm9.jsonl")) + for version in [joinpath(qmist, "veri_v1")] + df_qmist = ScreeningPlots.load_qmist_results(version) + df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) + ScreeningPlots.figure_parity(df, cols) |> MISTStyle.savefig(basename(version) * "_parity") + end end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index cef4a17d..218f1054 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -7,6 +7,8 @@ using JSON: JSON using SQLite: SQLite using PythonCall: Py, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef +using Format: format +using StatsBase: cor, mad using MISTStyle @@ -18,6 +20,7 @@ function __init__() __rdkit_chem[] = pyimport("rdkit.Chem") end +include("qmist.jl") include("collate.jl") include("sqlite.jl") include("pareto.jl") From 90586e96b511770ca244c0956fff8ba0cc23b15a Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 17 Jun 2025 11:34:31 -0400 Subject: [PATCH 118/147] move mol_surprise into electrolyte_fm --- electrolyte_fm/models/mol_surprise.py | 71 +++++++++++++++++++++++++ opt/synth_access/main.py | 76 ++------------------------- 2 files changed, 75 insertions(+), 72 deletions(-) create mode 100644 electrolyte_fm/models/mol_surprise.py diff --git a/electrolyte_fm/models/mol_surprise.py b/electrolyte_fm/models/mol_surprise.py new file mode 100644 index 00000000..62b34e8d --- /dev/null +++ b/electrolyte_fm/models/mol_surprise.py @@ -0,0 +1,71 @@ +import torch +from torch.nn import functional as F +from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding +from ..utils.tokenizer import load_tokenizer + + +class MolSurpriseFM(torch.nn.Module): + def __init__(self, encoder, tokenizer): + super().__init__() + self.encoder = encoder + self.tokenizer = tokenizer + self.collate_fn = DataCollatorWithPadding(self.tokenizer) + + def forward( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor, + special_tokens_mask: torch.Tensor, + per_token: bool = False, + ): + logits = self.encoder(input_ids, attention_mask).logits + B = input_ids.shape[0] + V = logits.shape[-1] + + labels = input_ids.detach().masked_fill(special_tokens_mask.bool(), -100) + + score = ( + F.cross_entropy(logits.view(-1, V), labels.view(-1), reduction="none") + .reshape(B, -1) + .sum(-1) + ) + if per_token: + score = score / attention_mask.sum(-1) + return score + + def score(self, smiles: list[str], per_token: bool = False) -> list[float]: + batch = self.tokenizer(smiles, return_special_tokens_mask=True) + batch = self.collate_fn(batch).to(self.encoder.device) + with torch.inference_mode(): + return self.forward( + batch["input_ids"], + batch["attention_mask"], + batch["special_tokens_mask"], + per_token, + ).to("cpu") + + @classmethod + def from_checkpoint(cls, ckpt: str, **kwargs): + from ..utils.ckpt import DeepSpeedMixin + + encoder = DeepSpeedMixin.load(ckpt).model + tokenizer = load_tokenizer(ckpt) + return cls(encoder, tokenizer, **kwargs) + + @classmethod + def from_pretrained(cls, name_or_path: str, dtype=None, **kwargs): + encoder = AutoModelForMaskedLM.from_pretrained( + name_or_path, + trust_remote_code=True, + # device_map="auto", + # torch_dtype="auto", + ) + tokenizer = load_tokenizer(name_or_path) + return cls(encoder, tokenizer, **kwargs) + + @classmethod + def from_untrained(cls, name_or_path: str, dtype=None, **kwargs): + config = AutoConfig.from_pretrained(name_or_path, trust_remote_code=True) + encoder = AutoModelForMaskedLM.from_config(config) + tokenizer = load_tokenizer(name_or_path) + return cls(encoder, tokenizer, **kwargs) diff --git a/opt/synth_access/main.py b/opt/synth_access/main.py index 74f48cf1..543f9090 100644 --- a/opt/synth_access/main.py +++ b/opt/synth_access/main.py @@ -20,14 +20,11 @@ from sklearn.metrics import roc_auc_score from syba.syba import SybaClassifier from smirk import SmirkTokenizerFast -from torch.nn import functional as F -from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding from vendor.scscore.scscore import SCScorer +from electrolyte_fm.models.mol_surprise import MolSurpriseFM from electrolyte_fm.data_modules.utils import MolEncoding, encode_molecules -from electrolyte_fm.models.model_utils import DeepSpeedMixin from electrolyte_fm.utils.cache import cached_download, extract_file -from electrolyte_fm.utils.tokenizer import load_tokenizer # Suppress DeprecationWarnings for MorganGenerator rdBase.DisableLog("rdApp.warning") @@ -94,71 +91,6 @@ def syba_scorer(): return syba -class SynthAccessFM(torch.nn.Module): - def __init__(self, encoder, tokenizer): - super().__init__() - self.encoder = encoder - self.tokenizer = tokenizer - self.collate_fn = DataCollatorWithPadding(self.tokenizer) - - def forward( - self, - input_ids: torch.Tensor, - attention_mask: torch.Tensor, - special_tokens_mask: torch.Tensor, - per_token: bool = False, - ): - logits = self.encoder(input_ids, attention_mask).logits - B = input_ids.shape[0] - V = logits.shape[-1] - - labels = input_ids.detach().masked_fill(special_tokens_mask.bool(), -100) - - score = ( - F.cross_entropy(logits.view(-1, V), labels.view(-1), reduction="none") - .reshape(B, -1) - .sum(-1) - ) - if per_token: - score = score / attention_mask.sum(-1) - return score - - def score(self, smiles: List[str], per_token: bool = False) -> List[float]: - batch = self.tokenizer(smiles, return_special_tokens_mask=True) - batch = self.collate_fn(batch).to(self.encoder.device) - with torch.inference_mode(): - return self.forward( - batch["input_ids"], - batch["attention_mask"], - batch["special_tokens_mask"], - per_token, - ).to("cpu") - - @classmethod - def from_checkpoint(cls, ckpt: str, **kwargs): - encoder = DeepSpeedMixin.load(ckpt).model - tokenizer = load_tokenizer(ckpt) - return cls(encoder, tokenizer, **kwargs) - - @classmethod - def from_pretrained(cls, name_or_path: str, dtype=None, **kwargs): - encoder = AutoModelForMaskedLM.from_pretrained( - name_or_path, - trust_remote_code=True, - device_map="auto", - torch_dtype="auto", - ) - tokenizer = load_tokenizer(name_or_path) - return cls(encoder, tokenizer, **kwargs) - - @classmethod - def from_untrained(cls, name_or_path: str, dtype=None, **kwargs): - config = AutoConfig.from_pretrained(name_or_path, trust_remote_code=True) - encoder = AutoModelForMaskedLM.from_config(config) - tokenizer = load_tokenizer(name_or_path) - return cls(encoder, tokenizer, **kwargs) - - @timeout(30) # molecular_assembly (v0.2.0) timeout flag doesn't timeout def molecular_assembly_timeout(smi: str) -> Optional[int]: mol = Chem.MolFromSmiles(smi) @@ -203,7 +135,7 @@ def map_batchsize_finder(ds, f, batch_size: int = 64, **kwargs): def eval_fm_model( - metric_name: str, model: SynthAccessFM, ds: Dataset, target: str | None = None + metric_name: str, model: MolSurpriseFM, ds: Dataset, target: str | None = None ): model = model.to("cuda") model = model.eval() @@ -269,8 +201,8 @@ def evaluate_dataset( for name, metric in metrics.items(): if isinstance(metric, str): for suffix, init_model in [ - ("", SynthAccessFM.from_pretrained), - ("-untrained", SynthAccessFM.from_untrained), + ("", MolSurpriseFM.from_pretrained), + ("-untrained", MolSurpriseFM.from_untrained), ]: ds, metric_runtime, metric_auroc = eval_fm_model( metric + suffix, From 022c335d6c3831c8b82b19f76119303caa85bd56 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 17 Jun 2025 11:35:49 -0400 Subject: [PATCH 119/147] Update figures for screening plots --- electrolyte_fm/models/prod_finetune.py | 4 +- opt/screening/.gitignore | 10 +- opt/screening/activate | 16 +- opt/screening/plots/plots.jl | 90 +++++++-- opt/screening/plots/src/ScreeningPlots.jl | 14 +- opt/screening/plots/src/collate.jl | 41 ++-- opt/screening/plots/src/creativity.jl | 6 + opt/screening/plots/src/pareto.jl | 36 ++-- opt/screening/plots/src/qmist.jl | 219 ++++++++++++++++++++++ opt/screening/plots/src/sqlite.jl | 1 + opt/screening/pyproject.toml | 1 + opt/screening/uv.lock | 21 ++- 12 files changed, 398 insertions(+), 61 deletions(-) create mode 100644 opt/screening/plots/src/creativity.jl create mode 100644 opt/screening/plots/src/qmist.jl diff --git a/electrolyte_fm/models/prod_finetune.py b/electrolyte_fm/models/prod_finetune.py index 21b122eb..2618362e 100644 --- a/electrolyte_fm/models/prod_finetune.py +++ b/electrolyte_fm/models/prod_finetune.py @@ -103,7 +103,7 @@ def embed(self, smi: list[str]): return hs.to("cpu") - def predict(self, smi: list[str]): + def predict(self, smi: list[str], return_dict=True): batch = self.tokenizer(smi) collate_fn = DataCollatorWithPadding(self.tokenizer) batch = collate_fn(batch) @@ -114,7 +114,7 @@ def predict(self, smi: list[str]): with torch.inference_mode(): out = self(**batch).cpu() - if self.channels is None: + if self.channels is None or not return_dict: return out return annotate_prediction(out, self.channels) diff --git a/opt/screening/.gitignore b/opt/screening/.gitignore index 9a33bd18..af2ec7e4 100644 --- a/opt/screening/.gitignore +++ b/opt/screening/.gitignore @@ -1,8 +1,6 @@ vendor/ *.smi *.frag -out/ -out fig/ *.csv *.sqlite @@ -11,3 +9,11 @@ models/ models fig/ *.csv + +# Output folders +out/ +out +runs/ +runs +initial-sweep/ +initial-sweep diff --git a/opt/screening/activate b/opt/screening/activate index aa006cf0..9603d5e6 100755 --- a/opt/screening/activate +++ b/opt/screening/activate @@ -1,14 +1,16 @@ #!/bin/bash # Source this to activate the environment -DIR="$(git rev-parse --show-toplevel)/opt/screening" + +# Locate Project Root +GIT_ROOT=$(git rev-parse --show-toplevel) +ROOT=${GIT_ROOT}/opt/screening # Activate virtual environment -source $DIR/.venv/bin/activate +source ${ROOT}/.venv/bin/activate -# Configure HF and Tokenizers -export HF_HOME="$(git rev-parse --show-toplevel)/.cache/huggingface" -export TOKENIZERS_PARALLELISM=false +# HuggingFace +export HF_HOME="$GIT_ROOT/.cache/huggingface" -# Configure julia +# Julia export JULIA_CONDAPKG_BACKEND=Null -export JULIA_PYTHONCALL_EXE="$DIR/.venv/bin/python" +export JULIA_PYTHONCALL_EXE="${ROOT}/.venv/bin/python" diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index b8de9069..6f6877c1 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -4,15 +4,32 @@ using MISTStyle using StatsBase using DataFrames using GLM -using RegressionTables: LatexTable, regtable +using RegressionTables: RegressionTables, LatexTable, regtable using JSON: JSON using CSV: CSV +using Format: format -ROOTDIR = joinpath(pkgdir(ScreeningPlots), "..") +using ScreeningPlots: searchfirst + +ROOTDIR = realpath(joinpath(pkgdir(ScreeningPlots), "..")) +GIT_ROOT = realpath(joinpath(ROOTDIR, "..", "..")) fig_dir = joinpath(ROOTDIR, "fig") isdir(fig_dir) || mkdir(fig_dir) -df = ScreeningPlots.collate_performance_stats("initial-sweep") +# Filter to screening runs +df = ScreeningPlots.collate_performance_stats(joinpath(ROOTDIR, "runs")) +df = filter(df) do row + config = row.config + get(config, "limit_walltime", nothing) == 300 || return false + models = Set([c["model_path"] for c in config["critics"]]) + expected_models = [ + "models/mist-26.9M-b302p09x-bp", + "models/mist-26.9M-y3ge5pf9-mp", + "models/mist-x4i8qzuq-qm9", + ] + models == Set(expected_models) || return false + return true +end df.limit_ref_fragments .= something.(df.limit_ref_fragments, 122) df.limit_db_fragments .= something.(df.limit_db_fragments, 88_800_000) df.limit_ref_fragments ./= 122 @@ -30,7 +47,7 @@ df.generation_efficiency = df.global_unique_throughput ./ df.global_throughput # Linear Models to guide scaling m_eff = lm( @formula(generation_efficiency ~ log(gpus) + epoch_size + epoch_size^2 + limit_ref_fragments + limit_db_fragments), - df + subset(df, :duration => ByRow(<(400))), ) display(m_eff) coef_m = Dict(zip(coefnames(m_eff), coef(m_eff))) @@ -40,7 +57,7 @@ ideal_rel_epoch = -coef_m["epoch_size"] / (2 * coef_m["epoch_size ^ 2"]) m_speed = lm( @formula(log(global_throughput) ~ log(gpus) + batch_size + +batch_size^2 + n_fragments + epoch_size), - df + subset(df, :duration => ByRow(<(400))), ) display(m_speed) coef_speed = Dict(zip(coefnames(m_speed), coef(m_speed))) @@ -51,41 +68,86 @@ regtable( m_speed, m_eff; file=joinpath(fig_dir, "screening_lm.tex"), render=LatexTable(), + regression_statistics=[ + RegressionTables.Nobs, + RegressionTables.DOF, + RegressionTables.R2, + (m -> mad(residuals(m))) => "MAE", + (m -> rmsd(predict(m), response(m))) => "RMSE", + ] ) # Plot generated molecules -production_run = (; pairs(first(sort!(df, :n_passing; rev=true)))...) -df_mol = ScreeningPlots.load_generated_molecules(production_run.path) -prod_config = JSON.parsefile(joinpath(production_run.path, "config.json")) +prod_id = "b7c6ceb2-2114-4ba5-bd2e-41b9bfa2d5df" +production_run_path = joinpath(ROOTDIR, "runs", prod_id) +df_mol = ScreeningPlots.load_generated_molecules(production_run_path) +prod_config = JSON.parsefile(joinpath(production_run_path, "config.json")) # Reference Molecules -df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes_predictions.csv"))) +df_ref = DataFrame(CSV.File(joinpath(ROOTDIR, "electrolytes_predictons.csv"))) df_mol.inchi_key = ScreeningPlots.inchi_key.(df_mol.smiles) df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi) df_novel = subset(df_mol, :inchi_key => ByRow(∉(df_ref.inchi_key))) df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df_mol.inchi_key))) @info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel) / nrow(df_mol) nrow(df_unfound) / nrow(df_ref) +@info "Prod. Perf" throughput=production_run.global_throughput / production_run.gpus uniq_throughput = production_run.global_unique_throughput / production_run.gpus # Generate Plots -prod_id = basename(production_run.path) -trace = DataFrame(production_run[:trace]) +trace, _ = ScreeningPlots.performance_trace(joinpath(production_run_path, "screen.jsonl")) +trace = DataFrame(trace) with_theme(MISTStyle.theme()) do f = ScreeningPlots.plot_pareto_front(df_mol, df_ref) - MISTStyle.savefig(joinpath("pareto", "production" * "-" * prod_id), f) + MISTStyle.savefig(joinpath("production" * "-" * prod_id), f) f = ScreeningPlots.plot_gen_trace(trace) MISTStyle.savefig(joinpath("gen-trace" * "-" * prod_id), f) + f = ScreeningPlots.weak_scaling(subset(df, :duration => ByRow(<(400)))) + MISTStyle.savefig(joinpath("scaling" * "-" * prod_id), f) + f = ScreeningPlots.figure_screening(trace, df_mol, df_ref, df) MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) # Verify qmist can reproduce QM9 calculations qmist = realpath(joinpath(pkgdir(ScreeningPlots), "..", "..", "qmist")) df_qm9 = ScreeningPlots.load_jsonl(joinpath(qmist, "qm9.jsonl")) - for version in [joinpath(qmist, "veri_v1")] + label = "QM9 (Ramakrishnan et al.)" => "Ours" + for version in [joinpath(qmist, "veri_v1"), joinpath(qmist, "veri_v2"), joinpath(qmist, "veri_v3")] df_qmist = ScreeningPlots.load_qmist_results(version) df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) - ScreeningPlots.figure_parity(df, cols) |> MISTStyle.savefig(basename(version) * "_parity") + μ, σ = mean_and_std(df_qmist.walltime) + walltime_p95 = quantile(df_qmist.walltime, 0.95) + @info basename(version) nrow(df) walltime=format("\\({:.0f} \\pm {:.0f}\\)", μ, σ) walltime_p95 + ScreeningPlots.figure_parity(df, cols; label) |> MISTStyle.savefig(basename(version) * "_parity") end + + # Parity Plots vs. QM9 Calculations + for (dir_name, label) in ["qm9" => "rdkit", "qm9_obabel" => "openbabel", "qm9_conf" => "conformer"] + f = ScreeningPlots.compare_qmist( + production_run_path, + joinpath(production_run_path, dir_name); + label="B3LYP/6-31G(2df,p)" => "MIST", + ) + MISTStyle.savefig(joinpath("parity-$(label)-$(prod_id)"), f) + end + + # Load the QM9 Model used for screening + qm9_model_name = searchfirst( + c -> occursin("qm9", c), + [ basename(c["model_path"]) for c in prod_config["critics"] ] + ) + mist_qm9 = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", qm9_model_name)) + mist_qm9 = mist_qm9.to("mps") + # + # Parity Plots with Chembl data + f = ScreeningPlots.compare_qmist( + production_run_path, + joinpath(production_run_path, "qm9_conf"), + joinpath(ROOTDIR, "veri_chembl"), + mist_qm9; + label="B3LYP/6-31G(2df,p)" => "MIST", + ) + MISTStyle.savefig(joinpath("parity-chembl-$(prod_id)"), f) + end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index 218f1054..8bc7d924 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -5,7 +5,7 @@ using DataFrames using Metaheuristics: Metaheuristics using JSON: JSON using SQLite: SQLite -using PythonCall: Py, pyimport, pyconvert +using PythonCall: Py, PyList, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef using Format: format using StatsBase: cor, mad @@ -15,12 +15,19 @@ using MISTStyle const HARTREE_TO_EV = 27.211_386_245_981 const __rdkit_chem = Ref{Py}() +const __mol_surprise = Ref{Py}() +const __prod_finetune = Ref{Py}() +const __data_utils = Ref{Py}() function __init__() __rdkit_chem[] = pyimport("rdkit.Chem") + __mol_surprise[] = pyimport("electrolyte_fm.models.mol_surprise") + __prod_finetune[] = pyimport("electrolyte_fm.models.prod_finetune") + __data_utils[] = pyimport("electrolyte_fm.data_modules.utils") end include("qmist.jl") +include("creativity.jl") include("collate.jl") include("sqlite.jl") include("pareto.jl") @@ -37,4 +44,9 @@ function inchi_key(smi::String) return pyconvert(String, __rdkit_chem[].MolToInchiKey(mol)) end +function searchfirst(f, x) + idx = findfirst(f, x) + return isnothing(idx) ? nothing : x[idx] +end + end diff --git a/opt/screening/plots/src/collate.jl b/opt/screening/plots/src/collate.jl index 69cf3bad..07bba668 100644 --- a/opt/screening/plots/src/collate.jl +++ b/opt/screening/plots/src/collate.jl @@ -6,27 +6,12 @@ function collate_performance_stats(sweep::String) (isfile(config_file) && isfile(logfile)) || continue config = JSON.parsefile(config_file) - trace = [] - rank_throughput = Dict{Int,Float64}() - for line in eachline(logfile) - msg = JSON.parse(line) - if haskey(msg, "n_passing_world") && get(msg, "global_rank", -1) == 0 - # Track global generation stats - push!(trace, (; - time=msg["elapsed_perf"], - n_passing=msg["n_passing_world"], - unique_molecules=msg["unique_molecules_world"], - )) - elseif haskey(msg, "passing_rank") - # Record the final throughput of each rank - rank = msg["global_rank"] - rank_throughput[rank] = msg["eval_throughput_rank"] - end - end + trace, rank_throughput = performance_trace(logfile) isempty(trace) && continue push!(rows, (; id=basename(run), path=joinpath(sweep, run), + config, batch_size=config["generation"]["batch_size"], limit_db_fragments=config["generation"]["limit_db_fragments"], limit_ref_fragments=config["generation"]["limit_ref_fragments"], @@ -42,3 +27,25 @@ function collate_performance_stats(sweep::String) end return DataFrame(rows) end + +function performance_trace(logfile::String) + trace = [] + rank_throughput = Dict{Int,Float64}() + for line in eachline(logfile) + msg = JSON.parse(line) + if haskey(msg, "n_passing_world") && get(msg, "global_rank", -1) == 0 + # Track global generation stats + push!(trace, (; + time=msg["elapsed_perf"], + n_passing=msg["n_passing_world"], + unique_molecules=msg["unique_molecules_world"], + )) + elseif haskey(msg, "passing_rank") + # Record the final throughput of each rank + rank = msg["global_rank"] + rank_throughput[rank] = msg["eval_throughput_rank"] + end + end + return trace, rank_throughput +end + diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl new file mode 100644 index 00000000..9aaa0359 --- /dev/null +++ b/opt/screening/plots/src/creativity.jl @@ -0,0 +1,6 @@ +function batch_score(model::Py, smiles::Vector{String}; batch_size=32) + _eval(batch) = pyconvert(Vector{Float64}, model.score(PyList(batch))) + ds = Iterators.partition(smiles, batch_size) + out = Iterators.map(_eval, ds) + return collect(Iterators.flatten(out)) +end diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl index d40225af..74070059 100644 --- a/opt/screening/plots/src/pareto.jl +++ b/opt/screening/plots/src/pareto.jl @@ -19,9 +19,7 @@ function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing) fe = Point2(frontier[end][1], limits.ly) vcat([fs], frontier, [fe]) end - @info frontier end - @info "frontier" frontier return frontier end @@ -54,17 +52,9 @@ function fit_exp_decay(x, y) return (; m, y_hat, A, τ) end -function figure_screening(trace, case, ref, df_speed) - f = Figure(; - size=(3.42inch, 2inch), - figure_padding=(2, 2, 2, 5) - ) - gl_perf = GridLayout(f[1, 1]) - plot_pareto_front!(GridLayout(f[2, 1]), case, ref) - plot_gen_trace!(GridLayout(gl_perf[1, 1]), trace) - - gl = GridLayout(gl_perf[1, 2]) - ax = Axis(gl[1, 1]; +weak_scaling(df_speed) = weak_scaling!(Figure(), df_speed) +function weak_scaling!(f, df_speed) + ax = Axis(f[1, 1]; limits=((nothing, 9), (0, nothing)), xlabel="GPUs", ylabel="Evaluated/GPU-sec", @@ -73,15 +63,27 @@ function figure_screening(trace, case, ref, df_speed) # x = df_speed.gpus .+ 0.1 * randn(nrow(df_speed)) x = df_speed.gpus .* (1 .+ 0.02 .* randn(nrow(df_speed))) h = scatter!(ax, - x, df_speed.global_unique_throughput ./ df_speed.gpus; + x, df_speed.global_throughput ./ df_speed.gpus; color=df_speed.batch_size, marker=:circle, colormap=:roma, alpha=0.8, ) - Colorbar(gl[1, 2], h; - label="Batch Size" + Colorbar(f[1, 2], h; label="Batch Size") + return f +end + +function figure_screening(trace, case, ref, df_speed) + f = Figure(; + size=(3.42inch, 2inch), + figure_padding=(2, 2, 2, 5) ) + gl_perf = GridLayout(f[1, 1]) + plot_pareto_front!(GridLayout(f[2, 1]), case, ref) + plot_gen_trace!(GridLayout(gl_perf[1, 1]), trace) + + gl = GridLayout(gl_perf[1, 2]) + weak_scaling!(gl, df_speed) colgap!(gl_perf, 4pt) sublabel!(gl_perf[1, 1, TopLeft()], "a"; left=27pt) @@ -154,7 +156,7 @@ function plot_pareto_front!(f, case, ref) case.dominated .= true case.dominated[nidx] .= false sort!(case, :dominated; rev=true) - @info "non-dominated" sort(case[nidx, :], :inchi) + @info "non-dominated" sort(case[nidx, :], :inchi_key) front = map(front) do p p[1] *= -1 p[2] *= -1 diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl new file mode 100644 index 00000000..e862e8d3 --- /dev/null +++ b/opt/screening/plots/src/qmist.jl @@ -0,0 +1,219 @@ +function load_qmist_results(folder::String) + rows = [] + for file in readdir(folder; join=true) + endswith(file, ".json") || continue + push!(rows, JSON.parsefile(file)) + end + df = DataFrame(rows) + rename!(df, "InChIKey" => "inchi_key") + return df +end + +load_mist_pretrained(folder::String) = __prod_finetune[].MISTFinetuned.from_pretrained(folder) + +function predict_mist(model::Py, smi::Vector{String}; batch_size=32) + model = model.eval() + transcode = __data_utils[].MolEncoding("smiles-kekule") + smi = map(x -> pyconvert(String, transcode(x)), smi) + ds = Iterators.partition(smi, batch_size) + channels = pyconvert(Vector{String}, [chn["name"] for chn in model.channels]) + out = Iterators.map(ds) do batch + y = model.predict(PyList(batch); return_dict=false) + yj = pyconvert(Matrix{Float64}, y) + return eachrow(yj) + end + data = stack(collect(Iterators.flatten(out)))' + df = DataFrame(data, channels) + df.smiles = smi + return df +end + +function load_jsonl(file::String) + rows = [] + open(file, "r") do io + for line in eachline(io) + push!(rows, JSON.parse(line)) + end + end + return DataFrame(rows) +end + +function merge_qmist_results(qmist::DataFrame, ref::DataFrame) + cols = filter(!=("smiles"), names(ref)) + df = innerjoin(qmist, ref; on=:smiles, renamecols = "_qmist" => "_qm9") + return df, cols +end + +figure_parity(args...; kwargs...) = figure_parity!(Figure(; size=(3.42inch, 2inch)), args...; kwargs...) +function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", other="_qmist", label::Union{Pair{String,String},Nothing}=nothing) + nrow = floor(Int, sqrt(length(cols))) + ncol = ceil(Int, length(cols) / nrow) + + gl = GridLayout(f[1, 1]) + for (idx, col) in enumerate(cols) + i, j = divrem(idx - 1, ncol) + i += 1 + x = df[!, col * ref] + y = df[!, col * other] + xlim = extrema(x) + ylim = extrema(y) + ax = Axis(gl[i, j]; + title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mad(x .- y)), + limits=MISTStyle.parity_limits(x, y), + xticks=WilkinsonTicks(2), + yticks=WilkinsonTicks(2), + ) + if "color$(ref)" in names(df) + sargs = (; alpha= 0.3, color=df[!, "color$(ref)"], colormap=MISTStyle.CAT_COLORS, colorrange=(1, 10)) + else + sargs = (; alpha=0.3) + end + scatter!(ax, x, y; marker=:circle, alpha=0.3, sargs...) + ablines!(ax, 0, 1; color=:black, linestyle=:dash) + end + + if !isnothing(label) + xlabel, ylabel = label + Label(f[:, 0], ylabel, rotation=pi/2, tellwidth=true, tellheight=false) + Label(f[end+1,:], xlabel,; tellwidth=false, tellheight=true) + end + + if "color$(ref)" in names(df) + @info "Group Sizes" combine(groupby(df, "color$(ref)"), DataFrames.nrow => :n) + ng = count(==(1), df[!, "color$(ref)"]) + ni = count(==(2), df[!, "color$(ref)"]) + elements = map(enumerate(["Generated (n=$ng)", "Inventory (n=$ni)"])) do (idx, label) + MarkerElement(; label, marker=:circle, color=MISTStyle.CAT_COLORS[idx]) + end + Legend(gl[begin, end], elements, MISTStyle.label.(elements); + fontsize=6pt, + margin=(2pt, 2pt, 2pt, 2pt), + padding=2pt, + valign=:bottom, + halign=:right, + ) + end + + resize_to_layout!(f) + return f +end + +# Compare MIST vs. qmist +function compare_qmist( + run::String, + qmist_dir::String, + cols::Vector{String} = ["gap", "lumo", "homo"]; + kwargs... +) + # Load Screening & QMist results + df_qmist = load_qmist_results(qmist_dir) + df_mist = load_generated_molecules(run) + select!(df_mist, ["inchi_key", cols...]) + select!(df_qmist, ["inchi_key", cols...]) + return compare_qmist(df_qmist, df_mist, cols; on="inchi_key", kwargs...) +end + +# Compare MIST vs. qmist +function compare_qmist( + run::String, + qmist_dir::String, + chembl_dir::String, + mist_qm9::Py; + cols::Vector{String} = ["gap", "lumo", "homo"], + kwargs... +) + # Load Screening & QMist results + df_qmist = load_qmist_results(qmist_dir) + df_mist = load_generated_molecules(run) + select!(df_mist, ["inchi_key", cols...]) + select!(df_qmist, ["inchi_key", cols...]) + df_qmist.color .= 1 + df_mist.color .= 1 + + # Load Chembl results + df_chembl = load_qmist_results(chembl_dir) + df_mist_chembl = predict_mist(mist_qm9, df_chembl.smiles) + df_mist_chembl.inchi_key = df_chembl.inchi_key + select!(df_mist_chembl, ["inchi_key", cols...]) + select!(df_chembl, ["inchi_key", cols...]) + df_chembl.color .= 2 + df_mist_chembl.color .= 2 + + df_qmist = vcat(df_qmist, df_chembl) + df_mist = vcat(df_mist, df_mist_chembl) + + return compare_qmist(df_qmist, df_mist, cols; on="inchi_key", kwargs...) +end + +function compare_qmist(df_qmist::DataFrame, df_mist::DataFrame, cols::Vector{String}; on="InChIKey" => "inchi", kwargs...) + df = innerjoin(df_qmist, df_mist; on, renamecols="_qmist" => "_mist") + disallowmissing!(df) + + f = Figure(; size=(3.42inch, 1inch)) + figure_parity!(f ,df, cols; ref="_qmist", other="_mist", kwargs...) + return f +end + +function hit_rate!(df::DataFrame, limits) + df_cols = names(df) + select!(df, filter(!endswith("_hit"), names(df))) + chns = String[] + models = String[] + for (col, limit) in limits + for dc in filter(x -> occursin(col, x), df_cols) + push!(chns, col) + push!(models, split(dc, "_")[2]) + df[!, Symbol(dc * "_hit")] = map(x -> inbounds(x, limit), df[!, dc]) + end + end + chns = unique(chns) + models = unique(models) + + # Mark overall hit rate + for model in models + transform!(df, + map(col -> "$(col)_$(model)_hit", chns) => ByRow((x...) -> all(x)) => "$(model)_hit" + ) + end + + return chns, models +end + +function retrival_quality(df::DataFrame, chns, models::Pair{String}) + hitcols = filter(endswith("_hit"), names(df)) + df_hit = select(df, hitcols) + ref_model, other_model = models + cf = Dict{String, NamedTuple}() + for chn in chns + x = df_hit[!, "$(chn)_$(ref_model)_hit"] + x_hat = df_hit[!, "$(chn)_$(other_model)_hit"] + cf[chn] = confmatrix(x_hat, x) + end + cf["all"] = confmatrix(df_hit[!, other_model * "_hit"], df_hit[!, ref_model * "_hit"]) + return retrival_quality(cf) +end + +retrival_quality(cfs::Dict) = Dict(k => retrival_quality(v) for (k, v) in cfs) + +function retrival_quality(cf::NamedTuple) + return (; + cf, + precision = cf.tp / (cf.tp + cf.fp), + ) +end + +function confmatrix(x_hat::Vector{Bool}, x::Vector{Bool}) + return (; + tp = sum(x_hat .& x), + tn = sum(.!x_hat .& .!x), + fp = sum(x_hat .& .!x), + fn = sum(.!x_hat .& x), + ) +end + +inbounds(x::Real, lim::Tuple) = inbounds(x, first(lim), last(lim)) +inbounds(x::Real, lb::Real, ub::Real) = x >= lb && x <= ub +inbounds(x::Real, lb::Nothing, ub::Real) = x <= ub +inbounds(x::Real, lb::Real, ub::Nothing) = x >= lb +inbounds(x::Real, lb::Nothing, ub::Nothing) = true + diff --git a/opt/screening/plots/src/sqlite.jl b/opt/screening/plots/src/sqlite.jl index fe53e0f8..379a56c8 100644 --- a/opt/screening/plots/src/sqlite.jl +++ b/opt/screening/plots/src/sqlite.jl @@ -17,6 +17,7 @@ function load_generated_molecules(path) cols = keys(JSON.parse(df[1, :props])) transform!(df, :props => ByRow(JSON.parse) => Symbol.(cols)) select!(df, Not(:props)) + rename!(df, "inchi" => "inchi_key") return df end diff --git a/opt/screening/pyproject.toml b/opt/screening/pyproject.toml index 89049c51..a19e5246 100644 --- a/opt/screening/pyproject.toml +++ b/opt/screening/pyproject.toml @@ -5,6 +5,7 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.10, <3.14" dependencies = [ + "accelerate>=1.7.0", "electrolyte_fm", "lightning>=2.5.1", "pandas>=2.2.3", diff --git a/opt/screening/uv.lock b/opt/screening/uv.lock index 9d5ce456..3d51c261 100644 --- a/opt/screening/uv.lock +++ b/opt/screening/uv.lock @@ -1,5 +1,4 @@ version = 1 -revision = 1 requires-python = ">=3.10, <3.14" resolution-markers = [ "python_full_version >= '3.13' and sys_platform == 'linux'", @@ -12,6 +11,24 @@ resolution-markers = [ "python_full_version < '3.11' and sys_platform != 'linux'", ] +[[package]] +name = "accelerate" +version = "1.7.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "huggingface-hub" }, + { name = "numpy" }, + { name = "packaging" }, + { name = "psutil" }, + { name = "pyyaml" }, + { name = "safetensors" }, + { name = "torch" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/97/33/47bbd507e3a851d33d19ce7b2141c5ea3689bfae91ba168044d7db24b0e9/accelerate-1.7.0.tar.gz", hash = "sha256:e8a2a5503d6237b9eee73cc8d36cf543f9c2d8dd2c6713450b322f5e6d53a610", size = 376026 } +wheels = [ + { url = "https://files.pythonhosted.org/packages/f8/bb/be8146c196ad6e4dec78385d91e92591f8a433576c4e04c342a636fcd811/accelerate-1.7.0-py3-none-any.whl", hash = "sha256:cf57165cca28769c6cf2650812371c81b18e05743dfa3c748524b1bb4f2b272f", size = 362095 }, +] + [[package]] name = "aiohappyeyeballs" version = "2.6.1" @@ -1887,6 +1904,7 @@ name = "screening" version = "0.1.0" source = { virtual = "." } dependencies = [ + { name = "accelerate" }, { name = "electrolyte-fm" }, { name = "lightning" }, { name = "pandas" }, @@ -1902,6 +1920,7 @@ dev = [ [package.metadata] requires-dist = [ + { name = "accelerate", specifier = ">=1.7.0" }, { name = "electrolyte-fm", editable = "../../" }, { name = "lightning", specifier = ">=2.5.1" }, { name = "pandas", specifier = ">=2.2.3" }, From 05a1873b26b5c286773103f09c788280a0b76d45 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Wed, 18 Jun 2025 14:55:46 -0400 Subject: [PATCH 120/147] Update and expand screening plots --- electrolyte_fm/models/mol_surprise.py | 13 ++ opt/MISTStyle/Project.toml | 8 +- opt/MISTStyle/src/MISTStyle.jl | 2 + opt/MISTStyle/src/nbins.jl | 17 ++ opt/screening/plots/Project.toml | 5 +- opt/screening/plots/plots.jl | 44 ++++- opt/screening/plots/src/ScreeningPlots.jl | 18 +- opt/screening/plots/src/creativity.jl | 206 +++++++++++++++++++++- opt/screening/plots/src/pareto.jl | 7 +- opt/screening/plots/src/qmist.jl | 3 +- opt/synth_access/plot.jl | 2 +- 11 files changed, 305 insertions(+), 20 deletions(-) create mode 100644 opt/MISTStyle/src/nbins.jl diff --git a/electrolyte_fm/models/mol_surprise.py b/electrolyte_fm/models/mol_surprise.py index 62b34e8d..907a4561 100644 --- a/electrolyte_fm/models/mol_surprise.py +++ b/electrolyte_fm/models/mol_surprise.py @@ -1,4 +1,5 @@ import torch +from torch.fx import has_side_effect from torch.nn import functional as F from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding from ..utils.tokenizer import load_tokenizer @@ -44,6 +45,18 @@ def score(self, smiles: list[str], per_token: bool = False) -> list[float]: per_token, ).to("cpu") + def embed(self, smiles: list[str]) -> torch.Tensor: + batch = self.tokenizer(smiles, return_special_tokens_mask=True) + batch = self.collate_fn(batch).to(self.encoder.device) + input_ids = batch["input_ids"] + attention_mask = batch["attention_mask"] + if hasattr(self.encoder, "roberta_prelayernorm"): + encoder = self.encoder.roberta_prelayernorm + else: + encoder = self.encoder.encoder + with torch.inference_mode(): + return encoder(input_ids, attention_mask=attention_mask).last_hidden_state + @classmethod def from_checkpoint(cls, ckpt: str, **kwargs): from ..utils.ckpt import DeepSpeedMixin diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index 36924d30..00c8259b 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -6,8 +6,10 @@ CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [compat] -Makie = "0.24" -CategoricalArrays = "0.10.8" -GLMakie = "0.11.3" +Makie = "0.22,0.23" +CairoMakie = "0.13,0.14" +CategoricalArrays = "0.10" +StatsBase = "0.33, 0.34" diff --git a/opt/MISTStyle/src/MISTStyle.jl b/opt/MISTStyle/src/MISTStyle.jl index e39e1658..4204e191 100644 --- a/opt/MISTStyle/src/MISTStyle.jl +++ b/opt/MISTStyle/src/MISTStyle.jl @@ -3,6 +3,7 @@ module MISTStyle using Makie using CategoricalArrays: levels using CairoMakie: CairoMakie +using StatsBase: StatsBase, AbstractWeights # Conversion from units into pixels const pt = 1 @@ -76,6 +77,7 @@ include("tantext.jl") include("quadrant.jl") include("asinh.jl") include("sci_notation.jl") +include("nbins.jl") const CAT_COLORS = cgrad( map(x -> RGBf(x ./ 255...), [ diff --git a/opt/MISTStyle/src/nbins.jl b/opt/MISTStyle/src/nbins.jl new file mode 100644 index 00000000..fa53bc33 --- /dev/null +++ b/opt/MISTStyle/src/nbins.jl @@ -0,0 +1,17 @@ +# Estimate Number of Histogram Bins from data +hist_nbins(x::AbstractVector) = hist_nbins(:scott, x) +hist_nbins(x::AbstractVector, w::AbstractWeights) = hist_nbins(:scott, x) +hist_nbins(method::Symbol, x::AbstractVector) = hist_nbins(method, x, StatsBase.UnitWeights{Int}(length(x))) + +hist_nbins(method, x, w) = hist_nbins(Val(Symbol(method)), x, w) +hist_nbins(method::Symbol, x::AbstractVector, w::AbstractWeights) = hist_nbins(Val(method), x, w) +function hist_nbins(::Val{:scott}, x, w) + σ = StatsBase.std(x, w) + h = 3.5 * σ / cbrt(length(x)) + n = (maximum(x) - minimum(x)) / h + return ceil(Int, n) +end +hist_nbins(::Val{:sqrt}, x, w) = ceil(Int, sqrt(length(x))) +hist_nbins(::Val{M}, args...) where {M} = MethodError(hist_nbins, M, args...) +hist_nbins(::Val{:sturges}, x, w) = ceil(Int, log2(length(x)) + 1) +hist_nbins(::Val{:sturges}, x, w::StatsBase.FrequencyWeights) = ceil(Int, log2(sum(w)) + 1) diff --git a/opt/screening/plots/Project.toml b/opt/screening/plots/Project.toml index 7cfc3530..33870f90 100644 --- a/opt/screening/plots/Project.toml +++ b/opt/screening/plots/Project.toml @@ -7,14 +7,17 @@ CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" +LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" +ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" Metaheuristics = "bcdb8e00-2c21-11e9-3065-2b553b22f898" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9" -StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" [sources] MISTStyle = {path = "../../MISTStyle"} diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 6f6877c1..21615de7 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -1,7 +1,7 @@ using ScreeningPlots using Makie using MISTStyle -using StatsBase +using Statistics using DataFrames using GLM using RegressionTables: RegressionTables, LatexTable, regtable @@ -72,8 +72,8 @@ regtable( RegressionTables.Nobs, RegressionTables.DOF, RegressionTables.R2, - (m -> mad(residuals(m))) => "MAE", - (m -> rmsd(predict(m), response(m))) => "RMSE", + (m -> ScreeningPlots.mae(residuals(m))) => "MAE", + (m -> ScreeningPlots.rmsd(predict(m), response(m))) => "RMSE", ] ) @@ -91,7 +91,7 @@ df_ref.inchi_key = ScreeningPlots.inchi_key.(df_ref.smi) df_novel = subset(df_mol, :inchi_key => ByRow(∉(df_ref.inchi_key))) df_unfound = subset(df_ref, :inchi_key => ByRow(∉(df_mol.inchi_key))) @info "Novel Molecules" nrow(df_novel) nrow(df_ref) nrow(df_novel) / nrow(df_mol) nrow(df_unfound) / nrow(df_ref) -@info "Prod. Perf" throughput=production_run.global_throughput / production_run.gpus uniq_throughput = production_run.global_unique_throughput / production_run.gpus +# @info "Prod. Perf" throughput=production_run.global_throughput / production_run.gpus uniq_throughput = production_run.global_unique_throughput / production_run.gpus # Generate Plots trace, _ = ScreeningPlots.performance_trace(joinpath(production_run_path, "screen.jsonl")) @@ -116,7 +116,8 @@ with_theme(MISTStyle.theme()) do for version in [joinpath(qmist, "veri_v1"), joinpath(qmist, "veri_v2"), joinpath(qmist, "veri_v3")] df_qmist = ScreeningPlots.load_qmist_results(version) df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) - μ, σ = mean_and_std(df_qmist.walltime) + μ = mean(df_qmist.walltime) + σ = std(df_qmist.walltime) walltime_p95 = quantile(df_qmist.walltime, 0.95) @info basename(version) nrow(df) walltime=format("\\({:.0f} \\pm {:.0f}\\)", μ, σ) walltime_p95 ScreeningPlots.figure_parity(df, cols; label) |> MISTStyle.savefig(basename(version) * "_parity") @@ -151,3 +152,36 @@ with_theme(MISTStyle.theme()) do MISTStyle.savefig(joinpath("parity-chembl-$(prod_id)"), f) end + +mol_surprise = ScreeningPlots.load_mol_surprise(joinpath(GIT_ROOT, "models", "mist-ti624ev1")) +mist_mp = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist-26.9M-y3ge5pf9-mp")) +mist_bp = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist-26.9M-b302p09x-bp")) + +f, df_surprise = ScreeningPlots.compare_creativity( + production_run_path, + joinpath(ROOTDIR, "veri_chembl"), + df_ref; + mol_surprise, + mist_mp, + mist_bp +) +MISTStyle.savefig("surprise_vs_utility", f) + +# Compute Renyi Entropy Metrics +df_surprise.embed_mean = eachrow(ScreeningPlots.mist_embedding(mol_surprise, df_surprise.smiles; pooling=ScreeningPlots.mean_pooling)) +dist_metrics = [ + "eculidean" => ScreeningPlots.eculidean_distance, + "cosine" => ScreeningPlots.cosine_distance, + "angular" => ScreeningPlots.angular_distance, +] +df_s = combine(groupby(df_surprise, :group)) do gdf + out = [] + n = nrow(gdf) + for (pool, emb) in ["first" => gdf.embed, "mean" => gdf.embed_mean] + for (metric, distance) in dist_metrics + o = ScreeningPlots.renyi_entropy_estimate(emb; distance) + push!(out, (; n, pool, metric, o...)) + end + end + return DataFrame(out) +end diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index 8bc7d924..e9e886f9 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -2,13 +2,17 @@ module ScreeningPlots using Makie using DataFrames +using Graphs: complete_graph, boruvka_mst using Metaheuristics: Metaheuristics using JSON: JSON using SQLite: SQLite using PythonCall: Py, PyList, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef using Format: format -using StatsBase: cor, mad +using Statistics: cor, mean +using CategoricalArrays: categorical, levelcode +using LinearAlgebra: norm, dot +using ManifoldLearning: ManifoldLearning, DiffMap, fit, predict, transform using MISTStyle @@ -26,6 +30,18 @@ function __init__() __data_utils[] = pyimport("electrolyte_fm.data_modules.utils") end +mae(x, y) = mean(x -> abs(-(x...)), zip(x, y)) +mae(x) = mean(abs, x) +rmsd(x, y) = sqrt(mean(x -> -(x...)^2, zip(x, y))) + +default_device() = Sys.isbsd() ? "mps" : "cuda" +function load_mist_pretrained(folder::String; device=default_device()) + return __prod_finetune[].MISTFinetuned.from_pretrained(folder).to(device).eval() +end +function load_mol_surprise(path::String; device=default_device()) + return __mol_surprise[].MolSurpriseFM.from_pretrained(path).to(device).eval() +end + include("qmist.jl") include("creativity.jl") include("collate.jl") diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index 9aaa0359..ec6b7758 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -1,6 +1,208 @@ -function batch_score(model::Py, smiles::Vector{String}; batch_size=32) +function molecular_surprise(model::Py, smiles::Vector{String}; batch_size=32) _eval(batch) = pyconvert(Vector{Float64}, model.score(PyList(batch))) + batch_transcode(smiles) do batch + pyconvert(Vector{Float64}, model.score(PyList(batch))) + end +end + +first_token(E::AbstractArray{<:Real, 3}) = eachrow(reshape(E[:, 1, :], size(E, 1), :)) +first_token(E::AbstractMatrix) = vec(E[1, :]) +mean_pooling(E::AbstractArray{<:Real, 3}) = eachrow(reshape(mean(E; dims=2), size(E, 1), :)) +mean_pooling(E::AbstractMatrix) = vec(mean(E; dims=1)) +no_pooling(E) = eachslice(E; dims=1) + +function mist_embedding( + model::Py, + smiles::Vector{String}; + batch_size=32, + pooling=first_token, + collect=x -> stack(x; dims=1), +) + batch_transcode(smiles; collect) do batch + E = pyconvert(Array{Float64}, model.embed(PyList(batch)).to("cpu")) + return pooling(E) + end +end +function batch_transcode(f, smiles; batch_size=32, collect=Base.collect) + transcode = __data_utils[].MolEncoding("smiles-kekule") + smiles = map(x -> pyconvert(String, transcode(x)), smiles) ds = Iterators.partition(smiles, batch_size) - out = Iterators.map(_eval, ds) + out = Iterators.map(f, ds) return collect(Iterators.flatten(out)) end + +function compare_creativity( + run::String, + chembl_dir::String, + df_ref::DataFrame; + mol_surprise::Py, + mist_mp::Py, + mist_bp::Py, + kwargs... +) + + + df_mol = load_generated_molecules(run) + df_chembl = load_qmist_results(chembl_dir) + df = vcat( + _select_creativity(df_mol, mol_surprise, "Generated"), + _select_creativity(df_chembl, mol_surprise, "ChEMBL"), + _select_creativity(df_ref, mol_surprise, "Electrolytes"), + ) + + mp_missing = ismissing.