diff --git a/electrolyte_fm/data_modules/molnet_dataset.py b/electrolyte_fm/data_modules/molnet_dataset.py index 7f898a66..d1fcb1b4 100644 --- a/electrolyte_fm/data_modules/molnet_dataset.py +++ b/electrolyte_fm/data_modules/molnet_dataset.py @@ -1,10 +1,7 @@ -import logging - -from datasets import Dataset, DatasetDict, load_dataset -from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles -from sklearn.model_selection import GroupShuffleSplit +from datasets import Dataset, load_dataset from .property_prediction_dataset import PropertyPredictionDataModule +from .utils import scaffold_split, train_val_test_split _URLS = { "qm8": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv", @@ -87,67 +84,3 @@ def _get_dataset(self): return ds else: raise ValueError(f"Unknown split {self.split}") - - -def train_val_test_split(ds, **kwargs): - ds_train_other = ds.train_test_split(test_size=0.2, seed=42, **kwargs) - ds_val_test = ds_train_other["test"].train_test_split( - test_size=0.5, seed=42, **kwargs - ) - return DatasetDict( - { - "train": ds_train_other["train"], - "validation": ds_val_test["train"], - "test": ds_val_test["test"], - } - ) - - -def scaffold_hash(smi: str) -> str: - try: - scaffold = MurckoScaffoldSmiles(smi) - except ValueError: - logging.warn("No scaffold for %s, using input smiles string", smi) - scaffold = smi - return scaffold - - -def scaffold_split(ds: Dataset, smi_column): - # Hash scaffolds and then bin into groups, maintains the scaffold split - # but reduces the compute - df = ds.map( - lambda x: {"scaffold": scaffold_hash(x)}, - input_columns=smi_column, - batched=False, - ).to_pandas(batched=False) - - # Split - train, other = next( - GroupShuffleSplit(n_splits=1, test_size=0.2, random_state=42).split( - df.index, groups=df["scaffold"].values - ) - ) - df_other = df.iloc[other] - val, test = next( - GroupShuffleSplit(n_splits=1, test_size=0.5, random_state=42).split( - df_other, groups=df.iloc[other]["scaffold"] - ) - ) - return DatasetDict( - { - "train": Dataset.from_pandas(df.iloc[train], preserve_index=False), - "validation": Dataset.from_pandas(df_other.iloc[val], preserve_index=False), - "test": Dataset.from_pandas(df_other.iloc[test], preserve_index=False), - } - ) - - -def strip_unk_tokens(encoding: dict, unk_token_id: int) -> dict: - """Remove unknown tokens from input""" - is_oov = [id == unk_token_id for id in encoding["input_ids"]] - out = {} - for k, v in encoding.items(): - assert len(v) == len(is_oov) - out[k] = [x for x, oov in zip(v, is_oov) if not oov] - out["is_oov"] = any(is_oov) - return out diff --git a/electrolyte_fm/data_modules/roberta_dataset.py b/electrolyte_fm/data_modules/roberta_dataset.py index 9fafde45..35db425a 100644 --- a/electrolyte_fm/data_modules/roberta_dataset.py +++ b/electrolyte_fm/data_modules/roberta_dataset.py @@ -24,6 +24,7 @@ def __init__( persistent_workers=False, canonical: Optional[bool] = None, # Deprecated: Use encoding instead encoding: str | MolEncoding = "smiles", + random: bool = False, ): super().__init__() @@ -48,6 +49,7 @@ def __init__( self.prefetch_factor = prefetch_factor self.persistent_workers = persistent_workers self.encoding = MolEncoding(encoding) + self.random = random self.hparams["vocab_size"] = self.vocab_size self.save_hyperparameters(logger=False) @@ -85,7 +87,7 @@ def setup(self, stage: str) -> None: ) # Transcode - ds = encode_molecules(ds, "text", encoding=self.encoding) + ds = encode_molecules(ds, "text", encoding=self.encoding, random=self.random) # Tokenize ds = ds.map( diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py index 7e5d421b..0dc3132a 100644 --- a/electrolyte_fm/data_modules/utils.py +++ b/electrolyte_fm/data_modules/utils.py @@ -1,11 +1,15 @@ +import logging import asyncio from enum import Enum import random +from asyncio import Semaphore from typing import TypeVar import torch from rdkit import Chem from datasets import Dataset, DatasetDict, IterableDatasetDict from datasets.distributed import split_dataset_by_node +from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles +from sklearn.model_selection import GroupShuffleSplit def is_fast(tokenizer): @@ -102,6 +106,7 @@ def encode_molecules( output_column: str | None = None, encoding: MolEncoding = MolEncoding.SMILES, random: bool = False, + max_workers: int = 8, **kwargs, ) -> AbstractDataset: """Convert SMILES encoding in `input_column` to desired `encoding` and save to `output_column`. @@ -110,15 +115,89 @@ def encode_molecules( """ assert isinstance(input_column, str) output_column = output_column or input_column - encode = encoding if not random else encoding.random + + tasks = Semaphore(max_workers) + + async def async_encode(batch: list[str]) -> dict: + async with tasks: + return {output_column: [encode(smi) for smi in batch]} + + async def async_filter(batch: list[str | None]) -> list[bool]: + async with tasks: + return [x is not None for x in batch] + ds = ds.map( - lambda smi: {output_column: encode(smi)}, + async_encode, input_columns=input_column, - batched=False, + batched=True, **kwargs, ) - return ds.filter(lambda x: x[output_column] is not None, batched=False, **kwargs) + return ds.filter(async_filter, batched=True, input_columns=output_column, **kwargs) + + +def train_val_test_split(ds, **kwargs): + ds_train_other = ds.train_test_split(test_size=0.2, seed=42, **kwargs) + ds_val_test = ds_train_other["test"].train_test_split( + test_size=0.5, seed=42, **kwargs + ) + return DatasetDict( + { + "train": ds_train_other["train"], + "validation": ds_val_test["train"], + "test": ds_val_test["test"], + } + ) + + +def scaffold_hash(smi: str) -> str: + try: + scaffold = MurckoScaffoldSmiles(smi) + except ValueError: + logging.warning("No scaffold for %s, using input smiles string", smi) + scaffold = smi + return scaffold + + +def scaffold_split(ds: Dataset, smi_column): + # Hash scaffolds and then bin into groups, maintains the scaffold split + # but reduces the compute + df = ds.map( + lambda x: {"scaffold": scaffold_hash(x)}, + input_columns=smi_column, + batched=False, + ).to_pandas(batched=False) + + # Split + train, other = next( + GroupShuffleSplit(n_splits=1, test_size=0.2, random_state=42).split( + df.index, groups=df["scaffold"].values + ) + ) + df_other = df.iloc[other] + val, test = next( + GroupShuffleSplit(n_splits=1, test_size=0.5, random_state=42).split( + df_other, groups=df.iloc[other]["scaffold"] + ) + ) + return DatasetDict( + { + "train": Dataset.from_pandas(df.iloc[train], preserve_index=False), + "validation": Dataset.from_pandas(df_other.iloc[val], preserve_index=False), + "test": Dataset.from_pandas(df_other.iloc[test], preserve_index=False), + } + ) + + +def strip_unk_tokens(encoding: dict, unk_token_id: int) -> dict: + """Remove unknown tokens from input""" + is_oov = [id == unk_token_id for id in encoding["input_ids"]] + out = {} + for k, v in encoding.items(): + assert len(v) == len(is_oov) + out[k] = [x for x, oov in zip(v, is_oov) if not oov] + out["is_oov"] = any(is_oov) + return out def stack_columns(batch, columns: list[str], output: str, dtype=None): diff --git a/electrolyte_fm/utils/cache.py b/electrolyte_fm/utils/cache.py index a45b2481..b8a753ee 100644 --- a/electrolyte_fm/utils/cache.py +++ b/electrolyte_fm/utils/cache.py @@ -42,17 +42,24 @@ def cached_github_archive(repo, commit, file): return cached_path -def cached_download(url: str, path: Path) -> Path: +def cached_download(url: str, path: Path, disable_ssl=False) -> Path: path = Path(path) cache = Path(__file__).parent.parent.parent.joinpath(".cache") cached_file = cache.joinpath(path) cached_file.parent.mkdir(exist_ok=True, parents=True) if not cached_file.exists(): - import urllib + import ssl + import urllib.request + + ctx = ( + ssl.create_default_context() + if not disable_ssl + else ssl._create_unverified_context() + ) user_agent = "Wget/1.19.5" # Pretend to be wget req = urllib.request.Request(url, headers={"User-Agent": user_agent}) - with urllib.request.urlopen(req) as fid: + with urllib.request.urlopen(req, context=ctx) as fid: cached_file.parent.mkdir(parents=True, exist_ok=True) with open(cached_file, "wb") as out: out.write(fid.read()) diff --git a/electrolyte_fm/tokenize/spe.py b/electrolyte_fm/utils/spe.py similarity index 99% rename from electrolyte_fm/tokenize/spe.py rename to electrolyte_fm/utils/spe.py index 2191e489..38a9828a 100644 --- a/electrolyte_fm/tokenize/spe.py +++ b/electrolyte_fm/utils/spe.py @@ -10,7 +10,7 @@ from transformers import PreTrainedTokenizerBase from transformers.tokenization_utils_base import BatchEncoding -from ..utils.cache import cached_download +from .cache import cached_download class PreTrainedSPETokenizer(PreTrainedTokenizerBase): diff --git a/electrolyte_fm/utils/tokenizer.py b/electrolyte_fm/utils/tokenizer.py index aa6721b3..a27fdc2c 100644 --- a/electrolyte_fm/utils/tokenizer.py +++ b/electrolyte_fm/utils/tokenizer.py @@ -8,7 +8,7 @@ def load_tokenizer(name: str, **kwargs) -> PreTrainedTokenizerBase: # Locate Tokeniser and dataset unk_name = RuntimeError(f"Unknown tokenizer: {name}") - if name.startswith("smirk"): + if name in ["smirk", "smirk-selfies", "smirk-cls"]: from smirk import SmirkTokenizerFast if name == "smirk": @@ -24,7 +24,7 @@ def load_tokenizer(name: str, **kwargs) -> PreTrainedTokenizerBase: raise unk_name elif name == "SmilesPE/SPE_ChEMBL": - from ..tokenize.spe import pretrained_spe_tokenizer + from .spe import pretrained_spe_tokenizer return pretrained_spe_tokenizer(cache_generated=True) @@ -319,6 +319,9 @@ def all_special_ids(self) -> list[int]: "SmirkTokenizer", fast_tokenizer_class=SmirkTokenizerFast ) + if Path(name).exists(): + name = str(Path(name).resolve()) + tok_tf = AutoTokenizer.from_pretrained( name, trust_remote_code=True, diff --git a/opt/TokenizerStats/.gitignore b/opt/TokenizerStats/.gitignore index d6d99908..60b42393 100644 --- a/opt/TokenizerStats/.gitignore +++ b/opt/TokenizerStats/.gitignore @@ -9,3 +9,7 @@ models smirk-gpe-* smirk-gpe-*/ fig/ +*.tar.* +archive/ +*.slurm +*.jld2 diff --git a/opt/TokenizerStats/.python-version b/opt/TokenizerStats/.python-version new file mode 100644 index 00000000..e4fba218 --- /dev/null +++ b/opt/TokenizerStats/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/opt/TokenizerStats/Project.toml b/opt/TokenizerStats/Project.toml index 1653f4e8..1a53de8a 100644 --- a/opt/TokenizerStats/Project.toml +++ b/opt/TokenizerStats/Project.toml @@ -12,13 +12,13 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688" MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195" MPIPreferences = "3da0fdf6-3ccc-4f1b-acd9-58baa6c99267" -NVTX = "5da4648a-3479-48b8-97b9-01cb529c0a1f" OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" SHA = "ea8e919c-243c-51af-8825-aaa63cd721ce" Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b" SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" +Tracy = "e689c965-62c8-4b79-b2c5-8359227902fd" [compat] ArgParse = "1.2.0" @@ -27,12 +27,12 @@ JSON = "0.21" LinearAlgebra = "1.10" MPI = "0.20" MPIPreferences = "0.1" -NVTX = "0.3.4" OnlineStats = "1.7" PythonCall = "0.9" Serialization = "1.10" SparseArrays = "1.10" StatsBase = "0.34" +Tracy = "0.1.4" [extras] ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823" diff --git a/opt/TokenizerStats/activate b/opt/TokenizerStats/activate index aaab6060..94d901db 100755 --- a/opt/TokenizerStats/activate +++ b/opt/TokenizerStats/activate @@ -5,7 +5,10 @@ DIR="$(git rev-parse --show-toplevel)/opt/TokenizerStats" # Load modules if command -v module > /dev/null; then module purge - module --ignore_cache load gcc python/3.11.5 openmpi/4.1.6 + module --ignore_cache load gcc/10.3.0 openmpi/4.1.6 python/3.11.5 + export SSL_CERT_DIR=/etc/pki/tls/certs + export SSL_CERT_FILE=/etc/pki/tls/cert.pem + export JULIA_CPU_TARGET="generic;znver4,clone_all;znver3,clone_all;haswell" fi # Activate virtual environment @@ -16,6 +19,5 @@ export HF_HOME="$(git rev-parse --show-toplevel)/.cache/huggingface" export TOKENIZERS_PARALLELISM=false # Configure julia -export JULIA_CPU_TARGET="generic;znver4,clone_all;znver3,clone_all;haswell" export JULIA_CONDAPKG_BACKEND=Null export JULIA_PYTHONCALL_EXE="$DIR/.venv/bin/python" diff --git a/opt/TokenizerStats/benchmark/bench_tokenizer.jl b/opt/TokenizerStats/benchmark/bench_tokenizer.jl index 6a8e49ef..9e909a53 100644 --- a/opt/TokenizerStats/benchmark/bench_tokenizer.jl +++ b/opt/TokenizerStats/benchmark/bench_tokenizer.jl @@ -1,5 +1,5 @@ using BenchmarkTools -using TokenizerStats +using TokenizerStats: DatasetConfig, dataset_split using PythonCall: pyconvert const suite = BenchmarkGroup() @@ -28,7 +28,14 @@ TOKENIZERS = [ "meta-llama/Meta-Llama-3-8B", ] -setup_dataset(tokenizer) = Iterators.take(TokenizerStats.molnet("freesolv"; tokenizer).train_dataset, 10) +function setup_dataset(tokenizer, encoding) + dc = DatasetConfig("qm9", tokenizer, encoding) + ds = dataset_split(dc, "val") + return Iterators.take(ds, 1000) +end for name in TOKENIZERS - suite[name] = @benchmarkable map(obs -> pyconvert(Vector{Int}, obs["input_ids"]), ds) setup=(ds = setup_dataset($name)) + suite[name] = tok_suite = BenchmarkGroup() + for encoding in ["smiles", "smiles-canonical", "smiles-kekule"] + tok_suite[encoding] = @benchmarkable foreach(identity, ds) setup=(ds = setup_dataset($name, $encoding)) + end end diff --git a/opt/TokenizerStats/check_ambi_smiles.py b/opt/TokenizerStats/check_ambi_smiles.py new file mode 100755 index 00000000..e258b77b --- /dev/null +++ b/opt/TokenizerStats/check_ambi_smiles.py @@ -0,0 +1,258 @@ +#!/usr/bin/env -S uv run --script +# /// script +# requires-python = ">=3.10" +# dependencies = [ +# "pandas", +# "rdkit", +# "typer", +# ] +# /// + +# python +import re +import csv +from pathlib import Path +import pandas as pd +from rdkit import Chem +import typer + +AMBIGUOUS_PAIRS = ["Sn", "Sb", "Co", "Os", "Po", "Sc", "Cs", "Cn"] + +app = typer.Typer() + + +def canonicalize_smiles(smiles: str): + """Return canonical SMILES or None if invalid.""" + mol = Chem.MolFromSmiles(smiles) + if mol: + return Chem.MolToSmiles(mol, canonical=True) + return None + + +def count_ambiguous_substrings(smiles: str): + """Count occurrences of each ambiguous substring in a SMILES string.""" + smiles = re.sub(r"\[[^\]]+]", "", smiles) # Ignore bracketed atoms + return {pair: len(re.findall(pair, smiles)) for pair in AMBIGUOUS_PAIRS} + + +def get_chunks(input_file, chunk_size): + """Read CSV file dynamically handling .csv and .csv.gz extensions in chunks.""" + if input_file.suffix == ".gz": + return pd.read_csv(input_file, compression="gzip", chunksize=chunk_size) + return pd.read_csv(input_file, chunksize=chunk_size) + + +def write_header(output_file, ambiguity_keys): + """Write the header to the output CSV file.""" + header = ["name", "cid", "smiles", "total_ambiguity_count"] + ambiguity_keys + with open(output_file, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(header) + + +def append_to_csv(output_file, row): + """Append a row to the output CSV file.""" + with open(output_file, "a", newline="") as f: + writer = csv.DictWriter(f, fieldnames=row.keys()) + writer.writerow(row) + + +def process_chunk(chunk, output_file): + """Process a single chunk and append results incrementally to the output file.""" + for _, row in chunk.iterrows(): + original = row["SMILES"] + canonical = canonicalize_smiles(original) + if canonical: + ambiguity_counts = count_ambiguous_substrings(canonical) + total_count = sum(ambiguity_counts.values()) + if total_count > 0: + output_row = { + "name": row["Name"], # Include Name column + "cid": row["Compound_CID"], # Include Compound_CID column + "smiles": canonical, # Canonical SMILES column + "total_ambiguity_count": total_count, # Total count column + } + output_row.update(ambiguity_counts) # Add individual counts + append_to_csv(output_file, output_row) + + +@app.command("filter") +def process_smiles( + input_file: str, + chunk_size: int = typer.Option(10000, help="Number of rows to process per chunk."), +): + """ + Process SMILES strings in the input file to identify ambiguities and write results incrementally. + """ + input_path = Path(input_file) + output_file = input_path.with_name(input_path.stem + "_ambiguous.csv") + + # Initialize output file with header + ambiguity_keys = list( + count_ambiguous_substrings("").keys() + ) # Keys for ambiguous pairs + header = [ + "name", + "cid", + "smiles", + "InChI", + "IUPAC_Name", + "total_ambiguity_count", + ] + ambiguity_keys + with open(output_file, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(header) + + total_molecules_checked = 0 # Counter for molecules + chunks = get_chunks(input_path, chunk_size) + for chunk in chunks: + if not {"SMILES", "Name", "Compound_CID", "InChI", "IUPAC_Name"}.issubset( + chunk.columns + ): + typer.echo( + "CSV must contain 'SMILES', 'Name', 'Compound_CID', 'InChI', and 'IUPAC_Name' columns.", + err=True, + ) + raise typer.Exit() + + for _, row in chunk.iterrows(): + total_molecules_checked += 1 # Increment counter + original = row["SMILES"] + canonical = canonicalize_smiles(original) + if canonical: + ambiguity_counts = count_ambiguous_substrings(canonical) + total_count = sum(ambiguity_counts.values()) + if total_count > 0: + output_row = { + "name": row["Name"], # Include Name column + "cid": row["Compound_CID"], # Include Compound_CID column + "smiles": canonical, # Canonical SMILES column + "InChI": row["InChI"], # InChI source column + "IUPAC_Name": row["IUPAC_Name"], # IUPAC Name column + "total_ambiguity_count": total_count, # Total count column + } + output_row.update(ambiguity_counts) # Add individual counts + with open(output_file, "a", newline="") as f: + writer = csv.DictWriter(f, fieldnames=output_row.keys()) + writer.writerow(output_row) + + # Print total molecules checked to stderr + typer.echo(f"Total molecules checked: {total_molecules_checked}", err=True) + typer.echo(f"Results written incrementally to: {output_file}") + + +@app.command("stats") +def tabulate_ambiguities( + output_file: str, + remove_nonbond: bool = True, + ignore_cn: bool = False, +): + """ + Tabulate the total counts of each ambiguity type and display shortest 5 examples of each. + Optionally, remove SMILES strings containing a period ("."), and ignore "Cn" in the distinct ambiguities calculation. + """ + output_path = Path(output_file) + if not output_path.exists(): + typer.echo(f"File {output_file} does not exist.") + raise typer.Exit() + + # Read the output file + df = pd.read_csv(output_file) + + # Deduplicate on the `smiles` column + df = df.drop_duplicates(subset=["smiles"]) + + # Optionally remove rows where `smiles` contains a period (".") + if remove_nonbond: + df = df[~df["smiles"].str.contains(r"\.", na=False)] + typer.echo("Removed SMILES