From 3f17cf8393e74baeda3aa8afafc505f21bfa1a39 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 2 May 2025 06:59:46 -0700
Subject: [PATCH 01/54] migrate TokenizerStats to uv

---
 opt/TokenizerStats/.python-version |    1 +
 opt/TokenizerStats/poetry.toml     |    3 -
 opt/TokenizerStats/pyproject.toml  |   31 +-
 opt/TokenizerStats/uv.lock         | 2753 ++++++++++++++++++++++++++++
 4 files changed, 2771 insertions(+), 17 deletions(-)
 create mode 100644 opt/TokenizerStats/.python-version
 delete mode 100644 opt/TokenizerStats/poetry.toml
 create mode 100644 opt/TokenizerStats/uv.lock

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/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..58faafa7 100644
--- a/opt/TokenizerStats/pyproject.toml
+++ b/opt/TokenizerStats/pyproject.toml
@@ -1,15 +1,18 @@
-[tool.poetry]
-package-mode = false
-authors = ["Alexius Wadell <awadell@umich.edu>"]
+[project]
+name = "TokenizerStats"
+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 = [
+    "mendeleev >= 0.16",
+    "selfies == 2.1.2",
+    "jsonnet ~= 0.20.0",
+    "electrolyte-fm",
+]
 
-[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 = ["poetry-core"]
-build-backend = "poetry.core.masonry.api"
+[tool.uv.sources]
+electrolyte-fm = { path = "../../" }
diff --git a/opt/TokenizerStats/uv.lock b/opt/TokenizerStats/uv.lock
new file mode 100644
index 00000000..6488ce45
--- /dev/null
+++ b/opt/TokenizerStats/uv.lock
@@ -0,0 +1,2753 @@
+version = 1
+revision = 1
+requires-python = ">=3.10, <3.14"
+resolution-markers = [
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+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux'",
+]
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From f0f1eceb37943b2d3fa5455651d82fb20951b594 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 6 May 2025 16:44:37 -0400
Subject: [PATCH 02/54] fix: ssl error causing download issues

Fixed by setting env in activate, but implemented a don't check
(unsafe) fix too
---
 electrolyte_fm/utils/cache.py | 9 ++++++---
 opt/TokenizerStats/activate   | 2 ++
 2 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/electrolyte_fm/utils/cache.py b/electrolyte_fm/utils/cache.py
index a45b2481..e7e2fb99 100644
--- a/electrolyte_fm/utils/cache.py
+++ b/electrolyte_fm/utils/cache.py
@@ -42,17 +42,20 @@ 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/opt/TokenizerStats/activate b/opt/TokenizerStats/activate
index aaab6060..0bcdd953 100755
--- a/opt/TokenizerStats/activate
+++ b/opt/TokenizerStats/activate
@@ -6,6 +6,8 @@ DIR="$(git rev-parse --show-toplevel)/opt/TokenizerStats"
 if command -v module > /dev/null; then
     module purge
     module --ignore_cache load gcc python/3.11.5 openmpi/4.1.6
+    export SSL_CERT_DIR=/etc/pki/tls/certs
+    export SSL_CERT_FILE=/etc/pki/tls/cert.pem
 fi
 
 # Activate virtual environment

From cded7facabb97e117034fdd50bd401e521a2dcb3 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 6 May 2025 16:45:31 -0400
Subject: [PATCH 03/54] fixes for running tokenizer stats with uv

---
 opt/TokenizerStats/pyproject.toml |  4 +-
 opt/TokenizerStats/src/cli.jl     |  3 +-
 opt/TokenizerStats/src/collect.jl |  7 ++-
 opt/TokenizerStats/uv.lock        | 85 +++++++++++++++++++++++++++----
 4 files changed, 87 insertions(+), 12 deletions(-)

diff --git a/opt/TokenizerStats/pyproject.toml b/opt/TokenizerStats/pyproject.toml
index 58faafa7..d2cc6c99 100644
--- a/opt/TokenizerStats/pyproject.toml
+++ b/opt/TokenizerStats/pyproject.toml
@@ -12,7 +12,9 @@ dependencies = [
     "selfies == 2.1.2",
     "jsonnet ~= 0.20.0",
     "electrolyte-fm",
+    "smilespe==0.0.3",
+    "apetokenizer @ git+https://github.com/mikemayuare/apetokenizer.git@8b070d6278503bcf8ad2eedc1756ac34f4bbd981",
 ]
 
 [tool.uv.sources]
-electrolyte-fm = { path = "../../" }
+electrolyte-fm = { path = "../../", editable = true }
diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index 13229b12..42633a4f 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -21,6 +21,7 @@ end
 
 @annotate function get_dataset(name_or_path, tokenizer, encoding)
     start = time()
+    tokenizer = isdir(tokenizer) ? realpath(tokenizer) : tokenizer
     if isdir(name_or_path)
         if "tmQM" in splitpath(name_or_path)
             dm = TokenizerStats.tmqm(name_or_path; tokenizer, encoding)
@@ -117,7 +118,7 @@ end
         # 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"])
+        dm = () -> first(get_dataset(args_cmd["dataset"], tokenizer, args_cmd["encoding"]))
         model_loss(dm, args_cmd["model"], args_cmd["output"])
 
     elseif args["%COMMAND%"] == "merge"
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index 3fd6d9c4..2249fd16 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -162,12 +162,17 @@ function tabulate_dataset(datamodule::Py, out_file::AbstractString; tokenizer_na
     return 0
 end
 
-function model_loss(datamodule::Py, ref_file::String, output::String)
+function model_loss(constructor, 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 ready" now()
+
+    # Create datamodule
+    datamodule = constructor()
+    MPI.Barrier(comm)
     @info "Rank $rank of $size is starting" now()
 
     # Load Reference Tokenizer / n-gram model
diff --git a/opt/TokenizerStats/uv.lock b/opt/TokenizerStats/uv.lock
index 6488ce45..36781d93 100644
--- a/opt/TokenizerStats/uv.lock
+++ b/opt/TokenizerStats/uv.lock
@@ -124,6 +124,11 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
 ]
 
+[[package]]
+name = "apetokenizer"
+version = "0.0.0"
+source = { git = "https://github.com/mikemayuare/apetokenizer.git?rev=8b070d6278503bcf8ad2eedc1756ac34f4bbd981#8b070d6278503bcf8ad2eedc1756ac34f4bbd981" }
+
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
@@ -332,7 +337,7 @@ wheels = [
 [[package]]
 name = "electrolyte-fm"
 version = "0.1.0"
-source = { virtual = "../../" }
+source = { editable = "../../" }
 dependencies = [
     { name = "datasets" },
     { name = "deepspeed" },
@@ -385,6 +390,15 @@ dev = [
 plotting = [{ name = "matplotlib", specifier = "~=3.9" }]
 tokenizers = [{ name = "smilespe", specifier = "==0.0.3" }]
 
+[[package]]
+name = "fastprogress"
+version = "1.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/95/52/77c827febdf8aac3b0840ce0edb7c34cbd330fab9bbe032c2a79ec8f2d51/fastprogress-1.0.3.tar.gz", hash = "sha256:7a17d2b438890f838c048eefce32c4ded47197ecc8ea042cecc33d3deb8022f5", size = 14920 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a7/8f/213223fdee199c55db81e2d0c669f30e8285c5be2526c4ed924de39247da/fastprogress-1.0.3-py3-none-any.whl", hash = "sha256:6dfea88f7a4717b0a8d6ee2048beae5dbed369f932a368c5dd9caff34796f7c5", size = 12744 },
+]
+
 [[package]]
 name = "filelock"
 version = "3.18.0"
@@ -502,6 +516,30 @@ http = [
     { name = "aiohttp" },
 ]
 
+[[package]]
+name = "gensim"
+version = "4.3.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12'" },
+    { name = "numpy", version = "2.2.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12'" },
+    { name = "scipy" },
+    { name = "smart-open" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/77/68/074333a52f6fa82402332054ca0dfa721f7dcfa7eace313f64cdb44bacde/gensim-4.3.2.tar.gz", hash = "sha256:99ac6af6ffd40682e70155ed9f92ecbf4384d59fb50af120d343ea5ee1b308ab", size = 23263982 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/43/21/21f993356303c4c92352d9e7c732f715e86e7b0bc04674be71bb1e9bb05b/gensim-4.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:31b3cb313939b6940ee21660177f6405e71b920da462dbf065b2458a24ab33e1", size = 24084903 },
+    { url = "https://files.pythonhosted.org/packages/d7/84/ec2f74713475b57b7d882f5160b0b7c9df40ecc409d793e87656a6f3ada0/gensim-4.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:67c41b15e19e4950f57124f633c45839b5c84268ffa58079c5b0c0f04d2a9cb9", size = 24035874 },
+    { url = "https://files.pythonhosted.org/packages/59/84/ed371ab548a02e16f83669dee5337ad3917d3bf980878608956817e0534b/gensim-4.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a9bf1a8ee2e8214499c517008a0fd175ce5c649954a88569358cfae6bfca42dc", size = 26398052 },
+    { url = "https://files.pythonhosted.org/packages/e8/d9/104988573fd2c1acdc64e66883b35fb8ae559310d2d9f77db78bf7de9add/gensim-4.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5e34ee6f8a318fbf0b65e6d39a985ecf9e9051febfd1221ae6255fff1972c547", size = 26506991 },
+    { url = "https://files.pythonhosted.org/packages/ab/b0/d58dc405fd60ab546ca714321235dc2d455b2dc06bfb4fc1092940c749fc/gensim-4.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:c46b7395dc57c83329932f3febed9660891fdcc75327d56f55000e3e08898983", size = 24012956 },
+    { url = "https://files.pythonhosted.org/packages/99/f5/8d2cb0b2628bb6482baafbf0ff7262c11fc46e98b23ee79234828b927e8d/gensim-4.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:a919493339cfad39d5e76768c1bc546cd507f715c5fca93165cc174a97657457", size = 24078719 },
+    { url = "https://files.pythonhosted.org/packages/63/46/5feab9c524a380bfa9f9f1c0d065743280dca30b216ab4c7a231f22dbed7/gensim-4.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8dcd1419266bd563c371d25530f4dce3505fe78059b2c0c08724e4f9e5479b38", size = 24030236 },
+    { url = "https://files.pythonhosted.org/packages/14/34/f1e056feda95330f7d8beef6771e3441ce0e8e2d1f55bf754b0b0594b234/gensim-4.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e3e8035ac3f54dca3a8ca56bec526ddfe5b23006e0134b7375ca5f5dbfaef70a", size = 26558413 },
+    { url = "https://files.pythonhosted.org/packages/22/40/7d2cce3ad4ad5d02aa68e253e6ea5f0acc381f02f594e235fe00a274faff/gensim-4.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c3b537c1fd4699c8e6d59c3ffa2fdd9918cd4e5555bf5ee7c1fbedd89b2d643", size = 26662667 },
+    { url = "https://files.pythonhosted.org/packages/ad/97/b8253236dfedb9094f4273393a3fd03997da81f27f15822e56128da894ae/gensim-4.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:5a52001226f9e89f7833503f99c9b4fd028fdf837002f24cdc1bc3cf901a4003", size = 24010152 },
+]
+
 [[package]]
 name = "gitdb"
 version = "4.0.12"
@@ -1171,7 +1209,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.5.1.17"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/2a/78/4535c9c7f859a64781e43c969a3a7e84c54634e319a996d43ef32ce46f83/nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:30ac3869f6db17d170e0e556dd6cc5eee02647abc31ca856634d5a40f82c15b2", size = 570988386 },
@@ -1182,7 +1220,7 @@ name = "nvidia-cufft-cu12"
 version = "11.3.0.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/8f/16/73727675941ab8e6ffd86ca3a4b7b47065edcca7a997920b831f8147c99d/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ccba62eb9cef5559abd5e0d54ceed2d9934030f51163df018532142a8ec533e5", size = 200221632 },
@@ -1211,9 +1249,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.7.1.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
-    { name = "nvidia-cusparse-cu12" },
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/f0/6e/c2cf12c9ff8b872e92b4a5740701e51ff17689c4d726fca91875b07f655d/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e9e49843a7707e42022babb9bcfa33c29857a93b88020c4e4434656a655b698c", size = 158229790 },
@@ -1225,7 +1263,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.5.4.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12" },
+    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/06/1e/b8b7c2f4099a37b96af5c9bb158632ea9e5d9d27d7391d7eb8fc45236674/nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7556d9eca156e18184b94947ade0fba5bb47d69cec46bf8660fd2c71a4b48b73", size = 216561367 },
@@ -2173,6 +2211,31 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274", size = 11050 },
 ]
 
+[[package]]
+name = "smart-open"
+version = "7.1.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "wrapt" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/21/30/1f41c3d3b8cec82024b4b277bfd4e5b18b765ae7279eb9871fa25c503778/smart_open-7.1.0.tar.gz", hash = "sha256:a4f09f84f0f6d3637c6543aca7b5487438877a21360e7368ccf1f704789752ba", size = 72044 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/7a/18/9a8d9f01957aa1f8bbc5676d54c2e33102d247e146c1a3679d3bd5cc2e3a/smart_open-7.1.0-py3-none-any.whl", hash = "sha256:4b8489bb6058196258bafe901730c7db0dcf4f083f316e97269c66f45502055b", size = 61746 },
+]
+
+[[package]]
+name = "smilespe"
+version = "0.0.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "fastprogress" },
+    { name = "gensim" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/5e/5c/a638fd96cdf4499eaed76d5dbcec734d98c4ddaf2a8f9e13e44e5151fa29/SmilesPE-0.0.3.tar.gz", hash = "sha256:7ceebc7d314e456a08f77d45f08fe4b638886901c0eac50f0cdb005b9f0912bc", size = 15625 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6d/f9/273f54d9d4b42779926291c82a5b3ffea30cff2492ebbe4ce08dccdcc949/SmilesPE-0.0.3-py3-none-any.whl", hash = "sha256:9f74279daa14945859546fb2de11c208b5116927ce5fe03b3cf46bcba96f5e58", size = 15723 },
+]
+
 [[package]]
 name = "smirk"
 version = "0.2.0"
@@ -2286,18 +2349,22 @@ name = "tokenizerstats"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "apetokenizer" },
     { name = "electrolyte-fm" },
     { name = "jsonnet" },
     { name = "mendeleev" },
     { name = "selfies" },
+    { name = "smilespe" },
 ]
 
 [package.metadata]
 requires-dist = [
-    { name = "electrolyte-fm", virtual = "../../" },
+    { name = "apetokenizer", git = "https://github.com/mikemayuare/apetokenizer.git?rev=8b070d6278503bcf8ad2eedc1756ac34f4bbd981" },
+    { name = "electrolyte-fm", editable = "../../" },
     { name = "jsonnet", specifier = "~=0.20.0" },
     { name = "mendeleev", specifier = ">=0.16" },
     { name = "selfies", specifier = "==2.1.2" },
+    { name = "smilespe", specifier = "==0.0.3" },
 ]
 
 [[package]]
@@ -2406,7 +2473,7 @@ name = "triton"
 version = "3.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "setuptools" },
+    { name = "setuptools", marker = "sys_platform == 'linux'" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/76/04/d54d3a6d077c646624dc9461b0059e23fd5d30e0dbe67471e3654aec81f9/triton-3.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fad99beafc860501d7fcc1fb7045d9496cbe2c882b1674640304949165a916e7", size = 156441993 },

From 8f7a8ba1aabf0a1ff40b900d672356c943557094 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 6 May 2025 16:48:09 -0400
Subject: [PATCH 04/54] tweak: compute loss using finetuned n-gram model as
 well

---
 opt/TokenizerStats/submit_jobs.py | 97 +++++++++++++------------------
 1 file changed, 41 insertions(+), 56 deletions(-)

diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 72f00461..185a4c84 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -1,4 +1,4 @@
-#!/usr/bin/env python
+#!/usr/bin/env -S uv run python
 import time
 import logging
 import subprocess
@@ -278,12 +278,17 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles"):
     )
 
 
-def ngram_loss(dataset, tokenizer, slurm=None, encoding="smiles", ds_name=None):
-    input = STATS_DIR.joinpath(tokenizer, "realspace", "usage.jld2")
+def ngram_loss(dataset, tokenizer, slurm=None, encoding="smiles", ngram="realspace", ds_name=None):
+    input = STATS_DIR.joinpath(tokenizer, ngram, "usage.jld2")
     ds_name = ds_name or str(dataset)
-    output = STATS_DIR.joinpath(tokenizer, ds_name, "model_loss.jld2")
+    outfile = "model_loss.jld2" if ngram == "realspace" else f"model_loss_{ngram}.jld2"
+    output = STATS_DIR.joinpath(tokenizer, ds_name, outfile)
     slurm = slurm or {}
-    slurm.setdefault("job-name", f"loss-{ds_name}")
+    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", "2G")
     slurm.setdefault("partition", "venkvis-cpu,venkvis-largemem")
     slurm.setdefault("ntasks", 4)
@@ -373,12 +378,12 @@ def set_logging_level(verbosity):
     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,
-        # )
+        # Tabulate OOVs
+        output = STATS_DIR.joinpath(tok_name, "oov.json")
+        p = wk.add_process(
+            ["submit_oov.sh", "--output", output, tok_name],
+            output=output,
+        )
 
         # Tokenize RealSpace
         wk.add_process(
@@ -401,65 +406,45 @@ def set_logging_level(verbosity):
         )
 
         # Tokenize MoleculeNet
-        for ds in MOLNET_DATASETS:
+        for ds in [*MOLNET_DATASETS, "tmqm"]:
+            dataset = ds if ds != "tmqm" else args.tmqm
+            ds_name = None if ds != "tmqm" else "tmqm"
             wk.add_process(
                 usage(
-                    ds,
+                    dataset,
                     tok_name,
+                    ds_name=ds_name,
                     encoding=tok["encoding"],
                 )
             )
-            wk.add_process(
-                ngram_loss(
-                    ds,
-                    tok_name,
-                    encoding=tok["encoding"],
+            for ngram in ["realspace", ds]:
+                wk.add_process(
+                    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
+
                 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"])
-                )
-
-        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"},
-            )
-        )
-        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
-
-            wk.add_process(
-                ngram_info_loss(
-                    args.tmqm,
-                    tok_name,
-                    ref,
-                    ds_name="tmqm",
-                    encoding=tok["encoding"],
-                    slurm={"job-name": "dist-tmqm"},
+                    ngram_info_loss(
+                        dataset,
+                        tok_name,
+                        ref,
+                        ds_name=ds_name,
+                        encoding=tok["encoding"],
+                    )
                 )
-            )
 
     wk.show()
     wk.run(dry_run=args.dry_run)

From 4046bad97cf4a0e058b7db579ef30c79ee2b5c9b Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 6 May 2025 16:48:42 -0400
Subject: [PATCH 05/54] fix: correct encoding for ChemGPT

---
 opt/TokenizerStats/tokenizers.json | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/tokenizers.json b/opt/TokenizerStats/tokenizers.json
index db8ca276..f358ae19 100644
--- a/opt/TokenizerStats/tokenizers.json
+++ b/opt/TokenizerStats/tokenizers.json
@@ -240,7 +240,7 @@
   {
     "name": "ChemGPT",
     "name_or_path": "ncfrey/ChemGPT-4.7M",
-    "encoding": "smiles",
+    "encoding": "selfies",
     "tokenizer_class": "bpe",
     "domain": "chemistry",
     "source": "huggingface",

From f03670745ce9b71d09aba6684a331eb941d2e402 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 6 May 2025 13:55:13 -0700
Subject: [PATCH 06/54] Summarize results with fixed effects

---
 opt/TokenizerStats/plots/Project.toml         |   1 +
 .../plots/src/tabulate_results.jl             | 180 +++++++++++++++---
 .../plots/src/tabulate_tokenizer.jl           |  29 +++
 3 files changed, 186 insertions(+), 24 deletions(-)

diff --git a/opt/TokenizerStats/plots/Project.toml b/opt/TokenizerStats/plots/Project.toml
index 85f022ee..34b502f2 100644
--- a/opt/TokenizerStats/plots/Project.toml
+++ b/opt/TokenizerStats/plots/Project.toml
@@ -19,6 +19,7 @@ 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"
 
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index dc1eddb4..f5ffe395 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, :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
@@ -260,21 +268,145 @@ 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)
 
+zscore(x) = (x .- mean(x)) ./ std(x)
+
+function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
+    df_ng, df_p, df_f = df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft)
     contrasts = Dict(
         :tokenizer_class => EffectsCoding(; base="atomwise"),
-        :encoding => EffectsCoding(; base="SMILES"),
+        :encoding => EffectsCoding(; base="smiles"),
     )
 
+    # Pretraining models
+    models = []
+    df_ng.val_loss = mean.(df_ng.loss_per_token_moments)
+    df_ng.wts = inv.(var.(df_ng.loss_per_token_moments))
+    # subset!(df_ng, :tokenizer_class => ByRow(in(unique(df_f.tokenizer_class))))
+    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)
+    df_predict = leftjoin(
+        select(df_ng_pt, :tokenizer, :val_loss => :ng_loss, :ng_est_loss),
+        select(df_p, :tokenizer, :val_loss => :fm_loss);
+        on=:tokenizer,
+    )
+    dropmissing!(df_predict)
+    rho = corspearman(df_predict.ng_loss, df_predict.fm_loss)
+    rho_est = corspearman(df_predict.ng_est_loss, df_predict.fm_loss)
+
+
+    push!(models, (; model, dataset = "REALSpace", ngram=true, metric="CE", rho, rho_est))
+    model = lm(@formula(val_loss ~ 1 + tokenizer_class + encoding), df_p; contrasts)
+    push!(models, (; model, dataset = "REALSpace", ngram=false, metric="CE"))
+
+    ft_ng = Dict{String,Any}()
+    ft_fm = Dict{String,Any}()
+    subset!(df_f, :dataset => ByRow(!=("freesolv"))) # fit issue
+    for dataset in unique(df_f.dataset)
+        df_f_fm = subset(df_f, :dataset => ByRow(==(dataset)))
+        df_f_ng = subset(df_ng, :dataset => ByRow(==(dataset)))
+        task = first(df_f_fm.task)
+        link = task == "regression" ? LogLink() : LogitLink()
+
+        # Foundation Model
+        df_f_fm.val_loss = zscore(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,
+        )
+        push!(models, (; model, dataset, ngram=false, metric=first(df_f_fm.metric)))
+
+        # NGram
+        df_f_ng.val_loss = zscore(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),
+        )
+
+        # Spearman's for n-gram fit vs. fm results
+        df_f_ng.ng_est_loss = predict(model)
+        df_predict = leftjoin(
+            select(df_f_ng, :tokenizer, :val_loss => :ng_loss, :ng_est_loss),
+            select(df_f_fm, :tokenizer, :val_loss => :fm_loss);
+            on=:tokenizer,
+        )
+        dropmissing!(df_predict)
+        rho = corspearman(df_predict.ng_loss, df_predict.fm_loss)
+        rho_est = corspearman(df_predict.ng_est_loss, df_predict.fm_loss)
+
+        push!(models, (; model, dataset, ngram=true, metric="CE", rho, rho_est))
+    end
+
+    models = map(models) do m
+        haskey(m, :rho) ? m : (; m..., rho=missing, rho_est=missing)
+    end
+    @show models = DataFrame(models)
+
+    tbl_datasets = ["REALSpace", "tmQM", "qm9", "bbbp", "hiv"]
+    models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
+    models_tbl.dataset .= categorical(models_tbl.dataset, levels=tbl_datasets, ordered=true)
+    sort!(models_tbl, [:dataset, :ngram])
+    transform!(models_tbl,
+        :metric => ByRow(m -> replace(m,
+            "auroc" => L"AUROC ($\uparrow$)",
+            "mae" => L"MAE ($\downarrow$)",
+            "CE" => L"CE ($\downarrow$)",
+        )) => :metric
+    )
+
+    return regtable(
+        models_tbl.model...;
+        groups=string.(models_tbl.dataset),
+        stat_below=false,
+        print_depvar = false,
+        number_regressions = false,
+        align=:r,
+        regression_statistics=[
+            nobs_nonwts => "N",
+            (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
+            (model -> cor(response(model), predict(model))) => L"R^2",
+
+        ],
+        extralines=[
+            ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
+            ["Metric:", models_tbl.metric...],
+            [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
+        ],
+        labels=Dict(
+           "tokenizer_class: spe" => "SPE/APE Tokenizers",
+           "tokenizer_class: bpe" => "BPE Tokenizers",
+           "tokenizer_class: smirk" => "Smirk",
+           "tokenizer_class: smirk-gpe" => "Smirk-GPE",
+           "tokenizer_class: character" => "Character-level",
+           "tokenizer_class: unigram" => "Unigram Tokenizers",
+            "encoding: selfies" => "SELFIES",
+            "encoding: smiles-canonical" => "Canonical SMILES",
+        ),
+        # render=LatexTable(),
+    )
+
+
+
     # 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
     )
+    @show tok_ngram = lm(
+        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
+        df; contrasts
+    )
     LogLikelihoodRatioTest(tok, null) |> display
+    LogLikelihoodRatioTest(tok_ngram, null) |> display
 