(df.mp) + df.mp[mp_missing] .= predict_mist(mist_mp, df.smiles[mp_missing]).mp + bp_missing = ismissing.(df.bp) + df.bp[bp_missing] .= predict_mist(mist_bp, df.smiles[bp_missing]).bp + + + return compare_creativity(df), df +end + +function compare_creativity(df::DataFrame, M = missing) + f = Figure(; size=(3.42inch, 1inch)) + df.group = categorical(df.group; levels=["Generated", "ChEMBL", "Electrolytes"]) + ax1 = Axis(f[1, 1]; + ylabel="Molecular Surprise", + xticks=(1:3, unique(df.group)), + limits=((0, nothing), nothing), + ygridvisible=true, + yminorticksvisible=true, + xticklabelrotation=0.3, + ) + rainclouds!(ax1, levelcode.(df.group), df.surprise; + gap=0.2, + clouds=hist, + jitter_width=0.1, + boxplot_width=0.15, + side_nudge=0.25, + strokewidth=0.5, + whiskerwidth=1.0, + color=map(g -> MISTStyle.CAT_COLORS[levelcode(g)], df.group), + ) + + df = combine(groupby(df, :group)) do gdf + pos = map(vcat, -gdf.gap, gdf.homo, gdf.mp, -gdf.bp) + idx = Metaheuristics.get_non_dominated_solutions_perm(pos) + @info first(gdf.group) mean(gdf.surprise) mean(gdf.surprise[idx]) + gdf.group_frontier .= false + gdf[idx, :group_frontier] .= true + return gdf + end + + + ax2 = Axis(f[1, 2]; + xlabel=L"$$HOMO [eV]", + ylabel=L"$$Molecular Surprise", + xticks=WilkinsonTicks(3), + ) + color = map(df.group, df.group_frontier) do g, gf + alpha = gf ? 1.0 : 0.1 + return (MISTStyle.CAT_COLORS[levelcode(g)], alpha) + end + marker = :circle + markersize = 3 + sargs = (; marker, color, markersize) + sort!(df, :group_frontier; rev=true) + scatter!(ax2, df.homo .* HARTREE_TO_EV, df.surprise; sargs...) + + ax3 = Axis(f[1, 3]; + xlabel=L"$$Gap [eV]", + ylabel=L"$$Molecular Surprise", + xticks=WilkinsonTicks(3), + yticks=ax1.yticks, + ygridvisible=ax1.ygridvisible, + ) + scatter!(ax3, df.gap .* HARTREE_TO_EV, df.surprise; sargs...) + + + ax4 = Axis(f[1, 4]; + xlabel=L"$$Melt [$\degree C$ ]", + xticks=WilkinsonTicks(3), + ylabel=L"$$Molecular Surprise", + yticks=ax1.yticks, + ygridvisible=ax1.ygridvisible, + ) + scatter!(ax4, df.mp, df.surprise; sargs...) + + ax5 = Axis(f[1, 5]; + xlabel=L"$$Boil [$\degree C$ ]", + xticks=WilkinsonTicks(3), + ylabel=L"$$Molecular Surprise", + yticks=ax1.yticks, + ygridvisible=ax1.ygridvisible, + ) + scatter!(ax5, df.bp, df.surprise; sargs...) + + hideydecorations!(ax2; grid=false) + hideydecorations!(ax3; grid=false) + hideydecorations!(ax4; grid=false) + hideydecorations!(ax5; grid=false) + linkyaxes!(ax1, ax2, ax3) + + return f +end + +function _select_creativity(df::DataFrame, model::Py, group::String) + df = _select_creativity(df, model) + df.group .= group + return df +end +function _select_creativity(df::DataFrame, model::Py) + if "smi" in names(df) + df = rename(df, "smi" => "smiles") + end + cols = ["inchi_key", "smiles", "homo", "gap", "mp", "bp"] + df = select(df, intersect(cols, names(df))) + df.surprise = molecular_surprise(model, df.smiles) + df.embed = eachrow(mist_embedding(model, df.smiles)) + for col in cols + if !(col in names(df)) + df[!, col] .= missing + end + end + return df +end + +bounding_diameter(embeddings::Matrix) = norm(map(x -> -(extrema(x)...), eachcol(e))) +function embedding_momemt(embeddings::Matrix, n=1) + center = vec(mean(embeddings; dims=1)) + @assert size(center) == (size(embeddings, 2),) == (512,) + return mean(eachrow(embeddings)) do emb + return norm(emb .- center)^n + end +end +function embedding_spread(embeddings::Matrix) + center = vec(mean(embeddings; dims=1)) + return map(e -> norm(e .- center), eachrow(embeddings)) +end + +eculidean_distance(a, b) = norm(a .- b) +cosine_similarity(a, b) = dot(a, b) / (norm(a) * norm(b)) +cosine_distance(a, b) = sqrt(2 * max(1 - cosine_similarity(a, b), 0)) +angular_distance(a, b) = acos(min(max(cosine_similarity(a, b), 0), 1)) + +function embedding_mst_distance(embeddings::Matrix; distance=eculidean_distance) + n = size(embeddings, 1) + dd = Matrix{Float64}(undef, n, n) + for I in eachindex(IndexCartesian(), dd) + x = embeddings[I[1], :] + y = embeddings[I[2], :] + @assert length(x) == length(y) + dd[I] = distance(x, y) + end + g = complete_graph(n) + o = boruvka_mst(g, dd) + return o.weight +end + +function renyi_entropy_estimate(embeddings; kwargs...) + E = stack(embeddings; dims=1) + @assert E isa AbstractMatrix + mst = embedding_mst_distance(E; kwargs...) + n, d = size(E) + γ = (d - 1) / d + renyi = (1 / (1-γ)) * (log(mst) - γ * log(n)) + return (; renyi, mst) +end + diff --git a/opt/screening/plots/src/pareto.jl b/opt/screening/plots/src/pareto.jl index 74070059..5a3eccff 100644 --- a/opt/screening/plots/src/pareto.jl +++ b/opt/screening/plots/src/pareto.jl @@ -1,10 +1,7 @@ function get_pareto_front(x::Vector, y::Vector; quad=:lt, ax=nothing) @assert quad == :lt "not implemented" - front = Metaheuristics.get_non_dominated_solutions(map(vcat, x, -1 .* y)) - front = map(front) do p - p[2] *= -1 - p - end + idx = Metaheuristics.get_non_dominated_solutions_perm(map(vcat, x, -1 .* y)) + front = Point2.(x[idx], y[idx]) frontier = Point2.(sort(front; by=first, rev=true)) if ax !== nothing limits = lift(ax.finallimits) do hr diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl index e862e8d3..eb9b4186 100644 --- a/opt/screening/plots/src/qmist.jl +++ b/opt/screening/plots/src/qmist.jl @@ -9,7 +9,6 @@ function load_qmist_results(folder::String) return df end -load_mist_pretrained(folder::String) = __prod_finetune[].MISTFinetuned.from_pretrained(folder) function predict_mist(model::Py, smi::Vector{String}; batch_size=32) model = model.eval() @@ -58,7 +57,7 @@ function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", othe xlim = extrema(x) ylim = extrema(y) ax = Axis(gl[i, j]; - title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mad(x .- y)), + title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mae(x .- y)), limits=MISTStyle.parity_limits(x, y), xticks=WilkinsonTicks(2), yticks=WilkinsonTicks(2), diff --git a/opt/synth_access/plot.jl b/opt/synth_access/plot.jl index 1d6ac5fe..63978af6 100644 --- a/opt/synth_access/plot.jl +++ b/opt/synth_access/plot.jl @@ -349,7 +349,7 @@ function create_figures() RegressionTables.Nobs, RegressionTables.DOF, RegressionTables.R2, - (m -> mad(residuals(m))) => "MAE", + (m -> mae(residuals(m))) => "MAE", (m -> rmsd(predict(m), response(m))) => "RMSE", ] ) From 44523edecb5eceea7392dc9ef813a5a710cb2b28 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Tue, 24 Jun 2025 11:18:34 -0400 Subject: [PATCH 121/147] add updates to screening --- opt/config_tf_oud_wood.yaml | 70 ++++++++++++ opt/screening/.gitignore | 5 +- opt/screening/electrolyte_sweep.py | 0 opt/screening/launch.sh | 4 +- opt/screening/src/generate.py | 174 ++++++++++++++++++++++++----- opt/screening/test/test_critic.py | 2 - 6 files changed, 218 insertions(+), 37 deletions(-) create mode 100644 opt/config_tf_oud_wood.yaml mode change 100755 => 100644 opt/screening/electrolyte_sweep.py diff --git a/opt/config_tf_oud_wood.yaml b/opt/config_tf_oud_wood.yaml new file mode 100644 index 00000000..16784f56 --- /dev/null +++ b/opt/config_tf_oud_wood.yaml @@ -0,0 +1,70 @@ +generation: + db_path: zinc_fragment/fragments.sqlite + n_fragments: 1000 + reference_fragments: electrolyte.smi.frag + epoch_size: 1000 + limit_db_fragments: null + limit_ref_fragments: null + batch_size: 512 +critics: + - model_path: models/mist-26.9M-b302p09x-bp + limits: + bp: + - 75 + - null + model_cls: MISTFinetuned + - model_path: models/mist-26.9M-y3ge5pf9-mp + model_cls: MISTFinetuned + limits: + mp: + - null + - 0 + - model_path: models/mist-26.9M-48kpooqf-odour + model_cls: MISTFinetuned + limits: + woody: + - 0 + - null + amber: + - 0 + - null + - model_path: models/mist-ti624ev1-moleculenet/tox21 + model_cls: MISTFinetuned + limits: + NR-AR: + - null + - 0 + NR-AR-LBD: + - null + - 0 + NR-AhR: + - null + - 0 + NR-Aromatase: + - null + - 0 + NR-ER: + - null + - 0 + NR-ER-LBD: + - null + - 0 + NR-PPAR-gamma: + - null + - 0 + SR-ARE: + - null + - 0 + SR-ATAD5: + - null + - 0 + SR-HSE: + - null + - 0 + SR-MMP: + - null + - 0 + SR-p53: + - null + - 0 + model_cls: MISTFinetuned diff --git a/opt/screening/.gitignore b/opt/screening/.gitignore index af2ec7e4..c0ec279c 100644 --- a/opt/screening/.gitignore +++ b/opt/screening/.gitignore @@ -1,16 +1,15 @@ vendor/ *.smi *.frag -fig/ *.csv *.sqlite *.bin_cache models/ models -fig/ -*.csv # Output folders +fig/ +fig out/ out runs/ diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100755 new mode 100644 diff --git a/opt/screening/launch.sh b/opt/screening/launch.sh index eb494dfe..41eeb4a1 100755 --- a/opt/screening/launch.sh +++ b/opt/screening/launch.sh @@ -27,9 +27,9 @@ $(which apptainer) run \ --env-file $ENV_FILE \ /lustre/fs0/shared/sqsh-files/mist+pytorch+25.01+v4.sif \ ../../submit/set_node_rank \ -python screen.py --gpus-per-node ${SLURM_GPUS_PER_NODE:-1} --num-nodes ${SLURM_NNODES:-1} $@ # nsys profile \ -# --output="nsys_multinode_%q{JOBID}_%q{NODE_RANK}" \ +# --output="nsys_multinode_%q{SLURM_JOB_ID}_%q{NODE_RANK}" \ # --trace=cuda,cudnn,cublas,nvtx \ # --cuda-memory-usage=true \ # --duration 120 \ +python screen.py --gpus-per-node ${SLURM_GPUS_PER_NODE:-1} --num-nodes ${SLURM_NNODES:-1} $@ diff --git a/opt/screening/src/generate.py b/opt/screening/src/generate.py index 7a985a08..f3a7c934 100644 --- a/opt/screening/src/generate.py +++ b/opt/screening/src/generate.py @@ -1,16 +1,44 @@ import logging import time +from abc import ABC, abstractmethod import torch from lightning.fabric import Fabric -from torch import nn +from torch import Tensor, nn -from electrolyte_fm.models.prod_finetune import MISTFinetuned, MISTMultiTask -from src.hyperloglog import HyperLogLogSet +from .hyperloglog import HyperLogLogSet +from .prod_finetune import MISTFinetuned, MISTMultiTask + + +class Critic(nn.Module, ABC): + """ + Abstract base class for any “Critic.” Subclasses must implement + `active_channels`, which returns a 1‐D boolean Tensor of length = C, + where C = number of channels. True means “that channel is active.” + + We override __repr__ (and __str__) so that printing any Critic object + will show its class name + the active‐channels mask. + """ + + @property + @abstractmethod + def active_channels(self) -> Tensor: + """ + Return a 1‐D boolean Tensor (length = num_channels) indicating + which channels are active. Subclasses must override this. + """ + ... + + @abstractmethod + def forward(self, y: Tensor) -> Tensor: + """ + Given a tensor (..., C), return a tensor (...) indicating which entries are passing + """ + ... class OracleCritic(nn.Module): - def __init__(self, oracle: nn.Module, critic): + def __init__(self, oracle: nn.Module, critic: Critic): super().__init__() self.oracle = oracle self.critic = critic @@ -25,43 +53,129 @@ def forward( @classmethod def from_pretrained( cls, - save_directory: str, - limits: dict, + model_path: str, + limits: dict | None = None, + all_passing: bool | None = None, + any_passing: bool | None = None, model_cls: str = "MISTFinetuned", + **kwargs, ): - model_cls = MISTFinetuned if model_cls == "MISTFinetuned" else MISTMultiTask - oracle = model_cls.from_pretrained(save_directory) - critic = QuadrantCritic( - limits, channels=[chn["name"] for chn in oracle.channels] - ) + oracle_cls = { + "MISTFinetuned": MISTFinetuned, + "MISTMultiTask": MISTMultiTask, + }.get(model_cls) + oracle = oracle_cls.from_pretrained(model_path) + channels = [chn["name"] for chn in oracle.channels] + if limits is not None: + critic = QuadrantCritic.from_limits(limits, channels) + elif all_passing is not None: + critic = QuadrantCritic.from_all_passing(channels, **kwargs) + elif any_passing is not None: + critic = AnyCritic.from_any_passing(channels, **kwargs) + else: + raise RuntimeError("Unknown critic type") + return cls(oracle, critic) -class QuadrantCritic(nn.Module): - def __init__(self, limits: dict[str, tuple[float, float]], channels: list[str]): +def logit_limits( + channels: list[str], + pass_positive: bool = True, + flip_channels: dict[str, bool] | None = None, +): + limits = dict() + flip_channels = flip_channels or {} + for chn in channels: + flip_channels[chn] = flip_channels.get(chn, False) + if (pass_positive and not flip_channels[chn]) or ( + not pass_positive and flip_channels[chn] + ): + limits[chn] = (0, None) + else: + limits[chn] = (None, 0) + return limits + + +def limits_to_bounds(limits: dict[str, tuple[float, float]], channels: list[str]): + lower = [] + upper = [] + assert limits.keys() <= set(channels), ( + f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" + ) + for chn in channels: + if chn in limits: + lb, ub = limits[chn] + else: + lb, ub = None, None + lower.append(-torch.inf if lb is None else lb) + upper.append(torch.inf if ub is None else ub) + return lower, upper + + +class QuadrantCritic(Critic): + def __init__(self, lower: Tensor, upper: Tensor): super().__init__() - lower = [] - upper = [] - assert limits.keys() <= set( - channels - ), f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" - for chn in channels: - if chn in limits: - lb, ub = limits[chn] - else: - lb, ub = None, None - lower.append(-torch.inf if lb is None else lb) - upper.append(torch.inf if ub is None else ub) + self.register_buffer("lower", lower.view(1, -1)) + self.register_buffer("upper", upper.view(1, -1)) - self.register_buffer("lower", torch.tensor(lower).view(1, -1)) - self.register_buffer("upper", torch.tensor(upper).view(1, -1)) + @classmethod + def from_limits(cls, limits: dict[str, tuple[float, float]], channels: list[str]): + lower, upper = limits_to_bounds(limits, channels) + return cls(torch.tensor(lower), torch.tensor(upper)) + + @classmethod + def from_all_passing( + cls, + channels: list[str], + pass_positive: bool = True, + flip_channels: dict[str, bool] | None = None, + ): + limits = logit_limits(channels, pass_positive, flip_channels) + return cls.from_limits(limits, channels) @property def active_channels(self): return ~(self.lower.isinf() & self.upper.isinf()).view(-1) - def __call__(self, y: torch.Tensor): - return (self.lower < y) & (y < self.upper) + def forward(self, y: torch.Tensor): + y = torch.atleast_2d(y) + return ((self.lower < y) & (y < self.upper)).all(-1) + + +class AnyCritic(Critic): + def __init__(self, lower: Tensor, upper: Tensor, mask: Tensor): + super().__init__() + self.register_buffer("lower", lower.view(1, -1)) + self.register_buffer("upper", upper.view(1, -1)) + self.register_buffer("mask", mask.to(dtype=bool).view(1, -1)) + + @classmethod + def from_any_passing( + cls, + channels: list[str], + pass_positive: bool = True, + flip_channels: dict[str, bool] | None = None, + subset: list[str] | None = None, + ): + limits = logit_limits(channels, pass_positive, flip_channels) + lower, upper = limits_to_bounds(limits, channels) + subset_mask = [] + for chn in channels: + if subset is None: + subset_mask.append(True) + else: + subset_mask.append(chn in subset) + return cls(torch.tensor(lower), torch.tensor(upper), torch.tensor(subset_mask)) + + @property + def active_channels(self): + active_limits = ~(self.lower.isinf() & self.upper.isinf()) + return (active_limits & self.mask).view(-1) + + def forward(self, y: torch.Tensor): + """Return True if any channel is active for the molecule""" + y = torch.atleast_2d(y) + return ((self.lower < y) & (y < self.upper) & (self.mask)).any(-1) class CriticPanel(nn.Module): @@ -82,7 +196,7 @@ def forward( y = [] for critic in self.critics: yc, score = critic(input_ids, attention_mask=attention_mask) - net_score &= score.all(-1) + net_score &= score y.append(yc) return torch.cat(y, dim=-1), net_score diff --git a/opt/screening/test/test_critic.py b/opt/screening/test/test_critic.py index 9985af01..a4f8df47 100644 --- a/opt/screening/test/test_critic.py +++ b/opt/screening/test/test_critic.py @@ -1,6 +1,4 @@ import torch - -# Adjust this import if your file structure is different: from src.generate import ( AnyCritic, CriticPanel, From 6b07bd017487b0b3859833ff0346b9e10576583f Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 28 Jun 2025 14:29:41 -0400 Subject: [PATCH 122/147] Updated creativity figures --- opt/design/plots.jl | 11 +++- opt/design/src/trends.jl | 41 ++++++++++----- opt/screening/plots/Project.toml | 3 ++ opt/screening/plots/plots.jl | 12 +++++ opt/screening/plots/src/ScreeningPlots.jl | 4 +- opt/screening/plots/src/creativity.jl | 64 +++++++++++++++++------ 6 files changed, 103 insertions(+), 32 deletions(-) diff --git a/opt/design/plots.jl b/opt/design/plots.jl index c28ec5de..070fda20 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -90,4 +90,13 @@ with_theme(MISTStyle.theme()) do "Baseline" => df_perm_ref, "Random" => df_perm_rand; name_df_order=("Baseline" => df_order, "Random" => df_order_rand) ) -end |> MISTStyle.savefig("permutations") \ No newline at end of file +end |> MISTStyle.savefig("permutations") + + +with_theme(MISTStyle.theme()) do + DesignRules.figure_double_bond_loc( + "Baseline" => df_perm_ref, "Random" => df_perm_rand; + name_df_order=("Baseline" => df_order, "Augmented" => df_order_rand) + ) +end |> MISTStyle.savefig("double_bond_loc") + diff --git a/opt/design/src/trends.jl b/opt/design/src/trends.jl index 2575f945..c2ae4832 100644 --- a/opt/design/src/trends.jl +++ b/opt/design/src/trends.jl @@ -325,20 +325,36 @@ function figure_permutations(name_df::Pair...; name_df_order) colsize!(gl_trends, 1, Relative(3 / 4)) colgap!(gl_trends, 1, 2) + figure_double_bond_loc!(gl_order, name_df...; name_df_order) + + + sublabel!(gl_order[2, 1, TopLeft()], "a"; left=15) + sublabel!(gl_order[3, 1, TopLeft()], "b"; left=15) + sublabel!(gl_trends[1, 1, TopLeft()], "c"; left=13) + + resize_to_layout!(f) + + + return f +end + +function figure_double_bond_loc(name_df::Pair...; name_df_order) + f = Figure(; size=(113, 136), figure_padding=(2,4,2,2)) + return figure_double_bond_loc!(f, name_df...; name_df_order) +end +function figure_double_bond_loc!(f, name_df::Pair...; name_df_order) # Order Sensitivity n_carbon_range = extrema(last(first(name_df_order)).n_carbon) - cb = Colorbar(gl_order[1, 1]; + cb = Colorbar(f[1, 1]; label="Number of Carbons", colorrange=n_carbon_range, vertical=false, tellwidth=false, - # flipaxis=false, ) axes = Axis[] - for (idx, (_, df)) in enumerate(name_df_order) + for (idx, (name, df)) in enumerate(name_df_order) is_last = idx == length(name_df_order) - ax = Axis(gl_order[1+idx, 1]; + ax = Axis(f[1+idx, 1]; xlabel=L"Double Bond Location$$", - ylabel=L"HOMO [eV]$$", limits=((0, 1), nothing), xtickformat="{:.0%}", xlabelvisible=is_last, @@ -346,9 +362,13 @@ function figure_permutations(name_df::Pair...; name_df_order) xticklabelsvisible=is_last, yticks=WilkinsonTicks(5), ) + text!(0.99, 0.0; + text=name, + align=(:right, :bottom), + space=:relative, + ) push!(axes, ax) df = subset(df, :n_carbon => ByRow(>(4))) - # df = subset(df, :n_carbon => ByRow(n -> n % 2 == 0)) foreach(groupby(df, :n_carbon)) do gdf n_carbon = gdf.n_carbon[1] homo = gdf.homo .* HARTREE_TO_EV @@ -365,15 +385,10 @@ function figure_permutations(name_df::Pair...; name_df_order) ) end end - linkyaxes!(axes...) - colgap!(f.layout, 1, 3) - colsize!(f.layout, 2, Relative(3 / 4)) + Label(f[:, 0]; text=L"HOMO [eV]$$", rotation=pi/2) - sublabel!(gl_order[2, 1, TopLeft()], "a"; left=15) - sublabel!(gl_order[3, 1, TopLeft()], "b"; left=15) - sublabel!(gl_trends[1, 1, TopLeft()], "c"; left=13) - + linkyaxes!(axes...) resize_to_layout!(f) diff --git a/opt/screening/plots/Project.toml b/opt/screening/plots/Project.toml index 33870f90..468a5858 100644 --- a/opt/screening/plots/Project.toml +++ b/opt/screening/plots/Project.toml @@ -4,6 +4,7 @@ version = "0.1.0" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +Clustering = "aaaa29a8-35af-508c-8bc3-b662a17a0fe5" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" @@ -15,9 +16,11 @@ Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" ManifoldLearning = "06eb3307-b2af-5a2a-abea-d33192699d32" Metaheuristics = "bcdb8e00-2c21-11e9-3065-2b553b22f898" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" SQLite = "0aa819cd-b072-5ff4-a722-6bc24af294d9" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" +StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [sources] MISTStyle = {path = "../../MISTStyle"} diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 21615de7..3d237bc4 100644 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -185,3 +185,15 @@ df_s = combine(groupby(df_surprise, :group)) do gdf end return DataFrame(out) end + +# Bar chart of top odor in generated molecules +odor_model = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist-26.9M-48kpooqf-odour")) +df_odor = select(df_surprise, :smiles, :group) +df_odor = innerjoin(df_odor, ScreeningPlots.predict_mist(odor_model, df_odor.smiles); on=:smiles) +f = ScreeningPlots.figure_odor_counts(subset(df_odor, :group => ByRow(!=("ChEMBL"))), odor_model) +MISTStyle.savefig("screening_odors", f) + +# Screen for odorless +df_so = innerjoin(df_surprise, df_odor; on=["smiles", "group"]) +f = ScreeningPlots.plot_pareto_front_scent(df_so, "odorless") +MISTStyle.savefig("electrolyte_odorless", f) diff --git a/opt/screening/plots/src/ScreeningPlots.jl b/opt/screening/plots/src/ScreeningPlots.jl index e9e886f9..0740d274 100644 --- a/opt/screening/plots/src/ScreeningPlots.jl +++ b/opt/screening/plots/src/ScreeningPlots.jl @@ -2,6 +2,7 @@ module ScreeningPlots using Makie using DataFrames +using Random: randperm using Graphs: complete_graph, boruvka_mst using Metaheuristics: Metaheuristics using JSON: JSON @@ -9,10 +10,11 @@ using SQLite: SQLite using PythonCall: Py, PyList, pyimport, pyconvert using GLM: @formula, lm, glm, Normal, LogLink, coef using Format: format -using Statistics: cor, mean +using Statistics: cor, mean, std using CategoricalArrays: categorical, levelcode using LinearAlgebra: norm, dot using ManifoldLearning: ManifoldLearning, DiffMap, fit, predict, transform +using Clustering: hclust using MISTStyle diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index ec6b7758..7767d670 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -61,10 +61,13 @@ end function compare_creativity(df::DataFrame, M = missing) f = Figure(; size=(3.42inch, 1inch)) - df.group = categorical(df.group; levels=["Generated", "ChEMBL", "Electrolytes"]) + df = subset(df, :group => ByRow(in(["Generated", "Electrolytes"]))) + df.group = categorical(df.group; levels=["Generated", "Electrolytes"]) + df.gap .*= HARTREE_TO_EV + df.homo .*= HARTREE_TO_EV ax1 = Axis(f[1, 1]; ylabel="Molecular Surprise", - xticks=(1:3, unique(df.group)), + xticks=(1:2, unique(df.group)), limits=((0, nothing), nothing), ygridvisible=true, yminorticksvisible=true, @@ -84,27 +87,26 @@ function compare_creativity(df::DataFrame, M = missing) df = combine(groupby(df, :group)) do gdf pos = map(vcat, -gdf.gap, gdf.homo, gdf.mp, -gdf.bp) idx = Metaheuristics.get_non_dominated_solutions_perm(pos) - @info first(gdf.group) mean(gdf.surprise) mean(gdf.surprise[idx]) + h_pareto = mean(gdf.surprise[idx]) + h_std_pareto = std(gdf.surprise[idx]) + h_dominated = mean(gdf.surprise[Not(idx)]) + h_std_dominated = std(gdf.surprise[Not(idx)]) + h_delta = h_pareto - h_dominated + h_std_delta = hypot(h_std_pareto, h_std_dominated) + @info first(gdf.group) mean(gdf.surprise) std(gdf.surprise) mean(gdf.surprise[idx]) h_delta h_std_delta gdf.group_frontier .= false gdf[idx, :group_frontier] .= true return gdf end - ax2 = Axis(f[1, 2]; xlabel=L"$$HOMO [eV]", ylabel=L"$$Molecular Surprise", xticks=WilkinsonTicks(3), ) - color = map(df.group, df.group_frontier) do g, gf - alpha = gf ? 1.0 : 0.1 - return (MISTStyle.CAT_COLORS[levelcode(g)], alpha) - end - marker = :circle - markersize = 3 - sargs = (; marker, color, markersize) + sargs = (; markersize = 3) sort!(df, :group_frontier; rev=true) - scatter!(ax2, df.homo .* HARTREE_TO_EV, df.surprise; sargs...) + _mark_creative!(ax2, df, :homo, :surprise, df.group_frontier; sargs...) ax3 = Axis(f[1, 3]; xlabel=L"$$Gap [eV]", @@ -113,8 +115,7 @@ function compare_creativity(df::DataFrame, M = missing) yticks=ax1.yticks, ygridvisible=ax1.ygridvisible, ) - scatter!(ax3, df.gap .* HARTREE_TO_EV, df.surprise; sargs...) - + _mark_creative!(ax3, df, :gap, :surprise, df.group_frontier; sargs...) ax4 = Axis(f[1, 4]; xlabel=L"$$Melt [$\degree C$ ]", @@ -123,7 +124,7 @@ function compare_creativity(df::DataFrame, M = missing) yticks=ax1.yticks, ygridvisible=ax1.ygridvisible, ) - scatter!(ax4, df.mp, df.surprise; sargs...) + _mark_creative!(ax4, df, :mp, :surprise, df.group_frontier; sargs...) ax5 = Axis(f[1, 5]; xlabel=L"$$Boil [$\degree C$ ]", @@ -132,7 +133,7 @@ function compare_creativity(df::DataFrame, M = missing) yticks=ax1.yticks, ygridvisible=ax1.ygridvisible, ) - scatter!(ax5, df.bp, df.surprise; sargs...) + _mark_creative!(ax5, df, :bp, :surprise, df.group_frontier; sargs...) hideydecorations!(ax2; grid=false) hideydecorations!(ax3; grid=false) @@ -143,6 +144,35 @@ function compare_creativity(df::DataFrame, M = missing) return f end +function _annotate_creative!(ax, smiles, df, x, y; label_pos=nothing, kwargs...) + key = inchi_key(smiles) + @show row = first(df[df.inchi_key .== key, :]) + if label_pos !== nothing + lx, ly = (row[x], row[y]) .+ label_pos + return annotation!(ax, label_pos..., row[x], row[y]; kwargs...) + else + return annotation!(ax, row[x], row[y]; kwargs...) + end +end + +function _mark_creative!(ax, df, x, y, pareto; kwargs...) + idx = findall(pareto) + df_other = df[Not(idx), :] + scatter!(ax, df_other[!, x], df_other[!, y]; + marker=:circle, + color=MISTStyle.CAT_COLORS[levelcode.(df_other.group)], + alpha=0.1, + kwargs... + ) + df_pareto = df[idx, :] + scatter!(ax, df_pareto[!, x], df_pareto[!, y]; + marker=:star5, + strokewidth=0.1, + color=MISTStyle.CAT_COLORS[levelcode.(df_pareto.group)], + kwargs... + ) +end + function _select_creativity(df::DataFrame, model::Py, group::String) df = _select_creativity(df, model) df.group .= group @@ -184,7 +214,7 @@ angular_distance(a, b) = acos(min(max(cosine_similarity(a, b), 0), 1)) function embedding_mst_distance(embeddings::Matrix; distance=eculidean_distance) n = size(embeddings, 1) - dd = Matrix{Float64}(undef, n, n) + dd = Matrix{}(undef, n, n) for I in eachindex(IndexCartesian(), dd) x = embeddings[I[1], :] y = embeddings[I[2], :] From f4b5f55f0f5b2e209a42799a01f0b69a7e348501 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Thu, 10 Jul 2025 14:45:30 -0400 Subject: [PATCH 123/147] Add shebang to screening plots --- opt/screening/plots/plots.jl | 1 + 1 file changed, 1 insertion(+) mode change 100644 => 100755 opt/screening/plots/plots.jl diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl old mode 100644 new mode 100755 index 3d237bc4..736317b6 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -1,3 +1,4 @@ +#!/usr/bin/env -S julia +release --color=auto --startup-file=no --project=@script using ScreeningPlots using Makie using MISTStyle From 75122deae2fe1362dd8598b85c38b8bf552ee770 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sun, 13 Jul 2025 13:06:53 -0400 Subject: [PATCH 124/147] fix pre-commit --- opt/config_tf_oud_wood.yaml | 70 ------------------------------ opt/screening/electrolyte_sweep.py | 0 2 files changed, 70 deletions(-) delete mode 100644 opt/config_tf_oud_wood.yaml mode change 100644 => 100755 opt/screening/electrolyte_sweep.py diff --git a/opt/config_tf_oud_wood.yaml b/opt/config_tf_oud_wood.yaml deleted file mode 100644 index 16784f56..00000000 --- a/opt/config_tf_oud_wood.yaml +++ /dev/null @@ -1,70 +0,0 @@ -generation: - db_path: zinc_fragment/fragments.sqlite - n_fragments: 1000 - reference_fragments: electrolyte.smi.frag - epoch_size: 1000 - limit_db_fragments: null - limit_ref_fragments: null - batch_size: 512 -critics: - - model_path: models/mist-26.9M-b302p09x-bp - limits: - bp: - - 75 - - null - model_cls: MISTFinetuned - - model_path: models/mist-26.9M-y3ge5pf9-mp - model_cls: MISTFinetuned - limits: - mp: - - null - - 0 - - model_path: models/mist-26.9M-48kpooqf-odour - model_cls: MISTFinetuned - limits: - woody: - - 0 - - null - amber: - - 0 - - null - - model_path: models/mist-ti624ev1-moleculenet/tox21 - model_cls: MISTFinetuned - limits: - NR-AR: - - null - - 0 - NR-AR-LBD: - - null - - 0 - NR-AhR: - - null - - 0 - NR-Aromatase: - - null - - 0 - NR-ER: - - null - - 0 - NR-ER-LBD: - - null - - 0 - NR-PPAR-gamma: - - null - - 0 - SR-ARE: - - null - - 0 - SR-ATAD5: - - null - - 0 - SR-HSE: - - null - - 0 - SR-MMP: - - null - - 0 - SR-p53: - - null - - 0 - model_cls: MISTFinetuned diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100644 new mode 100755 From bdbeba7c12d631e7757a796287f0dd44f81a8a42 Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 11 Aug 2025 17:04:01 -0400 Subject: [PATCH 125/147] tweaks to screening figures --- opt/screening/electrolyte_sweep.py | 0 opt/screening/plots/src/creativity.jl | 1 - opt/screening/plots/src/odor.jl | 375 ++++++++++++++++++++++++++ opt/screening/plots/src/qmist.jl | 1 - opt/screening/src/generate.py | 6 +- opt/synth_access/main.py | 2 +- 6 files changed, 379 insertions(+), 6 deletions(-) mode change 100755 => 100644 opt/screening/electrolyte_sweep.py create mode 100644 opt/screening/plots/src/odor.jl diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100755 new mode 100644 diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index 7767d670..47e7e2f4 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -235,4 +235,3 @@ function renyi_entropy_estimate(embeddings; kwargs...) renyi = (1 / (1-γ)) * (log(mst) - γ * log(n)) return (; renyi, mst) end - diff --git a/opt/screening/plots/src/odor.jl b/opt/screening/plots/src/odor.jl new file mode 100644 index 00000000..45f8c934 --- /dev/null +++ b/opt/screening/plots/src/odor.jl @@ -0,0 +1,375 @@ +function evaluate_odor(model::Py, smiles::Vector{String}; batch_size=32, filter_non_active=true) + df = predict_mist(model, smiles; batch_size) + + # Summarize odor activations + odor_summary = describe(df[!, Not("smiles")], :max, Base.Fix1(count, >(0)) => :nactive) + filter_non_active && subset!(odor_summary, :max => ByRow(>(0))) + sort!(odor_summary, :max; rev=true) + + df.max_odor = map(eachrow(df)) do row + maximum(Vector(row[Not("smiles")])) + end + select!(df, "smiles", "max_odor", odor_summary.variable...) + sort!(df, :max_odor; rev=true) + + @info "Top Odors" odor_summary + return df +end + +""" Return the names of the channels in the MIST model""" +channel_names(model::Py) = pyconvert(Vector{String}, [chn["name"] for chn in model.channels]) + +filter_non_active(df::DataFrame, odor_model::Py; kwargs...) = filter_non_active(df, channel_names(odor_model); kwargs...) + +function filter_non_active(df::DataFrame, odor_columns=Not("smiles"); limit=nothing) + odor_columns = intersect(odor_columns, names(df)) + odor_summary = describe(df[!, odor_columns], :max, Base.Fix1(count, >(0)) => :nactive) + non_odor = names(df[!, Not(odor_columns)]) + subset!(odor_summary, :max => ByRow(>(0))) + sort!(odor_summary, :max; rev=true) + if !isnothing(limit) + @show odor_summary = odor_summary[1:limit, :] + end + return select(df, non_odor..., odor_summary.variable...) +end + +function count_active(df, odor_model::Py) + scents = pyconvert(Vector{String}, [chn["name"] for chn in odor_model.channels]) + n = Dict() + for scent in scents + n[scent] = count(df[!, scent] .> 0) + end + s = collect(keys(n)) + counts = collect(values(n)) + return DataFrame(scent=s, active_count=counts) +end + +logistic(x) = inv(1 + exp(-x)) + +function correlation_matrix(df::DataFrame, columns; correlation=cor) + D = Matrix{Float64}(undef, length(columns), length(columns)) + for I in eachindex(IndexCartesian(), D) + if I[1] == I[2] + D[I] = 1 + elseif I[1] > I[2] + D[I] = correlation(df[!, columns[I.I[1]]], df[!, columns[I.I[2]]]) + D[I[2], I[1]] = D[I] + end + end + return D, columns +end + +function figure_hclust(dist::Matrix, labels::Vector; kwargs...) + c = hclust(dist; linkage=:single, branchorder=:barjoseph) + dist = dist[c.order, c.order] + labels = labels[c.order] + + f = Figure() + cb = Colorbar(f[1, 2]; + colorrange=(-1, 1), + colormap=:vik10, + tellheight=true, + tellwidth=true, + halign=:left, + valign=:top, + ) + ticks = (eachindex(labels), labels) + ax = Axis(f[1, 1]; + xticks=ticks, yticks=ticks, + xticklabelrotation=0.55, + xticklabelsvisible=false, + xticksvisible=false, + aspect=DataAspect(), + ) + heatmap!(ax, dist; MISTStyle.cb_attrs(cb, Heatmap)..., kwargs...) + return f +end + +function linear_correlation(df, cols, target; correlation=cor) + S = Matrix(df[!, cols]) + c = S \ df[!, target] + P = sortperm(abs.(c)) + rho = Vector{Float64}(undef, length(cols)) + for i in range(1, length(cols)) + Sp = @view S[:, P[1:i]] + c = Sp \ df[!, target] + rho[i] = correlation(Sp * c, df[!, target]) + end + return rho +end + +function higher_order_odors(df) + groups = Dict( + "fruity & Sweet" => ["fruity", "tropical", "apple", "banana", "pear", "pineapple", "sweet"], + "Floral" => ["floral", "rose", "green", "herbal", "ethereal"], + "Pungent" => ["sulfurous", "onion", "garlic", "cheesy", "phenolic", "vegetable"], + ) + out = Dict() + for (label, odors) in pairs(groups) + out[label] = vec(maximum(Matrix(df[!, odors]); dims=2)) + end + dfo = DataFrame(out) + dfo.smiles = df.smiles + return dfo +end + +function pick_odor(df, scent) + df = deepcopy(df) + df.group = categorical(df.group) + dfi = subset(df, scent => ByRow(>(0))) + dfo = subset(df, scent => ByRow(<(0))) + f = Figure(; size=(105, 187)) + ax = Axis(f[1, 1]; xlabel="Homo", ylabel="Gap", limits=((-0.4, -0.25), nothing)) + kwargs = (; colormap=:tab10, colorrange=(1, 10), marker=:circle) + scatter!(ax, dfo.homo, dfo.gap; color=levelcode.(dfo.group), alpha = 0.1, markersize=3pt, kwargs...) + scatter!(ax, dfi.homo, dfi.gap; color=levelcode.(dfi.group), kwargs...) + ax = Axis(f[2, 1]; xlabel="Melt", ylabel="Boil", limits=((-100, 0), (0, nothing))) + scatter!(ax, dfo.mp, dfo.bp; color=levelcode.(dfo.group), alpha = 0.1, markersize=3pt, kwargs...) + scatter!(ax, dfi.mp, dfi.bp; color=levelcode.(dfi.group), kwargs...) + return f +end + + +function plot_odor_act(df) + f = Figure(; size=(8inch, 8inch)) + odors = setdiff(names(df), ["smiles", "name", "class", "max_odor"]) + mols = map((n,c) -> "$n ($c)", df.name, df.class) + ax = Axis(f[1, 1]; + xticks=(1:length(odors), odors), + yticks=(1:length(mols), mols), + xticklabelrotation=pi/4, + ) + act = logistic.(Matrix(df[!, odors])) + heatmap!(ax, act'; colormap=:lipari) + return f +end + +function plot_odor_tsne(df::DataFrame, odor_columns=Not("smiles")) + act = Matrix(df[!, odor_columns]) + M = fit(ManifoldLearning.LLE, act') + f = plot_odor_tsne(M, df, act, odor_columns) + return f, M +end +plot_odor_tsne(M, df::DataFrame, model::Py; kwargs...) = plot_odor_tsne(M, df, channel_names(model); kwargs...) +function plot_odor_tsne(M, df::DataFrame, act, odor_columns=Not("smiles")) + act = Matrix(df[!, odor_columns]) + R = predict(M) + f = Figure(; size=(2inch, 2inch)) + ax = Axis(f[1, 1]) + hidedecorations!(ax) + dominate = map(argmax, eachrow(act)) + h = scatter!(ax, eachrow(R)...; + marker=:circle, + color=dominate, + markersize=2pt, + colormap=:tab10, + colorrange=(1, 10), + + ) + + # elements = map(enumerate(odor_columns)) do (i, label) + # PolyElement(; color=i, label, colormap=h.colormap, colorrange=h.colorrange) + # end + # Legend(f[1, 1], elements, MISTStyle.label.(elements); + # tellheight=false, tellwidth=false, + # # orientation=:horizontal, + # halign=:left, + # valign=:top, + # nbanks=2, + # ) + # ann = annotation!(ax, Point2.(eachcol(R)); text=df.name) + return f +end + +function figure_odor_tsne(df::DataFrame, odor_model::Py; limit=10) + scents = pyconvert(Vector{String}, [chn["name"] for chn in odor_model.channels]) + df_odor = filter_non_active(df, scents; limit) + active_scents = intersect(names(df_odor), scents) + df_odor.dominate = map(eachrow(df_odor)) do row + return argmax(row[active_scents]) + end + df_odor.embed = eachrow(mist_embedding(odor_model, df_odor.smiles; pooling=no_pooling)) + return df_odor +end + +function figure_odor_tsne(df::DataFrame) + E = stack(df.embed; dims=2) + M = fit(ManifoldLearning.Isomap, E) + # M = fit(ManifoldLearning.TSNE, E) + return M +end + +function figure_odor_tsne(df::DataFrame, M) + f = Figure(; size=(2inch, 2inch)) + R = predict(M) + ax = Axis(f[1, 1]) + df.dominate = categorical(string.(df.dominate)) + df = df[randperm(nrow(df)), :] + scatter!(ax, eachrow(R)...; + marker=:circle, + markersize=2pt, + color=levelcode.(df.dominate), + colormap=:tab10, + colorrange=(1, 10), + ) + return f +end + +function figure_odor_counts(df::DataFrame, odor_model::Py) + df_sum = combine(groupby(df, :group)) do gdf + nactive = ScreeningPlots.count_active(gdf, odor_model) + nactive.active_count ./= nrow(gdf) + return nactive + end + @info df_sum + groups = unique(df_sum.group) + df_sum = unstack(df_sum, :group, :active_count) + transform!(df_sum, groups => ByRow((x...) -> sum(x)) => :any_active) + subset!(df_sum, :any_active => ByRow(>(0))) + sort!(df_sum, :any_active; rev=true) + scents = df_sum.scent + @info df_sum + + df_sum = stack(df_sum, groups; variable_name="group") + df_sum.group = categorical(df_sum.group; levels=groups) + df_sum.scent = categorical(df_sum.scent; levels=scents) + + f = Figure(; size=(122, 200)) + ax = Axis(f[1, 1]; + limits=((2e-4, 1), nothing), + yticks=(1:length(scents), scents), + xlabel="Molecules with Odor", + xscale=log10, + xticks=([1e-3, 1e-2, 1e-1], ["0.1%", "1%", "10%"]), + xticklabelsize=7pt, + yticklabelsize=7pt, + ) + h = barplot!(ax, levelcode.(df_sum.scent), df_sum.value; + color=levelcode.(df_sum.group), + dodge=levelcode.(df_sum.group), + colormap=MISTStyle.CAT_COLORS, + colorrange=(1, length(MISTStyle.CAT_COLORS)), + direction=:x, + fillto=1e-5, + ) + + elements = map(enumerate(unique(df_sum.group))) do (i, label) + PolyElement(; color=i, label, colormap=h.colormap, colorrange=h.colorrange) + end + Legend(f[1, 1], elements, MISTStyle.label.(elements); + tellheight=false, tellwidth=false, + halign=:right, + valign=:top, + ) + resize_to_layout!(f) + + return f +end + +function plot_pareto_front_scent(df, scent) + df = subset(df, :group => ByRow(in(["Generated", "Electrolytes"]))) + df.group = categorical(df.group; levels=["Generated", "Electrolytes"]) + df = combine(first, groupby(df, [:inchi_key, :group])) + pareto_kwargs = (; + linewidth=1.5pt, + alpha=0.7, + ) + + # Get non-dominated solutions + df = combine(groupby(df, :group)) do gdf + pos = map(vcat, -gdf.gap, gdf.homo, gdf.mp, -gdf.bp) + idx = Metaheuristics.get_non_dominated_solutions_perm(pos) + h_pareto = mean(gdf.surprise[idx]) + h_std_pareto = std(gdf.surprise[idx]) + h_dominated = mean(gdf.surprise[Not(idx)]) + h_std_dominated = std(gdf.surprise[Not(idx)]) + h_delta = h_pareto - h_dominated + h_std_delta = hypot(h_std_pareto, h_std_dominated) + gdf.group_frontier .= false + gdf[idx, :group_frontier] .= true + return gdf + end + + + # Convert Units + df.gap .*= HARTREE_TO_EV + df.homo .*= HARTREE_TO_EV + + skwargs = (; + marker=:circle, + colormap=MISTStyle.CAT_COLORS, + colorrange=(1, length(MISTStyle.CAT_COLORS)), + ) + + f = Figure(; size=(100, 203)) + ax = Axis(f[1, 1]; + limits=((-10.5, -7), (5, 13)), + xlabel=L"HOMO [eV]$$", + ylabel=L"Gap [eV]$$", + xticks=WilkinsonTicks(5; k_max=7), + yticks=WilkinsonTicks(5; k_max=7), + ) + gen = subset(df, :group => ByRow(==("Generated"))) + w_scent = subset(df, scent => ByRow(>(0))) + stairs!(ax, get_pareto_front(gen.homo, gen.gap; ax); + color=MISTStyle.UM_COLORS.blue, + pareto_kwargs... + ) + scatter!(ax, df.homo, df.gap; color=levelcode.(df.group), alpha=0.2, markersize=3pt, skwargs...) + scatter!(ax, w_scent.homo, w_scent.gap; + color=levelcode.(w_scent.group), + markersize=4pt, + strokewidth=0.5, + skwargs... + ) + + ax = Axis(f[2, 1]; + limits=((minimum(df.mp), 0), (50, maximum(df.bp))), + xlabel=L"Melt [$\degree C$]", + ylabel=L"Boil [$\degree C$]", + ) + h_pareto = stairs!