containing a nonbond ('.').") + + # Ensure required ambiguity columns exist + ambiguity_keys = [key for key in AMBIGUOUS_PAIRS if key in df.columns] + + if not ambiguity_keys: + typer.echo("No ambiguity type columns found in the file.") + raise typer.Exit() + + if ignore_cn and "Cn" in ambiguity_keys: + ambiguity_keys.remove("Cn") + typer.echo("Ignoring 'Cn' in distinct ambiguities calculation.") + + # Calculate totals for each ambiguity type + ambiguity_totals = df[ambiguity_keys].sum().sort_values(ascending=False) + + # Display results in tabular form + typer.echo("\nTabulated Ambiguities:") + typer.echo("-" * 40) + for ambiguity, count in ambiguity_totals.items(): + typer.echo(f"{ambiguity}: {count}") + + # Extract shortest 5 SMILES examples for this ambiguity type + filtered_df = df[df[ambiguity] > 0] + shortest_examples = filtered_df.assign( + smiles_length=filtered_df["smiles"].str.len() + ).nsmallest(5, "smiles_length") + typer.echo(f"Shortest 5 examples for {ambiguity}:") + for _, row in shortest_examples.iterrows(): + typer.echo(f"\t{row['name']}: {row['smiles']}") + typer.echo("-" * 40) + + # Calculate the number of distinct ambiguity types per molecule + df["distinct_ambiguities"] = df[ambiguity_keys].gt(0).sum(axis=1) + + # Sort by number of distinct ambiguities and then by SMILES length + df = df.assign(smiles_length=df["smiles"].str.len()) + top_distinct = df.sort_values( + by=["distinct_ambiguities", "smiles_length"], ascending=[False, True] + ).head(5) + + # Print top 5 molecules with the greatest number of distinct ambiguities + typer.echo("\nTop 5 molecules with the greatest number of distinct ambiguities:") + typer.echo("-" * 40) + for _, row in top_distinct.iterrows(): + typer.echo( + f"{row['name']} (CID: {row['cid']}): {row['smiles']} - {row['distinct_ambiguities']} distinct ambiguities" + ) + typer.echo("-" * 40) + + +@app.command("merge") +def merge_ambiguous_files( + output_file: str, + input_files: list[str] = typer.Argument( + ..., help="List of ambiguous.csv files to merge." + ), +): + """ + Merge multiple ambiguous.csv files and deduplicate using InChIKey. + """ + combined_df = pd.DataFrame() + for file in input_files: + input_path = Path(file) + if not input_path.exists(): + typer.echo(f"File {file} does not exist.") + raise typer.Exit() + + typer.echo(f"Reading file: {file}") + df = pd.read_csv(file) + + if "InChI" not in df or "smiles" not in df: + typer.echo(f"File {file} must contain 'InChI' and 'smiles' columns.") + raise typer.Exit() + + combined_df = pd.concat([combined_df, df], ignore_index=True) + + # Deduplicate using InChIKey + if "InChI" in combined_df.columns: + combined_df = combined_df.drop_duplicates(subset=["InChI"]) + + typer.echo(f"Merged and deduplicated {len(combined_df)} rows based on InChIKey.") + + # Write to output file + output_path = Path(output_file) + combined_df.to_csv(output_path, index=False) + typer.echo(f"Final merged file written to: {output_path}") + + +if __name__ == "__main__": + app() diff --git a/opt/TokenizerStats/main.jl b/opt/TokenizerStats/main.jl index 0feb2539..c079f1df 100755 --- a/opt/TokenizerStats/main.jl +++ b/opt/TokenizerStats/main.jl @@ -1,3 +1,11 @@ #!/usr/bin/env -S julia --project --threads=4 --startup-file=no using TokenizerStats: main +using Tracy: wait_for_tracy + +if haskey(ENV, "TRACY_ENABLE") && get(ENV, "PMIX_RANK", -1) == 0 + @info "Waiting for tracy to connect..." + wait_for_tracy() + @info "Connected!" +end + exit(main(ARGS)) diff --git a/opt/TokenizerStats/plots/Project.toml b/opt/TokenizerStats/plots/Project.toml index 85f022ee..e80338d2 100644 --- a/opt/TokenizerStats/plots/Project.toml +++ b/opt/TokenizerStats/plots/Project.toml @@ -11,6 +11,7 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" Format = "1fa38f19-a742-5d3f-a2b9-30dd87b9d5f8" FreeTypeAbstraction = "663a7486-cb36-511b-a19d-713bb74d65c9" GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a" +GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a" HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5" JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" @@ -19,6 +20,7 @@ OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e" OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" Preferences = "21216c6a-2e73-6563-6e65-726566657250" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" +RegressionTables = "d519eb52-b820-54da-95a6-98e1306fdade" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" TokenizerStats = "ca7eb560-02c0-4cfb-b1b2-ae9b1b03b10c" @@ -26,9 +28,9 @@ TokenizerStats = "ca7eb560-02c0-4cfb-b1b2-ae9b1b03b10c" TokenizerStats = {path = "../"} [compat] -CairoMakie = "0.12" +Makie = "0.22" +GLM = "1.9" Clustering = "0.15" -Colors = "0.12.11" -DataFrames = "1.6" +DataFrames = "1.7" JSON = "0.21" julia = "1.11" diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl new file mode 100644 index 00000000..5b97d2f6 --- /dev/null +++ b/opt/TokenizerStats/plots/plots.jl @@ -0,0 +1,177 @@ +using DataFrames +using CairoMakie +using CSV: CSV +using SmirkPaperPlots +using TokenizerStats +using SmirkPaperPlots: savefig +using StatsBase +using JLD2: JLD2 +using Format + +stats_dir = joinpath(pkgdir(TokenizerStats), "stats") + +# Tokenizer Statistics +loss_stats = SmirkPaperPlots.model_loss_stats(stats_dir) +info_loss = SmirkPaperPlots.info_loss_stats(stats_dir) +token_usage = SmirkPaperPlots.usage_stats(stats_dir) +tok_info = SmirkPaperPlots.tokenizers_info(stats_dir) +JLD2.jldsave(joinpath(stats_dir, "tokenizer_stats.jld2"); loss_stats, info_loss, token_usage, tok_info) + +function intrinsic_metrics(token_usage) + tok_info = SmirkPaperPlots.tokenizers_info(stats_dir) + token_usage = transform(token_usage, + [:unk_count, :tokens_seen] => ByRow(/) => :oov_rate, + ) + wmean = (x, s) -> mean(x, fweights(s)) + df = combine(groupby(token_usage, [:tokenizer, :dataset]), + :fertility => (x -> mean(reduce(merge, x))) => :fertility, + [:divergence, :samples] => wmean => :divergence, + [:f95_all, :samples] => wmean => :f95, + [:oov_rate, :tokens_seen] => wmean => :oov_rate, + [:normalized_entropy, :samples] => wmean => :normalized_entropy, + [:entropy, :samples] => wmean => :entropy, + ) + transform!(df, + :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class, + :tokenizer => ByRow(x -> tok_info[x]["domain"]) => :tokenizer_domain, + :tokenizer => ByRow(x -> tok_info[x]["encoding"]) => :encoding, + ) + return df +end +df_intrinsic = intrinsic_metrics(token_usage) +CSV.write(joinpath("stats", "intrinsic_metrics.csv"), df_intrinsic) + +# Tokenizer Fertility by Dataset +function fertility_summary(token_usage) + tok_info = SmirkPaperPlots.tokenizers_info(stats_dir) + token_usage = transform(token_usage, + :dataset => ByRow(x -> x ∉ ["tmQM", "realspace"] ? "MoleculeNet" : x) => :dataset, + :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class, + ) + combine(groupby(token_usage, [:tokenizer_class, :dataset])) do gdf + dataset = first(gdf.dataset) + fertility = reduce(merge, gdf.fertility) + avg_fertility = mean(fertility) + std_fertility = std(fertility) + return (; + dataset=lowercase(dataset) in ("tmqm", "realspace") ? dataset : "MoleculeNet", + fertility, + avg_fertility, + std_fertility, + fmt_fertility=format("{}\\pm{}", round(avg_fertility; sigdigits=3), round(std_fertility; sigdigits=3)), + ) + end +end +fertility_summary(token_usage) |> display + +# Frequency of Unknown Tokens +function unk_freq(token_usage) + tok_info = SmirkPaperPlots.tokenizers_info(stats_dir) + token_usage = transform(token_usage, + :dataset => ByRow(x -> x ∉ ["tmQM", "realspace"] ? "MoleculeNet" : x) => :dataset, + :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class, + ) + combine(groupby(token_usage, [:tokenizer, :dataset])) do gdf + unk_count = sum(gdf.unk_count) + tokens_seen = sum(gdf.tokens_seen) + unk_freq = unk_count / tokens_seen + return (; + unk_freq, + fmt_unk_freq=format("{:.2f}%", round(100 * unk_freq; sigdigits=3)), + ) + end +end +unk_freq(token_usage) |> display + +# Normalized Entropy +function norm_entropy_summary(token_usage) + token_usage = subset(token_usage, + :dataset => ByRow(==("realspace")), + ) + # Combine splits + df = combine(groupby(token_usage, :tokenizer)) do gdf + return (; normalized_entropy=mean(gdf.normalized_entropy, fweights(gdf.tokens_seen))) + end + tok_info = SmirkPaperPlots.tokenizers_info(stats_dir) + df = transform(df, + :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class, + :tokenizer => ByRow(x -> tok_info[x]["domain"]) => :domain, + ) + # subset!(df, :domain => ByRow(==("chemistry"))) + df.tokenizer_class .= replace.(df.tokenizer_class, "smirk-gpe" => "smirk") + # Combine over tokenizer classes + df = combine(groupby(df, [:tokenizer_class, :domain])) do gdf + return (; + norm_entropy_avg=mean(gdf.normalized_entropy), + norm_entropy_std=std(gdf.normalized_entropy), + norm_entropy_n=nrow(gdf), + ) + end + sort!(df, [:norm_entropy_avg, :norm_entropy_std, :norm_entropy_n]) + return df +end +norm_entropy_summary(token_usage) |> display + + +# Transformer Models +dfp, dff, dft = SmirkPaperPlots.transformer_models(stats_dir) +df_ng, df_p, df_f = SmirkPaperPlots.df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft) + +# NGrams Stats vs. FM Performance +df_prog = SmirkPaperPlots.df_ngram_stats_v_fm_perf(tok_info, loss_stats, info_loss, df_f) +CSV.write(joinpath("stats", "ngram_stats_vs_fm.csv"), df_prog) + +# Predictive and Fixed-Effect Models +fe_models, df_predict = SmirkPaperPlots.ngram_vs_transformer_fits(df_ng, df_p, df_f) +prog_models = SmirkPaperPlots.ngram_prognostic_fits(df_prog) +display(prog_models[!, [:dataset, :finetuned, :all_rho, :all_rho_p]]) +JLD2.jldsave(joinpath("stats", "quality_models.jld2"); fe_models, df_predict, prog_models) +CSV.write(joinpath("stats", "ngram_vs_transformer.csv"), df_predict) + +# Tokenizer Summary +df_tok = SmirkPaperPlots.tokenizer_summary(stats_dir; k=5) + +# Tokenizer Summary +SmirkPaperPlots.report_tokenizer_summary_stats(stats_dir, loss_stats, info_loss, token_usage; k=3) + +with_theme(SmirkPaperPlots.theme()) do + # Token Usage + savefig("token_usage", SmirkPaperPlots.figure_token_usage(stats_dir)) + savefig("oov_rate", SmirkPaperPlots.figure_oov_rate(stats_dir)) + savefig("oov_rate_no_transcode", + SmirkPaperPlots.figure_oov_rate(stats_dir; include_transcode_errors=false) + ) + savefig("jaccard", SmirkPaperPlots.figure_jaccard(stats_dir)) + savefig("intrinsic_metrics", SmirkPaperPlots.figure_intrinsic(df_intrinsic; p=90)) + savefig("intrinsic_metrics_95", SmirkPaperPlots.figure_intrinsic(df_intrinsic; p=95)) + savefig("ngram_metrics", SmirkPaperPlots.figure_ngram_metrics(tok_info, loss_stats, info_loss)) + + # Transformers vs. N-Grams + savefig("prognostic", SmirkPaperPlots.figure_fe_models(fe_models, prog_models)) + savefig("prognostic_abs", SmirkPaperPlots.figure_fe_models(fe_models, prog_models; scale=:absolute)) + savefig("prognostic_std", SmirkPaperPlots.figure_fe_models(fe_models, prog_models; scale=:std)) + + # Transformer Model Summary + f, df = SmirkPaperPlots.figure_tf_finetune(stats_dir, dff, dft) + savefig("tf_finetune", f) + CSV.write(joinpath("stats", "tf_model_summary.csv"), df) + + # N-Gram Analysis + savefig("ngram_fits", SmirkPaperPlots.figure_ngram_fits(loss_stats, stats_dir)) + savefig("kl_v_info_loss", SmirkPaperPlots.figure_kl_v_info_loss(tok_info, loss_stats, info_loss, df_f)) + + # Example n-gram predictions + compunds = [ + "caffeine" => "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", + "lsd" => "CCN(CC)C(=O)[C@H]1CN([C@@H]2Cc3c[nH]c4c3c(ccc4)C2=C1)C", + "cortisol" => "O=C4\\C=C2/[C@]([C@H]1[C@@H](O)C[C@@]3([C@@](O)(C(=O)CO)CC[C@H]3[C@@H]1CC2)C)(C)CC4", + ] + for (name, smi) in compunds + for direction in [:forward, :bidirectional] + savefig( + "log_prob_$(direction)_$name", + SmirkPaperPlots.figure_ngram_prediction(smi, stats_dir; direction) + ) + end + end +end diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl index 194909f0..351aa5f0 100644 --- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl +++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl @@ -2,23 +2,25 @@ module SmirkPaperPlots using Makie using OnlineStats +using StatsBase using OnlineStats: Moments using GLM using DataFrames using CairoMakie: CairoMakie using Colors: distinguishable_colors, weighted_color_mean, RGBA +using CategoricalArrays: categorical using Format: format -using PythonCall: Py, pyconvert -using StatsBase: StatsBase, mean, stderr, mean_and_std, quantile, AbstractWeights, Weights using FreeTypeAbstraction: FreeTypeAbstraction, newface, FTFont +using PythonCall: Py, pyconvert using JLD2: jldopen using JSON: JSON using CategoricalArrays: categorical, levelcode using HypothesisTests: HypothesisTests, HypothesisTest, VarianceEqualityTest, pvalue -using Distributions: Chisq, FDist, Normal +using Distributions: Chisq, FDist, Normal, TDist using OrderedCollections: OrderedDict using CSV: CSV using Clustering: hclust +using RegressionTables: regtable, LatexTable using TokenizerStats using TokenizerStats: load_tokenizer, find @@ -27,15 +29,19 @@ using TokenizerStats: load_tokenizer, find include("tabulate_results.jl") include("tabulate_tokenizer.jl") include("tokenizer_summary.jl") +include("prognostics.jl") # Helper for plotting include("plot_utils.jl") +include("coeff_plot.jl") # Figures include("figures/info_loss.jl") include("figures/ngram.jl") include("figures/transfromer.jl") include("figures/jaccard.jl") +include("figures/fe_models.jl") +include("figures/intrinsic.jl") const CLASS_MARKER = Dict( @@ -73,51 +79,4 @@ function savefig(name::String, f::Figure; dpi=300) return nothing end -function (@main)(stats_dir=joinpath(pkgdir(TokenizerStats), "stats")) - CairoMakie.activate!() - - # Tokenizer Statistics - loss_stats = model_loss_stats(stats_dir) - info_loss = info_loss_stats(stats_dir) - token_usage = usage_stats(stats_dir) - - # Transformer Models - dfp, dff, dft = transformer_models(stats_dir) - - # Tokenizer Summary - report_tokenizer_summary_stats(stats_dir, loss_stats, info_loss, token_usage; k=3) - - with_theme(theme()) do - # Token Usage - savefig("token_usage", figure_token_usage(stats_dir)) - savefig("oov_rate", figure_oov_rate(stats_dir)) - savefig("jaccard", figure_jaccard(stats_dir)) - - # Transformers vs. N-Grams - df = ngram_vs_transformer_fits(stats_dir, loss_stats, dfp) - savefig("ngram_vs_transformer", figure_ngram_vs_transformer(stats_dir, df)) - - # Transformer Model Summary - f, df = figure_tf_finetune(stats_dir, dff, dft) - savefig("tf_finetune", f) - CSV.write(joinpath("stats/tf_model_summary.csv"), df) - - # N-Gram Analysis - savefig("ngram_fits", figure_ngram_fits(loss_stats, stats_dir)) - savefig("kl_v_info_loss", figure_kl_v_info_loss(stats_dir, loss_stats, info_loss, df)) - - # Example n-gram predictions - compunds = [ - "caffeine" => "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", - "lsd" => "CCN(CC)C(=O)[C@H]1CN([C@@H]2Cc3c[nH]c4c3c(ccc4)C2=C1)C", - "cortisol" => "O=C4\\C=C2/[C@]([C@H]1[C@@H](O)C[C@@]3([C@@](O)(C(=O)CO)CC[C@H]3[C@@H]1CC2)C)(C)CC4", - ] - for (name, smi) in compunds - for direction in [:forward, :bidirectional] - savefig("log_prob_$(direction)_$name", figure_ngram_prediction(smi, stats_dir; direction)) - end - end - end -end - end diff --git a/opt/TokenizerStats/plots/src/coeff_plot.jl b/opt/TokenizerStats/plots/src/coeff_plot.jl new file mode 100644 index 00000000..81ff2a8f --- /dev/null +++ b/opt/TokenizerStats/plots/src/coeff_plot.jl @@ -0,0 +1,128 @@ +struct CoeffInternal{T} + expectation::T + credible_interval::NTuple{2,T} +end + +lower(x::CoeffInternal) = first(x.credible_interval) +upper(x::CoeffInternal) = last(x.credible_interval) +StatsBase.mean(x::CoeffInternal) = x.expectation + +function CoeffInternal(μ::Real, σ²::Real; p=0.95) + ci = quantile(Normal(μ, σ²), [(1 - p) / 2, (1 + p) / 2]) + return CoeffInternal(μ, tuple(ci...)) +end + +function Base.:-(x::CoeffInternal, y::CoeffInternal) + μ = mean(x) - mean(y) + lb = lower(x) - upper(y) + ub = upper(x) - lower(y) + ci = lb <= ub ? (lb, ub) : (ub, lb) + return CoeffInternal(μ, ci) +end +Base.:-(::CoeffInternal, ::Missing) = missing +Base.:-(::Missing, ::CoeffInternal) = missing +Base.:/(::Missing, ::CoeffInternal) = missing +Base.:*(x::CoeffInternal, y::Real) = CoeffInternal(y * mean(x), y .* x.credible_interval) +Base.:*(x::Real, y::CoeffInternal) = y * x +Base.:/(x::CoeffInternal, y::Real) = x * inv(y) + +function Base.:/(x::CoeffInternal, y::CoeffInternal) + μ = mean(x) / mean(y) + limits = map(x -> /(x...), Iterators.product(x.credible_interval, y.credible_interval)) + return CoeffInternal(μ, extrema(limits)) +end + +function coefint(model::M; level=0.95) where {M<:StatsBase.StatisticalModel} + μ = coef(model) + ci = map(x -> tuple(x...), eachrow(confint(model; level))) + return CoeffInternal.(μ, ci) +end + +function coefint(model, names::Vector{String}; kwargs...) + ci = Dict(zip(coefnames(model), coefint(model))) + return [get(ci, name, missing) for name in names] +end + +@recipe(EffectBars, val, effect) do scene + Theme( + color = Makie.inherit(scene, :linecolor, :black), + colormap = Makie.inherit(scene, :colormap, :tab10), + direction = :x, + whiskerwidth = Makie.inherit(scene, (:RangeBars, :whiskerwidth), 10), + gap = 0.2, + dodge = Makie.automatic, + n_dodge = Makie.automatic, + dodge_gap = 0.03, + noeffect_visible = true, + noeffect_color = :black, + noeffect_linewidth = Makie.inherit(scene, :linewidth, 1), + noeffect_linestyle = :dash, + ) +end + +function Makie.plot!(plt::EffectBars) + effect = plt.effect + highs = @lift(map(upper, $effect)) + lows = @lift(map(lower, $effect)) + + val = first(Makie.compute_x_and_width(plt.val[], 1, plt.gap[], plt.dodge[], plt.n_dodge[], plt.dodge_gap[])) + + if plt[:noeffect_visible][] + vlines!(plt, 0; + color=plt[:noeffect_color], + linewidth=plt[:noeffect_linewidth], + linestyle=plt[:noeffect_linestyle], + ) + end + + rangebars!(plt, val, lows, highs; + color = plt[:color], + colormap=plt[:colormap], + direction = plt[:direction], + whiskerwidth = plt[:whiskerwidth], + Makie.shared_attributes(plt, Makie.Rangebars)... + ) + expecation = lift(plt[:direction], effect, val) do dir, effect, val + if dir == :x + return Point2.(mean.(effect), val) + else + return Point2.(val, mean.(effect)) + end + end + scatter!(plt, expecation; + color=plt[:color], + colormap=plt[:colormap], + Makie.shared_attributes(plt, Scatter)... + ) + + + + return plt +end + + +effect_size_plot(models; kwargs...) = effect_size_plot!(Figure(), models; kwargs...) +function effect_size_plot!(f::Figure, models; relative=true) + @show names = intersect(coefnames.(models)...) + ci = map(models) do model + cdx = map(in(names), coefnames(model)) + return coefint(model)[cdx] + end + ci = hcat(ci...) + if relative + ci = (ci .