     # Test impact of encoding on pretraining
     tokenizers = tokenizers_info(stats_dir)
diff --git a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
index 2232b497..3eadf63c 100644
--- a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
@@ -6,12 +6,24 @@ 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]
+
             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)
+
+                entropy = sum(values(unigram)) do c
+                    p = c / tokens_seen
+                    -p * log(p)
+                end
+                normalized_entropy = entropy / log(data["tokenizer"][:vocab_size])
+
                 push!(rows, (;
                     file,
                     tokenizer,
@@ -20,7 +32,11 @@ function usage_stats(stats_dir)
                     samples,
                     out_of_vocab=data[split]["out_of_vocab"],
                     fertility,
+                    entropy,
+                    normalized_entropy,
                     nunique,
+                    unk_count,
+                    tokens_seen,
                     avg_fertility=mean(fertility),
                     std_fertility=std(fertility),
                     max_fertility=maximum(keys(data[split]["fertility"])),
@@ -139,3 +155,16 @@ 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

From d6fbae06b2de854f929aa9d98476d1bfff429d51 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 6 May 2025 13:55:43 -0700
Subject: [PATCH 07/54] move plot code to a script

---
 opt/TokenizerStats/plots/plots.jl             | 131 ++++++++++++++++++
 .../plots/src/SmirkPaperPlots.jl              |  52 +------
 opt/TokenizerStats/submit_plots.sh            |   2 +-
 3 files changed, 135 insertions(+), 50 deletions(-)
 create mode 100644 opt/TokenizerStats/plots/plots.jl

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
new file mode 100644
index 00000000..30c95ac8
--- /dev/null
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -0,0 +1,131 @@
+using DataFrames
+using CairoMakie
+using CSV: CSV
+using SmirkPaperPlots
+using TokenizerStats
+using SmirkPaperPlots: savefig
+using StatsBase
+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)
+
+# 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(df_usage_summary, [: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
+    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)
+
+# 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("jaccard", SmirkPaperPlots.figure_jaccard(stats_dir))
+
+    # Transformers vs. N-Grams
+    df = SmirkPaperPlots.ngram_vs_transformer_fits(stats_dir, loss_stats, dfp)
+    savefig("ngram_vs_transformer", SmirkPaperPlots.figure_ngram_vs_transformer(stats_dir, df))
+
+    # 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(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",
+                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..e1284549 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -7,9 +7,10 @@ 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 StatsBase: StatsBase, mean, stderr, mean_and_std, quantile, AbstractWeights, Weights, aweights, corspearman, cor
 using FreeTypeAbstraction: FreeTypeAbstraction, newface, FTFont
 using JLD2: jldopen
 using JSON: JSON
@@ -19,6 +20,7 @@ using Distributions: Chisq, FDist, Normal
 using OrderedCollections: OrderedDict
 using CSV: CSV
 using Clustering: hclust
+using RegressionTables: regtable, LatexTable
 
 using TokenizerStats
 using TokenizerStats: load_tokenizer, find
@@ -73,51 +75,3 @@ 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/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

From 9481dde0ad1ba498301132c3abc917283a7378c6 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 7 May 2025 09:49:39 -0400
Subject: [PATCH 08/54] add script for finding ambiguous smiles

---
 opt/TokenizerStats/check_ambi_smiles.py | 296 ++++++++++++++++++++++++
 1 file changed, 296 insertions(+)
 create mode 100755 opt/TokenizerStats/check_ambi_smiles.py

diff --git a/opt/TokenizerStats/check_ambi_smiles.py b/opt/TokenizerStats/check_ambi_smiles.py
new file mode 100755
index 00000000..92661a36
--- /dev/null
+++ b/opt/TokenizerStats/check_ambi_smiles.py
@@ -0,0 +1,296 @@
+#!/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("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}")
+
+
+@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()

From 93a5527879bcf6db51c84b3103ce928bba46c4d9 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 7 May 2025 09:43:14 -0400
Subject: [PATCH 09/54] expand fe models to cover finetuned models

---
 opt/TokenizerStats/plots/plots.jl             | 27 ++++--
 .../plots/src/SmirkPaperPlots.jl              |  1 +
 .../plots/src/tabulate_results.jl             | 96 ++++++++++---------
 .../plots/src/tabulate_tokenizer.jl           |  4 +-
 4 files changed, 74 insertions(+), 54 deletions(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 30c95ac8..13f262c5 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -5,9 +5,10 @@ using SmirkPaperPlots
 using TokenizerStats
 using SmirkPaperPlots: savefig
 using StatsBase
+using JLD2: JLD2
 using Format
 
-stats_dir=joinpath(pkgdir(TokenizerStats), "stats")
+stats_dir = joinpath(pkgdir(TokenizerStats), "stats")
 
 # Tokenizer Statistics
 loss_stats = SmirkPaperPlots.model_loss_stats(stats_dir)
@@ -21,17 +22,17 @@ function fertility_summary(token_usage)
         :dataset => ByRow(x -> x ∉ ["tmQM", "realspace"] ? "MoleculeNet" : x) => :dataset,
         :tokenizer => ByRow(x -> tok_info[x]["tokenizer_class"]) => :tokenizer_class,
     )
-    combine(groupby(df_usage_summary, [:tokenizer_class, :dataset])) do gdf
+    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",
+            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)),
+            fmt_fertility=format("{}\\pm{}", round(avg_fertility; sigdigits=3), round(std_fertility; sigdigits=3)),
         )
     end
 end
@@ -50,7 +51,7 @@ function unk_freq(token_usage)
         unk_freq = unk_count / tokens_seen
         return (;
             unk_freq,
-            fmt_unk_freq = format("{:.2f}%", round(100*unk_freq; sigdigits=3)),
+            fmt_unk_freq=format("{:.2f}%", round(100 * unk_freq; sigdigits=3)),
         )
     end
 end
@@ -63,8 +64,9 @@ function norm_entropy_summary(token_usage)
     )
     # Combine splits
     df = combine(groupby(token_usage, :tokenizer)) do gdf
-        return (; normalized_entropy = mean(gdf.normalized_entropy, fweights(gdf.tokens_seen)))
+        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,
@@ -73,10 +75,10 @@ function norm_entropy_summary(token_usage)
     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),
+        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])
@@ -88,6 +90,11 @@ norm_entropy_summary(token_usage) |> display
 # Transformer Models
 dfp, dff, dft = SmirkPaperPlots.transformer_models(stats_dir)
 
+# Fixed Effects models for results
+fe_models = SmirkPaperPlots.ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
+JLD2.jldsave("fe_models.jld2"; fe_models)
+
+
 # Tokenizer Summary
 df_tok = SmirkPaperPlots.tokenizer_summary(stats_dir; k=5)
 
diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
index e1284549..e3073898 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -75,3 +75,4 @@ function savefig(name::String, f::Figure; dpi=300)
     return nothing
 end
 
+end
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index f5ffe395..29cf7f68 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -225,7 +225,7 @@ function df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft)
         :tokenizer => ByRow(x -> tokenizers[x]["tokenizer_class"]) => :tokenizer_class,
         :tokenizer => ByRow(x -> tokenizers[x]["encoding"]) => :encoding,
     )
-    select!(df_ng, :tokenizer, :tokenizer_class, :dataset, :encoding, :samples, :loss_per_token_moments)
+    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)
@@ -283,7 +283,6 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
     models = []
     df_ng.val_loss = mean.(df_ng.loss_per_token_moments)
     df_ng.wts = inv.(var.(df_ng.loss_per_token_moments))
-    # subset!(df_ng, :tokenizer_class => ByRow(in(unique(df_f.tokenizer_class))))
     df_ng_pt = subset(df_ng, :dataset => ByRow(==("realspace")))
     model = lm(@formula(val_loss ~ 1 + tokenizer_class + encoding), df_ng_pt;
         contrasts,
@@ -300,55 +299,68 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
     rho_est = corspearman(df_predict.ng_est_loss, df_predict.fm_loss)
 
 
-    push!(models, (; model, dataset = "REALSpace", ngram=true, metric="CE", rho, rho_est))
+    push!(models, (; model, dataset="REALSpace", ngram=true, metric="CE", rho, rho_est))
     model = lm(@formula(val_loss ~ 1 + tokenizer_class + encoding), df_p; contrasts)
-    push!(models, (; model, dataset = "REALSpace", ngram=false, metric="CE"))
+    push!(models, (; model, dataset="REALSpace", ngram=false, metric="CE"))
 
-    ft_ng = Dict{String,Any}()
-    ft_fm = Dict{String,Any}()
     subset!(df_f, :dataset => ByRow(!=("freesolv"))) # fit issue
     for dataset in unique(df_f.dataset)
         df_f_fm = subset(df_f, :dataset => ByRow(==(dataset)))
-        df_f_ng = subset(df_ng, :dataset => ByRow(==(dataset)))
         task = first(df_f_fm.task)
         link = task == "regression" ? LogLink() : LogitLink()
 
         # Foundation Model
-        df_f_fm.val_loss = zscore(df_f_fm.mean)
-        # df_f_fm.wts = inv.(df_f_fm.std .^2)
+        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
-        df_f_ng.val_loss = zscore(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),
-        )
-
-        # Spearman's for n-gram fit vs. fm results
-        df_f_ng.ng_est_loss = predict(model)
-        df_predict = leftjoin(
-            select(df_f_ng, :tokenizer, :val_loss => :ng_loss, :ng_est_loss),
-            select(df_f_fm, :tokenizer, :val_loss => :fm_loss);
-            on=:tokenizer,
-        )
-        dropmissing!(df_predict)
-        rho = corspearman(df_predict.ng_loss, df_predict.fm_loss)
-        rho_est = corspearman(df_predict.ng_est_loss, df_predict.fm_loss)
-
-        push!(models, (; model, dataset, ngram=true, metric="CE", rho, rho_est))
+        for finetuned in [true, false]
+            # NGram
+            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),
+            )
+
+            # Spearman's for n-gram fit vs. fm results
+            df_f_ng.ng_est_loss = predict(model)
+            df_predict = leftjoin(
+                select(df_f_ng, :tokenizer, :val_loss => :ng_loss, :ng_est_loss),
+                select(df_f_fm, :tokenizer, :val_loss => :fm_loss);
+                on=:tokenizer,
+            )
+            dropmissing!(df_predict)
+            fm_loss = first(df_f_fm.metric) in ["auroc"] ? -df_predict.fm_loss : df_predict.fm_loss
+            rho = corspearman(df_predict.ng_loss, fm_loss)
+            rho_est = corspearman(df_predict.ng_est_loss, fm_loss)
+
+            push!(models, (; model, dataset, ngram=true, finetuned, metric="CE", rho, rho_est))
+        end
     end
 
     models = map(models) do m
-        haskey(m, :rho) ? m : (; m..., rho=missing, rho_est=missing)
+        m = haskey(m, :rho) ? m : (; m..., rho=missing, rho_est=missing)
+        m = haskey(m, :finetuned) ? m : (; m..., finetuned=missing)
     end
-    @show models = DataFrame(models)
+
+    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,
+        :model => ByRow(confint) => :confint,
+        :model => ByRow(coef) => :coef,
+    )
+    return df_model
 
     tbl_datasets = ["REALSpace", "tmQM", "qm9", "bbbp", "hiv"]
     models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
@@ -366,27 +378,25 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
         models_tbl.model...;
         groups=string.(models_tbl.dataset),
         stat_below=false,
-        print_depvar = false,
-        number_regressions = false,
+        print_depvar=false,
+        number_regressions=false,
         align=:r,
         regression_statistics=[
             nobs_nonwts => "N",
             (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
-            (model -> cor(response(model), predict(model))) => L"R^2",
-
-        ],
+            (model -> cor(response(model), predict(model))) => L"R^2",],
         extralines=[
             ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
             ["Metric:", models_tbl.metric...],
             [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
         ],
         labels=Dict(
-           "tokenizer_class: spe" => "SPE/APE Tokenizers",
-           "tokenizer_class: bpe" => "BPE Tokenizers",
-           "tokenizer_class: smirk" => "Smirk",
-           "tokenizer_class: smirk-gpe" => "Smirk-GPE",
-           "tokenizer_class: character" => "Character-level",
-           "tokenizer_class: unigram" => "Unigram Tokenizers",
+            "tokenizer_class: spe" => "SPE/APE Tokenizers",
+            "tokenizer_class: bpe" => "BPE Tokenizers",
+            "tokenizer_class: smirk" => "Smirk",
+            "tokenizer_class: smirk-gpe" => "Smirk-GPE",
+            "tokenizer_class: character" => "Character-level",
+            "tokenizer_class: unigram" => "Unigram Tokenizers",
             "encoding: selfies" => "SELFIES",
             "encoding: smiles-canonical" => "Canonical SMILES",
         ),
diff --git a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
index 3eadf63c..7604c201 100644
--- a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
@@ -99,11 +99,12 @@ 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]
             dataset = basename(dirname(file))
             dataset = dataset == "tmqm" ? "tmQM" : dataset
+            finetuned = occursin(dataset, basename(file))
             for split in ["train", "val", "test"]
                 split ∉ keys(data) && continue
                 split_data = data[split]
@@ -114,6 +115,7 @@ function model_loss_stats(stats_dir)
                     push!(rows, (;
                         tokenizer,
                         dataset,
+                        finetuned,
                         split,
                         ngram,
                         vocab_size=data["tokenizer"][:vocab_size],

From 7876c6057455f76ee623e88f659a97b75e38cf02 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 7 May 2025 10:10:56 -0400
Subject: [PATCH 10/54] add script for compressing tok stats

---
 opt/TokenizerStats/submit_archive.sh | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 opt/TokenizerStats/submit_archive.sh

diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
new file mode 100644
index 00000000..844b9ab3
--- /dev/null
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+#SBATCH -p venkvis-cpu
+#SBATCH --cpus-per-task 8
+#SBATCH --mem-per-cpu 1800M
+#SBATCH --time=1:0:00
+set -x
+my_job_header
+
+# Add tokenizers.json
+cp tokenizers.json stats/
+
+# Compress stats
+export XZ_OPT="-e9 --memlimit=4000000000 --threads=${SLURM_CPUS_PER_TASK}"
+tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
+    --xz \
+    --exclude="*.slurm" \
+    --exclude="*.tmp" \
+    --exclude=".unmerged/" \
+    -C stats \
+    .

From dc30fcfbfe70d736c565cdc0d9dafaaa169bdf59 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 7 May 2025 20:36:12 -0400
Subject: [PATCH 11/54] feat: add fixed effects models comparing fits

---
 opt/TokenizerStats/plots/plots.jl             |  11 +-
 .../plots/src/SmirkPaperPlots.jl              |   1 +
 .../plots/src/figures/info_loss.jl            |   7 +-
 opt/TokenizerStats/plots/src/prognostics.jl   |  55 +++++++
 .../plots/src/tabulate_results.jl             | 134 +++---------------
 .../plots/src/tabulate_tokenizer.jl           |   3 +-
 opt/TokenizerStats/submit_archive.sh          |   7 +-
 opt/TokenizerStats/submit_jobs.py             |   6 +-
 8 files changed, 96 insertions(+), 128 deletions(-)
 create mode 100644 opt/TokenizerStats/plots/src/prognostics.jl

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 13f262c5..b8467695 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -14,6 +14,7 @@ stats_dir = joinpath(pkgdir(TokenizerStats), "stats")
 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)
 
 # Tokenizer Fertility by Dataset
 function fertility_summary(token_usage)
@@ -90,10 +91,14 @@ norm_entropy_summary(token_usage) |> display
 # Transformer Models
 dfp, dff, dft = SmirkPaperPlots.transformer_models(stats_dir)
 
-# Fixed Effects models for results
-fe_models = SmirkPaperPlots.ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
-JLD2.jldsave("fe_models.jld2"; fe_models)
+# 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(stats_dir, loss_stats, dfp, dff, dft)
+prog_models = SmirkPaperPlots.ngram_prognostic_fits(df_prog)
+JLD2.jldsave(joinpath("stats", "quality_models.jld2"); fe_models, df_predict, prog_models)
 
 # Tokenizer Summary
 df_tok = SmirkPaperPlots.tokenizer_summary(stats_dir; k=5)
diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
index e3073898..e549cbc7 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -29,6 +29,7 @@ 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")
diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index 3a253a1a..df920b20 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -221,13 +221,8 @@ 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)
diff --git a/opt/TokenizerStats/plots/src/prognostics.jl b/opt/TokenizerStats/plots/src/prognostics.jl
new file mode 100644
index 00000000..752b31d1
--- /dev/null
+++ b/opt/TokenizerStats/plots/src/prognostics.jl
@@ -0,0 +1,55 @@
+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)
+        return (; loss_and_info_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_only => ByRow(nobs_nonwts) => :nobs,
+        :loss_only => ByRow(StatsBase.variation ∘ response) => :response_cv,
+    )
+    return df
+end
+
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index 29cf7f68..6469589f 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -282,6 +282,7 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
     # 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;
@@ -289,25 +290,24 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
         wts=aweights(df_ng_pt.wts),
     )
     df_ng_pt.ng_est_loss = predict(model)
+
+    # Dataframe for predictions
     df_predict = leftjoin(
-        select(df_ng_pt, :tokenizer, :val_loss => :ng_loss, :ng_est_loss),
-        select(df_p, :tokenizer, :val_loss => :fm_loss);
-        on=:tokenizer,
+        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],
     )
-    dropmissing!(df_predict)
-    rho = corspearman(df_predict.ng_loss, df_predict.fm_loss)
-    rho_est = corspearman(df_predict.ng_est_loss, df_predict.fm_loss)
-
+    df_predict.fm_loss_std .= missing
+    df_predict.finetuned .= false
+    df_predict.dataset .= "realspace"
 
-    push!(models, (; model, dataset="REALSpace", ngram=true, metric="CE", rho, rho_est))
+    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"))
+    push!(models, (; model, dataset="realspace", ngram=false, metric="CE"))
 
-    subset!(df_f, :dataset => ByRow(!=("freesolv"))) # fit issue
     for dataset in unique(df_f.dataset)
         df_f_fm = subset(df_f, :dataset => ByRow(==(dataset)))
         task = first(df_f_fm.task)
-        link = task == "regression" ? LogLink() : LogitLink()
 
         # Foundation Model
         df_f_fm.val_loss = df_f_fm.mean
@@ -319,8 +319,8 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
         )
         push!(models, (; model, dataset, ngram=false, metric=first(df_f_fm.metric)))
 
+        # NGram
         for finetuned in [true, false]
-            # NGram
             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)
@@ -330,26 +330,22 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
                 contrasts,
                 wts=aweights(df_f_ng.wts),
             )
+            push!(models, (; model, dataset, ngram=true, finetuned, metric="CE"))
 
-            # Spearman's for n-gram fit vs. fm results
-            df_f_ng.ng_est_loss = predict(model)
-            df_predict = leftjoin(
-                select(df_f_ng, :tokenizer, :val_loss => :ng_loss, :ng_est_loss),
-                select(df_f_fm, :tokenizer, :val_loss => :fm_loss);
-                on=:tokenizer,
+            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],
             )
-            dropmissing!(df_predict)
-            fm_loss = first(df_f_fm.metric) in ["auroc"] ? -df_predict.fm_loss : df_predict.fm_loss
-            rho = corspearman(df_predict.ng_loss, fm_loss)
-            rho_est = corspearman(df_predict.ng_est_loss, fm_loss)
-
-            push!(models, (; model, dataset, ngram=true, finetuned, metric="CE", rho, rho_est))
+            ds_predict.dataset .= dataset
+            ds_predict.finetuned .= finetuned
+            df_predict = vcat(df_predict, ds_predict)
         end
+
     end
 
     models = map(models) do m
-        m = haskey(m, :rho) ? m : (; m..., rho=missing, rho_est=missing)
-        m = haskey(m, :finetuned) ? m : (; m..., finetuned=missing)
+        m = haskey(m, :finetuned) ? m : (; m..., finetuned=false)
     end
 
     df_model = DataFrame(models)
@@ -357,92 +353,8 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
         :model => ByRow(m -> cor(response(m), predict(m))) => :r2,
         :model => ByRow(m -> corspearman(response(m), predict(m))) => :spearman,
         :model => ByRow(nobs_nonwts) => :nobs,
-        :model => ByRow(confint) => :confint,
-        :model => ByRow(coef) => :coef,
-    )
-    return df_model
-
-    tbl_datasets = ["REALSpace", "tmQM", "qm9", "bbbp", "hiv"]
-    models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
-    models_tbl.dataset .= categorical(models_tbl.dataset, levels=tbl_datasets, ordered=true)
-    sort!(models_tbl, [:dataset, :ngram])
-    transform!(models_tbl,
-        :metric => ByRow(m -> replace(m,
-            "auroc" => L"AUROC ($\uparrow$)",
-            "mae" => L"MAE ($\downarrow$)",
-            "CE" => L"CE ($\downarrow$)",
-        )) => :metric
-    )
-
-    return regtable(
-        models_tbl.model...;
-        groups=string.(models_tbl.dataset),
-        stat_below=false,
-        print_depvar=false,
-        number_regressions=false,
-        align=:r,
-        regression_statistics=[
-            nobs_nonwts => "N",
-            (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
-            (model -> cor(response(model), predict(model))) => L"R^2",],
-        extralines=[
-            ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
-            ["Metric:", models_tbl.metric...],
-            [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
-        ],
-        labels=Dict(
-            "tokenizer_class: spe" => "SPE/APE Tokenizers",
-            "tokenizer_class: bpe" => "BPE Tokenizers",
-            "tokenizer_class: smirk" => "Smirk",
-            "tokenizer_class: smirk-gpe" => "Smirk-GPE",
-            "tokenizer_class: character" => "Character-level",
-            "tokenizer_class: unigram" => "Unigram Tokenizers",
-            "encoding: selfies" => "SELFIES",
-            "encoding: smiles-canonical" => "Canonical SMILES",
-        ),
-        # render=LatexTable(),
-    )
-
-
-
-    # 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
-    )
-    @show tok_ngram = lm(
-        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
-        df; contrasts
-    )
-    LogLikelihoodRatioTest(tok, null) |> display
-    LogLikelihoodRatioTest(tok_ngram, 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"
     )
-
-    null = lm(@formula(ngram_token_loss ~ 1), best_models)
-    @show tok = lm(
-        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
-        best_models; contrasts
-    )
-    LogLikelihoodRatioTest(tok, null) |> display
-
-
-    # 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
-
-    return df
+    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 7604c201..8e3014cf 100644
--- a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
@@ -102,9 +102,10 @@ function model_loss_stats(stats_dir)
     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]
diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index 844b9ab3..420a471b 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -1,8 +1,8 @@
 #!/bin/bash
 #SBATCH -p venkvis-cpu
-#SBATCH --cpus-per-task 8
+#SBATCH --cpus-per-task 3
 #SBATCH --mem-per-cpu 1800M
-#SBATCH --time=1:0:00
+#SBATCH --time=8:0:00
 set -x
 my_job_header
 