(ax, get_pareto_front(gen.mp, gen.bp; ax); + color=MISTStyle.UM_COLORS.blue, + pareto_kwargs... + ) + w_scent = subset(df, scent => ByRow(>(0))) + h_other = scatter!(ax, df.mp, df.bp; color=levelcode.(df.group), alpha=0.2, markersize=3pt, skwargs...) + h = scatter!(ax, w_scent.mp, w_scent.bp; + color=levelcode.(w_scent.group), + markersize=4pt, + strokewidth=0.5, + skwargs... + ) + + elements = map(enumerate(unique(df.group))) do (i, label) + PolyElement(; color=i, label, colormap=h.colormap, colorrange=h.colorrange) + end |> Vector{Any} + push!(elements, LineElement(; + color=h_pareto.color, + linestyle=h_pareto.linestyle, + linewidth=h_pareto.linewidth, + label="Pareto Front", + )) + push!(elements, MarkerElement(; + label="Odorless", + color=:gray, + marker=h.marker, + markersize=h.markersize, + strokewidth=h.strokewidth, + )) + # push!(elements, MarkerElement(; + # label="Other Scents", + # color=:gray, + # marker=h_other.marker, + # markersize=h_other.markersize, + # strokewidth=h_other.strokewidth, + # )) + Legend(f[2, 1], elements, MISTStyle.label.(elements); + tellheight=false, tellwidth=false, + halign=:left, + valign=:top, + ) + resize_to_layout!(f) + + return f +end diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl index eb9b4186..ddde005b 100644 --- a/opt/screening/plots/src/qmist.jl +++ b/opt/screening/plots/src/qmist.jl @@ -215,4 +215,3 @@ inbounds(x::Real, lb::Real, ub::Real) = x >= lb && x <= ub inbounds(x::Real, lb::Nothing, ub::Real) = x <= ub inbounds(x::Real, lb::Real, ub::Nothing) = x >= lb inbounds(x::Real, lb::Nothing, ub::Nothing) = true - diff --git a/opt/screening/src/generate.py b/opt/screening/src/generate.py index f3a7c934..b6107404 100644 --- a/opt/screening/src/generate.py +++ b/opt/screening/src/generate.py @@ -99,9 +99,9 @@ def logit_limits( def limits_to_bounds(limits: dict[str, tuple[float, float]], channels: list[str]): lower = [] upper = [] - assert limits.keys() <= set(channels), ( - f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" - ) + assert limits.keys() <= set( + channels + ), f"limits must be a subset of channels: {limits.keys()} ⊆ {channels}" for chn in channels: if chn in limits: lb, ub = limits[chn] diff --git a/opt/synth_access/main.py b/opt/synth_access/main.py index 543f9090..4d0646e9 100644 --- a/opt/synth_access/main.py +++ b/opt/synth_access/main.py @@ -7,7 +7,7 @@ from os import environ from pathlib import Path from time import perf_counter -from typing import Callable, List, Optional +from typing import Callable, Optional import torch import accelerate # noqa: F401 From c62a54c116e2da02c51e4ba4f8d8099b7927192e Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Sat, 30 Aug 2025 17:42:08 -0400 Subject: [PATCH 126/147] Linearly calibrate models --- opt/screening/plots/plots.jl | 27 +++++++++++------ opt/screening/plots/src/creativity.jl | 10 +++---- opt/screening/plots/src/qmist.jl | 42 +++++++++++++++++++++++---- 3 files changed, 60 insertions(+), 19 deletions(-) diff --git a/opt/screening/plots/plots.jl b/opt/screening/plots/plots.jl index 736317b6..12f88605 100755 --- a/opt/screening/plots/plots.jl +++ b/opt/screening/plots/plots.jl @@ -1,4 +1,5 @@ #!/usr/bin/env -S julia +release --color=auto --startup-file=no --project=@script +# Script to generate screening plots and analysis using ScreeningPlots using Makie using MISTStyle @@ -12,13 +13,18 @@ using Format: format using ScreeningPlots: searchfirst +# Input Files and Path +# ROOTDIR: opt/screening folder +# GIT_ROOT: MIST Project Root +# DATA_DIR: Path to the screening files (to be released in data drop) ROOTDIR = realpath(joinpath(pkgdir(ScreeningPlots), "..")) GIT_ROOT = realpath(joinpath(ROOTDIR, "..", "..")) +DATA_DIR = joinpath(GIT_ROOT, "data", "screening") fig_dir = joinpath(ROOTDIR, "fig") isdir(fig_dir) || mkdir(fig_dir) # Filter to screening runs -df = ScreeningPlots.collate_performance_stats(joinpath(ROOTDIR, "runs")) +df = ScreeningPlots.collate_performance_stats(joinpath(DATA_DIR, "runs")) df = filter(df) do row config = row.config get(config, "limit_walltime", nothing) == 300 || return false @@ -80,7 +86,7 @@ regtable( # Plot generated molecules prod_id = "b7c6ceb2-2114-4ba5-bd2e-41b9bfa2d5df" -production_run_path = joinpath(ROOTDIR, "runs", prod_id) +production_run_path = joinpath(DATA_DIR, "runs", prod_id) df_mol = ScreeningPlots.load_generated_molecules(production_run_path) prod_config = JSON.parsefile(joinpath(production_run_path, "config.json")) @@ -111,16 +117,19 @@ with_theme(MISTStyle.theme()) do MISTStyle.savefig(joinpath("panel" * "-" * prod_id), f) # Verify qmist can reproduce QM9 calculations - qmist = realpath(joinpath(pkgdir(ScreeningPlots), "..", "..", "qmist")) - df_qm9 = ScreeningPlots.load_jsonl(joinpath(qmist, "qm9.jsonl")) - label = "QM9 (Ramakrishnan et al.)" => "Ours" - for version in [joinpath(qmist, "veri_v1"), joinpath(qmist, "veri_v2"), joinpath(qmist, "veri_v3")] + df_qm9 = ScreeningPlots.load_jsonl(joinpath(DATA_DIR, "qm9.jsonl")) + for (version, vlabel) in [ + (joinpath(DATA_DIR, "veri_v1"), "v1"), + (joinpath(DATA_DIR, "veri_v2"), "v2"), + (joinpath(DATA_DIR, "veri_v3"), "v3"), + ] df_qmist = ScreeningPlots.load_qmist_results(version) df, cols = ScreeningPlots.merge_qmist_results(df_qmist, df_qm9) μ = mean(df_qmist.walltime) σ = std(df_qmist.walltime) walltime_p95 = quantile(df_qmist.walltime, 0.95) @info basename(version) nrow(df) walltime=format("\\({:.0f} \\pm {:.0f}\\)", μ, σ) walltime_p95 + label = "QM9 (Ramakrishnan et al.)" => "Ours ($vlabel)" ScreeningPlots.figure_parity(df, cols; label) |> MISTStyle.savefig(basename(version) * "_parity") end @@ -129,7 +138,7 @@ with_theme(MISTStyle.theme()) do f = ScreeningPlots.compare_qmist( production_run_path, joinpath(production_run_path, dir_name); - label="B3LYP/6-31G(2df,p)" => "MIST", + label="B3LYP/6-31G(2df,p) - Ours (v3)" => "MIST", ) MISTStyle.savefig(joinpath("parity-$(label)-$(prod_id)"), f) end @@ -146,7 +155,7 @@ with_theme(MISTStyle.theme()) do f = ScreeningPlots.compare_qmist( production_run_path, joinpath(production_run_path, "qm9_conf"), - joinpath(ROOTDIR, "veri_chembl"), + joinpath(DATA_DIR, "veri_chembl"), mist_qm9; label="B3LYP/6-31G(2df,p)" => "MIST", ) @@ -160,7 +169,7 @@ mist_bp = ScreeningPlots.load_mist_pretrained(joinpath(GIT_ROOT, "models", "mist f, df_surprise = ScreeningPlots.compare_creativity( production_run_path, - joinpath(ROOTDIR, "veri_chembl"), + joinpath(DATA_DIR, "veri_chembl"), df_ref; mol_surprise, mist_mp, diff --git a/opt/screening/plots/src/creativity.jl b/opt/screening/plots/src/creativity.jl index 47e7e2f4..53fcb3bc 100644 --- a/opt/screening/plots/src/creativity.jl +++ b/opt/screening/plots/src/creativity.jl @@ -100,16 +100,16 @@ function compare_creativity(df::DataFrame, M = missing) end ax2 = Axis(f[1, 2]; - xlabel=L"$$HOMO [eV]", + xlabel=L"$$HOMO (eV)", ylabel=L"$$Molecular Surprise", xticks=WilkinsonTicks(3), ) - sargs = (; markersize = 3) + sargs = (; markersize = 4) sort!(df, :group_frontier; rev=true) _mark_creative!(ax2, df, :homo, :surprise, df.group_frontier; sargs...) ax3 = Axis(f[1, 3]; - xlabel=L"$$Gap [eV]", + xlabel=L"$$Gap (eV)", ylabel=L"$$Molecular Surprise", xticks=WilkinsonTicks(3), yticks=ax1.yticks, @@ -118,7 +118,7 @@ function compare_creativity(df::DataFrame, M = missing) _mark_creative!(ax3, df, :gap, :surprise, df.group_frontier; sargs...) ax4 = Axis(f[1, 4]; - xlabel=L"$$Melt [$\degree C$ ]", + xlabel=L"$$Melt ($\degree C$)", xticks=WilkinsonTicks(3), ylabel=L"$$Molecular Surprise", yticks=ax1.yticks, @@ -127,7 +127,7 @@ function compare_creativity(df::DataFrame, M = missing) _mark_creative!(ax4, df, :mp, :surprise, df.group_frontier; sargs...) ax5 = Axis(f[1, 5]; - xlabel=L"$$Boil [$\degree C$ ]", + xlabel=L"$$Boil ($\degree C$)", xticks=WilkinsonTicks(3), ylabel=L"$$Molecular Surprise", yticks=ax1.yticks, diff --git a/opt/screening/plots/src/qmist.jl b/opt/screening/plots/src/qmist.jl index ddde005b..3e832fae 100644 --- a/opt/screening/plots/src/qmist.jl +++ b/opt/screening/plots/src/qmist.jl @@ -43,7 +43,7 @@ function merge_qmist_results(qmist::DataFrame, ref::DataFrame) return df, cols end -figure_parity(args...; kwargs...) = figure_parity!(Figure(; size=(3.42inch, 2inch)), args...; kwargs...) +figure_parity(args...; kwargs...) = figure_parity!(Figure(; size=(3.42inch, 3inch)), args...; kwargs...) function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", other="_qmist", label::Union{Pair{String,String},Nothing}=nothing) nrow = floor(Int, sqrt(length(cols))) ncol = ceil(Int, length(cols) / nrow) @@ -57,7 +57,7 @@ function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", othe xlim = extrema(x) ylim = extrema(y) ax = Axis(gl[i, j]; - title=format("{}\nρ: {:.2f}, MAE: {:.3f}", col, cor(x,y), mae(x .- y)), + title=format("{}\nρ: {:.2f}\nRMSD: {:.3f}\nMAE: {:.3f}", col, cor(x,y), rmsd(x, y), mae(x .- y)), limits=MISTStyle.parity_limits(x, y), xticks=WilkinsonTicks(2), yticks=WilkinsonTicks(2), @@ -84,12 +84,15 @@ function figure_parity!(f, df::DataFrame, cols::Vector{String}; ref="_qm9", othe elements = map(enumerate(["Generated (n=$ng)", "Inventory (n=$ni)"])) do (idx, label) MarkerElement(; label, marker=:circle, color=MISTStyle.CAT_COLORS[idx]) end - Legend(gl[begin, end], elements, MISTStyle.label.(elements); + Legend(f[end+1, :], elements, MISTStyle.label.(elements); + orientation=:horizontal, fontsize=6pt, margin=(2pt, 2pt, 2pt, 2pt), padding=2pt, valign=:bottom, - halign=:right, + halign=:center, + tellwidth=false, + tellheight=true, ) end @@ -144,11 +147,40 @@ function compare_qmist( return compare_qmist(df_qmist, df_mist, cols; on="inchi_key", kwargs...) end +function calibrate_qmist(df_qm9::DataFrame, df_qmist::DataFrame...) + """ Linearly Calibrate Qmist replication attempts to QM9 Ground truth """ + cols = filter(!in(["smiles", "inchi_key", "InChI", "walltime"]), names(df_qm9)) + smiles = df_qm9.smiles + for df in df_qmist + smiles = intersect(smiles, df.smiles) + end + df_qm9 = subset(df_qm9, :smiles => ByRow(∈(smiles))) + dropmissing!(df_qm9) + + Y = Matrix(df_qm9[!, cols]) + dfo = select(df_qm9, :smiles) + transform!(dfo, :smiles => ByRow(inchi_key) => :inchi_key) + kcols = vcat(["inchi_key"], cols) + for df in df_qmist + leftjoin!(dfo, select(df, kcols); on=:inchi_key, makeunique=true) + end + dropmissing!(dfo) + X = Matrix(select(dfo, Not([:smiles, :inchi_key]))) + X = hcat(X, ones(eltype(X), size(X, 1), 1)) + + # Fit General Linear Model + B = X \ Y + df = DataFrame(eachcol(X * B), cols) + df[!, :smiles] .= dfo.smiles + transform!(df, :smiles => ByRow(inchi_key) => :InChIKey) + return B, df +end + function compare_qmist(df_qmist::DataFrame, df_mist::DataFrame, cols::Vector{String}; on="InChIKey" => "inchi", kwargs...) df = innerjoin(df_qmist, df_mist; on, renamecols="_qmist" => "_mist") disallowmissing!(df) - f = Figure(; size=(3.42inch, 1inch)) + f = Figure(; size=(3.42inch, 1.4inch)) figure_parity!(f ,df, cols; ref="_qmist", other="_mist", kwargs...) return f end From 49c0968eb27ef1e493a281c371e4158310d0372b Mon Sep 17 00:00:00 2001 From: Alexius Wadell Date: Mon, 1 Sep 2025 16:40:29 -0400 Subject: [PATCH 127/147] fix pre-commit issues --- electrolyte_fm/models/mol_surprise.py | 1 - opt/design/plots.jl | 1 - opt/screening/electrolyte_sweep.py | 0 opt/screening/plots/src/collate.jl | 1 - 4 files changed, 3 deletions(-) mode change 100644 => 100755 opt/screening/electrolyte_sweep.py diff --git a/electrolyte_fm/models/mol_surprise.py b/electrolyte_fm/models/mol_surprise.py index 907a4561..98dee914 100644 --- a/electrolyte_fm/models/mol_surprise.py +++ b/electrolyte_fm/models/mol_surprise.py @@ -1,5 +1,4 @@ import torch -from torch.fx import has_side_effect from torch.nn import functional as F from transformers import AutoModelForMaskedLM, AutoConfig, DataCollatorWithPadding from ..utils.tokenizer import load_tokenizer diff --git a/opt/design/plots.jl b/opt/design/plots.jl index 070fda20..dc984d38 100644 --- a/opt/design/plots.jl +++ b/opt/design/plots.jl @@ -99,4 +99,3 @@ with_theme(MISTStyle.theme()) do name_df_order=("Baseline" => df_order, "Augmented" => df_order_rand) ) end |> MISTStyle.savefig("double_bond_loc") - diff --git a/opt/screening/electrolyte_sweep.py b/opt/screening/electrolyte_sweep.py old mode 100644 new mode 100755 diff --git a/opt/screening/plots/src/collate.jl b/opt/screening/plots/src/collate.jl index 07bba668..1d99c9a4 100644 --- a/opt/screening/plots/src/collate.jl +++ b/opt/screening/plots/src/collate.jl @@ -48,4 +48,3 @@ function performance_trace(logfile::String) end return trace, rank_throughput end - From c638b124091e835ae892f7ef5fe2de960e24fbe9 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Wed, 1 Oct 2025 12:34:47 -0400 Subject: [PATCH 128/147] correct task head name --- .../models/ionic_conductivity_model.py | 8 ++-- electrolyte_fm/models/physics_task_heads.py | 40 ------------------- 2 files changed, 3 insertions(+), 45 deletions(-) diff --git a/electrolyte_fm/models/ionic_conductivity_model.py b/electrolyte_fm/models/ionic_conductivity_model.py index 7313bf1b..3ed09f6f 100644 --- a/electrolyte_fm/models/ionic_conductivity_model.py +++ b/electrolyte_fm/models/ionic_conductivity_model.py @@ -10,7 +10,7 @@ from ..utils.metrics import get_metrics, masked_metric_update from ..utils.tokenizer import load_tokenizer from .model_utils import DeepSpeedMixin, LoggingMixin -from .physics_task_heads import VFTTaskHeadWithDecay +from .physics_task_heads import VFTTaskHead class IonicConductivityModel(LightningModule, DeepSpeedMixin, LoggingMixin): @@ -58,9 +58,7 @@ def __init__( assert ( self.encoder.config.vocab_size == vocab_size ), f"Expected vocab size to match. got {self.encoder.config.vocab_size} and {vocab_size}" - self.task_network = VFTTaskHeadWithDecay( - embed_dim=self.encoder.config.hidden_size - ) + self.task_network = VFTTaskHead(embed_dim=self.encoder.config.hidden_size) self.lossfn = torch.nn.MSELoss(reduction="mean") metrics = get_metrics( @@ -81,7 +79,7 @@ def forward(self, batch, transform=True, **kwargs): # type: ignore[override] attention_mask=batch[f"attention_mask_{i}"], return_dict=True, output_hidden_states=True, - ).last_hidden_state.mean(axis=1) + ).last_hidden_state[:, 0, :] embedding = torch.stack( [ diff --git a/electrolyte_fm/models/physics_task_heads.py b/electrolyte_fm/models/physics_task_heads.py index b204d350..0ffc7a5f 100644 --- a/electrolyte_fm/models/physics_task_heads.py +++ b/electrolyte_fm/models/physics_task_heads.py @@ -1,4 +1,3 @@ -import torch from torch import nn @@ -53,45 +52,6 @@ def forward(self, emb, temperature): class VFTTaskHead(nn.Module): - def __init__(self, embed_dim: int) -> None: - super().__init__() - self.desc_skip_connection = True - - self.fc1 = nn.Linear(embed_dim, embed_dim) - self.relu1 = nn.GELU() - self.fc2 = nn.Linear(embed_dim, embed_dim) - self.relu2 = nn.GELU() - self.fc3 = nn.Linear(embed_dim, int(0.5 * embed_dim)) - self.relu3 = nn.GELU() - self.final = nn.Linear(int(0.5 * embed_dim), 3) - - def forward(self, emb, temperature): - x_out = self.fc1(emb) - x_out = self.relu1(x_out) - - if self.desc_skip_connection is True: - x_out = x_out + emb - - z = self.fc2(x_out) - z = self.relu2(z) - z = self.fc3(z) - z = self.relu3(z) - z = self.final(z) - - logA = z[:, 0] - Ea = z[:, 1] - T_g = z[:, 2] - R = 8.63e-5 - - e = torch.exp(torch.tensor([1], device=logA.device)) - C = torch.log10(e) / R - - cond = logA - C * Ea / (temperature - T_g) - - return cond - - -class VFTTaskHeadWithDecay(nn.Module): def __init__(self, embed_dim: int) -> None: super().__init__() self.desc_skip_connection = True From 1d2494419826626e9eb5dd6c6bc246a80aea2454 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 16:23:43 -0400 Subject: [PATCH 129/147] feat: return intermidiate params --- .../models/ionic_conductivity_model.py | 45 ++++++---------- electrolyte_fm/models/physics_task_heads.py | 52 ++++++++++++++++++- 2 files changed, 67 insertions(+), 30 deletions(-) diff --git a/electrolyte_fm/models/ionic_conductivity_model.py b/electrolyte_fm/models/ionic_conductivity_model.py index 3ed09f6f..da3e0c5d 100644 --- a/electrolyte_fm/models/ionic_conductivity_model.py +++ b/electrolyte_fm/models/ionic_conductivity_model.py @@ -7,10 +7,10 @@ from lightning.pytorch.cli import LRSchedulerCallable, OptimizerCallable from lightning.pytorch.loggers import WandbLogger -from ..utils.metrics import get_metrics, masked_metric_update +from ..utils.metrics import get_metrics from ..utils.tokenizer import load_tokenizer from .model_utils import DeepSpeedMixin, LoggingMixin -from .physics_task_heads import VFTTaskHead +from .physics_task_heads import VFTDecayTaskHead class IonicConductivityModel(LightningModule, DeepSpeedMixin, LoggingMixin): @@ -58,7 +58,7 @@ def __init__( assert ( self.encoder.config.vocab_size == vocab_size ), f"Expected vocab size to match. got {self.encoder.config.vocab_size} and {vocab_size}" - self.task_network = VFTTaskHead(embed_dim=self.encoder.config.hidden_size) + self.task_network = VFTDecayTaskHead(embed_dim=self.encoder.config.hidden_size) self.lossfn = torch.nn.MSELoss(reduction="mean") metrics = get_metrics( @@ -71,7 +71,7 @@ def __init__( self.val_metrics = metrics.clone(prefix="val/") self.test_metrics = metrics.clone(prefix="test/") - def forward(self, batch, transform=True, **kwargs): # type: ignore[override] + def forward(self, batch, return_all=False, **kwargs): # type: ignore[override] mix_embedding = None for i in range(self.n_components): embedding = self.encoder( @@ -92,9 +92,12 @@ def forward(self, batch, transform=True, **kwargs): # type: ignore[override] else: mix_embedding += embedding - pred_unscaled, alpha, beta, lmbda = self.task_network( - mix_embedding, batch["temperature"] - ) + params = self.task_network(mix_embedding, batch["temperature"]) + + pred_unscaled = params["conductivity"] + alpha = params["alpha"] + beta = params["beta"] + lmbda = params["beta"] exponent = torch.div(-1 * alpha + batch["composition_4"], lmbda) pred_decay = torch.mul((1 - beta), torch.exp(exponent)) + beta @@ -104,7 +107,9 @@ def forward(self, batch, transform=True, **kwargs): # type: ignore[override] # else predicted conductivity pred = torch.where(batch["composition_4"] > alpha, pred, pred_unscaled) - return pred.view(-1, 1), alpha, beta + if return_all: + return pred.view(-1, 1), params + return pred.view(-1, 1), alpha def setup(self, stage: str) -> None: if isinstance(self.logger, WandbLogger): @@ -114,7 +119,7 @@ def setup(self, stage: str) -> None: def _scaled_pred_loss(self, batch): """Compute loss before transforming the model's predictions""" - preds, alpha, beta = self.forward(batch, transform=False) + preds, alpha = self.forward(batch, return_all=False) target = batch["target"] loss = self.lossfn(preds, target) + alpha.abs().mean() return preds, loss @@ -130,12 +135,7 @@ def training_step(self, batch, batch_idx: int) -> torch.FloatTensor: sync_dist=True, ) - masked_metric_update( - self.train_metrics, - preds, - batch["target"], - batch["target_mask"], - ) + self.train_metrics.update(preds, batch["target"]) return loss def on_train_epoch_end(self): @@ -155,13 +155,7 @@ def validation_step(self, batch, batch_idx: int) -> torch.FloatTensor: on_epoch=True, sync_dist=True, ) - - masked_metric_update( - self.val_metrics, - preds, - batch["target"], - batch["target_mask"], - ) + self.val_metrics.update(preds, batch["target"]) return loss def on_validation_epoch_end(self): @@ -181,12 +175,7 @@ def test_step(self, batch, batch_idx: int) -> torch.FloatTensor: on_epoch=True, sync_dist=True, ) - masked_metric_update( - self.test_metrics, - preds.to(dtype=torch.float32), - batch["target"].to(dtype=torch.float32), - batch["target_mask"], - ) + self.test_metrics.update(preds, batch["target"]) return loss def on_test_epoch_end(self): diff --git a/electrolyte_fm/models/physics_task_heads.py b/electrolyte_fm/models/physics_task_heads.py index 0ffc7a5f..d74f2984 100644 --- a/electrolyte_fm/models/physics_task_heads.py +++ b/electrolyte_fm/models/physics_task_heads.py @@ -1,3 +1,4 @@ +import torch from torch import nn @@ -52,6 +53,45 @@ def forward(self, emb, temperature): class VFTTaskHead(nn.Module): + def __init__(self, embed_dim: int) -> None: + super().__init__() + self.desc_skip_connection = True + + self.fc1 = nn.Linear(embed_dim, embed_dim) + self.relu1 = nn.GELU() + self.fc2 = nn.Linear(embed_dim, embed_dim) + self.relu2 = nn.GELU() + self.fc3 = nn.Linear(embed_dim, int(0.5 * embed_dim)) + self.relu3 = nn.GELU() + self.final = nn.Linear(int(0.5 * embed_dim), 3) + + def forward(self, emb, temperature): + x_out = self.fc1(emb) + x_out = self.relu1(x_out) + + if self.desc_skip_connection is True: + x_out = x_out + emb + + z = self.fc2(x_out) + z = self.relu2(z) + z = self.fc3(z) + z = self.relu3(z) + z = self.final(z) + + logA = z[:, 0] + Ea = z[:, 1] + T_g = z[:, 2] + R = 8.63e-5 + + e = torch.exp(torch.tensor([1], device=logA.device)) + C = torch.log10(e) / R + + cond = logA - C * Ea / (temperature - T_g) + + return cond + + +class VFTDecayTaskHead(nn.Module): def __init__(self, embed_dim: int) -> None: super().__init__() self.desc_skip_connection = True @@ -84,5 +124,13 @@ def forward(self, emb, temperature): alpha = self.sigmoid(z[:, 3]) beta = z[:, 4] lmbda = self.sigmoid(z[:, 5]) - - return ln_A - Ea / (temperature - Tg), alpha, beta, lmbda + params = { + "conductivity": ln_A - Ea / (temperature - Tg), + "ln_A": ln_A, + "Ea": Ea, + "Tg": Tg, + "alpha": alpha, + "beta": beta, + "lmbda": lmbda, + } + return params From 64b1fd35bf79924f3843827ca4a4ad5ea69d9b9d Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 16:48:52 -0400 Subject: [PATCH 130/147] add command for exporting mixture models --- opt/package/__main__.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 02cbd79d..1dd4f64f 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -15,6 +15,7 @@ sys.path.append(str(Path(__file__).parent.parent.parent)) from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer +from electrolyte_fm.models.model_utils import DeepSpeedMixin from electrolyte_fm.utils.tokenizer import load_tokenizer import utils @@ -84,6 +85,26 @@ def finetuned(ckpt: Path, name: Optional[str] = None, safe: bool = True): utils.create_tar_gz(save_dir) +@cli.command() +def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True): + """Export a mixture model""" + if Path(ckpt).joinpath("config.json").is_file(): + ckpt = get_best_ckpt(ckpt) + + model = DeepSpeedMixin.load(ckpt) + + name = utils.name_model( + model, + template=name or "mist-{model_size}-{ckpt}", + ckpt=ckpt_id(ckpt), + ) + save_dir = create_save_directory(name, ckpt) + utils.export_code(save_dir, model, model.transform, model.task_network) + utils.save_model(model, save_dir, safe) + logging.info("Saved model to %s", save_dir) + utils.create_tar_gz(save_dir) + + def export_multitask( encoder_ckpt: Path, task_ckpt: list[Path], From aaa91153ae156ff438a8b138c8be6e9cf31b7d57 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 17:30:31 -0400 Subject: [PATCH 131/147] export for mixture models --- opt/package/__main__.py | 20 ++++++++++++++++---- opt/package/utils.py | 2 -- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 1dd4f64f..70087d1f 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -16,10 +16,11 @@ sys.path.append(str(Path(__file__).parent.parent.parent)) from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer from electrolyte_fm.models.model_utils import DeepSpeedMixin +from electrolyte_fm.models.prod_finetune import save_model from electrolyte_fm.utils.tokenizer import load_tokenizer import utils -from utils import get_best_ckpt, create_save_directory, ckpt_id +from utils import get_best_ckpt, create_save_directory, ckpt_id, save_tokenizer cli = typer.Typer() @@ -92,15 +93,26 @@ def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True): ckpt = get_best_ckpt(ckpt) model = DeepSpeedMixin.load(ckpt) - name = utils.name_model( model, template=name or "mist-{model_size}-{ckpt}", ckpt=ckpt_id(ckpt), ) save_dir = create_save_directory(name, ckpt) - utils.export_code(save_dir, model, model.transform, model.task_network) - utils.save_model(model, save_dir, safe) + + # Some mixture model classes don't have a transform + if hasattr(model, "transform"): + utils.export_code(save_dir, model, model.transform, model.task_network) + else: + utils.export_code(save_dir, model, model.task_network) + + config = json.loads(Path(ckpt.parent.parent, "config.json").read_text()) + + if hasattr(model, "tokenizer"): + save_tokenizer(save_dir, model.tokenizer) + + Path(save_dir, "config.json").write_text(json.dumps(config, indent=4)) + save_model(model, save_dir, safe_serialization=True) logging.info("Saved model to %s", save_dir) utils.create_tar_gz(save_dir) diff --git a/opt/package/utils.py b/opt/package/utils.py index 4f1b3fac..cb3c9969 100644 --- a/opt/package/utils.py +++ b/opt/package/utils.py @@ -25,8 +25,6 @@ def get_best_ckpt(ckpt_dir: Path) -> Path: best = None CKPT_REGEX = re.compile(r".*step=(\d+?)-val_loss=([\d\.]+?)\.ckpt") for ckpt in Path(ckpt_dir, "checkpoints").iterdir(): - if not ckpt.is_dir(): - continue if m := CKPT_REGEX.match(ckpt.name): step = int(m.group(1)) loss = float(m.group(2)) From 0c365509aa00da4e2b14d104eaf2cc6952582155 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 2 Oct 2025 19:25:27 -0400 Subject: [PATCH 132/147] fix: mixture export --- electrolyte_fm/models/__init__.py | 3 +- electrolyte_fm/models/prod_mixture.py | 555 ++++++++++++++++++++++++++ opt/package/__main__.py | 44 +- 3 files changed, 583 insertions(+), 19 deletions(-) create mode 100644 electrolyte_fm/models/prod_mixture.py diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index ac7ac769..cfbca699 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -3,4 +3,5 @@ from .roberta_base import RoBERTa from .roberta_prelayernorm import RoBERTaPreLayerNorm from .roformer_base import RoFormer -from .prod_finetune import MISTFinetuned, MISTMultiTask \ No newline at end of file +from .prod_finetune import MISTFinetuned, MISTMultiTask +from .prod_mixture import MISTIonicConductivity, MISTExcessPhysics \ No newline at end of file diff --git a/electrolyte_fm/models/prod_mixture.py b/electrolyte_fm/models/prod_mixture.py new file mode 100644 index 00000000..d94fb19e --- /dev/null +++ b/electrolyte_fm/models/prod_mixture.py @@ -0,0 +1,555 @@ +import json +import torch +from pathlib import Path +from transformers import AutoConfig, AutoModel, AutoTokenizer +from transformers.data.data_collator import DataCollatorWithPadding +from .prod_finetune import load_model + + +class MISTIonicConductivity(torch.nn.Module): + def __init__(self, encoder, task_network, tokenizer, n_components=38): + super().__init__() + self.encoder = encoder + self.task_network = task_network + self.tokenizer = tokenizer + self.n_components = n_components + + def forward(self, batch, return_all=False): + """ + Forward pass for mixture ionic conductivity prediction. + + Args: + batch: Dictionary containing input_ids, attention_mask, composition, + and temperature for each component + return_all: If True, return all parameters along with prediction + + Returns: + Predicted conductivity and alpha (or all params if return_all=True) + """ + mix_embedding = None + for i in range(self.n_components): + embedding = self.encoder( + batch[f"input_ids_{i}"], + attention_mask=batch[f"attention_mask_{i}"], + return_dict=True, + output_hidden_states=True, + ).last_hidden_state[:, 0, :] + + embedding = torch.stack( + [ + torch.mul(embedding[j, :], batch[f"composition_{i}"][j]) + for j in range(embedding.shape[0]) + ] + ) + if mix_embedding is None: + mix_embedding = embedding + else: + mix_embedding += embedding + + params = self.task_network(mix_embedding, batch["temperature"]) + + pred_unscaled = params["conductivity"] + alpha = params["alpha"] + beta = params["beta"] + lmbda = params["beta"] + + exponent = torch.div(-1 * alpha + batch["composition_4"], lmbda) + pred_decay = torch.mul((1 - beta), torch.exp(exponent)) + beta + pred = torch.mul(pred_unscaled, pred_decay) + + # predicted conductivity*decay if salt molarity > alpha + # else predicted conductivity + pred = torch.where(batch["composition_4"] > alpha, pred, pred_unscaled) + + if return_all: + return pred.view(-1, 1), params + return pred.view(-1, 1), alpha + + def save_pretrained(self, save_directory, safe_serialization=False): + """Save model configuration and weights.""" + config = { + "architectures": [ + self.__class__.__name__, + ], + "tokenizer_class": self.tokenizer.__class__.__name__, + "encoder": self.encoder.config.to_diff_dict(), + "task_network": { + "embed_dim": self.encoder.config.hidden_size, + }, + "n_components": self.n_components, + } + + Path(save_directory).mkdir(parents=True, exist_ok=True) + Path(save_directory, "config.json").write_text(json.dumps(config, indent=4)) + + # Save model state dict + if safe_serialization: + from safetensors.torch import save_file + + save_file(self.state_dict(), Path(save_directory, "model.safetensors")) + else: + torch.save(self.state_dict(), Path(save_directory, "pytorch_model.bin")) + + # Save tokenizer + self.tokenizer.save_pretrained(save_directory) + + def embed_mixture( + self, smiles_list: list[list[str]], compositions: list[list[float]] + ): + """ + Generate embeddings for mixture components. + + Args: + smiles_list: List of SMILES lists, where each inner list contains + SMILES for all components in a mixture + compositions: List of composition lists corresponding to each mixture + + Returns: + Mixture embeddings tensor + """ + batch_size = len(smiles_list) + mix_embeddings = [] + + with torch.inference_mode(): + for batch_idx in range(batch_size): + mix_embedding = None + for comp_idx in range( + min(len(smiles_list[batch_idx]), self.n_components) + ): + smi = smiles_list[batch_idx][comp_idx] + comp = compositions[batch_idx][comp_idx] + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids = tokens["input_ids"].to(self.encoder.device) + attention_mask = tokens["attention_mask"].to(self.encoder.device) + + embedding = self.encoder( + input_ids, + attention_mask=attention_mask, + return_dict=True, + output_hidden_states=True, + ).last_hidden_state[:, 0, :] + + embedding = embedding * comp + + if mix_embedding is None: + mix_embedding = embedding + else: + mix_embedding += embedding + + mix_embeddings.append(mix_embedding) + + return torch.cat(mix_embeddings, dim=0).cpu() + + def predict( + self, + smiles_list: list[list[str]], + compositions: list[list[float]], + temperatures: list[float], + salt_molarities: list[float], + return_dict=True, + ): + """ + Predict ionic conductivity for mixtures. + + Args: + smiles_list: List of SMILES lists for mixture components + compositions: List of composition arrays for each mixture + temperatures: List of temperatures + salt_molarities: List of salt molarities (composition_4) + return_dict: If True, return dictionary with detailed predictions + + Returns: + Predictions (tensor or dict depending on return_dict) + """ + batch_size = len(smiles_list) + batch = { + "temperature": torch.tensor(temperatures).float().to(self.encoder.device), + "composition_4": torch.tensor(salt_molarities) + .float() + .to(self.encoder.device), + } + + # Tokenize and prepare batch for each component + for i in range(self.n_components): + input_ids_list = [] + attention_mask_list = [] + comp_list = [] + + for batch_idx in range(batch_size): + if i < len(smiles_list[batch_idx]): + smi = smiles_list[batch_idx][i] + comp = compositions[batch_idx][i] + else: + smi = "[H]" # Dummy molecule + comp = 0.0 + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids_list.append(tokens["input_ids"].squeeze(0)) + attention_mask_list.append(tokens["attention_mask"].squeeze(0)) + comp_list.append(comp) + + # Collate with padding + collator = DataCollatorWithPadding(self.tokenizer) + collated = collator( + [ + {"input_ids": ids, "attention_mask": mask} + for ids, mask in zip(input_ids_list, attention_mask_list) + ] + ) + + batch[f"input_ids_{i}"] = collated["input_ids"].to(self.encoder.device) + batch[f"attention_mask_{i}"] = collated["attention_mask"].to( + self.encoder.device + ) + batch[f"composition_{i}"] = ( + torch.tensor(comp_list).float().to(self.encoder.device) + ) + + with torch.inference_mode(): + pred, params = self(**batch, return_all=return_dict) + + if not return_dict: + return pred.cpu() + + result = { + "conductivity": pred.cpu(), + "alpha": params["alpha"].cpu(), + "beta": params["beta"].cpu(), + "conductivity_unscaled": params["conductivity"].cpu(), + } + return result + + @classmethod + def from_pretrained(cls, save_directory: str): + """Load model from saved directory.""" + config = json.loads(Path(save_directory, "config.json").read_text()) + + encoder_config = AutoConfig.for_model( + config["encoder"]["model_type"] + ).from_dict(config["encoder"]) + encoder = AutoModel.from_config(encoder_config, add_pooling_layer=False) + tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) + + from .physics_task_heads import VFTDecayTaskHead + + task_network = VFTDecayTaskHead(embed_dim=config["task_network"]["embed_dim"]) + + tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) + n_components = config.get("n_components", 5) + + model = cls(encoder, task_network, tokenizer, n_components) + load_model(model, save_directory) + return model + + +class MISTExcessPhysics(torch.nn.Module): + def __init__( + self, + encoder, + task_network, + transform, + tokenizer, + n_components=2, + temperature_normalization=(273, 400), + ): + super().__init__() + self.encoder = encoder + self.task_network = task_network + self.transform = transform + self.tokenizer = tokenizer + self.n_components = n_components + self.temperature_normalization = temperature_normalization + + def forward(self, batch, transform=True): + """ + Forward pass for mixture property prediction. + + Args: + batch: Dictionary containing input_ids, attention_mask for each component, + temperature, and composition data + transform: If True, apply normalization transform to predictions + + Returns: + Predicted property values + """ + mn, mx = self.temperature_normalization + # Normalize temperature once per mixture + temperature = (batch["temperature"] - mn) / (mx - mn) # (B,) + batch["temperature"] = temperature + + for i in range(self.n_components): + enc_out = self.encoder( + input_ids=batch[f"input_ids_{i}"], + attention_mask=batch[f"attention_mask_{i}"], + return_dict=True, + output_hidden_states=False, + ) + + token_seq = enc_out.last_hidden_state # (B, L_i, d) + padmask = batch[f"attention_mask_{i}"] == 0 # (B, L_i) bool + + # Save for cross-attention fusion + batch[f"tokens_{i}"] = token_seq.float() + batch[f"padmask_{i}"] = padmask + + # Mean-pool tokens_i: single-molecule embedding + pooled = token_seq.masked_fill(padmask.unsqueeze(-1), 0).mean(dim=1) + batch[f"embedding_{i}"] = pooled + + # Property prediction + pred = self.task_network(batch) # (B, 1) + + if transform: + pred = self.transform.forward(pred) # Rescale to original units + return pred + + def save_pretrained(self, save_directory, safe_serialization=False): + """Save model configuration and weights.""" + config = { + "architectures": [ + self.__class__.__name__, + ], + "tokenizer_class": self.tokenizer.__class__.__name__, + "encoder": self.encoder.config.to_diff_dict(), + "task_network": { + "embed_dim": self.encoder.config.hidden_size, + "polynomial_order": getattr(self.task_network, "polynomial_order", 4), + "n_components": self.n_components, + "num_heads": getattr(self.task_network, "num_heads", 4), + "include_linear_mixing": getattr( + self.task_network, "include_linear_mixing", True + ), + "fusion": getattr(self.task_network, "fusion", "attention"), + "basis": self.task_network.__class__.__name__, + }, + "transform": self.transform.to_config(), + "n_components": self.n_components, + "temperature_normalization": self.temperature_normalization, + } + + Path(save_directory).mkdir(parents=True, exist_ok=True) + Path(save_directory, "config.json").write_text(json.dumps(config, indent=4)) + + # Save model state dict + if safe_serialization: + from safetensors.torch import save_file + + save_file(self.state_dict(), Path(save_directory, "model.safetensors")) + else: + torch.save(self.state_dict(), Path(save_directory, "pytorch_model.bin")) + + # Save tokenizer + self.tokenizer.save_pretrained(save_directory) + + def embed_components( + self, smiles_list: list[list[str]], compositions: list[list[float]] + ): + """ + Generate embeddings for mixture components. + + Args: + smiles_list: List of SMILES lists, where each inner list contains + SMILES for all components in a mixture + compositions: List of composition lists corresponding to each mixture + + Returns: + Dictionary with embeddings for each component + """ + batch_size = len(smiles_list) + component_embeddings = {i: [] for i in range(self.n_components)} + + with torch.inference_mode(): + for batch_idx in range(batch_size): + for comp_idx in range(self.n_components): + if comp_idx < len(smiles_list[batch_idx]): + smi = smiles_list[batch_idx][comp_idx] + else: + smi = "[H]" # Dummy molecule + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids = tokens["input_ids"].to(self.encoder.device) + attention_mask = tokens["attention_mask"].to(self.encoder.device) + + enc_out = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + output_hidden_states=False, + ) + + token_seq = enc_out.last_hidden_state + padmask = attention_mask == 0 + pooled = token_seq.masked_fill(padmask.unsqueeze(-1), 0).mean(dim=1) + + component_embeddings[comp_idx].append(pooled) + + # Concatenate all embeddings per component + result = {} + for comp_idx in range(self.n_components): + result[f"component_{comp_idx}"] = torch.cat( + component_embeddings[comp_idx], dim=0 + ).cpu() + + return result + + def predict( + self, + smiles_list: list[list[str]], + compositions: list[list[float]], + temperatures: list[float], + return_dict=False, + ): + """ + Predict mixture properties. + + Args: + smiles_list: List of SMILES lists for mixture components + e.g., [["CCO", "CC"], ["CCCO", "CCC"]] for 2 binary mixtures + compositions: List of composition arrays for each mixture + e.g., [[0.5, 0.5], [0.3, 0.7]] + temperatures: List of temperatures (in Kelvin) + return_dict: If True, return dictionary with detailed information + + Returns: + Predictions (tensor or dict depending on return_dict) + """ + batch_size = len(smiles_list) + + # Validate inputs + assert len(compositions) == batch_size, "Mismatch in batch sizes" + assert len(temperatures) == batch_size, "Mismatch in batch sizes" + + for i, (smiles, comps) in enumerate(zip(smiles_list, compositions)): + assert len(smiles) == len( + comps + ), f"Mixture {i}: SMILES and composition lengths don't match" + assert ( + len(smiles) <= self.n_components + ), f"Mixture {i}: Too many components (max {self.n_components})" + + batch = { + "temperature": torch.tensor(temperatures, dtype=torch.float32).to( + self.encoder.device + ) + } + + # Tokenize and prepare batch for each component + for i in range(self.n_components): + input_ids_list = [] + attention_mask_list = [] + comp_list = [] + + for batch_idx in range(batch_size): + if i < len(smiles_list[batch_idx]): + smi = smiles_list[batch_idx][i] + comp = compositions[batch_idx][i] + else: + smi = "[H]" # Dummy molecule for padding + comp = 0.0 + + tokens = self.tokenizer([smi], return_tensors="pt", padding=True) + input_ids_list.append(tokens["input_ids"].squeeze(0)) + attention_mask_list.append(tokens["attention_mask"].squeeze(0)) + comp_list.append(comp) + + # Collate with padding + collator = DataCollatorWithPadding(self.tokenizer) + collated = collator( + [ + {"input_ids": ids, "attention_mask": mask} + for ids, mask in zip(input_ids_list, attention_mask_list) + ] + ) + + batch[f"input_ids_{i}"] = collated["input_ids"].to(self.encoder.device) + batch[f"attention_mask_{i}"] = collated["attention_mask"].to( + self.encoder.device + ) + batch[f"composition_{i}"] = torch.tensor(comp_list, dtype=torch.float32).to( + self.encoder.device + ) + + with torch.inference_mode(): + pred = self(batch, transform=True) + + if not return_dict: + return pred.cpu() + + result = { + "prediction": pred.cpu(), + "smiles": smiles_list, + "compositions": compositions, + "temperatures": temperatures, + } + return result + + def predict_single( + self, smiles: list[str], composition: list[float], temperature: float + ): + """ + Convenience method to predict for a single mixture. + + Args: + smiles: List of SMILES for the mixture components + composition: List of mole fractions (should sum to 1.0) + temperature: Temperature in Kelvin + + Returns: + Predicted property value (scalar tensor) + """ + pred = self.predict([smiles], [composition], [temperature]) + return pred.squeeze() + + @classmethod + def from_pretrained(cls, save_directory: str): + """Load model from saved directory.""" + config = json.loads(Path(save_directory, "config.json").read_text()) + + encoder_config = AutoConfig.for_model( + config["encoder"]["model_type"] + ).from_dict(config["encoder"]) + encoder = AutoModel.from_config(encoder_config, add_pooling_layer=False) + + from .polynomial_task_head import PolynomialHead + + basis_name = config["task_network"]["basis"] + task_network_config = { + "embed_dim": config["task_network"]["embed_dim"], + "polynomial_order": config["task_network"]["polynomial_order"], + "n_components": config["task_network"]["n_components"], + "num_heads": config["task_network"]["num_heads"], + "include_linear_mixing": config["task_network"]["include_linear_mixing"], + "fusion": config["task_network"]["fusion"], + } + + # Instantiate the appropriate polynomial head + # This assumes you have a way to get the class from the basis name + # Adjust based on your actual implementation + task_network = PolynomialHead.get_class(basis_name)(**task_network_config) + + # Load transform + from your_module.normalize import AbstractNormalizer # Update this import + + transform = AbstractNormalizer.get( + config["transform"]["class"], config["transform"]["num_outputs"] + ) + + tokenizer = AutoTokenizer.from_pretrained(save_directory, use_fast=True) + n_components = config.get("n_components", 2) + temperature_normalization = tuple( + config.get("temperature_normalization", (273, 400)) + ) + + model = cls( + encoder, + task_network, + transform, + tokenizer, + n_components, + temperature_normalization, + ) + + model = cls(encoder, task_network, tokenizer, n_components) + load_model(model, save_directory) + return model diff --git a/opt/package/__main__.py b/opt/package/__main__.py index 70087d1f..c4b43f71 100755 --- a/opt/package/__main__.py +++ b/opt/package/__main__.py @@ -15,8 +15,6 @@ sys.path.append(str(Path(__file__).parent.parent.parent)) from electrolyte_fm.utils.ckpt import SaveConfigWithCkpts, get_ckpt_tokenizer -from electrolyte_fm.models.model_utils import DeepSpeedMixin -from electrolyte_fm.models.prod_finetune import save_model from electrolyte_fm.utils.tokenizer import load_tokenizer import utils @@ -86,33 +84,43 @@ def finetuned(ckpt: Path, name: Optional[str] = None, safe: bool = True): utils.create_tar_gz(save_dir) +def export_conductivity(ckpt: Path): + from electrolyte_fm.models import MISTIonicConductivity + + model = SaveConfigWithCkpts.load(ckpt) + model_config = json.loads(ckpt.parent.parent.joinpath("config.json").read_text()) + tokenizer_name = model_config["data"]["init_args"]["tokenizer"] + tokenizer = load_tokenizer(tokenizer_name) + return MISTIonicConductivity( + model.encoder, + model.task_network, + tokenizer=tokenizer, + n_components=model_config["model"]["init_args"]["n_components"], + ) + + @cli.command() -def mixtures(ckpt: Path, name: Optional[str] = None, safe: bool = True): - """Export a mixture model""" +def conductivity(ckpt: Path, name: Optional[str] = None, safe: bool = True): + """Export a mixture conductivity model""" + if Path(ckpt).joinpath("config.json").is_file(): ckpt = get_best_ckpt(ckpt) + model = export_conductivity(ckpt) - model = DeepSpeedMixin.load(ckpt) name = utils.name_model( model, - template=name or "mist-{model_size}-{ckpt}", + template=name or "mist-conductivity-{model_size}-{ckpt}", ckpt=ckpt_id(ckpt), ) - save_dir = create_save_directory(name, ckpt) - - # Some mixture model classes don't have a transform - if hasattr(model, "transform"): - utils.export_code(save_dir, model, model.transform, model.task_network) - else: - utils.export_code(save_dir, model, model.task_network) - - config = json.loads(Path(ckpt.parent.parent, "config.json").read_text()) + save_dir = create_save_directory(name, ckpt) + utils.export_code(save_dir, model, model.task_network) if hasattr(model, "tokenizer"): save_tokenizer(save_dir, model.tokenizer) - - Path(save_dir, "config.json").write_text(json.dumps(config, indent=4)) - save_model(model, save_dir, safe_serialization=True) + model.save_pretrained(save_dir, safe_serialization=safe) + # Validate + model.