- ci[[1], :]) ./ ci[[1], :] + ci = ci[2:end, :] + names = names[2:end] + end + + ax = Axis(f[1, 1]; + yticks=(eachindex(names), names), + ) + ci = vec(ci) + val = vec(repeat(1:length(names), 1, length(models))) + dodge = vec(repeat((1:length(models))', length(names))) + + effectbars!(ax, val, vec(ci); dodge, color=dodge) + + return f +end diff --git a/opt/TokenizerStats/plots/src/figures/fe_models.jl b/opt/TokenizerStats/plots/src/figures/fe_models.jl new file mode 100644 index 00000000..3a6f8830 --- /dev/null +++ b/opt/TokenizerStats/plots/src/figures/fe_models.jl @@ -0,0 +1,206 @@ +function figure_fe_models(models, prog_models; scale=:relative, colormap=:tab10, positive_is_better=true) + f = Figure(size=(7inch, 2.5inch)) + + datasets = OrderedDict( + "realspace" => "REALSpace", + "tmQM" => "tmQM", + "qm9" => "QM9", + # "qm8" => "QM8", + "lipo" => "Lipo.", + "hiv" => "HIV", + "toxcast" => "ToxCast", + "clintox" => "ClinTox", + # "freesolv" => "FreeSolv", + # "esol" => "ESOL", + ) + coefnames = OrderedDict( + "(Intercept)" => missing, + "tokenizer_class: bpe" => "BPE", + "tokenizer_class: smirk" => "Smirk", + "tokenizer_class: smirk-gpe" => "Smirk-GPE", + "tokenizer_class: spe" => "SPE/APE", + "encoding: selfies" => "SELFIES", + ) + ds_metric = Dict(eachrow(subset(models, :ngram => ByRow(!))[!, [:dataset, :metric]])) + + function label_model(ngram, finetuned) + if !ngram + return "Transformer" + elseif ismissing(finetuned) || !finetuned + return "N-Gram" + else + return "N-Gram, Finetuned" + end + end + models = transform(models, + [:ngram, :finetuned] => ByRow(label_model) => :label + ) + models.label = categorical(models.label, + levels=["Transformer", "N-Gram", "N-Gram, Finetuned"], + ordered=true, + ) + + # Block out figure + n_levels = length(unique(models.label)) + n_datasets = length(datasets) + colorrange = (1, n_levels) + gl_coef = GridLayout(f[1, 1]) #range(1; length=n_datasets)]) + gl_prog_effect = GridLayout(f[2, 1]) + gl_parity = GridLayout(f[2, 1]) #range(1; length=n_datasets-1)]) + label_kwargs = (; + fontsize=10pt, font=:bold, + halign=:right, + tellheight=false, + ) + Label(f[1, 1, TopLeft()], "a)"; padding=(0, 15, 3, 0), label_kwargs...) + Label(f[2, 1, TopLeft()], "b)"; padding=(0, 15, 3, 0), label_kwargs...) + # Label(f[2, 2, TopLeft()], "c)"; padding=(0, 15, 5, 0), label_kwargs...) + + for (adx, ds) in enumerate(keys(datasets)) + plt_ds = datasets[ds] + ds_models = subset(models, :dataset => ByRow(==(ds))) + metric = uppercase(ds_metric[ds]) + better_sign = map(ds_models.metric) do metric + metric in ["auroc"] ? 1 : -1 + end + + val, ci, kwargs = val_ci_dodge(ds_models.model; + scale, + better_sign = positive_is_better ? better_sign : nothing, + dodge=levelcode.(ds_models.label), + coefs=keys(coefnames), + ) + + # Plot Effect sizes + lb, ub = round.(kwargs.baseline.credible_interval; sigdigits=3) + if lb != ub + title = "$plt_ds\n$metric: $lb - $ub" + else + title = "$plt_ds\n$metric: $lb" + end + yticks = (first(kwargs.yticks), map(k -> get(coefnames, k, k), last(kwargs.yticks))) + ax = Axis(gl_coef[1, adx]; + title, + yticks=yticks, + yticklabelsvisible = adx == 1, + yticksvisible=adx == 1, + xlabel=kwargs.effectlabel, + xticks=WilkinsonTicks(3), + xminorticksvisible=true, + xtickformat=kwargs.effectformat, + ) + effectbars!(ax, val, ci; dodge=kwargs.dodge, color=kwargs.dodge, colormap, colorrange) + + if adx == 1 + h = map(enumerate(levels(models.label))) do (color, label) + PolyElement(; label, color, colormap, colorrange) + end + Legend(gl_coef[1, 1], h, labels(h); + tellheight=false, + tellwidth=false, + valign=:bottom, + halign=positive_is_better ? :left : :right, + margin=(2, 2, 2, 2), + alignmode=Outside(), + labelsize=6pt, + patchsize=(6pt, 6pt), + ) + end + + # Plot Predictions + if adx != 1 + row = subset(prog_models, + :dataset => ByRow(==(ds)), + :finetuned => ByRow(==(true)), + ) |> only + qm = row.loss_and_info_and_ft + spearman = SpearmanTTest(qm) + rho = round(spearman.rho; sigdigits=3) + qm_r2 = round(r2(qm); sigdigits=3) + ax = Axis(gl_parity[1, adx-1]; + title = L"%$plt_ds, $R^2: %$qm_r2$ $\rho: %$rho$", + ylabel="Transformer - $metric", + xlabel="N-Gram Based Estimate $metric", + xticks=WilkinsonTicks(3), + yticks=WilkinsonTicks(3), + ) + ablines!(ax, 0, 1; color=:black, linestyle=:dash, label="Parity") + scatter!(ax, predict(qm), response(qm)) + end + end + + # # Plot Prog Effect Size + # prog_effect = stack(coefint, prog_models.loss_and_info_and_ft) + # val, ci, kwargs = val_ci_dodge(prog_models.loss_and_info_and_ft; + # scale=:std2, + # better_sign=map(ds -> ds in ["tmQM", "sider", "QM9", "lipo"] ? -1 : 1, prog_models.dataset) + # ) + # ax = Axis(gl_prog_effect[1,1]; + # yticks=kwargs.yticks, + # limits=(nothing, (0, 4)), + # ) + # effectbars!(ax, val, ci; + # dodge=kwargs.dodge, + # color=kwargs.dodge, + # colormap, + # colorrange=(1, size(prog_effect, 2)) + # ) + + return f +end + +function val_ci_dodge(models; dodge::Union{AbstractVector,Nothing}=nothing, scale=:absolute, coefs=Colon(), better_sign=nothing) + names = coefs isa Colon ? union(coefnames.(models)...) : collect(coefs) + ci = stack(m -> coefint(m, names), models) + baseline = ci[1] + + # Rescale coefficients + effectlabel = "Effect Size" + effectformat = "{:.2f}" + if scale == :relative + ci = ci ./ ci[[1], :] + effectlabel = "Relative Effect Size" + effectformat = "{:.0%}" + elseif scale == :std + ci = ci ./ map(std∘response, models)' + effectlabel = "Effect Size (Std. Dev.)" + elseif scale == :std2 + response_std = map(std∘response, models)' + coef_std = stack(m -> vec(std(modelmatrix(m); dims=1)), models) + ci = ci .* (coef_std ./ response_std) + elseif scale == :coef_scale + ci = ci ./ span.(ci[1, :])' + elseif scale != :absolute + error("unknown scale $scale") + end + + # Drop intercept + ci = ci[2:end, :] + names = names[2:end] + + # Flip so positive is better + if !isnothing(better_sign) + ci = ci .* vec(better_sign)' + effectlabel = "Pos. " * effectlabel + end + + # Pack results + val = vec(repeat(1:size(ci, 1), 1, size(ci, 2))) + if isnothing(dodge) + dodge = 1:size(ci, 2) + end + dodge = vec(repeat(dodge', size(ci, 1), 1)) + ci = vec(ci) + val = val[(!ismissing).(ci)] + dodge = dodge[(!ismissing).(ci)] + ci = ci[(!ismissing).(ci)] + + kwargs = (; + dodge, + effectlabel, + effectformat, + yticks = (1:length(names), names), + baseline, + ) + return val, ci, kwargs +end diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl index 3a253a1a..46b98f5b 100644 --- a/opt/TokenizerStats/plots/src/figures/info_loss.jl +++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl @@ -31,7 +31,7 @@ end function tok_log_prob!(f, cb, file, name, smi, max_vocab=50; direction=:forward) ngram, tok, info = TokenizerStats.load_ngram_model(file) - code = pyconvert(Vector{Int}, tok(smi)["input_ids"]) + code = pyconvert(Vector{UInt32}, tok(smi)["input_ids"]) if direction == :forward P = TokenizerStats.log_probability(ngram, code) @@ -86,7 +86,7 @@ function figure_ngram_info_loss(; # Load model ref_file = joinpath(@__DIR__, "stats", "character", "realspace/usage.jld2") ngram, ref_tok, ref_info = TokenizerStats.load_ngram_model(ref_file) - ref_code = pyconvert(Vector{Int}, ref_tok(smi)["input_ids"]) + ref_code = pyconvert(Vector{UInt32}, ref_tok(smi)["input_ids"]) kwargs = (; ngram, ref_tok, ref_code, token_color) tok_info_loss!(f[1, 1], cb, "smirk", smi; kwargs...) @@ -165,7 +165,7 @@ function tok_info_loss!(f, cb, tok::String, smi::String; ngram, ref_tok, ref_cod # Load model name = tokenizers_info()[tok]["name"] tok = TokenizerStats.load_tokenizer(tok) - code = pyconvert(Vector{Int}, tok(smi)["input_ids"]) + code = pyconvert(Vector{UInt32}, tok(smi)["input_ids"]) # Align both tokenizations smi_tokens = pyconvert(Vector{String}, tok.tokenize(smi)) @@ -211,7 +211,6 @@ function tok_info_loss!(f, cb, tok::String, smi::String; ngram, ref_tok, ref_cod ) # Highlight the correct token - @info token_color for (code_pos, token_id) in enumerate(ref_code) color = ismissing(token_color) ? :black : token_color[code_pos] box_token!(ax, token_id, code_pos; linewidth=0.5, color) @@ -221,13 +220,8 @@ function tok_info_loss!(f, cb, tok::String, smi::String; ngram, ref_tok, ref_cod end -function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; reference="character") - tokenizers = tokenizers_info(stats_dir) +function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference="character") - model_loss = subset(model_loss, - :dataset => ByRow(∉(["realspace"])), - :split => ByRow(==("val")), - ) model_loss.dataset = map(d -> d == "tmQM" ? d : "MoleculeNet", model_loss.dataset) model_loss = combine(groupby(model_loss, [:tokenizer, :split, :ngram, :dataset])) do gdf loss_per_token_moments = reduce(merge, gdf.loss_per_token_moments) @@ -254,14 +248,13 @@ function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; referenc stderr_info_loss=std(info_loss_moments) / sqrt(nobs(info_loss_moments)), ) end - @info info_loss # Summarize Transformer Model df_tf = deepcopy(df_tf) df_tf.dataset = map(d -> d == "tmQM" ? d : "MoleculeNet", string.(df_tf.dataset)) df_tf = combine(groupby(df_tf, [:tokenizer, :dataset])) do gdf - i = argmin(gdf.test_loss) - return (; test_loss=gdf.test_loss[i], test_loss_std=gdf.test_loss_std[i]) + i = argmin(gdf.mean) + return (; test_loss=gdf.mean[i], test_loss_std=gdf.std[i]) end df = innerjoin(model_loss, info_loss, on=[:tokenizer, :ngram, :dataset]) @@ -296,12 +289,13 @@ function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; referenc display(df) - f = Figure(; size=72 .* (4.5, 3), figure_padding=(1, 1, 1, 4)) + f = Figure(; size=(3.42inch, 2.1inch), figure_padding=(1, 2, 1, 4)) + gl = GridLayout(f[1, 2]) ax = Axis(f[1, 1]; - limits=(nothing, (-0.1, nothing)), + limits=(nothing, (-0.1, 2048)), xlabel="Cross Entropy Loss [nats/token]", ylabel="Information Loss [nats/molecule]", - yticks=[0, 0.5, 2, 4, 16, 64, 256, 512], + yticks=[0, 0.5, 2, 4, 16, 64, 256, 512, 1024], yscale=Asinh(1), yminorticksvisible=true, yminorticks=IntervalsBetween(4), @@ -318,12 +312,10 @@ function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; referenc marker=df.marker ) - ax_info = Axis(f[1, 2]; - limits=(nothing, (0, 1)), - ylabel="tmQM Test R2", + ax_info = Axis(gl[1, 1]; + ylabel="tmQM Avg. MAE", xlabel="Information Loss [nats/molecule]", yticks=LinearTicks(5), - ytickformat="{:.0%}", ) df = subset(df, :dataset => ByRow(==("tmQM"))) @@ -335,9 +327,8 @@ function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; referenc ) # Check correlation + @info "info loss vs. test loss spearman" SpearmanTTest(df.avg_info_loss, df.test_loss) @show t = HypothesisTests.CorrelationTest(df.avg_info_loss, df.test_loss) - @show pvalue(t) - Label(f[1, 1, TopLeft()], "a)"; font=:bold, @@ -348,7 +339,7 @@ function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; referenc Label(f[1, 2, TopLeft()], "b)"; font=:bold, halign=:right, - padding=(0, 25, -3, 0), + padding=(0, 10, -3, 0), tellwidth=false, ) @@ -359,26 +350,36 @@ function figure_kl_v_info_loss(stats_dir, model_loss, info_loss, df_tf; referenc ngram_elements = map(enumerate(levels(df.dataset))) do (gdx, label) PolyElement(; label, color=gdx, colorrange=h.colorrange, colormap=h.colormap) end - ngram_labels = [x.label[] for x in ngram_elements] tokenizer_elements = map(levels(df.tokenizer)) do name MarkerElement(; marker=Dict(plt_tokenizers)[name], color=:black) end tokenizer_labels = map(levels(df.tokenizer)) do name_or_path - return tokenizers[name_or_path]["name"] + return tok_info[name_or_path]["name"] end - Legend(f[1, 2], - [ngram_elements, tokenizer_elements], - [ngram_labels, tokenizer_labels], - ["Dataset", "Tokenizer"]; + Legend(gl[2, 1], + [tokenizer_elements], + [tokenizer_labels], + ["Tokenizer"]; + nbanks=2, + tellheight=true, + tellwidth=false, + halign=:center, + valign=:center, + margin=(0, 0, -15, 0), + ) + Legend(f[1, 1], + [ngram_elements], + [labels(ngram_elements)], + ["Datset"]; nbanks=2, tellheight=false, tellwidth=false, - halign=:right, - valign=:bottom, + halign=:left, + valign=:top, margin=(2, 2, 2, 2), ) - + resize_to_layout!(f) return f end @@ -473,3 +474,107 @@ function figure_info_loss_ref_tokenizer(model_loss) return f end + +function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5) + mergereduce(x) = reduce(merge, x) + df_loss = subset(df_loss, :finetuned => ByRow(==(false)), :split=>ByRow(==("val"))) + df = leftjoin(df_loss, select(df_info, Not(:vocab_size)); + on=[:tokenizer, :dataset, :ngram], + makeunique=true, + ) + transform!(df, + :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class, + :tokenizer => ByRow(x -> tok_info[x]["domain"]) => :tokenizer_domain, + :dataset => ByRow(x -> x ∈ ["realspace", "tmQM"] ? x : "MoleculeNet") => :dataset, + ) + label_tokenizers!(df) + label_datasets!(df) + + df_loss = combine(groupby(df, [:tokenizer_class, :dataset, :ngram]), + :loss_per_token_moments => mean∘mergereduce => :ng_loss, + :samples => sum => :samples, + ) + + plt_label = map(eachrow(combine(groupby(df, :tokenizer_class), :tokenizer => length∘unique => :nclass))) do r + (; tokenizer_class, nclass) = r + tokenizer_class => "$tokenizer_class (n=$nclass)" + end |> Dict + + f = Figure(; size=(3.42inch, 2.1inch), figure_padding=(1, 8, 1, 4)) + yticks = (1:length(plt_label), [plt_label[k] for k in levels(df.tokenizer_class)]) + ax = Axis(f[1, 1]; + xlabel="N-Gram Cross Entropy [nats/token]", + limits=((0, nothing), nothing), + yticks, + xminorticks=IntervalsBetween(4), + xminorticksvisible=true, + yticksvisible=false, + ) + colormap=:Set1_3 + colorrange=(1, 3) + + barplot!(ax, levelcode.(df_loss.tokenizer_class), df_loss.ng_loss; + dodge=levelcode.(df_loss.dataset), + color=levelcode.(df_loss.dataset), + stack=df_loss.ngram, + direction=:x, + colormap, + colorrange, + strokewidth=0.5pt, + strokecolor=:white, + bar_labels=format.("{:d}", df_loss.ngram), + label_position=:center, + label_align=(:center, :center), + label_color=:white, + label_size=6pt, + label_font=:bold, + ) + power2ticks(r) = (2.0 .^r, [L"2^{%$p}" for p in r]) + xticks = power2ticks(-1:2:12) + xticks = ([0, xticks[1]...], [L"0", xticks[2]...]) + ax = Axis(f[1, 2]; + xscale=Asinh(1), + limits=((0, 1024), nothing), + xticks=[0, 1, 4, 16, 64, 256, 1024], + xlabel="Information Loss [nats/molecule]", + xminorticksvisible=true, + xminorticks=IntervalsBetween(4), + ylabelvisible=false, + yticksvisible=false, + yticklabelsvisible=false, + ) + df_info = dropmissing(df, :info_loss_moments) + df_info = combine(groupby(df_info, [:tokenizer_class, :dataset, :ngram]), + :info_loss_moments => mean∘mergereduce => :info_loss, + :samples => sum => :samples, + ) + df_info = subset(df_info, :ngram => ByRow(==(ngram))) + barplot!(ax, levelcode.(df_info.tokenizer_class), df_info.info_loss; + dodge=levelcode.(df_info.dataset), + color=levelcode.(df_info.dataset), + # stack=df_info.ngram, + direction=:x, + colormap, + colorrange, + ) + + + ds_elements = map(enumerate(levels(df.dataset))) do (color, label) + PolyElement(; label, color, colorrange, colormap) + end + Legend(f[1, 1], ds_elements, labels(ds_elements); + tellheight=false, tellwidth=false, orientation=:vertical, + framevisible=true, + margin=(2, 2, 2, 2), + fontsize=6pt, + patchsize=(6pt, 6pt), + padding=2pt, + rowgap=1pt, + valign=:top, + halign=:right, + ) + + resize_to_layout!(f) + + return f +end diff --git a/opt/TokenizerStats/plots/src/figures/intrinsic.jl b/opt/TokenizerStats/plots/src/figures/intrinsic.jl new file mode 100644 index 00000000..b9e1be08 --- /dev/null +++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl @@ -0,0 +1,146 @@ +function figure_intrinsic(df; p=90) + f = Figure(; size=(3.42inch, 1.5inch)) + colgap!(f.layout, 3pt) + tokenizer_classes = OrderedDict( + "nlp" => "NLP", + "character" => "Character", + "unigram" => "Unigram", + "bpe" => "BPE", + "atomwise" => "Atom-wise", + "spe" => "SPE/APE", + "smirk-gpe" => "Smirk-GPE", + "smirk" => "Smirk", + ) + df = deepcopy(df) + df.tokenizer_class = map(df.tokenizer_domain, df.tokenizer_class) do domain, tokenizer_class + if domain == "chemistry" + return tokenizer_class + elseif domain in ["nlp", "nlp-science"] + return "nlp" + else + return "$tokenizer_class, $domain" + end + end + ckeys = collect∘keys + subset!(df, :tokenizer_class => ByRow(in(ckeys(tokenizer_classes)))) + df.tokenizer_class = categorical(df.tokenizer_class, levels=ckeys(tokenizer_classes), ordered=true) + + # Label classes with the number of examples + foreach(eachrow(combine(groupby(df, :tokenizer_class), :tokenizer => length∘unique => :nclass))) do r + (; tokenizer_class, nclass) = r + plt_class = tokenizer_classes[tokenizer_class] + tokenizer_classes[tokenizer_class] = "$plt_class (n=$nclass)" + end + + df.dataset = map(ds -> ds in ["realspace", "tmQM"] ? ds : "MoleculeNet", df.dataset) + df.dataset = map(ds -> ds == "realspace" ? "REALSpace" : ds, df.dataset) + df.dataset = categorical(df.dataset, levels=["REALSpace", "tmQM", "MoleculeNet"], ordered=true) + colormap=:Set1_3 + colorrange=(1, 3) + + metrics = [:fertility, :divergence, :normalized_entropy, :oov_rate] + ylims = (0.5, length(tokenizer_classes) + 0.5) + ax_kwargs = Dict( + :fertility => (; + xlabel="Fertility", + limits=((0, nothing), ylims), + xticks=WilkinsonTicks(3), + ), + :divergence => (; + xlabel=L"Imbalance ($D$)", + limits=((0, nothing), ylims), + xtickformat="{:.0%}", + xticks=WilkinsonTicks(3), + ), + :oov_rate => (; + xlabel="UNK Freq.", + limits=((0, 1), ylims), + xtickformat="{:.0%}", + xscale=sqrt, + xticks=[0.25, 0.5, 1], + ), + :normalized_entropy => (; + xlabel=L"Normalized Entropy ($\eta$)", + limits=((0, 1), ylims), + # xticks=[0.25, 0.75], + xticks=WilkinsonTicks(3), + xtickformat="{:.0%}", + ), + ) + + for (mdx, metric) in enumerate(metrics) + kwargs = get(ax_kwargs, metric, (;)) + ax = Axis(f[1, mdx]; + xlabelfont=:regular, + yticks=(1:length(tokenizer_classes), collect(values(tokenizer_classes))), + yticksvisible=false, + yticklabelsvisible=mdx == 1, + # xticklabelrotation=-pi/4, + xminorticks=IntervalsBetween(4), + xminorticksvisible=true, + kwargs... + ) + vspan!(ax, + percentile(df[!, metric], (100 - p) / 2), + percentile(df[!, metric], (100 + p) / 2), + color=:black, + alpha=0.2, + ) + vlines!(ax, mean(df[!, metric]); + color=:black, + linestyle=:dash, + ) + for (gdx, gdf) in enumerate(keys(tokenizer_classes)) + gdf = subset(df, :tokenizer_class => ByRow(==(gdf))) + x = gdx * ones(nrow(gdf)) + y = collect(gdf[!, metric]) + h = boxplot!(ax, x, y; + orientation=:horizontal, + dodge=levelcode.(gdf.dataset), + color=levelcode.