@@ -10,11 +10,10 @@ my_job_header
 cp tokenizers.json stats/
 
 # Compress stats
-export XZ_OPT="-e9 --memlimit=4000000000 --threads=${SLURM_CPUS_PER_TASK}"
+export XZ_OPT="-9 -v --extreme --memlimit=4000000000 --threads=${SLURM_CPUS_PER_TASK}"
 tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
     --xz \
     --exclude="*.slurm" \
     --exclude="*.tmp" \
-    --exclude=".unmerged/" \
     -C stats \
     .
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 185a4c84..46ee7bf2 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -231,9 +231,9 @@ def show(self):
 
 REF_INFO_LOSS = [
     "character",
-    # "meta-llama/Meta-Llama-3.1-8B",
-    # "google/gemma-7b",
-    # "Xenova/gpt-4o",
+    "meta-llama/Meta-Llama-3.1-8B",
+    "google/gemma-7b",
+    "Xenova/gpt-4o",
 ]
 
 

From 8f2a1e100607538eea01d9fc642297b95d5e7908 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 7 May 2025 20:37:29 -0400
Subject: [PATCH 12/54] add fixed effects figure

---
 opt/TokenizerStats/activate                   |   2 +-
 opt/TokenizerStats/fe_models.jld2             | Bin 0 -> 884366 bytes
 opt/TokenizerStats/plots/Project.toml         |   1 +
 opt/TokenizerStats/plots/plots.jl             |   3 +
 .../plots/src/SmirkPaperPlots.jl              |   2 +
 opt/TokenizerStats/plots/src/coeff_plot.jl    | 122 +++++++++++++
 .../plots/src/figures/fe_models.jl            | 156 ++++++++++++++++
 opt/TokenizerStats/plots/src/figures/ngram.jl |   1 -
 opt/TokenizerStats/plots/src/plot_utils.jl    |   9 +
 .../plots/src/tabulate_results.jl             | 170 ++++++++++++++++++
 10 files changed, 464 insertions(+), 2 deletions(-)
 create mode 100644 opt/TokenizerStats/fe_models.jld2
 create mode 100644 opt/TokenizerStats/plots/src/coeff_plot.jl
 create mode 100644 opt/TokenizerStats/plots/src/figures/fe_models.jl

diff --git a/opt/TokenizerStats/activate b/opt/TokenizerStats/activate
index 0bcdd953..6c072d92 100755
--- a/opt/TokenizerStats/activate
+++ b/opt/TokenizerStats/activate
@@ -8,6 +8,7 @@ if command -v module > /dev/null; then
     module --ignore_cache load gcc python/3.11.5 openmpi/4.1.6
     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
@@ -18,6 +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="$DIR/.venv/bin/python"
diff --git a/opt/TokenizerStats/fe_models.jld2 b/opt/TokenizerStats/fe_models.jld2
new file mode 100644
index 0000000000000000000000000000000000000000..d5fc8e24facf6436ff9a11471976e3ad487f0c2b
GIT binary patch
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zPZIrxyE{KmYkqiPK4IJx{bSK@iT;V`w?)4r`d!gK75y{OKNtNA(Z3Y^E789e{TtEm
ziT<tV_eK9s^zTLgLG&L*|4H<pMSmdrFQWe{`fsBDF8Uv$n|}+)JaIku5j~aYsYOpC
zdRo!biJo5c45DWgJ(K8}Mb9F7R?)MGo?Ucb(Q}BNQ}kS-=N3JW=y^rYCwhL-3y5A&
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literal 0
HcmV?d00001

diff --git a/opt/TokenizerStats/plots/Project.toml b/opt/TokenizerStats/plots/Project.toml
index 34b502f2..4274e2e0 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"
diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index b8467695..6b646c3c 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -116,6 +116,9 @@ with_theme(SmirkPaperPlots.theme()) do
     df = SmirkPaperPlots.ngram_vs_transformer_fits(stats_dir, loss_stats, dfp)
     savefig("ngram_vs_transformer", SmirkPaperPlots.figure_ngram_vs_transformer(stats_dir, df))
 
+    savefig("prognostic", SmirkPaperPlots.figure_fe_models(fe_models, prog_models))
+    savefig("prognostic_abs", SmirkPaperPlots.figure_fe_models(fe_models, prog_models; relative=false))
+
     # Transformer Model Summary
     f, df = SmirkPaperPlots.figure_tf_finetune(stats_dir, dff, dft)
     savefig("tf_finetune", f)
diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
index e549cbc7..93cf21e3 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -33,12 +33,14 @@ 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")
 
 
 const CLASS_MARKER = Dict(
diff --git a/opt/TokenizerStats/plots/src/coeff_plot.jl b/opt/TokenizerStats/plots/src/coeff_plot.jl
new file mode 100644
index 00000000..51f2814c
--- /dev/null
+++ b/opt/TokenizerStats/plots/src/coeff_plot.jl
@@ -0,0 +1,122 @@
+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 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
+
+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..5550a3c1
--- /dev/null
+++ b/opt/TokenizerStats/plots/src/figures/fe_models.jl
@@ -0,0 +1,156 @@
+function figure_fe_models(models, prog_models; relative=true, colormap=:tab10)
+    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",
+    )
+    @show 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,
+    )
+    n_levels = length(unique(models.label))
+    colorrange = (1, n_levels)
+    gl_coef = GridLayout(f[1, 1])
+    gl_parity = GridLayout(f[2, 1])
+
+    for (adx, ds) in enumerate(keys(datasets))
+        plt_ds = datasets[ds]
+        ds_models = subset(models, :dataset => ByRow(==(ds)))
+        metric = uppercase(ds_metric[ds])
+        ci = hcat(map(m -> coefint(m, collect(keys(coefnames))), ds_models.model)...)
+        yticklabels = collect(values(coefnames))
+        baseline = ci[1]
+        if relative
+            ci = ci ./ ci[[1], :]
+        end
+
+        # Drop intercept
+        ci = ci[2:end, :]
+        yticklabels = yticklabels[2:end]
+
+        # # Flip so positive is better
+        # ci_sign = map(ds_models.metric) do metric
+        #     metric in ["auroc"] ? 1 : -1
+        # end
+        # ci = ci .* ci_sign'
+
+        # Plot Effect sizes
+        lb, ub = round.(baseline.credible_interval; sigdigits=3)
+        if lb != ub
+            title = "$plt_ds\n$metric: $lb - $ub"
+        else
+            title = "$plt_ds\n$metric: $lb"
+        end
+        ax = Axis(gl_coef[1, adx];
+            title,
+            yticks=(eachindex(yticklabels), yticklabels),
+            yticklabelsvisible = adx == 1,
+            yticksvisible=adx == 1,
+            xlabel="Relative Effect Size",
+            xticks=WilkinsonTicks(3),
+            xminorticksvisible=true,
+            xtickformat="{:.0%}",
+        )
+        val = vec(repeat(1:size(ci, 1), 1, size(ci, 2)))
+        dodge = levelcode.(ds_models.label)
+        dodge = vec(repeat(dodge', size(ci, 1), 1))
+        ci = vec(ci)
+        val = val[(!ismissing).(ci)]
+        dodge = dodge[(!ismissing).(ci)]
+        ci = ci[(!ismissing).(ci)]
+        effectbars!(ax, val, ci; dodge, color=dodge, colormap, colorrange)
+
+        if adx == 1
+            h = map(enumerate(levels(models.label))) do (color, label)
+                PolyElement(; label, color, colormap, colorrange)
+            end
+            Legend(gl_coef[1, 1], h, labels(h);
+                tellheight=false,
+                tellwidth=false,
+                valign=:bottom,
+                halign=:right,
+                margin=(2, 2, 2, 2),
+                alignmode=Outside(),
+                labelsize=6pt,
+                patchsize=(6pt, 6pt),
+            )
+        end
+
+        # Plot Predictions
+        if adx != 1
+            qm = subset(prog_models,
+                :dataset => ByRow(==(ds)),
+                :finetuned => ByRow(==(true)),
+            ) |> only
+            qm = qm.loss_and_info_and_ft
+            rho = round(corspearman(predict(qm), response(qm)); sigdigits=3)
+            qm_r2 = round(r2(qm); sigdigits=3)
+            ax = Axis(gl_parity[1, adx-1];
+                title = L"%$plt_ds, $R^2: %$qm_r2$ $\rho: %$rho$",
+                ylabel="Transformer - $metric",
+                xlabel="N-Gram Based Estimate $metric",
+            )
+            ablines!(ax, 0, 1; color=:black, linestyle=:dash, label="Parity")
+            scatter!(ax, predict(qm), response(qm))
+        end
+
+    end
+
+    # Add Labels
+    label_kwargs = (;
+        fontsize=12pt, font=:bold,
+        halign=:right,
+        tellheight=false,
+    )
+    Label(f[1, 1, TopLeft()], "A)"; padding=(0, 15, -5, 0), label_kwargs...)
+    Label(f[2, 1, TopLeft()], "B)"; padding=(0, 15, -5, 0), label_kwargs...)
+    # Label(f[1, 2, TopLeft()], "C)"; padding=(0, 5, -5, 0), label_kwargs...)
+
+    # Label(f[2, :], "Effect Size";
+    #     tellwidth=false, tellheight=true,
+    #     font=:bold,
+    #     fontsize=10pt,
+    # )
+
+    # h = map(enumerate(levels(models.label))) do (color, label)
+    #     PolyElement(; label, color, colormap, colorrange)
+    # end
+    # Legend(f[end, 1], h, labels(h);
+    #     # orientation=:horizontal,
+    #     tellheight=false,
+    #     tellwidth=false,
+    # )
+
+    return f
+end
diff --git a/opt/TokenizerStats/plots/src/figures/ngram.jl b/opt/TokenizerStats/plots/src/figures/ngram.jl
index 5c390a74..6ab317ea 100644
--- a/opt/TokenizerStats/plots/src/figures/ngram.jl
+++ b/opt/TokenizerStats/plots/src/figures/ngram.jl
@@ -351,7 +351,6 @@ 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/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index 5da3131b..73809004 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -61,6 +61,14 @@ function theme()
         ErrrorBar=(;
             whiskerwidth=2,
             linewidth=0.5,
+        ),
+        EffectBars=(;
+            linewidth=1pt,
+            marker=:circle,
+            markersize=3pt,
+            colormap=[:red, :blue],
+            whiskerwidth=8pt,
+            noeffect_linewidth=1pt,
         )
     )
 end
@@ -79,6 +87,7 @@ 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)
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index 6469589f..a378ed2b 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -354,7 +354,177 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
         :model => ByRow(m -> corspearman(response(m), predict(m))) => :spearman,
         :model => ByRow(nobs_nonwts) => :nobs,
     )
+<<<<<<< Updated upstream
     return df_model, df_predict
+||||||| Stash base
+    return df_model
+
+    tbl_datasets = ["REALSpace", "tmQM", "qm9", "bbbp", "hiv"]
+    models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
+    models_tbl.dataset .= categorical(models_tbl.dataset, levels=tbl_datasets, ordered=true)
+    sort!(models_tbl, [:dataset, :ngram])
+    transform!(models_tbl,
+        :metric => ByRow(m -> replace(m,
+            "auroc" => L"AUROC ($\uparrow$)",
+            "mae" => L"MAE ($\downarrow$)",
+            "CE" => L"CE ($\downarrow$)",
+        )) => :metric
+    )
+
+    return regtable(
+        models_tbl.model...;
+        groups=string.(models_tbl.dataset),
+        stat_below=false,
+        print_depvar=false,
+        number_regressions=false,
+        align=:r,
+        regression_statistics=[
+            nobs_nonwts => "N",
+            (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
+            (model -> cor(response(model), predict(model))) => L"R^2",],
+        extralines=[
+            ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
+            ["Metric:", models_tbl.metric...],
+            [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
+        ],
+        labels=Dict(
+            "tokenizer_class: spe" => "SPE/APE Tokenizers",
+            "tokenizer_class: bpe" => "BPE Tokenizers",
+            "tokenizer_class: smirk" => "Smirk",
+            "tokenizer_class: smirk-gpe" => "Smirk-GPE",
+            "tokenizer_class: character" => "Character-level",
+            "tokenizer_class: unigram" => "Unigram Tokenizers",
+            "encoding: selfies" => "SELFIES",
+            "encoding: smiles-canonical" => "Canonical SMILES",
+        ),
+        # render=LatexTable(),
+    )
+
+
+
+    # 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
+    )
+    @show tok_ngram = lm(
+        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
+        df; contrasts
+    )
+    LogLikelihoodRatioTest(tok, null) |> display
+    LogLikelihoodRatioTest(tok_ngram, 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"
+    )
+
+    null = lm(@formula(ngram_token_loss ~ 1), best_models)
+    @show tok = lm(
+        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
+        best_models; contrasts
+    )
+    LogLikelihoodRatioTest(tok, null) |> display
+
+
+    # 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
+
+    return df
+=======
+    return df_model
+
+    tbl_datasets = ["realspace", "tmQM", "qm9", "bbbp", "hiv"]
+    models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
+    models_tbl.dataset .= categorical(models_tbl.dataset, levels=tbl_datasets, ordered=true)
+    sort!(models_tbl, [:dataset, :ngram])
+    transform!(models_tbl,
+        :metric => ByRow(m -> replace(m,
+            "auroc" => L"AUROC ($\uparrow$)",
+            "mae" => L"MAE ($\downarrow$)",
+            "CE" => L"CE ($\downarrow$)",
+        )) => :metric
+    )
+
+    return regtable(
+        models_tbl.model...;
+        groups=string.(models_tbl.dataset),
+        stat_below=false,
+        print_depvar=false,
+        number_regressions=false,
+        align=:r,
+        regression_statistics=[
+            nobs_nonwts => "N",
+            (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
+            (model -> cor(response(model), predict(model))) => L"R^2",],
+        extralines=[
+            ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
+            ["Metric:", models_tbl.metric...],
+            [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
+        ],
+        labels=Dict(
+            "tokenizer_class: spe" => "SPE/APE Tokenizers",
+            "tokenizer_class: bpe" => "BPE Tokenizers",
+            "tokenizer_class: smirk" => "Smirk",
+            "tokenizer_class: smirk-gpe" => "Smirk-GPE",
+            "tokenizer_class: character" => "Character-level",
+            "tokenizer_class: unigram" => "Unigram Tokenizers",
+            "encoding: selfies" => "SELFIES",
+            "encoding: smiles-canonical" => "Canonical SMILES",
+        ),
+        # render=LatexTable(),
+    )
+
+
+
+    # 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
+    )
+    @show tok_ngram = lm(
+        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
+        df; contrasts
+    )
+    LogLikelihoodRatioTest(tok, null) |> display
+    LogLikelihoodRatioTest(tok_ngram, 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"
+    )
+
+    null = lm(@formula(ngram_token_loss ~ 1), best_models)
+    @show tok = lm(
+        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
+        best_models; contrasts
+    )
+    LogLikelihoodRatioTest(tok, null) |> display
+
+
+    # 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
+
+    return df
+>>>>>>> Stashed changes
 end
 
 function tmqm_finetune(stats_dir, dff, dft)

From 724887515c10a5fe42cf7b47866d80399d65e736 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 8 May 2025 10:41:31 -0400
Subject: [PATCH 13/54] refactor fe figure, add more scale options

---
 opt/TokenizerStats/fe_models.jld2             | Bin 884366 -> 0 bytes
 opt/TokenizerStats/plots/plots.jl             |   3 +-
 opt/TokenizerStats/plots/src/coeff_plot.jl    |   1 +
 .../plots/src/figures/fe_models.jl            | 156 ++++++++++------
 .../plots/src/tabulate_results.jl             | 170 ------------------
 5 files changed, 105 insertions(+), 225 deletions(-)
 delete mode 100644 opt/TokenizerStats/fe_models.jld2

diff --git a/opt/TokenizerStats/fe_models.jld2 b/opt/TokenizerStats/fe_models.jld2
deleted file mode 100644
index d5fc8e24facf6436ff9a11471976e3ad487f0c2b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 6b646c3c..624df7ee 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -117,7 +117,8 @@ with_theme(SmirkPaperPlots.theme()) do
     savefig("ngram_vs_transformer", SmirkPaperPlots.figure_ngram_vs_transformer(stats_dir, df))
 
     savefig("prognostic", SmirkPaperPlots.figure_fe_models(fe_models, prog_models))
-    savefig("prognostic_abs", SmirkPaperPlots.figure_fe_models(fe_models, prog_models; relative=false))
+    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)
diff --git a/opt/TokenizerStats/plots/src/coeff_plot.jl b/opt/TokenizerStats/plots/src/coeff_plot.jl
index 51f2814c..716a7479 100644
--- a/opt/TokenizerStats/plots/src/coeff_plot.jl
+++ b/opt/TokenizerStats/plots/src/coeff_plot.jl
@@ -19,6 +19,7 @@ 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)
diff --git a/opt/TokenizerStats/plots/src/figures/fe_models.jl b/opt/TokenizerStats/plots/src/figures/fe_models.jl
index 5550a3c1..80f042f1 100644
--- a/opt/TokenizerStats/plots/src/figures/fe_models.jl
+++ b/opt/TokenizerStats/plots/src/figures/fe_models.jl
@@ -1,4 +1,4 @@
-function figure_fe_models(models, prog_models; relative=true, colormap=:tab10)
+function figure_fe_models(models, prog_models; scale=:relative, colormap=:tab10, positive_is_better=true)
     f = Figure(size=(7inch, 2.5inch))
 
     datasets = OrderedDict(
@@ -21,7 +21,7 @@ function figure_fe_models(models, prog_models; relative=true, colormap=:tab10)
         "tokenizer_class: spe" => "SPE/APE",
         "encoding: selfies" => "SELFIES",
     )
-    @show ds_metric = Dict(eachrow(subset(models, :ngram => ByRow(!))[!, [:dataset, :metric]]))
+    ds_metric = Dict(eachrow(subset(models, :ngram => ByRow(!))[!, [:dataset, :metric]]))
 
     function label_model(ngram, finetuned)
         if !ngram
@@ -39,57 +39,57 @@ function figure_fe_models(models, prog_models; relative=true, colormap=:tab10)
         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])
-    gl_parity = GridLayout(f[2, 1])
+    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])
-        ci = hcat(map(m -> coefint(m, collect(keys(coefnames))), ds_models.model)...)
-        yticklabels = collect(values(coefnames))
-        baseline = ci[1]
-        if relative
-            ci = ci ./ ci[[1], :]
+        better_sign =  map(ds_models.metric) do metric
+            metric in ["auroc"] ? 1 : -1
         end
 
-        # Drop intercept
-        ci = ci[2:end, :]
-        yticklabels = yticklabels[2:end]
-
-        # # Flip so positive is better
-        # ci_sign = map(ds_models.metric) do metric
-        #     metric in ["auroc"] ? 1 : -1
-        # end
-        # ci = ci .* ci_sign'
+        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.(baseline.credible_interval; sigdigits=3)
+        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=(eachindex(yticklabels), yticklabels),
+            yticks=yticks,
             yticklabelsvisible = adx == 1,
             yticksvisible=adx == 1,
-            xlabel="Relative Effect Size",
+            xlabel=kwargs.effectlabel,
             xticks=WilkinsonTicks(3),
             xminorticksvisible=true,
-            xtickformat="{:.0%}",
+            xtickformat=kwargs.effectformat,
         )
-        val = vec(repeat(1:size(ci, 1), 1, size(ci, 2)))
-        dodge = levelcode.(ds_models.label)
-        dodge = vec(repeat(dodge', size(ci, 1), 1))
-        ci = vec(ci)
-        val = val[(!ismissing).(ci)]
-        dodge = dodge[(!ismissing).(ci)]
-        ci = ci[(!ismissing).(ci)]
-        effectbars!(ax, val, ci; dodge, color=dodge, colormap, colorrange)
+        effectbars!(ax, val, ci; dodge=kwargs.dodge, color=kwargs.dodge, colormap, colorrange)
 
         if adx == 1
             h = map(enumerate(levels(models.label))) do (color, label)
@@ -99,7 +99,7 @@ function figure_fe_models(models, prog_models; relative=true, colormap=:tab10)
                 tellheight=false,
                 tellwidth=false,
                 valign=:bottom,
-                halign=:right,
+                halign=positive_is_better ? :left : :right,
                 margin=(2, 2, 2, 2),
                 alignmode=Outside(),
                 labelsize=6pt,
@@ -124,33 +124,81 @@ function figure_fe_models(models, prog_models; relative=true, colormap=:tab10)
             ablines!(ax, 0, 1; color=:black, linestyle=:dash, label="Parity")
             scatter!(ax, predict(qm), response(qm))
         end
-
     end
 