__class__.from_pretrained(save_dir) + shutil.move(Path(save_dir, "prod_mixture.py"), Path(save_dir, "model.py")) logging.info("Saved model to %s", save_dir) utils.create_tar_gz(save_dir) From e35c5636624343ec18d1471a018e290dcc821beb Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 17:38:01 -0400 Subject: [PATCH 133/147] fix: precommit and spliter target distribution test --- electrolyte_fm/data_modules/utils.py | 2 +- electrolyte_fm/models/__init__.py | 8 -------- electrolyte_fm/utils/metrics.py | 8 ++------ test/test_spliters.py | 5 ++++- 4 files changed, 7 insertions(+), 16 deletions(-) diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py index 07c551ab..7e5d421b 100644 --- a/electrolyte_fm/data_modules/utils.py +++ b/electrolyte_fm/data_modules/utils.py @@ -126,7 +126,7 @@ def stack_columns(batch, columns: list[str], output: str, dtype=None): if dtype is None: convert = torch.tensor else: - convert = lambda x: torch.tensor(x, dtype=dtype) + convert = lambda x: torch.tensor(x, dtype=dtype) # noqa: E731 return {output: [convert([batch[col][i] for col in columns]) for i in range(n)]} diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index 8dab7014..e69de29b 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -1,8 +0,0 @@ -from .ionic_conductivity_model import IonicConductivityModel -from .lm_finetuning import LMFinetuning -from .mixture_model import MixtureModel -from .roberta_base import RoBERTa -from .roberta_prelayernorm import RoBERTaPreLayerNorm -from .roformer_base import RoFormer -from .prod_finetune import MISTFinetuned, MISTMultiTask -from .prod_mixture import MISTIonicConductivity, MISTExcessPhysics diff --git a/electrolyte_fm/utils/metrics.py b/electrolyte_fm/utils/metrics.py index 927b1e4d..c388e348 100644 --- a/electrolyte_fm/utils/metrics.py +++ b/electrolyte_fm/utils/metrics.py @@ -443,12 +443,8 @@ def __init__(self, reduction="mean", **kwargs): self.add_state("distance", default=torch.tensor(0.0), dist_reduce_fx="sum") self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum") - def __init__(self, *args, threshold: Optional[int] = None, **kwargs): - super().__init__(*args, **kwargs) - self.threshold = threshold - def compute(self): - return self.distance / self.total + return self.distance / self.total def update(self, preds: torch.Tensor, targets: torch.Tensor): dists = self.procrustes_disparity(preds, targets) @@ -493,4 +489,4 @@ def procrustes_alignment(pc1: torch.Tensor, pc2: torch.Tensor) -> torch.Tensor: s = s.to(u) R = u.matmul(s).matmul(v).mT.to(pc1) - return R \ No newline at end of file + return R diff --git a/test/test_spliters.py b/test/test_spliters.py index 9f4a421a..a5f21f3d 100644 --- a/test/test_spliters.py +++ b/test/test_spliters.py @@ -116,10 +116,13 @@ def test_target_distribution(synthetic_data): train_counts = Counter(y[train_idx]) test_counts = Counter(y[test_idx]) # For each bitstring, the fraction in train/test should be within ±10% of overall + tolerance = 0.1 for bitstr, overall_count in overall_counts.items(): overall_frac = overall_count / len(y) test_frac = test_counts.get(bitstr, 0) / len(test_idx) - assert abs(test_frac - overall_frac) < 0.5 + train_frac = train_counts.get(bitstr, 0) / len(train_idx) + assert abs(test_frac - overall_frac) < tolerance + assert abs(train_frac - overall_frac) < tolerance def test_multiple_splits_randomness(synthetic_data): From b91e124b1600b316e47c33530b7e5cdd14f4ed18 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 17:56:05 -0400 Subject: [PATCH 134/147] fix test_ckpt: lightining 2.5.5 cli automatically links matching parameter names between datamodule and model --- test/test_ckpt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_ckpt.py b/test/test_ckpt.py index b77f03e3..2f0a5054 100644 --- a/test/test_ckpt.py +++ b/test/test_ckpt.py @@ -79,7 +79,7 @@ def test_ckpt(cli): assert model_config["lightning_module"] == { "_class_path": __name__ + ".MockedModel", "_instantiator": "lightning.pytorch.cli.instantiate_module", - "vocab_size": None, + "vocab_size": cli.datamodule.vocab_size, } assert model_config["datamodule"] == { "_instantiator": "lightning.pytorch.cli.instantiate_module", From ded218d65f41cf759615f345c426c64f8a341c62 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 18:00:53 -0400 Subject: [PATCH 135/147] fix: merge conflict erased module level imports --- electrolyte_fm/models/__init__.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/electrolyte_fm/models/__init__.py b/electrolyte_fm/models/__init__.py index e69de29b..72ccd611 100644 --- a/electrolyte_fm/models/__init__.py +++ b/electrolyte_fm/models/__init__.py @@ -0,0 +1,7 @@ +from .ionic_conductivity_model import IonicConductivityModel +from .lm_finetuning import LMFinetuning +from .roberta_base import RoBERTa +from .roberta_prelayernorm import RoBERTaPreLayerNorm +from .roformer_base import RoFormer +from .prod_finetune import MISTFinetuned, MISTMultiTask +from .prod_mixture import MISTIonicConductivity, MISTExcessPhysics From c97b932cb989a025552b9c3e50041ec21702e5ac Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 18:04:44 -0400 Subject: [PATCH 136/147] fix: formatting --- .../interpretibility/functional_groups.py | 2 +- electrolyte_fm/models/model_utils.py | 3 - electrolyte_fm/utils/ckpt.py | 1 - opt/design/src/DesignRules.jl | 2 +- opt/mixtures/optimization/utils.py | 7 +- opt/mixtures/panel.jl | 876 +++++++++--------- opt/mixtures/plot_excess.jl | 386 ++++---- opt/mixtures/plot_mae.jl | 6 +- opt/mixtures/python/excess.py | 6 +- test/test_mixture_dataset.py | 6 +- 10 files changed, 647 insertions(+), 648 deletions(-) diff --git a/electrolyte_fm/interpretibility/functional_groups.py b/electrolyte_fm/interpretibility/functional_groups.py index 6841c67b..efc750eb 100644 --- a/electrolyte_fm/interpretibility/functional_groups.py +++ b/electrolyte_fm/interpretibility/functional_groups.py @@ -25,7 +25,7 @@ "azo compound": "N=N", "thiol": "[SH]", "aqueous": "[OH2]", - "salt": "[+1,+2,+3].[-1,-2,-3]" + "salt": "[+1,+2,+3].[-1,-2,-3]", } # Compile SMARTS into RDKit molecules diff --git a/electrolyte_fm/models/model_utils.py b/electrolyte_fm/models/model_utils.py index bfa59bb3..bd407000 100644 --- a/electrolyte_fm/models/model_utils.py +++ b/electrolyte_fm/models/model_utils.py @@ -28,9 +28,6 @@ def load(checkpoint_dir, **kwargs): def load_state(self, checkpoint_dir): print("Loading state for checkpoint:", checkpoint_dir) - from deepspeed.utils.zero_to_fp32 import ( - get_fp32_state_dict_from_zero_checkpoint, - ) state = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) self.load_state_dict(state, strict=False, assign=True) diff --git a/electrolyte_fm/utils/ckpt.py b/electrolyte_fm/utils/ckpt.py index ded96d91..f48978e5 100644 --- a/electrolyte_fm/utils/ckpt.py +++ b/electrolyte_fm/utils/ckpt.py @@ -1,7 +1,6 @@ import logging import importlib import json -import logging import os from typing import Optional from pathlib import Path diff --git a/opt/design/src/DesignRules.jl b/opt/design/src/DesignRules.jl index 5100e9cf..a9102e58 100644 --- a/opt/design/src/DesignRules.jl +++ b/opt/design/src/DesignRules.jl @@ -23,4 +23,4 @@ include("hydrocarbons.jl") include("plot_utils.jl") include("trends.jl") -end \ No newline at end of file +end diff --git a/opt/mixtures/optimization/utils.py b/opt/mixtures/optimization/utils.py index a7c039d8..92282988 100644 --- a/opt/mixtures/optimization/utils.py +++ b/opt/mixtures/optimization/utils.py @@ -1,5 +1,6 @@ import pandas as pd + def process_prediction_with_ref(iter, targets): rows = [] @@ -14,7 +15,9 @@ def process_prediction_with_ref(iter, targets): out[target] = row["y"][idx] out[f"excess {target}"] = row["y_excess"][idx] out[f"linear {target}"] = row["y_linear"][idx] - out[f"relative excess {target}"] = row["y_excess"][idx]/ (row["y_excess"][idx] + row["y_linear"][idx]) + out[f"relative excess {target}"] = row["y_excess"][idx] / ( + row["y_excess"][idx] + row["y_linear"][idx] + ) rows.append(out) - return pd.DataFrame(rows) \ No newline at end of file + return pd.DataFrame(rows) diff --git a/opt/mixtures/panel.jl b/opt/mixtures/panel.jl index c6a5d946..7b6e1aa2 100644 --- a/opt/mixtures/panel.jl +++ b/opt/mixtures/panel.jl @@ -15,19 +15,19 @@ using Mixtures: Mixtures, clean_target_name, titlecase const DATA_DIR = realpath(joinpath(pkgdir(Mixtures), "..", "..", "data")) function label_smi(smi::AbstractString) - known = Dict( - "O" => "Water", - "CC#N" => "ACN", # Acetonitrile - "CC(=O)OCCOC(=O)C" => "EDGA", # Ethylene Glycol Diacetate - "CN1CCN(C)C1=O" => "DMI", # 1,3-Dimethyl-2-imidazolidinone - "CN(CCO)CCO" => "MDEA", # N-Methyldiethanolamine - "NCCN" => "1,2-DE", # 1,2-Diaminoethane - "CC1COC(=O)O1" => "PC", # Propylene carbonate - "OCCO" => "1,2-ED", # 1,2-Ethanediol - "C(CO)O" => "1,2-ED", # 1,2-Ethanediol - "ClC(Cl)Cl" => "Chloroform", - ) - return get(known, smi, smi) + known = Dict( + "O" => "Water", + "CC#N" => "ACN", # Acetonitrile + "CC(=O)OCCOC(=O)C" => "EDGA", # Ethylene Glycol Diacetate + "CN1CCN(C)C1=O" => "DMI", # 1,3-Dimethyl-2-imidazolidinone + "CN(CCO)CCO" => "MDEA", # N-Methyldiethanolamine + "NCCN" => "1,2-DE", # 1,2-Diaminoethane + "CC1COC(=O)O1" => "PC", # Propylene carbonate + "OCCO" => "1,2-ED", # 1,2-Ethanediol + "C(CO)O" => "1,2-ED", # 1,2-Ethanediol + "ClC(Cl)Cl" => "Chloroform", + ) + return get(known, smi, smi) end function plot_experimental_data!(f, model; xaxisvisible=true) @@ -43,466 +43,466 @@ function plot_experimental_data!(f, model; xaxisvisible=true) tellwidth=true ) - mixtures = [ - Dict("compounds" => ["CC#N", "CC(=O)OCCOC(=O)C"], "temperature" => 299.25), - Dict("compounds" => ["CC#N", "CCC1COC(=O)O1"], "temperature" => 299.25), - ] - dfs = Mixtures.evaluate_mixtures(model, mixtures; gradients = false) - dfs.x1 = first.(dfs.composition) - for gdf in groupby(dfs, ["compounds"]) - lines!(ax, gdf.x1, gdf.density_excess ./ gdf.density; linestyle = :solid) - end - - exp_dfs = [ - DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "experimental_ACN_EGDA.csv"))) => "ACN & EGDA", - DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "experimental_ACN_BC.csv"))) => "ACN & BC", - ] - - elems = PolyElement[] - for (df, label) in exp_dfs - h = scatter!(ax, df.x1, df[!, "Excess Density [g/cm3]"] ./ df[!, "Density (g/cm^3)"]; label) - push!(elems, PolyElement(; color = h.color, label = h.label)) - end - - - - Legend(f[1, 1], elems, MISTStyle.label.(elems); - halign = :left, - valign = :top, - orientation = :horizontal, - ) - - return f, ax + mixtures = [ + Dict("compounds" => ["CC#N", "CC(=O)OCCOC(=O)C"], "temperature" => 299.25), + Dict("compounds" => ["CC#N", "CCC1COC(=O)O1"], "temperature" => 299.25), + ] + dfs = Mixtures.evaluate_mixtures(model, mixtures; gradients = false) + dfs.x1 = first.(dfs.composition) + for gdf in groupby(dfs, ["compounds"]) + lines!(ax, gdf.x1, gdf.density_excess ./ gdf.density; linestyle = :solid) + end + + exp_dfs = [ + DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "experimental_ACN_EGDA.csv"))) => "ACN & EGDA", + DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "experimental_ACN_BC.csv"))) => "ACN & BC", + ] + + elems = PolyElement[] + for (df, label) in exp_dfs + h = scatter!(ax, df.x1, df[!, "Excess Density [g/cm3]"] ./ df[!, "Density (g/cm^3)"]; label) + push!(elems, PolyElement(; color = h.color, label = h.label)) + end + + + + Legend(f[1, 1], elems, MISTStyle.label.(elems); + halign = :left, + valign = :top, + orientation = :horizontal, + ) + + return f, ax end plot_excess_skewness(df) = plot_excess_skewness!(Figure(), df) function plot_excess_skewness!(f, df) - skew = Mixtures.excess_skew(df) - ax = Axis(f[1, 1]; - xlabel = "Reference Asymmetry", - ylabel = "Predicted Asymmetry", - xtickformat = "{:.0%}", - ytickformat = "{:.0%}", - limits = ((0, 0.5), (0, 0.5)), - xticks = WilkinsonTicks(5), - xminorticks = IntervalsBetween(5), - xminorticksvisible = true, - yticks = WilkinsonTicks(5), - yminorticks = IntervalsBetween(5), - yminorticksvisible = true, - xscale = sqrt, - yscale = sqrt, - ) - - cb = Colorbar(f[1, 2]; - label = "Max Abs. Rel. Excess", - colormap = Reverse(MISTStyle.CONTINUOUS_COLORS), - colorrange = (0, 0.1), - tickformat = "{:.0%}", - tellheight = true, - minorticksvisible = true, - minorticks = IntervalsBetween(2), - flip_vertical_label = true, - vertical = true, - ) - targets = [ - ("Molar Volume", "molar_volume", :circle), - ("Density", "density", :rect), - ("Molar Enthalpy", "molar_enthalpy", :utriangle), - ] - for (label, target, marker) in targets - df_target = select(skew, "compound_id", target, "$(target)_ref", "$(target)_rel_ref") - dropmissing!(df_target) - transform!(df_target, "$(target)_rel_ref" => ByRow(abs) => "$(target)_rel_ref") - sort!(df_target, "$(target)_rel_ref"; rev = false) - x = df_target[!, "$(target)_ref"] - y = df_target[!, target] - color = df_target[!, "$(target)_rel_ref"] - nrow(df_target) == 0 && continue - scatter!(ax, x, y; - label, - marker, - color, - alpha=0.6, - MISTStyle.cb_attrs(cb, Scatter)..., - ) - - # Compute and report correlations - weight = weights(color) - pearson = cor(x, y) - pearson_weighted = cor(hcat(x, y), weight)[1, end] - spearman = corspearman(x, y) - spearman_weighted = cor(hcat(tiedrank(x), tiedrank(y)), weight)[1, end] - @info "Excess Skew Correlations: $target" pearson spearman pearson_weighted spearman_weighted - end - - margin = something(theme(:Legend), (; margin = (1, 1, 1, 1))).margin - axislegend(ax; position = :lt, orientation = :horizontal, margin) - return f + skew = Mixtures.excess_skew(df) + ax = Axis(f[1, 1]; + xlabel = "Reference Asymmetry", + ylabel = "Predicted Asymmetry", + xtickformat = "{:.0%}", + ytickformat = "{:.0%}", + limits = ((0, 0.5), (0, 0.5)), + xticks = WilkinsonTicks(5), + xminorticks = IntervalsBetween(5), + xminorticksvisible = true, + yticks = WilkinsonTicks(5), + yminorticks = IntervalsBetween(5), + yminorticksvisible = true, + xscale = sqrt, + yscale = sqrt, + ) + + cb = Colorbar(f[1, 2]; + label = "Max Abs. Rel. Excess", + colormap = Reverse(MISTStyle.CONTINUOUS_COLORS), + colorrange = (0, 0.1), + tickformat = "{:.0%}", + tellheight = true, + minorticksvisible = true, + minorticks = IntervalsBetween(2), + flip_vertical_label = true, + vertical = true, + ) + targets = [ + ("Molar Volume", "molar_volume", :circle), + ("Density", "density", :rect), + ("Molar Enthalpy", "molar_enthalpy", :utriangle), + ] + for (label, target, marker) in targets + df_target = select(skew, "compound_id", target, "$(target)_ref", "$(target)_rel_ref") + dropmissing!(df_target) + transform!(df_target, "$(target)_rel_ref" => ByRow(abs) => "$(target)_rel_ref") + sort!(df_target, "$(target)_rel_ref"; rev = false) + x = df_target[!, "$(target)_ref"] + y = df_target[!, target] + color = df_target[!, "$(target)_rel_ref"] + nrow(df_target) == 0 && continue + scatter!(ax, x, y; + label, + marker, + color, + alpha=0.6, + MISTStyle.cb_attrs(cb, Scatter)..., + ) + + # Compute and report correlations + weight = weights(color) + pearson = cor(x, y) + pearson_weighted = cor(hcat(x, y), weight)[1, end] + spearman = corspearman(x, y) + spearman_weighted = cor(hcat(tiedrank(x), tiedrank(y)), weight)[1, end] + @info "Excess Skew Correlations: $target" pearson spearman pearson_weighted spearman_weighted + end + + margin = something(theme(:Legend), (; margin = (1, 1, 1, 1))).margin + axislegend(ax; position = :lt, orientation = :horizontal, margin) + return f end function plot_soap!(f) - x_min = 0.3 - x_max = 1.1 - y_min = 0.0 - y_max = 0.035 - sim_threshold = 0.6 - excess_threshold = 0.025 - ax1 = Axis(f[1, 1]; - limits = ((x_min, 1), (0, y_max)), - xlabel = "ReMATCH Similarity", - ylabel = L"Max $\left| V^{E}_m \right|$", - xtickformat = "{:.0%}", - yticks = WilkinsonTicks(3; k_min = 3, k_max = 5), - ) - df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "soap_similarity.csv"))) - - df[!, :abs_target] = abs.( - df[!, :"excess molar volume [centimeter ** 3 / mole]"] ./ - df[!, :"molar volume [centimeter ** 3 / mole]"], - ) - transform!(df, ["smi1", "smi2"] => ByRow((x, y) -> sort([x, y])) => :compound_id) - df = combine(groupby(df, [:compound_id])) do gdf - idx = argmax(gdf.abs_target) - return (; - max_abs_relative_vol = gdf.abs_target[idx], - name1 = titlecase(first(gdf.name1)), - name2 = titlecase(first(gdf.name2)), - similarity = first(gdf.similarity), - temperature = gdf[idx, "temperature [kelvin]"], - ) - end - - dropmissing!(df) - transform!(df, :compound_id => ByRow(x -> "$(label_smi(x[1])) & $(label_smi(x[2]))") => :labels) - - scatter!( - ax1, df[!, "similarity"], df[!, "max_abs_relative_vol"]; - color = (MISTStyle.UM_COLORS.blue, 0.5), marker = :circle, - strokewidth = 0.25pt, - strokecolor = :black, - markersize = 4pt, - ) - - df_up = subset(df, - :similarity => ByRow(>(sim_threshold)), - :max_abs_relative_vol => ByRow(>(excess_threshold)), - ) - - points = Point2.(df_up.similarity, df_up.max_abs_relative_vol) - annotation!(ax1, points; text = df_up.labels, fontsize = 5pt, shrink = (2.0, 2.0)) - - df_left = subset(df, - :similarity => ByRow(<(sim_threshold)), - :max_abs_relative_vol => ByRow(<(excess_threshold)), - ) - - points = Point2.(df_left.similarity, df_left.max_abs_relative_vol) - annotation!(ax1, points; text = df_left.labels, fontsize = 5pt, shrink = (2.0, 2.0)) - return f + x_min = 0.3 + x_max = 1.1 + y_min = 0.0 + y_max = 0.035 + sim_threshold = 0.6 + excess_threshold = 0.025 + ax1 = Axis(f[1, 1]; + limits = ((x_min, 1), (0, y_max)), + xlabel = "ReMATCH Similarity", + ylabel = L"Max $\left| V^{E}_m \right|$", + xtickformat = "{:.0%}", + yticks = WilkinsonTicks(3; k_min = 3, k_max = 5), + ) + df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "soap_similarity.csv"))) + + df[!, :abs_target] = abs.( + df[!, :"excess molar volume [centimeter ** 3 / mole]"] ./ + df[!, :"molar volume [centimeter ** 3 / mole]"], + ) + transform!(df, ["smi1", "smi2"] => ByRow((x, y) -> sort([x, y])) => :compound_id) + df = combine(groupby(df, [:compound_id])) do gdf + idx = argmax(gdf.abs_target) + return (; + max_abs_relative_vol = gdf.abs_target[idx], + name1 = titlecase(first(gdf.name1)), + name2 = titlecase(first(gdf.name2)), + similarity = first(gdf.similarity), + temperature = gdf[idx, "temperature [kelvin]"], + ) + end + + dropmissing!(df) + transform!(df, :compound_id => ByRow(x -> "$(label_smi(x[1])) & $(label_smi(x[2]))") => :labels) + + scatter!( + ax1, df[!, "similarity"], df[!, "max_abs_relative_vol"]; + color = (MISTStyle.UM_COLORS.blue, 0.5), marker = :circle, + strokewidth = 0.25pt, + strokecolor = :black, + markersize = 4pt, + ) + + df_up = subset(df, + :similarity => ByRow(>(sim_threshold)), + :max_abs_relative_vol => ByRow(>(excess_threshold)), + ) + + points = Point2.(df_up.similarity, df_up.max_abs_relative_vol) + annotation!(ax1, points; text = df_up.labels, fontsize = 5pt, shrink = (2.0, 2.0)) + + df_left = subset(df, + :similarity => ByRow(<(sim_threshold)), + :max_abs_relative_vol => ByRow(<(excess_threshold)), + ) + + points = Point2.(df_left.similarity, df_left.max_abs_relative_vol) + annotation!(ax1, points; text = df_left.labels, fontsize = 5pt, shrink = (2.0, 2.0)) + return f end function plot_soap_outliers!(f, model; xaxisvisible = true) - ax = Axis(f[1, 1]; - limits = ((0, 1), (-2.5, nothing)), - xlabel = L"$x_1$", - ylabel = L"$V^{E}_m \;$ (cm$^3$/mol)", - xtickformat = "{:.0%}", - xticksvisible = xaxisvisible, - xlabelvisible = xaxisvisible, - xticklabelsvisible = xaxisvisible, - ) - mixtures = [ - Dict("compounds" => ["CC1COC(=O)O1", "ClC(Cl)Cl"], "temperature" => 298.15), - Dict("compounds" => ["NCCN", "OCCO"], "temperature" => 298.15), - Dict("compounds" => ["CN(CCO)CCO", "O"], "temperature" => 298.15), - Dict("compounds" => ["CN1CCN(C)C1=O", "O"], "temperature" => 298.15), - ] - df = Mixtures.evaluate_mixtures(model, mixtures; n = 50, gradients = false) - df.x1 = first.(df.composition) - - data = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "soap_similarity.csv"))) - subset!(data, "temperature [kelvin]" => ByRow(==(298.15))) - mix_elems = PolyElement[] - for gdf in groupby(df, ["compounds"]) - compounds = first(gdf.compounds) - df_ref = subset(data, - :smi1 => ByRow(in(compounds)), - :smi2 => ByRow(in(compounds)), - ) - name1, name2 = label_smi.(compounds) - label = "$name1 & $name2" - h = lines!(ax, gdf.x1, gdf.molar_volume_excess; linestyle = :solid, label) - push!(mix_elems, PolyElement(; color = h.color, label = h.label)) - - if nrow(df_ref) > 2 - dropmissing!(df_ref, "excess molar volume [centimeter ** 3 / mole]") - scatter!(ax, df_ref[!, "x1"], df_ref[!, "excess molar volume [centimeter ** 3 / mole]"]; - label, - color = h.color, - ) - end - end - - data_elems = [ - LineElement(; color = :black, label = "MIST"), - MarkerElement(; color = :black, label = "MIST", marker = :x), - ] - - Legend(f[1, 1], - mix_elems, - MISTStyle.label.(mix_elems); - nbanks = 2, - valign = :bottom, - halign = :right, - titlesize = 5pt, - ) - return f, ax + ax = Axis(f[1, 1]; + limits = ((0, 1), (-2.5, nothing)), + xlabel = L"$x_1$", + ylabel = L"$V^{E}_m \;$ (cm$^3$/mol)", + xtickformat = "{:.0%}", + xticksvisible = xaxisvisible, + xlabelvisible = xaxisvisible, + xticklabelsvisible = xaxisvisible, + ) + mixtures = [ + Dict("compounds" => ["CC1COC(=O)O1", "ClC(Cl)Cl"], "temperature" => 298.15), + Dict("compounds" => ["NCCN", "OCCO"], "temperature" => 298.15), + Dict("compounds" => ["CN(CCO)CCO", "O"], "temperature" => 298.15), + Dict("compounds" => ["CN1CCN(C)C1=O", "O"], "temperature" => 298.15), + ] + df = Mixtures.evaluate_mixtures(model, mixtures; n = 50, gradients = false) + df.x1 = first.(df.composition) + + data = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "soap_similarity.csv"))) + subset!(data, "temperature [kelvin]" => ByRow(==(298.15))) + mix_elems = PolyElement[] + for gdf in groupby(df, ["compounds"]) + compounds = first(gdf.compounds) + df_ref = subset(data, + :smi1 => ByRow(in(compounds)), + :smi2 => ByRow(in(compounds)), + ) + name1, name2 = label_smi.(compounds) + label = "$name1 & $name2" + h = lines!(ax, gdf.x1, gdf.molar_volume_excess; linestyle = :solid, label) + push!(mix_elems, PolyElement(; color = h.color, label = h.label)) + + if nrow(df_ref) > 2 + dropmissing!(df_ref, "excess molar volume [centimeter ** 3 / mole]") + scatter!(ax, df_ref[!, "x1"], df_ref[!, "excess molar volume [centimeter ** 3 / mole]"]; + label, + color = h.color, + ) + end + end + + data_elems = [ + LineElement(; color = :black, label = "MIST"), + MarkerElement(; color = :black, label = "MIST", marker = :x), + ] + + Legend(f[1, 1], + mix_elems, + MISTStyle.label.(mix_elems); + nbanks = 2, + valign = :bottom, + halign = :right, + titlesize = 5pt, + ) + return f, ax end function plot_ionic_conductivity!(f) - cb = Colorbar(f[1, 2]; - label = L"Temperature (K)$$", - colormap = MISTStyle.CONTINUOUS_COLORS, - colorrange = (240, 340), - vertical = true, - tellheight = true, - tellwidth = true, - flipaxis = true, - flip_vertical_label = true, - ) - - df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "ionic_conductivity_curves.csv"))) - df = df[1:2:end, :] - salts = unique(df.salt_name) - line_styles = [:solid, :dot] - # Sort temperatures and build a continuous, dark‐cropped colormap - temps = sort(unique(df.temperature)) - - ax = Axis(f[1, 1]; - xlabel = L"x_{Li}", - ylabel = L"$\sigma$ (mS/cm)", - limits = ((0, 0.20), (0, 0.35)), - yticksvisible = true, - yticklabelsvisible = true, - ) - x_min = 0.160369437447523 - x_max = 0.20 - y_min = 0.0 - y_max = 0.35 - poly!(ax, Point2f[(x_min, y_min), (x_min, y_max), (x_max, y_max), (x_max, y_min)]; - color = MISTStyle.UM_COLORS.maize, - alpha = 0.2, - strokewidth = 0, - ) - # Plot each temperature curve in both panels - for (idx, salt) in enumerate(salts), T in temps - mask = (df.salt_name .== salt) .& (df.temperature .== T) - sub = df[mask, :] - label = (salt == "LiPF6") ? L"LiPF$_6$" : salt - lines!( - ax, - sub.composition, - sub.predictions; - linestyle = line_styles[idx], - color = sub.temperature, - label, - MISTStyle.cb_attrs(cb, Lines)..., - ) - end - axislegend(ax, position = :rt, padding = (1, 1, 1, 1), margin = (1, 1, 1, 1), unique = true) - return f + cb = Colorbar(f[1, 2]; + label = L"Temperature (K)$$", + colormap = MISTStyle.CONTINUOUS_COLORS, + colorrange = (240, 340), + vertical = true, + tellheight = true, + tellwidth = true, + flipaxis = true, + flip_vertical_label = true, + ) + + df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "ionic_conductivity_curves.csv"))) + df = df[1:2:end, :] + salts = unique(df.salt_name) + line_styles = [:solid, :dot] + # Sort temperatures and build a continuous, dark‐cropped colormap + temps = sort(unique(df.temperature)) + + ax = Axis(f[1, 1]; + xlabel = L"x_{Li}", + ylabel = L"$\sigma$ (mS/cm)", + limits = ((0, 0.20), (0, 0.35)), + yticksvisible = true, + yticklabelsvisible = true, + ) + x_min = 0.160369437447523 + x_max = 0.20 + y_min = 0.0 + y_max = 0.35 + poly!(ax, Point2f[(x_min, y_min), (x_min, y_max), (x_max, y_max), (x_max, y_min)]; + color = MISTStyle.UM_COLORS.maize, + alpha = 0.2, + strokewidth = 0, + ) + # Plot each temperature curve in both panels + for (idx, salt) in enumerate(salts), T in temps + mask = (df.salt_name .== salt) .& (df.temperature .== T) + sub = df[mask, :] + label = (salt == "LiPF6") ? L"LiPF$_6$" : salt + lines!( + ax, + sub.composition, + sub.predictions; + linestyle = line_styles[idx], + color = sub.temperature, + label, + MISTStyle.cb_attrs(cb, Lines)..., + ) + end + axislegend(ax, position = :rt, padding = (1, 1, 1, 1), margin = (1, 1, 1, 1), unique = true) + return f end plot_thermal_alpha(model; kwargs...) = plot_thermal_alpha!(Figure(), model; kwargs...) function plot_thermal_alpha!(f, model; xaxisvisible = true) - mixture_map = Dict( - ["CC#N", "C(CO)O"] => "ACN+1,2-ED", # Acetonitrile and 1,2-ethanediol - ["CC#N", "CC(CO)O"] => "ACN+PEG", # ACN and 1,2-propanediol - ["CC#N", "C(CO)CO"] => "ACN+1,3-PD", # ACN and 1,3-propanediol - ) - mixtures = Dict[] - for other in ["C(CO)O", "CC(CO)O", "C(CO)CO"] - push!(mixtures, Dict("compounds" => ["CC#N", other], "temperature" => 298.15)) - end - df = Mixtures.evaluate_mixtures(model, mixtures; n = 50, gradients = true) - df.x1 .= first.(df.composition) - @. df.alpha_excess = df.molar_volume_excess_dT / df.molar_volume - - # Parse reference data - ref = read(joinpath(DATA_DIR, "mixtures", "alpha_data.jsonc"), String) - ref = replace(ref, r"//.*" => "") - ref = JSON.parse(ref) - ref = Dict(x["mixture"] => x for x in ref) - - # scale = 1e3 - scale = 3e-4 - ax = Axis(f[1, 1]; - limits = ((0, 1), (-2.25e-4, 0.5e-4)), - xlabel = L"x_1", - xtickformat = "{:.0%}", - ylabel = L"$\alpha^{\mathrm{E}}$ (K$^{-1}$)", - ytickformat = MISTStyle.sci_notation(), - yticks = [0, -0.5, -1.0, -1.5] .* 1e-4, - xminorticks = IntervalsBetween(5), - xminorticksvisible = xaxisvisible, - xticksvisible = xaxisvisible, - xlabelvisible = xaxisvisible, - xticklabelsvisible = xaxisvisible, - ) - - result_scale = 0 - foreach(groupby(df, [:compounds, :temperature])) do gdf - sort!(gdf, :x1) - row = ref[mixture_map[first(gdf.compounds)]] - itp = LinearInterpolation(gdf.x1, gdf.alpha_excess ) - result_scale += mean(itp(row["x1"])./row["alpha"]) - end - rel_scale = 3e-4 - - ax_left = Axis(f[1, 1]; - limits = lift(x -> (x[1], rel_scale .* x[2]), ax.limits), - yaxisposition = :right, - ylabel = L"Est. $\alpha^{\mathrm{E}}$ (K$^{-1}$)", - ytickformat = ax.ytickformat, - flip_ylabel = true, - yticks = @lift($(ax.yticks) .* rel_scale), - ) - hideydecorations!(ax_left; label = false, ticklabels = false, ticks = false) - hidexdecorations!(ax_left) - linkxaxes!(ax, ax_left) - - elems = PolyElement[] - for mixture in ["ACN+PEG", "ACN+1,2-ED", "ACN+1,3-PD"] - row = ref[mixture] - h = scatter!(ax, row["x1"], row["alpha"]; label = replace(mixture, "+" => " & ")) - push!(elems, PolyElement(; color = h.color, label = h.label)) - end - - foreach(groupby(df, [:compounds, :temperature])) do gdf - sort!(gdf, :x1) - lines!(ax_left, gdf.x1, gdf.alpha_excess) - end - - Legend(f[1, 1], elems, MISTStyle.label.(elems); - orientation = :horizontal, - halign = :right, - valign = :bottom, - ) - return f, ax + mixture_map = Dict( + ["CC#N", "C(CO)O"] => "ACN+1,2-ED", # Acetonitrile and 1,2-ethanediol + ["CC#N", "CC(CO)O"] => "ACN+PEG", # ACN and 1,2-propanediol + ["CC#N", "C(CO)CO"] => "ACN+1,3-PD", # ACN and 1,3-propanediol + ) + mixtures = Dict[] + for other in ["C(CO)O", "CC(CO)O", "C(CO)CO"] + push!(mixtures, Dict("compounds" => ["CC#N", other], "temperature" => 298.15)) + end + df = Mixtures.evaluate_mixtures(model, mixtures; n = 50, gradients = true) + df.x1 .= first.(df.composition) + @. df.alpha_excess = df.molar_volume_excess_dT / df.molar_volume + + # Parse reference data + ref = read(joinpath(DATA_DIR, "mixtures", "alpha_data.jsonc"), String) + ref = replace(ref, r"//.*" => "") + ref = JSON.parse(ref) + ref = Dict(x["mixture"] => x for x in ref) + + # scale = 1e3 + scale = 3e-4 + ax = Axis(f[1, 1]; + limits = ((0, 1), (-2.25e-4, 0.5e-4)), + xlabel = L"x_1", + xtickformat = "{:.0%}", + ylabel = L"$\alpha^{\mathrm{E}}$ (K$^{-1}$)", + ytickformat = MISTStyle.sci_notation(), + yticks = [0, -0.5, -1.0, -1.5] .* 1e-4, + xminorticks = IntervalsBetween(5), + xminorticksvisible = xaxisvisible, + xticksvisible = xaxisvisible, + xlabelvisible = xaxisvisible, + xticklabelsvisible = xaxisvisible, + ) + + result_scale = 0 + foreach(groupby(df, [:compounds, :temperature])) do gdf + sort!(gdf, :x1) + row = ref[mixture_map[first(gdf.compounds)]] + itp = LinearInterpolation(gdf.x1, gdf.alpha_excess ) + result_scale += mean(itp(row["x1"])./row["alpha"]) + end + rel_scale = 3e-4 + + ax_left = Axis(f[1, 1]; + limits = lift(x -> (x[1], rel_scale .* x[2]), ax.limits), + yaxisposition = :right, + ylabel = L"Est. $\alpha^{\mathrm{E}}$ (K$^{-1}$)", + ytickformat = ax.ytickformat, + flip_ylabel = true, + yticks = @lift($(ax.yticks) .* rel_scale), + ) + hideydecorations!(ax_left; label = false, ticklabels = false, ticks = false) + hidexdecorations!(ax_left) + linkxaxes!(ax, ax_left) + + elems = PolyElement[] + for mixture in ["ACN+PEG", "ACN+1,2-ED", "ACN+1,3-PD"] + row = ref[mixture] + h = scatter!(ax, row["x1"], row["alpha"]; label = replace(mixture, "+" => " & ")) + push!(elems, PolyElement(; color = h.color, label = h.label)) + end + + foreach(groupby(df, [:compounds, :temperature])) do gdf + sort!(gdf, :x1) + lines!(ax_left, gdf.x1, gdf.alpha_excess) + end + + Legend(f[1, 1], elems, MISTStyle.label.(elems); + orientation = :horizontal, + halign = :right, + valign = :bottom, + ) + return f, ax end function plot_diffmix_enthalpy!(f, model) - col = "molar_enthalpy_excess" - smiles_to_name = Dict( - "CC1COC(=O)O1" => "PC", - "CCCCC(=O)OCC" => "EP", # Ethyl pentanoate - "CCCOC(=O)CCC" => "PP", # Propyl Propanoate - "CC(=O)OCC(C)C" => "2-MA", # 2-methylpropyl acetate - "CS(C)=O" => "DMS", # Dimethyl Sulfoxide - "CCOc1ccccc1" => "Phenetole", - "CCOc1ccccc1" => "Anisole", - "CCCCCCC(=O)OCC" => "EH", # Ethyl Heptanoate - "CCCCC(=O)OC" => "MP", # Methy Petanoate - "CCCCOC(=O)CCC" => "Butanoic", # Butonic acid - "CC(=O)OC(C)(C)C" => "Acetic", - "CCCCCCCC(=O)OCC" => "Octanoic", - "CCOC(=O)C(C)C" => "Propanoic", - "CCCCCC(=O)OCC" => "Hexanoic", - "CCOC(=O)OCC" => "EC", - "CCCOC(=O)CC" => "PE", # Propyl Ester - "COC(=O)OC" => "DMC" - ) - - excess_dataset = joinpath(DATA_DIR, "mixtures", "diffmix_enthalpy.csv") - df_excess = Mixtures.evaluate_binary_csv(model, excess_dataset) - df_excess = subset(df_excess, - :temperature => ByRow(<(300.)), - ) - @info unique(first.(df_excess.temperature)) - @info unique(last.(df_excess.compounds)) - ax2 = Axis(f[1, :]; - limits = ((0, 1), (0, nothing)), - xlabel = L"x_1", - ylabel = L"$H_m$", - tellwidth = true, - ) + col = "molar_enthalpy_excess" + smiles_to_name = Dict( + "CC1COC(=O)O1" => "PC", + "CCCCC(=O)OCC" => "EP", # Ethyl pentanoate + "CCCOC(=O)CCC" => "PP", # Propyl Propanoate + "CC(=O)OCC(C)C" => "2-MA", # 2-methylpropyl acetate + "CS(C)=O" => "DMS", # Dimethyl Sulfoxide + "CCOc1ccccc1" => "Phenetole", + "CCOc1ccccc1" => "Anisole", + "CCCCCCC(=O)OCC" => "EH", # Ethyl Heptanoate + "CCCCC(=O)OC" => "MP", # Methy Petanoate + "CCCCOC(=O)CCC" => "Butanoic", # Butonic acid + "CC(=O)OC(C)(C)C" => "Acetic", + "CCCCCCCC(=O)OCC" => "Octanoic", + "CCOC(=O)C(C)C" => "Propanoic", + "CCCCCC(=O)OCC" => "Hexanoic", + "CCOC(=O)OCC" => "EC", + "CCCOC(=O)CC" => "PE", # Propyl Ester + "COC(=O)OC" => "DMC" + ) + + excess_dataset = joinpath(DATA_DIR, "mixtures", "diffmix_enthalpy.csv") + df_excess = Mixtures.evaluate_binary_csv(model, excess_dataset) + df_excess = subset(df_excess, + :temperature => ByRow(<(300.)), + ) + @info unique(first.(df_excess.temperature)) + @info unique(last.(df_excess.compounds)) + ax2 = Axis(f[1, :]; + limits = ((0, 1), (0, nothing)), + xlabel = L"x_1", + ylabel = L"$H_m$", + tellwidth = true, + ) df_excess.x1 = first.(df_excess.composition) - df_excess.smi2 = last.(df_excess.compounds) - sort!(df_excess, :smi2) + df_excess.smi2 = last.(df_excess.compounds) + sort!(df_excess, :smi2) - counter = 0 - elems = PolyElement[] + counter = 0 + elems = PolyElement[] for gdf in groupby(df_excess, [ "compounds"]) - if counter > 3 - break - end - name1 = smiles_to_name[first(gdf.compounds)[1]] - name2 = smiles_to_name[first(gdf.compounds)[2]] - label = "$name2" - h = lines!(ax2, gdf.x1, gdf[!, "$col"]; + if counter > 3 + break + end + name1 = smiles_to_name[first(gdf.compounds)[1]] + name2 = smiles_to_name[first(gdf.compounds)[2]] + label = "$name2" + h = lines!(ax2, gdf.x1, gdf[!, "$col"]; linestyle = :solid, - label, + label, ) - + scatter!(ax2, gdf.x1, gdf[!, "$(col)_ref"]; ) - push!(elems, PolyElement(; color = h.color, label = h.label)) - counter += 1 - end - Legend(f[1, 1], elems, MISTStyle.label.(elems); - orientation = :horizontal, - halign = :center, - valign = :bottom, - nbanks=2 - ) + push!(elems, PolyElement(; color = h.color, label = h.label)) + counter += 1 + end + Legend(f[1, 1], elems, MISTStyle.label.(elems); + orientation = :horizontal, + halign = :center, + valign = :bottom, + nbanks=2 + ) return f, ax2 end function plot_viscosity!(f, df_excess) - print(names(df_excess)) - col = "density_excess" - - @info unique(countmap(df_excess.temperature)) - # @info unique(last.(df_excess.compounds)) - ax2 = Axis(f[1, :]; - # limits = ((0, 1), (0, nothing)), - xlabel = L"x_1", - ylabel = L"$\rho^E_m$ (g/cm$^3$)", - tellwidth = true, - ) + print(names(df_excess)) + col = "density_excess" + + @info unique(countmap(df_excess.temperature)) + # @info unique(last.(df_excess.compounds)) + ax2 = Axis(f[1, :]; + # limits = ((0, 1), (0, nothing)), + xlabel = L"x_1", + ylabel = L"$\rho^E_m$ (g/cm$^3$)", + tellwidth = true, + ) df_excess.x1 = first.(df_excess.composition) - df_excess.smi2 = last.(df_excess.compounds) + df_excess.smi2 = last.(df_excess.compounds) - counter = 0 - elems = PolyElement[] + counter = 0 + elems = PolyElement[] for gdf in groupby(df_excess, ["temperature", "compounds"]) - if counter > 5 - break - end - sort!(gdf, :x1) - name1 = first(gdf.compounds)[1] - name2 = first(gdf.compounds)[2] - label = "$name1 & $name2" - h = lines!(ax2, gdf.x1, gdf[!, "$col"]; + if counter > 5 + break + end + sort!(gdf, :x1) + name1 = first(gdf.compounds)[1] + name2 = first(gdf.compounds)[2] + label = "$name1 & $name2" + h = lines!(ax2, gdf.x1, gdf[!, "$col"]; linestyle = :solid, - label, + label, ) - + scatter!(ax2, gdf.x1, gdf[!, "$(col)_ref"]; ) - push!(elems, PolyElement(; color = h.color, label = h.label)) - counter += 1 - end - Legend(f[1, 1], elems, MISTStyle.label.(elems); - orientation = :horizontal, - halign = :center, - valign = :bottom, - nbanks=2 - ) + push!(elems, PolyElement(; color = h.color, label = h.label)) + counter += 1 + end + Legend(f[1, 1], elems, MISTStyle.label.(elems); + orientation = :horizontal, + halign = :center, + valign = :bottom, + nbanks=2 + ) return f, ax2 end @@ -540,12 +540,12 @@ function mixture_panel(model_id::String, model_strict_id::String) # Full Binary mixture dataset excess_dataset = joinpath(DATA_DIR, "mixtures", "excess_dataset_v6", "k-compound-0") - # Model Trained on Random Split - model = Mixtures.load_excess_model(joinpath(DATA_DIR, "models", model_id)).to("mps") - df_excess = Mixtures.evaluate_dataset(model, excess_dataset) + # Model Trained on Random Split + model = Mixtures.load_excess_model(joinpath(DATA_DIR, "models", model_id)).to("mps") + df_excess = Mixtures.evaluate_dataset(model, excess_dataset) - # Model Trained on Compound Split - model_strict = Mixtures.load_excess_model(joinpath(DATA_DIR, "models", model_strict_id)).to("mps") + # Model Trained on Compound Split + model_strict = Mixtures.load_excess_model(joinpath(DATA_DIR, "models", model_strict_id)).to("mps") - return mixture_panel(model, model_strict, df_excess) + return mixture_panel(model, model_strict, df_excess) end diff --git a/opt/mixtures/plot_excess.jl b/opt/mixtures/plot_excess.jl index 74f567d4..8d8898f4 100644 --- a/opt/mixtures/plot_excess.jl +++ b/opt/mixtures/plot_excess.jl @@ -34,24 +34,24 @@ markers_labels = [ function load_reference(path) - df = DataFrame(CSV.File(path)) + df = DataFrame(CSV.File(path)) end function dataframe_inspector(df, colums) - function mixture_inspector(plot, idx, pos) - entries = map(colums) do col - data = df[idx, col] - if data isa Real - return string(col) * ": " * format(df[idx, col]) - elseif data isa AbstractVector{<:Real} - return string(col) * ": " * join(format.