(gdf.dataset), + outliercolor=:black, + colormap, + colorrange, + ) + end + end + ds_elements = map(enumerate(levels(df.dataset))) do (color, label) + PolyElement(; label, color, colorrange, colormap) + end + Legend(f[1, end], ds_elements, labels(ds_elements); + tellheight=false, tellwidth=false, orientation=:vertical, + framevisible=true, + margin=(2, 2, 2, 2), + fontsize=6pt, + patchsize=(6pt, 6pt), + padding=2pt, + rowgap=1pt, + valign=:top, + halign=:right, + ) + + return f +end + +function vspan!(ax, lb, ub, n; kwargs...) + points = Point2f[(lb, 0), (lb, n), (ub, n), (ub, 0)] + poly!(ax, points; kwargs...) +end + +@recipe(VSpan, lb, ub) do scene + Attributes(; + ) +end + +function Makie.plot!(plt::VSpan) + ax= Makie.current_axis() + limits = ax.finallimits + p = lift(limits, plt[:lb], plt[:ub]) do limits, lb, ub + ly = limits.origin[2] + uy = limits.origin[2] + limits.widths[2] + ly, uy = ly < uy ? (ly, uy) : (uy, ly) + Point2f[(lb, ly), (lb, uy), (ub, uy), (ub, ly)] + end + poly!(plt, p; Makie.shared_attributes(plt, Poly)...) + return plt +end diff --git a/opt/TokenizerStats/plots/src/figures/ngram.jl b/opt/TokenizerStats/plots/src/figures/ngram.jl index 5c390a74..cd379869 100644 --- a/opt/TokenizerStats/plots/src/figures/ngram.jl +++ b/opt/TokenizerStats/plots/src/figures/ngram.jl @@ -105,7 +105,7 @@ function collate_atomic_oov(key::Regex, results::Dict) return (covered / nobs), (failed_encode / nobs) end -function figure_oov_rate(stats_dir) +function figure_oov_rate(stats_dir; include_transcode_errors=true) datasets = OrderedDict( "Elements" => "elements", "Bonds" => "bonds", @@ -156,10 +156,17 @@ function figure_oov_rate(stats_dir) tok_info["oov_rate"] = Dict{String,Float64}() for (gdx, group_key) in enumerate(collect(values(datasets))) group_oov_rate, group_encode_rate = collate_atomic_oov(group_key, oov_stats) - append!(tok_id, (idx, idx)) - append!(oov_rate, (group_oov_rate, group_encode_rate)) - append!(ds_grp, (gdx, gdx)) - append!(enc_grp, (0, 1)) + if include_transcode_errors + append!(tok_id, (idx, idx)) + append!(oov_rate, (group_oov_rate, group_encode_rate)) + append!(ds_grp, (gdx, gdx)) + append!(enc_grp, (0, 1)) + else + push!(tok_id, idx) + push!(oov_rate, group_oov_rate) + push!(ds_grp, gdx) + push!(enc_grp, 0) + end end end @@ -351,7 +358,6 @@ function figure_ngram_fits(df, stats_dir; colormap=:Set2_5) colorrange=h.colorrange ) end - labels(x) = map(e -> e.label[], x) Legend(f[2, 1:2], [domains, classes], diff --git a/opt/TokenizerStats/plots/src/figures/transfromer.jl b/opt/TokenizerStats/plots/src/figures/transfromer.jl index 7260b2c2..6846cd1d 100644 --- a/opt/TokenizerStats/plots/src/figures/transfromer.jl +++ b/opt/TokenizerStats/plots/src/figures/transfromer.jl @@ -113,7 +113,7 @@ function figure_tf_finetune(stats_dir, dff, dft) ytickformat="{:.0%}", xgridvisible=false, ) - h = _finetune_results!(ax, dfr.dataset, dfr.test_loss, dfr.tokenizer; + h = barploterrors!(ax, dfr.dataset, dfr.test_loss, dfr.tokenizer; std=dfr.test_loss_std, colormap, colorrange=(1, length(colormap)), @@ -130,7 +130,7 @@ function figure_tf_finetune(stats_dir, dff, dft) yticks=LinearTicks(5), ytickformat="{:.0%}", ) - _finetune_results!(ax, dfc.dataset, dfc.test_loss, dfc.tokenizer; + barploterrors!(ax, dfc.dataset, dfc.test_loss, dfc.tokenizer; std=dfc.test_loss_std, colormap=h.colormap, colorrange=h.colorrange @@ -151,35 +151,6 @@ function figure_tf_finetune(stats_dir, dff, dft) return f, dff_all end -function _finetune_results!(ax, x, y, dodge; std=nothing, colormap, colorrange=nothing) - if isnothing(colorrange) - colorrange = extrema(levelcode.(dodge)) - end - - h = barplot!(ax, levelcode.(x), y; - dodge=levelcode.(dodge), - colormap, - colorrange, - color=levelcode.(dodge), - ) - - if !isnothing(std) - SmirkPaperPlots.dodgederrorbars!(ax, levelcode.(x), y, std; - dodge=h.dodge, - width=h.width, - n_dodge=h.n_dodge, - gap=h.gap, - dodge_gap=h.dodge_gap, - linewidth=1, - color=:black, - ) - end - - return h -end - -categorical_ticks(x) = (1:length(levels(x)), levels(x)) - function table_finetuned_models(stats_dir, dff, dft) dff = select(dff, [:id, :pretrained_id, :tokenizer, :task, :dataset, :encoding]) dff.benchmark = map(dff.dataset, dff.task) do dataset, task diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl index 5da3131b..2b853de7 100644 --- a/opt/TokenizerStats/plots/src/plot_utils.jl +++ b/opt/TokenizerStats/plots/src/plot_utils.jl @@ -22,7 +22,10 @@ function theme() Lines=(; cycle=Cycle([:color, :linestyle], covary=true), ), + markersize=4pt, + linewidth=1pt, Axis=(; + titlegap=2pt, spinewidth=0.5, ylabelpadding=3pt, yticksize=3, @@ -32,7 +35,7 @@ function theme() xtickwidth=0.5, xticksize=3, xminortickwidth=0.5, - xminorticksize=2, + xminorticksize=1.5, xgridwidth=0.5, ygridwidth=0.5, xminorgridwidth=0.5, @@ -58,10 +61,23 @@ function theme() markersize=8pt, marker=:x, ), + BarPlot=(; + whiskerwidth=1pt, + markersize=2pt, + medianlinewidth=0.5pt, + ), ErrrorBar=(; whiskerwidth=2, linewidth=0.5, - ) + ), + EffectBars=(; + linewidth=1pt, + marker=:circle, + markersize=3pt, + colormap=[:red, :blue], + whiskerwidth=8pt, + noeffect_linewidth=1pt, + ), ) end @@ -75,10 +91,16 @@ Makie.inverse_transform(m::Asinh) = x -> m.a * sinh(x / m.a) Makie.defined_interval(::Asinh) = Makie.defined_interval(identity) Makie.defaultlimits(m::Asinh) = (0.0, 10 * m.a) +my_sqrt(x) = sqrt(x) +Makie.inverse_transform(::typeof(my_sqrt)) = x -> x^2 +Makie.defaultlimits(::typeof(my_sqrt)) = (0.0, 10.0) +Makie.defined_interval(::typeof(my_sqrt)) = Makie.defined_interval(sqrt) + Makie.inverse_transform(::typeof(asinh)) = sinh Makie.defined_interval(::typeof(asinh)) = Makie.defined_interval(identity) Makie.defaultlimits(::typeof(asinh)) = (0.0, 10.0) +labels(x) = map(e -> e.label[], x) # Estimate Number of Histogram Bins from data hist_nbins(x::AbstractVector, w::AbstractWeights) = hist_nbins(:scott, x) @@ -118,8 +140,8 @@ function collate_token_usage(ids::AbstractVector{<:Integer}, counts::Dict{<:Abst counts = Dict((parse(Int, k) => v for (k, v) in pairs(counts))) return collate_token_usage(ids, counts; smoothing) end -collate_token_usage(counts::Dict, vocab_size::Integer; kwargs...) = collate_token_usage(0:vocab_size-1, counts; kwargs...) -function collate_token_usage(ids::AbstractVector{T}, counts::Dict{T,<:Integer}; smoothing) where {T} +collate_token_usage(counts::Dict, vocab_size::Integer; kwargs...) = collate_token_usage(collect(0:vocab_size-1), counts; kwargs...) +function collate_token_usage(ids::AbstractVector{T}, counts::Dict; smoothing) where {T} usage = Vector{Int}(undef, length(ids)) for (idx, token_id) in enumerate(ids) usage[idx] = get(counts, token_id, 0) + smoothing @@ -146,6 +168,7 @@ Makie.@recipe(DodgedErrorBars, x, y, error) do scene n_dodge=Makie.inherit(scene, :BarPlot, :n_dodge), gap=Makie.inherit(scene, :BarPlot, :gap), dodge_gap=Makie.inherit(scene, :BarPlot, :dodge_gap), + direction=:y, ) end @@ -154,7 +177,11 @@ function Makie.plot!(plt::DodgedErrorBars) first(Makie.compute_x_and_width(x, width, gap, dodge, n_dodge, dodge_gap)) end attr = Makie.shared_attributes(plt, Errorbars) - errorbars!(plt, x, plt[:y], plt[:error]; attr...) + if plt[:direction][] == :y + errorbars!(plt, x, plt[:y], plt[:error]; direction=:y, attr...) + else + errorbars!(plt, plt[:y], plt[:x], plt[:error]; direction=:x, attr...) + end return plt end @@ -215,3 +242,80 @@ function Makie.plot!(plt::Powerlaw) lines!(plt, points; Makie.shared_attributes(plt, Lines)...) return plt end + +function siglevel(p::Real; cutoff=[0.05, 0.01, 0.001], symbol="*") + l = findlast(sort(cutoff; rev=true) .>= p) + return isnothing(l) ? "" : symbol ^ l +end + +function label_tokenizers!(df; include_count=true) + tokenizer_classes = OrderedDict( + "nlp" => "NLP", + "character" => "Character", + "unigram" => "Unigram", + "bpe" => "BPE", + "atomwise" => "Atom-wise", + "spe" => "SPE/APE", + "smirk-gpe" => "Smirk-GPE", + "smirk" => "Smirk", + ) + df.tokenizer_class = map(df.tokenizer_domain, df.tokenizer_class) do domain, tokenizer_class + if domain == "chemistry" + return tokenizer_class + elseif domain in ["nlp", "nlp-science"] + return "nlp" + else + return "$tokenizer_class, $domain" + end + end + ckeys = collect∘keys + subset!(df, :tokenizer_class => ByRow(in(ckeys(tokenizer_classes)))) + transform!(df, :tokenizer_class => ByRow(x -> tokenizer_classes[x]) => :tokenizer_class) + df.tokenizer_class = categorical(df.tokenizer_class, levels=(collect∘values)(tokenizer_classes), ordered=true) + + # if include_count + # map(eachrow(combine(groupby(df, :tokenizer_class), :tokenizer => length∘unique => :nclass))) do r + # (; tokenizer_class, nclass) = r + # plt_class = tokenizer_classes[tokenizer_class] + # tokenizer_class => "$plt_class (n=$nclass)" + # end + # end + + return df +end + +function label_datasets!(df) + df.dataset = map(ds -> ds in ["realspace", "tmQM"] ? ds : "MoleculeNet", df.dataset) + df.dataset = map(ds -> ds == "realspace" ? "REALSpace" : ds, df.dataset) + df.dataset = categorical(df.dataset, levels=["REALSpace", "tmQM", "MoleculeNet"], ordered=true) + return df +end + +categorical_ticks(x) = (1:length(levels(x)), levels(x)) + +function barploterrors!(ax, x, y, dodge; std=nothing, colormap, colorrange=nothing) + if isnothing(colorrange) + colorrange = extrema(levelcode.(dodge)) + end + + h = barplot!(ax, levelcode.(x), y; + dodge=levelcode.(dodge), + colormap, + colorrange, + color=levelcode.(dodge), + ) + + if !isnothing(std) + SmirkPaperPlots.dodgederrorbars!(ax, levelcode.(x), y, std; + dodge=h.dodge, + width=h.width, + n_dodge=h.n_dodge, + gap=h.gap, + dodge_gap=h.dodge_gap, + linewidth=1, + color=:black, + ) + end + + return h +end diff --git a/opt/TokenizerStats/plots/src/prognostics.jl b/opt/TokenizerStats/plots/src/prognostics.jl new file mode 100644 index 00000000..3b625006 --- /dev/null +++ b/opt/TokenizerStats/plots/src/prognostics.jl @@ -0,0 +1,86 @@ +function df_ngram_stats_v_fm_perf(tok_info, model_loss, info_loss, df_f; reference="character") + info_loss = subset(info_loss, + :ngram => ByRow(==(5)), + :ref_tokenizer => ByRow(==(reference)), + ) + model_loss = subset(model_loss, + :dataset => ByRow(!=("realspace")), + :split => ByRow(==("val")), + :ngram => ByRow(==(5)), + ) + df = leftjoin( + select(info_loss, :tokenizer, :dataset, :info_loss_moments), + select(model_loss, :tokenizer, :dataset, :finetuned, :loss_per_token_moments); + on=[:tokenizer, :dataset], + ) + disallowmissing!(df) + transform!(df, + :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class, + :tokenizer => ByRow(x -> tok_info[x]["encoding"]) => :encoding, + :info_loss_moments => ByRow(mean) => :info_loss_avg, + :info_loss_moments => ByRow(std) => :info_loss_std, + :loss_per_token_moments => ByRow(mean) => :ng_loss_avg, + :loss_per_token_moments => ByRow(std) => :ng_loss_std, + ) + select!(df, Not([:info_loss_moments, :loss_per_token_moments])) + df_f = select(df_f, :tokenizer, :dataset, :encoding, :metric, :mean => :fm_loss_avg, :std => :fm_loss_std) + return leftjoin!(df, df_f; on=[:tokenizer, :dataset, :encoding]) +end + +function ngram_prognostic_fits(df_prog) + df_prog = select(df_prog, [:finetuned, :dataset, :encoding, :ng_loss_avg, :info_loss_avg, :fm_loss_avg, :fm_loss_std]) + dropmissing!(df_prog) + df = combine(groupby(df_prog, [:dataset, :finetuned])) do gdf + wts = aweights(inv.(gdf.fm_loss_std .^ 2)) + loss_only = lm(@formula(fm_loss_avg ~ 1 + ng_loss_avg), gdf; wts) + loss_and_info = lm(@formula(fm_loss_avg ~ 1 + ng_loss_avg + info_loss_avg), gdf; wts) + return (; loss_only, loss_and_info) + end + df_ft = combine(groupby(df_prog, :dataset)) do gdf + gdf = unstack(gdf, :finetuned, :ng_loss_avg, renamecols=x -> x ? :ng_ft_loss_avg : :ng_loss_avg) + wts = aweights(inv.(gdf.fm_loss_std .^ 2)) + loss_and_info_and_ft = lm(@formula(fm_loss_avg ~ 1 + ng_loss_avg + ng_ft_loss_avg + info_loss_avg), gdf; wts) + loss_and_ft = lm(@formula(fm_loss_avg ~ 1 + ng_loss_avg + ng_ft_loss_avg), gdf; wts) + return (; loss_and_info_and_ft, loss_and_ft) + end + leftjoin!(df, df_ft; on=:dataset) + transform!(df, + :loss_only => ByRow(r2), + :loss_and_info => ByRow(r2), + :loss_and_info_and_ft => ByRow(r2), + :loss_and_ft => ByRow(r2), + # [:loss_only, :loss_and_info_and_ft] => ByRow(ftests) => :ftests, + :loss_and_info_and_ft => ByRow(m -> SpearmanTTest(m).rho) => :all_rho, + :loss_and_info_and_ft => ByRow(pvalue∘SpearmanTTest) => :all_rho_p, + :loss_only => ByRow(nobs_nonwts) => :nobs, + :loss_only => ByRow(StatsBase.variation ∘ response) => :response_cv, + ) + return df +end + +function ftests(models...) + ftest(getfield.(models, :model)...) +end + +struct SpearmanTTest + rho::Float64 + t::Float64 + nobs::Int +end + +SpearmanTTest(model::StatsBase.RegressionModel) = SpearmanTTest(response(model), predict(model)) +function SpearmanTTest(x::AbstractVector, y::AbstractVector) + rho = corspearman(x, y) + nobs = length(x) + t = rho * sqrt((nobs - 2) / (1 - rho ^ 2)) + return SpearmanTTest(rho, t, nobs) +end + +HypothesisTests.pvalue(stt::SpearmanTTest) = pvalue(TDist(stt.nobs-2), stt.t) + +function Base.show(io::IO, mime::MIME"text/plain", stt::SpearmanTTest) + println(io, "ρ: $(round(stt.rho; sigdigits=3))") + println(io, "n: $(stt.nobs)") + println(io, "t: $(round(stt.t; sigdigits=3))") + println(io, "p-value: $(round(pvalue(stt); sigdigits=3))") +end diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl index dc1eddb4..0fb51625 100644 --- a/opt/TokenizerStats/plots/src/tabulate_results.jl +++ b/opt/TokenizerStats/plots/src/tabulate_results.jl @@ -136,7 +136,7 @@ end function task_metrics(task, metrics; dataset=nothing) if task == "regression" - metric = "r2" + metric = dataset in ["esol", "freesolv", "lipo", "rmse"] ? "rmse" : "mae" elseif task == "binary" metric = dataset == "muv" ? "avg-precision" : "auroc" else @@ -153,7 +153,7 @@ end function get_metric(metrics::Vector, name::String; split, tok_group="all", type="best") for metric in metrics - if metric["metric"] == name && metric["split"] == split && metric["tok_group"] == tok_group && metric["type"] == type + if metric["metric"] == name && metric["split"] == split && coalesce(metric["tok_group"] == tok_group, false) && coalesce(metric["type"] == type, false) return metric["value"] end end @@ -214,31 +214,39 @@ function link_training_runs(runs) return linked end -function df_ngrams_vs_transformer(stats_dir, loss_stats, dfp) +function df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft) tokenizers = tokenizers_info(stats_dir) - val_loss = subset(loss_stats, + df_ng = subset(loss_stats, :split => ByRow(==("val")), - :dataset => ByRow(==("realspace")), + :ngram => ByRow(==(5)), :tokenizer => ByRow(x -> haskey(tokenizers, x)), ) - best_models = combine(groupby(val_loss, :tokenizer)) do gdf - sort!(gdf, :avg_model_loss; rev=false) - return gdf[1, :] - end - select!(best_models, :tokenizer, :ngram, :avg_model_loss => :ngram_loss, :avg_model_token_loss => :ngram_token_loss) - - # Merge with pretraining data - dfp = leftjoin(dfp, best_models, on=[:tokenizer]) - sort!(dfp, :val_loss) - dfp.tokenizer = categorical(dfp.tokenizer, levels=unique(dfp.tokenizer)) - replace!(dfp.encoding, - "smiles" => "SMILES", - "smiles-canonical" => "Canonical SMILES", - "selfies" => "SELFIES" + transform!(df_ng, + :tokenizer => ByRow(x -> tokenizers[x]["tokenizer_class"]) => :tokenizer_class, + :tokenizer => ByRow(x -> tokenizers[x]["encoding"]) => :encoding, + ) + select!(df_ng, :tokenizer, :tokenizer_class, :dataset, :encoding, :finetuned, :samples, :loss_per_token_moments) + + # Combine Molecular Foundation Models + dfp = select(dfp, :id => :pretrained_id, :id, :tokenizer, :encoding, :tokenizer_class, :val_loss, :train_loss) + dfp.task .= "mlm" + dfp.metric .= "cross-entropy" + dff = subset(dff, + :frozen => ByRow(!), + [:encoding, :pretrained_encoding] => ByRow(==), + :dataset => ByRow(!=("muv")), ) - dfp.encoding = categorical(dfp.encoding, levels=["SMILES", "Canonical SMILES", "SELFIES"]) + select!(dff, Not([:frozen, :train_oov_loss, :val_oov_loss, :train_loss, :val_loss, :step])) + select!(dff, Not([:pretrained_encoding])) + + # Get preferred MoleculeNet Metrics + dft = subset(dft, :tok_group => ByRow(==("all")), :channel => ByRow(isnothing)) + select!(dft, :ckpt_id, :metric, :mean, :std) + leftjoin!(dff, dft; on=[:id => :ckpt_id, :metric]) + dropmissing!(dff) + - return dfp, best_models + return df_ng, dfp, dff end struct LogLikelihoodRatioTest @@ -260,47 +268,90 @@ function Base.show(io::IO, mime::MIME"text/plain", lrt::LogLikelihoodRatioTest) println(io, "p-value: $(round(pvalue(lrt); sigdigits=3))") end -function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp) - df, best_models = df_ngrams_vs_transformer(stats_dir, loss_stats, dfp) +nobs_nonwts(model) = size(model.model.pp.X, 1) +function ngram_vs_transformer_fits(df_ng, df_p, df_f) contrasts = Dict( :tokenizer_class => EffectsCoding(; base="atomwise"), - :encoding => EffectsCoding(; base="SMILES"), + :encoding => EffectsCoding(; base="smiles"), ) - # Test impact of encoding on pretraining - null = lm(@formula(val_loss ~ 1), df) - @show tok = lm( - @formula(val_loss ~ 1 + tokenizer_class + encoding), - df; contrasts - ) - LogLikelihoodRatioTest(tok, null) |> display - - # Test impact of encoding on pretraining - tokenizers = tokenizers_info(stats_dir) - best_models.tokenizer_class = map(tok -> tokenizers[tok]["tokenizer_class"], best_models.tokenizer) - best_models.encoding = map(tok -> tokenizers[tok]["encoding"], best_models.tokenizer) - replace!(best_models.encoding, - "smiles" => "SMILES", - "smiles-canonical" => "Canonical SMILES", - "selfies" => "SELFIES" + # Pretraining models + models = [] + df_ng.val_loss = mean.(df_ng.loss_per_token_moments) + df_ng.ng_loss_std = std.(df_ng.loss_per_token_moments) + df_ng.wts = inv.(var.(df_ng.loss_per_token_moments)) + df_ng_pt = subset(df_ng, :dataset => ByRow(==("realspace"))) + model = lm(@formula(val_loss ~ 1 + tokenizer_class + encoding), df_ng_pt; + contrasts, + wts=aweights(df_ng_pt.wts), ) + df_ng_pt.ng_est_loss = predict(model) - null = lm(@formula(ngram_token_loss ~ 1), best_models) - @show tok = lm( - @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding), - best_models; contrasts + # Dataframe for predictions + df_predict = leftjoin( + select(df_ng_pt, :tokenizer, :val_loss => :ng_loss_avg, :ng_loss_std, :encoding), + select(df_p, :tokenizer, :val_loss => :fm_loss_avg, :encoding); + on=[:tokenizer, :encoding], ) - LogLikelihoodRatioTest(tok, null) |> display + df_predict.fm_loss_std .= missing + df_predict.finetuned .= false + df_predict.dataset .= "realspace" + + push!(models, (; model, dataset="realspace", ngram=true, metric="CE")) + model = lm(@formula(val_loss ~ 1 + tokenizer_class + encoding), df_p; contrasts) + push!(models, (; model, dataset="realspace", ngram=false, metric="CE")) + + for dataset in unique(df_f.dataset) + df_f_fm = subset(df_f, :dataset => ByRow(==(dataset))) + task = first(df_f_fm.task) + + # Foundation Model + df_f_fm.val_loss = df_f_fm.mean + df_f_fm.wts = inv.