-    # Add Labels
-    label_kwargs = (;
-        fontsize=12pt, font=:bold,
-        halign=:right,
-        tellheight=false,
-    )
-    Label(f[1, 1, TopLeft()], "A)"; padding=(0, 15, -5, 0), label_kwargs...)
-    Label(f[2, 1, TopLeft()], "B)"; padding=(0, 15, -5, 0), label_kwargs...)
-    # Label(f[1, 2, TopLeft()], "C)"; padding=(0, 5, -5, 0), label_kwargs...)
-
-    # Label(f[2, :], "Effect Size";
-    #     tellwidth=false, tellheight=true,
-    #     font=:bold,
-    #     fontsize=10pt,
+    # # 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)
     # )
-
-    # h = map(enumerate(levels(models.label))) do (color, label)
-    #     PolyElement(; label, color, colormap, colorrange)
-    # end
-    # Legend(f[end, 1], h, labels(h);
-    #     # orientation=:horizontal,
-    #     tellheight=false,
-    #     tellwidth=false,
+    # 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 $scale")
+    end
+
+    # Drop intercept
+    ci = ci[2:end, :]
+    names = names[2:end]
+
+    # Flip so positive is better
+    if !isnothing(better_sign)
+        ci = ci .* vec(better_sign)'
+        effectlabel = "Pos. " * effectlabel
+    end
+
+    # Pack results
+    val = vec(repeat(1:size(ci, 1), 1, size(ci, 2)))
+    if isnothing(dodge)
+        dodge = 1:size(ci, 2)
+    end
+    dodge = vec(repeat(dodge', size(ci, 1), 1))
+    ci = vec(ci)
+    val = val[(!ismissing).(ci)]
+    dodge = dodge[(!ismissing).(ci)]
+    ci = ci[(!ismissing).(ci)]
+
+    kwargs = (;
+        dodge,
+        effectlabel,
+        effectformat,
+        yticks = (1:length(names), names),
+        baseline,
+    )
+    return val, ci, kwargs
+end
+
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index a378ed2b..6469589f 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -354,177 +354,7 @@ function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
         :model => ByRow(m -> corspearman(response(m), predict(m))) => :spearman,
         :model => ByRow(nobs_nonwts) => :nobs,
     )
-<<<<<<< Updated upstream
     return df_model, df_predict
-||||||| Stash base
-    return df_model
-
-    tbl_datasets = ["REALSpace", "tmQM", "qm9", "bbbp", "hiv"]
-    models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
-    models_tbl.dataset .= categorical(models_tbl.dataset, levels=tbl_datasets, ordered=true)
-    sort!(models_tbl, [:dataset, :ngram])
-    transform!(models_tbl,
-        :metric => ByRow(m -> replace(m,
-            "auroc" => L"AUROC ($\uparrow$)",
-            "mae" => L"MAE ($\downarrow$)",
-            "CE" => L"CE ($\downarrow$)",
-        )) => :metric
-    )
-
-    return regtable(
-        models_tbl.model...;
-        groups=string.(models_tbl.dataset),
-        stat_below=false,
-        print_depvar=false,
-        number_regressions=false,
-        align=:r,
-        regression_statistics=[
-            nobs_nonwts => "N",
-            (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
-            (model -> cor(response(model), predict(model))) => L"R^2",],
-        extralines=[
-            ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
-            ["Metric:", models_tbl.metric...],
-            [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
-        ],
-        labels=Dict(
-            "tokenizer_class: spe" => "SPE/APE Tokenizers",
-            "tokenizer_class: bpe" => "BPE Tokenizers",
-            "tokenizer_class: smirk" => "Smirk",
-            "tokenizer_class: smirk-gpe" => "Smirk-GPE",
-            "tokenizer_class: character" => "Character-level",
-            "tokenizer_class: unigram" => "Unigram Tokenizers",
-            "encoding: selfies" => "SELFIES",
-            "encoding: smiles-canonical" => "Canonical SMILES",
-        ),
-        # render=LatexTable(),
-    )
-
-
-
-    # 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
-    )
-    @show tok_ngram = lm(
-        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
-        df; contrasts
-    )
-    LogLikelihoodRatioTest(tok, null) |> display
-    LogLikelihoodRatioTest(tok_ngram, 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"
-    )
-
-    null = lm(@formula(ngram_token_loss ~ 1), best_models)
-    @show tok = lm(
-        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
-        best_models; contrasts
-    )
-    LogLikelihoodRatioTest(tok, null) |> display
-
-
-    # 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
-
-    return df
-=======
-    return df_model
-
-    tbl_datasets = ["realspace", "tmQM", "qm9", "bbbp", "hiv"]
-    models_tbl = subset(models, :dataset => ByRow(in(tbl_datasets)))
-    models_tbl.dataset .= categorical(models_tbl.dataset, levels=tbl_datasets, ordered=true)
-    sort!(models_tbl, [:dataset, :ngram])
-    transform!(models_tbl,
-        :metric => ByRow(m -> replace(m,
-            "auroc" => L"AUROC ($\uparrow$)",
-            "mae" => L"MAE ($\downarrow$)",
-            "CE" => L"CE ($\downarrow$)",
-        )) => :metric
-    )
-
-    return regtable(
-        models_tbl.model...;
-        groups=string.(models_tbl.dataset),
-        stat_below=false,
-        print_depvar=false,
-        number_regressions=false,
-        align=:r,
-        regression_statistics=[
-            nobs_nonwts => "N",
-            (model -> corspearman(response(model), predict(model))) => L"Spearman's $\rho$",
-            (model -> cor(response(model), predict(model))) => L"R^2",],
-        extralines=[
-            ["N-Gram:", map(x -> x ? "Yes" : "No", models_tbl.ngram)...],
-            ["Metric:", models_tbl.metric...],
-            [L"Spearman's $\rho$, N-Gram vs. FM:", models_tbl.rho...],
-        ],
-        labels=Dict(
-            "tokenizer_class: spe" => "SPE/APE Tokenizers",
-            "tokenizer_class: bpe" => "BPE Tokenizers",
-            "tokenizer_class: smirk" => "Smirk",
-            "tokenizer_class: smirk-gpe" => "Smirk-GPE",
-            "tokenizer_class: character" => "Character-level",
-            "tokenizer_class: unigram" => "Unigram Tokenizers",
-            "encoding: selfies" => "SELFIES",
-            "encoding: smiles-canonical" => "Canonical SMILES",
-        ),
-        # render=LatexTable(),
-    )
-
-
-
-    # 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
-    )
-    @show tok_ngram = lm(
-        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
-        df; contrasts
-    )
-    LogLikelihoodRatioTest(tok, null) |> display
-    LogLikelihoodRatioTest(tok_ngram, 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"
-    )
-
-    null = lm(@formula(ngram_token_loss ~ 1), best_models)
-    @show tok = lm(
-        @formula(ngram_token_loss ~ 1 + tokenizer_class + encoding),
-        best_models; contrasts
-    )
-    LogLikelihoodRatioTest(tok, null) |> display
-
-
-    # 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
-
-    return df
->>>>>>> Stashed changes
 end
 
 function tmqm_finetune(stats_dir, dff, dft)

From d9177eab7ce115080d24502cf14b44005b97c32a Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 8 May 2025 15:52:46 -0400
Subject: [PATCH 14/54] add precompile flag to submit jobs

---
 opt/TokenizerStats/submit_jobs.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 46ee7bf2..6992f41d 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -155,7 +155,7 @@ def active_jobs(self, file: Path) -> Optional[int]:
         job_file.unlink()
         return None
 
-    def run(self, dry_run=False, rate_limit=100):
+    def run(self, dry_run=False, rate_limit=100, preflight=list[Process]):
         outputs = {
             process.output: process
             for process in self.processes
@@ -166,6 +166,11 @@ 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:
@@ -183,6 +188,8 @@ def run(self, dry_run=False, rate_limit=100):
             # Get the process that writes to this file
             process = outputs[file]
             deps = self._get_deps(process, jobs)
+            deps.extend(preflight_ids)
+            deps = list(set(deps))
 
             # Launch the process
             job_id = process.launch(deps, dry_run)
@@ -361,6 +368,7 @@ def set_logging_level(verbosity):
 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"
@@ -447,4 +455,8 @@ def set_logging_level(verbosity):
                 )
 
     wk.show()
-    wk.run(dry_run=args.dry_run)
+    preflight = []
+    if args.precompile:
+        preflight.append(Process(["submit_precompile.sh"], []))
+
+    wk.run(dry_run=args.dry_run, preflight=preflight)

From 6d5bd934ffc008c8632ef6bd62342c2d47c1d96d Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 8 May 2025 17:07:42 -0400
Subject: [PATCH 15/54] update figure

---
 opt/TokenizerStats/plots/plots.jl             | 38 +++++++++++--
 .../plots/src/SmirkPaperPlots.jl              |  4 +-
 .../plots/src/figures/info_loss.jl            | 56 +++++++++++--------
 opt/TokenizerStats/plots/src/figures/ngram.jl | 17 ++++--
 opt/TokenizerStats/plots/src/plot_utils.jl    |  4 +-
 .../plots/src/tabulate_results.jl             |  5 +-
 .../plots/src/tabulate_tokenizer.jl           | 24 +++++---
 opt/TokenizerStats/submit_precompile.sh       |  1 +
 8 files changed, 100 insertions(+), 49 deletions(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 624df7ee..cc788aeb 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -16,6 +16,30 @@ info_loss = SmirkPaperPlots.info_loss_stats(stats_dir)
 token_usage = SmirkPaperPlots.usage_stats(stats_dir)
 tok_info = SmirkPaperPlots.tokenizers_info(stats_dir)
 
+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)
@@ -90,15 +114,17 @@ 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(stats_dir, loss_stats, dfp, dff, dft)
+fe_models, df_predict = SmirkPaperPlots.ngram_vs_transformer_fits(df_ng, df_p, df_f)
 prog_models = SmirkPaperPlots.ngram_prognostic_fits(df_prog)
 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)
@@ -110,12 +136,12 @@ 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))
 
     # Transformers vs. N-Grams
-    df = SmirkPaperPlots.ngram_vs_transformer_fits(stats_dir, loss_stats, dfp)
-    savefig("ngram_vs_transformer", SmirkPaperPlots.figure_ngram_vs_transformer(stats_dir, df))
-
     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))
@@ -123,11 +149,11 @@ with_theme(SmirkPaperPlots.theme()) do
     # 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)
+    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(stats_dir, loss_stats, info_loss, df))
+    savefig("kl_v_info_loss", SmirkPaperPlots.figure_kl_v_info_loss(tok_info, loss_stats, info_loss, df_f))
 
     # Example n-gram predictions
     compunds = [
diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
index 93cf21e3..50e284bd 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -2,6 +2,7 @@ module SmirkPaperPlots
 
 using Makie
 using OnlineStats
+using StatsBase
 using OnlineStats: Moments
 using GLM
 using DataFrames
@@ -9,9 +10,8 @@ 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, aweights, corspearman, cor
 using FreeTypeAbstraction: FreeTypeAbstraction, newface, FTFont
+using PythonCall: Py, pyconvert
 using JLD2: jldopen
 using JSON: JSON
 using CategoricalArrays: categorical, levelcode
diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index df920b20..533780ff 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -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)
@@ -249,14 +248,13 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
             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])
@@ -291,12 +289,13 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
     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),
@@ -313,12 +312,10 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
         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")))
@@ -330,9 +327,12 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
     )
 
     # Check correlation
+    @show rho = corspearman(df.avg_info_loss, df.test_loss)
+    @info length(df.avg_info_loss) factorial(length(df.avg_info_loss))
+    @show rho_p_value = mean(HypothesisTests.permutations(df.avg_info_loss)) do x
+        rho <= corspearman(x, df.test_loss)
+    end
     @show t = HypothesisTests.CorrelationTest(df.avg_info_loss, df.test_loss)
-    @show pvalue(t)
-
 
     Label(f[1, 1, TopLeft()], "a)";
         font=:bold,
@@ -343,7 +343,7 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
     Label(f[1, 2, TopLeft()], "b)";
         font=:bold,
         halign=:right,
-        padding=(0, 25, -3, 0),
+        padding=(0, 10, -3, 0),
         tellwidth=false,
     )
 
@@ -354,26 +354,36 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
     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
diff --git a/opt/TokenizerStats/plots/src/figures/ngram.jl b/opt/TokenizerStats/plots/src/figures/ngram.jl
index 6ab317ea..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
 
diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index 73809004..a2e1ffc8 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -127,8 +127,8 @@ 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
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index 6469589f..0fb51625 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -270,10 +270,7 @@ end
 
 nobs_nonwts(model) = size(model.model.pp.X, 1)
 
-zscore(x) = (x .- mean(x)) ./ std(x)
-
-function ngram_vs_transformer_fits(stats_dir, loss_stats, dfp, dff, dft)
-    df_ng, df_p, df_f = df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft)
+function ngram_vs_transformer_fits(df_ng, df_p, df_f)
     contrasts = Dict(
         :tokenizer_class => EffectsCoding(; base="atomwise"),
         :encoding => EffectsCoding(; base="smiles"),
diff --git a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
index 8e3014cf..45ed2af9 100644
--- a/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_tokenizer.jl
@@ -7,6 +7,7 @@ function usage_stats(stats_dir)
             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
@@ -18,12 +19,22 @@ function usage_stats(stats_dir)
                 tokens_seen = sum(values(unigram))
                 nunique = CountMap(data[split]["fertility"], samples)
 
-                entropy = sum(values(unigram)) do c
-                    p = c / tokens_seen
+                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,
@@ -33,15 +44,14 @@ function usage_stats(stats_dir)
                     out_of_vocab=data[split]["out_of_vocab"],
                     fertility,
                     entropy,
+                    divergence,
+                    f95,
+                    f95_all,
+                    f95_smooth,
                     normalized_entropy,
                     nunique,
                     unk_count,
                     tokens_seen,
-                    avg_fertility=mean(fertility),
-                    std_fertility=std(fertility),
-                    max_fertility=maximum(keys(data[split]["fertility"])),
-                    avg_nunique=mean(nunique),
-                    std_nunique=std(nunique),
                 ))
             end
         end
diff --git a/opt/TokenizerStats/submit_precompile.sh b/opt/TokenizerStats/submit_precompile.sh
index 5db9a3a9..64f7ac77 100755
--- a/opt/TokenizerStats/submit_precompile.sh
+++ b/opt/TokenizerStats/submit_precompile.sh
@@ -18,3 +18,4 @@ 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.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

From 934043124a0ed46a6250508034fca9d30a2f3484 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 8 May 2025 17:08:21 -0400
Subject: [PATCH 16/54] update deps and add intrinsic plots

---
 opt/TokenizerStats/plots/Project.toml         |  6 +-
 opt/TokenizerStats/plots/plots.jl             |  1 +
 .../plots/src/SmirkPaperPlots.jl              |  1 +
 .../plots/src/figures/intrinsic.jl            | 99 +++++++++++++++++++
 opt/TokenizerStats/plots/src/plot_utils.jl    | 11 ++-
 5 files changed, 113 insertions(+), 5 deletions(-)
 create mode 100644 opt/TokenizerStats/plots/src/figures/intrinsic.jl

diff --git a/opt/TokenizerStats/plots/Project.toml b/opt/TokenizerStats/plots/Project.toml
index 4274e2e0..e80338d2 100644
--- a/opt/TokenizerStats/plots/Project.toml
+++ b/opt/TokenizerStats/plots/Project.toml
@@ -28,9 +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
index cc788aeb..bce6a26f 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -140,6 +140,7 @@ with_theme(SmirkPaperPlots.theme()) do
         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))
 
     # Transformers vs. N-Grams
     savefig("prognostic", SmirkPaperPlots.figure_fe_models(fe_models, prog_models))
diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
index 50e284bd..f747f6eb 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -41,6 +41,7 @@ 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(
diff --git a/opt/TokenizerStats/plots/src/figures/intrinsic.jl b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
new file mode 100644
index 00000000..2cad2a83
--- /dev/null
+++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
@@ -0,0 +1,99 @@
+function figure_intrinsic(df)
+    f = Figure(; size=(3.42inch, 1.5inch))
+    colgap!(f.layout, 3pt)
+    tokenizer_classes = OrderedDict(
+        "bpe, nlp" => "BPE, NLP",
+        "bpe" => "BPE, Chemistry",
+        "atomwise" => "Atom-wise",
+        "spe" => "SPE/APE",
+        "character" => "Character",
+        "unigram" => "Unigram",
+        "smirk-gpe" => "Smirk-GPE",
+        "smirk" => "Smirk",
+    )
+    df = subset(df, :tokenizer_domain => ByRow(in(["nlp", "chemistry"])))
+    df.tokenizer_class = map(df.tokenizer_domain, df.tokenizer_class) do domain, tokenizer_class
+        if domain == "chemistry"
+            return tokenizer_class
+        else
+            return "$tokenizer_class, $domain"
+        end
+    end
+    df.tokenizer_class = categorical(df.tokenizer_class, levels=(collect∘keys)(tokenizer_classes), ordered=true)
+
+    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]
+    ax_kwargs = Dict(
+        :fertility => (;
+            title="Fertility",
+            limits=((0, nothing), nothing),
+        ),
+        :divergence => (;
+            title="Imbalance",
+            limits=((0.4, nothing), nothing),
+            xtickformat="{:.0%}",
+        ),
+        :oov_rate => (;
+            title="UNK Freq.",
+            limits=((0, 1), nothing),
+            xtickformat="{:.0%}",
+            xscale=my_sqrt,
+        ),
+        :normalized_entropy => (;
+            title=L"\eta",
+            limits=((0, 1), nothing),
+            xticks=[0.25, 0.75],
+            xtickformat="{:.0%}",
+        ),
+    )
+    # ax_kwargs = Dict()
+
+    for (mdx, metric) in enumerate(metrics)
+        kwargs = get(ax_kwargs, metric, (;))
+        ax = Axis(f[1, mdx];
+            yticks=(1:length(tokenizer_classes), collect(values(tokenizer_classes))),
+            yticksvisible=false,
+            yticklabelsvisible=mdx == 1,
+            xticklabelrotation=-pi/4,
+            xminorticks=IntervalsBetween(4),
+            xminorticksvisible=true,
+            kwargs...
+        )
+        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),
+                show_outliers=false,
+                whiskerwidth=1pt,
+                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,
+    )
+    resize_to_layout!(f)
+
+    return f
+end
diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index a2e1ffc8..5fb9b045 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -22,6 +22,8 @@ function theme()
         Lines=(;
             cycle=Cycle([:color, :linestyle], covary=true),
         ),
+        markersize=4pt,
+        linewidth=1pt,
         Axis=(;
             spinewidth=0.5,
             ylabelpadding=3pt,
@@ -32,7 +34,7 @@ function theme()
             xtickwidth=0.5,
             xticksize=3,
             xminortickwidth=0.5,
-            xminorticksize=2,
+            xminorticksize=1.5,
             xgridwidth=0.5,
             ygridwidth=0.5,
             xminorgridwidth=0.5,
@@ -69,7 +71,7 @@ function theme()
             colormap=[:red, :blue],
             whiskerwidth=8pt,
             noeffect_linewidth=1pt,
-        )
+        ),
     )
 end
 
@@ -83,6 +85,11 @@ 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)

From 1132d6085c4c6422b1de81761faf83b534fb8511 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 11:04:19 -0400
Subject: [PATCH 17/54] Add 90% percentile span to intrinsic metric plots

---
 opt/TokenizerStats/plots/plots.jl             |  1 +
 .../plots/src/figures/intrinsic.jl            | 66 ++++++++++++++++---
 opt/TokenizerStats/plots/src/plot_utils.jl    |  1 +
 opt/TokenizerStats/submit_jobs.py             |  6 +-
 4 files changed, 61 insertions(+), 13 deletions(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index bce6a26f..16556666 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -141,6 +141,7 @@ with_theme(SmirkPaperPlots.theme()) do
     )
     savefig("jaccard", SmirkPaperPlots.figure_jaccard(stats_dir))
     savefig("intrinsic_metrics", SmirkPaperPlots.figure_intrinsic(df_intrinsic))
+    savefig("intrinsic_metrics_95", SmirkPaperPlots.figure_intrinsic(df_intrinsic; p=95))
 
     # Transformers vs. N-Grams
     savefig("prognostic", SmirkPaperPlots.figure_fe_models(fe_models, prog_models))
diff --git a/opt/TokenizerStats/plots/src/figures/intrinsic.jl b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
index 2cad2a83..41a1abbe 100644
--- a/opt/TokenizerStats/plots/src/figures/intrinsic.jl
+++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
@@ -1,25 +1,38 @@
-function figure_intrinsic(df)
+function figure_intrinsic(df; p=90)
     f = Figure(; size=(3.42inch, 1.5inch))
     colgap!(f.layout, 3pt)
     tokenizer_classes = OrderedDict(
-        "bpe, nlp" => "BPE, NLP",
-        "bpe" => "BPE, Chemistry",
-        "atomwise" => "Atom-wise",
-        "spe" => "SPE/APE",
+        "nlp" => "NLP",
         "character" => "Character",
         "unigram" => "Unigram",
+        "bpe" => "BPE",
+        "atomwise" => "Atom-wise",
+        "spe" => "SPE/APE",
         "smirk-gpe" => "Smirk-GPE",
         "smirk" => "Smirk",
     )
-    df = subset(df, :tokenizer_domain => ByRow(in(["nlp", "chemistry"])))
+    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, $domain"
         end
     end
-    df.tokenizer_class = categorical(df.tokenizer_class, levels=(collect∘keys)(tokenizer_classes), ordered=true)
+    ckeys = collect∘keys
+    @info unique(df.tokenizer_class)
+    subset!(df, :tokenizer_class => ByRow(in(ckeys(tokenizer_classes))))
+    df.tokenizer_class = categorical(df.tokenizer_class, levels=ckeys(tokenizer_classes), ordered=true)
+
+    # Label classes with the number of examples
+    foreach(eachrow(combine(groupby(df, :tokenizer_class), :tokenizer => length∘unique => :nclass))) do r
+        (; tokenizer_class, nclass) = r
+        plt_class = tokenizer_classes[tokenizer_class]
+        tokenizer_classes[tokenizer_class] = "$plt_class (n=$nclass)"
+    end
+    @info tokenizer_classes
 
     df.dataset = map(ds -> ds in ["realspace", "tmQM"] ? ds : "MoleculeNet", df.dataset)
     df.dataset = map(ds -> ds == "realspace" ? "REALSpace" : ds, df.dataset)
@@ -34,7 +47,7 @@ function figure_intrinsic(df)
             limits=((0, nothing), nothing),
         ),
         :divergence => (;
-            title="Imbalance",
+            title=L"Imbalance ($D$)",
             limits=((0.4, nothing), nothing),
             xtickformat="{:.0%}",
         ),
@@ -51,11 +64,11 @@ function figure_intrinsic(df)
             xtickformat="{:.0%}",
         ),
     )
-    # ax_kwargs = Dict()
 
     for (mdx, metric) in enumerate(metrics)
         kwargs = get(ax_kwargs, metric, (;))
         ax = Axis(f[1, mdx];
+            titlefont=:regular,
             yticks=(1:length(tokenizer_classes), collect(values(tokenizer_classes))),
             yticksvisible=false,
             yticklabelsvisible=mdx == 1,
@@ -64,6 +77,16 @@ function figure_intrinsic(df)
             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))
@@ -93,7 +116,30 @@ function figure_intrinsic(df)
         valign=:top,
         halign=:right,
     )
-    resize_to_layout!(f)
 
     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/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index 5fb9b045..e555405c 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -25,6 +25,7 @@ function theme()
         markersize=4pt,
         linewidth=1pt,
         Axis=(;
+            titlegap=2pt,
             spinewidth=0.5,
             ylabelpadding=3pt,
             yticksize=3,
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 6992f41d..16b9fb41 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -238,9 +238,9 @@ def show(self):
 
 REF_INFO_LOSS = [
     "character",
-    "meta-llama/Meta-Llama-3.1-8B",
-    "google/gemma-7b",
-    "Xenova/gpt-4o",
+    # "meta-llama/Meta-Llama-3.1-8B",
+    # "google/gemma-7b",
+    # "Xenova/gpt-4o",
 ]
 
 

From 519bb60a95ad1cd05098d0d173cf497d1d5660e1 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 13:46:08 -0400
Subject: [PATCH 18/54] update plots