(data), ", ") - else - return string(col) * ": " * string(data) - end - end - return join(entries, "\n") - end - return mixture_inspector + function mixture_inspector(plot, idx, pos) + entries = map(colums) do col + data = df[idx, col] + if data isa Real + return string(col) * ": " * format(df[idx, col]) + elseif data isa AbstractVector{<:Real} + return string(col) * ": " * join(format.(data), ", ") + else + return string(col) * ": " * string(data) + end + end + return join(entries, "\n") + end + return mixture_inspector end mae(x, y) = mean(@.(abs(x - y))) @@ -61,189 +61,189 @@ unzip(a) = map(x->getfield.(a, x), fieldnames(eltype(a))) skipmissingpairs(x...) = Iterators.filter(x -> all(!ismissing, x), zip(x...)) |> collect |> unzip function compound_stats(df) - compounds = Set(Iterators.flatten(df.compounds)) - rows = [] - for c in compounds - df_c = subset(df, :compounds => ByRow(x -> c in x)) - row = Dict{String, Any}( - "compound" => c, - "nobs" => nrow(df_c), - ) - for (name, rmetric) in [("mae", mae), ("rmse", rmse)] - metric = (x, y) -> rmetric(skipmissingpairs(x, y)...) - row["density_$(name)"] = metric(df_c.density, df_c.density_ref) - row["density_excess_$(name)"] = metric(df_c.density_excess, df_c.density_excess_ref) - row["molar_volume_$(name)"] = metric(df_c.molar_volume, df_c.molar_volume_ref) - row["molar_enthalpy_$(name)"] = metric(df_c.molar_enthalpy, df_c.molar_enthalpy_ref) - row["molar_volume_excess_$(name)"] = metric(df_c.molar_volume_excess, df_c.molar_volume_excess_ref) - end - push!(rows, row) - end - return DataFrame(rows) + compounds = Set(Iterators.flatten(df.compounds)) + rows = [] + for c in compounds + df_c = subset(df, :compounds => ByRow(x -> c in x)) + row = Dict{String, Any}( + "compound" => c, + "nobs" => nrow(df_c), + ) + for (name, rmetric) in [("mae", mae), ("rmse", rmse)] + metric = (x, y) -> rmetric(skipmissingpairs(x, y)...) + row["density_$(name)"] = metric(df_c.density, df_c.density_ref) + row["density_excess_$(name)"] = metric(df_c.density_excess, df_c.density_excess_ref) + row["molar_volume_$(name)"] = metric(df_c.molar_volume, df_c.molar_volume_ref) + row["molar_enthalpy_$(name)"] = metric(df_c.molar_enthalpy, df_c.molar_enthalpy_ref) + row["molar_volume_excess_$(name)"] = metric(df_c.molar_volume_excess, df_c.molar_volume_excess_ref) + end + push!(rows, row) + end + return DataFrame(rows) end function parity_plots(df, model) - targets = clean_target_name.(pyconvert(Vector{String}, model.config.target_columns)) - compounds = unique(Iterators.flatten(df.compounds)) - - mixture_selection = Observable((first(compounds), first(compounds), 293.15)) - function di(df, idx_obs, args...) - df_inspect = dataframe_inspector(df, args...) - function label(plot, idx, pos) - smi1 = df[idx, :compounds][1] - smi2 = df[idx, :compounds][2] - temp = df[idx, :temperature] - mixture_selection[] = (smi1, smi2, temp) - return df_inspect(plot, idx, pos) - end - return label - end - - f = Figure() - gl_parity = GridLayout(f[1, 1]) - gl = GridLayout(gl_parity[1, 1]) - cb = Colorbar(gl_parity[1, 2]; colormap = :managua, colorrange = extrema(df.temperature), flip_vertical_label = true) - temperature = df[!, "temperature"] - inspector_columns = ["temperature", "compounds", "composition"] - for (tdx, target) in enumerate(targets) - ax_y = Axis(gl[1, tdx]) - ablines!(ax_y, 0, 1; color = :black, linestyle = :dash) - scatter!(ax_y, df[!, "$(target)_ref"], df[!, target]; - color = temperature, - inspectable = true, - inspector_label = di(df, mixture_selection, [target, "$(target)_ref", inspector_columns...]), - MISTStyle.cb_attrs(cb, Scatter)..., - ) - - ax_e = Axis(gl[2, tdx]; xlabel = target) - ablines!(ax_e, 0, 1; color = :black, linestyle = :dash) - scatter!(ax_e, df[!, "$(target)_excess_ref"], df[!, "$(target)_excess"]; - color = temperature, - inspectable = true, - inspector_label = di(df, mixture_selection, [target, "$(target)_ref", inspector_columns...]), - MISTStyle.cb_attrs(cb, Scatter)..., - ) - - if tdx == 1 - ax_y.ylabel[] = "Total" - ax_e.ylabel[] = "Excess" - end - end - - gl_predict = GridLayout(f[2, 1]) - df = transform(df, :composition => ByRow(first) => :x1) - dfs = lift(mixture_selection) do (smi1, smi2, temp) - out = subset(df, :compounds => ByRow(x -> smi1 in x && smi2 in x), :temperature => ByRow(==(temp))) - # transform!(out, - # ["molar_volume_excess", "molar_volume"] => ByRow(/) => "molar_volume_excess", - # ["molar_volume_excess_ref", "molar_volume_ref"] => ByRow(/) => "molar_volume_excess_ref", - # ["density_excess", "density"] => ByRow(/) => "density_excess", - # ["density_excess_ref", "density_ref"] => ByRow(/) => "density_excess_ref", - # # ["molar_enthalpy_excess" "molar_enthalpy"] => ByRow(/) => "molar_enthalpy_excess", - # # ["molar_enthalpy_excess_ref", "molar_enthalpy_ref"] => ByRow(/) => "molar_enthalpy_excess_ref", - # ) - return out - end - for (tdx, target) in enumerate(targets) - ax = Axis(gl_predict[1, tdx]; limits = ((0, 1), extrema(df[!, target]))) - h = scatter!(ax, lift_points(dfs, "x1", target); color = :red) - h = scatter!(ax, lift_points(dfs, "x1", "$(target)_ref"); color = :blue) - on(_ -> autolimits!(ax), mixture_selection) - - ax = Axis(gl_predict[2, tdx]; limits = ((0, 1), nothing)) - scatter!(ax, lift_points(dfs, "x1", "$(target)_excess"); color = :red) - scatter!(ax, lift_points(dfs, "x1", "$(target)_excess_ref"); color = :blue) - on(_ -> autolimits!(ax), mixture_selection) - end - predict_label = lift(mixture_selection) do (smi1, smi2, temp) - return "$smi1 vs. $smi2 at $(format("{:.2f}", temp)) K" - end - Label(gl_predict[0, :], predict_label) - - elems = [ - LineElement(; color = :red, label = "Prediction"), - LineElement(; color = :blue, label = "Reference"), - ] - Legend(gl_predict[3, :], elems, MISTStyle.label.(elems); - nbanks = 2, - tellheight = true, - tellwidth = false, - ) - rowsize!(gl_predict, 0, Auto(0.2)) - rowsize!(gl_predict, 3, Auto(0.2)) - - DataInspector(f) - return f + targets = clean_target_name.(pyconvert(Vector{String}, model.config.target_columns)) + compounds = unique(Iterators.flatten(df.compounds)) + + mixture_selection = Observable((first(compounds), first(compounds), 293.15)) + function di(df, idx_obs, args...) + df_inspect = dataframe_inspector(df, args...) + function label(plot, idx, pos) + smi1 = df[idx, :compounds][1] + smi2 = df[idx, :compounds][2] + temp = df[idx, :temperature] + mixture_selection[] = (smi1, smi2, temp) + return df_inspect(plot, idx, pos) + end + return label + end + + f = Figure() + gl_parity = GridLayout(f[1, 1]) + gl = GridLayout(gl_parity[1, 1]) + cb = Colorbar(gl_parity[1, 2]; colormap = :managua, colorrange = extrema(df.temperature), flip_vertical_label = true) + temperature = df[!, "temperature"] + inspector_columns = ["temperature", "compounds", "composition"] + for (tdx, target) in enumerate(targets) + ax_y = Axis(gl[1, tdx]) + ablines!(ax_y, 0, 1; color = :black, linestyle = :dash) + scatter!(ax_y, df[!, "$(target)_ref"], df[!, target]; + color = temperature, + inspectable = true, + inspector_label = di(df, mixture_selection, [target, "$(target)_ref", inspector_columns...]), + MISTStyle.cb_attrs(cb, Scatter)..., + ) + + ax_e = Axis(gl[2, tdx]; xlabel = target) + ablines!(ax_e, 0, 1; color = :black, linestyle = :dash) + scatter!(ax_e, df[!, "$(target)_excess_ref"], df[!, "$(target)_excess"]; + color = temperature, + inspectable = true, + inspector_label = di(df, mixture_selection, [target, "$(target)_ref", inspector_columns...]), + MISTStyle.cb_attrs(cb, Scatter)..., + ) + + if tdx == 1 + ax_y.ylabel[] = "Total" + ax_e.ylabel[] = "Excess" + end + end + + gl_predict = GridLayout(f[2, 1]) + df = transform(df, :composition => ByRow(first) => :x1) + dfs = lift(mixture_selection) do (smi1, smi2, temp) + out = subset(df, :compounds => ByRow(x -> smi1 in x && smi2 in x), :temperature => ByRow(==(temp))) + # transform!(out, + # ["molar_volume_excess", "molar_volume"] => ByRow(/) => "molar_volume_excess", + # ["molar_volume_excess_ref", "molar_volume_ref"] => ByRow(/) => "molar_volume_excess_ref", + # ["density_excess", "density"] => ByRow(/) => "density_excess", + # ["density_excess_ref", "density_ref"] => ByRow(/) => "density_excess_ref", + # # ["molar_enthalpy_excess" "molar_enthalpy"] => ByRow(/) => "molar_enthalpy_excess", + # # ["molar_enthalpy_excess_ref", "molar_enthalpy_ref"] => ByRow(/) => "molar_enthalpy_excess_ref", + # ) + return out + end + for (tdx, target) in enumerate(targets) + ax = Axis(gl_predict[1, tdx]; limits = ((0, 1), extrema(df[!, target]))) + h = scatter!(ax, lift_points(dfs, "x1", target); color = :red) + h = scatter!(ax, lift_points(dfs, "x1", "$(target)_ref"); color = :blue) + on(_ -> autolimits!(ax), mixture_selection) + + ax = Axis(gl_predict[2, tdx]; limits = ((0, 1), nothing)) + scatter!(ax, lift_points(dfs, "x1", "$(target)_excess"); color = :red) + scatter!(ax, lift_points(dfs, "x1", "$(target)_excess_ref"); color = :blue) + on(_ -> autolimits!(ax), mixture_selection) + end + predict_label = lift(mixture_selection) do (smi1, smi2, temp) + return "$smi1 vs. $smi2 at $(format("{:.2f}", temp)) K" + end + Label(gl_predict[0, :], predict_label) + + elems = [ + LineElement(; color = :red, label = "Prediction"), + LineElement(; color = :blue, label = "Reference"), + ] + Legend(gl_predict[3, :], elems, MISTStyle.label.(elems); + nbanks = 2, + tellheight = true, + tellwidth = false, + ) + rowsize!(gl_predict, 0, Auto(0.2)) + rowsize!(gl_predict, 3, Auto(0.2)) + + DataInspector(f) + return f end function lift_points(df, x, y) - lift(df) do df - df = dropmissing(df[!, [x, y]]) - points = Point2f.(df[!, x], df[!, y]) - return points - end + lift(df) do df + df = dropmissing(df[!, [x, y]]) + points = Point2f.(df[!, x], df[!, y]) + return points + end end function plot_mixture(df, model) - targets = clean_target_name.(pyconvert(Vector{String}, model.config.target_columns)) + targets = clean_target_name.(pyconvert(Vector{String}, model.config.target_columns)) - f = Figure() - gl = GridLayout(f[1, 1]) - ui = GridLayout(f[2, 1]) - compounds = unique(Iterators.flatten(df.compounds)) - smi1 = Menu(ui[1, 1], options = compounds) + f = Figure() + gl = GridLayout(f[1, 1]) + ui = GridLayout(f[2, 1]) + compounds = unique(Iterators.flatten(df.compounds)) + smi1 = Menu(ui[1, 1], options = compounds) - smi2_options = lift(smi1.selection) do smi1 - dfs = subset(df, :compounds => ByRow(x -> smi1 in x)) - unique(Iterators.flatten(dfs.compounds)) - end - smi2 = Menu(ui[1, 2], options = smi2_options) + smi2_options = lift(smi1.selection) do smi1 + dfs = subset(df, :compounds => ByRow(x -> smi1 in x)) + unique(Iterators.flatten(dfs.compounds)) + end + smi2 = Menu(ui[1, 2], options = smi2_options) - df = transform(df, :composition => ByRow(first) => :x1) + df = transform(df, :composition => ByRow(first) => :x1) - dfs = lift(smi1.selection, smi2.selection) do smi1, smi2 - subset(df, :compounds => ByRow(x -> smi1 in x && smi2 in x)) - end + dfs = lift(smi1.selection, smi2.selection) do smi1, smi2 + subset(df, :compounds => ByRow(x -> smi1 in x && smi2 in x)) + end - for (tdx, target) in enumerate(targets) - ax = Axis(gl[1, tdx]; limits = ((0, 1), nothing)) - scatter!(ax, lift_points(dfs, "x1", target); label = "Prediction") - scatter!(ax, lift_points(dfs, "x1", "$(target)_ref"); label = "Reference") - on(_ -> autolimits!(ax), smi2.selection) + for (tdx, target) in enumerate(targets) + ax = Axis(gl[1, tdx]; limits = ((0, 1), nothing)) + scatter!(ax, lift_points(dfs, "x1", target); label = "Prediction") + scatter!(ax, lift_points(dfs, "x1", "$(target)_ref"); label = "Reference") + on(_ -> autolimits!(ax), smi2.selection) - ax = Axis(gl[2, tdx]; limits = ((0, 1), nothing)) - scatter!(ax, lift_points(dfs, "x1", "$(target)_excess")) - scatter!(ax, lift_points(dfs, "x1", "$(target)_excess_ref")) - on(_ -> autolimits!(ax), smi2.selection) - end + ax = Axis(gl[2, tdx]; limits = ((0, 1), nothing)) + scatter!(ax, lift_points(dfs, "x1", "$(target)_excess")) + scatter!(ax, lift_points(dfs, "x1", "$(target)_excess_ref")) + on(_ -> autolimits!(ax), smi2.selection) + end - notify(smi1.selection) - notify(smi2.selection) + notify(smi1.selection) + notify(smi2.selection) - return f + return f end function plot_ionic_temperature!(f) - ax = Axis(f[1, 1]; - xlabel = "Temperature (K)", - ylabel = L"$\sigma$ (mS/cm)", - ) - df = DataFrame(CSV.File("panel_data/temperature_w8phe03q.csv")) - errorbars!(ax, df[!, "temperature"], df[!, "predicted"], df[!, "st_dev"], color = :black) - scatter!(ax, df[!, "temperature"], df[!, "predicted"], markersize = 3) - data_T_max = 293.150000 - x_min, x_max = extrema(df[!, "temperature"]) - y_min, y_max = extrema(df[!, "predicted"]) - poly!( - Point2f[(x_min, y_min), (x_min, y_max), (data_T_max, y_max), (data_T_max, y_min)]; - color = MISTStyle.UM_COLORS.maize, - alpha = 0.2, - strokewidth = 0, - ) - - return f + ax = Axis(f[1, 1]; + xlabel = "Temperature (K)", + ylabel = L"$\sigma$ (mS/cm)", + ) + df = DataFrame(CSV.File("panel_data/temperature_w8phe03q.csv")) + errorbars!(ax, df[!, "temperature"], df[!, "predicted"], df[!, "st_dev"], color = :black) + scatter!(ax, df[!, "temperature"], df[!, "predicted"], markersize = 3) + data_T_max = 293.150000 + x_min, x_max = extrema(df[!, "temperature"]) + y_min, y_max = extrema(df[!, "predicted"]) + poly!( + Point2f[(x_min, y_min), (x_min, y_max), (data_T_max, y_max), (data_T_max, y_min)]; + color = MISTStyle.UM_COLORS.maize, + alpha = 0.2, + strokewidth = 0, + ) + + return f end metadata = Dict( @@ -254,14 +254,14 @@ metadata = Dict( function diffmix_validation(model_id) f = Figure(; figure_padding=(2,2,2,2)) cb = Colorbar(f[3, 1:2]; - label = "Temperature (K)", + label = "Temperature (K)", colormap = :managua, colorrange = (298, 309), - tellheight = true, + tellheight = true, tellwidth = true, - vertical = false, + vertical = false, flip_vertical_label=true - ) + ) for (idx, excess_dataset) in enumerate([ "diffmix/excess_molar_volume.csv", "diffmix/excess_molar_enthalpy.csv"]) diffmix_validation!(model_id, f[idx, :], excess_dataset, cb) @@ -273,33 +273,33 @@ function diffmix_validation!(model_id, f, excess_dataset, cb) quantity = metadata[excess_dataset][1] col = metadata[excess_dataset][2] model = Mixtures.load_excess_model(joinpath(DATA_DIR, "models", model_id)).to("mps") - df_excess = Mixtures.evaluate_binary_csv(model, excess_dataset) + df_excess = Mixtures.evaluate_binary_csv(model, excess_dataset) df_ref = load_reference(excess_dataset) ax1 = Axis(f[1, 1]; - xlabel = "Exp", - ylabel = "MIST", - ) + xlabel = "Exp", + ylabel = "MIST", + ) scatter!(ax1, df_excess[!, "$(col)_ref"], df_excess[!, "$col"]; color = df_excess[!, "temperature"], MISTStyle.cb_attrs(cb, Scatter)..., - ) + ) lines!(ax1, df_excess[!, "$(col)_ref"], df_excess[!, "$(col)_ref"]; color=:black ) ax2 = Axis(f[1, 2]; - limits = ((0, 1), nothing), - xlabel = L"x_1", - ylabel = quantity, - tellwidth = true, - ) + limits = ((0, 1), nothing), + xlabel = L"x_1", + ylabel = quantity, + tellwidth = true, + ) if length(df_excess.composition) > 500 df_excess = df_excess[1: 1: 500, :] end df_excess.x1 = first.(df_excess.composition) marker_lookup = Dict(zip(unique(df_excess.compounds), first.(markers_labels))) for gdf in groupby(df_excess, ["compounds", "temperature"]) - lines!(ax2, gdf.x1, gdf[!, "$col"]; + lines!(ax2, gdf.x1, gdf[!, "$col"]; linestyle = :solid, color = gdf.temperature, MISTStyle.cb_attrs(cb, Lines)..., @@ -309,9 +309,9 @@ function diffmix_validation!(model_id, f, excess_dataset, cb) color = gdf.temperature, marker = marker_lookup[first(gdf.compounds)], strokewidth = 0.25pt, - strokecolor = MISTStyle.UM_COLORS.ash, + strokecolor = MISTStyle.UM_COLORS.ash, MISTStyle.cb_attrs(cb, Scatter)..., ) - end + end return f end diff --git a/opt/mixtures/plot_mae.jl b/opt/mixtures/plot_mae.jl index 5ee6147c..7b50bb94 100644 --- a/opt/mixtures/plot_mae.jl +++ b/opt/mixtures/plot_mae.jl @@ -22,7 +22,7 @@ function plot_mae!(f, df, target, units) :smi2 => ByRow(Mixtures.label_functional_groups) => :fg2, ) transform!(df, [target, "$(target)_ref"] => ByRow(-) => target) - + fg = unique(Iterators.flatten(df.fg1)) union!(fg, Iterators.flatten(df.fg2)) pairs = Matrix{Float32}(undef, length(fg), length(fg)) @@ -94,7 +94,7 @@ function plot_mae!(f, df, target, units) mask = fill(NaN, length(fg), length(fg)) triu!(mask, 1) - + cb = Colorbar(f[1, 5]; colormap=MISTStyle.CONTINUOUS_COLORS, @@ -154,4 +154,4 @@ function plot_mae(df) plot_mae!(gl[b, :], df, target, units) end return f -end \ No newline at end of file +end diff --git a/opt/mixtures/python/excess.py b/opt/mixtures/python/excess.py index 46058c4d..98a4e0f1 100644 --- a/opt/mixtures/python/excess.py +++ b/opt/mixtures/python/excess.py @@ -33,9 +33,9 @@ def __iter__(self): temperature = mixture.get("temperature", 293.15) if n_compounds is None: n_compounds = len(compounds) - assert len(compounds) == n_compounds, ( - "Number of compounds must be consistent" - ) + assert ( + len(compounds) == n_compounds + ), "Number of compounds must be consistent" obs = { "compounds": compounds, "temperature": torch.tensor(temperature), diff --git a/test/test_mixture_dataset.py b/test/test_mixture_dataset.py index b7438009..a5d3fb8f 100644 --- a/test/test_mixture_dataset.py +++ b/test/test_mixture_dataset.py @@ -138,9 +138,9 @@ def check_mixture_batch(dm: ComponentDataModule, batch: dict): comp_sum = batch["composition"].sum(axis=1) assert comp_sum.shape == (dm.batch_size,) - assert torch.allclose(comp_sum, torch.ones(dm.batch_size).to(comp_sum)), ( - f"Composition sums to 1: {comp_sum}" - ) + assert torch.allclose( + comp_sum, torch.ones(dm.batch_size).to(comp_sum) + ), f"Composition sums to 1: {comp_sum}" if dm.temperature_column is not None: assert batch["temperature"].shape == (dm.batch_size,) From 3e9e2fae5b6dc286e0683df2ebdbfeab59075d2f Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 18:16:29 -0400 Subject: [PATCH 137/147] increase spit test tolerance and disable wandb in CI --- .github/workflows/CI.yaml | 2 ++ test/test_spliters.py | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/CI.yaml b/.github/workflows/CI.yaml index d785997d..11c2b7da 100644 --- a/.github/workflows/CI.yaml +++ b/.github/workflows/CI.yaml @@ -17,4 +17,6 @@ jobs: - name: install project run: uv sync --all-extras --dev - name: pytest + env: + WANDB_MODE: disabled run: uv run pytest --durations=0 diff --git a/test/test_spliters.py b/test/test_spliters.py index a5f21f3d..fd547600 100644 --- a/test/test_spliters.py +++ b/test/test_spliters.py @@ -115,8 +115,8 @@ def test_target_distribution(synthetic_data): train_idx, test_idx = next(splitter.split(X, y, groups)) train_counts = Counter(y[train_idx]) test_counts = Counter(y[test_idx]) - # For each bitstring, the fraction in train/test should be within ±10% of overall - tolerance = 0.1 + # For each bitstring, the fraction in train/test should be within ±15% of overall + tolerance = 0.15 for bitstr, overall_count in overall_counts.items(): overall_frac = overall_count / len(y) test_frac = test_counts.get(bitstr, 0) / len(test_idx) From 9842289297e5fbdc95bdf26bcfc34fe92e877f46 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 18:22:33 -0400 Subject: [PATCH 138/147] splitter test tolerance --- test/test_spliters.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_spliters.py b/test/test_spliters.py index fd547600..7950b9de 100644 --- a/test/test_spliters.py +++ b/test/test_spliters.py @@ -115,8 +115,8 @@ def test_target_distribution(synthetic_data): train_idx, test_idx = next(splitter.split(X, y, groups)) train_counts = Counter(y[train_idx]) test_counts = Counter(y[test_idx]) - # For each bitstring, the fraction in train/test should be within ±15% of overall - tolerance = 0.15 + # For each bitstring, the fraction in train/test should be within ±20% of overall + tolerance = 0.20 for bitstr, overall_count in overall_counts.items(): overall_frac = overall_count / len(y) test_frac = test_counts.get(bitstr, 0) / len(test_idx) From de224337a926496491497633db4ceda5a9408a34 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sat, 4 Oct 2025 22:13:29 -0400 Subject: [PATCH 139/147] python call for inference of conductivity model --- opt/MISTStyle/Project.toml | 1 - opt/mixtures/Project.toml | 1 + opt/mixtures/src/Mixtures.jl | 3 +++ opt/mixtures/src/python.jl | 38 ++++++++++++++++++++++++++++++++++++ opt/mixtures/uv.lock | 11 ++++++++--- 5 files changed, 50 insertions(+), 4 deletions(-) diff --git a/opt/MISTStyle/Project.toml b/opt/MISTStyle/Project.toml index 00c8259b..50137742 100644 --- a/opt/MISTStyle/Project.toml +++ b/opt/MISTStyle/Project.toml @@ -4,7 +4,6 @@ uuid = "0a43f4a4-3db7-4939-b247-26273f54b603" [deps] CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" -GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" diff --git a/opt/mixtures/Project.toml b/opt/mixtures/Project.toml index 15f4cca3..58f7bf74 100644 --- a/opt/mixtures/Project.toml +++ b/opt/mixtures/Project.toml @@ -23,3 +23,4 @@ MISTStyle = {path = "../MISTStyle"} Interpolations = "0.15.1" JSON = "0.21" LinearAlgebra = "1.11.0" +StatsBase = "0.34.6" diff --git a/opt/mixtures/src/Mixtures.jl b/opt/mixtures/src/Mixtures.jl index fa4b09b4..53d5a763 100644 --- a/opt/mixtures/src/Mixtures.jl +++ b/opt/mixtures/src/Mixtures.jl @@ -7,9 +7,12 @@ using DataFrames using PythonCall: PythonCall, Py, pyconvert, pyimport pyexcess = Ref{Py}() +pyionic = Ref{Py}() + function __init__() pyexcess[] = pyimport("excess") + pyionic[] = pyimport("ionic_conductivity") return nothing end diff --git a/opt/mixtures/src/python.jl b/opt/mixtures/src/python.jl index 33c803ad..e8283523 100644 --- a/opt/mixtures/src/python.jl +++ b/opt/mixtures/src/python.jl @@ -1,6 +1,7 @@ clean_target_name(x::String) = replace((strip∘first∘split)(x, "["), " " => "_") load_excess_model(ckpt) = pyexcess[].load_excess_model(ckpt) +load_conductivity_model(ckpt) = pyionic[].load_conductivity_model(ckpt) function evaluate_mixtures(model::Py, mixtures::Vector{<:Dict}; kwargs...) pymixtures = map(mixtures) do mixture @@ -86,3 +87,40 @@ function label_functional_groups(smi::String) groups = pyconvert(Vector{String}, pyfg.identify_functional_groups(smi)) length(groups) == 0 ? ["Other"] : groups end + + +function evaluate_conductivity(model::Py, mixtures::Vector{<:Dict}; kwargs...) + pymixtures = map(mixtures) do mixture + PythonCall.pydict(; + solvents=PythonCall.pylist(mixture["solvents"]), + salt=mixture["salt"], + temperature=mixture["temperature"], + ) + end + return evaluate_conductivity(model, pymixtures; kwargs...) +end + +function evaluate_conductivity(model::Py, compounds::String...; temperature::Real=298.15, kwargs...) + evaluate_conductivity(model, [Dict("solvents" => solvents, "salt" => salt, "temperature" => temperature)]; kwargs...) +end + +function evaluate_conductivity(model::Py, mixtures::Union{Py, Vector{Py}}; n = 20) + rows = map(pyionic[].evaluate_mixtures(model, mixtures; n)) do row + row = pyconvert(Dict{String, Union{Float64, String, Vector}}, row) + out = Dict( + "components" => row["components"], + "composition" => row["composition"], + "temperature" => row["temperature"], + ) + + out["conductivity"] = row["conductivity"] + out["ln_A"] = row["ln_A"] + out["Ea"] = row["Ea"] + out["Tg"] = row["Tg"] + out["alpha"] = row["alpha"] + out["beta"] = row["beta"] + out["lmbda"] = row["lmbda"] + return out + end + return DataFrame(rows) +end diff --git a/opt/mixtures/uv.lock b/opt/mixtures/uv.lock index 8bac22ec..b92cb703 100644 --- a/opt/mixtures/uv.lock +++ b/opt/mixtures/uv.lock @@ -566,6 +566,9 @@ dependencies = [ { name = "jsonargparse", extra = ["jsonnet", "signatures"] }, { name = "jsonnet" }, { name = "lightning" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" }, + { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" }, + { name = "pandas" }, { name = "pydoe2" }, { name = "pytorch-lightning" }, { name = "rdkit" }, @@ -588,6 +591,8 @@ requires-dist = [ { name = "jsonargparse", extras = ["signatures", "jsonnet"], specifier = ">=4.27" }, { name = "jsonnet", specifier = ">=0.21.0" }, { name = "lightning", specifier = "~=2.5" }, + { name = "numpy", specifier = ">=2.2.4" }, + { name = "pandas", specifier = ">=2.2.3" }, { name = "pydoe2", specifier = ">=1.3" }, { name = "pytorch-lightning", specifier = "~=2.5" }, { name = "rdkit", specifier = ">=2024.3.5" }, @@ -595,7 +600,7 @@ requires-dist = [ { name = "safetensors", specifier = ">=0.5.1" }, { name = "scikit-learn", specifier = ">=1.5.1" }, { name = "selfies", specifier = ">=2.1.0" }, - { name = "smirk", git = "ssh://git@github.com/BattModels/smirk.git?rev=dfdd9dc27c066f956b69d1f55c2d4717beb7838d" }, + { name = "smirk", git = "https://github.com/BattModels/smirk?rev=main" }, { name = "torch", specifier = "~=2.6" }, { name = "torchmetrics", specifier = "~=1.7" }, { name = "transformers", specifier = ">=4.40" }, @@ -2806,8 +2811,8 @@ wheels = [ [[package]] name = "smirk" -version = "0.2.0" -source = { git = "ssh://git@github.com/BattModels/smirk.git?rev=dfdd9dc27c066f956b69d1f55c2d4717beb7838d#dfdd9dc27c066f956b69d1f55c2d4717beb7838d" } +version = "0.2.0.dev0" +source = { git = "https://github.com/BattModels/smirk?rev=main#742e81c85acc123873f3c58ab84edc6ce7e90c18" } dependencies = [ { name = "transformers" }, ] From 103ac7456df27dfdf5f093fd9f48ed1f66f80846 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sun, 5 Oct 2025 21:00:41 -0400 Subject: [PATCH 140/147] feat: ternary plots --- opt/mixtures/Project.toml | 2 + opt/mixtures/TernaryPlots.jl | 186 +++++++++++++++++ opt/mixtures/ionic_conductivity.jl | 134 ++++++++++++ opt/mixtures/python/excess.py | 44 ++-- opt/mixtures/python/ionic_conductivity.py | 239 ++++++++++++++++++++++ opt/mixtures/src/python.jl | 88 +++++--- 6 files changed, 649 insertions(+), 44 deletions(-) create mode 100644 opt/mixtures/TernaryPlots.jl create mode 100644 opt/mixtures/ionic_conductivity.jl create mode 100644 opt/mixtures/python/ionic_conductivity.py diff --git a/opt/mixtures/Project.toml b/opt/mixtures/Project.toml index 58f7bf74..e39b8c74 100644 --- a/opt/mixtures/Project.toml +++ b/opt/mixtures/Project.toml @@ -6,6 +6,7 @@ version = "0.1.0" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" +DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" @@ -20,6 +21,7 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" MISTStyle = {path = "../MISTStyle"} [compat] +DelaunayTriangulation = "1.6.4" Interpolations = "0.15.1" JSON = "0.21" LinearAlgebra = "1.11.0" diff --git a/opt/mixtures/TernaryPlots.jl b/opt/mixtures/TernaryPlots.jl new file mode 100644 index 00000000..452b5f8c --- /dev/null +++ b/opt/mixtures/TernaryPlots.jl @@ -0,0 +1,186 @@ +using DelaunayTriangulation +using Statistics: mean + +""" + ternary(a, b, c, values) + +Plots a pseudocolor (tripcolor) plot on a ternary diagram using Delaunay triangulation +and filled triangles, similar to mpltern's tripcolor. The input vectors `a`, `b`, and `c` +represent the three compositional components, and `values` contains the scalar data to be +visualized with color. Components will be automatically normalized so that a + b + c = 1. +""" + +@recipe(Ternary, a, b, c, values) do scene + Theme( + colormap = :viridis, + colorrange = Makie.automatic, + show_triangle = true, + triangle_color = :black, + triangle_linewidth = 2, + show_grid = false, + grid_color = (:gray, 0.3), + grid_linestyle = :dash, + grid_steps = 5, + label_a = "A", + label_b = "B", + label_c = "C", + label_fontsize = 16, + label_offset = 25, + show_axis_labels = true, + axis_label_fontsize = 12, + axis_label_offset = 15, + axis_ticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + show_ticks = true, + tick_fontsize = 10, + interpolate = true, # If true, interpolate colors across triangles + ) +end + +function Makie.plot!(plt::Ternary) + # Get the input data + a = plt.a[] + b = plt.b[] + c = plt.c[] + values = plt.values[] + + # Normalize so a + b + c = 1 + total = a .+ b .+ c + a_norm = a ./ total + b_norm = b ./ total + c_norm = c ./ total + + # Convert ternary coordinates to Cartesian + x = @. 0.5 * (2 * b_norm + c_norm) + y = @. (√3 / 2) * c_norm + + # Perform Delaunay triangulation + points = [Point2(xi, yi) for (xi, yi) in zip(x, y)] + tri = triangulate(points) + + # Determine color range + crange = plt.colorrange[] === Makie.automatic ? extrema(values) : plt.colorrange[] + + # Plot each triangle + for triangle in each_solid_triangle(tri) + i, j, k = triangle_vertices(triangle) + + # Triangle vertices + tri_points = [points[i], points[j], points[k]] + + if plt.interpolate[] + # Use vertex colors for interpolation + tri_colors = [values[i], values[j], values[k]] + # Note: Makie's poly doesn't support per-vertex colors directly + # Use mean color as approximation + tri_value = mean(tri_colors) + else + # Use mean value + tri_value = mean([values[i], values[j], values[k]]) + end + + # Plot filled triangle + poly!(plt, tri_points, + color = tri_value, + colormap = plt.colormap, + colorrange = crange, + strokewidth = 0) + end + + # Plot the triangle boundary if requested + if plt.show_triangle[] + corners = [Point2(0.0, 0.0), Point2(1.0, 0.0), Point2(0.5, √3/2), Point2(0.0, 0.0)] + lines!(plt, corners, + color = plt.triangle_color, + linewidth = plt.triangle_linewidth) + end + + # Add grid lines if requested + if plt.show_grid[] + n_steps = plt.grid_steps[] + for i in 1:(n_steps-1) + val = i / n_steps + + # Lines parallel to bottom edge (constant C) + p1 = Point2(0.5 * (2 * (1-val) + 0), (√3/2) * val) + p2 = Point2(0.5 * (2 * 0 + val), (√3/2) * val) + lines!(plt, [p1, p2], + color = plt.grid_color, + linestyle = plt.grid_linestyle) + + # Lines parallel to left edge (constant B) + p1 = Point2(0.5 * (2 * val + 0), (√3/2) * 0) + p2 = Point2(0.5 * (2 * val + (1-val)), (√3/2) * (1-val)) + lines!(plt, [p1, p2], + color = plt.grid_color, + linestyle = plt.grid_linestyle) + + # Lines parallel to right edge (constant A) + p1 = Point2(0.5 * (2 * 0 + val), (√3/2) * val) + p2 = Point2(0.5 * (2 * (1-val) + val), (√3/2) * val) + lines!(plt, [p1, p2], + color = plt.grid_color, + linestyle = plt.grid_linestyle) + end + end + + # Add corner labels + text!(plt, 0.0, 0.0, + text = plt.label_a[], + align = (:center, :top), + offset = (0, -plt.label_offset[]), + fontsize = plt.label_fontsize[]) + + text!(plt, 1.0, 0.0, + text = plt.label_b[], + align = (:center, :top), + offset = (0, -plt.label_offset[]), + fontsize = plt.label_fontsize[]) + + text!(plt, 0.5, √3/2, + text = plt.label_c[], + align = (:center, :bottom), + offset = (0, plt.label_offset[]), + fontsize = plt.label_fontsize[]) + + # Add tick labels along each axis if requested + if plt.show_ticks[] + ticks = plt.axis_ticks[] + tick_fs = plt.tick_fontsize[] + + for t in ticks[2:end-1] # Skip 0 and 1 (corners) + # Bottom edge ticks (A to B, showing B values) + x_bottom = t + y_bottom = 0.0 + text!(plt, x_bottom, y_bottom, + text = string(round(t, digits=1)), + align = (:center, :top), + offset = (0, -5), + fontsize = tick_fs) + + # Left edge ticks (A to C, showing C values) + x_left, y_left = ternary_to_cartesian(1-t, 0, t) + text!(plt, x_left, y_left, + text = string(round(t, digits=1)), + align = (:right, :center), + offset = (-5, 0), + fontsize = tick_fs) + + # Right edge ticks (B to C, showing A values) + x_right, y_right = ternary_to_cartesian(1-t, t, 0) + text!(plt, x_right, y_right, + text = string(round(t, digits=1)), + align = (:left, :center), + offset = (5, 0), + fontsize = tick_fs) + end + end + + return plt +end + +# Helper function for coordinate conversion (used for tick placement) +function ternary_to_cartesian(a, b, c) + x = 0.5 * (2b + c) + y = (√3 / 2) * c + return x, y +end diff --git a/opt/mixtures/ionic_conductivity.jl b/opt/mixtures/ionic_conductivity.jl new file mode 100644 index 00000000..dbbda744 --- /dev/null +++ b/opt/mixtures/ionic_conductivity.jl @@ -0,0 +1,134 @@ +using DataFrames +using Makie +using CSV: CSV +using MISTStyle +using Format: format +using GLM +using Interpolations +using Statistics: mean +using Mixtures: Mixtures, titlecase +include("TernaryPlots.jl") + +const DATA_DIR = realpath(joinpath(pkgdir(Mixtures), "..", "..", "data")) + +function label_smi(smi::AbstractString) + known = Dict( + "O=C1OCC(F)O1" => "FEC", + "CC1COC(=O)O1" => "PC", + "CCOC(=O)OC" => "EMC", + "CCOC(=O)OCC" => "DEC", + "O=C1OCCO1" => "EC", + "COC(=O)OC" => "DMC" + ) + return get(known, smi, smi) +end + +function plot_angell_solvents() + + df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "ionic_conductivity_inference.csv"))) + + fig = Figure(size=(53mm, 32mm), figure_padding=(2, 2, 2, 2)) + + ax = Axis(fig[1, 1], + xlabel = L"T_g/T", + ylabel = L"ln $\sigma$", + xgridvisible = false, + ygridvisible = false, + topspinevisible = true, + rightspinevisible = true + ) + + + # Get unique solvents and create color mapping + solvents = unique(df[!, "Solvent"]) + n_solvents = length(solvents) + colors = MISTStyle.CAT_COLORS[1:n_solvents] # Use Wong colorblind-friendly palette + + # Plot each solvent separately for discrete coloring + for (i, solvent) in enumerate(solvents) + mask = df[!, "Solvent"] .== solvent + scatter!(ax, df[mask, "Tg/T"], df[mask, "conductivity"], + color = colors[i], + marker = :circle, + markersize = 2, + alpha = 0.6, + label = string(solvent) + ) + end + + # Add legend + Legend(fig[1, 2], ax, + framevisible = true, + ) + + return fig +end + +function plot_solvent_ternary(f, solvent) + return +end + +function ternary_activation_energy!(fig, df::DataFrame) + + # Get solvent names for axis labels + solvent_names = mixtures[1]["solvents"] + + # Determine global colorrange for consistent coloring + global_crange = extrema(df.Ea) + println(global_crange) + @info "Activation Energy Range" global_crange + + ax = Axis(fig[1, 1], aspect=DataAspect()) + + # Extract first 3 components (solvents) from composition + comp_matrix = reduce(hcat, df.composition)' # Convert to matrix + a = comp_matrix[:, 1] + b = comp_matrix[:, 2] + c = comp_matrix[:, 3] + + ternary!(ax, a, b, c, df.Ea, + colormap = :viridis, + colorrange = global_crange, + label_a = solvent_names[1], + label_b = solvent_names[2], + label_c = solvent_names[3], + show_ticks = true) + + hidedecorations!(ax) + hidespines!(ax) + + # Add title showing salt composition + salt_comp = first(last.(df.composition)) + Label(fig[0, 1], "Salt: $(round(salt_comp, digits=3))", fontsize=14) + + return fig +end + +function ternary_activation_energy(model_id::String) + model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") + + fig = Figure(size=(300*3, 400)) + + mixtures = [ + Dict( + "solvents" => ["O=C1OCC(F)O1", "CCOC(=O)OC", "O=C1OCCO1"], + "temperature" => 298.15, + "salt" => ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "[Li+]"] + ), + ] + + for (idx, salt_comp) in enumerate([0.06, 0.1, 0.14]) + df = Mixtures.evaluate_conductivity(model, mixtures; n = 140, fixed_salt=salt_comp) + ternary_activation_energy!(fig[:, idx], df) + end + # Add colorbar + Colorbar( + fig[:, 4], + tellheight = true, + tellwidth = true, + colormap = :viridis, + limits = (100, 200), + label = L"$E_a$" + ) + return fig +end diff --git a/opt/mixtures/python/excess.py b/opt/mixtures/python/excess.py index 98a4e0f1..c00cc85d 100644 --- a/opt/mixtures/python/excess.py +++ b/opt/mixtures/python/excess.py @@ -154,7 +154,7 @@ def generate_simplex_grid(n, grid_size): """ grid_values = (i / grid_size for i in range(grid_size + 1)) - # Generate all combinations of grid values for the n-1 dimensions + # Generate all combinations of grid values for the n dimensions grid_combinations = itertools.product(grid_values, repeat=n - 1) # For each combination, scale it such that the sum of the coordinates is 1 @@ -272,23 +272,27 @@ def evaluate(model: ExcessPhysicsModel, dataloader, gradients: bool = False): yield out +# if __name__ == "__main__": +# dataset = "/Users/alexwadell/electrolyte-fm/mixtures/excess_dataset_v5/random" +# name_or_path = ( +# "~/Downloads/z8ido8hw/checkpoints/epoch=61-step=2232-val_loss=1.232.ckpt" +# ) +# model = load_excess_model(name_or_path) +# for out in evaluate_binary_csv( +# model, +# "/Users/alexwadell/Documents/repos/excess_density/excess_v5.csv", +# gradients=True, +# ): +# print(out) + +# mixtures = [ +# {"compounds": ["CC#N", "CCCO"], "temperature": 293.15}, +# {"compounds": ["CC#N", "CO"], "temperature": 293.15}, +# {"compounds": ["CC#N", "CCCCCCCCCCO"], "temperature": 293.15}, +# ] +# for out in evaluate_mixtures(model, mixtures, gradients=True): +# print(out) + if __name__ == "__main__": - dataset = "/Users/alexwadell/electrolyte-fm/mixtures/excess_dataset_v5/random" - name_or_path = ( - "~/Downloads/z8ido8hw/checkpoints/epoch=61-step=2232-val_loss=1.232.ckpt" - ) - model = load_excess_model(name_or_path) - for out in evaluate_binary_csv( - model, - "/Users/alexwadell/Documents/repos/excess_density/excess_v5.csv", - gradients=True, - ): - print(out) - - mixtures = [ - {"compounds": ["CC#N", "CCCO"], "temperature": 293.15}, - {"compounds": ["CC#N", "CO"], "temperature": 293.15}, - {"compounds": ["CC#N", "CCCCCCCCCCO"], "temperature": 293.15}, - ] - for out in evaluate_mixtures(model, mixtures, gradients=True): - print(out) + for comp in generate_simplex_grid(4, 5): + print(comp) diff --git a/opt/mixtures/python/ionic_conductivity.py b/opt/mixtures/python/ionic_conductivity.py new file mode 100644 index 00000000..53321e97 --- /dev/null +++ b/opt/mixtures/python/ionic_conductivity.py @@ -0,0 +1,239 @@ +import itertools +import torch +from typing import Optional +from accelerate import Accelerator +from pathlib import Path + +from smirk import SmirkTokenizerFast + +from electrolyte_fm.data_modules.mixture_dataset import ( + ComponentDataModule, +) +from transformers import DataCollatorWithPadding +from torch.utils.data import IterableDataset +from electrolyte_fm.models.prod_mixture import MISTIonicConductivity + + +def generate_simplex_grid(n, grid_size, fixed_last=None): + g = grid_size + + if fixed_last is None: + # full simplex + for counts in itertools.product(range(g + 1), repeat=n - 1): + s = sum(counts) + if s <= g: + last = g - s + yield [*(c / g for c in counts), last / g] + else: + # Fix last coordinate + if isinstance(fixed_last, int): + k = fixed_last + else: + # float → nearest grid step + k = int(round(float(fixed_last) * g)) + if not (0 <= k <= g): + raise ValueError(f"fixed_last={fixed_last} maps to k={k}, expected 0..