(df_f_fm.std .^ 2) + model = lm( + @formula(val_loss ~ 1 + tokenizer_class + encoding), df_f_fm; + contrasts, + wts=aweights(df_f_fm.wts), + ) + push!(models, (; model, dataset, ngram=false, metric=first(df_f_fm.metric))) + + # NGram + for finetuned in [true, false] + df_f_ng = subset(df_ng, :dataset => ByRow(==(dataset)), :finetuned => ByRow(==(finetuned))) + nrow(df_f_ng) == 0 && continue + df_f_ng.val_loss = mean.(df_f_ng.loss_per_token_moments) + df_f_ng.wts = inv.(var.(df_f_ng.loss_per_token_moments)) + model = lm( + @formula(val_loss ~ 1 + tokenizer_class + encoding), df_f_ng; + contrasts, + wts=aweights(df_f_ng.wts), + ) + push!(models, (; model, dataset, ngram=true, finetuned, metric="CE")) + + ds_predict = leftjoin( + select(df_f_ng, :tokenizer, :val_loss => :ng_loss_avg, :ng_loss_std, :encoding), + select(df_f_fm, :tokenizer, :val_loss => :fm_loss_avg, :std => :fm_loss_std, :encoding); + on=[:tokenizer, :encoding], + ) + ds_predict.dataset .= dataset + ds_predict.finetuned .= finetuned + df_predict = vcat(df_predict, ds_predict) + end + end - # Predict pretraining loss using n-gram model - null = glm(@formula(val_loss ~ 1), df, Normal(), LogLink()) - @show ngram = glm(@formula(val_loss ~ 1 + log(ngram_token_loss)), df, Normal(), LogLink(); contrasts) - LogLikelihoodRatioTest(ngram, null) |> display - r2(ngram.model, :devianceratio) |> display + models = map(models) do m + m = haskey(m, :finetuned) ? m : (; m..., finetuned=false) + end - return df + df_model = DataFrame(models) + transform!(df_model, + :model => ByRow(m -> cor(response(m), predict(m))) => :r2, + :model => ByRow(m -> corspearman(response(m), predict(m))) => :spearman, + :model => ByRow(nobs_nonwts) => :nobs, + ) + return df_model, df_predict end function tmqm_finetune(stats_dir, dff, dft) diff --git a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl index 2232b497..45ed2af9 100644 --- a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl +++ b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl @@ -6,12 +6,35 @@ function usage_stats(stats_dir) tokenizer = joinpath(splitpath(file)[1:end-2]) dataset = splitpath(file)[end-1] dataset = dataset == "tmqm" ? "tmQM" : dataset + unk_id = data["tokenizer"][:unk_token_id] + vocab_size = data["tokenizer"][:vocab_size] + for split in ["train", "val", "test"] split ∉ keys(data) && continue Set(keys(data[split])) >= Set(["samples", "out_of_vocab", "fertility"]) || continue samples = data[split]["samples"] fertility = CountMap(data[split]["fertility"], samples) + unigram = data[split]["ngrams"]["1"] + unk_count = get(unigram, (unk_id,), 0) + tokens_seen = sum(values(unigram)) nunique = CountMap(data[split]["fertility"], samples) + + token_prob = values(unigram) ./ tokens_seen + entropy = sum(token_prob) do p + -p * log(p) + end + normalized_entropy = entropy / log(data["tokenizer"][:vocab_size]) + + divergence = 0.5 * sum(token_prob) do p + abs(p - (1 / vocab_size)) + end + + c_all = collate_token_usage(unigram, vocab_size; smoothing=0) + f95_all = tokens_seen .* percentile(c_all ./ tokens_seen, 5) + p_smooth = @. (c_all + 1) / (tokens_seen + length(c_all)) + f95_smooth = tokens_seen .* percentile(p_smooth, 5) + f95 = tokens_seen .* percentile(token_prob, 5) + push!(rows, (; file, tokenizer, @@ -20,12 +43,15 @@ function usage_stats(stats_dir) samples, out_of_vocab=data[split]["out_of_vocab"], fertility, + entropy, + divergence, + f95, + f95_all, + f95_smooth, + normalized_entropy, nunique, - avg_fertility=mean(fertility), - std_fertility=std(fertility), - max_fertility=maximum(keys(data[split]["fertility"])), - avg_nunique=mean(nunique), - std_nunique=std(nunique), + unk_count, + tokens_seen, )) end end @@ -83,11 +109,13 @@ end function model_loss_stats(stats_dir) rows = [] - for file in find(stats_dir, r"/model_loss\.jld2$") + for file in find(stats_dir, r"/model_loss.*\.jld2$") jldopen(file) do data tokenizer = data["ref_tokenizer"][:name] + tokenizer = dirname(tokenizer) == "." ? basename(tokenizer) : tokenizer dataset = basename(dirname(file)) - dataset = dataset == "tmqm" ? "tmQM" : dataset + finetuned = occursin(dataset, basename(file)) + dataset = lowercase(dataset) == "tmqm" ? "tmQM" : dataset for split in ["train", "val", "test"] split ∉ keys(data) && continue split_data = data[split] @@ -98,6 +126,7 @@ function model_loss_stats(stats_dir) push!(rows, (; tokenizer, dataset, + finetuned, split, ngram, vocab_size=data["tokenizer"][:vocab_size], @@ -139,3 +168,16 @@ function find_oov_samples(results::Dict) sort!(df, :ntokenizers; rev=true) return df end + +function fertility_summary(df_tokenizer, df_usage) + df_usage = transform(df_usage, + :dataset => ByRow(x -> lowercase(x) in ["tmqm", "realspace"] ? x : "MoleculeNet") => :dataset + ) + df_usage = combine(groupby(df_usage, [:tokenizer, :dataset, :encoding])) do gdf + return (; + loss_per_token_moments=reduce(merge, gdf.loss_per_token_moments), + loss_moments=reduce(merge, gdf.loss_per_token_moments), + ) + end + return df_usage +end diff --git a/opt/TokenizerStats/plots/src/tokenizer_summary.jl b/opt/TokenizerStats/plots/src/tokenizer_summary.jl index 55e4f95e..0d24b4b9 100644 --- a/opt/TokenizerStats/plots/src/tokenizer_summary.jl +++ b/opt/TokenizerStats/plots/src/tokenizer_summary.jl @@ -2,7 +2,7 @@ function top_k_tokens(ngram_file; k=5) # Load unigram statistics unigram, tok = jldopen(ngram_file, "r") do data # Load tokenizer - name = data["tokenizer"][:name] + name = data["tokenizer"][:tokenizer_name] name = startswith(name, "smirk-gpe") ? "./" * name : name tok = load_tokenizer(name) @@ -121,34 +121,14 @@ function report_tokenizer_summary_stats(stats_dir, model_loss, info_loss, usage_ replace.(top_k_tokens, "#" => "\\#", "" => "[UNK]") end - leftjoin!(df_out, - select(subset(df, :dataset => ByRow(==("realspace"))), :tokenizer, :cross_entropy => :cross_entropy_realspace); - on="name_or_path" => "tokenizer" - ) - leftjoin!(df_out, - select(subset(df, :dataset => ByRow(==("realspace"))), :tokenizer, :fertility => :fertility_realspace); - on="name_or_path" => "tokenizer" - ) - - # MoleculeNet - leftjoin!(df_out, - select(subset(df, :dataset => ByRow(==("MoleculeNet"))), :tokenizer, :cross_entropy => :cross_entropy_molnet); - on="name_or_path" => "tokenizer" - ) - leftjoin!(df_out, - select(subset(df, :dataset => ByRow(==("MoleculeNet"))), :tokenizer, :info_loss => :info_loss_molnet); - on="name_or_path" => "tokenizer" - ) - - # tmQM - leftjoin!(df_out, - select(subset(df, :dataset => ByRow(==("tmQM"))), :tokenizer, :cross_entropy => :cross_entropy_tmqm); - on="name_or_path" => "tokenizer" - ) - leftjoin!(df_out, - select(subset(df, :dataset => ByRow(==("tmQM"))), :tokenizer, :info_loss => :info_loss_tmqm); - on="name_or_path" => "tokenizer" - ) + # Stack dataset specific metrics + @info describe(df) + rename_cols(col, x) = Symbol(col, "_", lowercase(x)) + for col in [:info_loss, :cross_entropy, :fertility] + df_col = select(df, :dataset, :tokenizer, col) + df_col = unstack(df_col, :dataset, col; renamecols=Base.Fix1(rename_cols, col)) + leftjoin!(df_out, df_col; on="name_or_path" => "tokenizer") + end select!(df_out, Not("name_or_path")) fig_dir = joinpath(pkgdir(TokenizerStats), "fig") @@ -158,24 +138,28 @@ function report_tokenizer_summary_stats(stats_dir, model_loss, info_loss, usage_ \\begin{landscape} \\begin{table} \\resizebox{\\linewidth}{!}{% - \begin{tabular}{llllc|cc|cc|cc} + \\begin{tabular}{llllrc|cc|ccc|ccc} & & & & & - \\multicolumn{2}{c|}{REAL Space} & - \\multicolumn{2}{c|}{MoleculeNet} & - \\multicolumn{2}{c}{tmQM} \\\\ + & + \\multicolumn{2}{c|}{REALSpace} & + \\multicolumn{3}{c|}{MoleculeNet} & + \\multicolumn{3}{c}{tmQM} \\\\ Tokenizer & Domain & Encoding & Class & + Vocab. Size & Top-$k & + Fertility & \\(H\\) & Fertility & \\(H\\) & \\(D_{KL}\\) & + Fertility & \\(H\\) & \\(D_{KL}\\) \\\\ \\hline """ @@ -192,11 +176,14 @@ function report_tokenizer_summary_stats(stats_dir, model_loss, info_loss, usage_ $(row["Domain"]) & $(row["Encoding"]) & $(row["Class"]) & + $(format("{:,d}", row["Vocab. Size"])) & $top_k_tokens & - $(row["cross_entropy_realspace"]) & $(row["fertility_realspace"]) & - $(row["cross_entropy_molnet"]) & - $(coalesce(row["info_loss_molnet"], "---")) & + $(row["cross_entropy_realspace"]) & + $(row["fertility_moleculenet"]) & + $(row["cross_entropy_moleculenet"]) & + $(coalesce(row["info_loss_moleculenet"], "---")) & + $(row["fertility_tmqm"]) & $(row["cross_entropy_tmqm"]) & $(coalesce(row["info_loss_tmqm"], "---")) \\\\ """ diff --git a/opt/TokenizerStats/poetry.toml b/opt/TokenizerStats/poetry.toml deleted file mode 100644 index 53b35d37..00000000 --- a/opt/TokenizerStats/poetry.toml +++ /dev/null @@ -1,3 +0,0 @@ -[virtualenvs] -create = true -in-project = true diff --git a/opt/TokenizerStats/pyproject.toml b/opt/TokenizerStats/pyproject.toml index 098c5099..afcbb620 100644 --- a/opt/TokenizerStats/pyproject.toml +++ b/opt/TokenizerStats/pyproject.toml @@ -1,15 +1,35 @@ -[tool.poetry] -package-mode = false -authors = ["Alexius Wadell "] +[project] +name = "tokenizer_stats" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +authors = [ + {name = "Alexius Wadell", email = "awadell@umich.edu"}, +] +requires-python = ">=3.10, <3.14" +dependencies = [ + "smirk", + "mendeleev >= 0.16", + "selfies == 2.1.2", + "jsonnet ~= 0.20.0", + "smilespe==0.0.3", + "datasets~=3.5", + "apetokenizer @ git+https://github.com/mikemayuare/apetokenizer.git@8b070d6278503bcf8ad2eedc1756ac34f4bbd981", + "transformers>=4.51.3", + "rdkit>=2025.3.1", + "scikit-learn>=1.6.1", + "networkx>=3.4.2", +] -[tool.poetry.dependencies] -python = ">=3.10,<3.13" -mendeleev = "^0.16" -electrolyte_fm = {path = "../..", extras = ["tokenizers"], develop = true} -selfies = "^2.1.2" -jsonnet = "^0.20.0" +[build-system] +requires = ["setuptools>=42"] +build-backend = "setuptools.build_meta" +[tool.setuptools] +package-dir = {"" = "python"} -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +[tool.setuptools.packages.find] +where = ["python"] + +[tool.uv.sources] +smirk = { git = "ssh://git@github.com/BattModels/smirk.git", rev = "dfdd9dc27c066f956b69d1f55c2d4717beb7838d" } diff --git a/opt/TokenizerStats/python/helper/__init__.py b/opt/TokenizerStats/python/helper/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/opt/TokenizerStats/src/atomic_oov.py b/opt/TokenizerStats/python/helper/atomic_oov.py similarity index 97% rename from opt/TokenizerStats/src/atomic_oov.py rename to opt/TokenizerStats/python/helper/atomic_oov.py index e916fc5b..ece78d5c 100644 --- a/opt/TokenizerStats/src/atomic_oov.py +++ b/opt/TokenizerStats/python/helper/atomic_oov.py @@ -10,7 +10,7 @@ from typing import Callable, Iterable, Optional import selfies -from build_vocab import ( +from .build_vocab import ( ALIPHATIC_ORGANIC, AROMATIC_ORGANIC, AROMATIC_SYMBOLS, @@ -21,7 +21,7 @@ from datasets import load_dataset from mendeleev import element -from electrolyte_fm.utils.tokenizer import PreTrainedTokenizerBase, load_tokenizer +from .tokenizer import PreTrainedTokenizerBase, load_tokenizer logging.basicConfig( level=logging.INFO, @@ -347,6 +347,11 @@ def process_tokenizer(dataset_name: str, tokenizer: dict[str, str]) -> dict: parser.add_argument("--workers", type=int, default=None) parser.add_argument("-d", "--dataset", type=str, default=None, action="append") parser.add_argument("--output", type=argparse.FileType("w"), default="-") + parser.add_argument( + "--tokenizers", + type=Path, + default=Path(__file__).parent.parent.parent.joinpath("tokenizers.json"), + ) args = parser.parse_args() # Lookup tokenizer information @@ -355,7 +360,7 @@ def process_tokenizer(dataset_name: str, tokenizer: dict[str, str]) -> dict: "name_or_path": args.name_or_path, "encoding": args.encoding, } - tokenizers = Path(__file__).parent.parent.joinpath("tokenizers.json").read_text() + tokenizers = args.tokenizers.read_text() for tok in json.loads(tokenizers): if tok["name_or_path"] == args.name_or_path: tokenizer["name"] = args.name or tok["name"] diff --git a/opt/TokenizerStats/src/build_vocab.py b/opt/TokenizerStats/python/helper/build_vocab.py similarity index 100% rename from opt/TokenizerStats/src/build_vocab.py rename to opt/TokenizerStats/python/helper/build_vocab.py diff --git a/opt/TokenizerStats/python/helper/cache.py b/opt/TokenizerStats/python/helper/cache.py new file mode 120000 index 00000000..3f2ac92b --- /dev/null +++ b/opt/TokenizerStats/python/helper/cache.py @@ -0,0 +1 @@ +../../../../electrolyte_fm/utils/cache.py \ No newline at end of file diff --git a/opt/TokenizerStats/python/helper/loader.py b/opt/TokenizerStats/python/helper/loader.py new file mode 100644 index 00000000..9433c799 --- /dev/null +++ b/opt/TokenizerStats/python/helper/loader.py @@ -0,0 +1,127 @@ +from pathlib import Path +from dataclasses import dataclass +from datasets import load_dataset +from .tokenizer import load_tokenizer + +from .utils import ( + MolEncoding, + AbstractDataset, + maybe_shard_dataset, + encode_molecules, + is_fast, + scaffold_split, + train_val_test_split, +) + + +@dataclass +class GlobalComm: + global_rank: int = 0 + world_size: int = 1 + + +MOLNET_URLS = { + "qm8": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm8.csv", + "qm9": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/qm9.csv", + "esol": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/delaney-processed.csv", + "freesolv": "https://deepchemdata.s3.us-west-1.amazonaws.com/datasets/freesolv.csv.gz", + "lipo": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/Lipophilicity.csv", + "muv": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/muv.csv.gz", + "hiv": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/HIV.csv", + "bace": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/bace.csv", + "bbbp": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/BBBP.csv", + "tox21": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/tox21.csv.gz", + "toxcast": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/toxcast_data.csv.gz", + "sider": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/sider.csv.gz", + "clintox": "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/clintox.csv.gz", +} + + +def tokenizer_dataset( + tokenizer: str, + name_or_path: str, + encoding: str, + world_size: int = 1, + global_rank: int = 0, + limit: int | None = None, + max_workers: int = 8, +) -> AbstractDataset: + tokenizer = load_tokenizer(tokenizer) + encoding = MolEncoding(encoding) + ds = get_dataset(name_or_path) + ds = maybe_shard_dataset(GlobalComm(global_rank, world_size), ds) + + ds = encode_molecules(ds, "smi", encoding=encoding, max_workers=max_workers) + ds = ds.map( + tokenizer, + batched=is_fast(tokenizer), + input_columns="smi", + ) + + if limit is not None: + return ds.shuffle(seed=42).take(limit) + else: + return ds + + +def get_dataset(name_or_path: str): + if Path(name_or_path).is_dir(): + if "tmQM" in Path(name_or_path).parts: + return tmqm(name_or_path) + else: + return smiles_dataset(name_or_path) + else: + assert name_or_path in MOLNET_URLS.keys() + return molnet(name_or_path) + + +def molnet(name: str): + ds = load_dataset( + "csv", + name=name, + data_files=[MOLNET_URLS[name]], + split="train", + streaming=False, + save_infos=False, + ) + ds = ds.rename_column("smiles" if name != "bace" else "mol", "smi") + ds = ds.select_columns("smi") + + if name in ["hiv", "bace", "bbbp"]: + return scaffold_split(ds, "smi") + else: + return train_val_test_split(ds) + + +def tmqm(name_or_path: str): + path = Path(name_or_path) + ds = load_dataset( + "arrow", + name=path.name, + data_files={ + "train": str(path.joinpath("train/*.arrow")), + "validation": str(path.joinpath("validation/*.arrow")), + "test": str(path.joinpath("test/*.arrow")), + }, + keep_in_memory=False, + streaming=True, + save_infos=False, + ) + return ds.select_columns("smiles").rename_column("smiles", "smi") + + +def smiles_dataset(name_or_path: str): + path = Path(name_or_path) + ds = load_dataset( + "text", + name=str(path.name), + data_files={ + "train": str(path.joinpath("data/train/*.txt")), + "validation": str(path.joinpath("data/val/*.txt")), + "test": str(path.joinpath("data/test/*.txt")), + }, + keep_in_memory=False, + streaming=True, + save_infos=False, + ) + return ds.rename_column("text", "smi") diff --git a/opt/TokenizerStats/python/helper/spe.py b/opt/TokenizerStats/python/helper/spe.py new file mode 120000 index 00000000..71ff814d --- /dev/null +++ b/opt/TokenizerStats/python/helper/spe.py @@ -0,0 +1 @@ +../../../../electrolyte_fm/utils/spe.py \ No newline at end of file diff --git a/opt/TokenizerStats/python/helper/tokenizer.py b/opt/TokenizerStats/python/helper/tokenizer.py new file mode 120000 index 00000000..b053eac1 --- /dev/null +++ b/opt/TokenizerStats/python/helper/tokenizer.py @@ -0,0 +1 @@ +../../../../electrolyte_fm/utils/tokenizer.py \ No newline at end of file diff --git a/opt/TokenizerStats/python/helper/utils.py b/opt/TokenizerStats/python/helper/utils.py new file mode 120000 index 00000000..85c980d8 --- /dev/null +++ b/opt/TokenizerStats/python/helper/utils.py @@ -0,0 +1 @@ +../../../../electrolyte_fm/data_modules/utils.py \ No newline at end of file diff --git a/opt/TokenizerStats/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl index b4bc9d37..c8bdcb75 100644 --- a/opt/TokenizerStats/src/TokenizerStats.jl +++ b/opt/TokenizerStats/src/TokenizerStats.jl @@ -1,5 +1,6 @@ module TokenizerStats +using Tracy using PythonCall: Py, pyimport, pyconvert, @pyconst using ArgParse: ArgParseSettings, parse_args, @add_arg_table! using OnlineStats: OnlineStats, CountMap, HyperLogLog, Extrema, KHist, Counter, fit!, merge!, value @@ -13,7 +14,6 @@ using Dates: now using SparseArrays: sparse using LogExpFunctions: logsumexp, log1pexp, xexpy using Serialization: serialize, deserialize -using NVTX: @annotate function find(dir, pattern) found = String[] @@ -28,32 +28,50 @@ function find(dir, pattern) return found end -@annotate load_tokenizer(args...; kwargs...) = @pyconst(pyimport("electrolyte_fm.utils.tokenizer")).load_tokenizer(args...; kwargs...) -function split_dataset_by_node(args...; kwargs...) - m = @pyconst(pyimport("datasets.distributed")) - return m.split_dataset_by_node(args...; kwargs...) + +const __loader = Ref{Py}() +const __tokenizer = Ref{Py}() +function __init__() + __loader[] = pyimport("helper.loader") + __tokenizer[] = pyimport("helper.tokenizer") + return nothing +end + +tokenizer_dataset(args...; kwargs...) = __loader[].tokenizer_dataset(args...; kwargs...) +load_tokenizer(name_or_path) = __tokenizer[].load_tokenizer(name_or_path) + +struct DatasetConfig + name_or_path::String + tokenizer::String + encoding::String end -function molnet(name::AbstractString; tokenizer="smirk", encoding::String="smiles") - data_modules = @pyconst(pyimport("electrolyte_fm.data_modules")) - target_columns = String[] - dm = data_modules.MolNetDataModule(name; tokenizer, encoding, target_columns, include_encoding=true) - dm.prepare_data() - return dm +function dataset_split(dc::DatasetConfig, split::String; kwargs...) + split = split == "val" ? "validation" : split + ds = tokenizer_dataset(dc.tokenizer, dc.name_or_path, dc.encoding; kwargs...)