---
 opt/TokenizerStats/plots/plots.jl             |  1 +
 .../plots/src/SmirkPaperPlots.jl              |  2 +-
 .../plots/src/figures/fe_models.jl            |  9 +++--
 .../plots/src/figures/info_loss.jl            |  6 +---
 .../plots/src/figures/intrinsic.jl            |  5 +--
 opt/TokenizerStats/plots/src/plot_utils.jl    |  5 +++
 opt/TokenizerStats/plots/src/prognostics.jl   | 34 ++++++++++++++++++-
 .../plots/src/tabulate_results.jl             |  3 +-
 .../plots/src/tokenizer_summary.jl            |  2 +-
 9 files changed, 53 insertions(+), 14 deletions(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 16556666..20059477 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -123,6 +123,7 @@ 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)
 
diff --git a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
index f747f6eb..351aa5f0 100644
--- a/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
+++ b/opt/TokenizerStats/plots/src/SmirkPaperPlots.jl
@@ -16,7 +16,7 @@ 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
diff --git a/opt/TokenizerStats/plots/src/figures/fe_models.jl b/opt/TokenizerStats/plots/src/figures/fe_models.jl
index 80f042f1..76ab19ae 100644
--- a/opt/TokenizerStats/plots/src/figures/fe_models.jl
+++ b/opt/TokenizerStats/plots/src/figures/fe_models.jl
@@ -109,17 +109,20 @@ function figure_fe_models(models, prog_models; scale=:relative, colormap=:tab10,
 
         # Plot Predictions
         if adx != 1
-            qm = subset(prog_models,
+            row = subset(prog_models,
                 :dataset => ByRow(==(ds)),
                 :finetuned => ByRow(==(true)),
             ) |> only
-            qm = qm.loss_and_info_and_ft
-            rho = round(corspearman(predict(qm), response(qm)); sigdigits=3)
+            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))
diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index 533780ff..5b253686 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -327,11 +327,7 @@ function figure_kl_v_info_loss(tok_info, model_loss, info_loss, df_tf; reference
     )
 
     # Check correlation
-    @show rho = corspearman(df.avg_info_loss, df.test_loss)
-    @info length(df.avg_info_loss) factorial(length(df.avg_info_loss))
-    @show rho_p_value = mean(HypothesisTests.permutations(df.avg_info_loss)) do x
-        rho <= corspearman(x, df.test_loss)
-    end
+    @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)
 
     Label(f[1, 1, TopLeft()], "a)";
diff --git a/opt/TokenizerStats/plots/src/figures/intrinsic.jl b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
index 41a1abbe..340679a0 100644
--- a/opt/TokenizerStats/plots/src/figures/intrinsic.jl
+++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
@@ -55,10 +55,11 @@ function figure_intrinsic(df; p=90)
             title="UNK Freq.",
             limits=((0, 1), nothing),
             xtickformat="{:.0%}",
-            xscale=my_sqrt,
+            xscale=sqrt,
+            xticks=[0.25, 0.5, 1],
         ),
         :normalized_entropy => (;
-            title=L"\eta",
+            title=L"Normalized Entropy ($\eta$)",
             limits=((0, 1), nothing),
             xticks=[0.25, 0.75],
             xtickformat="{:.0%}",
diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index e555405c..fca3f51e 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -232,3 +232,8 @@ 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
diff --git a/opt/TokenizerStats/plots/src/prognostics.jl b/opt/TokenizerStats/plots/src/prognostics.jl
index 752b31d1..ebe95e92 100644
--- a/opt/TokenizerStats/plots/src/prognostics.jl
+++ b/opt/TokenizerStats/plots/src/prognostics.jl
@@ -40,16 +40,48 @@ function ngram_prognostic_fits(df_prog)
         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)
-        return (; loss_and_info_and_ft)
+        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 0fb51625..aeafe837 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -270,7 +270,8 @@ end
 
 nobs_nonwts(model) = size(model.model.pp.X, 1)
 
-function ngram_vs_transformer_fits(df_ng, df_p, df_f)
+function ngram_vs_transformer_fits(df_ng, df_p,)
+    df_ng, df_p, df_f = df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft)
     contrasts = Dict(
         :tokenizer_class => EffectsCoding(; base="atomwise"),
         :encoding => EffectsCoding(; base="smiles"),
diff --git a/opt/TokenizerStats/plots/src/tokenizer_summary.jl b/opt/TokenizerStats/plots/src/tokenizer_summary.jl
index 55e4f95e..bb068e93 100644
--- a/opt/TokenizerStats/plots/src/tokenizer_summary.jl
+++ b/opt/TokenizerStats/plots/src/tokenizer_summary.jl
@@ -158,7 +158,7 @@ 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}{llllc|cc|cc|cc}
                 &
                 &
                 &

From b83b137c630e3c43e5916ab1b844628942037a21 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 17:28:18 -0400
Subject: [PATCH 19/54] add ngram statistics plot

---
 opt/TokenizerStats/plots/plots.jl             |  3 +-
 opt/TokenizerStats/plots/src/coeff_plot.jl    |  5 +
 .../plots/src/figures/info_loss.jl            | 94 +++++++++++++++++++
 .../plots/src/figures/intrinsic.jl            |  2 +-
 .../plots/src/figures/transfromer.jl          | 32 +------
 opt/TokenizerStats/plots/src/plot_utils.jl    | 74 ++++++++++++++-
 6 files changed, 177 insertions(+), 33 deletions(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 20059477..324d85ad 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -141,8 +141,9 @@ with_theme(SmirkPaperPlots.theme()) do
         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))
+    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))
diff --git a/opt/TokenizerStats/plots/src/coeff_plot.jl b/opt/TokenizerStats/plots/src/coeff_plot.jl
index 716a7479..81ff2a8f 100644
--- a/opt/TokenizerStats/plots/src/coeff_plot.jl
+++ b/opt/TokenizerStats/plots/src/coeff_plot.jl
@@ -7,6 +7,11 @@ 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)
diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index 5b253686..b8c5ae30 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -474,3 +474,97 @@ 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)
+
+    f = Figure(; size=(3.42inch, 2.1inch), figure_padding=(1, 8, 1, 4))
+    ax = Axis(f[1, 1];
+        xlabel="N-Gram Cross Entropy [nats/token]",
+        limits=((0, nothing), nothing),
+        yticks=categorical_ticks(df.tokenizer_class)
+    )
+    colormap=:Set1_3
+    colorrange=(1, 3)
+
+    df_loss = combine(groupby(df, [:tokenizer_class, :dataset, :ngram]),
+        :loss_per_token_moments => mean∘mergereduce => :ng_loss,
+        :samples => sum => :samples,
+    )
+    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_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, 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,
+    )
+
+    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
index 340679a0..8649c15f 100644
--- a/opt/TokenizerStats/plots/src/figures/intrinsic.jl
+++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
@@ -96,7 +96,7 @@ function figure_intrinsic(df; p=90)
                 orientation=:horizontal,
                 dodge=levelcode.(gdf.dataset),
                 color=levelcode.(gdf.dataset),
-                show_outliers=false,
+                outliercolor=:black,
                 whiskerwidth=1pt,
                 colormap,
                 colorrange,
diff --git a/opt/TokenizerStats/plots/src/figures/transfromer.jl b/opt/TokenizerStats/plots/src/figures/transfromer.jl
index 7260b2c2..3a1bbc86 100644
--- a/opt/TokenizerStats/plots/src/figures/transfromer.jl
+++ b/opt/TokenizerStats/plots/src/figures/transfromer.jl
@@ -113,7 +113,8 @@ 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;
+        dodge=dfr.tokenizer,
         std=dfr.test_loss_std,
         colormap,
         colorrange=(1, length(colormap)),
@@ -151,35 +152,6 @@ 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 fca3f51e..67efb21c 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -163,6 +163,7 @@ 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
 
@@ -171,7 +172,11 @@ 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
 
@@ -237,3 +242,70 @@ 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)
+    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)
+    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=Makie.automatic(), std=nothing, colormap, colorrange=nothing, direction=:y)
+    if isnothing(colorrange)
+        colorrange = extrema(levelcode.(dodge))
+    end
+
+    h = barplot!(ax, levelcode.(x), y;
+        dodge=levelcode.(dodge),
+        colormap,
+        colorrange,
+        color=levelcode.(dodge),
+        direction,
+    )
+
+    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,
+            direction,
+        )
+    end
+
+    return h
+end
+
+

From c8e97ec1f9d8c906c6d101304edb6d130ce8d1ec Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 17:28:56 -0400
Subject: [PATCH 20/54] fixup and save tokenizer stats

---
 opt/TokenizerStats/plots/plots.jl                | 1 +
 opt/TokenizerStats/plots/src/tabulate_results.jl | 3 +--
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 324d85ad..1ab2a644 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -15,6 +15,7 @@ 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)
diff --git a/opt/TokenizerStats/plots/src/tabulate_results.jl b/opt/TokenizerStats/plots/src/tabulate_results.jl
index aeafe837..0fb51625 100644
--- a/opt/TokenizerStats/plots/src/tabulate_results.jl
+++ b/opt/TokenizerStats/plots/src/tabulate_results.jl
@@ -270,8 +270,7 @@ end
 
 nobs_nonwts(model) = size(model.model.pp.X, 1)
 
-function ngram_vs_transformer_fits(df_ng, df_p,)
-    df_ng, df_p, df_f = df_ngrams_vs_transformer(stats_dir, loss_stats, dfp, dff, dft)
+function ngram_vs_transformer_fits(df_ng, df_p, df_f)
     contrasts = Dict(
         :tokenizer_class => EffectsCoding(; base="atomwise"),
         :encoding => EffectsCoding(; base="smiles"),

From 839fb3edf2f95b904f4480673d431de9baafdfd5 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 17:35:16 -0400
Subject: [PATCH 21/54] fix barploterrors

---
 opt/TokenizerStats/plots/src/figures/info_loss.jl   | 5 ++++-
 opt/TokenizerStats/plots/src/figures/transfromer.jl | 5 ++---
 opt/TokenizerStats/plots/src/plot_utils.jl          | 4 +---
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index b8c5ae30..a95965e9 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -494,7 +494,10 @@ function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5)
     ax = Axis(f[1, 1];
         xlabel="N-Gram Cross Entropy [nats/token]",
         limits=((0, nothing), nothing),
-        yticks=categorical_ticks(df.tokenizer_class)
+        yticks=categorical_ticks(df.tokenizer_class),
+        xminorticks=IntervalsBetween(4),
+        xminorticksvisible=true,
+        yticksvisible=false,
     )
     colormap=:Set1_3
     colorrange=(1, 3)
diff --git a/opt/TokenizerStats/plots/src/figures/transfromer.jl b/opt/TokenizerStats/plots/src/figures/transfromer.jl
index 3a1bbc86..6846cd1d 100644
--- a/opt/TokenizerStats/plots/src/figures/transfromer.jl
+++ b/opt/TokenizerStats/plots/src/figures/transfromer.jl
@@ -113,8 +113,7 @@ function figure_tf_finetune(stats_dir, dff, dft)
         ytickformat="{:.0%}",
         xgridvisible=false,
     )
-    h = barploterrors!(ax, dfr.dataset, dfr.test_loss;
-        dodge=dfr.tokenizer,
+    h = barploterrors!(ax, dfr.dataset, dfr.test_loss, dfr.tokenizer;
         std=dfr.test_loss_std,
         colormap,
         colorrange=(1, length(colormap)),
@@ -131,7 +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
diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index 67efb21c..15713efb 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -279,7 +279,7 @@ end
 
 categorical_ticks(x) = (1:length(levels(x)), levels(x))
 
-function barploterrors!(ax, x, y; dodge=Makie.automatic(), std=nothing, colormap, colorrange=nothing, direction=:y)
+function barploterrors!(ax, x, y, dodge; std=nothing, colormap, colorrange=nothing)
     if isnothing(colorrange)
         colorrange = extrema(levelcode.(dodge))
     end
@@ -289,7 +289,6 @@ function barploterrors!(ax, x, y; dodge=Makie.automatic(), std=nothing, colormap
         colormap,
         colorrange,
         color=levelcode.(dodge),
-        direction,
     )
 
     if !isnothing(std)
@@ -301,7 +300,6 @@ function barploterrors!(ax, x, y; dodge=Makie.automatic(), std=nothing, colormap
             dodge_gap=h.dodge_gap,
             linewidth=1,
             color=:black,
-            direction,
         )
     end
 

From 687dae5089b648df066b2b699c7c2deeb9f0f767 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 17:31:15 -0400
Subject: [PATCH 22/54] fix typo

---
 opt/TokenizerStats/plots/plots.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index 1ab2a644..cdaa84ff 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -144,7 +144,7 @@ with_theme(SmirkPaperPlots.theme()) do
     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)
+    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))

From bc4f55d785f1463c22ebab62b758d596910a9bb0 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 9 May 2025 23:15:48 -0400
Subject: [PATCH 23/54] plot tweaks

---
 .../plots/src/figures/info_loss.jl            | 20 +++++++++----
 .../plots/src/figures/intrinsic.jl            | 29 ++++++++++---------
 opt/TokenizerStats/plots/src/plot_utils.jl    | 16 +++++++++-
 3 files changed, 44 insertions(+), 21 deletions(-)

diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index a95965e9..725d0494 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -490,11 +490,22 @@ function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5)
     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=categorical_ticks(df.tokenizer_class),
+        yticks,
         xminorticks=IntervalsBetween(4),
         xminorticksvisible=true,
         yticksvisible=false,
@@ -502,10 +513,6 @@ function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5)
     colormap=:Set1_3
     colorrange=(1, 3)
 
-    df_loss = combine(groupby(df, [:tokenizer_class, :dataset, :ngram]),
-        :loss_per_token_moments => mean∘mergereduce => :ng_loss,
-        :samples => sum => :samples,
-    )
     barplot!(ax, levelcode.(df_loss.tokenizer_class), df_loss.ng_loss;
         dodge=levelcode.(df_loss.dataset),
         color=levelcode.(df_loss.dataset),
@@ -517,6 +524,7 @@ function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5)
         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,
@@ -554,7 +562,7 @@ function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5)
     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);
+    Legend(f[1, 1], ds_elements, labels(ds_elements);
         tellheight=false, tellwidth=false, orientation=:vertical,
         framevisible=true,
         margin=(2, 2, 2, 2),
diff --git a/opt/TokenizerStats/plots/src/figures/intrinsic.jl b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
index 8649c15f..192d9c35 100644
--- a/opt/TokenizerStats/plots/src/figures/intrinsic.jl
+++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
@@ -22,7 +22,6 @@ function figure_intrinsic(df; p=90)
         end
     end
     ckeys = collect∘keys
-    @info unique(df.tokenizer_class)
     subset!(df, :tokenizer_class => ByRow(in(ckeys(tokenizer_classes))))
     df.tokenizer_class = categorical(df.tokenizer_class, levels=ckeys(tokenizer_classes), ordered=true)
 
@@ -32,7 +31,6 @@ function figure_intrinsic(df; p=90)
         plt_class = tokenizer_classes[tokenizer_class]
         tokenizer_classes[tokenizer_class] = "$plt_class (n=$nclass)"
     end
-    @info tokenizer_classes
 
     df.dataset = map(ds -> ds in ["realspace", "tmQM"] ? ds : "MoleculeNet", df.dataset)
     df.dataset = map(ds -> ds == "realspace" ? "REALSpace" : ds, df.dataset)
@@ -41,27 +39,31 @@ function figure_intrinsic(df; p=90)
     colorrange=(1, 3)
 
     metrics = [:fertility, :divergence, :normalized_entropy, :oov_rate]
+    ylims = (0.5, length(tokenizer_classes) + 0.5)
     ax_kwargs = Dict(
         :fertility => (;
-            title="Fertility",
-            limits=((0, nothing), nothing),
+            xlabel="Fertility",
+            limits=((0, nothing), ylims),
+            xticks=WilkinsonTicks(3),
         ),
         :divergence => (;
-            title=L"Imbalance ($D$)",
-            limits=((0.4, nothing), nothing),
+            xlabel=L"Imbalance ($D$)",
+            limits=((0, nothing), ylims),
             xtickformat="{:.0%}",
+            xticks=WilkinsonTicks(3),
         ),
         :oov_rate => (;
-            title="UNK Freq.",
-            limits=((0, 1), nothing),
+            xlabel="UNK Freq.",
+            limits=((0, 1), ylims),
             xtickformat="{:.0%}",
             xscale=sqrt,
             xticks=[0.25, 0.5, 1],
         ),
         :normalized_entropy => (;
-            title=L"Normalized Entropy ($\eta$)",
-            limits=((0, 1), nothing),
-            xticks=[0.25, 0.75],
+            xlabel=L"Normalized Entropy ($\eta$)",
+            limits=((0, 1), ylims),
+            # xticks=[0.25, 0.75],
+            xticks=WilkinsonTicks(3),
             xtickformat="{:.0%}",
         ),
     )
@@ -69,11 +71,11 @@ function figure_intrinsic(df; p=90)
     for (mdx, metric) in enumerate(metrics)
         kwargs = get(ax_kwargs, metric, (;))
         ax = Axis(f[1, mdx];
-            titlefont=:regular,
+            xlabelfont=:regular,
             yticks=(1:length(tokenizer_classes), collect(values(tokenizer_classes))),
             yticksvisible=false,
             yticklabelsvisible=mdx == 1,
-            xticklabelrotation=-pi/4,
+            # xticklabelrotation=-pi/4,
             xminorticks=IntervalsBetween(4),
             xminorticksvisible=true,
             kwargs...
@@ -97,7 +99,6 @@ function figure_intrinsic(df; p=90)
                 dodge=levelcode.(gdf.dataset),
                 color=levelcode.(gdf.dataset),
                 outliercolor=:black,
-                whiskerwidth=1pt,
                 colormap,
                 colorrange,
             )
diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index 15713efb..46fd4a25 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -61,6 +61,11 @@ function theme()
             markersize=8pt,
             marker=:x,
         ),
+        BarPlot=(;
+            whiskerwidth=1pt,
+            markersize=2pt,
+            medianlinewidth=0.5pt,
+        ),
         ErrrorBar=(;
             whiskerwidth=2,
             linewidth=0.5,
@@ -243,7 +248,7 @@ function siglevel(p::Real; cutoff=[0.05, 0.01, 0.001], symbol="*")
     return isnothing(l) ? "" : symbol ^ l
 end
 
-function label_tokenizers!(df)
+function label_tokenizers!(df; include_count=true)
     tokenizer_classes = OrderedDict(
         "nlp" => "NLP",
         "character" => "Character",
@@ -267,6 +272,15 @@ function label_tokenizers!(df)
     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
 

From cda20bcdefc6e0a429d6306e65aa0cc9a8dc826e Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Sat, 10 May 2025 12:44:10 -0400
Subject: [PATCH 24/54] refactor: dedicated tokenizer dataset loading

Instead of hooking into the training code, just do it manually
- Way faster loading
- Delay loading till mpi init
- More smaller functions instead of repeat code
---
 electrolyte_fm/data_modules/molnet_dataset.py |   71 +-
 electrolyte_fm/data_modules/utils.py          |   66 ++
 opt/TokenizerStats/pyproject.toml             |   20 +-
 opt/TokenizerStats/python/helper/__init__.py  |    0
 opt/TokenizerStats/python/helper/cache.py     |    1 +
 opt/TokenizerStats/python/helper/loader.py    |  112 ++
 opt/TokenizerStats/python/helper/tokenizer.py |    1 +
 opt/TokenizerStats/python/helper/utils.py     |    1 +
 opt/TokenizerStats/src/TokenizerStats.jl      |   44 +-
 opt/TokenizerStats/src/atomic_oov.py          |    4 +-
 opt/TokenizerStats/src/cli.jl                 |   46 +-
 opt/TokenizerStats/src/collect.jl             |  159 +--
 opt/TokenizerStats/uv.lock                    | 1030 ++---------------
 13 files changed, 404 insertions(+), 1151 deletions(-)
 create mode 100644 opt/TokenizerStats/python/helper/__init__.py
 create mode 120000 opt/TokenizerStats/python/helper/cache.py
 create mode 100644 opt/TokenizerStats/python/helper/loader.py
 create mode 120000 opt/TokenizerStats/python/helper/tokenizer.py
 create mode 120000 opt/TokenizerStats/python/helper/utils.py

diff --git a/electrolyte_fm/data_modules/molnet_dataset.py b/electrolyte_fm/data_modules/molnet_dataset.py
index f60d029e..dd3e86f7 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",
@@ -85,67 +82,3 @@ def _get_dataset(self):
             return train_val_test_split(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/utils.py b/electrolyte_fm/data_modules/utils.py
index 5a05ae6a..08139259 100644
--- a/electrolyte_fm/data_modules/utils.py
+++ b/electrolyte_fm/data_modules/utils.py
@@ -2,6 +2,8 @@
 import random
 from typing import Optional, TypeVar
 from rdkit import Chem
+from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles
+from sklearn.model_selection import GroupShuffleSplit
 from datasets import Dataset, DatasetDict, IterableDatasetDict
 from datasets.distributed import split_dataset_by_node
 
@@ -104,3 +106,67 @@ def encode_molecules(
         **kwargs,
     )
     return ds.filter(lambda x: x[output_column] is not None, batched=False, **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.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/opt/TokenizerStats/pyproject.toml b/opt/TokenizerStats/pyproject.toml
index d2cc6c99..792f6f77 100644
--- a/opt/TokenizerStats/pyproject.toml
+++ b/opt/TokenizerStats/pyproject.toml
@@ -1,5 +1,5 @@
 [project]
-name = "TokenizerStats"
+name = "tokenizer_stats"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"
@@ -8,13 +8,27 @@ authors = [
 ]
 requires-python = ">=3.10, <3.14"
 dependencies = [
+    "smirk",
     "mendeleev >= 0.16",
     "selfies == 2.1.2",
     "jsonnet ~= 0.20.0",
-    "electrolyte-fm",
     "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",
 ]
 
+[build-system]
+requires = ["setuptools>=42"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+package-dir = {"" = "python"}
+
+[tool.setuptools.packages.find]
+where = ["python"]
+
 [tool.uv.sources]
-electrolyte-fm = { path = "../../", editable = true }
+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/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..93cac039
--- /dev/null
+++ b/opt/TokenizerStats/python/helper/loader.py
@@ -0,0 +1,112 @@
+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,
+)
+
+
+@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,
+) -> 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)
+    return ds.map(
+        tokenizer,
+        batched=is_fast(tokenizer),
+        input_columns="smi",
+    )
+
+
+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=True,
+        save_infos=False,
+    )
+    ds = ds.rename_column("smiles" if name != "bace" else "mol", "smi")
+    return ds.select_columns("smi")
+
+
+def tmqm(name_or_path: str):
+    path = Path(name_or_path)
+    ds = load_dataset(
+        "arrow",
+        name=self.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/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..06ecfb52 100644
--- a/opt/TokenizerStats/src/TokenizerStats.jl
+++ b/opt/TokenizerStats/src/TokenizerStats.jl
@@ -28,7 +28,49 @@ function find(dir, pattern)
     return found
 end
 