{g}") + + target_sum = g - k + # Enumerate only solvent counts summing to target_sum + # (n-1 solvents; last coord is fixed to k/g) + for counts in itertools.product(range(target_sum + 1), repeat=n - 1): + if sum(counts) == target_sum: + yield [*(c / g for c in counts), k / g] + + +def move_to_device(obj, device, non_blocking=False): + if torch.is_tensor(obj): + return obj.to(device, non_blocking=non_blocking) + if isinstance(obj, dict): + return {k: move_to_device(v, device, non_blocking) for k, v in obj.items()} + return obj + + +class MixtureDataset(IterableDataset): + def __init__( + self, + mixtures: list[dict], + tokenizer=None, + n: int = 50, + fixed_salt: Optional[float] = None, + ): + self.tokenizer = tokenizer or SmirkTokenizerFast() + self.token_collator = DataCollatorWithPadding(self.tokenizer) + self.mixtures = mixtures + self.n = n + self.fixed_salt = fixed_salt + + def __iter__(self): + n_components = 5 + for mixture in self.mixtures: + components = list(mixture["solvents"]) + salt = mixture["salt"] + assert len(salt) == 2, "Specify cation and anion SMILES" + components.extend(salt) + + temperature = mixture.get("temperature", 293.15) + + assert ( + len(components) == n_components + ), "Number of components must be consistent" + obs = { + "components": components, + "temperature": torch.tensor(temperature), + } + + # Anion and cation have the same mole ratio + for comp in generate_simplex_grid( + n_components - 1, self.n, fixed_last=self.fixed_salt + ): + yield {**obs, "composition": torch.tensor(comp)} + + def get_dataloader(self, batch_size=16): + return torch.utils.data.DataLoader( + self, + batch_size=batch_size, + collate_fn=self.collate_fn, + ) + + def collate_fn(self, batch): + out = { + "temperature": torch.tensor( + [x["temperature"] for x in batch], dtype=torch.float32 + ) + } + + # Get number of components from first sample + n_components = len(batch[0]["components"]) + + for idx in range(n_components): + # Collect SMILES for this component across all samples in batch + smiles_list = [x["components"][idx] for x in batch] + + # Collect composition for this component across all samples + if idx == n_components - 1: + # salt molarity: + composition = [x["composition"][-1] for x in batch] + else: + composition = [x["composition"][idx] for x in batch] + + # Tokenize all SMILES for this component + enc = self.tokenizer(smiles_list) + enc = self.token_collator(enc) + + out[f"input_ids_{idx}"] = enc["input_ids"] + out[f"attention_mask_{idx}"] = enc["attention_mask"] + out[f"composition_{idx}"] = torch.tensor(composition, dtype=torch.float32) + + out["components"] = [x["components"] for x in batch] + out["composition"] = [x["composition"] for x in batch] + # Add target if present + if "target" in batch[0]: + out["target"] = torch.tensor( + [x["target"] for x in batch], dtype=torch.float32 + ) + + return out + + +def evaluate_mixtures(model, mixtures: list[dict], n=15, fixed_salt=None, **kwargs): + ds = MixtureDataset(mixtures, n=n, fixed_salt=fixed_salt) + dl = ds.get_dataloader() + return evaluate(model, dl, **kwargs) + + +def evaluate(model, dataloader): + accelerator = Accelerator() + device = accelerator.device + model = accelerator.prepare_model(model.eval(), device_placement=True) + + for batch in dataloader: + batch = move_to_device(batch, device) + comps = torch.stack([c for c in batch["composition"]]) + assert torch.isfinite(comps).all(), "Non-finite in batch composition." + assert (comps >= -1e-6).all() and ( + comps <= 1 + 1e-6 + ).all(), "Composition out of [0,1]." + assert torch.allclose( + comps.sum(-1), torch.ones(comps.shape[0]), atol=1e-6 + ), "Row sum != 1." + + output = model.forward(batch, return_all=True)[-1] + targets = list(output.keys()) + for bdx in range(batch["input_ids_0"].shape[0]): + out = {} + out["components"] = batch["components"][bdx] + out["composition"] = batch["composition"][bdx].tolist() + out["temperature"] = batch["temperature"][bdx].item() + for target in targets: + out[target] = output[target][bdx].tolist() + # print(out) + yield out + + +def load_conductivity_model(ckpt): + return MISTIonicConductivity.from_pretrained(ckpt) + + +def evaluate_at_composition(model, mixture, composition): + accelerator = Accelerator() + device = accelerator.device + model = accelerator.prepare_model(model.eval(), device_placement=True) + + tokenizer = SmirkTokenizerFast() + collator = DataCollatorWithPadding(tokenizer) + + solvents = list(mixture["solvents"]) + salt = list(mixture["salt"]) + assert len(solvents) == 3, "Expected three solvents." + assert len(salt) == 2, "Specify [anion, cation] SMILES." + components = solvents + salt # [s1, s2, s3, anion, cation] + temperature = float(mixture.get("temperature", 293.15)) + + comp = torch.tensor(list(composition), dtype=torch.float32) + assert comp.numel() == 4, "composition must be length 4: [s1, s2, s3, salt]" + comp = torch.clamp(comp, 0.0, 1.0) + comp[-1] = 1.0 - comp[:-1].sum() # enforce exact sum==1.0 in fp32 + + batch = {"temperature": torch.tensor([temperature], dtype=torch.float32)} + for idx in range(5): + smiles = [components[idx]] + enc = tokenizer(smiles) + enc = collator(enc) + batch[f"input_ids_{idx}"] = enc["input_ids"] + batch[f"attention_mask_{idx}"] = enc["attention_mask"] + if idx < 4: + batch[f"composition_{idx}"] = comp[idx].reshape(1) + else: + batch[f"composition_{idx}"] = comp[-1].reshape(1) + + batch["components"] = [components] + batch["composition"] = [comp] + batch = move_to_device(batch, device) + + with torch.no_grad(): + outputs = model.forward(batch, return_all=True)[-1] + targets = list(outputs.keys()) + + result = { + "components": components, + "composition": comp.tolist(), + "temperature": temperature, + } + for t in targets: + result[t] = outputs[t][0].detach().cpu().tolist() + return result + + +if __name__ == "__main__": + name_or_path = "/Users/anoushka/VSCodeProjects/electrolyte-fm/data/models/mist-conductivity-27.0M-2mpg8dcd/" + model = MISTIonicConductivity.from_pretrained(name_or_path) + mixtures = [ + { + "solvents": ["O=C1OCC(F)O1", "CCOC(=O)OC", "O=C1OCCO1"], + "salt": ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "[Li+]"], + "temperature": 293.15, + }, + ] + # print(evaluate_at_composition(model, mixtures[0], [0.9, 0, 0, 0.1])) + for out in evaluate_mixtures(model, mixtures, fixed_salt=0.1): + print(out["composition"]) diff --git a/opt/mixtures/src/python.jl b/opt/mixtures/src/python.jl index e8283523..8a24571c 100644 --- a/opt/mixtures/src/python.jl +++ b/opt/mixtures/src/python.jl @@ -89,38 +89,78 @@ function label_functional_groups(smi::String) end -function evaluate_conductivity(model::Py, mixtures::Vector{<:Dict}; kwargs...) +function evaluate_conductivity( + model::Py, + mixtures::Vector{<:Dict}; + n::Integer=20, + fixed_salt::Union{Nothing,Real}=nothing, + kwargs...) + pymixtures = map(mixtures) do mixture PythonCall.pydict(; - solvents=PythonCall.pylist(mixture["solvents"]), - salt=mixture["salt"], - temperature=mixture["temperature"], + solvents = PythonCall.pylist(mixture["solvents"]), + salt = PythonCall.pylist(mixture["salt"]), + temperature = mixture["temperature"], ) end - return evaluate_conductivity(model, pymixtures; kwargs...) -end - -function evaluate_conductivity(model::Py, compounds::String...; temperature::Real=298.15, kwargs...) - evaluate_conductivity(model, [Dict("solvents" => solvents, "salt" => salt, "temperature" => temperature)]; kwargs...) + return evaluate_conductivity(model, pymixtures; n=n, fixed_salt=fixed_salt, kwargs...) end -function evaluate_conductivity(model::Py, mixtures::Union{Py, Vector{Py}}; n = 20) - rows = map(pyionic[].evaluate_mixtures(model, mixtures; n)) do row - row = pyconvert(Dict{String, Union{Float64, String, Vector}}, row) - out = Dict( - "components" => row["components"], - "composition" => row["composition"], - "temperature" => row["temperature"], +function evaluate_conductivity( + model::Py, + mixtures::Union{Py, Vector{Py}}; + n::Integer=20, + fixed_salt::Union{Nothing,Real}=nothing, + kwargs...) + rows = map(pyionic[].evaluate_mixtures(model, mixtures; n, fixed_salt)) do row + row_dict = pyconvert(Dict{String,Any}, row) + out = Dict{String, Any}( + "components" => pyconvert(Vector, row_dict["components"]), + "composition" => pyconvert(Vector, row_dict["composition"]), + "temperature" => pyconvert(Float64, row_dict["temperature"]), + "conductivity" => pyconvert(Float64, row_dict["conductivity"]), + "ln_A" => pyconvert(Float64, row_dict["ln_A"]), + "Ea" => pyconvert(Float64, row_dict["Ea"]), + "Tg" => pyconvert(Float64, row_dict["Tg"]), + "alpha" => pyconvert(Float64, row_dict["alpha"]), + "beta" => pyconvert(Float64, row_dict["beta"]), + "lmbda" => pyconvert(Float64, row_dict["lmbda"]) ) - - out["conductivity"] = row["conductivity"] - out["ln_A"] = row["ln_A"] - out["Ea"] = row["Ea"] - out["Tg"] = row["Tg"] - out["alpha"] = row["alpha"] - out["beta"] = row["beta"] - out["lmbda"] = row["lmbda"] return out end return DataFrame(rows) end + +function evaluate_at_composition(model::Py, mixture::Dict, composition::Vector{Float64}) + pymixture = PythonCall.pydict(; + solvents=PythonCall.pylist(mixture["solvents"]), + salt=PythonCall.pylist(mixture["salt"]), + temperature=mixture["temperature"], + ) + pycomposition = PythonCall.pylist(composition) + return evaluate_at_composition(model, pymixture, pycomposition) +end + +function evaluate_at_composition(model::Py, mixture::Py, composition::Py) + pymixture = PythonCall.pydict(; + solvents=PythonCall.pylist(mixture["solvents"]), + salt=PythonCall.pylist(mixture["salt"]), + temperature=mixture["temperature"], + ) + pycomposition = PythonCall.pylist(composition) + pred = pyionic[].evaluate_at_composition(model, solvents, pycomposition) + pred = pyconvert(Dict{String,Any}, pred) + out = Dict{String, Any}( + "components" => pyconvert(Vector, pred["components"]), + "composition" => pyconvert(Vector, pred["composition"]), + "temperature" => pyconvert(Float64, pred["temperature"]), + "conductivity" => pyconvert(Float64, pred["conductivity"]), + "ln_A" => pyconvert(Float64, pred["ln_A"]), + "Ea" => pyconvert(Float64, pred["Ea"]), + "Tg" => pyconvert(Float64, pred["Tg"]), + "alpha" => pyconvert(Float64, pred["alpha"]), + "beta" => pyconvert(Float64, pred["beta"]), + "lmbda" => pyconvert(Float64, pred["lmbda"]) + ) + return out +end From af1cfba44511951cee310c7ef9d084dd33159f39 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 6 Oct 2025 18:53:41 -0400 Subject: [PATCH 141/147] feat: ternary plots for activation energy and excess Ea --- opt/mixtures/Project.toml | 2 + opt/mixtures/TernaryPlots.jl | 135 ++++++++-------- opt/mixtures/ionic_conductivity.jl | 188 ++++++++++++++++------ opt/mixtures/python/ionic_conductivity.py | 18 ++- opt/mixtures/src/python.jl | 2 +- 5 files changed, 226 insertions(+), 119 deletions(-) diff --git a/opt/mixtures/Project.toml b/opt/mixtures/Project.toml index e39b8c74..c5939fc2 100644 --- a/opt/mixtures/Project.toml +++ b/opt/mixtures/Project.toml @@ -7,6 +7,7 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df" +FStrings = "1e2663ae-5621-49ef-9c0a-672f85872094" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" @@ -22,6 +23,7 @@ MISTStyle = {path = "../MISTStyle"} [compat] DelaunayTriangulation = "1.6.4" +FStrings = "0.1.2" Interpolations = "0.15.1" JSON = "0.21" LinearAlgebra = "1.11.0" diff --git a/opt/mixtures/TernaryPlots.jl b/opt/mixtures/TernaryPlots.jl index 452b5f8c..cf909c7f 100644 --- a/opt/mixtures/TernaryPlots.jl +++ b/opt/mixtures/TernaryPlots.jl @@ -1,4 +1,5 @@ using DelaunayTriangulation +DelaunayTriangulation.toggle_warn_on_dupes!() using Statistics: mean """ @@ -16,23 +17,20 @@ visualized with color. Components will be automatically normalized so that a + b colorrange = Makie.automatic, show_triangle = true, triangle_color = :black, - triangle_linewidth = 2, - show_grid = false, - grid_color = (:gray, 0.3), - grid_linestyle = :dash, + triangle_linewidth = 1, + show_grid = true, + grid_color = (:black, 0.7), + grid_linestyle = :solid, grid_steps = 5, label_a = "A", label_b = "B", label_c = "C", - label_fontsize = 16, - label_offset = 25, + label_offset = 0, show_axis_labels = true, - axis_label_fontsize = 12, - axis_label_offset = 15, + axis_label_offset = 2, axis_ticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], show_ticks = true, - tick_fontsize = 10, - interpolate = true, # If true, interpolate colors across triangles + interpolate = true, ) end @@ -68,17 +66,12 @@ function Makie.plot!(plt::Ternary) tri_points = [points[i], points[j], points[k]] if plt.interpolate[] - # Use vertex colors for interpolation tri_colors = [values[i], values[j], values[k]] - # Note: Makie's poly doesn't support per-vertex colors directly - # Use mean color as approximation tri_value = mean(tri_colors) else - # Use mean value tri_value = mean([values[i], values[j], values[k]]) end - # Plot filled triangle poly!(plt, tri_points, color = tri_value, colormap = plt.colormap, @@ -86,7 +79,7 @@ function Makie.plot!(plt::Ternary) strokewidth = 0) end - # Plot the triangle boundary if requested + # Plot the triangle boundary if plt.show_triangle[] corners = [Point2(0.0, 0.0), Point2(1.0, 0.0), Point2(0.5, √3/2), Point2(0.0, 0.0)] lines!(plt, corners, @@ -94,14 +87,14 @@ function Makie.plot!(plt::Ternary) linewidth = plt.triangle_linewidth) end - # Add grid lines if requested + # Add grid lines if plt.show_grid[] n_steps = plt.grid_steps[] for i in 1:(n_steps-1) val = i / n_steps # Lines parallel to bottom edge (constant C) - p1 = Point2(0.5 * (2 * (1-val) + 0), (√3/2) * val) + p1 = Point2(0.5 * (2 * (1-val) + val), (√3/2) * val) p2 = Point2(0.5 * (2 * 0 + val), (√3/2) * val) lines!(plt, [p1, p2], color = plt.grid_color, @@ -115,70 +108,76 @@ function Makie.plot!(plt::Ternary) linestyle = plt.grid_linestyle) # Lines parallel to right edge (constant A) - p1 = Point2(0.5 * (2 * 0 + val), (√3/2) * val) - p2 = Point2(0.5 * (2 * (1-val) + val), (√3/2) * val) + p1 = Point2(0.5 * (1 - val), (√3/2) * (1 - val)) + p2 = Point2(1.0 - val, 0.0) lines!(plt, [p1, p2], color = plt.grid_color, linestyle = plt.grid_linestyle) end end - # Add corner labels - text!(plt, 0.0, 0.0, - text = plt.label_a[], - align = (:center, :top), - offset = (0, -plt.label_offset[]), - fontsize = plt.label_fontsize[]) - - text!(plt, 1.0, 0.0, - text = plt.label_b[], - align = (:center, :top), - offset = (0, -plt.label_offset[]), - fontsize = plt.label_fontsize[]) - - text!(plt, 0.5, √3/2, - text = plt.label_c[], - align = (:center, :bottom), - offset = (0, plt.label_offset[]), - fontsize = plt.label_fontsize[]) - - # Add tick labels along each axis if requested if plt.show_ticks[] ticks = plt.axis_ticks[] - tick_fs = plt.tick_fontsize[] - - for t in ticks[2:end-1] # Skip 0 and 1 (corners) - # Bottom edge ticks (A to B, showing B values) - x_bottom = t - y_bottom = 0.0 - text!(plt, x_bottom, y_bottom, - text = string(round(t, digits=1)), - align = (:center, :top), - offset = (0, -5), - fontsize = tick_fs) - - # Left edge ticks (A to C, showing C values) - x_left, y_left = ternary_to_cartesian(1-t, 0, t) - text!(plt, x_left, y_left, - text = string(round(t, digits=1)), - align = (:right, :center), - offset = (-5, 0), - fontsize = tick_fs) - - # Right edge ticks (B to C, showing A values) - x_right, y_right = ternary_to_cartesian(1-t, t, 0) - text!(plt, x_right, y_right, - text = string(round(t, digits=1)), - align = (:left, :center), - offset = (5, 0), - fontsize = tick_fs) + + for t in ticks + + x_bottom, y_bottom = ternary_to_cartesian(1 - t, t, 0) + text!(plt, x_bottom, y_bottom; + text = string(round(t, digits=1)), + align = (:center, :top), + rotation = π/3, + fontsize = 6pt, + offset = (0, -4pt)) + + x_left, y_left = ternary_to_cartesian(1 - t, 0, t) + text!(plt, x_left, y_left; + text = string(round(1- t, digits=1)), + rotation = -π/3, + align = (:center, :center), + fontsize = 6pt, + offset = (-6pt, 4pt)) + + x_right, y_right = ternary_to_cartesian(0, 1 - t, t) + text!(plt, x_right, y_right; + text = string(round(t, digits=1)), + align = (:left, :center), + rotation = 0, + fontsize = 6pt, + offset = (2pt, 2pt)) end end + if plt.show_axis_labels[] + off = plt.axis_label_offset[] + + xb, yb = ternary_to_cartesian(0.5, 0.5, 0.0) + text!(plt, xb, yb; + text = plt.label_b[], + align = (:center, :top), + fontsize=7pt, + rotation = 0.0, + offset = (0, -9pt)) + + xl, yl = ternary_to_cartesian(0.5, 0.0, 0.5) + text!(plt, xl, yl; + text = plt.label_a[], + align = (:center, :center), + fontsize=7pt, + rotation = π/3, + offset = (-22pt, 2pt)) + + xr, yr = ternary_to_cartesian(0.0, 0.5, 0.5) + text!(plt, xr, yr; + text = plt.label_c[], + align = (:center, :center), + fontsize=7pt, + rotation = -π/3, + offset = (22pt, 2pt) + ) + end return plt end -# Helper function for coordinate conversion (used for tick placement) function ternary_to_cartesian(a, b, c) x = 0.5 * (2b + c) y = (√3 / 2) * c diff --git a/opt/mixtures/ionic_conductivity.jl b/opt/mixtures/ionic_conductivity.jl index dbbda744..e406dd5f 100644 --- a/opt/mixtures/ionic_conductivity.jl +++ b/opt/mixtures/ionic_conductivity.jl @@ -7,12 +7,16 @@ using GLM using Interpolations using Statistics: mean using Mixtures: Mixtures, titlecase +using Printf + include("TernaryPlots.jl") const DATA_DIR = realpath(joinpath(pkgdir(Mixtures), "..", "..", "data")) function label_smi(smi::AbstractString) known = Dict( + "O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F" => "TFSI", + "F[P-](F)(F)(F)(F)F" => "PF6", "O=C1OCC(F)O1" => "FEC", "CC1COC(=O)O1" => "PC", "CCOC(=O)OC" => "EMC", @@ -64,71 +68,161 @@ function plot_angell_solvents() return fig end -function plot_solvent_ternary(f, solvent) - return -end -function ternary_activation_energy!(fig, df::DataFrame) +function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer) + + df = Mixtures.evaluate_conductivity(model, [mixture]; n = n, fixed_salt=salt_comp) - # Get solvent names for axis labels - solvent_names = mixtures[1]["solvents"] + comp_matrix = reduce(hcat, df.composition)' + # update salt composition to account for grid discretization + salt_comp = first(comp_matrix[: , 4]) + pure_solvent_Ea = zeros(Float64, 3) + pure_solvent_comp = 1 - salt_comp - # Determine global colorrange for consistent coloring - global_crange = extrema(df.Ea) - println(global_crange) - @info "Activation Energy Range" global_crange + for (idx, solvent) in enumerate(mixture["solvents"]) + comp = zeros(4) + comp[idx] = pure_solvent_comp + comp[4] = salt_comp + preds = Mixtures.evaluate_at_composition(model, mixture, comp) + pure_solvent_Ea[idx] = preds["Ea"] + end + + + solvent_comps = comp_matrix[:, 1:3] + solvent_fractions = solvent_comps ./ sum(solvent_comps, dims=2) + df[!, "ideal_mixing_Ea"] = solvent_fractions * pure_solvent_Ea + df[!, "excess_Ea"] = df.Ea .- df.ideal_mixing_Ea + @assert sum(abs.(collect(df.excess_Ea[1:4]))) < 1e-4 "Zero excess for expected degenerate case" + return df +end - ax = Axis(fig[1, 1], aspect=DataAspect()) +function ternary_activation_energy!( + ax::Axis, df::DataFrame, + solvent_names::Vector{String}, global_crange::NTuple{2, Real}; + excess:: Bool = false + ) - # Extract first 3 components (solvents) from composition - comp_matrix = reduce(hcat, df.composition)' # Convert to matrix + comp_matrix = reduce(hcat, df.composition)' a = comp_matrix[:, 1] b = comp_matrix[:, 2] c = comp_matrix[:, 3] - ternary!(ax, a, b, c, df.Ea, - colormap = :viridis, + if excess + values = df.excess_Ea + else + values = df.Ea + end + ternary!(ax, a, b, c, values, + colormap = MISTStyle.CONTINUOUS_COLORS, colorrange = global_crange, - label_a = solvent_names[1], - label_b = solvent_names[2], - label_c = solvent_names[3], + label_a = label_smi(solvent_names[1]), + label_b = label_smi(solvent_names[2]), + label_c = label_smi(solvent_names[3]), show_ticks = true) - hidedecorations!(ax) - hidespines!(ax) +end + +function ternary_activation_energy(model, mixture::Dict; excess::Bool=false, n::Integer = 64) - # Add title showing salt composition - salt_comp = first(last.(df.composition)) - Label(fig[0, 1], "Salt: $(round(salt_comp, digits=3))", fontsize=14) + fig = Figure(; size=(183mm, 55mm), figure_padding=(1, 1, 1, 1)) + solvent_names = mixture["solvents"] + mixtures = [mixture] + + all_dfs = [] + for salt_comp in [0.05, 0.1, 0.15] + if excess + df = calculate_excess(model, mixture, salt_comp, n ) + else + df = Mixtures.evaluate_conductivity(model, mixtures; n = n, fixed_salt=salt_comp) + end + push!(all_dfs, df) + end + + if excess + all_ea = vcat([df.excess_Ea for df in all_dfs]...) + else + all_ea = vcat([df.Ea for df in all_dfs]...) + end + + global_crange = extrema(all_ea) + + for (idx, df) in enumerate(all_dfs) + ax = Axis( + fig[1, idx]; + aspect=DataAspect(), + tellwidth=true, + tellheight=true, + limits=((-0.1, 1.1), (-0.2, 1.0)), + ) + ternary_activation_energy!(ax, df, solvent_names, global_crange; excess = excess) + salt_comp = first(last.(df.composition)) + hidedecorations!(ax) + hidespines!(ax) + + val = @sprintf("%.2f", salt_comp) + label = L"$x_{\mathrm{Li}^{+}}$ = %$val" + Label( + fig[0, idx], label; + tellheight=true, fontsize=8pt, lineheight=0.1, padding=(0, 0, 0, 0) + ) + end + + label = excess ? L"excess $E_a$" : L"$E_a$" + + Colorbar(fig[1, 4], + colormap = MISTStyle.CONTINUOUS_COLORS, + limits = global_crange, + label = label, + height = Relative(0.8), + tellheight=true, + flip_vertical_label = true, + vertical = true, + ) + + colsize!(fig.layout, 1, Aspect(1, 1.0)) + colsize!(fig.layout, 2, Aspect(1, 1.0)) + colsize!(fig.layout, 3, Aspect(1, 1.0)) + colgap!(fig.layout, 10) + resize_to_layout!(fig) return fig end -function ternary_activation_energy(model_id::String) - model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") - - fig = Figure(size=(300*3, 400)) - mixtures = [ - Dict( - "solvents" => ["O=C1OCC(F)O1", "CCOC(=O)OC", "O=C1OCCO1"], - "temperature" => 298.15, - "salt" => ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "[Li+]"] - ), - ] +function plot_ternary_activation_energies() + model_id = "mist-conductivity-27.0M-2mpg8dcd" + model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") + for solvent in ["O=C1OCC(F)O1", "CCOC(=O)OC", "CCOC(=O)OCC", "COC(=O)OC"] + for salt in ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + fn_name = "$(label_smi(solvent))_$(label_smi(salt))" + mixture = Dict( + "solvents" => [ "CC1COC(=O)O1", "O=C1OCCO1", solvent], + "temperature" => 298.15, + "salt" => [ salt, "[Li+]" ] + ) + with_theme(MISTStyle.theme()) do + ternary_activation_energy(model, mixture) + end |> MISTStyle.savefig(fn_name) + end + end + return +end - for (idx, salt_comp) in enumerate([0.06, 0.1, 0.14]) - df = Mixtures.evaluate_conductivity(model, mixtures; n = 140, fixed_salt=salt_comp) - ternary_activation_energy!(fig[:, idx], df) +function plot_excess_activation_energies() + model_id = "mist-conductivity-27.0M-2mpg8dcd" + model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") + for solvent in ["O=C1OCC(F)O1", "CCOC(=O)OC", "CCOC(=O)OCC", "COC(=O)OC"] + for salt in ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + fn_name = "excess_$(label_smi(solvent))_$(label_smi(salt))" + mixture = Dict( + "solvents" => [ "CC1COC(=O)O1", "O=C1OCCO1", solvent], + "temperature" => 298.15, + "salt" => [salt, "[Li+]"] + ) + with_theme(MISTStyle.theme()) do + ternary_activation_energy(model, mixture; excess = true) + end |> MISTStyle.savefig(fn_name) + end end - # Add colorbar - Colorbar( - fig[:, 4], - tellheight = true, - tellwidth = true, - colormap = :viridis, - limits = (100, 200), - label = L"$E_a$" - ) - return fig + return end diff --git a/opt/mixtures/python/ionic_conductivity.py b/opt/mixtures/python/ionic_conductivity.py index 53321e97..bbb4ea73 100644 --- a/opt/mixtures/python/ionic_conductivity.py +++ b/opt/mixtures/python/ionic_conductivity.py @@ -35,6 +35,19 @@ def generate_simplex_grid(n, grid_size, fixed_last=None): raise ValueError(f"fixed_last={fixed_last} maps to k={k}, expected 0..{g}") target_sum = g - k + + # Degenerate slice: salt = 1.0 -> only one point + if target_sum == 0: + yield [*(0.0 for _ in range(n - 1)), k / g] + return + + # edge vertices first + # one solvent takes all remaining fraction, others 0 + for i in range(n - 1): + counts = [0] * (n - 1) + counts[i] = target_sum + yield [*(c / g for c in counts), k / g] + # Enumerate only solvent counts summing to target_sum # (n-1 solvents; last coord is fixed to k/g) for counts in itertools.product(range(target_sum + 1), repeat=n - 1): @@ -166,8 +179,7 @@ def evaluate(model, dataloader): out["temperature"] = batch["temperature"][bdx].item() for target in targets: out[target] = output[target][bdx].tolist() - # print(out) - yield out + yield out def load_conductivity_model(ckpt): @@ -235,5 +247,5 @@ def evaluate_at_composition(model, mixture, composition): }, ] # print(evaluate_at_composition(model, mixtures[0], [0.9, 0, 0, 0.1])) - for out in evaluate_mixtures(model, mixtures, fixed_salt=0.1): + for out in evaluate_mixtures(model, mixtures, n=5, fixed_salt=0.1): print(out["composition"]) diff --git a/opt/mixtures/src/python.jl b/opt/mixtures/src/python.jl index 8a24571c..312c170e 100644 --- a/opt/mixtures/src/python.jl +++ b/opt/mixtures/src/python.jl @@ -148,7 +148,7 @@ function evaluate_at_composition(model::Py, mixture::Py, composition::Py) temperature=mixture["temperature"], ) pycomposition = PythonCall.pylist(composition) - pred = pyionic[].evaluate_at_composition(model, solvents, pycomposition) + pred = pyionic[].evaluate_at_composition(model, pymixture, pycomposition) pred = pyconvert(Dict{String,Any}, pred) out = Dict{String, Any}( "components" => pyconvert(Vector, pred["components"]), From 99b01a7b93ac4db8ea10748ddfae51ae15dd8b83 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 6 Oct 2025 19:31:18 -0400 Subject: [PATCH 142/147] refactor: allow both mixture data modules (pre/post mixture-splits --- electrolyte_fm/data_modules/__init__.py | 2 +- .../data_modules/mixture_dataset.py | 231 +++++++++++++++++- electrolyte_fm/models/excess_physics_model.py | 4 +- opt/mixtures/python/excess.py | 4 +- opt/mixtures/python/ionic_conductivity.py | 3 - test/test_mixture_dataset.py | 8 +- 6 files changed, 237 insertions(+), 15 deletions(-) diff --git a/electrolyte_fm/data_modules/__init__.py b/electrolyte_fm/data_modules/__init__.py index a5af553f..c58a4906 100644 --- a/electrolyte_fm/data_modules/__init__.py +++ b/electrolyte_fm/data_modules/__init__.py @@ -2,4 +2,4 @@ from .property_prediction_dataset import PropertyPredictionDataModule from .roberta_dataset import RobertaDataSet from .tmqm_dataset import tmQMDataModule -from .mixture_dataset import ComponentDataModule +from .mixture_dataset import ComponentDataModule, ComponentDataModuleFast diff --git a/electrolyte_fm/data_modules/mixture_dataset.py b/electrolyte_fm/data_modules/mixture_dataset.py index 2895db34..559faef8 100644 --- a/electrolyte_fm/data_modules/mixture_dataset.py +++ b/electrolyte_fm/data_modules/mixture_dataset.py @@ -1,18 +1,243 @@ from pathlib import Path from itertools import chain +from typing import List, Optional, Union import torch import typer -from datasets import Dataset, DatasetDict, IterableDatasetDict, load_dataset -from torch.utils.data import default_collate +from lightning import LightningDataModule +from datasets import Dataset, IterableDatasetDict, load_dataset +from torch.utils.data import default_collate, DataLoader +from transformers import DataCollatorWithPadding, PreTrainedModel +from ..utils.tokenizer import load_tokenizer from .utils import MolEncoding, stack_columns, collate_target from .property_prediction_dataset import PropertyPredictionDataModule cli = typer.Typer() -class ComponentDataModule(PropertyPredictionDataModule): +def collate_components_and_environment( + *args, + include_temperature, + tokenizer, + randomize, + encoding, + n_components, + encoder: PreTrainedModel = None, + collate: DataCollatorWithPadding = None, +): + output = {} + if include_temperature: + temperature = torch.tensor(args[-1]) + output = {"temperature": temperature} + for i in range(n_components): + idx = 2 * i + smiles = args[idx] + if randomize: + smiles = encoding.random(smiles) + composition = args[idx + 1] + batch = tokenizer(smiles) + batch = collate(batch) + output[f"input_ids_{i}"] = batch["input_ids"] + output[f"attention_mask_{i}"] = batch["attention_mask"] + output[f"composition_{i}"] = composition + + return output + + +def collate_target_stacked(x, target_columns): + """Stack multiple target columns into a single vector, + recording unknown elements to be masked out during training + """ + target = [] + mask = [] + for k in target_columns: + v = x[k] + if v is None: + target.append(torch.tensor(0)) # Placeholder, should be masked out + mask.append(torch.tensor(True)) + else: + target.append(torch.tensor(v)) + mask.append(torch.tensor(False)) + + return {"target": torch.stack(target), "target_mask": torch.stack(mask)} + + +class ComponentDataModule(LightningDataModule): + def __init__( + self, + path: str, + target_col: Union[str, List], + n_components: int = 2, + tokenizer: Optional[str] = None, + batch_size: int = 64, + val_batch_size: Optional[int] = None, + num_workers: int = 0, + prefetch_factor: Optional[int] = None, + include_temperature: str | bool = False, + encoding: Optional[str | MolEncoding] = "smiles", + iterable: bool = False, + randomize: bool = False, + max_length: int = 512, + ): + super().__init__() + + # Locate Tokeniser and dataset + self.tokenizer = load_tokenizer(tokenizer) + self.iterable = iterable + self.vocab_size = len(self.tokenizer) + self.path: Path = Path(path) + self.encoding = MolEncoding(encoding) + self.randomize = randomize + if isinstance(target_col, str): + target_col = [ + target_col, + ] + self.target_col = target_col + assert self.path.is_dir() or self.path.is_file() + + self.batch_size = batch_size + self.n_components = n_components + self.temperature = bool(include_temperature) + self.val_batch_size = val_batch_size or batch_size + self.num_workers = num_workers + self.prefetch_factor = prefetch_factor + self.save_hyperparameters(logger=False) + self.data_collator = DataCollatorWithPadding( + self.tokenizer, max_length=max_length, padding="max_length" + ) + + def prepare_data(self): + self.dataset + + @property + def dataset(self): + if hasattr(self, "_dataset"): + return self._dataset + if self.iterable: + self._dataset = load_dataset( + "arrow", + name=str(self.path.name), + data_files={ + "train": str(self.path.joinpath("train/*.arrow")), + "validation": str(self.path.joinpath("validation/*.arrow")), + "test": str(self.path.joinpath("test/*.arrow")), + }, + keep_in_memory=False, + streaming=True, + save_infos=False, + ) + assert isinstance(self._dataset, IterableDatasetDict) + else: + self._dataset = self._dataset = load_dataset( + "csv", + name=str(self.path.name), + data_files={ + "train": str(self.path.joinpath("train.csv")), + "validation": str(self.path.joinpath("val.csv")), + "test": str(self.path.joinpath("test.csv")), + }, + ) + return self._dataset + + def setup(self, stage: str) -> None: + # Extract per molecule hidden states + input_columns = [] + for i in range(self.n_components): + input_columns.extend([f"smi{i+1}", f"x{i+1}"]) + if self.temperature: + input_columns.append("temperature") + + ds = self.dataset + ds = ds.map( + collate_target_stacked, + batched=False, + fn_kwargs={"target_columns": self.target_col}, + remove_columns=self.target_col, + ) + ds = ds.map( + collate_components_and_environment, + batched=False, + fn_kwargs={ + "include_temperature": self.temperature, + "tokenizer": self.tokenizer, + "randomize": self.randomize, + "encoding": self.encoding, + "collate": self.data_collator, + "n_components": self.n_components, + }, + input_columns=input_columns, + ) + + self.train_dataset: Dataset = ds["train"].shuffle() + self.val_dataset: Dataset = ds["validation"] + self.test_dataset: Dataset = ds["test"] + self.target_dataset = ds["train"].select_columns(["target", "target_mask"]) + + def collator(self, batch): + output = {} + + for i in range(self.n_components): + output[f"input_ids_{i}"] = torch.stack( + [torch.tensor(x[f"input_ids_{i}"], dtype=int) for x in batch] + ) + output[f"attention_mask_{i}"] = torch.stack( + [torch.tensor(x[f"attention_mask_{i}"], dtype=int) for x in batch] + ) + output[f"composition_{i}"] = torch.stack( + [ + torch.tensor(x[f"composition_{i}"], dtype=torch.float32) + for x in batch + ] + ) + + output["target"] = torch.stack( + [torch.tensor(x["target"], dtype=torch.float32) for x in batch] + ) + output["target_mask"] = torch.stack( + [torch.tensor(x["target_mask"], dtype=bool) for x in batch] + ) + + if self.temperature: + output["temperature"] = torch.stack( + [torch.tensor(x["temperature"], dtype=torch.float32) for x in batch] + ) + return output + + def train_dataloader(self): + return DataLoader( + self.train_dataset, + num_workers=self.num_workers, + collate_fn=self.collator, + prefetch_factor=self.prefetch_factor, + batch_size=self.batch_size, + pin_memory=True, + persistent_workers=self.num_workers > 0, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + num_workers=self.num_workers, + collate_fn=self.collator, + prefetch_factor=self.prefetch_factor, + batch_size=self.val_batch_size, + pin_memory=True, + persistent_workers=self.num_workers > 0, + ) + + def test_dataloader(self): + return DataLoader( + self.test_dataset, + collate_fn=self.collator, + num_workers=self.num_workers, + prefetch_factor=self.prefetch_factor, + batch_size=self.val_batch_size, + persistent_workers=self.num_workers > 0, + ) + + +class ComponentDataModuleFast(PropertyPredictionDataModule): def __init__( self, path: str | Path, diff --git a/electrolyte_fm/models/excess_physics_model.py b/electrolyte_fm/models/excess_physics_model.py index d17acb29..add8b883 100644 --- a/electrolyte_fm/models/excess_physics_model.py +++ b/electrolyte_fm/models/excess_physics_model.py @@ -571,7 +571,7 @@ def configure_optimizers(self): from electrolyte_fm.models.excess_physics_model import ExcessPhysicsConfig from electrolyte_fm.utils.lr_schedule import RelativeCosineWarmup - from ..data_modules.mixture_dataset import ComponentDataModule + from ..data_modules.mixture_dataset import ComponentDataModuleFast parser = ArgumentParser() parser.add_argument("--config", type=str, default=None, required=False) @@ -641,7 +641,7 @@ def configure_optimizers(self): config["model"] = model.config.to_dict() config["data"]["target_columns"] = model.config.target_columns - dm = ComponentDataModule(**config["data"]) + dm = ComponentDataModuleFast(**config["data"]) lit = ExcessPhysicsLightningModel( model=model, diff --git a/opt/mixtures/python/excess.py b/opt/mixtures/python/excess.py index c00cc85d..da1c4527 100644 --- a/opt/mixtures/python/excess.py +++ b/opt/mixtures/python/excess.py @@ -11,7 +11,7 @@ ) from electrolyte_fm.data_modules.utils import MolEncoding, collate_target from electrolyte_fm.data_modules.mixture_dataset import ( - ComponentDataModule, + ComponentDataModuleFast, encode_and_tokenize_mixture, ) from transformers import DataCollatorWithPadding @@ -187,7 +187,7 @@ def evaluate_binary_csv(model: ExcessPhysicsModel, path: str | Path, **kwargs): def evaluate_dataset(model: ExcessPhysicsModel, path, **kwargs): assert Path(path).exists() - dm = ComponentDataModule( + dm = ComponentDataModuleFast( path, batch_size=16, target_columns=model.config.target_columns, diff --git a/opt/mixtures/python/ionic_conductivity.py b/opt/mixtures/python/ionic_conductivity.py index bbb4ea73..d7160110 100644 --- a/opt/mixtures/python/ionic_conductivity.py +++ b/opt/mixtures/python/ionic_conductivity.py @@ -6,9 +6,6 @@ from smirk import SmirkTokenizerFast -from electrolyte_fm.data_modules.mixture_dataset import ( - ComponentDataModule, -) from transformers import DataCollatorWithPadding from torch.utils.data import IterableDataset from electrolyte_fm.models.prod_mixture import MISTIonicConductivity diff --git a/test/test_mixture_dataset.py b/test/test_mixture_dataset.py index a5d3fb8f..da9d19a1 100644 --- a/test/test_mixture_dataset.py +++ b/test/test_mixture_dataset.py @@ -10,7 +10,7 @@ from transformers import DataCollatorWithPadding from electrolyte_fm.data_modules.mixture_dataset import ( - ComponentDataModule, + ComponentDataModuleFast, encode_and_tokenize_mixture, ) from electrolyte_fm.data_modules.utils import MolEncoding @@ -100,7 +100,7 @@ def write_split(split: str, n_rows: int): ] ) def datamodule(request, tmp_dataset): - dm = ComponentDataModule( + dm = ComponentDataModuleFast( path=str(tmp_dataset), target_columns=["propA", "propB"], batch_size=8, @@ -116,7 +116,7 @@ def datamodule(request, tmp_dataset): return dm -def check_mixture_batch(dm: ComponentDataModule, batch: dict): +def check_mixture_batch(dm: ComponentDataModuleFast, batch: dict): assert batch["target"].shape == (dm.batch_size, len(dm.target_columns)) assert batch["target"].dtype == torch.float32 assert batch["target_mask"].shape == (dm.batch_size, len(dm.target_columns)) @@ -239,7 +239,7 @@ def test_dataloader_iteration(datamodule): break -def test_sparsity_weights(datamodule: ComponentDataModule): +def test_sparsity_weights(datamodule: ComponentDataModuleFast): columns = ["target_mask"] if datamodule.excess_columns is not None: columns.append("target_excess_mask") From 8f41a75c49fb6f209c4738e73bb6ffc3cd30e708 Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Mon, 6 Oct 2025 19:35:11 -0400 Subject: [PATCH 143/147] remove old mixture plotting code --- opt/mixtures/analysis/__init__.py | 0 opt/mixtures/analysis/__main__.py | 25 -- opt/mixtures/analysis/cond_trends.py | 88 ------- opt/mixtures/analysis/lipari_cm.py | 268 -------------------- opt/mixtures/analysis/mist.mplstyle | 15 -- opt/mixtures/analysis/pred_vs_actual.py | 85 ------- opt/mixtures/analysis/ternary_plots.py | 309 ------------------------ 7 files changed, 790 deletions(-) delete mode 100644 opt/mixtures/analysis/__init__.py delete mode 100644 opt/mixtures/analysis/__main__.py delete mode 100644 opt/mixtures/analysis/cond_trends.py delete mode 100644 opt/mixtures/analysis/lipari_cm.py delete mode 100644 opt/mixtures/analysis/mist.mplstyle delete mode 100644 opt/mixtures/analysis/pred_vs_actual.py delete mode 100644 opt/mixtures/analysis/ternary_plots.py diff --git a/opt/mixtures/analysis/__init__.py b/opt/mixtures/analysis/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/opt/mixtures/analysis/__main__.py b/opt/mixtures/analysis/__main__.py deleted file mode 100644 index 39ce4638..00000000 --- a/opt/mixtures/analysis/__main__.py +++ /dev/null @@ -1,25 +0,0 @@ -from ternary_plots import plot_vector_fields, generate_dataset, run_inference -import glob - - -if __name__ == "__main__": - run_id = "8aqjzwmr" - pretrained_ckpt = ( - f"/home/abhutani/electrolyte-fm/mist/{run_id}/checkpoints/last.ckpt" - ) - for target in ["transferance", "conductivity"]: - for salt_composition in [0.05, 0.1, 0.15]: - for salt in ["LiPF6", "TFSI"]: - dir_name = f"{salt}_{int(salt_composition*100)}" - generate_dataset( - salt_mole_fraction=salt_composition, save_dir=dir_name, salt=salt - ) - run_inference( - pretrained_ckpt, - data_dir=dir_name, - val_batch_size=165, - target=target, - ) - - for target in ["transferance", "conductivity", "product", "diffusioncoeff"]: - plot_vector_fields(data_files=glob.glob(f"{target}_*.csv"), target=target) diff --git a/opt/mixtures/analysis/cond_trends.py b/opt/mixtures/analysis/cond_trends.py deleted file mode 100644 index e723a902..00000000 --- a/opt/mixtures/analysis/cond_trends.py +++ /dev/null @@ -1,88 +0,0 @@ -import glob -import os -import re - -import matplotlib.pyplot as plt -import numpy as np -from excess_property_plots import interpolate_color -from lipari_cm import lipari10_cmap -from matplotlib.colors import Normalize - -from electrolyte_fm.data_modules import ComponentDataModule -from electrolyte_fm.models.model_utils import DeepSpeedMixin - -plt.style.use("./mist.mplstyle") - - -def plot(solvent_name, pretrained_ckpt, model, base_dir): - """ - Plot ionic conductivity as a function of mole fraction. - """ - - fig, axes = plt.subplots(1, 2, figsize=(4, 1), sharey=True) - - salts = ["LiPF6", "LiTFSI"] - - for sidx, salt_name in enumerate(salts): - ax = axes[sidx] - dir_list = glob.glob(os.path.join(base_dir, f"{solvent_name}_{salt_name}_*")) - - for cidx, directory in enumerate(dir_list): - temperature = re.search(r"_([0-9]+)$", directory).group(1) - color = interpolate_color(float(temperature), 243, 293) - solvent_name = directory.split("/")[-1].split("_")[0] - dm = ComponentDataModule( - path=directory, - target_col="Cond(mS)2", - n_components=5, - val_batch_size=20, - tokenizer=pretrained_ckpt, - include_temperature=True, - ) - - dm.setup(stage="test") - - for idx, batch in enumerate(dm.test_dataloader()): - pred = model(batch)[0].detach().numpy() - pred = np.exp(pred) - if pred.max() > 100: - continue - else: - ax.plot( - batch["composition_4"].numpy(), - pred, - label=solvent_name, - color=color, - ) - - ax.grid(True, linestyle="--", linewidth=0.5, alpha=0.7) - - ax.spines["top"].set_visible(True) - ax.spines["top"].set_visible(True) - ax.set_xlabel("$x_{Li}$") - if sidx < 1: - ax.set_ylabel("$\sigma$ [mS/cm]") - ax.set_xticks(np.around(np.linspace(0, 0.2, 10), 1)) - ax.set_xticklabels(np.around(np.linspace(0, 0.2, 10), 1)) - - # Create a single colorbar for the entire figure - norm = Normalize(243, 293) - sm = plt.cm.ScalarMappable(cmap=lipari10_cmap, norm=norm) - colorbar_ax = fig.add_axes([0.05, -0.02, 0.9, 0.05]) # x, y, width, height - cbar = plt.colorbar(sm, cax=colorbar_ax, orientation="horizontal") - cbar.set_label("Temperature [K]") - cbar.solids.set_rasterized(True) - cbar.ax.tick_params(width=0.2) - - # Save figure - plt.savefig(f"ic_solvent_{solvent_name}.png", bbox_inches="tight", dpi=500) - - -if __name__ == "__main__": - run_id = "ur7v2a6y" # "z4ni8hcj" # "c7ssptg7" - pretrained_ckpt = ( - f"/home/abhutani/electrolyte-fm/mist/{run_id}/checkpoints/last.ckpt" - ) - model = DeepSpeedMixin.load(pretrained_ckpt) - solvent_name = "PC" - plot(solvent_name, pretrained_ckpt, model) diff --git a/opt/mixtures/analysis/lipari_cm.py b/opt/mixtures/analysis/lipari_cm.py deleted file mode 100644 index c985268e..00000000 --- a/opt/mixtures/analysis/lipari_cm.py +++ /dev/null @@ -1,268 +0,0 @@ -from matplotlib.colors import ListedColormap - -cm_data = [ - [0.01137, 0.07324, 0.14828], - [0.013965, 0.079062, 0.15537], - [0.015899, 0.084718, 0.16252], - [0.017234, 0.090035, 0.16973], - [0.018046, 0.095138, 0.17697], - [0.018987, 0.1, 0.18427], - [0.01997, 0.10491, 0.19161], - [0.021, 0.10996, 0.19896], - [0.022084, 0.11488, 0.20639], - [0.023227, 0.11992, 0.21378], - [0.02444, 0.12498, 0.22121], - [0.025732, 0.1301, 0.22866], - [0.027114, 0.13518, 0.2361], - [0.028602, 0.14029, 0.24354], - [0.030209, 0.14548, 0.25102], - [0.031951, 0.15062, 0.25848], - [0.033828, 0.15583, 0.26596], - [0.036137, 0.16105, 