[split] + return ds end -function tmqm(path::AbstractString; tokenizer="smirk", encoding::String="smiles") - data_modules = @pyconst(pyimport("electrolyte_fm.data_modules")) - dm = data_modules.tmQMDataModule(path; tokenizer, encoding, include_encoding=true) - dm.prepare_data() - return dm +tokenizer_name(dc::DatasetConfig) = isdir(dc.tokenizer) ? basename(dc.tokenizer) : dc.tokenizer +function tokenizer(dc::DatasetConfig) + tok = load_tokenizer(dc.tokenizer) + info = (; + tokenizer_name = isdir(dc.tokenizer) ? basename(dc.tokenizer) : dc.tokenizer, + vocab_size=pyconvert(Int, length(tok)), + unk_token_id=pyconvert(Union{Int,Nothing}, tok.unk_token_id), + ) + return tok, info end -function pretrain(path::AbstractString; tokenizer="smirk", encoding::String="smiles") - data_modules = @pyconst(pyimport("electrolyte_fm.data_modules")) - dm = data_modules.RobertaDataSet(path; tokenizer, encoding) - dm.prepare_data() - return dm +function dataset_name(dc::DatasetConfig) + if isdir(dc.name_or_path) + if "tmQM" in splitpath(dc.name_or_path) + return "tmQM" + else + return basename(dc.name_or_path) + end + end + return dc.name_or_path end include("ngrams.jl") diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl index 13229b12..4d4e5404 100644 --- a/opt/TokenizerStats/src/cli.jl +++ b/opt/TokenizerStats/src/cli.jl @@ -18,25 +18,6 @@ function common_args!(s) end end - -@annotate function get_dataset(name_or_path, tokenizer, encoding) - start = time() - if isdir(name_or_path) - if "tmQM" in splitpath(name_or_path) - dm = TokenizerStats.tmqm(name_or_path; tokenizer, encoding) - dataset_name = "tmQM" - else - dm = TokenizerStats.pretrain(name_or_path; tokenizer, encoding) - dataset_name = basename(name_or_path) - end - else - dm = TokenizerStats.molnet(name_or_path; tokenizer, encoding) - dataset_name = name_or_path - end - @info "loaded $dataset_name in $(time() - start) s" - return dm, dataset_name -end - function maybe_parse_env(T::Type, x::String) env = get(ENV, x, nothing) if !isnothing(env) @@ -45,7 +26,7 @@ function maybe_parse_env(T::Type, x::String) return parse(T, x) end -@annotate function main(args::Vector{String}) +@tracepoint function main(args::Vector{String}) s = ArgParseSettings() @add_arg_table! s begin "usage" @@ -92,7 +73,7 @@ end end @add_arg_table! s["merge"] begin "--pattern" - default = r"usage.+?_rank_\d+\.jld2" + default = r"usage.+?_rank_\d+\.jld2$" arg_type = Regex "directory" help = "Directory to search for files to merge" @@ -108,17 +89,13 @@ end # Run command if args["%COMMAND%"] == "distortion" - tokenizer = args_cmd["tokenizer"] - tokenizer_name = isdir(tokenizer) ? basename(tokenizer) : tokenizer - dm, dataset = get_dataset(args_cmd["dataset"], tokenizer, args_cmd["encoding"]) - avg_information_loss(dm, args_cmd["reference"], args_cmd["output"]) + ds = DatasetConfig(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"]) + avg_information_loss(ds, args_cmd["reference"], args_cmd["output"]) elseif args["%COMMAND%"] == "loss" # Load the tokenizer - tokenizer = args_cmd["tokenizer"] - tokenizer_name = isdir(tokenizer) ? basename(tokenizer) : tokenizer - dm, dataset = get_dataset(args_cmd["dataset"], tokenizer, args_cmd["encoding"]) - model_loss(dm, args_cmd["model"], args_cmd["output"]) + ds = DatasetConfig(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"]) + model_loss(ds, args_cmd["model"], args_cmd["output"]) elseif args["%COMMAND%"] == "merge" directory = args_cmd["directory"] @@ -129,19 +106,17 @@ end merge_usage_stats(files; output) elseif args["%COMMAND%"] == "usage" - tokenizer = args_cmd["tokenizer"] - tokenizer_name = isdir(tokenizer) ? basename(tokenizer) : tokenizer - dm, dataset = get_dataset(args_cmd["dataset"], tokenizer, args_cmd["encoding"]) - splits = split(args_cmd["splits"], ",") + ds = DatasetConfig(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"]) + splits = parse_splits(args_cmd["splits"]) # Distribute computation if args_cmd["mode"] == "mpi" - tabulate_dataset(dm, args_cmd["output"]; tokenizer_name, splits) + tabulate_dataset(ds, args_cmd["output"], splits) elseif args_cmd["mode"] == "batch" - size = maybe_parse_env(Int, args_cmd["size"]) - rank = maybe_parse_env(Int, args_cmd["rank"]) - @info "Using batch mode: $rank of $size (0-indexed)" - job_array_usage_stats(dm, args_cmd["output"]; tokenizer_name, splits, size, rank) + world_size = maybe_parse_env(Int, args_cmd["size"]) + global_rank = maybe_parse_env(Int, args_cmd["rank"]) + @info "Using batch mode: $global_rank of $world_size (0-indexed)" + job_array_usage_stats(ds, args_cmd["output"]; splits, world_size, global_rank) else error("Unknown mode $(args_cmd["mode"])") end @@ -150,44 +125,67 @@ end return 0 end +parse_splits(x::String) = parse_splits(split(x, ",")) +function parse_splits(x::Vector{<:AbstractString}) + if length(x) == 1 && first(x) == "all" + return ["val", "train", "test"] + else + return x + end +end + function merge_usage_stats(files::Vector{String}; output::String="merged.jld2", splits::Vector{String}=["train", "val", "test"]) - merged = jldopen(output, "w") + merged_stats = Dict{String, Int}() + rm(output * ".tmp", force=true) for file in files - jldopen(file, "r") do other - @info "Merging $file" other - if haskey(other, "tokenizer") - if haskey(merged, "tokenizer") - @assert other["tokenizer"] == merged["tokenizer"] "N-gram models must use the same tokenizer" - else - merged["tokenizer"] = other["tokenizer"] + jldopen(output * ".tmp", "a+") do merged + jldopen(file, "r") do other + @info "Merging $file" other + if haskey(other, "tokenizer") + if haskey(merged, "tokenizer") + @assert other["tokenizer"] == merged["tokenizer"] "N-gram models must use the same tokenizer" + else + merged["tokenizer"] = other["tokenizer"] + end end - end - for split in splits - if haskey(merged, split) - merged[split]["out_of_vocab"] += other["out_of_vocab"] - merged[split]["samples"] += other["samples"] - merged[split]["out_of_vocab"] += other["out_of_vocab"] - merged[split]["fertility"] = Dict(mergewith(+, merged[split]["fertility"], other["fertility"])) - merged[split]["nunique"] = Dict(mergewith(+, merged[split]["nunique"], other["nunique"])) - for n in 1:length(merged[split]["ngrams"]) - a_ngram = merged[split]["ngrams"]["$n"] - b_ngram = compact_ngrams(other["ngrams"]["$n"]) - merged[split]["ngrams"]["$n"] = Dict(mergewith(+, a_ngram, b_ngram)) - end - else - merged[split]["out_of_vocab"] = other[split]["out_of_vocab"] - merged[split]["samples"] = other[split]["samples"] - merged[split]["out_of_vocab"] = other[split]["out_of_vocab"] - merged[split]["fertility"] = other[split]["fertility"] - merged[split]["nunique"] = other[split]["nunique"] - for n in 1:length(other[split]["ngrams"]) - merged[split]["ngrams"]["$n"] = compact_ngrams(other[split]["ngrams"]["$n"]) + for split in splits + if haskey(merged, split) + merged_stats[joinpath(split, "out_of_vocab")] += other[split]["out_of_vocab"] + merged_stats[joinpath(split, "samples")] += other[split]["samples"] + mergewith!(+, merged[split]["fertility"], other[split]["fertility"]) + mergewith!(+, merged[split]["nunique"], other[split]["nunique"]) + for n in 1:length(merged[split]["ngrams"]) + b_ngram = compact_ngrams(other[split]["ngrams"]["$n"]) + mergewith!(+, merged[split]["ngrams"]["$n"], b_ngram) + end + else + merged_stats[joinpath(split, "out_of_vocab")] = other[split]["out_of_vocab"] + merged_stats[joinpath(split, "samples")] = other[split]["samples"] + merged[joinpath(split, "fertility")] = other[split]["fertility"] + merged[joinpath(split, "nunique")] = other[split]["nunique"] + for n in 1:length(other[split]["ngrams"]) + merged[joinpath(split, "ngrams", string(n))] = compact_ngrams(other[split]["ngrams"]["$n"]) + end end end end end end + # Write merged single-value stats + jldopen(output * ".tmp", "a+") do merged + for split in splits + for k in ["out_of_vocab", "samples"] + merged[joinpath(split, k)] = merged_stats[joinpath(split, k)] + end + end + end + + # Finalize merged file + mv(output * ".tmp", output; force=true) + @info "saved results to $output" now() + chmod(output, 0o444) + return output end diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl index 3fd6d9c4..15ddf596 100644 --- a/opt/TokenizerStats/src/collect.jl +++ b/opt/TokenizerStats/src/collect.jl @@ -1,18 +1,14 @@ -# Maximum number of oov samples to track -const MAX_OOV_SAMPLES = 100 - - function tracked_stats() return (; - fertility=CountMap(Int), - nunique=CountMap(Int), + fertility=CountMap(Dict{Int,Int}()), + nunique=CountMap(Dict{Int,Int}()), out_of_vocab=Counter(Int), - ngrams=ntuple(i -> CountMap(NTuple{i,Int}), 5), + ngrams=ntuple(i -> CountMap(Dict{NTuple{i,UInt32}, Int}()), 5), ) end usage_stats(example, is_oov) = usage_stats!(tracked_stats(), example, is_oov) -function usage_stats!(stats, code::Vector{Int}, is_oov::Bool) +function usage_stats!(stats, code::Vector{UInt32}, is_oov::Bool) # Track usage stats fit!(stats.fertility, length(code)) @@ -31,7 +27,7 @@ function usage_stats!(stats, code::Vector{Int}, is_oov::Bool) return stats end -function leader_reduce(f, x; comm=MPI.COMM_WORLD) +@tracepoint function leader_reduce(f, x; comm=MPI.COMM_WORLD) g = MPI.gather(x, comm; root=0) if MPI.Comm_rank(comm) == 0 @assert length(g) == MPI.Comm_size(comm) @@ -40,40 +36,25 @@ function leader_reduce(f, x; comm=MPI.COMM_WORLD) return nothing end -function setup_dm_mpi(dm::Py, split::AbstractString; rank::Int=0, size::Int=1) - dm.trainer = (; global_rank=rank, world_size=size) - dm.setup("fit") - if split == "train" - ds = dm.train_dataset - elseif split == "val" - ds = dm.val_dataset - elseif split == "test" - ds = dm.test_dataset - else - throw(ArgumentError(lazy"Invalid split: $split")) - end - return ds -end - -function rank_usage_stats(datamodule, split; rank, size) - ds = setup_dm_mpi(datamodule, split; rank, size) - tokenizer = datamodule.tokenizer - unk_token_id = pyconvert(Int, tokenizer.unk_token_id) +function rank_usage_stats(dataset::DatasetConfig, split::String; global_rank::Int, world_size::Int) + ds = dataset_split(dataset, split; global_rank, world_size) + _, tok_info = tokenizer(dataset) + unk_token_id = tok_info.unk_token_id local_stats = tracked_stats() start_time = time() - @info "rank $rank: started processing $split" now() + @info "rank $global_rank: started processing $split" now() for (idx, example) in enumerate(ds) - input_ids = pyconvert(Vector{Int}, example["input_ids"]) + input_ids = pyconvert(Vector{UInt32}, example["input_ids"]) is_oov = unk_token_id in input_ids usage_stats!(local_stats, input_ids, is_oov) if idx % 1_000_000 == 0 elapsed = time() - start_time - @info "rank $rank on molecule $idx" idx elapsed idx / elapsed now() + @info "rank $global_rank on molecule $idx" idx elapsed idx / elapsed now() end end n_obs = nobs(local_stats[:fertility]) elapsed = time() - start_time - @info "Rank $rank has finished tokenizer stats" n_obs elapsed n_obs / elapsed now() + @info "Rank $global_rank has finished tokenizer stats" n_obs elapsed n_obs / elapsed now() return OnlineStats.Series(; fertility=local_stats.fertility, @@ -83,25 +64,26 @@ function rank_usage_stats(datamodule, split; rank, size) ) end -function job_array_usage_stats(datamodule::Py, out_file::AbstractString; tokenizer_name::AbstractString="", splits=["train"], size::Int=1, rank::Int=0) +function job_array_usage_stats(dataset::DatasetConfig, out_file::AbstractString; splits=["train"], world_size::Int=1, global_rank::Int=0) # Load Dataset and Tokenizer - tokenizer = datamodule.tokenizer - tokenizer_info = (; - name=tokenizer_name, - vocab_size=pyconvert(Int, length(tokenizer)), - unk_token_id=pyconvert(Union{Int,Nothing}, tokenizer.unk_token_id), - ) - - splits = (length(splits) == 1 && first(splits) == "all") ? ["val", "train", "test"] : splits - out_file = out_file * "_split_$(join(splits, "_"))_rank_$rank.jld2" + _, tok_info = tokenizer(dataset) + out_file = out_file * "_split_$(join(splits, "_"))_rank_$global_rank.jld2" mkpath(dirname(out_file)) + + # Early exit if file already exists + if isfile(out_file) + @info "Found existing file $out_file, skipping" + return 0 + end + jldopen(out_file * ".tmp", "w+") do f - f["tokenizer"] = tokenizer_info + f["tokenizer"] = tok_info + f["dataset"] = (; dataset_name=dataset_name(dataset), encoding=dataset.encoding) end for split in splits - tokenizer_stats = rank_usage_stats(datamodule, split; rank, size) - @info "Saving results for $split on rank $rank" now() + tokenizer_stats = rank_usage_stats(dataset, split; global_rank, world_size) + @info "Saving results for $split on rank $global_rank" now() jldopen(out_file * ".tmp", "a+") do f serialize_usage!(f, split, tokenizer_stats) end @@ -114,82 +96,85 @@ function job_array_usage_stats(datamodule::Py, out_file::AbstractString; tokeniz return 0 end -function tabulate_dataset(datamodule::Py, out_file::AbstractString; tokenizer_name::AbstractString="", splits=["train"]) +function tabulate_dataset(dataset::DatasetConfig, out_file::AbstractString, splits::Vector{String}=["train"]) # Setup mpi MPI.Init() comm = MPI.COMM_WORLD - rank = MPI.Comm_rank(comm) - size = MPI.Comm_size(comm) - @info "Rank $rank of $size is starting" now() + global_rank = MPI.Comm_rank(comm) + world_size = MPI.Comm_size(comm) + @info "Rank $global_rank of $world_size is starting" now() # Load Dataset and Tokenizer - tokenizer = datamodule.tokenizer - tokenizer_info = (; - name=tokenizer_name, - vocab_size=pyconvert(Int, length(tokenizer)), - unk_token_id=pyconvert(Union{Int,Nothing}, tokenizer.unk_token_id), - ) - splits = (length(splits) == 1 && first(splits) == "all") ? ["val", "train", "test"] : splits - if rank == 0 - @debug "rank $rank: created $out_file" now() + _, tok_info = tokenizer(dataset) + if global_rank == 0 + @debug "rank $global_rank: created $out_file" now() mkpath(dirname(out_file)) jldopen(out_file * ".tmp", "w+") do f - f["tokenizer"] = tokenizer_info + f["tokenizer"] = tok_info + f["dataset"] = (; dataset_name=dataset_name(dataset), encoding=dataset.encoding) end end MPI.Barrier(comm) + start_time = time() for split in splits - rank_stats = rank_usage_stats(datamodule, split; rank, size) + rank_stats = rank_usage_stats(dataset, split; global_rank, world_size) tokenizer_stats = leader_reduce(merge!, rank_stats; comm) - if rank == 0 + if global_rank == 0 jldopen(out_file * ".tmp", "a+") do f serialize_usage!(f, split, tokenizer_stats) end - @info "rank $rank: saved results for $split" now() + @info "rank $global_rank: saved results for $split" now() end end - if rank == 0 + if global_rank == 0 + jldopen(out_file * ".tmp", "a+") do f + f["walltime"] = time() - start_time + f["world_size"] = world_size + end mv(out_file * ".tmp", out_file; force=true) chmod(out_file, 0o444) - @info "Saved stats on rank $rank to $out_file" now() + @info "Saved stats on rank $global_rank to $out_file" now() end MPI.Barrier(comm) MPI.Finalize() - @debug "rank $rank: finished" now() + @debug "rank $global_rank: finished" now() return 0 end -function model_loss(datamodule::Py, ref_file::String, output::String) +function model_loss(dataset::DatasetConfig, ref_file::String, output::String) # Init MPI MPI.Init() comm = MPI.COMM_WORLD - rank = MPI.Comm_rank(comm) - size = MPI.Comm_size(comm) - @info "Rank $rank of $size is starting" now() + global_rank = MPI.Comm_rank(comm) + world_size = MPI.Comm_size(comm) + @info "Rank $global_rank of $world_size is ready" now() + + # Create datamodule + tok, tok_info = tokenizer(dataset) + MPI.Barrier(comm) + @info "Rank $global_rank of $world_size is starting" now() # Load Reference Tokenizer / n-gram model ngram, _, ref_info = load_ngram_model(ref_file) - rank == 0 && @info "Loaded n-gram model for $(ref_info.name) from $ref_file ($(ref_info.sha256[1:8]))" + global_rank == 0 && @info "Loaded n-gram model for $(ref_info.name) from $ref_file ($(ref_info.sha256[1:8]))" # Init Fit Stats - tok = datamodule.tokenizer - if rank == 0 + if global_rank == 0 mkpath(dirname(output)) jldopen(output * ".tmp", "w+") do f - f["tokenizer"] = (; - vocab_size=pyconvert(Int, length(tok)), - unk_token_id=pyconvert(Union{Int,Nothing}, tok.unk_token_id), - ) + f["tokenizer"] = tok_info f["ref_tokenizer"] = ref_info + f["dataset"] = (; dataset_name=dataset_name(dataset), encoding=dataset.encoding) end end MPI.Barrier(comm) + start_time = time() for split in ["val", "train", "test"] - ds = setup_dm_mpi(datamodule, split; rank, size) + ds = dataset_split(dataset, split; global_rank, world_size) stats = map(1:length(ngram)) do _ OnlineStats.Series(; moments=OnlineStats.Moments(), @@ -199,39 +184,45 @@ function model_loss(datamodule::Py, ref_file::String, output::String) end |> OnlineStats.Group stats = (; cross_entropy=deepcopy(stats), cross_entropy_per_token=deepcopy(stats)) - @info "rank $rank: started processing $split" now() + @info "rank $global_rank: started processing $split" now() loss = zeros(length(ngram)) start_time = time() for (idx, encoding) in enumerate(ds) - code = pyconvert(Vector{Int}, encoding["input_ids"]) + code = pyconvert(Vector{UInt32}, encoding["input_ids"]) for N in 1:length(ngram) loss[N] = cross_entropy(ngram, code; N) end fit!(stats.cross_entropy, tuple(loss)) fit!