-@annotate load_tokenizer(args...; kwargs...) = @pyconst(pyimport("electrolyte_fm.utils.tokenizer")).load_tokenizer(args...; kwargs...)
+
+const __loader = 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(args...; kwargs...) = __tokenizer[].load_tokenizer(args...; kwargs...)
+
+struct DatasetConfig
+    name_or_path::String
+    tokenizer::String
+    encoding::String
+end
+
+function dataset_split(dc::DatasetConfig, split::String; kwargs...)
+    return tokenizer_dataset(dc.tokenizer, dc.name_or_path, dc.encoding; kwargs...)[split]
+end
+
+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(tokenizer)),
+            unk_token_id=pyconvert(Union{Int,Nothing}, tokenizer.unk_token_id),
+    )
+    return tok, info
+end
+
+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
+
 function split_dataset_by_node(args...; kwargs...)
     m = @pyconst(pyimport("datasets.distributed"))
     return m.split_dataset_by_node(args...; kwargs...)
diff --git a/opt/TokenizerStats/src/atomic_oov.py b/opt/TokenizerStats/src/atomic_oov.py
index e916fc5b..1ae9d90a 100644
--- a/opt/TokenizerStats/src/atomic_oov.py
+++ b/opt/TokenizerStats/src/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,
diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index 42633a4f..283ffca4 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -19,23 +19,16 @@ function common_args!(s)
 end
 
 
-@annotate function get_dataset(name_or_path, tokenizer, encoding)
-    start = time()
-    tokenizer = isdir(tokenizer) ? realpath(tokenizer) : tokenizer
-    if isdir(name_or_path)
+@annotate function get_dataset(name_or_path::String, tokenizer::String, encoding::String)
+    dataset_name = name_or_path
+    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
+    return DatasetConfig(name_or_path, tokenizer, encoding), dataset_name
 end
 
 function maybe_parse_env(T::Type, x::String)
@@ -109,17 +102,13 @@ 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, _ = get_dataset(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 = () -> first(get_dataset(args_cmd["dataset"], tokenizer, args_cmd["encoding"]))
-        model_loss(dm, args_cmd["model"], args_cmd["output"])
+        ds, _ = get_dataset(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"]
@@ -130,19 +119,17 @@ 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, _ = get_dataset(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)
+            job_array_usage_stats(ds, args_cmd["output"]; splits, size, rank)
         else
             error("Unknown mode $(args_cmd["mode"])")
         end
@@ -151,6 +138,15 @@ end
     return 0
 end
 
+parse_splits(x::String) = parse_splits(split(x, ","))
+function parse_splits(x::Vector{String})
+    if length(x) == 1 && first(x)
+        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")
     for file in files
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index 2249fd16..428067fc 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -1,7 +1,3 @@
-# Maximum number of oov samples to track
-const MAX_OOV_SAMPLES = 100
-
-
 function tracked_stats()
     return (;
         fertility=CountMap(Int),
@@ -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)
+    tokenizer, 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"])
         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 +64,19 @@ 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"
+    tokenizer, tokenizer_info = tokenizer(dataset)
+    out_file = out_file * "_split_$(join(splits, "_"))_rank_$global_rank.jld2"
     mkpath(dirname(out_file))
     jldopen(out_file * ".tmp", "w+") do f
         f["tokenizer"] = tokenizer_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
@@ -114,87 +89,80 @@ 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()
+    tokenizer, tokenizer_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["dataset"] = (; dataset_name=dataset_name(dataset), encoding=dataset.encoding)
         end
     end
 
     MPI.Barrier(comm)
     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
         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(constructor, 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 ready" 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
-    datamodule = constructor()
+    ds = tokenizer_dataset(dataset)
+    tok, tok_info = tokenizer(dataset)
     MPI.Barrier(comm)
-    @info "Rank $rank of $size is starting" now()
+    @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)
     for split in ["val", "train", "test"]
-        ds = setup_dm_mpi(datamodule, split; rank, size)
         stats = map(1:length(ngram)) do _
             OnlineStats.Series(;
                 moments=OnlineStats.Moments(),
@@ -204,7 +172,7 @@ function model_loss(constructor, 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)
@@ -214,29 +182,32 @@ function model_loss(constructor, ref_file::String, output::String)
             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
         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)
@@ -245,28 +216,25 @@ function model_loss(constructor, ref_file::String, output::String)
 
 end
 
-@annotate function avg_information_loss(datamodule::Py, ref_file::String, output::String)
+@annotate 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
+    tokenizer, 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"] = tokenizer_info
             f["ref_tokenizer"] = ref_info
+            f["system"] = (; world_size)
         end
     end
 
@@ -280,25 +248,26 @@ 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, tokenizer, 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/uv.lock b/opt/TokenizerStats/uv.lock
index 36781d93..06c86c4f 100644
--- a/opt/TokenizerStats/uv.lock
+++ b/opt/TokenizerStats/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 1
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
@@ -217,18 +216,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/20/94/c5790835a017658cbfabd07f3bfb549140c3ac458cfc196323996b10095a/charset_normalizer-3.4.2-py3-none-any.whl", hash = "sha256:7f56930ab0abd1c45cd15be65cc741c28b1c9a34876ce8c17a2fa107810c0af0", size = 52626 },
 ]
 
-[[package]]
-name = "click"
-version = "8.1.8"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/b9/2e/0090cbf739cee7d23781ad4b89a9894a41538e4fcf4c31dcdd705b78eb8b/click-8.1.8.tar.gz", hash = "sha256:ed53c9d8990d83c2a27deae68e4ee337473f6330c040a31d4225c9574d16096a", size = 226593 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/7e/d4/7ebdbd03970677812aac39c869717059dbb71a4cfc033ca6e5221787892c/click-8.1.8-py3-none-any.whl", hash = "sha256:63c132bbbed01578a06712a2d1f497bb62d9c1c0d329b7903a866228027263b2", size = 98188 },
-]
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -240,10 +227,9 @@ wheels = [
 
 [[package]]
 name = "datasets"
-version = "3.5.1"
+version = "3.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "aiohttp" },
     { name = "dill" },
     { name = "filelock" },
     { name = "fsspec", extra = ["http"] },
@@ -259,28 +245,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "xxhash" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/10/af/8c1d10daf37383c32ab0a7461eaa4d5c7a3c47808fe5a8563744de002df7/datasets-3.5.1.tar.gz", hash = "sha256:f835b45dbbd7065c1191734b6f7c8d96fdf8c5751feaa5aa52b2a0dc43eea58f", size = 568915 }
+sdist = { url = "https://files.pythonhosted.org/packages/1a/89/d3d6fef58a488f8569c82fd293ab7cbd4250244d67f425dcae64c63800ea/datasets-3.6.0.tar.gz", hash = "sha256:1b2bf43b19776e2787e181cfd329cb0ca1a358ea014780c3581e0f276375e041", size = 569336 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e3/f5/668b3444a2f487b0052b908af631fe39eeb2bdb2359d9bbc2c3b80b71119/datasets-3.5.1-py3-none-any.whl", hash = "sha256:4074dda8dd6e9ece242b1580a8ef3928777d59ae1db144d911229e443a093cbb", size = 491436 },
-]
-
-[[package]]
-name = "deepspeed"
-version = "0.16.4+e2dc3eeb"
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-
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-name = "typeshed-client"
-version = "2.7.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "importlib-resources" },
-    { name = "typing-extensions" },
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 name = "typing-extensions"
 version = "4.13.2"
@@ -2550,39 +1701,6 @@ wheels = [
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-    { name = "protobuf" },
-    { name = "psutil" },
-    { name = "pydantic" },
-    { name = "pyyaml" },
-    { name = "requests" },
-    { name = "sentry-sdk" },
-    { name = "setproctitle" },
-    { name = "setuptools" },
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From eedcad854f2f8ab809ec5dad925b43323327caa1 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Sat, 10 May 2025 13:23:51 -0400
Subject: [PATCH 25/54] fixup typos

---
 opt/TokenizerStats/pyproject.toml        |  1 +
 opt/TokenizerStats/src/TokenizerStats.jl |  2 +-
 opt/TokenizerStats/src/cli.jl            | 19 +++-------------
 opt/TokenizerStats/submit_jobs.py        | 28 +++++++++++++++++-------
 opt/TokenizerStats/uv.lock               | 12 ++++++++++
 5 files changed, 37 insertions(+), 25 deletions(-)

diff --git a/opt/TokenizerStats/pyproject.toml b/opt/TokenizerStats/pyproject.toml
index 792f6f77..afcbb620 100644
--- a/opt/TokenizerStats/pyproject.toml
+++ b/opt/TokenizerStats/pyproject.toml
@@ -18,6 +18,7 @@ dependencies = [
     "transformers>=4.51.3",
     "rdkit>=2025.3.1",
     "scikit-learn>=1.6.1",
+    "networkx>=3.4.2",
 ]
 
 [build-system]
diff --git a/opt/TokenizerStats/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl
index 06ecfb52..3ec41fce 100644
--- a/opt/TokenizerStats/src/TokenizerStats.jl
+++ b/opt/TokenizerStats/src/TokenizerStats.jl
@@ -61,7 +61,7 @@ function tokenizer(dc::DatasetConfig)
 end
 
 function dataset_name(dc::DatasetConfig)
-    if isdir(dc.name_or_path):
+    if isdir(dc.name_or_path)
         if "tmQM" in splitpath(dc.name_or_path)
             return "tmQM"
         else
diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index 283ffca4..fba94d90 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -18,19 +18,6 @@ function common_args!(s)
     end
 end
 
-
-@annotate function get_dataset(name_or_path::String, tokenizer::String, encoding::String)
-    dataset_name = name_or_path
-    if isdir(name_or_path):
-        if "tmQM" in splitpath(name_or_path)
-            dataset_name = "tmQM"
-        else
-            dataset_name = basename(name_or_path)
-        end
-    end
-    return DatasetConfig(name_or_path, tokenizer, encoding), dataset_name
-end
-
 function maybe_parse_env(T::Type, x::String)
     env = get(ENV, x, nothing)
     if !isnothing(env)
@@ -102,12 +89,12 @@ end
 
     # Run command
     if args["%COMMAND%"] == "distortion"
-        ds, _ = get_dataset(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"])
+        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
-        ds, _ = get_dataset(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"])
+        ds = DatasetConfig(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"])
         model_loss(ds, args_cmd["model"], args_cmd["output"])
 
     elseif args["%COMMAND%"] == "merge"
@@ -119,7 +106,7 @@ end
         merge_usage_stats(files; output)
 
     elseif args["%COMMAND%"] == "usage"
-        ds, _ = get_dataset(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"])
+        ds = DatasetConfig(args_cmd["dataset"], args_cmd["tokenizer"], args_cmd["encoding"])
         splits = parse_splits(args_cmd["splits"])
 
         # Distribute computation
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 16b9fb41..f3f3919b 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -22,7 +22,7 @@
 @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)
@@ -285,7 +285,9 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles"):
     )
 
 
-def ngram_loss(dataset, tokenizer, slurm=None, encoding="smiles", ngram="realspace", ds_name=None):
+def ngram_loss(
+    dataset, tokenizer, slurm=None, encoding="smiles", ngram="realspace", ds_name=None
+):
     input = STATS_DIR.joinpath(tokenizer, ngram, "usage.jld2")
     ds_name = ds_name or str(dataset)
     outfile = "model_loss.jld2" if ngram == "realspace" else f"model_loss_{ngram}.jld2"
@@ -309,13 +311,13 @@ def ngram_loss(dataset, tokenizer, slurm=None, encoding="smiles", ngram="realspa
             "--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"},
@@ -345,9 +347,9 @@ def ngram_info_loss(
             "--encoding",
             encoding,
             "--output",
-            output,
+            str(output),
             "--reference",
-            ref_usage,
+            str(ref_usage),
             dataset,
             tokenizer,
         ],
@@ -412,6 +414,16 @@ def set_logging_level(verbosity):
                 slurm={"ntasks": 32, "time": "8:0:0"},
             )
         )
+        wk.add_process(
+            ngram_info_loss(
+                args.realspace,
+                tok_name,
+                ref="character",
+                ds_name="realspace",
+                encoding=tok["encoding"],
+                slurm={"ntasks": 48, "time": "1-0:0:0"},
+            )
+        )
 
         # Tokenize MoleculeNet
         for ds in [*MOLNET_DATASETS, "tmqm"]:
@@ -439,7 +451,7 @@ def set_logging_level(verbosity):
 
             for ref in REF_INFO_LOSS:
                 if ds == "tmqm" and tok["encoding"] == "selfies":
-                    continue # Majority of selfies fail
+                    continue  # Majority of selfies fail
 
                 if tok["name_or_path"] == "ncfrey/ChemGPT-4.7M":
                     continue  # Token Alignment will fail
diff --git a/opt/TokenizerStats/uv.lock b/opt/TokenizerStats/uv.lock
index 06c86c4f..a00fbd5b 100644
--- a/opt/TokenizerStats/uv.lock
+++ b/opt/TokenizerStats/uv.lock
@@ -1,4 +1,5 @@
 version = 1
+revision = 1
 requires-python = ">=3.10, <3.14"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
@@ -665,6 +666,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/da/d9/f7f9379981e39b8c2511c9e0326d212accacb82f12fbfdc1aa2ce2a7b2b6/multiprocess-0.70.16-py39-none-any.whl", hash = "sha256:a0bafd3ae1b732eac64be2e72038231c1ba97724b60b09400d68f229fcc2fbf3", size = 133351 },
 ]
 
+[[package]]
+name = "networkx"
+version = "3.4.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fd/1d/06475e1cd5264c0b870ea2cc6fdb3e37177c1e565c43f56ff17a10e3937f/networkx-3.4.2.tar.gz", hash = "sha256:307c3669428c5362aab27c8a1260aa8f47c4e91d3891f48be0141738d8d053e1", size = 2151368 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b9/54/dd730b32ea14ea797530a4479b2ed46a6fb250f682a9cfb997e968bf0261/networkx-3.4.2-py3-none-any.whl", hash = "sha256:df5d4365b724cf81b8c6a7312509d0c22386097011ad1abe274afd5e9d3bbc5f", size = 1723263 },
+]
+
 [[package]]
 name = "numpy"
 version = "1.26.4"
@@ -1581,6 +1591,7 @@ dependencies = [
     { name = "datasets" },
     { name = "jsonnet" },
     { name = "mendeleev" },
+    { name = "networkx" },
     { name = "rdkit" },
     { name = "scikit-learn" },
     { name = "selfies" },
@@ -1595,6 +1606,7 @@ requires-dist = [
     { name = "datasets", specifier = "~=3.5" },
     { name = "jsonnet", specifier = "~=0.20.0" },
     { name = "mendeleev", specifier = ">=0.16" },
+    { name = "networkx", specifier = ">=3.4.2" },
     { name = "rdkit", specifier = ">=2025.3.1" },
     { name = "scikit-learn", specifier = ">=1.6.1" },
     { name = "selfies", specifier = "==2.1.2" },

From 039d8e0d746663b0289052c5b785ee970110cbbf Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 12 May 2025 12:00:35 -0400
Subject: [PATCH 26/54] fix spe and expand tokenizer summary table

---
 electrolyte_fm/{tokenize => utils}/spe.py     |  2 +-
 electrolyte_fm/utils/tokenizer.py             |  2 +-
 .../plots/src/tokenizer_summary.jl            | 57 +++++++------------
 opt/TokenizerStats/python/helper/spe.py       |  1 +
 opt/TokenizerStats/src/TokenizerStats.jl      | 33 ++---------
 opt/TokenizerStats/src/collect.jl             | 19 +++----
 opt/TokenizerStats/submit_archive.sh          | 13 +++++
 7 files changed, 51 insertions(+), 76 deletions(-)
 rename electrolyte_fm/{tokenize => utils}/spe.py (99%)
 create mode 120000 opt/TokenizerStats/python/helper/spe.py

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..529d7e06 100644
--- a/electrolyte_fm/utils/tokenizer.py
+++ b/electrolyte_fm/utils/tokenizer.py
@@ -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)
 
diff --git a/opt/TokenizerStats/plots/src/tokenizer_summary.jl b/opt/TokenizerStats/plots/src/tokenizer_summary.jl
index bb068e93..4ab840ab 100644
--- a/opt/TokenizerStats/plots/src/tokenizer_summary.jl
+++ b/opt/TokenizerStats/plots/src/tokenizer_summary.jl
@@ -121,34 +121,14 @@ function report_tokenizer_summary_stats(stats_dir, model_loss, info_loss, usage_
         replace.(top_k_tokens, "#" => "\\#", "<unk>" => "[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/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/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl
index 3ec41fce..d0117319 100644
--- a/opt/TokenizerStats/src/TokenizerStats.jl
+++ b/opt/TokenizerStats/src/TokenizerStats.jl
@@ -30,6 +30,7 @@ end
 
 
 const __loader = Ref{Py}()
+const __tokenizer = Ref{Py}()
 function __init__()
     __loader[] = pyimport("helper.loader")
     __tokenizer[] = pyimport("helper.tokenizer")
@@ -46,6 +47,7 @@ struct DatasetConfig
 end
 
 function dataset_split(dc::DatasetConfig, split::String; kwargs...)
+    split = split == "val" ? "validation" : split
     return tokenizer_dataset(dc.tokenizer, dc.name_or_path, dc.encoding; kwargs...)[split]
 end
 
@@ -54,8 +56,8 @@ 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(tokenizer)),
-            unk_token_id=pyconvert(Union{Int,Nothing}, tokenizer.unk_token_id),
+        vocab_size=pyconvert(Int, length(tok)),
+        unk_token_id=pyconvert(Union{Int,Nothing}, tok.unk_token_id),
     )
     return tok, info
 end
@@ -71,33 +73,6 @@ function dataset_name(dc::DatasetConfig)
     return dc.name_or_path
 end
 
-function split_dataset_by_node(args...; kwargs...)
-    m = @pyconst(pyimport("datasets.distributed"))
-    return m.split_dataset_by_node(args...; kwargs...)
-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
-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
-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
-end
-
 include("ngrams.jl")
 include("serialize.jl")
 include("collect.jl")
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index 428067fc..cc4429b4 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -38,7 +38,7 @@ end
 
 function rank_usage_stats(dataset::DatasetConfig, split::String; global_rank::Int, world_size::Int)
     ds = dataset_split(dataset, split; global_rank, world_size)
-    tokenizer, tok_info = tokenizer(dataset)
+    _, tok_info = tokenizer(dataset)
     unk_token_id = tok_info.unk_token_id
     local_stats = tracked_stats()
     start_time = time()
@@ -66,11 +66,11 @@ end
 
 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, tokenizer_info = tokenizer(dataset)
+    _, tok_info = tokenizer(dataset)
     out_file = out_file * "_split_$(join(splits, "_"))_rank_$global_rank.jld2"
     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
 
@@ -98,12 +98,12 @@ function tabulate_dataset(dataset::DatasetConfig, out_file::AbstractString, spli
     @info "Rank $global_rank of $world_size is starting" now()
 
     # Load Dataset and Tokenizer
-    tokenizer, tokenizer_info = tokenizer(dataset)
+    _, 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
@@ -141,7 +141,6 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
     @info "Rank $global_rank of $world_size is ready" now()
 
     # Create datamodule
-    ds = tokenizer_dataset(dataset)
     tok, tok_info = tokenizer(dataset)
     MPI.Barrier(comm)
     @info "Rank $global_rank of $world_size is starting" now()
@@ -151,7 +150,6 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
     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 global_rank == 0
         mkpath(dirname(output))
         jldopen(output * ".tmp", "w+") do f
@@ -163,6 +161,7 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
 
     MPI.Barrier(comm)
     for split in ["val", "train", "test"]
+        ds = dataset_split(dataset, split; global_rank, world_size)
         stats = map(1:length(ngram)) do _
             OnlineStats.Series(;
                 moments=OnlineStats.Moments(),
@@ -225,14 +224,14 @@ end
     @info "Rank $global_rank of $world_size is starting" now()
 
     # Load Reference Tokenizer / n-gram model
-    tokenizer, tok_info = tokenizer(dataset)
+    tok, tok_info = tokenizer(dataset)
     ngram, ref_tok, ref_info = load_ngram_model(ref_file)
     global_rank == 0 && @info "Loaded n-gram model for $(ref_info.name) from $ref_file ($(ref_info.sha256[1:8]))"
 
     if global_rank == 0
         mkpath(dirname(output))
         jldopen(output * ".tmp", "w+") do f
-            f["tokenizer"] = tokenizer_info
+            f["tokenizer"] = tok_info
             f["ref_tokenizer"] = ref_info
             f["system"] = (; world_size)
         end
@@ -254,7 +253,7 @@ end
     MPI.Barrier(comm)
     start_time = time()
     for (idx, encoding) in enumerate(ds)
-        info_loss = unk_information_loss(ngram, ref_tok, tokenizer, encoding; smi_column="smi")
+        info_loss = unk_information_loss(ngram, ref_tok, tok, encoding; smi_column="smi")
         fit!(stats, tuple(info_loss))
         if idx % 100 == 0 && global_rank == 0
             elapsed = time() - start_time
diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index 420a471b..33bb34d7 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -15,5 +15,18 @@ tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
     --xz \
     --exclude="*.slurm" \
     --exclude="*.tmp" \
+    --exclude=".unmerged" \
     -C stats \
     .
+
+# Compress Code (TokenizerStats)
+{ git ls-files; find fig/ -type f; find -name Manifest.toml; } | \
+    tar -cav \
+    -f "$(realpath ~/scratch)/TokenizerStats.jl_$(date +"%d%m%Y").tar.xz" \
+    --dereference \
+    --files-from -
+
+# Compress Code (Smirk)
+SMIRK_VERSION="v0.1.1"
+curl -L https://github.com/BattModels/smirk/archive/refs/tags/$(SMIRK_VERSION).tar.gz \
+    --output "$(realpath ~/scratch)/smrik_${SMIRK_VERSION}.tar.xz"

From f08c03e04e13451cd02d0d62d2911b6544d25404 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 12 May 2025 12:06:22 -0400
Subject: [PATCH 27/54] don't archive unmerged files

---
 opt/TokenizerStats/submit_archive.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index 33bb34d7..da28cd6b 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -15,7 +15,7 @@ tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
     --xz \
     --exclude="*.slurm" \
     --exclude="*.tmp" \
-    --exclude=".unmerged" \
+    --exclude='*/.unmerged/*'
     -C stats \
     .
 

From 2e88bef95c6d0a0b8580db5567b88c267f44ece2 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 12 May 2025 12:10:40 -0400
Subject: [PATCH 28/54] fix: replace paths with real ones when loading

---
 opt/TokenizerStats/src/TokenizerStats.jl | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl
index d0117319..d4a0764e 100644
--- a/opt/TokenizerStats/src/TokenizerStats.jl
+++ b/opt/TokenizerStats/src/TokenizerStats.jl
@@ -38,7 +38,12 @@ function __init__()
 end
 
 tokenizer_dataset(args...; kwargs...) = __loader[].tokenizer_dataset(args...; kwargs...)
-load_tokenizer(args...; kwargs...) = __tokenizer[].load_tokenizer(args...; kwargs...)
+function load_tokenizer(name_or_path)
+    if isdir(name_or_path)
+        name_or_path = realpath(name_or_path)
+    end
+    return __tokenizer[].load_tokenizer(name_or_path)
+end
 
 struct DatasetConfig
     name_or_path::String

From a94f0a282792e5e4e329868a4074eabfc052948e Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 12 May 2025 12:21:07 -0400
Subject: [PATCH 29/54] fix paths in archive

---
 opt/TokenizerStats/submit_archive.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index da28cd6b..53a68b37 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -20,7 +20,7 @@ tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
     .
 