0.27341], - [0.038425, 0.16628, 0.28084], - [0.040951, 0.17154, 0.28828], - [0.043537, 0.17682, 0.2957], - [0.046467, 0.18211, 0.30308], - [0.049594, 0.18747, 0.31047], - [0.052785, 0.19279, 0.31779], - [0.056373, 0.19814, 0.32507], - [0.060123, 0.20352, 0.33234], - [0.064059, 0.20889, 0.33952], - [0.068323, 0.21429, 0.34664], - [0.072795, 0.21969, 0.35369], - [0.077499, 0.22506, 0.36066], - [0.082595, 0.23046, 0.36755], - [0.087912, 0.23587, 0.37435], - [0.093441, 0.24124, 0.38103], - [0.099307, 0.24659, 0.38756], - [0.10541, 0.25194, 0.39397], - [0.11181, 0.25723, 0.40024], - [0.11843, 0.26248, 0.40634], - [0.12524, 0.26767, 0.41226], - [0.13231, 0.27284, 0.41798], - [0.13952, 0.27792, 0.42351], - [0.14698, 0.28292, 0.42882], - [0.15457, 0.2878, 0.43389], - [0.16232, 0.29261, 0.43873], - [0.17014, 0.2973, 0.44331], - [0.17807, 0.30184, 0.44762], - [0.18603, 0.30629, 0.45168], - [0.19404, 0.31056, 0.45545], - [0.20201, 0.31466, 0.45894], - [0.21, 0.31863, 0.46214], - [0.21794, 0.32242, 0.46506], - [0.22581, 0.32601, 0.4677], - [0.23354, 0.32945, 0.47006], - [0.24121, 0.33269, 0.47215], - [0.24874, 0.33574, 0.47396], - [0.25609, 0.3386, 0.47554], - [0.26333, 0.34129, 0.47686], - [0.27038, 0.34378, 0.47795], - [0.27724, 0.34611, 0.47882], - [0.28392, 0.34828, 0.4795], - [0.29042, 0.35028, 0.47998], - [0.29677, 0.3521, 0.48028], - [0.3029, 0.3538, 0.48042], - [0.3089, 0.35537, 0.48042], - [0.31468, 0.35679, 0.48029], - [0.32035, 0.35809, 0.48003], - [0.32583, 0.35927, 0.47967], - [0.33119, 0.36036, 0.47921], - [0.33642, 0.36136, 0.47866], - [0.34153, 0.36227, 0.47804], - [0.34652, 0.3631, 0.47735], - [0.35141, 0.36386, 0.47661], - [0.35622, 0.36456, 0.47581], - [0.36093, 0.36521, 0.47497], - [0.3656, 0.36581, 0.47408], - [0.37019, 0.36636, 0.47315], - [0.37475, 0.36687, 0.47222], - [0.37926, 0.36734, 0.47124], - [0.38375, 0.36778, 0.47023], - [0.3882, 0.36821, 0.46921], - [0.39264, 0.36861, 0.46815], - [0.39707, 0.36898, 0.4671], - [0.4015, 0.36934, 0.466], - [0.40595, 0.36968, 0.46491], - [0.41039, 0.37001, 0.4638], - [0.41484, 0.37033, 0.46266], - [0.41932, 0.37064, 0.46153], - [0.42381, 0.37095, 0.46037], - [0.42834, 0.37125, 0.45921], - [0.43289, 0.37154, 0.45803], - [0.43747, 0.37183, 0.45684], - [0.44208, 0.37212, 0.45564], - [0.44672, 0.37241, 0.45443], - [0.4514, 0.3727, 0.4532], - [0.45611, 0.37299, 0.45197], - [0.46086, 0.37327, 0.45072], - [0.46564, 0.37356, 0.44947], - [0.47048, 0.37385, 0.4482], - [0.47535, 0.37414, 0.44692], - [0.48024, 0.37443, 0.44563], - [0.48517, 0.37472, 0.44435], - [0.49015, 0.375, 0.44304], - [0.49516, 0.37529, 0.44173], - [0.50021, 0.37559, 0.44039], - [0.5053, 0.37589, 0.43905], - [0.51043, 0.37619, 0.43772], - [0.51559, 0.3765, 0.43636], - [0.52081, 0.3768, 0.435], - [0.52606, 0.3771, 0.43361], - [0.53134, 0.37741, 0.43225], - [0.53668, 0.37772, 0.43085], - [0.54204, 0.37803, 0.42944], - [0.54745, 0.37835, 0.42803], - [0.5529, 0.37867, 0.4266], - [0.55839, 0.37899, 0.42518], - [0.56394, 0.37932, 0.42372], - [0.5695, 0.37965, 0.42227], - [0.57513, 0.37998, 0.42081], - [0.58079, 0.38032, 0.41935], - [0.5865, 0.38067, 0.41786], - [0.59225, 0.38102, 0.41637], - [0.59803, 0.38138, 0.41487], - [0.60388, 0.38173, 0.41337], - [0.60975, 0.38209, 0.41185], - [0.61568, 0.38247, 0.41034], - [0.62164, 0.38286, 0.4088], - [0.62766, 0.38326, 0.40726], - [0.63373, 0.38366, 0.40573], - [0.63983, 0.38407, 0.40417], - [0.64598, 0.3845, 0.40262], - [0.65219, 0.38495, 0.40108], - [0.65843, 0.38541, 0.3995], - [0.66472, 0.3859, 0.39796], - [0.67106, 0.38641, 0.3964], - [0.67745, 0.38695, 0.39485], - [0.68388, 0.38752, 0.3933], - [0.69035, 0.38812, 0.39177], - [0.69688, 0.38877, 0.39023], - [0.70344, 0.38945, 0.38873], - [0.71005, 0.39017, 0.38722], - [0.71669, 0.39097, 0.38575], - [0.72336, 0.39181, 0.3843], - [0.73008, 0.39271, 0.38288], - [0.73681, 0.3937, 0.3815], - [0.74358, 0.39477, 0.38015], - [0.75036, 0.39593, 0.37885], - [0.75715, 0.39717, 0.3776], - [0.76395, 0.39854, 0.37642], - [0.77075, 0.40002, 0.37529], - [0.77754, 0.40162, 0.37425], - [0.78431, 0.40337, 0.37327], - [0.79105, 0.40525, 0.37239], - [0.79775, 0.40729, 0.3716], - [0.8044, 0.4095, 0.37093], - [0.81097, 0.41188, 0.37036], - [0.81747, 0.41444, 0.36992], - [0.82388, 0.4172, 0.36961], - [0.83018, 0.42014, 0.36944], - [0.83635, 0.42329, 0.36941], - [0.84239, 0.42665, 0.36953], - [0.84826, 0.43021, 0.3698], - [0.85397, 0.43398, 0.37025], - [0.85949, 0.43797, 0.37086], - [0.8648, 0.44216, 0.37164], - [0.8699, 0.44654, 0.3726], - [0.87477, 0.45112, 0.37375], - [0.87939, 0.45588, 0.37505], - [0.88376, 0.46082, 0.37655], - [0.88786, 0.46592, 0.3782], - [0.89169, 0.47118, 0.38003], - [0.89525, 0.47657, 0.38202], - [0.89851, 0.48208, 0.38417], - [0.9015, 0.48769, 0.38647], - [0.9042, 0.49337, 0.38893], - [0.90662, 0.49914, 0.39151], - [0.90876, 0.50497, 0.3942], - [0.91063, 0.51082, 0.39702], - [0.91223, 0.5167, 0.39994], - [0.91357, 0.52259, 0.40297], - [0.91467, 0.52848, 0.40608], - [0.91554, 0.53435, 0.40925], - [0.91619, 0.54019, 0.4125], - [0.91664, 0.546, 0.4158], - [0.91689, 0.55176, 0.41915], - [0.91696, 0.55748, 0.42253], - [0.91687, 0.56313, 0.42597], - [0.91664, 0.56872, 0.42943], - [0.91626, 0.57425, 0.43291], - [0.91578, 0.57973, 0.43641], - [0.91519, 0.58514, 0.43993], - [0.91451, 0.59048, 0.44346], - [0.91375, 0.59576, 0.447], - [0.91293, 0.60098, 0.45057], - [0.91206, 0.60614, 0.45414], - [0.91115, 0.61126, 0.45772], - [0.91021, 0.61632, 0.46133], - [0.90925, 0.62133, 0.46495], - [0.90829, 0.62631, 0.4686], - [0.90733, 0.63125, 0.47228], - [0.90637, 0.63616, 0.47599], - [0.90545, 0.64104, 0.47973], - [0.90454, 0.6459, 0.48351], - [0.90367, 0.65075, 0.48735], - [0.90285, 0.65559, 0.49125], - [0.90208, 0.66043, 0.49519], - [0.90137, 0.66527, 0.49921], - [0.90072, 0.67012, 0.5033], - [0.90014, 0.67498, 0.50749], - [0.89964, 0.67985, 0.51176], - [0.89922, 0.68476, 0.51612], - [0.89889, 0.68969, 0.5206], - [0.89865, 0.69465, 0.52519], - [0.89852, 0.69965, 0.52989], - [0.89848, 0.70469, 0.53471], - [0.89855, 0.70978, 0.53966], - [0.89873, 0.71491, 0.54476], - [0.89903, 0.72009, 0.54999], - [0.89945, 0.72533, 0.55536], - [0.89998, 0.73061, 0.56088], - [0.90063, 0.73594, 0.56656], - [0.90141, 0.74134, 0.57238], - [0.90232, 0.7468, 0.57835], - [0.90335, 0.75231, 0.58449], - [0.9045, 0.75787, 0.59077], - [0.90579, 0.76349, 0.59722], - [0.90719, 0.76916, 0.60381], - [0.90871, 0.77489, 0.61054], - [0.91037, 0.78067, 0.61743], - [0.91213, 0.7865, 0.62446], - [0.91402, 0.79237, 0.63162], - [0.91601, 0.79828, 0.63892], - [0.91812, 0.80424, 0.64635], - [0.92032, 0.81023, 0.65391], - [0.92264, 0.81627, 0.66158], - [0.92505, 0.82234, 0.66937], - [0.92755, 0.82844, 0.67726], - [0.93014, 0.83456, 0.68525], - [0.93281, 0.84071, 0.69334], - [0.93556, 0.84688, 0.7015], - [0.93839, 0.85308, 0.70976], - [0.94127, 0.85929, 0.71809], - [0.94423, 0.86551, 0.72648], - [0.94723, 0.87175, 0.73494], - [0.95029, 0.87799, 0.74345], - [0.95339, 0.88424, 0.75201], - [0.95653, 0.89049, 0.76061], - [0.95971, 0.89674, 0.76925], - [0.96291, 0.90299, 0.77792], - [0.96614, 0.90924, 0.78662], - [0.96939, 0.91548, 0.79532], - [0.97265, 0.92173, 0.80406], - [0.97592, 0.92795, 0.81279], - [0.9792, 0.93418, 0.82155], - [0.98248, 0.9404, 0.83031], - [0.98576, 0.94661, 0.83907], - [0.98904, 0.95282, 0.84784], - [0.99231, 0.95902, 0.85661], -] - -lipari10 = [ - "#{:02x}{:02x}{:02x}".format(int(r * 255), int(g * 255), int(b * 255)) - for r, g, b in cm_data -] - - -lipari10_cmap = ListedColormap(cm_data) diff --git a/opt/mixtures/analysis/mist.mplstyle b/opt/mixtures/analysis/mist.mplstyle deleted file mode 100644 index 24f98f29..00000000 --- a/opt/mixtures/analysis/mist.mplstyle +++ /dev/null @@ -1,15 +0,0 @@ -figure.constrained_layout.use : True -xtick.labelsize : 4 -ytick.labelsize : 4 -lines.markersize : 2 -lines.linewidth : 1 -font.size : 5 -font.weight : bold -axes.titlesize : 4 -axes.labelsize : 5 -legend.fontsize : 4 -font.family : Serif -grid.linewidth : 0.1 -figure.dpi : 500 -savefig.dpi : 500 -mathtext.fontset : stix diff --git a/opt/mixtures/analysis/pred_vs_actual.py b/opt/mixtures/analysis/pred_vs_actual.py deleted file mode 100644 index 8a025d70..00000000 --- a/opt/mixtures/analysis/pred_vs_actual.py +++ /dev/null @@ -1,85 +0,0 @@ -from electrolyte_fm.data_modules import ComponentDataModule -import matplotlib.pyplot as plt -from lipari_cm import lipari10_cmap -from electrolyte_fm.models.model_utils import DeepSpeedMixin -import numpy as np - -plt.rcParams["figure.constrained_layout.use"] = True -plt.rcParams["xtick.labelsize"] = 5 -plt.rcParams["ytick.labelsize"] = 5 -# plt.rcParams['lines.markersize'] = 2 -# plt.rcParams['lines.linewidth'] = 1 -plt.rcParams["font.size"] = 6 -plt.rcParams["axes.titlesize"] = 5 -plt.rcParams["axes.labelsize"] = 5 -plt.rcParams["xtick.labelsize"] = 5 -plt.rcParams["ytick.labelsize"] = 5 -plt.rcParams["legend.fontsize"] = 4 -plt.rcParams["font.family"] = "Serif" -plt.rcParams["grid.linewidth"] = 0.1 -plt.rcParams["figure.dpi"] = 500 -plt.rcParams["savefig.dpi"] = 500 -plt.rcParams["mathtext.fontset"] = "stix" - -run_id = "ur7v2a6y" # "c7ssptg7" # -pretrained_ckpt = f"/home/abhutani/electrolyte-fm/mist/{run_id}/checkpoints/last.ckpt" -model = DeepSpeedMixin.load(pretrained_ckpt) - -dm = ComponentDataModule( - path="/home/abhutani/electrolyte-fm/diffmix_data/Ion_Cond_Tle20/", - target_col="ln k", - n_components=5, - val_batch_size=20, - tokenizer=pretrained_ckpt, - include_temperature=True, - iterable=False, -) - -fig, ax = plt.subplots(figsize=(2, 2)) - -dm.setup(stage="test") -sc = None # Initialize scatter plot in case of using outside loop later - -for idx, batch in enumerate(dm.test_dataloader()): - # Assuming temperature is already the actual value, no need to divide again if normalized is not required - colors = batch[ - "temperature" - ].numpy() # Use unnormalized temperature directly for coloring - sc = ax.scatter( - np.exp(batch["target"].numpy()), - np.exp(model(batch)[0].detach().numpy()), - c=colors, - cmap=lipari10_cmap, - ) - if idx > 10: - break - -# Add the diagonal line -ax.plot([0, 14], [0, 14], c="black") -ax.grid(True, linestyle="--", linewidth=0.5, alpha=0.7) - -# Set the spines and axes customization -ax.set_xticks(np.arange(0, 16, 2)) -ax.set_yticks(np.arange(0, 16, 2)) -ax.set_xticklabels(np.arange(0, 16, 2)) -ax.set_yticklabels(np.arange(0, 16, 2)) -ax.set_xlim(-0.5, 14.5) -ax.set_ylim(-0.5, 14.5) -ax.spines["top"].set_visible(True) -ax.spines["right"].set_visible(True) - -# Create a colorbar with unnormalized temperature labels -cbar = plt.colorbar(sc, ax=ax) -# Set the label to reflect physical units like Kelvin or Celsius -cbar.set_label("Temperature [K]", rotation=-90, labelpad=15) -cbar.ax.yaxis.set_label_position("left") # Align label position - -# Rotate label to face inward -cbar.ax.yaxis.set_label_position("right") -cbar.ax.yaxis.label.set_rotation(270) - -# Set the axis labels -ax.set_xlabel("AEM $\sigma$ [mS/cm]") -ax.set_ylabel("MIST $\sigma$ [mS/cm]") - -plt.savefig(f"ionic_cond_{run_id}", bbox_inches="tight", dpi=500) diff --git a/opt/mixtures/analysis/ternary_plots.py b/opt/mixtures/analysis/ternary_plots.py deleted file mode 100644 index 7a0876da..00000000 --- a/opt/mixtures/analysis/ternary_plots.py +++ /dev/null @@ -1,309 +0,0 @@ -import itertools -import os -from typing import List, Union - -import matplotlib.pyplot as plt -import mpltern # noqa -import numpy as np -import pandas as pd -import torch -import tqdm -from electrolyte_fm.data_modules import ComponentDataModule -from electrolyte_fm.models.model_utils import DeepSpeedMixin -from matplotlib.colors import Normalize - -from lipari_cm import lipari10_cmap - -plt.style.use("./mist.mplstyle") - - -def softmax_rowwise(arr): - """ - Computes the softmax function for each row of a NumPy array. - - Args: - arr: A 2D NumPy array. - - Returns: - A NumPy array with the same shape as arr, where each row is the softmax of the - corresponding row in arr. - """ - # Subtract the maximum value in each row for numerical stability - row_maxes = arr.max(axis=1, keepdims=True) - shifted_arr = arr - row_maxes - - # Calculate the exponential of each element - exp_arr = np.exp(shifted_arr) - - # Divide by the sum of exponentials in each row - row_sums = exp_arr.sum(axis=1, keepdims=True) - return exp_arr / row_sums - - -def get_triangular_grid(n=31, prec=1e-6): - """Triangular grid - - Parameters - ---------- - n : int, optional - Number of grid points along one ternary axis, by default 11 - prec : float, optional - Tolerance for triangular points, by default 1e-6 - - Returns - ------- - (t, l, r) : tuple[np.ndarray] - Ternary coordinates. - """ - # top axis in descending order to start from the top point - t = np.linspace(1, 0, n) - points = [] - for tmp in itertools.product(t, repeat=3): - if abs(sum(tmp) - 1.0) > prec: - continue - points.append(tmp) - points = np.array(points) - return points - - -def generate_dataset( - salt_mole_fraction: float = 0.1, - save_dir: str = "ternary", - # PC, DMC, - solvents: List[str] = ["CC1COC(=O)O1", "COC(=O)OC", "O=C1OCCO1"], - salt: str = "LiPF6", -): - os.makedirs(save_dir, exist_ok=True) - compositions = (1 - salt_mole_fraction) * get_triangular_grid() - df = pd.DataFrame( - { - "x1": compositions[:, 0], - "x2": compositions[:, 1], - "x3": compositions[:, 2], - "x4": salt_mole_fraction * np.ones_like(compositions[:, 0]), - "x5": salt_mole_fraction * np.ones_like(compositions[:, 0]), - } - ) - for i in range(1, 4): - df[f"smi{i}"] = solvents[i - 1] - if salt == "LiPF6": - df["smi4"] = "F[P-](F)(F)(F)(F)F" - elif salt == "TFSI": - df["smi4"] = "O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F" - df["smi5"] = "[Li+]" - df["temperature"] = 298.15 - df["target"] = 0 - df.to_csv(os.path.join(save_dir, "train.csv")) - df.to_csv(os.path.join(save_dir, "val.csv")) - df.to_csv(os.path.join(save_dir, "test.csv")) - - -def transferance(pred): - return pred[-2] - - -def conductivity(pred): - return pred[-3] - - -def product(pred): - return pred[-2] * pred[-3] - - -def diffusioncoeff(pred): - return pred[-4] - - -targets = { - "transferance": transferance, - "conductivity": conductivity, - "product": product, - "diffusioncoeff": diffusioncoeff, -} - -target_names = { - "transferance": "$t_+$", - "conductivity": "$\sigma$ [mS/cm]", - "product": "$t_+ \cdot \sigma$", - "diffusioncoeff": "$\mathcal{D}_{Li} [cm^2/s]$", -} - - -def run_inference( - pretrained_ckpt: str, data_dir: str, val_batch_size: int, target: str -): - target_fn = targets[target] - model = DeepSpeedMixin.load(pretrained_ckpt) - model.to(torch.device("cpu")) - model.eval() - dm = ComponentDataModule( - path=data_dir, - target_col="target", - n_components=5, - val_batch_size=val_batch_size, - tokenizer=pretrained_ckpt, - include_temperature=True, - ) - dm.setup(stage="train") - - results = [] - dx = [] - - for idx, batch in tqdm.tqdm(enumerate(dm.test_dataloader())): - for i in range(3): - batch[f"composition_{i}"].requires_grad = True - - pred = model(batch)[0] - _dx = torch.autograd.grad( - [target_fn(pred[i, :]) for i in range(pred.shape[0])], - [batch[f"composition_{i}"] for i in range(3)], - retain_graph=True, - ) - - _dx = torch.stack(_dx, dim=1) - for bdx in range(pred.shape[0]): - dx.append(_dx[bdx, :].tolist()) - results.append(target_fn(pred[bdx, :]).item()) - - v = np.array(results) - c = get_triangular_grid() - x1 = c[:, 0] - x2 = c[:, 1] - x3 = c[:, 2] - dx = np.array(dx) - - df = pd.DataFrame( - { - "x1": x1, - "x2": x2, - "x3": x3, - "transferance": v, - "dx1": dx[:, 0], - "dx2": dx[:, 1], - "dx3": dx[:, 2], - } - ) - inference_filename = os.path.basename(data_dir) - inference_filename, extension = os.path.splitext(inference_filename) - print(f"Saving inference data to {inference_filename}") - df.to_csv(f"{target}_{inference_filename}.csv") - - -def cartesian_to_barycentric(d_cartesian): - """ - Converts a gradient vector from Cartesian coordinates to barycentric coordinates. - - Parameters: - - d_cartesian: A 2D numpy array where each row represents a vector (dx, dy, dz) - in Cartesian coordinates. - - Returns: - - np.ndarray: Corresponding rows of vectors in barycentric coordinates. - """ - - if len(d_cartesian.shape) != 2 or d_cartesian.shape[1] != 3: - raise ValueError("Input should be a 2D array with shape (n, 3).") - - # Assuming d_cartesian comes in the form of n x 3 array (dx, dy, dz) - d_barycentric = np.zeros_like(d_cartesian) - - # Convert each vector - for i, (dx, dy, dz) in enumerate(d_cartesian): - # Calculate the change in barycentric coordinates - d_barycentric[i, 0] = dx - (dy + dz) / 2 - d_barycentric[i, 1] = dy - (dx + dz) / 2 - d_barycentric[i, 2] = dz - (dx + dy) / 2 - - return d_barycentric - - -def plot_vector_fields(data_files: Union[List, str], target: str): - print("plotting") - if isinstance(data_files, str): - data_files = [ - data_files, - ] - - n_files = len(data_files) - n_cols = max(1, int(0.5 * n_files)) - fig, axes = plt.subplots( - 2, n_cols, figsize=(0.8 * n_files, 3), subplot_kw={"projection": "ternary"} - ) - fig.subplots_adjust(left=0.075, right=0.85, wspace=1.0, hspace=0.5) - - data_files = sorted(data_files, key=lambda x: x.split("_")[1]) - - # Collect transferance values to determine global min and max - all_transferance = [] - - for data_file in data_files: - df = pd.read_csv(data_file) - all_transferance.extend(df.transferance) - - normalize = Normalize(vmin=min(all_transferance), vmax=max(all_transferance)) - - for i, data_file in enumerate(data_files): - _, salt, composition = data_file.split("_") - salt = "LiPF_6" if salt == "LiPF6" else "LiTFSI" - composition = float(composition.split(".")[0]) - col = int(composition // 5) - 1 - composition = 1e-2 * composition - composition = "$\mathbf{x_{" + salt + "}}$" + f"$= {composition:.2f}$" - row = i // n_cols - - ax = axes[row, col] if n_files > 1 else axes - - df = pd.read_csv(data_file) - - cs = ax.tricontour( - df.x1, - df.x2, - df.x3, - df.transferance, - levels=40, - colors="#989C97", - linewidths=0.1, - zorder=1, - ) - - max_bound = 0.98 - min_bound = 0.02 - - df = df[(df.x1 < max_bound) & (df.x1 > min_bound)] - df = df[(df.x2 < max_bound) & (df.x2 > min_bound)] - df = df[(df.x3 < max_bound) & (df.x3 > min_bound)] - - dx = cartesian_to_barycentric(df[["dx1", "dx2", "dx3"]].values) - - cs = ax.quiver( - df.x1, - df.x2, - df.x3, - dx[:, 0], - dx[:, 1], - dx[:, 2], - df.transferance, - units="dots", - width=4, - zorder=2, - # scale=1, - # scale_units="xy", - cmap=lipari10_cmap, - norm=normalize, - ) - - ax.set_tlabel("PC", fontweight="bold") - ax.set_llabel("DMC", fontweight="bold") - ax.set_rlabel("EC", fontweight="bold") - ax.set_title(composition, size=8) - - # Add a common color bar outside of the loop, aligned to the right of all plots - cbar_axis = fig.add_axes([1.0, 0.1, 0.015, 0.9]) # Adjust position as needed - cbar = fig.colorbar(cs, cax=cbar_axis) - cbar.set_label( - target_names[target], - weight="bold", - ) - cbar.ax.tick_params() - cbar.solids.set_rasterized(True) - plt.savefig(f"{target}_salt_composition.pdf", bbox_inches="tight", dpi=500) From da0e352396284931111cc101544037d77243ab9f Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Thu, 9 Oct 2025 13:55:09 -0400 Subject: [PATCH 144/147] increase dataset panel size --- opt/mixtures/dataset.jl | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/opt/mixtures/dataset.jl b/opt/mixtures/dataset.jl index bcdc5c24..54f1b43a 100644 --- a/opt/mixtures/dataset.jl +++ b/opt/mixtures/dataset.jl @@ -105,7 +105,7 @@ end function mixture_dataset(df) prop_columns = ["Density", "Molar Volume", "Molar Enthalpy"] - f = Figure(; size=(4.5inch, 3inch)) + f = Figure(; size=(180mm, 100mm)) plot_mixture_properties!(f[1, 1:2], df, prop_columns) Label(f[1, 1, Left()]; text=L"$\ln$ Examples", @@ -137,7 +137,7 @@ function mixture_dataset(df) return f end -function dataset_stats(df, prop_columns) +function dataset_stats(df) compounds = unique(Iterators.flatten([df.smi1, df.smi2])) @info "Number of compounds" length(compounds) From c8fb927414c14837dd6fa3abfed5bf2278b2dc4c Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Sun, 12 Oct 2025 20:51:53 -0400 Subject: [PATCH 145/147] excess Ea plot --- opt/mixtures/Project.toml | 4 + opt/mixtures/TernaryPlots.jl | 185 ------------- opt/mixtures/ionic_conductivity.jl | 421 +++++++++++++++++++---------- opt/mixtures/pyproject.toml | 1 + opt/mixtures/src/Mixtures.jl | 3 + opt/mixtures/uv.lock | 135 +++++++++ opt/screening/uv.lock | 40 ++- 7 files changed, 452 insertions(+), 337 deletions(-) delete mode 100644 opt/mixtures/TernaryPlots.jl diff --git a/opt/mixtures/Project.toml b/opt/mixtures/Project.toml index c5939fc2..4f01ccd1 100644 --- a/opt/mixtures/Project.toml +++ b/opt/mixtures/Project.toml @@ -5,6 +5,7 @@ version = "0.1.0" [deps] CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" +ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df" FStrings = "1e2663ae-5621-49ef-9c0a-672f85872094" @@ -16,15 +17,18 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" MISTStyle = "0a43f4a4-3db7-4939-b247-26273f54b603" Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" [sources] MISTStyle = {path = "../MISTStyle"} [compat] +ColorSchemes = "3.31.0" DelaunayTriangulation = "1.6.4" FStrings = "0.1.2" Interpolations = "0.15.1" JSON = "0.21" LinearAlgebra = "1.11.0" +Statistics = "1.10" StatsBase = "0.34.6" diff --git a/opt/mixtures/TernaryPlots.jl b/opt/mixtures/TernaryPlots.jl deleted file mode 100644 index cf909c7f..00000000 --- a/opt/mixtures/TernaryPlots.jl +++ /dev/null @@ -1,185 +0,0 @@ -using DelaunayTriangulation -DelaunayTriangulation.toggle_warn_on_dupes!() -using Statistics: mean - -""" - ternary(a, b, c, values) - -Plots a pseudocolor (tripcolor) plot on a ternary diagram using Delaunay triangulation -and filled triangles, similar to mpltern's tripcolor. The input vectors `a`, `b`, and `c` -represent the three compositional components, and `values` contains the scalar data to be -visualized with color. Components will be automatically normalized so that a + b + c = 1. -""" - -@recipe(Ternary, a, b, c, values) do scene - Theme( - colormap = :viridis, - colorrange = Makie.automatic, - show_triangle = true, - triangle_color = :black, - triangle_linewidth = 1, - show_grid = true, - grid_color = (:black, 0.7), - grid_linestyle = :solid, - grid_steps = 5, - label_a = "A", - label_b = "B", - label_c = "C", - label_offset = 0, - show_axis_labels = true, - axis_label_offset = 2, - axis_ticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], - show_ticks = true, - interpolate = true, - ) -end - -function Makie.plot!(plt::Ternary) - # Get the input data - a = plt.a[] - b = plt.b[] - c = plt.c[] - values = plt.values[] - - # Normalize so a + b + c = 1 - total = a .+ b .+ c - a_norm = a ./ total - b_norm = b ./ total - c_norm = c ./ total - - # Convert ternary coordinates to Cartesian - x = @. 0.5 * (2 * b_norm + c_norm) - y = @. (√3 / 2) * c_norm - - # Perform Delaunay triangulation - points = [Point2(xi, yi) for (xi, yi) in zip(x, y)] - tri = triangulate(points) - - # Determine color range - crange = plt.colorrange[] === Makie.automatic ? extrema(values) : plt.colorrange[] - - # Plot each triangle - for triangle in each_solid_triangle(tri) - i, j, k = triangle_vertices(triangle) - - # Triangle vertices - tri_points = [points[i], points[j], points[k]] - - if plt.interpolate[] - tri_colors = [values[i], values[j], values[k]] - tri_value = mean(tri_colors) - else - tri_value = mean([values[i], values[j], values[k]]) - end - - poly!(plt, tri_points, - color = tri_value, - colormap = plt.colormap, - colorrange = crange, - strokewidth = 0) - end - - # Plot the triangle boundary - if plt.show_triangle[] - corners = [Point2(0.0, 0.0), Point2(1.0, 0.0), Point2(0.5, √3/2), Point2(0.0, 0.0)] - lines!(plt, corners, - color = plt.triangle_color, - linewidth = plt.triangle_linewidth) - end - - # Add grid lines - if plt.show_grid[] - n_steps = plt.grid_steps[] - for i in 1:(n_steps-1) - val = i / n_steps - - # Lines parallel to bottom edge (constant C) - p1 = Point2(0.5 * (2 * (1-val) + val), (√3/2) * val) - p2 = Point2(0.5 * (2 * 0 + val), (√3/2) * val) - lines!(plt, [p1, p2], - color = plt.grid_color, - linestyle = plt.grid_linestyle) - - # Lines parallel to left edge (constant B) - p1 = Point2(0.5 * (2 * val + 0), (√3/2) * 0) - p2 = Point2(0.5 * (2 * val + (1-val)), (√3/2) * (1-val)) - lines!(plt, [p1, p2], - color = plt.grid_color, - linestyle = plt.grid_linestyle) - - # Lines parallel to right edge (constant A) - p1 = Point2(0.5 * (1 - val), (√3/2) * (1 - val)) - p2 = Point2(1.0 - val, 0.0) - lines!(plt, [p1, p2], - color = plt.grid_color, - linestyle = plt.grid_linestyle) - end - end - - if plt.show_ticks[] - ticks = plt.axis_ticks[] - - for t in ticks - - x_bottom, y_bottom = ternary_to_cartesian(1 - t, t, 0) - text!(plt, x_bottom, y_bottom; - text = string(round(t, digits=1)), - align = (:center, :top), - rotation = π/3, - fontsize = 6pt, - offset = (0, -4pt)) - - x_left, y_left = ternary_to_cartesian(1 - t, 0, t) - text!(plt, x_left, y_left; - text = string(round(1- t, digits=1)), - rotation = -π/3, - align = (:center, :center), - fontsize = 6pt, - offset = (-6pt, 4pt)) - - x_right, y_right = ternary_to_cartesian(0, 1 - t, t) - text!(plt, x_right, y_right; - text = string(round(t, digits=1)), - align = (:left, :center), - rotation = 0, - fontsize = 6pt, - offset = (2pt, 2pt)) - end - end - - if plt.show_axis_labels[] - off = plt.axis_label_offset[] - - xb, yb = ternary_to_cartesian(0.5, 0.5, 0.0) - text!(plt, xb, yb; - text = plt.label_b[], - align = (:center, :top), - fontsize=7pt, - rotation = 0.0, - offset = (0, -9pt)) - - xl, yl = ternary_to_cartesian(0.5, 0.0, 0.5) - text!(plt, xl, yl; - text = plt.label_a[], - align = (:center, :center), - fontsize=7pt, - rotation = π/3, - offset = (-22pt, 2pt)) - - xr, yr = ternary_to_cartesian(0.0, 0.5, 0.5) - text!(plt, xr, yr; - text = plt.label_c[], - align = (:center, :center), - fontsize=7pt, - rotation = -π/3, - offset = (22pt, 2pt) - ) - end - return plt -end - -function ternary_to_cartesian(a, b, c) - x = 0.5 * (2b + c) - y = (√3 / 2) * c - return x, y -end diff --git a/opt/mixtures/ionic_conductivity.jl b/opt/mixtures/ionic_conductivity.jl index e406dd5f..d2b9e078 100644 --- a/opt/mixtures/ionic_conductivity.jl +++ b/opt/mixtures/ionic_conductivity.jl @@ -1,17 +1,18 @@ using DataFrames using Makie +using JSON using CSV: CSV using MISTStyle using Format: format using GLM using Interpolations using Statistics: mean -using Mixtures: Mixtures, titlecase +using Mixtures: Mixtures using Printf - -include("TernaryPlots.jl") +using ColorSchemes const DATA_DIR = realpath(joinpath(pkgdir(Mixtures), "..", "..", "data")) +similarity_data = JSON.parsefile("solvent_rematch.json") function label_smi(smi::AbstractString) known = Dict( @@ -22,207 +23,337 @@ function label_smi(smi::AbstractString) "CCOC(=O)OC" => "EMC", "CCOC(=O)OCC" => "DEC", "O=C1OCCO1" => "EC", - "COC(=O)OC" => "DMC" + "COC(=O)OC" => "DMC", ) return get(known, smi, smi) end -function plot_angell_solvents() - - df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "ionic_conductivity_inference.csv"))) +function get_similarity(smiles1, smiles2) - fig = Figure(size=(53mm, 32mm), figure_padding=(2, 2, 2, 2)) - - ax = Axis(fig[1, 1], - xlabel = L"T_g/T", - ylabel = L"ln $\sigma$", - xgridvisible = false, - ygridvisible = false, - topspinevisible = true, - rightspinevisible = true - ) + key1 = "$(smiles1)_$(smiles2)" + key2 = "$(smiles2)_$(smiles1)" + if haskey(similarity_data, key1) + return similarity_data[key1] + elseif haskey(similarity_data, key2) + return similarity_data[key2] + else + return nothing + end +end - # Get unique solvents and create color mapping - solvents = unique(df[!, "Solvent"]) - n_solvents = length(solvents) - colors = MISTStyle.CAT_COLORS[1:n_solvents] # Use Wong colorblind-friendly palette +function weighted_ternary_similarity(solvent_list, compositions) + weighted_sum = 0.0 - # Plot each solvent separately for discrete coloring - for (i, solvent) in enumerate(solvents) - mask = df[!, "Solvent"] .== solvent - scatter!(ax, df[mask, "Tg/T"], df[mask, "conductivity"], - color = colors[i], - marker = :circle, - markersize = 2, - alpha = 0.6, - label = string(solvent) - ) + for i in 1:length(solvent_list) + for j in (i+1):length(solvent_list) + sim = get_similarity(solvent_list[i], solvent_list[j]) + weighted_sum += (compositions[i] + compositions[j]) * sim + total_weight += weight + end end - # Add legend - Legend(fig[1, 2], ax, - framevisible = true, - ) - - return fig + return weighted_sum end +function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer, parameter::String) -function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer) - - df = Mixtures.evaluate_conductivity(model, [mixture]; n = n, fixed_salt=salt_comp) + df = Mixtures.evaluate_conductivity(model, [mixture]; n = n, fixed_salt = salt_comp) comp_matrix = reduce(hcat, df.composition)' - # update salt composition to account for grid discretization - salt_comp = first(comp_matrix[: , 4]) - pure_solvent_Ea = zeros(Float64, 3) + # Update salt composition to account for grid discretization + salt_comp = first(comp_matrix[:, 4]) + pure_solvent_param = zeros(Float64, 3) pure_solvent_comp = 1 - salt_comp for (idx, solvent) in enumerate(mixture["solvents"]) comp = zeros(4) comp[idx] = pure_solvent_comp comp[4] = salt_comp - preds = Mixtures.evaluate_at_composition(model, mixture, comp) - pure_solvent_Ea[idx] = preds["Ea"] + preds = Mixtures.evaluate_at_composition(model, mixture, comp) + pure_solvent_param[idx] = preds[parameter] end solvent_comps = comp_matrix[:, 1:3] - solvent_fractions = solvent_comps ./ sum(solvent_comps, dims=2) - df[!, "ideal_mixing_Ea"] = solvent_fractions * pure_solvent_Ea - df[!, "excess_Ea"] = df.Ea .- df.ideal_mixing_Ea - @assert sum(abs.(collect(df.excess_Ea[1:4]))) < 1e-4 "Zero excess for expected degenerate case" + solvent_fractions = solvent_comps ./ sum(solvent_comps, dims = 2) + df[!, "ideal_mixing_$(parameter)"] = solvent_fractions * pure_solvent_Ea + df[!, "excess_$(parameter)"] = df[!, parameter] .- df[!, "ideal_mixing_$(parameter)"] + + @assert sum(abs.(collect(df.excess_Ea[1:4]))) < 1e-4 "Non-zero excess for expected degenerate case" return df end -function ternary_activation_energy!( - ax::Axis, df::DataFrame, - solvent_names::Vector{String}, global_crange::NTuple{2, Real}; - excess:: Bool = false +function conductivity_composition_curve(model, mixture; n = 50) + x1 = 1.0 + x2 = 0.0 + x3 = 0.0 + composition = range(0.02, stop = 0.20, length = n) + conductivity = [ + Mixtures.evaluate_at_composition(model, mixture, [x1 - x, x2, x3, x])["conductivity"] for x in + composition + ] + return composition, conductivity +end + +function plot_composition_curves(model, solvent) + + temperatures = [260, 280, 300, 320, 340] + f = Figure(; size = (61mm, 36mm), figure_padding = (2, 2, 2, 2)) + cb = Colorbar(f[1, 2]; + label = L"Temperature (K)$$", + colormap = MISTStyle.CONTINUOUS_COLORS, + colorrange = extrema(temperatures), + vertical = true, tellheight = true, tellwidth = true, + flipaxis = true, flip_vertical_label = true, ) + salts = ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + line_styles = [:solid, :dot] - comp_matrix = reduce(hcat, df.composition)' - a = comp_matrix[:, 1] - b = comp_matrix[:, 2] - c = comp_matrix[:, 3] + ax = Axis(f[1, 1]; + xlabel = L"x_{Li}", ylabel = L"$\sigma$ (mS/cm)", + limits = ((0, 0.20), (0, nothing)), + ) - if excess - values = df.excess_Ea - else - values = df.Ea + for (idx, salt) in enumerate(salts), T in temperatures + label = (label_smi(salt) == "PF6") ? L"LiPF$_6$" : L"$$LiTFSI" + mixture = Dict( + "solvents" => solvent, + "temperature" => T, + "salt" => [salt, "[Li+]"], + ) + composition, conductivity = conductivity_composition_curve(model, mixture) + lines!( + ax, + composition, + exp.(conductivity); + linestyle = line_styles[idx], + color = T, + label, + MISTStyle.cb_attrs(cb, Lines)..., + ) end - ternary!(ax, a, b, c, values, - colormap = MISTStyle.CONTINUOUS_COLORS, - colorrange = global_crange, - label_a = label_smi(solvent_names[1]), - label_b = label_smi(solvent_names[2]), - label_c = label_smi(solvent_names[3]), - show_ticks = true) -end + xlims = ax.limits[][1] # (xmin, xmax) + ylims = (0, 10) # (ymin, ymax) + x_min = 0.160369437447523 # Maximum composition in training data + poly!(ax, Point2f[(x_min, ylims[1]), (x_min, ylims[2]), (xlims[2], ylims[2]), (xlims[2], ylims[1])]; + color = MISTStyle.UM_COLORS.maize, alpha = 0.2, strokewidth = 0 + ) -function ternary_activation_energy(model, mixture::Dict; excess::Bool=false, n::Integer = 64) + axislegend(ax, position = :rt, padding = (1, 1, 1, 1), margin = (1, 1, 1, 1), unique = true) + return f +end - fig = Figure(; size=(183mm, 55mm), figure_padding=(1, 1, 1, 1)) +function plot_composition_curves() - solvent_names = mixture["solvents"] - mixtures = [mixture] + solvents = [ + ["O=C1OCC(F)O1", "CCOC(=O)OC", "O=C1OCCO1"], + ["CC1COC(=O)O1", "CCOC(=O)OCC", "CCOC(=O)OC"], + ["CC1COC(=O)O1", "CCOC(=O)OC", "COC(=O)OC"], + ["CCOC(=O)OCC", "CCOC(=O)OC", "O=C1OCCO1"], + ] + model_id = "mist-conductivity-27.0M-2mpg8dcd" + model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") + for solvent in solvents + fn_name = "cond_curve_$(label_smi(solvent[1]))_$(label_smi(solvent[2]))_$(label_smi(solvent[3]))" + with_theme(MISTStyle.theme()) do + plot_composition_curves(model, solvent) + end |> MISTStyle.savefig(fn_name) + end +end - all_dfs = [] - for salt_comp in [0.05, 0.1, 0.15] - if excess - df = calculate_excess(model, mixture, salt_comp, n ) - else - df = Mixtures.evaluate_conductivity(model, mixtures; n = n, fixed_salt=salt_comp) +function calculate_delta_ea(model, solvent, temperatures, salts, salt_comps) + mixtures = [ + Dict("solvents" => solvent, "temperature" => temp, "salt" => [salt, "[Li+]"]) + for temp in temperatures for salt in salts + ] + all_data = [] + + for salt_comp in salt_comps + df = Mixtures.evaluate_conductivity(model, mixtures; n = 25, fixed_salt = salt_comp) + df[!, "salt_mol"] = last.(df.composition) + df[!, "salt_anion"] = [comp[4] for comp in df.components] + df[!, "Ea_over_T"] = df.Ea ./ df.temperature + + for temp in temperatures + temp_df = filter(row -> row.temperature == temp, df) + temp_gdf = groupby(temp_df, :salt_anion) + groups = collect(temp_gdf) + lipf6_idx = findfirst(g -> g[1, :salt_anion] == "F[P-](F)(F)(F)(F)F", groups) + litfsi_idx = findfirst(g -> g[1, :salt_anion] != "F[P-](F)(F)(F)(F)F", groups) + + delta_Ea_over_T = groups[lipf6_idx].Ea_over_T - groups[litfsi_idx].Ea_over_T + push!(all_data, (salt_comp = salt_comp, temperature = temp, + delta_Ea_over_T = delta_Ea_over_T)) end - push!(all_dfs, df) end + return all_data +end - if excess - all_ea = vcat([df.excess_Ea for df in all_dfs]...) - else - all_ea = vcat([df.Ea for df in all_dfs]...) +function plot_composition!(ax, all_data, comp, temperatures, dodge_width) + + n_temps = length(temperatures) + colorscheme = ColorSchemes.colorschemes[MISTStyle.CONTINUOUS_COLORS] + + for (t_idx, temperature) in enumerate(temperatures) + comp_temp_data = filter(d -> d.salt_comp == comp && d.temperature == temperature, all_data) + isempty(comp_temp_data) && continue + + values = vcat([d.delta_Ea_over_T for d in comp_temp_data]...) + position = comp + (t_idx - (n_temps+1)/2) * dodge_width + positions = fill(position, length(values)) + + temp_range = maximum(temperatures) - minimum(temperatures) + color_val = temp_range > 0 ? (temperature - minimum(temperatures)) / temp_range : 0.5 + box_color = get(colorscheme, color_val) + + boxplot!(ax, positions, values; + width = dodge_width * 0.7, color = (box_color, 0.9), + strokecolor = :black, strokewidth = 0.2, show_outliers = false, + ) end +end - global_crange = extrema(all_ea) +function plot_delta_Ea(model) + solvents = [ + ["CC1COC(=O)O1", "O=C1OCCO1", "O=C1OCC(F)O1"], + ["CC1COC(=O)O1", "CCOC(=O)OCC", "O=C1OCC(F)O1"], + ["CCOC(=O)OC", "O=C1OCCO1", "O=C1OCC(F)O1"], + ["CC1COC(=O)O1", "CCOC(=O)OC", "O=C1OCC(F)O1"], + ] + salts = ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + temperatures = [260, 280, 300, 320, 340] + salt_comps = range(start = 0.05, stop = 0.2, length = 5) + + fig = Figure(size = (90mm, 100mm), figure_padding = (2, 2, 2, 2)) + cb = Colorbar(fig[length(solvents) + 1, 1]; + label = L"$$Temperature [K]", + colormap = MISTStyle.CONTINUOUS_COLORS, colorrange = extrema(temperatures), + tellheight = true, tellwidth = false, flipaxis = false, vertical = false + ) - for (idx, df) in enumerate(all_dfs) + for (idx, solvent) in enumerate(solvents) ax = Axis( - fig[1, idx]; - aspect=DataAspect(), - tellwidth=true, - tellheight=true, - limits=((-0.1, 1.1), (-0.2, 1.0)), - ) - ternary_activation_energy!(ax, df, solvent_names, global_crange; excess = excess) - salt_comp = first(last.(df.composition)) - hidedecorations!(ax) - hidespines!(ax) - - val = @sprintf("%.2f", salt_comp) - label = L"$x_{\mathrm{Li}^{+}}$ = %$val" - Label( - fig[0, idx], label; - tellheight=true, fontsize=8pt, lineheight=0.1, padding=(0, 0, 0, 0) + fig[idx, 1], + title = "$(label_smi(solvent[1])) | $(label_smi(solvent[2])) | $(label_smi(solvent[3]))", + xlabel = idx == length(solvents) ? L"$x_{Li^+}$" : "", + ylabel = L"$\frac{E_{a,LiPF_6}}{T} - \frac{E_{a,LiTFSI}}{T}$", + xticksvisible = idx==length(solvents), + xticklabelsvisible = idx==length(solvents), + titlesize = 6pt, + xgridvisible = false, + ygridvisible = false, ) + + all_data = calculate_delta_ea(model, solvent, temperatures, salts, salt_comps) + comp_positions = unique([d.salt_comp for d in all_data]) + dodge_width = (comp_positions[2] - comp_positions[1]) * 0.5 / length(temperatures) + + for comp in comp_positions + plot_composition!(ax, all_data, comp, temperatures, dodge_width) + end + + rowgap!(fig.layout, 2) end - label = excess ? L"excess $E_a$" : L"$E_a$" - - Colorbar(fig[1, 4], - colormap = MISTStyle.CONTINUOUS_COLORS, - limits = global_crange, - label = label, - height = Relative(0.8), - tellheight=true, - flip_vertical_label = true, - vertical = true, - ) - - colsize!(fig.layout, 1, Aspect(1, 1.0)) - colsize!(fig.layout, 2, Aspect(1, 1.0)) - colsize!(fig.layout, 3, Aspect(1, 1.0)) - colgap!(fig.layout, 10) - resize_to_layout!(fig) return fig end -function plot_ternary_activation_energies() +function plot_delta_Ea() model_id = "mist-conductivity-27.0M-2mpg8dcd" model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") - for solvent in ["O=C1OCC(F)O1", "CCOC(=O)OC", "CCOC(=O)OCC", "COC(=O)OC"] - for salt in ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] - fn_name = "$(label_smi(solvent))_$(label_smi(salt))" - mixture = Dict( - "solvents" => [ "CC1COC(=O)O1", "O=C1OCCO1", solvent], - "temperature" => 298.15, - "salt" => [ salt, "[Li+]" ] - ) - with_theme(MISTStyle.theme()) do - ternary_activation_energy(model, mixture) - end |> MISTStyle.savefig(fn_name) - end - end + with_theme(MISTStyle.theme()) do + plot_delta_Ea(model) + end |> MISTStyle.savefig("delta_Ea") return end -function plot_excess_activation_energies() + +function plot_angell_solvents() + df = DataFrame(CSV.File(joinpath(DATA_DIR, "mixtures", "ionic_conductivity_inference.csv"))) + fig = Figure(size = (53mm, 32mm), figure_padding = (2, 2, 2, 2)) + + ax = Axis(fig[1, 1], + xlabel = L"T_g/T", + ylabel = L"ln $\sigma$", + ) + + solvents = unique(df[!, "Solvent"]) + n_solvents = length(solvents) + colors = MISTStyle.CAT_COLORS[1:n_solvents] + + for (i, solvent) in enumerate(solvents) + mask = df[!, "Solvent"] .== solvent + scatter!(ax, df[mask, "Tg/T"], df[mask, "conductivity"], + color = colors[i], + marker = :circle, + markersize = 2, + alpha = 0.6, + label = string(solvent), + ) + end + + Legend(fig[1, 2], ax, framevisible = true) + return fig +end + + +function plot_ternary_vft_parameter!( + ax::Axis, df::DataFrame, col_name::String, + solvent_names::Vector{String}, global_crange::NTuple{2, Real}, +) + + comp_matrix = reduce(hcat, df.composition)' + a = comp_matrix[:, 1] + b = comp_matrix[:, 2] + c = comp_matrix[:, 3] + + values = df[!, col_name] + Mixtures.ternary!(ax, a, b, c, values, + colormap = MISTStyle.CONTINUOUS_COLORS, + colorrange = global_crange, + label_a = label_smi(solvent_names[1]), + label_b = label_smi(solvent_names[2]), + label_c = label_smi(solvent_names[3]), + show_ticks = true) + +end + +function plot_all_vft_ternaries() model_id = "mist-conductivity-27.0M-2mpg8dcd" model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") - for solvent in ["O=C1OCC(F)O1", "CCOC(=O)OC", "CCOC(=O)OCC", "COC(=O)OC"] - for salt in ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] - fn_name = "excess_$(label_smi(solvent))_$(label_smi(salt))" - mixture = Dict( - "solvents" => [ "CC1COC(=O)O1", "O=C1OCCO1", solvent], - "temperature" => 298.15, - "salt" => [salt, "[Li+]"] - ) - with_theme(MISTStyle.theme()) do - ternary_activation_energy(model, mixture; excess = true) - end |> MISTStyle.savefig(fn_name) + + for excess in [true, false] + for parameter in ["Tg", "Ea"] + for solvent in ["O=C1OCC(F)O1", "CCOC(=O)OC", "CCOC(=O)OCC", "COC(=O)OC"] + for salt in ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + fn_name = "$(label_smi(solvent))_$(label_smi(salt))" + fn_name = excess ? "excess_$(fn_name)" : fn_name + mixture = Dict( + "solvents" => ["CC1COC(=O)O1", "O=C1OCCO1", solvent], + "temperature" => 298.15, + "salt" => [salt, "[Li+]"], + ) + with_theme(MISTStyle.theme()) do + plot_ternary_vft_parameter(model, mixture, parameter, excess) + end |> MISTStyle.savefig(fn_name) + end + end end end return end + +function plot_soap_similarity_correlation!