(stats.cross_entropy_per_token, tuple(loss ./ length(code))) - if idx % 1_000_000 == 0 && rank == 0 + if idx % 1_000_000 == 0 && global_rank == 0 elapsed = time() - start_time - @info "rank $rank on molecule $idx" idx elapsed idx / elapsed + @info "rank $global_rank on molecule $idx" idx elapsed idx / elapsed end end # Reduce stats over ranks stats = leader_reduce(merge!, OnlineStats.Group(; stats...); comm) - if rank == 0 + split_wall_time = time() - start_time + if global_rank == 0 jldopen(output * ".tmp", "a+") do f f[split] = (; samples=nobs(stats), cross_entropy=map(value, stats[:cross_entropy]), cross_entropy_per_token=map(value, stats[:cross_entropy_per_token]), + walltime=split_wall_time, + world_size, ) end end MPI.Barrier(comm) end - if rank == 0 + if global_rank == 0 + jldopen(output * ".tmp", "a+") do f + f["walltime"] = time() - start_time + end mv(output * ".tmp", output; force=true) chmod(output, 0o444) - @info "rank $rank: saved stats to $output" now() + @info "rank $global_rank: saved stats to $output" now() end MPI.Barrier(comm) @@ -240,28 +231,25 @@ function model_loss(datamodule::Py, ref_file::String, output::String) end -@annotate function avg_information_loss(datamodule::Py, ref_file::String, output::String) +@tracepoint function avg_information_loss(dataset::DatasetConfig, ref_file::String, output::String; split="val") # Init MPI MPI.Init() comm = MPI.COMM_WORLD - rank = MPI.Comm_rank(comm) - size = MPI.Comm_size(comm) - @info "Rank $rank of $size is starting" now() + global_rank = MPI.Comm_rank(comm) + world_size = MPI.Comm_size(comm) + @info "Rank $global_rank of $world_size is starting" now() # Load Reference Tokenizer / n-gram model - tokenizer = datamodule.tokenizer + tok, tok_info = tokenizer(dataset) ngram, ref_tok, ref_info = load_ngram_model(ref_file) - rank == 0 && @info "Loaded n-gram model for $(ref_info.name) from $ref_file ($(ref_info.sha256[1:8]))" + global_rank == 0 && @info "Loaded n-gram model for $(ref_info.name) from $ref_file ($(ref_info.sha256[1:8]))" - if rank == 0 + if global_rank == 0 mkpath(dirname(output)) jldopen(output * ".tmp", "w+") do f - tok = datamodule.tokenizer - f["tokenizer"] = (; - vocab_size=pyconvert(Int, length(tok)), - unk_token_id=pyconvert(Union{Int,Nothing}, tok.unk_token_id), - ) + f["tokenizer"] = tok_info f["ref_tokenizer"] = ref_info + f["system"] = (; world_size) end end @@ -275,25 +263,26 @@ end end |> OnlineStats.Group info_loss = zeros(length(ngram)) - @info "rank $rank: started processing" now() - ds = setup_dm_mpi(datamodule, "val"; rank, size) + @info "rank $global_rank: started processing" now() + ds = dataset_split(dataset, split; global_rank, world_size) MPI.Barrier(comm) - smi_column = pyconvert(String, datamodule.smi_column) start_time = time() for (idx, encoding) in enumerate(ds) - info_loss = unk_information_loss(ngram, ref_tok, tokenizer, encoding; smi_column) + info_loss = unk_information_loss(ngram, ref_tok, tok, encoding; smi_column="smi") fit!(stats, tuple(info_loss)) - if idx % 100 == 0 && rank == 0 + if idx % 100 == 0 && global_rank == 0 elapsed = time() - start_time - @info "rank $rank on molecule $idx" idx elapsed idx / elapsed now() + @info "rank $global_rank on molecule $idx" idx elapsed idx / elapsed now() end end stats = leader_reduce(merge!, stats; comm) - if rank == 0 + walltime = time() - start_time + if global_rank == 0 jldopen(output * ".tmp", "a+") do f f["samples"] = nobs(stats) f["info_loss"] = map(value, stats) + f["walltime"] = walltime end mv(output * ".tmp", output; force=true) @info "saved results to $output" now() diff --git a/opt/TokenizerStats/src/ngrams.jl b/opt/TokenizerStats/src/ngrams.jl index 319850dd..673985e7 100644 --- a/opt/TokenizerStats/src/ngrams.jl +++ b/opt/TokenizerStats/src/ngrams.jl @@ -38,14 +38,14 @@ function NGramModel(tokenizer::Py, ngrams::Union{Tuple,Vector}) # Don't remove in-use special tokens unk_token_id = pyconvert(Union{Nothing,Int}, tokenizer.unk_token_id) - used_special = pyconvert(Vector{Int}, tokenizer("")["input_ids"]) + used_special = pyconvert(Vector{UInt32}, tokenizer("")["input_ids"]) !isnothing(unk_token_id) && setdiff!(special_tokens, unk_token_id) setdiff!(special_tokens, used_special) return NGramModel(ngrams, vocab_size; special_tokens) end -function NGramModel(ngrams::Union{Tuple,Vector}, vocab_size::Int; special_tokens::Vector{Int}=Int[]) +function NGramModel(ngrams::Union{Tuple,Vector}, vocab_size::Int; special_tokens::AbstractVector{<:Integer}=Int[]) total = sum(values(first(ngrams)); init=0) ngrams = ntuple(i -> ngram_counts(ngrams[i], vocab_size), length(ngrams)) N = length(ngrams) @@ -59,7 +59,8 @@ function load_ngram_model(file::String, split="train") jldopen(file, "r") do data # Extract tokenizer info sha256 = bytes2hex(open(SHA.sha256, file)) - name = data["tokenizer"][:name] + tok_info = data["tokenizer"] + name = haskey(tok_info, :name) ? tok_info[:name] : tok_info[:tokenizer_name] name = startswith(name, "smirk-gpe") ? "./" * name : name tok = load_tokenizer(name) vocab_size = pyconvert(Int, length(tok)) @@ -90,11 +91,11 @@ end """ Return the conditional n-gram `(..., x_i-1)` for the given n-gram `(..., x_i)`""" -condgram(gram::NTuple{N,Int}) where {N} = reverse(Base.tail(reverse(gram))) +condgram(gram::NTuple{N,<:Integer}) where {N} = reverse(Base.tail(reverse(gram))) condgram(code::AbstractVector{<:Integer}, edx::Int, length::Int) = ngram(code, edx - 1, length - 1) """ Return the backward conditional n-gram `(x_i+1, ...)` for the given n-gram `(x_i, ...)`""" -condgram_backward(gram::NTuple{N,Int}) where {N} = Base.tail(gram) +condgram_backward(gram::NTuple{N,<:Integer}) where {N} = Base.tail(gram) condgram_backward(code::AbstractVector{<:Integer}, edx::Int, length::Int) = ngram(code, range(; start=edx + 1, length=length - 1)) """ Return the n-gram starting at `edx` of at most `length` """ @@ -108,9 +109,14 @@ struct MaskedCode{T,C<:AbstractVector{T},M<:AbstractVector{Bool}} <: AbstractVec code::C mask::M mask_value::T + function MaskedCode{T,C,M}(code, mask, mask_value) where {T,C,M} + @assert length(mask) == length(code) "length of code and mask must match" + @assert !any(==(mask_value), code[.!mask]) "mask value ($mask_value) occurs in unmasked code" + new(code, mask, mask_value) + end end -MaskedCode(code::Vector{T}, mask::Union{BitVector,Vector{Bool}}, mask_value::T) where {T} = MaskedCode{T}(code, BitVector(mask), mask_value) -MaskedCode{T}(code::C, mask::M, mask_value) where {T,C<:AbstractVector{T},M<:AbstractVector{Bool}} = MaskedCode{T,C,M}(code, mask, mask_value) +MaskedCode(code::AbstractVector{T}, mask::Union{BitVector,AbstractVector{Bool}}, mask_value::Integer=typemax(T)) where {T} = MaskedCode{T}(code, BitVector(mask), mask_value) +MaskedCode{T}(code::C, mask::M, mask_value) where {T,C<:AbstractVector{T},M<:AbstractVector{Bool}} = MaskedCode{T,C,M}(code, mask, T(mask_value)) Base.getindex(mc::MaskedCode, i::Int) = mc.mask[i] ? mc.mask_value : mc.code[i] Base.eltype(::MaskedCode{T}) where {T} = T @@ -320,7 +326,7 @@ function forward_odds(m::NGramModel, code::AbstractVector; mask::Integer=-100, N return counts, marginal, n_masked end -function forward_counts!(P::Vector{T}, m::NGramModel, cgram::NTuple{N,Int}; mask::Integer=-100) where {T,N} +function forward_counts!(P::Vector{T}, m::NGramModel, cgram::NTuple{N,<:Integer}; mask::Integer=-100) where {T,N} if isempty(cgram) fill_unigram_dist!(P, m) return P @@ -361,7 +367,7 @@ function backward_odds(m::NGramModel, code::AbstractVector; mask::Integer=-100, return counts, marginal, n_masked end -function backward_counts!(P::Vector{T}, m::NGramModel, cgram::NTuple{N,Int}; mask::Integer=-100) where {T,N} +function backward_counts!(P::Vector{T}, m::NGramModel, cgram::NTuple{N,<:Integer}; mask::Integer=-100) where {T,N} if isempty(cgram) fill_unigram_dist!(P, m) return P @@ -409,14 +415,14 @@ end Computes the Information Loss (KL-Divergence) from masking out tokens in an input code for a given n-gram model """ -@annotate function information_loss(m::NGramModel, code::Vector{<:Integer}, mask::Union{BitVector,Vector{Bool}}; N=length(m)) +@tracepoint function information_loss(m::NGramModel, code::Vector{<:Integer}, mask::Union{BitVector,Vector{Bool}}; N=length(m)) @assert 0 < N <= length(m) @assert length(code) == length(mask) loss = 0.0 ctype = valtype(m.ngrams[N]) isa Integer ? UInt64 : Float64 P = Vector{ctype}(undef, m.vocab_size) Q = similar(P) - code = MaskedCode(code, mask, -100) + code = MaskedCode(code, mask) for idx in eachindex(code) # If nothing is masked, the information # loss is zero @@ -446,10 +452,11 @@ function fb_log_dist!(Pf::Vector{Float64}, m::NGramModel, code::AbstractVector{< fgram = condgram(code, idx, N) bgram = condgram_backward(code, idx, N) V = nonspecial_vocab_size(m) + T = eltype(code) for (idx, id) in enumerate(token_ids(m)) - fc = first(gram_odds(m, (fgram..., id))) + fc = first(gram_odds(m, (fgram..., T(id)))) Pf[idx] = fc - bc = first(gram_odds(m, (id, bgram...))) + bc = first(gram_odds(m, (T(id), bgram...))) Pb[idx] = bc end @@ -501,9 +508,9 @@ end """ Compute the information_loss from unknown tokens using a character-tokenizer as a reference """ -@annotate function unk_information_loss(ngram::NGramModel, ref_tok::Py, tok::Py, encoding::Py; N=1:length(ngram), smi_column="smiles") +@tracepoint function unk_information_loss(ngram::NGramModel, ref_tok::Py, tok::Py, encoding::Py; N=1:length(ngram), smi_column="smiles") unk_token_id = pyconvert(Int, tok.unk_token_id) - code = pyconvert(Vector{Int}, encoding["input_ids"]) + code = pyconvert(Vector{UInt32}, encoding["input_ids"]) (unk_token_id ∉ code) && return zeros(length(N)) # Mask out unknown tokens @@ -511,7 +518,7 @@ Compute the information_loss from unknown tokens using a character-tokenizer as !(any(masked)) && return zeros(length(N)) # Unexpected, but possible if unk is from whitespace # Compute information_loss from unknown tokens - ref_code = pyconvert(Vector{Int}, ref_tok(encoding[smi_column])["input_ids"]) + ref_code = pyconvert(Vector{UInt32}, ref_tok(encoding[smi_column])["input_ids"]) @assert length(masked) == length(ref_code) return map(n -> information_loss(ngram, ref_code, masked; N=n), N) end @@ -562,7 +569,7 @@ function compute_unknown_mask(tok, ref_tok, smi) end end -function decode_non_special(tok::Py, ids::Vector{Int}) +function decode_non_special(tok::Py, ids::Vector{<:Integer}) ids = rm_special_tokens(tok, ids) tokens = pyconvert(Vector{String}, tok.convert_ids_to_tokens(ids)) return rm_special_tokens(tok, tokens) @@ -577,11 +584,11 @@ function _maybe_tokenize_offset(tok, smi) end if haskey(out, "offset_mapping") offsets = pyconvert(Vector{Tuple{Int,Int}}, out["offset_mapping"]) - ids = pyconvert(Vector{Int}, out["input_ids"]) + ids = pyconvert(Vector{UInt32}, out["input_ids"]) return (; ids, offsets) else @assert haskey(out, "input_ids") - ids = pyconvert(Vector{Int}, out["input_ids"]) + ids = pyconvert(Vector{UInt32}, out["input_ids"]) return (; ids, offsets=nothing) end end diff --git a/opt/TokenizerStats/src/serialize.jl b/opt/TokenizerStats/src/serialize.jl index d04d84ab..7796da17 100644 --- a/opt/TokenizerStats/src/serialize.jl +++ b/opt/TokenizerStats/src/serialize.jl @@ -1,10 +1,10 @@ function serialize_usage!(f::JLD2.JLDFile, path::AbstractString, x::OnlineStats.Series) f[joinpath(path, "samples")] = nobs(x) f[joinpath(path, "out_of_vocab")] = value(x[:out_of_vocab]) - f[joinpath(path, "fertility")] = Dict(value(x[:fertility])) - f[joinpath(path, "nunique")] = Dict(value(x[:nunique])) + f[joinpath(path, "fertility")] = value(x[:fertility]) + f[joinpath(path, "nunique")] = value(x[:nunique]) for n in eachindex(x[:ngrams].stats) - f[joinpath(path, "ngrams", string(n))] = Dict(value(x[:ngrams][n])) + f[joinpath(path, "ngrams", string(n))] = value(x[:ngrams][n]) end return nothing end diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh new file mode 100755 index 00000000..f319be7d --- /dev/null +++ b/opt/TokenizerStats/submit_archive.sh @@ -0,0 +1,49 @@ +#!/bin/bash +#SBATCH -p venkvis-cpu +#SBATCH --cpus-per-task 3 +#SBATCH --mem-per-cpu 1800M +#SBATCH --time=8:0:00 +set -x +my_job_header + +# Locate directory with tokenizers stats +STATS_DIR=$(realpath stats/) + +# Add tokenizers.json +cp tokenizers.json $STATS_DIR + +# Compress stats +export XZ_OPT="-9 -v --extreme --memlimit=4000000000 --threads=${SLURM_CPUS_PER_TASK}" +stats_archive="$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" +tar -cavf "$stats_archive" \ + --xz \ + --exclude="*.slurm" \ + --exclude="*.tmp" \ + --exclude='*/.unmerged/*' \ + -C "$STATS_DIR" \ + . +mkdir -p ./archive +ln -sf $stats_archive ./archive/ngram_tokenizer_stats.tar.xz + +# Compress Code (TokenizerStats) +code_archive="$(realpath ~/scratch)/TokenizerStats.jl_$(date +"%d%m%Y").tar.xz" +{ git ls-files; find fig/ -type f | sed 's|^\./||'; find -name Manifest.toml | sed 's|^\./||'; } | \ + tar -cavf $code_archive \ + --dereference \ + --files-from - +ln -sf $code_archive ./archive/TokenizerStats.tar.xz + +# Compress Code (Smirk) +SMIRK_VERSION="v0.1.1" +curl -L https://github.com/BattModels/smirk/archive/refs/tags/$(SMIRK_VERSION).tar.gz \ + --output "./archive/smirk_${SMIRK_VERSION}.tar.gz" + +# Archive to DataDen +SRC="3242c149-a2b9-4dba-9406-ae3717981621" # Artemis +DST="ab65757f-00f5-4e5b-aa21-133187732a01" # DataDen +if [[ $(type -t globus) ]]; then + find $(realpath ./archive) -not -type d -printf '%f %f\n' | \ + globus transfer --batch - \ + $SRC:$(realpath ./archive) \ + $DST:"/coe-venkvis/awadell/smirk-paper-archive_$(date +"%d%m%Y")/" +fi diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py index 72f00461..8a0baeae 100755 --- a/opt/TokenizerStats/submit_jobs.py +++ b/opt/TokenizerStats/submit_jobs.py @@ -1,19 +1,20 @@ -#!/usr/bin/env python -import time +#!/usr/bin/env -S uv run python +import argparse +import json import logging import subprocess -import json -import argparse +import time +from collections import defaultdict +from dataclasses import dataclass, field from pathlib import Path -from typing import Optional, Any from random import randint -from dataclasses import dataclass, field -from collections import defaultdict +from typing import Any, Optional + from networkx import DiGraph, topological_sort logging.basicConfig(level=logging.INFO) -STATS_DIR = Path(__file__).joinpath("..", "stats").resolve() +STATS_DIR = Path(__file__).joinpath("..", "stats-encoding").resolve() LOG_FILE = Path(__file__).parent.joinpath("logs", "slurm-%x-%j.log") LOG_FILE.parent.mkdir(exist_ok=True, parents=True) @@ -22,7 +23,7 @@ @dataclass class Process: cmd: list[str] - inputs: list[str] = field(default_factory=list) + inputs: list[Path] = field(default_factory=list) output: Optional[Path] = None slurm: Optional[dict] = None meta: dict[str, Any] = field(default_factory=dict) @@ -32,6 +33,8 @@ def __post_init__(self): self.inputs = [self.inputs] self.inputs = [Path(x) for x in self.inputs] self.output = Path(self.output) if self.output is not None else None + if self.output is not None: + self.output = self.output.resolve() self.slurm = self.slurm or {} def launch(self, deps: list[int], dry_run: bool = False) -> int: @@ -55,9 +58,51 @@ def launch(self, deps: list[int], dry_run: bool = False) -> int: print(p.stdout) print(p.stderr) assert p.returncode == 0, f"Non-zero return code: {p.returncode}" - return int(p.stdout.split(" ")[-1]) + + # Add a marker file with the job id + job_id = int(p.stdout.split(" ")[-1]) + if output := self.output: + output.parent.mkdir(exist_ok=True, parents=True) + output.with_suffix(output.suffix + ".slurm").write_text(str(job_id)) + + return job_id + return randint(1, 967_296) + def active_job(self) -> int | None: + if self.output is None: + return None + + job_file = self.output.with_suffix(self.output.suffix + ".slurm") + if not job_file.exists(): + return None + + # Get the job id + job_id = job_file.read_text() + if not job_id: + job_file.unlink(missing_ok=True) + return None + job_id = int(job_id) + + # Get the job state + p = subprocess.run( + ["scontrol", "show", "--json", "job", str(job_id)], capture_output=True + ) + job_info = json.loads(p.stdout) + try: + state = job_info["jobs"][0]["job_state"][0] + if state in ["RUNNING", "PENDING"]: + return job_id + except KeyError: + pass + + except IndexError: + pass + + # Job is no longer active + job_file.unlink() + return None + class Workflow: def __init__(self): @@ -124,38 +169,20 @@ def _get_deps(self, process: Process, jobs: dict[Path, int]) -> list[int]: return deps - def active_jobs(self, file: Path) -> Optional[int]: - job_file = file.with_suffix(file.suffix + ".slurm") - if not job_file.exists(): - return None - - # Get the job id - job_id = job_file.read_text() - if not job_id: - job_file.unlink(missing_ok=True) - return None - job_id = int(job_id) - - # Get the job state - p = subprocess.run( - ["scontrol", "show", "--json", "job", str(job_id)], capture_output=True - ) - job_info = json.loads(p.stdout) - try: - state = job_info["jobs"][0]["job_state"][0] - if state in ["RUNNING", "PENDING"]: - return job_id - except KeyError: - pass - - except IndexError: - pass + def launch(self, process: Process, deps, dry_run: bool = False): + # Launch the process + job_id = process.launch(deps, dry_run) + assert job_id is not None and isinstance(job_id, int) - # Job is no longer active - job_file.unlink() - return None + return job_id - def run(self, dry_run=False, rate_limit=100): + def run( + self, + dry_run=False, + rate_limit=100, + preflight=list[Process], + postflight=list[Process], + ): outputs = { process.output: process for process in self.processes @@ -166,13 +193,19 @@ def run(self, dry_run=False, rate_limit=100): last_launch = time.time() jobs_launched = 0 tasks_launched = defaultdict(int) + + preflight_ids = [] + for process in preflight: + preflight_ids.append(process.launch([], dry_run)) + for file in topological_sort(self.work): # Skip input files if file not in outputs: continue + process = outputs[file] # Check for an active job and clean up marker files - if job_id := self.active_jobs(file): + if job_id := process.active_job(): logging.info("found active job for %s", file) jobs[file] = job_id continue @@ -180,22 +213,16 @@ def run(self, dry_run=False, rate_limit=100): if file.exists(): continue - # Get the process that writes to this file - process = outputs[file] + # Get the dependencies for this process deps = self._get_deps(process, jobs) + deps.extend(preflight_ids) + deps = list(set(deps)) # Launch the process - job_id = process.launch(deps, dry_run) - assert job_id is not None and isinstance(job_id, int) - jobs[file] = job_id + jobs[file] = self.launch(process, deps, dry_run) jobs_launched += 1 tasks_launched[process.meta.get("task", "misc")] += 1 - # Add a marker file with the job id - if not dry_run: - file.parent.mkdir(exist_ok=True, parents=True) - file.with_suffix(file.suffix + ".slurm").write_text(str(job_id)) - # Sleep to avoid hitting the rate limit sleep_time = max(1 / rate_limit - (time.time() - last_launch), 0) logging.debug(f"ratelimiter: sleeping for {sleep_time} seconds") @@ -203,6 +230,14 @@ def run(self, dry_run=False, rate_limit=100): time.sleep(sleep_time) last_launch = time.time() + # Launch postflight jobs + for process in postflight: + if process.active_job() is not None: + logging.info("found active job for %s", process.output) + continue + + process.launch(jobs.values(), dry_run) + print(f"Launched {jobs_launched} jobs") for task, count in tasks_launched.items(): print(f" {task}: {count}") @@ -236,35 +271,54 @@ def show(self): # "Xenova/gpt-4o", ] +LARGE_MEM_TOKENIZERS = [ + "mikemayuare/SMILYAPE", + "mikemayuare/SELFYAPE", + "mikemayuare/SMILYBPE", + "mikemayuare/SELFYBPE", + "SmilesPE/SPE_ChEMBL", +] + def large_mem(tokenizer: str, slurm: dict) -> dict: - if tokenizer