 # Compress Code (TokenizerStats)
-{ git ls-files; find fig/ -type f; find -name Manifest.toml; } | \
+{ git ls-files; find fig/ -type f |  sed 's|^\./||'; find -name Manifest.toml | sed 's|^\./||'; } | \
     tar -cav \
     -f "$(realpath ~/scratch)/TokenizerStats.jl_$(date +"%d%m%Y").tar.xz" \
     --dereference \

From e79a73bbf16cd116e925e8f866b54371087e3e05 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 12 May 2025 12:23:25 -0400
Subject: [PATCH 30/54] fix typo in archive

---
 opt/TokenizerStats/submit_archive.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index 53a68b37..9f0d8453 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -29,4 +29,4 @@ tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
 # Compress Code (Smirk)
 SMIRK_VERSION="v0.1.1"
 curl -L https://github.com/BattModels/smirk/archive/refs/tags/$(SMIRK_VERSION).tar.gz \
-    --output "$(realpath ~/scratch)/smrik_${SMIRK_VERSION}.tar.xz"
+    --output "$(realpath ~/scratch)/smirk_${SMIRK_VERSION}.tar.gz"

From ce866f8a4c55a8d10b84637987179ee25bbcd165 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Sat, 17 May 2025 20:02:48 -0400
Subject: [PATCH 31/54] misc fixes

---
 electrolyte_fm/utils/tokenizer.py          |   5 +-
 opt/TokenizerStats/.gitignore              |   3 +
 opt/TokenizerStats/python/helper/loader.py |  15 ++-
 opt/TokenizerStats/src/TokenizerStats.jl   |   7 +-
 opt/TokenizerStats/src/collect.jl          |   2 +-
 opt/TokenizerStats/submit_archive.sh       |  12 ++-
 opt/TokenizerStats/submit_jobs.py          | 116 +++++++++++++--------
 7 files changed, 103 insertions(+), 57 deletions(-)

diff --git a/electrolyte_fm/utils/tokenizer.py b/electrolyte_fm/utils/tokenizer.py
index 529d7e06..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":
@@ -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..60128efc 100644
--- a/opt/TokenizerStats/.gitignore
+++ b/opt/TokenizerStats/.gitignore
@@ -9,3 +9,6 @@ models
 smirk-gpe-*
 smirk-gpe-*/
 fig/
+*.tar.*
+archive/
+*.slurm
diff --git a/opt/TokenizerStats/python/helper/loader.py b/opt/TokenizerStats/python/helper/loader.py
index 93cac039..7ccc17fe 100644
--- a/opt/TokenizerStats/python/helper/loader.py
+++ b/opt/TokenizerStats/python/helper/loader.py
@@ -41,11 +41,16 @@ def tokenizer_dataset(
     encoding: str,
     world_size: int = 1,
     global_rank: int = 0,
+    limit: int | None = None,
 ) -> AbstractDataset:
     tokenizer = load_tokenizer(tokenizer)
     encoding = MolEncoding(encoding)
     ds = get_dataset(name_or_path)
     ds = maybe_shard_dataset(GlobalComm(global_rank, world_size), ds)
+
+    if limit is not None:
+        ds = ds.shuffle(seed=42).take(limit)
+
     ds = encode_molecules(ds, "smi", encoding=encoding)
     return ds.map(
         tokenizer,
@@ -75,7 +80,7 @@ def molnet(name: str):
         save_infos=False,
     )
     ds = ds.rename_column("smiles" if name != "bace" else "mol", "smi")
-    return ds.select_columns("smi")
+    ds = ds.select_columns("smi")
 
 
 def tmqm(name_or_path: str):
@@ -92,7 +97,13 @@ def tmqm(name_or_path: str):
         streaming=True,
         save_infos=False,
     )
-    return ds.select_columns("smiles").rename_column("smiles", "smi")
+    ds = ds.select_columns("smiles").rename_column("smiles", "smi")
+
+    # Split dataset
+    if name_or_path in ["bace", "bbbp", "hiv"]:
+        return scaffold_split(ds, "smi")
+    else:
+        return train_val_test_split(ds)
 
 
 def smiles_dataset(name_or_path: str):
diff --git a/opt/TokenizerStats/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl
index d4a0764e..a73b25b8 100644
--- a/opt/TokenizerStats/src/TokenizerStats.jl
+++ b/opt/TokenizerStats/src/TokenizerStats.jl
@@ -38,12 +38,7 @@ function __init__()
 end
 
 tokenizer_dataset(args...; kwargs...) = __loader[].tokenizer_dataset(args...; kwargs...)
-function load_tokenizer(name_or_path)
-    if isdir(name_or_path)
-        name_or_path = realpath(name_or_path)
-    end
-    return __tokenizer[].load_tokenizer(name_or_path)
-end
+load_tokenizer(name_or_path) = __tokenizer[].load_tokenizer(name_or_path)
 
 struct DatasetConfig
     name_or_path::String
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index cc4429b4..e8be4167 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -248,7 +248,7 @@ end
     info_loss = zeros(length(ngram))
 
     @info "rank $global_rank: started processing" now()
-    ds = dataset_split(dataset, split; global_rank, world_size)
+    ds = dataset_split(dataset, split; global_rank, world_size, limit=1000)
 
     MPI.Barrier(comm)
     start_time = time()
diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index 9f0d8453..a2c36cec 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -11,22 +11,26 @@ cp tokenizers.json stats/
 
 # Compress stats
 export XZ_OPT="-9 -v --extreme --memlimit=4000000000 --threads=${SLURM_CPUS_PER_TASK}"
-tar -cavf "$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz" \
+stats_archive="$(realpath ~/scratch)/tokenizer_stats_$(date +"%d%m%Y").tar.xz"
+tar -cavf  $stats_archive \
     --xz \
     --exclude="*.slurm" \
     --exclude="*.tmp" \
     --exclude='*/.unmerged/*'
     -C stats \
     .
+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 -cav \
-    -f "$(realpath ~/scratch)/TokenizerStats.jl_$(date +"%d%m%Y").tar.xz" \
+    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/$(SMIRK_VERSION).tar.gz \
-    --output "$(realpath ~/scratch)/smirk_${SMIRK_VERSION}.tar.gz"
+    --output "./archive/smirk_${SMIRK_VERSION}.tar.gz"
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index f3f3919b..02a2bfdf 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -1,4 +1,5 @@
 #!/usr/bin/env -S uv run python
+from os import posix_spawn
 import time
 import logging
 import subprocess
@@ -55,9 +56,51 @@ 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 +167,20 @@ 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
+    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)
 
-        # 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)
+        return 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
-
-    def run(self, dry_run=False, rate_limit=100, preflight=list[Process]):
+    def run(
+        self,
+        dry_run=False,
+        rate_limit=100,
+        preflight=list[Process],
+        postflight=list[Process],
+    ):
         outputs = {
             process.output: process
             for process in self.processes
@@ -175,9 +200,10 @@ def run(self, dry_run=False, rate_limit=100, preflight=list[Process]):
             # 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
@@ -185,24 +211,16 @@ def run(self, dry_run=False, rate_limit=100, preflight=list[Process]):
             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")
@@ -210,6 +228,14 @@ def run(self, dry_run=False, rate_limit=100, preflight=list[Process]):
                 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}")
@@ -471,4 +497,8 @@ def set_logging_level(verbosity):
     if args.precompile:
         preflight.append(Process(["submit_precompile.sh"], []))
 
-    wk.run(dry_run=args.dry_run, preflight=preflight)
+    postflight = [
+        Process(["submit_archive.sh"], output=Path("archive")),
+    ]
+
+    wk.run(dry_run=args.dry_run, preflight=preflight, postflight=postflight)

From 00de458a50e2892bec06831ea039a14eb492cd98 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 21 May 2025 11:10:27 -0400
Subject: [PATCH 32/54] more tests and benchmark loader

---
 electrolyte_fm/data_modules/utils.py          | 19 +++++++---
 .../benchmark/bench_tokenizer.jl              | 13 +++++--
 opt/TokenizerStats/python/helper/loader.py    | 31 +++++++++-------
 opt/TokenizerStats/test/cli_tests.jl          |  8 +++++
 opt/TokenizerStats/test/python_tests.jl       | 36 +++++++++++++++++++
 5 files changed, 86 insertions(+), 21 deletions(-)
 create mode 100644 opt/TokenizerStats/test/python_tests.jl

diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py
index 08139259..c00bdf09 100644
--- a/electrolyte_fm/data_modules/utils.py
+++ b/electrolyte_fm/data_modules/utils.py
@@ -1,11 +1,13 @@
-from enum import Enum
 import random
+from asyncio import Semaphore
+from enum import Enum
 from typing import Optional, TypeVar
+
+from datasets import Dataset, DatasetDict, IterableDatasetDict
+from datasets.distributed import split_dataset_by_node
 from rdkit import Chem
 from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles
 from sklearn.model_selection import GroupShuffleSplit
-from datasets import Dataset, DatasetDict, IterableDatasetDict
-from datasets.distributed import split_dataset_by_node
 
 
 def is_fast(tokenizer):
@@ -89,6 +91,7 @@ def encode_molecules(
     output_column: Optional[str] = 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`.
@@ -97,10 +100,16 @@ 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(smi):
+        async with tasks:
+            return {output_column: encode(smi)}
+
     ds = ds.map(
-        lambda smi: {output_column: encode(smi)},
+        async_encode,
         input_columns=input_column,
         batched=False,
         **kwargs,
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/python/helper/loader.py b/opt/TokenizerStats/python/helper/loader.py
index 7ccc17fe..97de06ab 100644
--- a/opt/TokenizerStats/python/helper/loader.py
+++ b/opt/TokenizerStats/python/helper/loader.py
@@ -1,3 +1,4 @@
+from itertools import islice
 from pathlib import Path
 from dataclasses import dataclass
 from datasets import load_dataset
@@ -9,6 +10,8 @@
     maybe_shard_dataset,
     encode_molecules,
     is_fast,
+    scaffold_split,
+    train_val_test_split,
 )
 
 
@@ -42,22 +45,25 @@ def tokenizer_dataset(
     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)
 
-    if limit is not None:
-        ds = ds.shuffle(seed=42).take(limit)
-
-    ds = encode_molecules(ds, "smi", encoding=encoding)
-    return ds.map(
+    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():
@@ -76,12 +82,17 @@ def molnet(name: str):
         name=name,
         data_files=[MOLNET_URLS[name]],
         split="train",
-        streaming=True,
+        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)
@@ -97,13 +108,7 @@ def tmqm(name_or_path: str):
         streaming=True,
         save_infos=False,
     )
-    ds = ds.select_columns("smiles").rename_column("smiles", "smi")
-
-    # Split dataset
-    if name_or_path in ["bace", "bbbp", "hiv"]:
-        return scaffold_split(ds, "smi")
-    else:
-        return train_val_test_split(ds)
+    return ds.select_columns("smiles").rename_column("smiles", "smi")
 
 
 def smiles_dataset(name_or_path: str):
diff --git a/opt/TokenizerStats/test/cli_tests.jl b/opt/TokenizerStats/test/cli_tests.jl
index c5b1b54b..13be4421 100644
--- a/opt/TokenizerStats/test/cli_tests.jl
+++ b/opt/TokenizerStats/test/cli_tests.jl
@@ -4,3 +4,11 @@
         @test main(["--help"]) == 0
     end
 end
+
+@testitem "parse_splits" begin
+    using TokenizerStats: parse_splits
+    @test parse_splits("val") == ["val"]
+    @test parse_splits("val,train") == ["val", "train"]
+    @test parse_splits("all") == ["train", "val", "test"]
+end
+
diff --git a/opt/TokenizerStats/test/python_tests.jl b/opt/TokenizerStats/test/python_tests.jl
new file mode 100644
index 00000000..950be7d8
--- /dev/null
+++ b/opt/TokenizerStats/test/python_tests.jl
@@ -0,0 +1,36 @@
+@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
+
+    @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 item.haskey("input_ids")
+        @test pyconvert(Vector{Int}, item["input_ids"]) isa Vector{Int}
+        @test item.haskey("smi")
+        @test pyconvert(String, item["smi"]) isa String
+
+        # Check val => validation remapping
+        @test dataset_split(dc, "val") == dataset_split(dc, "validation")
+        @test dataset_split(dc, "train") != dataset_split(dc, "validation")
+    end
+end

From 5f4805b2e9744bef4628fa9e9514810ae5dc61fd Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 21 May 2025 12:14:51 -0400
Subject: [PATCH 33/54] manage batch jobs in submit_jobs

---
 opt/TokenizerStats/src/cli.jl      |   4 +-
 opt/TokenizerStats/src/collect.jl  |  11 +-
 opt/TokenizerStats/submit_jobs.py  | 256 ++++++++++++++++++-----------
 opt/TokenizerStats/tokenizers.json |  16 +-
 4 files changed, 179 insertions(+), 108 deletions(-)

diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index fba94d90..e02fac2c 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -126,8 +126,8 @@ end
 end
 
 parse_splits(x::String) = parse_splits(split(x, ","))
-function parse_splits(x::Vector{String})
-    if length(x) == 1 && first(x)
+function parse_splits(x::Vector{<:AbstractString})
+    if length(x) == 1 && first(x) == "all"
         return ["val", "train", "test"]
     else
         return x
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index e8be4167..1b2ffcf0 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -109,6 +109,7 @@ function tabulate_dataset(dataset::DatasetConfig, out_file::AbstractString, spli
     end
 
     MPI.Barrier(comm)
+    start_time = time()
     for split in splits
         rank_stats = rank_usage_stats(dataset, split; global_rank, world_size)
         tokenizer_stats = leader_reduce(merge!, rank_stats; comm)
@@ -121,6 +122,10 @@ function tabulate_dataset(dataset::DatasetConfig, out_file::AbstractString, spli
     end
 
     if global_rank == 0
+        jldopen(out_file * ".tmp", "a+") do f
+            out_file["walltime"] = time() - start_time
+            out_file["world_size"] = world_size
+        end
         mv(out_file * ".tmp", out_file; force=true)
         chmod(out_file, 0o444)
         @info "Saved stats on rank $global_rank to $out_file" now()
@@ -160,6 +165,7 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
     end
 
     MPI.Barrier(comm)
+    start_time = time()
     for split in ["val", "train", "test"]
         ds = dataset_split(dataset, split; global_rank, world_size)
         stats = map(1:length(ngram)) do _
@@ -204,6 +210,9 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
     end
 
     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 $global_rank: saved stats to $output" now()
@@ -248,7 +257,7 @@ end
     info_loss = zeros(length(ngram))
 
     @info "rank $global_rank: started processing" now()
-    ds = dataset_split(dataset, split; global_rank, world_size, limit=1000)
+    ds = dataset_split(dataset, split; global_rank, world_size)
 
     MPI.Barrier(comm)
     start_time = time()
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 02a2bfdf..1e73ad46 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -1,15 +1,15 @@
 #!/usr/bin/env -S uv run python
-from os import posix_spawn
-import time
+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)
@@ -33,6 +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:
@@ -269,35 +271,42 @@ 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", "3600M")
+    slurm.setdefault("partition", "venkvis-cpu,venkvis-largemem")
+
+    if mode == "batch":
+        output = Path(output.parent, ".unmerged", f"usage_{encoding}", output.name)
 
     return Process(
         [
             "submit_tok_stats.sh",
             "usage",
+            f"--mode={mode}",
             "--splits=all",
             "--encoding",
             encoding,
@@ -305,18 +314,48 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles"):
             str(dataset),
             tokenizer,
         ],
-        output=output,
+        output=output.parent if mode == "batch" else output,
         slurm=slurm,
         meta={"dataset": dataset, "tokenizer": tokenizer, "task": "usage"},
     )
 
 
+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, output],
+        inputs=[p.output],
+        output=output,
+        slurm=slurm,
+        meta=p.meta,
+    )
+
+
 def ngram_loss(
     dataset, tokenizer, slurm=None, encoding="smiles", ngram="realspace", ds_name=None
 ):
-    input = STATS_DIR.joinpath(tokenizer, ngram, "usage.jld2")
+    input = STATS_DIR.joinpath(tokenizer, ngram, f"usage_{encoding}.jld2")
     ds_name = ds_name or str(dataset)
-    outfile = "model_loss.jld2" if ngram == "realspace" else f"model_loss_{ngram}.jld2"
+    outfile = f"model_loss_{ngram}_{encoding}.jld2"
     output = STATS_DIR.joinpath(tokenizer, ds_name, outfile)
     slurm = slurm or {}
     if ngram == "realspace":
@@ -324,7 +363,7 @@ def ngram_loss(
     else:
         slurm.setdefault("job-name", f"loss-{ds_name}-{ngram}")
 
-    slurm.setdefault("mem-per-cpu", "2G")
+    slurm.setdefault("mem-per-cpu", "1800M")
     slurm.setdefault("partition", "venkvis-cpu,venkvis-largemem")
     slurm.setdefault("ntasks", 4)
     slurm.setdefault("time", "2:0:0")
@@ -354,13 +393,15 @@ 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")
 
@@ -393,104 +434,125 @@ 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("--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:
-        tok_name = tok["name_or_path"]
+def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
+    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,
+    # 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"},
         )
-
-        # Tokenize RealSpace
+    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"},
+                slurm={"ntasks": 128, "time": "1-0:0:0"},
             )
         )
-        wk.add_process(
-            ngram_loss(
-                args.realspace,
-                tok_name,
-                ds_name="realspace",
-                encoding=tok["encoding"],
-                slurm={"ntasks": 32, "time": "8:0:0"},
-            )
+    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"},
+        )
+    )
+    return
+
+    # 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_info_loss(
-                args.realspace,
+            usage(
+                dataset,
                 tok_name,
-                ref="character",
-                ds_name="realspace",
+                ds_name=ds_name,
                 encoding=tok["encoding"],
-                slurm={"ntasks": 48, "time": "1-0:0:0"},
             )
         )
+        for ngram in ["realspace", ds]:
+            wk.add_process(
+                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
 
-        # Tokenize MoleculeNet
-        for ds in [*MOLNET_DATASETS, "tmqm"]:
-            dataset = ds if ds != "tmqm" else args.tmqm
-            ds_name = None if ds != "tmqm" else "tmqm"
             wk.add_process(
-                usage(
+                ngram_info_loss(
                     dataset,
                     tok_name,
+                    ref,
                     ds_name=ds_name,
                     encoding=tok["encoding"],
                 )
             )
-            for ngram in ["realspace", ds]:
-                wk.add_process(
-                    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
 
-                if tok["name_or_path"] == "ncfrey/ChemGPT-4.7M":
-                    continue  # Token Alignment will fail
+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(
+            ["submit_oov.sh", "--output", output, tok["name_or_path"]],
+            output=output,
+        )
+
+        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(
-                        dataset,
-                        tok_name,
-                        ref,
-                        ds_name=ds_name,
-                        encoding=tok["encoding"],
-                    )
-                )
+        for encoding in encodings:
+            tok["encoding"] = encoding
+            tokenizer_jobs(wk, tok, args.realspace, args.tmqm)
 
     wk.show()
     preflight = []
diff --git a/opt/TokenizerStats/tokenizers.json b/opt/TokenizerStats/tokenizers.json
index f358ae19..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",

From d266329fb4e0e58e01b7c0993c8c8cf0202d8e05 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Wed, 21 May 2025 20:47:54 -0400
Subject: [PATCH 34/54] switch to UInt32 for token ids and squash bug

---
 electrolyte_fm/data_modules/utils.py          |  3 ++-
 .../plots/src/figures/info_loss.jl            |  6 ++---
 .../plots/src/tokenizer_summary.jl            |  2 +-
 opt/TokenizerStats/python/helper/loader.py    |  2 +-
 opt/TokenizerStats/src/TokenizerStats.jl      |  3 ++-
 opt/TokenizerStats/src/cli.jl                 |  8 +++----
 opt/TokenizerStats/src/collect.jl             | 14 +++++------
 opt/TokenizerStats/src/ngrams.jl              | 24 +++++++++----------
 opt/TokenizerStats/submit_jobs.py             | 11 +++++----
 9 files changed, 38 insertions(+), 35 deletions(-)

diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py
index c00bdf09..53fab1e4 100644
--- a/electrolyte_fm/data_modules/utils.py
+++ b/electrolyte_fm/data_modules/utils.py
@@ -1,3 +1,4 @@
+import logging
 import random
 from asyncio import Semaphore
 from enum import Enum
@@ -135,7 +136,7 @@ def scaffold_hash(smi: str) -> str:
     try:
         scaffold = MurckoScaffoldSmiles(smi)
     except ValueError:
-        logging.warn("No scaffold for %s, using input smiles string", smi)
+        logging.warning("No scaffold for %s, using input smiles string", smi)
         scaffold = smi
     return scaffold
 
diff --git a/opt/TokenizerStats/plots/src/figures/info_loss.jl b/opt/TokenizerStats/plots/src/figures/info_loss.jl
index 725d0494..4ae27369 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))
diff --git a/opt/TokenizerStats/plots/src/tokenizer_summary.jl b/opt/TokenizerStats/plots/src/tokenizer_summary.jl
index 4ab840ab..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)
 
diff --git a/opt/TokenizerStats/python/helper/loader.py b/opt/TokenizerStats/python/helper/loader.py
index 97de06ab..3fffcbf4 100644
--- a/opt/TokenizerStats/python/helper/loader.py
+++ b/opt/TokenizerStats/python/helper/loader.py
@@ -98,7 +98,7 @@ def tmqm(name_or_path: str):
     path = Path(name_or_path)
     ds = load_dataset(
         "arrow",
-        name=self.path.name,
+        name=path.name,
         data_files={
             "train": str(path.joinpath("train/*.arrow")),
             "validation": str(path.joinpath("validation/*.arrow")),
diff --git a/opt/TokenizerStats/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl
index a73b25b8..0b65bf8c 100644
--- a/opt/TokenizerStats/src/TokenizerStats.jl
+++ b/opt/TokenizerStats/src/TokenizerStats.jl
@@ -48,7 +48,8 @@ end
 
 function dataset_split(dc::DatasetConfig, split::String; kwargs...)
     split = split == "val" ? "validation" : split
-    return tokenizer_dataset(dc.tokenizer, dc.name_or_path, dc.encoding; kwargs...)[split]
+    ds = tokenizer_dataset(dc.tokenizer, dc.name_or_path, dc.encoding; kwargs...)[split]
+    return ds
 end
 
 tokenizer_name(dc::DatasetConfig) = isdir(dc.tokenizer) ? basename(dc.tokenizer) : dc.tokenizer
diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index e02fac2c..a34aa93b 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -113,10 +113,10 @@ end
         if args_cmd["mode"] == "mpi"
             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(ds, args_cmd["output"]; 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
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index 1b2ffcf0..de1339e5 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -3,12 +3,12 @@ function tracked_stats()
         fertility=CountMap(Int),
         nunique=CountMap(Int),
         out_of_vocab=Counter(Int),
-        ngrams=ntuple(i -> CountMap(NTuple{i,Int}), 5),
+        ngrams=ntuple(i -> CountMap(NTuple{i,UInt32}), 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))
@@ -44,10 +44,10 @@ function rank_usage_stats(dataset::DatasetConfig, split::String; global_rank::In
     start_time = time()
     @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
+        if idx % 10 == 0
             elapsed = time() - start_time
             @info "rank $global_rank on molecule $idx" idx elapsed idx / elapsed now()
         end
@@ -123,8 +123,8 @@ function tabulate_dataset(dataset::DatasetConfig, out_file::AbstractString, spli
 
     if global_rank == 0
         jldopen(out_file * ".tmp", "a+") do f
-            out_file["walltime"] = time() - start_time
-            out_file["world_size"] = world_size
+            f["walltime"] = time() - start_time
+            f["world_size"] = world_size
         end
         mv(out_file * ".tmp", out_file; force=true)
         chmod(out_file, 0o444)
@@ -181,7 +181,7 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
         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
diff --git a/opt/TokenizerStats/src/ngrams.jl b/opt/TokenizerStats/src/ngrams.jl
index 319850dd..37b2a6f8 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 +59,7 @@ 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]
+        name = data["tokenizer"][:tokenizer_name]
         name = startswith(name, "smirk-gpe") ? "./" * name : name
         tok = load_tokenizer(name)
         vocab_size = pyconvert(Int, length(tok))
@@ -90,11 +90,11 @@ 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` """
@@ -109,7 +109,7 @@ struct MaskedCode{T,C<:AbstractVector{T},M<:AbstractVector{Bool}} <: AbstractVec
     mask::M
     mask_value::T
 end
-MaskedCode(code::Vector{T}, mask::Union{BitVector,Vector{Bool}}, mask_value::T) where {T} = MaskedCode{T}(code, BitVector(mask), mask_value)
+MaskedCode(code::Vector{T}, mask::Union{BitVector,Vector{Bool}}, mask_value::Integer) where {T} = MaskedCode{T}(code, BitVector(mask), T(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)
 