(f, parameter, model, temperature) + return +end + +function plot_soap_similarity_correlation(model, temperature) + fig = Figure(size = (150mm, 75mm), figure_padding = (2, 2, 2, 2)) + for (idx, parameter) in enumerate(["Tg", "Ea"]) + plot_soap_similarity_correlation!(f[idx], parameter) + end + return +end diff --git a/opt/mixtures/pyproject.toml b/opt/mixtures/pyproject.toml index 81acf795..b9b49722 100644 --- a/opt/mixtures/pyproject.toml +++ b/opt/mixtures/pyproject.toml @@ -8,6 +8,7 @@ dependencies = [ "botorch>=0.15.1", "dataset>=1.6.2", "difftopk>=0.2.0", + "dscribe>=2.1.2", "electrolyte_fm", "h5py>=3.14.0", "matplotlib>=3.10.5", diff --git a/opt/mixtures/src/Mixtures.jl b/opt/mixtures/src/Mixtures.jl index 53d5a763..f8a72f2d 100644 --- a/opt/mixtures/src/Mixtures.jl +++ b/opt/mixtures/src/Mixtures.jl @@ -5,6 +5,8 @@ using LinearAlgebra: tril! using MISTStyle: MISTStyle, pt using DataFrames using PythonCall: PythonCall, Py, pyconvert, pyimport +using DelaunayTriangulation +using Statistics: mean pyexcess = Ref{Py}() pyionic = Ref{Py}() @@ -19,5 +21,6 @@ end include("python.jl") include("skew.jl") include("dataset.jl") +include("TernaryPlots.jl") end diff --git a/opt/mixtures/uv.lock b/opt/mixtures/uv.lock index b92cb703..a03800c2 100644 --- a/opt/mixtures/uv.lock +++ b/opt/mixtures/uv.lock @@ -160,6 +160,22 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 }, ] +[[package]] 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opt/mixtures/Project.toml | 2 + opt/mixtures/ionic_conductivity.jl | 257 ++++++++++++++++++++++++----- 2 files changed, 215 insertions(+), 44 deletions(-) diff --git a/opt/mixtures/Project.toml b/opt/mixtures/Project.toml index 4f01ccd1..3550711c 100644 --- a/opt/mixtures/Project.toml +++ b/opt/mixtures/Project.toml @@ -6,6 +6,7 @@ version = "0.1.0" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597" ColorSchemes = "35d6a980-a343-548e-a6ea-1d62b119f2f4" +Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DelaunayTriangulation = "927a84f5-c5f4-47a5-9785-b46e178433df" FStrings = "1e2663ae-5621-49ef-9c0a-672f85872094" @@ -25,6 +26,7 @@ MISTStyle = {path = "../MISTStyle"} [compat] ColorSchemes = "3.31.0" +Combinatorics = "1.0.3" DelaunayTriangulation = "1.6.4" FStrings = "0.1.2" Interpolations = "0.15.1" diff --git a/opt/mixtures/ionic_conductivity.jl b/opt/mixtures/ionic_conductivity.jl index d2b9e078..63f0f0f9 100644 --- a/opt/mixtures/ionic_conductivity.jl +++ b/opt/mixtures/ionic_conductivity.jl @@ -2,17 +2,25 @@ using DataFrames using Makie using JSON using CSV: CSV -using MISTStyle +using MISTStyle: MISTStyle, label using Format: format -using GLM -using Interpolations using Statistics: mean using Mixtures: Mixtures using Printf using ColorSchemes +using Combinatorics +using CategoricalArrays: categorical, levelcode const DATA_DIR = realpath(joinpath(pkgdir(Mixtures), "..", "..", "data")) -similarity_data = JSON.parsefile("solvent_rematch.json") +const similarity_data = JSON.parsefile("solvent_rematch.json") +const solvents = [ + ["CC1COC(=O)O1", "O=C1OCCO1", "O=C1OCC(F)O1"], + ["CC1COC(=O)O1", "CCOC(=O)OCC", "O=C1OCC(F)O1"], + ["CCOC(=O)OC", "O=C1OCCO1", "O=C1OCC(F)O1"], + ["CC1COC(=O)O1", "CCOC(=O)OC", "O=C1OCC(F)O1"], +] +const salts = ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + function label_smi(smi::AbstractString) known = Dict( @@ -28,7 +36,7 @@ function label_smi(smi::AbstractString) return get(known, smi, smi) end -function get_similarity(smiles1, smiles2) +function get_similarity(smiles1::AbstractString, smiles2::AbstractString) key1 = "$(smiles1)_$(smiles2)" key2 = "$(smiles2)_$(smiles1)" @@ -49,21 +57,23 @@ function weighted_ternary_similarity(solvent_list, compositions) for j in (i+1):length(solvent_list) sim = get_similarity(solvent_list[i], solvent_list[j]) weighted_sum += (compositions[i] + compositions[j]) * sim - total_weight += weight end end return weighted_sum end -function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer, parameter::String) +function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer) df = Mixtures.evaluate_conductivity(model, [mixture]; n = n, fixed_salt = salt_comp) comp_matrix = reduce(hcat, df.composition)' - # Update salt composition to account for grid discretization + # Update salt composition to account for discretization salt_comp = first(comp_matrix[:, 4]) - pure_solvent_param = zeros(Float64, 3) + pure_solvent_param = Dict( + "Ea" => zeros(Float64, 3), + "Tg" => zeros(Float64, 3), + ) pure_solvent_comp = 1 - salt_comp for (idx, solvent) in enumerate(mixture["solvents"]) @@ -71,16 +81,20 @@ function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer, comp[idx] = pure_solvent_comp comp[4] = salt_comp preds = Mixtures.evaluate_at_composition(model, mixture, comp) - pure_solvent_param[idx] = preds[parameter] + pure_solvent_param["Ea"][idx] = preds["Ea"] + pure_solvent_param["Tg"][idx] = preds["Tg"] end solvent_comps = comp_matrix[:, 1:3] solvent_fractions = solvent_comps ./ sum(solvent_comps, dims = 2) - df[!, "ideal_mixing_$(parameter)"] = solvent_fractions * pure_solvent_Ea - df[!, "excess_$(parameter)"] = df[!, parameter] .- df[!, "ideal_mixing_$(parameter)"] - - @assert sum(abs.(collect(df.excess_Ea[1:4]))) < 1e-4 "Non-zero excess for expected degenerate case" + for parameter in ["Ea", "Tg"] + pure_solvent_values = pure_solvent_param[parameter] + df[!, "ideal_mixing_$(parameter)"] = solvent_fractions * pure_solvent_values + df[!, "excess_$(parameter)"] = df[!, parameter] .- df[!, "ideal_mixing_$(parameter)"] + df[!, "relative_excess_$(parameter)"] = abs.(df[!, "excess_$(parameter)"] ./ df[!, parameter]) + @assert sum(abs.(collect(df[1:4, "excess_$(parameter)"]))) < 1e-3 "Zero excess for expected degenerate case" + end return df end @@ -107,7 +121,7 @@ function plot_composition_curves(model, solvent) vertical = true, tellheight = true, tellwidth = true, flipaxis = true, flip_vertical_label = true, ) - salts = ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + line_styles = [:solid, :dot] ax = Axis(f[1, 1]; @@ -138,7 +152,7 @@ function plot_composition_curves(model, solvent) ylims = (0, 10) # (ymin, ymax) x_min = 0.160369437447523 # Maximum composition in training data poly!(ax, Point2f[(x_min, ylims[1]), (x_min, ylims[2]), (xlims[2], ylims[2]), (xlims[2], ylims[1])]; - color = MISTStyle.UM_COLORS.maize, alpha = 0.2, strokewidth = 0 + color = MISTStyle.UM_COLORS.maize, alpha = 0.2, strokewidth = 0, ) axislegend(ax, position = :rt, padding = (1, 1, 1, 1), margin = (1, 1, 1, 1), unique = true) @@ -147,12 +161,6 @@ end function plot_composition_curves() - solvents = [ - ["O=C1OCC(F)O1", "CCOC(=O)OC", "O=C1OCCO1"], - ["CC1COC(=O)O1", "CCOC(=O)OCC", "CCOC(=O)OC"], - ["CC1COC(=O)O1", "CCOC(=O)OC", "COC(=O)OC"], - ["CCOC(=O)OCC", "CCOC(=O)OC", "O=C1OCCO1"], - ] model_id = "mist-conductivity-27.0M-2mpg8dcd" model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") for solvent in solvents @@ -216,21 +224,15 @@ function plot_composition!(ax, all_data, comp, temperatures, dodge_width) end function plot_delta_Ea(model) - solvents = [ - ["CC1COC(=O)O1", "O=C1OCCO1", "O=C1OCC(F)O1"], - ["CC1COC(=O)O1", "CCOC(=O)OCC", "O=C1OCC(F)O1"], - ["CCOC(=O)OC", "O=C1OCCO1", "O=C1OCC(F)O1"], - ["CC1COC(=O)O1", "CCOC(=O)OC", "O=C1OCC(F)O1"], - ] - salts = ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] + temperatures = [260, 280, 300, 320, 340] salt_comps = range(start = 0.05, stop = 0.2, length = 5) - fig = Figure(size = (90mm, 100mm), figure_padding = (2, 2, 2, 2)) - cb = Colorbar(fig[length(solvents) + 1, 1]; + fig = Figure(size = (95mm, 100mm), figure_padding = (2, 2, 2, 2)) + cb = Colorbar(fig[length(solvents)+1, 1]; label = L"$$Temperature [K]", colormap = MISTStyle.CONTINUOUS_COLORS, colorrange = extrema(temperatures), - tellheight = true, tellwidth = false, flipaxis = false, vertical = false + tellheight = true, tellwidth = false, flipaxis = false, vertical = false, ) for (idx, solvent) in enumerate(solvents) @@ -280,11 +282,11 @@ function plot_angell_solvents() ylabel = L"ln $\sigma$", ) - solvents = unique(df[!, "Solvent"]) - n_solvents = length(solvents) + unique_solvents = unique(df[!, "Solvent"]) + n_solvents = length(unique_solvents) colors = MISTStyle.CAT_COLORS[1:n_solvents] - for (i, solvent) in enumerate(solvents) + for (i, solvent) in enumerate(unique_solvents) mask = df[!, "Solvent"] .== solvent scatter!(ax, df[mask, "Tg/T"], df[mask, "conductivity"], color = colors[i], @@ -321,6 +323,72 @@ function plot_ternary_vft_parameter!( end +function plot_ternary_vft_parameter( + model, mixture::Dict; parameter::String = "Ea", excess::Bool = false, n::Integer = 64, +) + + fig = Figure(; size = (183mm, 55mm), figure_padding = (1, 1, 1, 1)) + + solvent_names = mixture["solvents"] + mixtures = [mixture] + + all_dfs = [] + for salt_comp in [0.05, 0.1, 0.15] + if excess + df = calculate_excess(model, mixture, salt_comp, n) + else + df = Mixtures.evaluate_conductivity(model, mixtures; n = n, fixed_salt = salt_comp) + end + push!(all_dfs, df) + end + col_name = excess ? "excess_$(parameter)" : parameter + + all_ea = vcat([df[!, col_name] for df in all_dfs]...) + + global_crange = extrema(all_ea) + + for (idx, df) in enumerate(all_dfs) + ax = Axis( + fig[1, idx]; + aspect = DataAspect(), + tellwidth = true, + tellheight = true, + limits = ((-0.1, 1.1), (-0.2, 1.0)), + ) + plot_ternary_vft_parameter!(ax, df, col_name, solvent_names, global_crange) + salt_comp = first(last.(df.composition)) + hidedecorations!(ax) + hidespines!(ax) + + val = @sprintf("%.2f", salt_comp) + label = L"$x_{\mathrm{Li}^{+}}$ = %$val" + Label( + fig[0, idx], label; + tellheight = true, fontsize = 8pt, lineheight = 0.1, padding = (0, 0, 0, 0), + ) + end + # Note: parameter T_0 is mislabelled Tg in model code + label = parameter == "Tg" ? L"T_0" : L"$E_a$" + label = excess ? L"excess %$(label) [K]" : L"%$(label) [K]" + + Colorbar(fig[1, 4], + colormap = MISTStyle.CONTINUOUS_COLORS, + limits = global_crange, + label = label, + height = Relative(0.8), + tellheight = true, + flip_vertical_label = true, + vertical = true, + ) + + colsize!(fig.layout, 1, Aspect(1, 1.0)) + colsize!(fig.layout, 2, Aspect(1, 1.0)) + colsize!(fig.layout, 3, Aspect(1, 1.0)) + colgap!(fig.layout, 10) + resize_to_layout!(fig) + return fig +end + function plot_all_vft_ternaries() model_id = "mist-conductivity-27.0M-2mpg8dcd" model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") @@ -328,8 +396,8 @@ function plot_all_vft_ternaries() for excess in [true, false] for parameter in ["Tg", "Ea"] for solvent in ["O=C1OCC(F)O1", "CCOC(=O)OC", "CCOC(=O)OCC", "COC(=O)OC"] - for salt in ["O=S(=O)([N-]S(=O)(=O)C(F)(F)F)C(F)(F)F", "F[P-](F)(F)(F)(F)F"] - fn_name = "$(label_smi(solvent))_$(label_smi(salt))" + for salt in salts + fn_name = "$(parameter)_$(label_smi(solvent))_$(label_smi(salt))" fn_name = excess ? "excess_$(fn_name)" : fn_name mixture = Dict( "solvents" => ["CC1COC(=O)O1", "O=C1OCCO1", solvent], @@ -337,7 +405,7 @@ function plot_all_vft_ternaries() "salt" => [salt, "[Li+]"], ) with_theme(MISTStyle.theme()) do - plot_ternary_vft_parameter(model, mixture, parameter, excess) + plot_ternary_vft_parameter(model, mixture; parameter, excess) end |> MISTStyle.savefig(fn_name) end end @@ -346,14 +414,115 @@ function plot_all_vft_ternaries() return end -function plot_soap_similarity_correlation!(f, parameter, model, temperature) - return +function calculate_maximum_relative_excess(model, temperature, solvents) + dfs = DataFrame[] + for salt_comp in range(0.02, stop = 0.16, length = 8) + for salt in salts + mixture = Dict( + "solvents" => solvents, + "temperature" => temperature, + "salt" => [salt, "[Li+]"], + ) + df = calculate_excess(model, mixture, salt_comp, 15) + push!(dfs, df) + end + end + df_all = vcat(dfs...) + df_all[!, "salt_anion"] = [comp[4] for comp in df_all.components] + for parameter in ["Tg", "Ea"] + transform!( + groupby(df_all, :salt_anion), + "relative_excess_$(parameter)" => maximum => "maximum_abs_rel_excess_$(parameter)", + ) + end + return df_all +end + +function plot_soap_similarity_correlation!(f, model, temperature) + + xlabel = "ReMATCH Similarity" + ax_Ea = Axis(f[1, 1]; + xlabel = xlabel, ylabel = L"max \left| \frac{E_a^{excess}}{E_a} \right|",) + ax_T0 = Axis(f[1, 2]; + xlabel = xlabel, ylabel = L"max \left| \frac{T_0^{excess}}{T_0} \right|", + ) + axs = Dict( + "Ea" => ax_Ea, + "Tg" => ax_T0, + ) + all_solvents = [ + "O=C1OCC(F)O1", "CC1COC(=O)O1", "CCOC(=O)OC", + "CCOC(=O)OCC", "O=C1OCCO1", "COC(=O)OC", + ] + + solvent_combinations = collect(combinations(all_solvents, 3)) + dfs = DataFrame[] + for solvents in solvent_combinations + df = calculate_maximum_relative_excess(model, temperature, solvents) + solvent_nms = label_smi.(solvents) + transform!(df, + [:components, :composition] => ByRow((solv, comp) -> + weighted_ternary_similarity(collect(solv[1:3]), collect(comp[1:3])) + ) => :similarity, + ) + push!(dfs, df) + end + df_all = vcat(dfs...) + unique!(df_all, :components) + + df_all[!, :solvent_label] .= map(row -> join(row[:components][1:3], ", "), eachrow(df_all)) + anion_labels = categorical([comp[4] for comp in df_all.components]) + + for (idx, parameter) in enumerate(["Ea", "Tg"]) + h = scatter!(axs[parameter], df_all.similarity, df_all[!, "maximum_abs_rel_excess_$(parameter)"]; + color = levelcode.(anion_labels), + colormap = MISTStyle.CAT_COLORS, + colorrange=(1, 10), + marker=:circle, + alpha = 0.5, + ) + points = collect(zip(df_all.similarity, df_all[!, "maximum_abs_rel_excess_$(parameter)"])) + + annotation!(axs[parameter], points, text = df_all.solvent_label) + + if idx == 1 + elements = map(enumerate(levels(anion_labels))) do (i, label) + MarkerElement( + markersize=4pt, + marker=h.marker, + color=MISTStyle.CAT_COLORS[i], + label=label_smi(label) + ) + end + + Legend(f[1, 1], elements, label.(elements); + labelsize=5pt, + tellheight=false, + tellwidth=false, + padding=(1, 1, 1, 1), + margin=(1, 1, 1, 1), + patchlabelgap=0, + rowgap=0, + colgap=0, + halign=:left, + valign=:bottom, + alignmode=Outside(), + ) + end + end + + return f end -function plot_soap_similarity_correlation(model, temperature) - fig = Figure(size = (150mm, 75mm), figure_padding = (2, 2, 2, 2)) - for (idx, parameter) in enumerate(["Tg", "Ea"]) - plot_soap_similarity_correlation!(f[idx], parameter) +function plot_soap_similarity_correlation() + model_id = "mist-conductivity-27.0M-2mpg8dcd" + model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") + fig = Figure(size = (150mm, 40mm), figure_padding = (2, 2, 2, 2)) + for temperature in [260, 298, 330] + fn_name = "soap_corr_T_$(temperature)" + with_theme(MISTStyle.theme()) do + plot_soap_similarity_correlation!(fig, model, temperature) + end |> MISTStyle.savefig(fn_name) end return end From 8509c041d38624687be9977ad4840fd7ae59049c Mon Sep 17 00:00:00 2001 From: Anoushka Bhutani Date: Tue, 14 Oct 2025 13:48:58 -0400 Subject: [PATCH 147/147] normalize similarity calculations, move calculation utils into Mixtures module and tests --- opt/mixtures/ionic_conductivity.jl | 161 ++++++----------- opt/mixtures/soap_similarities.py | 59 +++++++ opt/mixtures/src/Mixtures.jl | 2 + opt/mixtures/src/TernaryPlots.jl | 183 ++++++++++++++++++++ opt/mixtures/src/similarity.jl | 30 ++++ opt/mixtures/src/vft_conductivity.jl | 36 ++++ opt/mixtures/test/similarity_tests.jl | 61 +++++++ opt/mixtures/test/vft_conductivity_tests.jl | 118 +++++++++++++ 8 files changed, 538 insertions(+), 112 deletions(-) create mode 100644 opt/mixtures/soap_similarities.py create mode 100644 opt/mixtures/src/TernaryPlots.jl create mode 100644 opt/mixtures/src/similarity.jl create mode 100644 opt/mixtures/src/vft_conductivity.jl create mode 100644 opt/mixtures/test/similarity_tests.jl create mode 100644 opt/mixtures/test/vft_conductivity_tests.jl diff --git a/opt/mixtures/ionic_conductivity.jl b/opt/mixtures/ionic_conductivity.jl index 63f0f0f9..1c8a1c58 100644 --- a/opt/mixtures/ionic_conductivity.jl +++ b/opt/mixtures/ionic_conductivity.jl @@ -12,7 +12,7 @@ using Combinatorics using CategoricalArrays: categorical, levelcode const DATA_DIR = realpath(joinpath(pkgdir(Mixtures), "..", "..", "data")) -const similarity_data = JSON.parsefile("solvent_rematch.json") +const similarity_data = JSON.parsefile(joinpath(DATA_DIR, "mixtures", "solvent_rematch.json")) const solvents = [ ["CC1COC(=O)O1", "O=C1OCCO1", "O=C1OCC(F)O1"], ["CC1COC(=O)O1", "CCOC(=O)OCC", "O=C1OCC(F)O1"], @@ -36,79 +36,6 @@ function label_smi(smi::AbstractString) return get(known, smi, smi) end -function get_similarity(smiles1::AbstractString, smiles2::AbstractString) - - key1 = "$(smiles1)_$(smiles2)" - key2 = "$(smiles2)_$(smiles1)" - - if haskey(similarity_data, key1) - return similarity_data[key1] - elseif haskey(similarity_data, key2) - return similarity_data[key2] - else - return nothing - end -end - -function weighted_ternary_similarity(solvent_list, compositions) - weighted_sum = 0.0 - - for i in 1:length(solvent_list) - for j in (i+1):length(solvent_list) - sim = get_similarity(solvent_list[i], solvent_list[j]) - weighted_sum += (compositions[i] + compositions[j]) * sim - end - end - - return weighted_sum -end - -function calculate_excess(model, mixture::Dict, salt_comp::Float64, n::Integer) - - df = Mixtures.evaluate_conductivity(model, [mixture]; n = n, fixed_salt = salt_comp) - - comp_matrix = reduce(hcat, df.composition)' - # Update salt composition to account for discretization - salt_comp = first(comp_matrix[:, 4]) - pure_solvent_param = Dict( - "Ea" => zeros(Float64, 3), - "Tg" => zeros(Float64, 3), - ) - pure_solvent_comp = 1 - salt_comp - - for (idx, solvent) in enumerate(mixture["solvents"]) - comp = zeros(4) - comp[idx] = pure_solvent_comp - comp[4] = salt_comp - preds = Mixtures.evaluate_at_composition(model, mixture, comp) - pure_solvent_param["Ea"][idx] = preds["Ea"] - pure_solvent_param["Tg"][idx] = preds["Tg"] - end - - - solvent_comps = comp_matrix[:, 1:3] - solvent_fractions = solvent_comps ./ sum(solvent_comps, dims = 2) - for parameter in ["Ea", "Tg"] - pure_solvent_values = pure_solvent_param[parameter] - df[!, "ideal_mixing_$(parameter)"] = solvent_fractions * pure_solvent_values - df[!, "excess_$(parameter)"] = df[!, parameter] .- df[!, "ideal_mixing_$(parameter)"] - df[!, "relative_excess_$(parameter)"] = abs.(df[!, "excess_$(parameter)"] ./ df[!, parameter]) - @assert sum(abs.(collect(df[1:4, "excess_$(parameter)"]))) < 1e-3 "Zero excess for expected degenerate case" - end - return df -end - -function conductivity_composition_curve(model, mixture; n = 50) - x1 = 1.0 - x2 = 0.0 - x3 = 0.0 - composition = range(0.02, stop = 0.20, length = n) - conductivity = [ - Mixtures.evaluate_at_composition(model, mixture, [x1 - x, x2, x3, x])["conductivity"] for x in - composition - ] - return composition, conductivity -end function plot_composition_curves(model, solvent) @@ -136,7 +63,7 @@ function plot_composition_curves(model, solvent) "temperature" => T, "salt" => [salt, "[Li+]"], ) - composition, conductivity = conductivity_composition_curve(model, mixture) + composition, conductivity = Mixtures.conductivity_composition_curve(model, mixture) lines!( ax, composition, @@ -159,6 +86,7 @@ function plot_composition_curves(model, solvent) return f end + function plot_composition_curves() model_id = "mist-conductivity-27.0M-2mpg8dcd" @@ -228,7 +156,7 @@ function plot_delta_Ea(model) temperatures = [260, 280, 300, 320, 340] salt_comps = range(start = 0.05, stop = 0.2, length = 5) - fig = Figure(size = (95mm, 100mm), figure_padding = (2, 2, 2, 2)) + fig = Figure(size = (90mm, 120mm), figure_padding = (5, 5, 5, 5)) cb = Colorbar(fig[length(solvents)+1, 1]; label = L"$$Temperature [K]", colormap = MISTStyle.CONTINUOUS_COLORS, colorrange = extrema(temperatures), @@ -237,7 +165,7 @@ function plot_delta_Ea(model) for (idx, solvent) in enumerate(solvents) ax = Axis( - fig[idx, 1], + fig[idx, 1], limits = (nothing, (0.0, nothing)), title = "$(label_smi(solvent[1])) | $(label_smi(solvent[2])) | $(label_smi(solvent[3]))", xlabel = idx == length(solvents) ? L"$x_{Li^+}$" : "", ylabel = L"$\frac{E_{a,LiPF_6}}{T} - \frac{E_{a,LiTFSI}}{T}$", @@ -245,7 +173,7 @@ function plot_delta_Ea(model) xticklabelsvisible = idx==length(solvents), titlesize = 6pt, xgridvisible = false, - ygridvisible = false, + ygridvisible = true, ) all_data = calculate_delta_ea(model, solvent, temperatures, salts, salt_comps) @@ -323,6 +251,7 @@ function plot_ternary_vft_parameter!( end + function plot_ternary_vft_parameter( model, mixture::Dict; parameter::String = "Ea", excess::Bool = false, n::Integer = 64, ) @@ -334,10 +263,9 @@ function plot_ternary_vft_parameter( all_dfs = [] for salt_comp in [0.05, 0.1, 0.15] + df = Mixtures.evaluate_conductivity(model, mixtures; n = n, fixed_salt = salt_comp) if excess - df = calculate_excess(model, mixture, salt_comp, n) - else - df = Mixtures.evaluate_conductivity(model, mixtures; n = n, fixed_salt = salt_comp) + Mixtures.calculate_excess!(df) end push!(all_dfs, df) end @@ -389,6 +317,7 @@ function plot_ternary_vft_parameter( return fig end + function plot_all_vft_ternaries() model_id = "mist-conductivity-27.0M-2mpg8dcd" model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") @@ -414,6 +343,7 @@ function plot_all_vft_ternaries() return end + function calculate_maximum_relative_excess(model, temperature, solvents) dfs = DataFrame[] for salt_comp in range(0.02, stop = 0.16, length = 8) @@ -423,7 +353,8 @@ function calculate_maximum_relative_excess(model, temperature, solvents) "temperature" => temperature, "salt" => [salt, "[Li+]"], ) - df = calculate_excess(model, mixture, salt_comp, 15) + df = Mixtures.evaluate_conductivity(model, [mixture]; n = 15, fixed_salt = salt_comp) + Mixtures.calculate_excess!(df) push!(dfs, df) end end @@ -438,12 +369,13 @@ function calculate_maximum_relative_excess(model, temperature, solvents) return df_all end + function plot_soap_similarity_correlation!(f, model, temperature) - xlabel = "ReMATCH Similarity" - ax_Ea = Axis(f[1, 1]; - xlabel = xlabel, ylabel = L"max \left| \frac{E_a^{excess}}{E_a} \right|",) - ax_T0 = Axis(f[1, 2]; + xlabel = "REMatch Similarity" + ax_Ea = Axis(f[1, 1]; limits = (nothing, (0.0, 0.18)), + xlabel = xlabel, ylabel = L"max \left| \frac{E_a^{excess}}{E_a} \right|") + ax_T0 = Axis(f[2, 1]; limits = (nothing, (0.0, 0.18)), xlabel = xlabel, ylabel = L"max \left| \frac{T_0^{excess}}{T_0} \right|", ) axs = Dict( @@ -462,7 +394,7 @@ function plot_soap_similarity_correlation!(f, model, temperature) solvent_nms = label_smi.(solvents) transform!(df, [:components, :composition] => ByRow((solv, comp) -> - weighted_ternary_similarity(collect(solv[1:3]), collect(comp[1:3])) + Mixtures.weighted_ternary_similarity(collect(solv[1:3]), collect(comp[1:3]), similarity_data) ) => :similarity, ) push!(dfs, df) @@ -470,43 +402,47 @@ function plot_soap_similarity_correlation!(f, model, temperature) df_all = vcat(dfs...) unique!(df_all, :components) - df_all[!, :solvent_label] .= map(row -> join(row[:components][1:3], ", "), eachrow(df_all)) + df_all[!, :solvent_label] .= map(row -> join(label_smi.(row[:components][1:3]), ", "), eachrow(df_all)) anion_labels = categorical([comp[4] for comp in df_all.components]) + single_salt = unique(df_all, :solvent_label) for (idx, parameter) in enumerate(["Ea", "Tg"]) - h = scatter!(axs[parameter], df_all.similarity, df_all[!, "maximum_abs_rel_excess_$(parameter)"]; + h = scatter!( + axs[parameter], df_all.similarity, df_all[!, "maximum_abs_rel_excess_$(parameter)"]; color = levelcode.(anion_labels), colormap = MISTStyle.CAT_COLORS, - colorrange=(1, 10), - marker=:circle, - alpha = 0.5, + colorrange = (1, 10), + marker = :circle, + alpha = 0.6, ) - points = collect(zip(df_all.similarity, df_all[!, "maximum_abs_rel_excess_$(parameter)"])) - annotation!(axs[parameter], points, text = df_all.solvent_label) + points = collect( + zip(single_salt[idx:2:end, :similarity], single_salt[idx:2:end, "maximum_abs_rel_excess_$(parameter)"]), + ) + annotation!(axs[parameter], points; text = single_salt[idx:2:end, :solvent_label], shrink = (0.0, 0.0)) if idx == 1 elements = map(enumerate(levels(anion_labels))) do (i, label) MarkerElement( - markersize=4pt, - marker=h.marker, - color=MISTStyle.CAT_COLORS[i], - label=label_smi(label) + markersize = 4pt, + marker = h.marker, + color = MISTStyle.CAT_COLORS[i], + label = label_smi(label), ) end Legend(f[1, 1], elements, label.(elements); - labelsize=5pt, - tellheight=false, - tellwidth=false, - padding=(1, 1, 1, 1), - margin=(1, 1, 1, 1), - patchlabelgap=0, - rowgap=0, - colgap=0, - halign=:left, - valign=:bottom, - alignmode=Outside(), + labelsize = 5pt, + tellheight = false, + tellwidth = false, + padding = (1, 1, 1, 1), + margin = (1, 1, 1, 1), + patchlabelgap = 0, + rowgap = 0, + colgap = 0, + halign = :left, + valign = :bottom, + alignmode = Outside(), ) end end @@ -514,13 +450,14 @@ function plot_soap_similarity_correlation!(f, model, temperature) return f end + function plot_soap_similarity_correlation() model_id = "mist-conductivity-27.0M-2mpg8dcd" model = Mixtures.load_conductivity_model(joinpath(DATA_DIR, "models", model_id)).to("mps") - fig = Figure(size = (150mm, 40mm), figure_padding = (2, 2, 2, 2)) - for temperature in [260, 298, 330] + fig = Figure(size = (90mm, 120mm), figure_padding = (2, 2, 2, 2)) + for temperature in [298] # 298, 330] fn_name = "soap_corr_T_$(temperature)" - with_theme(MISTStyle.theme()) do + with_theme(MISTStyle.theme(); fontsize = 1pt) do plot_soap_similarity_correlation!(fig, model, temperature) end |> MISTStyle.savefig(fn_name) end diff --git a/opt/mixtures/soap_similarities.py b/opt/mixtures/soap_similarities.py new file mode 100644 index 00000000..d7b9257b --- /dev/null +++ b/opt/mixtures/soap_similarities.py @@ -0,0 +1,59 @@ +import json +import itertools + +from dscribe.descriptors import SOAP +from dscribe.kernels import REMatchKernel +from sklearn.preprocessing import normalize +from ase.data.pubchem import pubchem_atoms_search + +solvent_molecules = { + "O=C1OCC(F)O1": "FEC", + "CC1COC(=O)O1": "PC", + "CCOC(=O)OC": "EMC", + "CCOC(=O)OCC": "DEC", + "O=C1OCCO1": "EC", + "COC(=O)OC": "DMC", +} + + +def calculate_similarity(smi1, smi2): + """ + Calculate SOAP fingerprint and REMatch Kernel similarity + """ + # Note: this only takes the first conformer, if there are multiple + mol1 = pubchem_atoms_search(smiles=smi1) + mol2 = pubchem_atoms_search(smiles=smi2) + + # Create SOAP descriptors + # Parameters as in Kelly, C. (2024). + # Excess Density as a Descriptor for Electrolyte Solvent Design (No. arXiv:2410.14689). + soap = SOAP( + species=["H", "C", "O", "F"], + periodic=False, + r_cut=10.0, + n_max=15, + l_max=15, + rbf="gto", + sigma=0.1, + ) + descriptors1 = normalize(soap.create(mol1)) + descriptors2 = normalize(soap.create(mol2)) + + # Calculate REMatch kernel + kernel = REMatchKernel(metric="linear", alpha=1, threshold=1e-6) + similarity = kernel.create([descriptors1, descriptors2]) + similarity = similarity[0, 1] + return similarity + + +if __name__ == "__main__": + results = {} + for pair in list(itertools.combinations(list(solvent_molecules.keys()), 2)): + results["_".join(pair)] = calculate_similarity(*pair) + + for smi in list(solvent_molecules.keys()): + pair = (smi, smi) + results["_".join(pair)] = calculate_similarity(*pair) + + with open("solvent_rematch.json", "w") as json_file: + json.dump(results, json_file, indent=4) diff --git a/opt/mixtures/src/Mixtures.jl b/opt/mixtures/src/Mixtures.jl index f8a72f2d..8bb0ae5a 100644 --- a/opt/mixtures/src/Mixtures.jl +++ b/opt/mixtures/src/Mixtures.jl @@ -21,6 +21,8 @@ end include("python.jl") include("skew.jl") include("dataset.jl") +include("similarity.jl") +include("vft_conductivity.jl") include("TernaryPlots.jl") end diff --git a/opt/mixtures/src/TernaryPlots.jl b/opt/mixtures/src/TernaryPlots.jl new file mode 100644 index 00000000..61ab93c6 --- /dev/null +++ b/opt/mixtures/src/TernaryPlots.jl @@ -0,0 +1,183 @@ +DelaunayTriangulation.toggle_warn_on_dupes!() + +""" + ternary(a, b, c, values) + +Plots a pseudocolor (tripcolor) plot on a ternary diagram using Delaunay triangulation +and filled triangles, similar to mpltern's tripcolor. The input vectors `a`, `b`, and `c` +represent the three compositional components, and `values` contains the scalar data to be +visualized with color. Components will be automatically normalized so that a + b + c = 1. +""" + +@recipe(Ternary, a, b, c, values) do scene + Theme( + colormap = :viridis, + colorrange = Makie.automatic, + show_triangle = true, + triangle_color = :black, + triangle_linewidth = 1, + show_grid = true, + grid_color = (:black, 0.7), + grid_linestyle = :solid, + grid_steps = 5, + label_a = "A", + label_b = "B", + label_c = "C", + label_offset = 0, + show_axis_labels = true, + axis_label_offset = 2, + axis_ticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0], + show_ticks = true, + interpolate = true, + ) +end + +function Makie.plot!(plt::Ternary) + # Get the input data + a = plt.a[] + b = plt.b[] + c = plt.c[] + values = plt.values[] + + # Normalize so a + b + c = 1 + total = a .+ b .+ c + a_norm = a ./ total + b_norm = b ./ total + c_norm = c ./ total + + # Convert ternary coordinates to Cartesian + x = @. 0.5 * (2 * b_norm + c_norm) + y = @. (√3 / 2) * c_norm + + # Perform Delaunay triangulation + points = [Point2(xi, yi) for (xi, yi) in zip(x, y)] + tri = triangulate(points) + + # Determine color range + crange = plt.colorrange[] === Makie.automatic ? extrema(values) : plt.colorrange[] + + # Plot each triangle + for triangle in each_solid_triangle(tri) + i, j, k = triangle_vertices(triangle) + + # Triangle vertices + tri_points = [points[i], points[j], points[k]] + + if plt.interpolate[] + tri_colors = [values[i], values[j], values[k]] + tri_value = mean(tri_colors) + else + tri_value = mean([values[i], values[j], values[k]]) + end + + poly!(plt, tri_points, + color = tri_value, + colormap = plt.colormap, + colorrange = crange, + strokewidth = 0) + end + + # Plot the triangle boundary + if plt.show_triangle[] + corners = [Point2(0.0, 0.0), Point2(1.0, 0.0), Point2(0.5, √3/2), Point2(0.0, 0.0)] + lines!(plt, corners, + color = plt.triangle_color, + linewidth = plt.triangle_linewidth) + end + + # Add grid lines + if plt.show_grid[] + n_steps = plt.grid_steps[] + for i in 1:(n_steps-1) + val = i / n_steps + + # Lines parallel to bottom edge (constant C) + p1 = Point2(0.5 * (2 * (1-val) + val), (√3/2) * val) + p2 = Point2(0.5 * (2 * 0 + val), (√3/2) * val) + lines!(plt, [p1, p2], + color = plt.grid_color, + linestyle = plt.grid_linestyle) + + # Lines parallel to left edge (constant B) + p1 = Point2(0.5 * (2 * val + 0), (√3/2) * 0) + p2 = Point2(0.5 * (2 * val + (1-val)), (√3/2) * (1-val)) + lines!(plt, [p1, p2], + color = plt.grid_color, + linestyle = plt.grid_linestyle) + + # Lines parallel to right edge (constant A) + p1 = Point2(0.5 * (1 - val), (√3/2) * (1 - val)) + p2 = Point2(1.0 - val, 0.0) + lines!(plt, [p1, p2], + color = plt.grid_color, + linestyle = plt.grid_linestyle) + end + end + + if plt.show_ticks[] + ticks = plt.axis_ticks[] + + for t in ticks + + x_bottom, y_bottom = ternary_to_cartesian(1 - t, t, 0) + text!(plt, x_bottom, y_bottom; + text = string(round(t, digits=1)), + align = (:center, :top), + rotation = π/3, + fontsize = 6pt, + offset = (0, -4pt)) + + x_left, y_left = ternary_to_cartesian(1 - t, 0, t) + text!(plt, x_left, y_left; + text = string(round(1- t, digits=1)), + rotation = -π/3, + align = (:center, :center), + fontsize = 6pt, + offset = (-6pt, 4pt)) + + x_right, y_right = ternary_to_cartesian(0, 1 - t, t) + text!(plt, x_right, y_right; + text = string(round(t, digits=1)), + align = (:left, :center), + rotation = 0, + fontsize = 6pt, + offset = (2pt, 2pt)) + end + end + + if plt.show_axis_labels[] + off = plt.axis_label_offset[] + + xb, yb = ternary_to_cartesian(0.5, 0.5, 0.0) + text!(plt, xb, yb; + text = plt.label_b[], + align = (:center, :top), + fontsize=7pt, + rotation = 0.0, + offset = (0, -9pt)) + + xl, yl = ternary_to_cartesian(0.5, 0.0, 0.5) + text!(plt, xl, yl; + text = plt.label_a[], + align = (:center, :center), + fontsize=7pt, + rotation = π/3, + offset = (-22pt, 2pt)) + + xr, yr = ternary_to_cartesian(0.0, 0.5, 0.5) + text!(plt, xr, yr; + text = plt.label_c[], + align = (:center, :center), + fontsize=7pt, + rotation = -π/3, + offset = (22pt, 2pt) + ) + end + return plt +end + +function ternary_to_cartesian(a, b, c) + x = 0.5 * (2b + c) + y = (√3 / 2) * c + return x, y +end diff --git a/opt/mixtures/src/similarity.jl b/opt/mixtures/src/similarity.jl new file mode 100644 index 00000000..edfc2256 --- /dev/null +++ b/opt/mixtures/src/similarity.jl @@ -0,0 +1,30 @@ +function get_similarity(smiles1::AbstractString, smiles2::AbstractString, similarity_data::Dict) + + key1 = "$(smiles1)_$(smiles2)" + key2 = "$(smiles2)_$(smiles1)" + + if haskey(similarity_data, key1) + return similarity_data[key1] + elseif haskey(similarity_data, key2) + return similarity_data[key2] + else + return nothing + end +end + +function weighted_ternary_similarity(solvent_list::Vector{String}, compositions::Vector{Float64}, similarity_data::Dict) + compositions = compositions ./ sum(compositions) + weighted_sum = 0.0 + total_weight = 0.0 + + for i in 1:length(solvent_list) + for j in (i+1):length(solvent_list) + sim = get_similarity(solvent_list[i], solvent_list[j], similarity_data::Dict) + weight = compositions[i] + compositions[j] + weighted_sum += weight * sim + total_weight += weight + end + end + + return weighted_sum / total_weight +end diff --git a/opt/mixtures/src/vft_conductivity.jl b/opt/mixtures/src/vft_conductivity.jl new file mode 100644 index 00000000..bb7304c3 --- /dev/null +++ b/opt/mixtures/src/vft_conductivity.jl @@ -0,0 +1,36 @@ +function calculate_excess!(df::DataFrame) + comp_matrix = reduce(hcat, df.composition)' + + # Update salt composition to account for discretization + salt_comp = first(comp_matrix[:, 4]) + solvent_comps = comp_matrix[:, 1:3] + @assert all(count(!iszero, solvent_comps[i, :]) == 1 for i in 1:3) "First three rows must be single solvent mixtures" + + pure_solvent_param = Dict( + "Ea" => df[1:3, "Ea"], + "Tg" => df[1:3, "Tg"], + ) + + solvent_fractions = solvent_comps ./ sum(solvent_comps, dims = 2) + for parameter in ["Ea", "Tg"] + pure_solvent_values = pure_solvent_param[parameter] + df[!, "ideal_mixing_$(parameter)"] = solvent_fractions * pure_solvent_values + df[!, "excess_$(parameter)"] = df[!, parameter] .- df[!, "ideal_mixing_$(parameter)"] + df[!, "relative_excess_$(parameter)"] = abs.(df[!, "excess_$(parameter)"] ./ df[!, parameter]) + @assert sum(abs.(collect(df[1:3, "excess_$(parameter)"]))) < 1e-6 "Zero excess for expected degenerate case" + end + return df +end + + +function conductivity_composition_curve(model, mixture; n = 50) + x1 = 1.0 + x2 = 0.0 + x3 = 0.0 + composition = range(0.02, stop = 0.20, length = n) + conductivity = [ + evaluate_at_composition(model, mixture, [x1 - x, x2, x3, x])["conductivity"] for x in + composition + ] + return composition, conductivity +end diff --git a/opt/mixtures/test/similarity_tests.jl b/opt/mixtures/test/similarity_tests.jl new file mode 100644 index 00000000..479ec02c --- /dev/null +++ b/opt/mixtures/test/similarity_tests.jl @@ -0,0 +1,61 @@ +using Test +using Mixtures + +@testset "weighted_ternary_similarity" begin + + @testset "Binary mixture - same molecule" begin + solvents = ["O=C1OCCO1", "O=C1OCCO1"] + compositions = [0.3, 0.4] + + result = weighted_ternary_similarity(solvents, compositions) + + expected = 1.0 + @test result ≈ expected + end + + @testset "Ternary mixture - same molecule" begin + solvents = ["O=C1OCCO1", "O=C1OCCO1", "O=C1OCCO1"] + compositions = [0.1, 0.1, 0.1] + result = weighted_ternary_similarity(solvents, compositions) + + sim12 = get_similarity(solvents[1], solvents[2]) + sim13 = get_similarity(solvents[1], solvents[3]) + sim23 = get_similarity(solvents[2], solvents[3]) + + expected = 1.0 + + @test sim12 ≈ expected + @test sim13 ≈ expected + @test sim23 ≈ expected + @test result ≈ expected + end + + @testset "Ternary mixture - unequal compositions" begin + solvents = ["O=C1OCCO1", "O=C1OCC(F)O1", "CCOC(=O)OCC"] + compositions = [0.2, 0.3, 0.1] + + result = weighted_ternary_similarity(solvents, compositions) + + sim12 = get_similarity(solvents[1], solvents[2]) + sim13 = get_similarity(solvents[1], solvents[3]) + sim23 = get_similarity(solvents[2], solvents[3]) + + expected = 0.5*((0.5/0.6) * sim12 + (0.3/0.6) * sim13 + (0.4/0.6) * sim23) + @test result ≈ expected + end + + + @testset "Zero composition handling" begin + solvents = ["O=C1OCCO1", "COC(=O)OC", "O=C1OCC(F)O1"] + compositions = [0.3, 0.3, 0.0] + + result = weighted_ternary_similarity(solvents, compositions) + + sim12 = get_similarity(solvents[1], solvents[2]) + sim13 = get_similarity(solvents[1], solvents[3]) + sim23 = get_similarity(solvents[2], solvents[3]) + + expected = (sim12 + 0.5 * sim13 + 0.5 * sim23)/2.0 + @test result ≈ expected + end +end diff --git a/opt/mixtures/test/vft_conductivity_tests.jl b/opt/mixtures/test/vft_conductivity_tests.jl new file mode 100644 index 00000000..777c7225 --- /dev/null +++ b/opt/mixtures/test/vft_conductivity_tests.jl @@ -0,0 +1,118 @@ +using Test +using DataFrames +using Mixtures: calculate_excess! + +@testset "calculate_excess!" begin + + @testset "Binary mixture" begin + # 3 pure solvents + 1 binary mixture + df = DataFrame( + composition = [ + [0.9, 0.0, 0.0, 0.1], + [0.0, 0.9, 0.0, 0.1], + [0.0, 0.0, 0.9, 0.1], + [0.4, 0.4, 0.0, 0.1], + ], + Ea = [10.0, 20.0, 10.0, 15.0], + Tg = [200.0, 300.0, 210.0, 250.0], + ) + + result = calculate_excess!(df) + + @test hasproperty(result, :ideal_mixing_Ea) + @test hasproperty(result, :excess_Ea) + @test hasproperty(result, :relative_excess_Ea) + @test hasproperty(result, :ideal_mixing_Tg) + @test hasproperty(result, :excess_Tg) + @test hasproperty(result, :relative_excess_Tg) + + expected_Ea = 0.5 * 10.0 + 0.5 * 20.0 + @test result[4, :ideal_mixing_Ea] ≈ expected_Ea + + expected_Tg = 0.5 * 200.0 + 0.5 * 300.0 + @test result[4, :ideal_mixing_Tg] ≈ expected_Tg + + # Check excess calculation + @test result[4, :excess_Ea] ≈ 15.0 - expected_Ea + @test result[4, :excess_Tg] ≈ 250.0 - expected_Tg + + # Check degenerate cases + @test all(abs.(result[1:4, :excess_Ea]) .< 1e-3) + @test all(abs.(result[1:4, :excess_Tg]) .< 1e-3) + end + + @testset "Ternary mixture ideal mixing" begin + df = DataFrame( + composition = [ + [0.9, 0.0, 0.0, 0.1], + [0.0, 0.9, 0.0, 0.1], + [0.0, 0.0, 0.9, 0.1], + [0.3, 0.3, 0.3, 0.1], + ], + Ea = [10.0, 20.0, 30.0, 20.0], + Tg = [100.0, 200.0, 300.0, 200.0], + ) + + result = calculate_excess!(df) + + expected_Ea = (10.0 + 20.0 + 30.0) / 3.0 + @test result[4, :ideal_mixing_Ea] ≈ expected_Ea + + expected_Tg = (100.0 + 200.0 + 300.0) / 3.0 + @test result[4, :ideal_mixing_Tg] ≈ expected_Tg + + # Check excess + @test abs(result[4, :excess_Ea] - (20.0 - expected_Ea)) < 1e-10 + @test abs(result[4, :excess_Tg] - (200.0 - expected_Tg)) < 1e-10 + end + + @testset "Ternary mixture positive excess" begin + df = DataFrame( + composition = [ + [0.9, 0.0, 0.0, 0.1], + [0.0, 0.9, 0.0, 0.1], + [0.0, 0.0, 0.9, 0.1], + [0.4, 0.4, 0.1, 0.1], + ], + Ea = [10.0, 20.0, 15.0, 20.0], + Tg = [200.0, 300.0, 250.0, 275.0], + ) + + result = calculate_excess!(df) + + # Ideal mixing + expect_Ea = 10.0*(4/9) + 20.0*(4/9) + 15.0*(1/9) + @test result[4, :ideal_mixing_Ea] ≈ expect_Ea + expect_Tg = 200.0*(4/9) + 300.0*(4/9) + 250.0*(1/9) + @test result[4, :ideal_mixing_Tg] ≈ expect_Tg + + # Positive excess + @test result[4, :excess_Ea] ≈ 20.0 - expect_Ea + @test result[4, :excess_Tg] ≈ 275.0 - expect_Tg + + end + + @testset "Ternary mixture negative excess" begin + df = DataFrame( + composition = [ + [0.9, 0.0, 0.0, 0.1], + [0.0, 0.9, 0.0, 0.1], + [0.0, 0.0, 0.9, 0.1], + [0.4, 0.4, 0.1, 0.1], + ], + Ea = [10.0, 20.0, 15.0, 12.0], + Tg = [200.0, 300.0, 250.0, 125.0], + ) + + result = calculate_excess!(df) + + expect_Ea = 10.0*(4/9) + 20.0*(4/9) + 15.0*(1/9) + @test result[4, :excess_Ea] ≈ 12.0 - expect_Ea + expect_Tg = 200.0*(4/9) + 300.0*(4/9) + 250.0*(1/9) + @test result[4, :excess_Tg] ≈ 125.0 - expect_Tg + + @test result[4, :relative_excess_Ea] ≈ abs(12.0 - expect_Ea) / 12.0 + @test result[4, :relative_excess_Tg] ≈ abs(125.0 - expect_Tg) / 125.0 + end + +end