in [ - "mikemayuare/SMILYAPE", - "mikemayuare/SELFYAPE", - "mikemayuare/SMILYBPE", - "mikemayuare/SELFYBPE", - "SmilesPE/SPE_ChEMBL", - ]: + if tokenizer in LARGE_MEM_TOKENIZERS: slurm["mem-per-cpu"] = "32G" slurm["partition"] = "venkvis-largemem,venkvis-cpu" return slurm -def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles"): +def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles", mode="mpi"): ds_name = ds_name or str(dataset) - output = STATS_DIR.joinpath(tokenizer, ds_name, "usage.jld2") + output = STATS_DIR.joinpath(tokenizer, ds_name, f"usage_{encoding}.jld2") slurm = slurm or {} slurm["job-name"] = slurm.get("job-name", f"usage-{ds_name}") slurm.setdefault("job-name", f"usage-{ds_name}") slurm.setdefault("ntasks", 4) slurm.setdefault("time", "1-0:0:0") - slurm.setdefault("mem-per-cpu", "4G") + slurm.setdefault("mem-per-cpu", "1800M") + slurm.setdefault("partition", "venkvis-cpu,venkvis-largemem") + + if mode == "batch": + output = Path(output.parent, ".unmerged", f"usage_{encoding}", output.name) + slurm["array"] = f"0-{slurm['ntasks'] - 1}" + + # Check if all array outputs are present + n_complete = len(list(output.parent.glob("*.jld2"))) + logging.debug("found %d array output files for %s", n_complete, output.parent) + witness = output.parent.with_suffix(".witness") + if n_complete == slurm["ntasks"]: + witness.touch() + else: + witness.unlink(missing_ok=True) + + slurm["ntasks"] = 1 return Process( [ "submit_tok_stats.sh", "usage", + f"--mode={mode}", "--splits=all", "--encoding", encoding, @@ -272,19 +326,56 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles"): str(dataset), tokenizer, ], - output=output, + output=output.parent.with_suffix(".witness") if mode == "batch" else output, slurm=slurm, meta={"dataset": dataset, "tokenizer": tokenizer, "task": "usage"}, ) -def ngram_loss(dataset, tokenizer, slurm=None, encoding="smiles", ds_name=None): - input = STATS_DIR.joinpath(tokenizer, "realspace", "usage.jld2") +def usage_array( + wk: Workflow, dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles" +): + p = usage( + dataset, + tokenizer, + ds_name=ds_name, + slurm=slurm, + encoding=encoding, + mode="batch", + ) + output = p.output.parent.parent.joinpath(f"usage_{encoding}.jld2") + slurm = { + "job-name": "merge-usage", + "partition": "venkvis-cpu,venkvis-largemem", + "mem-per-cpu": "64G", + "cpus-per-task": 1, + "ntasks": 1, + "time": "2:0:0", + } + wk._add_process(p) + wk.add_process( + ["submit_tok_stats.sh", "merge", p.output.with_suffix(""), output], + inputs=[p.output], + output=output, + slurm=slurm, + meta={**p.meta, "task": "merge"}, + ) + + +def ngram_loss( + dataset, tokenizer, slurm=None, encoding="smiles", ngram="realspace", ds_name=None +): + input = STATS_DIR.joinpath(tokenizer, ngram, f"usage_{encoding}.jld2") ds_name = ds_name or str(dataset) - output = STATS_DIR.joinpath(tokenizer, ds_name, "model_loss.jld2") + outfile = f"model_loss_{ngram}_{encoding}.jld2" + output = STATS_DIR.joinpath(tokenizer, ds_name, outfile) slurm = slurm or {} - slurm.setdefault("job-name", f"loss-{ds_name}") - slurm.setdefault("mem-per-cpu", "2G") + if ngram == "realspace": + slurm.setdefault("job-name", f"loss-{ds_name}") + else: + slurm.setdefault("job-name", f"loss-{ds_name}-{ngram}") + + slurm.setdefault("mem-per-cpu", "1800M") slurm.setdefault("partition", "venkvis-cpu,venkvis-largemem") slurm.setdefault("ntasks", 4) slurm.setdefault("time", "2:0:0") @@ -297,13 +388,13 @@ def ngram_loss(dataset, tokenizer, slurm=None, encoding="smiles", ds_name=None): "--encoding", encoding, "--output", - output, + str(output), "--model", - input, + str(input), dataset, tokenizer, ], - inputs=input, + inputs=[input], output=output, slurm=slurm, meta={"dataset": dataset, "tokenizer": tokenizer, "task": "model_loss"}, @@ -314,13 +405,15 @@ def ngram_info_loss( dataset, tokenizer, ref, slurm=None, encoding="smiles", ds_name=None ): ref_name = ref.replace("/", "--") - ref_usage = STATS_DIR.joinpath(ref, "realspace", "usage.jld2") + ref_usage = STATS_DIR.joinpath(ref, "realspace", f"usage_{encoding}.jld2") ds_name = ds_name or str(dataset) - output = STATS_DIR.joinpath(tokenizer, ds_name, f"{ref_name}_info_loss.jld2") + output = STATS_DIR.joinpath( + tokenizer, ds_name, f"{ref_name}_info_loss_{encoding}.jld2" + ) slurm = slurm or {} slurm.setdefault("job-name", f"dist-{ds_name}") - slurm.setdefault("mem-per-cpu", "4G") - slurm.setdefault("partition", "venkvis-cpu") + slurm.setdefault("mem-per-cpu", "3600M") + slurm.setdefault("partition", "venkvis-cpu,venkvis-largemem") slurm.setdefault("ntasks", 4) slurm.setdefault("time", "1-0:0:0") @@ -333,9 +426,9 @@ def ngram_info_loss( "--encoding", encoding, "--output", - output, + str(output), "--reference", - ref_usage, + str(ref_usage), dataset, tokenizer, ], @@ -353,113 +446,134 @@ def set_logging_level(verbosity): logging.basicConfig(level=level) -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--dry-run", "-n", action="store_true") - parser.add_argument("-v", "--verbose", action="count", default=0) - parser.add_argument( - "--realspace", type=str, default="/nfs/turbo/coe-venkvis/mist/realspace_v4_dev2" - ) - parser.add_argument( - "--tmqm", - type=str, - default=Path(__file__).parent.parent.joinpath("tmQM", "data"), - ) - args = parser.parse_args() - set_logging_level(args.verbose) - - tokenizers = json.loads(Path("tokenizers.json").read_text()) - wk = Workflow() - for tok in tokenizers: - tok_name = tok["name_or_path"] - - # # Tabulate OOVs - # output = STATS_DIR.joinpath(tok_name, "oov.json") - # p = wk.add_process( - # ["submit_oov.sh", "--output", output, tok_name], - # output=output, - # ) +def tokenizer_jobs(wk, tok, realspace_path, tmqm_path): + tok_name = tok["name_or_path"] - # Tokenize RealSpace + # Tokenize RealSpace + if tok_name in LARGE_MEM_TOKENIZERS: + usage_array( + wk, + realspace_path, + tok_name, + ds_name="realspace", + encoding=tok["encoding"], + slurm={"ntasks": 128, "time": "1-0:0:0", "mem-per-cpu": "6G"}, + ) + else: wk.add_process( usage( - args.realspace, + realspace_path, tok_name, ds_name="realspace", encoding=tok["encoding"], slurm={"ntasks": 32, "time": "1-0:0:0"}, ) ) + + wk.add_process( + ngram_loss( + realspace_path, + tok_name, + ds_name="realspace", + encoding=tok["encoding"], + slurm={"ntasks": 128, "time": "8:0:0"}, + ) + ) + wk.add_process( + ngram_info_loss( + realspace_path, + tok_name, + ref="character", + ds_name="realspace", + encoding=tok["encoding"], + slurm={"ntasks": 128, "time": "1-0:0:0"}, + ) + ) + + # Tokenize MoleculeNet + for ds in [*MOLNET_DATASETS, "tmqm"]: + dataset = ds if ds != "tmqm" else tmqm_path + ds_name = None if ds != "tmqm" else "tmqm" wk.add_process( - ngram_loss( - args.realspace, + usage( + dataset, tok_name, - ds_name="realspace", + ds_name=ds_name, encoding=tok["encoding"], - slurm={"ntasks": 32, "time": "8:0:0"}, ) ) - - # Tokenize MoleculeNet - for ds in MOLNET_DATASETS: + for ngram in ["realspace", ds]: wk.add_process( - usage( - ds, + ngram_loss( + dataset, tok_name, encoding=tok["encoding"], + ngram=ngram, + ds_name=ds_name, + slurm={"time": "8:0:0" if ds == "tmqm" else "4:0:0"}, ) ) + + for ref in REF_INFO_LOSS: + if ds == "tmqm" and tok["encoding"] == "selfies": + continue # Majority of selfies fail + wk.add_process( - ngram_loss( - ds, + ngram_info_loss( + dataset, tok_name, + ref, + ds_name=ds_name, encoding=tok["encoding"], ) ) - for ref in REF_INFO_LOSS: - if tok["name_or_path"] == "ncfrey/ChemGPT-4.7M": - continue # Token Alignment will fail - wk.add_process( - ngram_info_loss(ds, tok_name, ref, encoding=tok["encoding"]) - ) +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", "-n", action="store_true") + parser.add_argument("--precompile", action="store_true") + parser.add_argument("-v", "--verbose", action="count", default=0) + parser.add_argument( + "--realspace", type=str, default="/nfs/turbo/coe-venkvis/mist/realspace_v4_dev2" + ) + parser.add_argument( + "--tmqm", + type=str, + default=Path(__file__).parent.parent.joinpath("tmQM", "data"), + ) + args = parser.parse_args() + set_logging_level(args.verbose) + tokenizers = json.loads(Path("tokenizers.json").read_text()) + wk = Workflow() + for tok in tokenizers: + # Tabulate OOVs + output = STATS_DIR.joinpath(tok["name_or_path"], "oov.json") wk.add_process( - usage( - args.tmqm, - tok_name, - ds_name="tmqm", - encoding=tok["encoding"], - slurm={"ntasks": 4, "time": "8:0:0", "job-name": "usage-tmqm"}, - ) - ) - wk.add_process( - ngram_loss( - args.tmqm, - tok_name, - ds_name="tmqm", - encoding=tok["encoding"], - slurm={"ntasks": 4, "time": "8:0:0", "job-name": "loss-tmqm"}, - ) + ["submit_oov.sh", "--output", output, tok["name_or_path"]], + output=output, + meta={"task": "oov"}, ) - for ref in REF_INFO_LOSS: - if tok["encoding"] == "selfies": - continue - if tok["name_or_path"] == "ncfrey/ChemGPT-4.7M": - continue # Token Alignment will fail + if tok["name_or_path"] == "character": + encodings = ["smiles", "selfies", "smiles-canonical", "smiles-kekule"] + elif tok["encoding"] == "selfies": + encodings = ["selfies"] + else: + encodings = ["smiles", "smiles-canonical", "smiles-kekule"] - wk.add_process( - ngram_info_loss( - args.tmqm, - tok_name, - ref, - ds_name="tmqm", - encoding=tok["encoding"], - slurm={"job-name": "dist-tmqm"}, - ) - ) + for encoding in encodings: + tok["encoding"] = encoding + tokenizer_jobs(wk, tok, args.realspace, args.tmqm) wk.show() - wk.run(dry_run=args.dry_run) + preflight = [] + if args.precompile: + preflight.append(Process(["submit_precompile.sh"], [])) + + postflight = [ + Process(["submit_archive.sh"], output=Path("archive")), + ] + + wk.run(dry_run=args.dry_run, preflight=preflight, postflight=postflight) diff --git a/opt/TokenizerStats/submit_oov.sh b/opt/TokenizerStats/submit_oov.sh index 6dfb85af..918a5599 100755 --- a/opt/TokenizerStats/submit_oov.sh +++ b/opt/TokenizerStats/submit_oov.sh @@ -15,7 +15,9 @@ cd "$(git rev-parse --show-toplevel)/opt/TokenizerStats" source ./activate env -python src/atomic_oov.py $@ +python -m python.helper.atomic_oov \ + --tokenizers $(realpath ./tokenizers.json) \ + $@ exit_code=$? echo "`date`: done" diff --git a/opt/TokenizerStats/submit_plots.sh b/opt/TokenizerStats/submit_plots.sh index ed25e4be..4f06f367 100755 --- a/opt/TokenizerStats/submit_plots.sh +++ b/opt/TokenizerStats/submit_plots.sh @@ -13,4 +13,4 @@ cd "$(git rev-parse --show-toplevel)/opt/TokenizerStats" source ./activate env -julia --color=no --startup-file=no --project=plots -e 'using SmirkPaperPlots; SmirkPaperPlots.main()' +julia --color=no --startup-file=no --project=plots ./plots/plots.jl diff --git a/opt/TokenizerStats/submit_precompile.sh b/opt/TokenizerStats/submit_precompile.sh index 5db9a3a9..d7701a40 100755 --- a/opt/TokenizerStats/submit_precompile.sh +++ b/opt/TokenizerStats/submit_precompile.sh @@ -15,6 +15,7 @@ export JULIA_PKG_USE_CLI_GIT=true export JULIA_NUM_PRECOMPILE_TASKS=$SLURM_CPUS_ON_NODE env -julia --color=no --startup-file=no --project -e 'using MPIPreferences; MPIPreferences.use_system_binary()' +julia --color=no --startup-file=no --project -e 'using Pkg; Pkg.instantiate(); using MPIPreferences; MPIPreferences.use_system_binary()' julia --color=no --startup-file=no --project -e 'using Pkg; Pkg.resolve(); Pkg.instantiate(); Pkg.precompile(timing=true)' julia --color=no --startup-file=no --project=plots -e 'using Pkg; Pkg.resolve(); Pkg.instantiate(); Pkg.precompile(timing=true)' +exit 0 diff --git a/opt/TokenizerStats/submit_tok_stats.sh b/opt/TokenizerStats/submit_tok_stats.sh index ab2028cb..8b5e23e0 100755 --- a/opt/TokenizerStats/submit_tok_stats.sh +++ b/opt/TokenizerStats/submit_tok_stats.sh @@ -13,8 +13,15 @@ cd "$(git rev-parse --show-toplevel)/opt/TokenizerStats" source ./activate env -# module load cuda/12.2.1 -# nsys profile -o "nsys_multinode_%q{SLURM_JOB_ID}_%q{PMIX_RANK}" --trace=mpi,nvtx \ +# # Start Tracy Profiler +# module --ignore_cache load spack +# module --ignore_cache load tracy +# export TRACY_WORKLOAD="tracy_${SLURM_JOB_ID}.tracy" +# export TRACY_PORT=$(( 9000 + $SLURM_JOB_ID % 1024 )) +# export TRACY_ENABLE=1 +# tracy-capture --output-path $TRACY_WORKLOAD --port $TRACY_PORT + +# Launch the job srun --mpi=pmix \ julia --project --threads=${SLURM_CPUS_PER_TASK:-1} --color=no --startup-file=no -- \ ./main.jl $@ diff --git a/opt/TokenizerStats/test/cli_tests.jl b/opt/TokenizerStats/test/cli_tests.jl index c5b1b54b..7b7fcc8c 100644 --- a/opt/TokenizerStats/test/cli_tests.jl +++ b/opt/TokenizerStats/test/cli_tests.jl @@ -4,3 +4,10 @@ @test main(["--help"]) == 0 end end + +@testitem "parse_splits" begin + using TokenizerStats: parse_splits + @test Set(parse_splits("val")) == Set(["val"]) + @test Set(parse_splits("val,train")) == Set(["val", "train"]) + @test Set(parse_splits("all")) == Set(["train", "val", "test"]) +end diff --git a/opt/TokenizerStats/test/masked_tests.jl b/opt/TokenizerStats/test/masked_tests.jl index 0c056453..0c8e4353 100644 --- a/opt/TokenizerStats/test/masked_tests.jl +++ b/opt/TokenizerStats/test/masked_tests.jl @@ -9,6 +9,11 @@ mcv = view(mc, 1:5) @test length(mcv) == 5 @test eltype(mcv) == Int + + # Check MaskedCode assertions hold + @test_throws AssertionError MaskedCode(rand(1:32, 10), rand(Bool, 9)) + @test_throws AssertionError MaskedCode([1, 2, 3, 4], falses(4), 3) + @test_throws InexactError MaskedCode(rand(UInt32, 10), rand(Bool, 10), -100) end @testitem "indexing" begin @@ -17,3 +22,9 @@ end @test mc[1] == mc.code[1] @test mc[1:5] == mc.code[1:5] end + +@testitem "UInt32" begin + using TokenizerStats: MaskedCode + mc = MaskedCode(rand(UInt32, 10), falses(10)) + @test mc[1:5] == mc.code[1:5] +end diff --git a/opt/TokenizerStats/test/ngram_tests.jl b/opt/TokenizerStats/test/ngram_tests.jl index 0dc3b923..bacd6f86 100644 --- a/opt/TokenizerStats/test/ngram_tests.jl +++ b/opt/TokenizerStats/test/ngram_tests.jl @@ -21,9 +21,9 @@ using OnlineStats export randngram # Build random n-gram counts -function randngram(N=3; vocab_size=9) - stats = map(n -> CountMap(NTuple{n,Int}), 1:N) - corpus = rand(range(0; length=vocab_size), 8, 32) +function randngram(N=3; vocab_size=9, T=Int) + stats = map(n -> CountMap(NTuple{n,T}), 1:N) + corpus = rand(range(T(0); length=T(vocab_size)), 8, 32) for i in axes(corpus, 2) for (n, s) in enumerate(stats) fit!(s, SlidingWindow(corpus[:, i], n)) @@ -55,6 +55,14 @@ end @test 1 <= n <= m.total @test d == m.total end + @testset "UInt32" begin + m = NGramModel(randngram(N; T=UInt32), 9) + @test m.total == 8 * 32 + @test length(m) == N + @test TokenizerStats.nonspecial_vocab_size(m) == 9 + @test isempty(setdiff(token_ids(m), 0:9)) + c, m = gram_odds(m, (0,)) + end end @testitem "ngram/condgram" begin diff --git a/opt/TokenizerStats/test/python_tests.jl b/opt/TokenizerStats/test/python_tests.jl new file mode 100644 index 00000000..b0b9fc19 --- /dev/null +++ b/opt/TokenizerStats/test/python_tests.jl @@ -0,0 +1,33 @@ +@testitem "smirk" begin + using TokenizerStats: load_tokenizer, pyconvert + using PythonCall: Py + tok = load_tokenizer("smirk") + @test tok isa Py + @test pyconvert(Vector{String}, tok.tokenize("CO")) == ["C", "O"] +end + +@testitem "dataset loader" begin + using TokenizerStats: DatasetConfig, dataset_split, dataset_name, tokenizer + using PythonCall: Py, pyconvert + + @testset "tokenizer" begin + dc = DatasetConfig("qm9", "smirk", "smiles") + @test dataset_name(dc) == "qm9" + tok, info = tokenizer(dc) + @test info.tokenizer_name == "smirk" + @test info.vocab_size == pyconvert(Int, length(tok)) && info.vocab_size > 0 + @test info.unk_token_id == pyconvert(Union{Int}, tok.unk_token_id) # Smirk has an unk_token_id + end + + @testset "dataset" begin + dc = DatasetConfig("qm9", "smirk", "smiles") + @test dataset_name(dc) == "qm9" + ds = dataset_split(dc, "train") + item = first(ds) + @test item isa Py + @test haskey(item, "input_ids") + @test pyconvert(Vector{Int}, item["input_ids"]) isa Vector{Int} + @test haskey(item, "smi") + @test pyconvert(String, item["smi"]) isa String + end +end diff --git a/opt/TokenizerStats/test/run_with_tracy.jl b/opt/TokenizerStats/test/run_with_tracy.jl new file mode 100755 index 00000000..7575e816 --- /dev/null +++ b/opt/TokenizerStats/test/run_with_tracy.jl @@ -0,0 +1,11 @@ +#!/usr/bin/env -S julia --project --startup-file=no +using Tracy +@info "Waiting for tracy to connect..." +wait_for_tracy() +@info "Tracy connected!" + +using TokenizerStats +using ReTestItems +@tracepoint "runtests" begin + runtests(TokenizerStats) +end diff --git a/opt/TokenizerStats/test/serialize_tests.jl b/opt/TokenizerStats/test/serialize_tests.jl index cb199904..d239180d 100644 --- a/opt/TokenizerStats/test/serialize_tests.jl +++ b/opt/TokenizerStats/test/serialize_tests.jl @@ -1,10 +1,17 @@ @testitem "Usage" begin - using TokenizerStats: tracked_stats, serialize_usage! + using TokenizerStats: tracked_stats, serialize_usage!, usage_stats! using JLD2: jldopen using OnlineStats: Series # Create some stats ts = tracked_stats() + for i in 1:10000 + code = rand(UInt32, rand(40:100)) + is_oov = rand(Bool) + usage_stats!(ts, code, is_oov) + end + + # Populate stats s = Series(; fertility=ts.fertility, nunique=ts.nunique, @@ -15,9 +22,22 @@ @test length(s[:ngrams].stats) == length(ts[:ngrams]) mktemp() do file, io + # Precompile jldopen(file, "w") do f serialize_usage!(f, "val", s) end + # Record serialization overhead + stats = @timed jldopen(file, "w") do f + serialize_usage!(f, "val", s) + end + + # Check for regressions in serialization overhead + io_bytes = stats.bytes + file_bytes = stat(file).size + mem_overhead = io_bytes / file_bytes + @test mem_overhead < 4.5 + + # Validate data jldopen(file, "r") do f @test Set(keys(f["val"])) == Set(["samples", string.(keys(s.stats))...]) @test f["val"]["samples"] isa Int diff --git a/opt/TokenizerStats/tokenizers.json b/opt/TokenizerStats/tokenizers.json index db8ca276..07c0a65b 100644 --- a/opt/TokenizerStats/tokenizers.json +++ b/opt/TokenizerStats/tokenizers.json @@ -1,4 +1,12 @@ [ + { + "name": "Character", + "name_or_path": "character", + "encoding": "smiles", + "tokenizer_class": "character", + "domain": "chemistry", + "source": "ours" + }, { "name": "Smirk", "name_or_path": "smirk", @@ -31,14 +39,6 @@ "domain": "chemistry", "source": "ours" }, - { - "name": "Character", - "name_or_path": "character", - "encoding": "smiles", - "tokenizer_class": "character", - "domain": "chemistry", - "source": "ours" - }, { "name": "ChemBERTa v1", "name_or_path": "seyonec/ChemBERTa-zinc-base-v1", @@ -240,7 +240,7 @@ { "name": "ChemGPT", "name_or_path": "ncfrey/ChemGPT-4.7M", - "encoding": "smiles", + "encoding": "selfies", "tokenizer_class": "bpe", "domain": "chemistry", "source": "huggingface", diff 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