 Base.getindex(mc::MaskedCode, i::Int) = mc.mask[i] ? mc.mask_value : mc.code[i]
@@ -320,7 +320,7 @@ 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 +361,7 @@ 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
@@ -503,7 +503,7 @@ Compute the information_loss from unknown tokens using a character-tokenizer as
 """
 @annotate 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 +511,7 @@ 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
@@ -577,11 +577,11 @@ 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/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 1e73ad46..39fb09a8 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -14,7 +14,7 @@
 
 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)
@@ -296,11 +296,13 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles", mode=
     slurm.setdefault("job-name", f"usage-{ds_name}")
     slurm.setdefault("ntasks", 4)
     slurm.setdefault("time", "1-0:0:0")
-    slurm.setdefault("mem-per-cpu", "3600M")
+    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}"
+        slurm["ntasks"] = 1
 
     return Process(
         [
@@ -445,7 +447,7 @@ def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
             tok_name,
             ds_name="realspace",
             encoding=tok["encoding"],
-            slurm={"ntasks": 128, "time": "1-0:0:0"},
+            slurm={"ntasks": 128, "time": "1-0:0:0", "mem-per-cpu": "6G"},
         )
     else:
         wk.add_process(
@@ -454,7 +456,7 @@ def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
                 tok_name,
                 ds_name="realspace",
                 encoding=tok["encoding"],
-                slurm={"ntasks": 128, "time": "1-0:0:0"},
+                slurm={"ntasks": 32, "time": "1-0:0:0"},
             )
         )
     wk.add_process(
@@ -476,7 +478,6 @@ def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
             slurm={"ntasks": 128, "time": "1-0:0:0"},
         )
     )
-    return
 
     # Tokenize MoleculeNet
     for ds in [*MOLNET_DATASETS, "tmqm"]:

From ec2f8e113564bd9b85df30f9e42ed7e74b295fd5 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 22 May 2025 10:10:05 -0400
Subject: [PATCH 35/54] fix oov launcher

---
 opt/TokenizerStats/{src => python/helper}/atomic_oov.py  | 7 ++++++-
 opt/TokenizerStats/{src => python/helper}/build_vocab.py | 0
 opt/TokenizerStats/submit_jobs.py                        | 1 +
 opt/TokenizerStats/submit_oov.sh                         | 4 +++-
 4 files changed, 10 insertions(+), 2 deletions(-)
 rename opt/TokenizerStats/{src => python/helper}/atomic_oov.py (98%)
 rename opt/TokenizerStats/{src => python/helper}/build_vocab.py (100%)

diff --git a/opt/TokenizerStats/src/atomic_oov.py b/opt/TokenizerStats/python/helper/atomic_oov.py
similarity index 98%
rename from opt/TokenizerStats/src/atomic_oov.py
rename to opt/TokenizerStats/python/helper/atomic_oov.py
index 1ae9d90a..ece78d5c 100644
--- a/opt/TokenizerStats/src/atomic_oov.py
+++ b/opt/TokenizerStats/python/helper/atomic_oov.py
@@ -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/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 39fb09a8..6e6f2bf8 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -542,6 +542,7 @@ def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
         wk.add_process(
             ["submit_oov.sh", "--output", output, tok["name_or_path"]],
             output=output,
+            meta={"task": "oov"},
         )
 
         if tok["name_or_path"] == "character":
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"

From cff438b3ec210dbeb5489c59e9d1c15ff638e317 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 22 May 2025 10:10:38 -0400
Subject: [PATCH 36/54] feat: resume partially completed job arrays

---
 opt/TokenizerStats/src/collect.jl |  7 +++++++
 opt/TokenizerStats/src/ngrams.jl  |  3 ++-
 opt/TokenizerStats/submit_jobs.py | 13 ++++++++++++-
 3 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index de1339e5..bfa71ff7 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -69,6 +69,13 @@ function job_array_usage_stats(dataset::DatasetConfig, out_file::AbstractString;
     _, 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"] = tok_info
         f["dataset"] = (; dataset_name=dataset_name(dataset), encoding=dataset.encoding)
diff --git a/opt/TokenizerStats/src/ngrams.jl b/opt/TokenizerStats/src/ngrams.jl
index 37b2a6f8..322ec1f6 100644
--- a/opt/TokenizerStats/src/ngrams.jl
+++ b/opt/TokenizerStats/src/ngrams.jl
@@ -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"][: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))
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 6e6f2bf8..5f0d2b26 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -302,6 +302,16 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles", mode=
     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(
@@ -316,7 +326,7 @@ def usage(dataset, tokenizer, ds_name=None, slurm=None, encoding="smiles", mode=
             str(dataset),
             tokenizer,
         ],
-        output=output.parent if mode == "batch" else output,
+        output=output.parent.with_suffix(".witness") if mode == "batch" else output,
         slurm=slurm,
         meta={"dataset": dataset, "tokenizer": tokenizer, "task": "usage"},
     )
@@ -459,6 +469,7 @@ def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
                 slurm={"ntasks": 32, "time": "1-0:0:0"},
             )
         )
+    return
     wk.add_process(
         ngram_loss(
             realspace_path,

From 45152540f274b496d5e2c5fed3d02085b00560fc Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 22 May 2025 10:17:46 -0400
Subject: [PATCH 37/54] submit all jobs

---
 opt/TokenizerStats/submit_jobs.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index 5f0d2b26..d25c8fa0 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -469,7 +469,7 @@ def tokenizer_jobs(wk, tok, realspace_path, tmqm_path):
                 slurm={"ntasks": 32, "time": "1-0:0:0"},
             )
         )
-    return
+
     wk.add_process(
         ngram_loss(
             realspace_path,

From c83740da586960e78da3bfe45c58de5326408fba Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 22 May 2025 10:41:24 -0400
Subject: [PATCH 38/54] support UInt32 for MaskedCode

---
 opt/TokenizerStats/src/ngrams.jl        | 11 ++++++++---
 opt/TokenizerStats/test/masked_tests.jl | 11 +++++++++++
 2 files changed, 19 insertions(+), 3 deletions(-)

diff --git a/opt/TokenizerStats/src/ngrams.jl b/opt/TokenizerStats/src/ngrams.jl
index 322ec1f6..43035a3b 100644
--- a/opt/TokenizerStats/src/ngrams.jl
+++ b/opt/TokenizerStats/src/ngrams.jl
@@ -109,9 +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::Integer) where {T} = MaskedCode{T}(code, BitVector(mask), T(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::Vector{T}, mask::Union{BitVector,Vector{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
@@ -417,7 +422,7 @@ given n-gram model
     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
diff --git a/opt/TokenizerStats/test/masked_tests.jl b/opt/TokenizerStats/test/masked_tests.jl
index 0c056453..715149e8 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, 1:32, 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

From fbc9cda12da3bf18c1a64d2fbacb14b8a8a8835d Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 22 May 2025 11:08:19 -0400
Subject: [PATCH 39/54] reduce log rate

---
 opt/TokenizerStats/src/collect.jl | 2 +-
 opt/TokenizerStats/submit_jobs.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index bfa71ff7..29135e03 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -47,7 +47,7 @@ function rank_usage_stats(dataset::DatasetConfig, split::String; global_rank::In
         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 % 10 == 0
+        if idx % 1_000_000 == 0
             elapsed = time() - start_time
             @info "rank $global_rank on molecule $idx" idx elapsed idx / elapsed now()
         end
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index d25c8fa0..b7255ac6 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -358,7 +358,7 @@ def usage_array(
         inputs=[p.output],
         output=output,
         slurm=slurm,
-        meta=p.meta,
+        meta={**p.meta, "task": "merge"},
     )
 
 

From df48d843abbbe5e876c2c3b5ecdf2dead6ecfc28 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Thu, 22 May 2025 11:27:01 -0400
Subject: [PATCH 40/54] squash some bugs

---
 opt/TokenizerStats/src/ngrams.jl        |  9 +++++----
 opt/TokenizerStats/test/cli_tests.jl    |  6 +++---
 opt/TokenizerStats/test/masked_tests.jl |  2 +-
 opt/TokenizerStats/test/ngram_tests.jl  | 14 +++++++++++---
 opt/TokenizerStats/test/python_tests.jl |  9 +++------
 5 files changed, 23 insertions(+), 17 deletions(-)

diff --git a/opt/TokenizerStats/src/ngrams.jl b/opt/TokenizerStats/src/ngrams.jl
index 43035a3b..62c04158 100644
--- a/opt/TokenizerStats/src/ngrams.jl
+++ b/opt/TokenizerStats/src/ngrams.jl
@@ -115,7 +115,7 @@ struct MaskedCode{T,C<:AbstractVector{T},M<:AbstractVector{Bool}} <: AbstractVec
         new(code, mask, mask_value)
     end
 end
-MaskedCode(code::Vector{T}, mask::Union{BitVector,Vector{Bool}}, mask_value::Integer=typemax(T)) where {T} = MaskedCode{T}(code, BitVector(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]
@@ -452,10 +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
 
@@ -568,7 +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)
diff --git a/opt/TokenizerStats/test/cli_tests.jl b/opt/TokenizerStats/test/cli_tests.jl
index 13be4421..c15cc797 100644
--- a/opt/TokenizerStats/test/cli_tests.jl
+++ b/opt/TokenizerStats/test/cli_tests.jl
@@ -7,8 +7,8 @@ end
 
 @testitem "parse_splits" begin
     using TokenizerStats: parse_splits
-    @test parse_splits("val") == ["val"]
-    @test parse_splits("val,train") == ["val", "train"]
-    @test parse_splits("all") == ["train", "val", "test"]
+    @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 715149e8..0c8e4353 100644
--- a/opt/TokenizerStats/test/masked_tests.jl
+++ b/opt/TokenizerStats/test/masked_tests.jl
@@ -13,7 +13,7 @@
     # 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, 1:32, 10), rand(Bool, 10), -100)
+    @test_throws InexactError MaskedCode(rand(UInt32, 10), rand(Bool, 10), -100)
 end
 
 @testitem "indexing" begin
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
index 950be7d8..b0b9fc19 100644
--- a/opt/TokenizerStats/test/python_tests.jl
+++ b/opt/TokenizerStats/test/python_tests.jl
@@ -8,6 +8,7 @@ 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")
@@ -24,13 +25,9 @@ end
         ds = dataset_split(dc, "train")
         item = first(ds)
         @test item isa Py
-        @test item.haskey("input_ids")
+        @test haskey(item, "input_ids")
         @test pyconvert(Vector{Int}, item["input_ids"]) isa Vector{Int}
-        @test item.haskey("smi")
+        @test haskey(item, "smi")
         @test pyconvert(String, item["smi"]) isa String
-
-        # Check val => validation remapping
-        @test dataset_split(dc, "val") == dataset_split(dc, "validation")
-        @test dataset_split(dc, "train") != dataset_split(dc, "validation")
     end
 end

From 66c532e99da2a43b646b4d7f33f6cc667ba4fda6 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Sun, 23 Feb 2025 14:24:43 -0800
Subject: [PATCH 41/54] feat: switch dgx to apptainer

Now that apptainer builds on dgx, switch to using apptainer
---
 container/build.sh     |  6 ++++++
 container/training.def | 38 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 44 insertions(+)
 create mode 100644 container/build.sh
 create mode 100644 container/training.def

diff --git a/container/build.sh b/container/build.sh
new file mode 100644
index 00000000..8a5e5745
--- /dev/null
+++ b/container/build.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+
+apptainer build \
+    --build-arg SSH_AUTH_SOCK=$SSH_AUTH_SOCK \
+    /tmp/mist.sif \
+    container/training.def
diff --git a/container/training.def b/container/training.def
new file mode 100644
index 00000000..8d57a45d
--- /dev/null
+++ b/container/training.def
@@ -0,0 +1,38 @@
+Bootstrap: docker
+From: nvcr.io/nvidia/pytorch:{{ NGC_VERSION }}
+
+%arguments
+    NGC_VERSION=25.01-py3
+    POETRY_VERSION=1.8.3
+    PROJECT_DIR=/mist
+
+%files
+    pyproject.toml {{ PROJECT_DIR }}/pyproject.toml
+    poetry.lock {{ PROJECT_DIR }}/poetry.lock
+    README.md {{ PROJECT_DIR }}/README.md
+
+%post
+    apt-get update
+    apt-get install -y python3-venv cargo
+
+    # Install Poetry
+    export POETRY_HOME=/opt/poetry
+    export POETRY_VERSION={{ POETRY_VERSION }}
+    export POETRY_VIRTUALENVS_IN_PROJECT=1
+    export POETRY_VIRTUALENVS_CREATE=1 \
+    export POETRY_NO_INTERACTION=1
+    export POETRY_CACHE_DIR=/tmp/poetry_cache
+    curl -sSL https://install.python-poetry.org | python3 -
+
+    # Setup SSH
+    export SSH_AUTH_SOCK={{ SSH_AUTH_SOCK }}
+    mkdir -p -m 0600 ~/.ssh
+    ssh-keyscan -H github.com >> ~/.ssh/known_hosts
+
+    # Install project
+    $POETRY_HOME/bin/poetry install --compile --no-root --all-extras --directory {{ PROJECT_DIR }}
+    rm -rf $POETRY_CACHE_DIR
+
+%runscript
+    source {{ PROJECT_DIR }}/.venv/bin/activate
+    exec $@

From a45e300985028c7aa58f0dec04195e05c1720a52 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 2 Jun 2025 08:03:14 -0700
Subject: [PATCH 42/54] feat: batched async molecular transcoding

---
 electrolyte_fm/data_modules/utils.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py
index 53fab1e4..744d00c3 100644
--- a/electrolyte_fm/data_modules/utils.py
+++ b/electrolyte_fm/data_modules/utils.py
@@ -105,17 +105,21 @@ def encode_molecules(
 
     tasks = Semaphore(max_workers)
 
-    async def async_encode(smi):
+    async def async_encode(batch: list[str]) -> dict:
         async with tasks:
-            return {output_column: encode(smi)}
+            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(
         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):

From 87eae38b156b1f91bb876c09e239dbb478e28f8e Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 2 Jun 2025 08:20:29 -0700
Subject: [PATCH 43/54] feat: add randomization support to pretraining

---
 .../data_modules/roberta_dataset.py           |  4 +-
 submit/default.jsonnet                        |  1 +
 submit/dgx.j2                                 | 46 ++++++-------------
 submit/dgx.jsonnet                            |  1 +
 4 files changed, 20 insertions(+), 32 deletions(-)

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/submit/default.jsonnet b/submit/default.jsonnet
index b4d43733..eb25c34c 100644
--- a/submit/default.jsonnet
+++ b/submit/default.jsonnet
@@ -1,4 +1,5 @@
 {
+  stage: 'fit',
   env: {
     // Set cache directories to the working directory
     TORCH_EXTENSIONS_DIR: '${PWD}/.cache/torch_extensions',
diff --git a/submit/dgx.j2 b/submit/dgx.j2
index 8e29fff0..0c88d7d3 100644
--- a/submit/dgx.j2
+++ b/submit/dgx.j2
@@ -29,40 +29,25 @@ module load slurm
 cd $(git rev-parse --show-toplevel)
 
 # Set env variables
+ENV_FILE="${TMPDIR}/env-${SLURM_JOB_ID}.sh"
+cat > $ENV_FILE<<EOF
 {{- export_env( env ) }}
-
-# Create job-specific temp dir
-TMPDIR=${TMPDIR:-/tmp}/mist-${SLURM_JOB_ID}/
-mkdir -p $TMPDIR
-
-{%- if job_queue is defined %}
-submit/sweep.py scheduler --num-workers {{ job_queue.tasks }} {{ job_queue.queue }}
-
-{%- else -%}
-{% if deepspeed is defined %}
-# Write out deepspeed config
-cat > $TMPDIR/deepspeed.json<<EOF
-{{ deepspeed | tojson(indent=4) }}
 EOF
-export PL_DEEPSPEED_CONFIG_PATH="$TMPDIR/deepspeed.json"
-export DEEPSPEED_CONFIG=$(cat $PL_DEEPSPEED_CONFIG_PATH)
-sbcast -fp $PL_DEEPSPEED_CONFIG_PATH $PL_DEEPSPEED_CONFIG_PATH
-{%- endif %}
+source $ENV_FILE
+sbcast -fp $ENV_FILE $ENV_FILE
 
-{% if job_array is defined %}
-export PL_CONFIG=$TMPDIR/lightning_${SLURM_JOB_ID}_${SLURM_ARRAY_TASK_ID}.json
-sed "${SLURM_ARRAY_TASK_ID}q;d" {{ job_array.file }} > $PL_CONFIG
-export JOB_CONFIG=$(cat $PL_CONFIG)
-{% else %}
-
-{{- export_job_config( __config__ ) }}
+{{ export_job_config( __config__ ) }}
 
 # Write out  lightning config
-export PL_CONFIG=$TMPDIR/lightning.json
+export PL_CONFIG=$TMPDIR/lightning-${SLURM_JOB_ID}.json
 cat > ${PL_CONFIG}<<EOF
-{{ {"fit": train } | tojson }}
+{%- if stage is not none %}
+{{ {stage: train } | tojson }}
+{%- else %}
+{{ train | tojson }}
+{%- endif %}
 EOF
-{% endif %}
+
 sbcast -fp ${PL_CONFIG} ${PL_CONFIG}
 
 {%- if requeue %}
@@ -76,8 +61,8 @@ fi
 {%- endif %}
 
 srun --mpi=pmix \
-$(which apptainer) exec \
---bind /lustre/fs0,/tmp,$TMPDIR \
+$(which apptainer) run \
+--bind /lustre/fs0,${TMPDIR:-/tmp} \
 --nv \
 {{ container }} \
 ${PWD}/submit/set_node_rank \
@@ -85,5 +70,4 @@ ${PWD}/submit/set_node_rank \
 nsys profile \
       {{- cliopts( nsys ) | indent }} \
 {%- endif %}
-    /mist/.venv/bin/python {{ program or "${PWD}/train.py" }} --config ${PL_CONFIG}
-{%- endif %}
+    python {{ program or "${PWD}/train.py" }} --config ${PL_CONFIG}
diff --git a/submit/dgx.jsonnet b/submit/dgx.jsonnet
index 6d82beaa..9d81ec66 100644
--- a/submit/dgx.jsonnet
+++ b/submit/dgx.jsonnet
@@ -13,5 +13,6 @@
     PMIX_MCA_gds: 'hash',
     NCCL_TOPO_FILE: '/cm/shared/etc/ndv4-topo.xml',
     MELLANOC_VISIBLE_DEVICES: 'all',
+    MASTER_PORT: '$(( 53394 + $SLURM_JOB_ID % 1024 ))',
   },
 }

From 396ddd259e3e5ff19c989c503f650692d354d4ed Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Tue, 3 Jun 2025 12:26:16 -0400
Subject: [PATCH 44/54] feat: tracy support

---
 opt/TokenizerStats/.gitignore             |  1 +
 opt/TokenizerStats/Project.toml           |  4 ++--
 opt/TokenizerStats/main.jl                |  8 ++++++++
 opt/TokenizerStats/src/TokenizerStats.jl  |  2 +-
 opt/TokenizerStats/src/cli.jl             |  2 +-
 opt/TokenizerStats/src/collect.jl         |  4 ++--
 opt/TokenizerStats/src/ngrams.jl          |  4 ++--
 opt/TokenizerStats/test/run_with_tracy.jl | 11 +++++++++++
 8 files changed, 28 insertions(+), 8 deletions(-)
 create mode 100755 opt/TokenizerStats/test/run_with_tracy.jl

diff --git a/opt/TokenizerStats/.gitignore b/opt/TokenizerStats/.gitignore
index 60128efc..60b42393 100644
--- a/opt/TokenizerStats/.gitignore
+++ b/opt/TokenizerStats/.gitignore
@@ -12,3 +12,4 @@ fig/
 *.tar.*
 archive/
 *.slurm
+*.jld2
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/main.jl b/opt/TokenizerStats/main.jl
index 0feb2539..70e9f21d 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, "TRACYJL_WAIT_FOR_TRACY")
+    @info "Waiting for tracy to connect..."
+    wait_for_tracy()
+    @info "Connected!"
+end
+
 exit(main(ARGS))
diff --git a/opt/TokenizerStats/src/TokenizerStats.jl b/opt/TokenizerStats/src/TokenizerStats.jl
index 0b65bf8c..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[]
diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index a34aa93b..60a83761 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -26,7 +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"
diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index 29135e03..54e1b934 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -27,7 +27,7 @@ function usage_stats!(stats, code::Vector{UInt32}, 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)
@@ -231,7 +231,7 @@ function model_loss(dataset::DatasetConfig, ref_file::String, output::String)
 
 end
 
-@annotate function avg_information_loss(dataset::DatasetConfig, ref_file::String, output::String; split="val")
+@tracepoint function avg_information_loss(dataset::DatasetConfig, ref_file::String, output::String; split="val")
     # Init MPI
     MPI.Init()
     comm = MPI.COMM_WORLD
diff --git a/opt/TokenizerStats/src/ngrams.jl b/opt/TokenizerStats/src/ngrams.jl
index 62c04158..673985e7 100644
--- a/opt/TokenizerStats/src/ngrams.jl
+++ b/opt/TokenizerStats/src/ngrams.jl
@@ -415,7 +415,7 @@ 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
@@ -508,7 +508,7 @@ 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{UInt32}, encoding["input_ids"])
     (unk_token_id ∉ code) && return zeros(length(N))
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

From 5b397a1c537fdba8a76a0eb710d3b63490d62416 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 2 Jun 2025 15:36:28 -0400
Subject: [PATCH 45/54] fix: avoid converting ngrams to dict during
 serialization

Doing so causes a large increase in peak memory usage (Dict effectively
is copied). Keeping it as a Dict from the get-go avoids this

Added a test to check for regressions in serialization overhead
---
 opt/TokenizerStats/src/collect.jl          |  6 +++---
 opt/TokenizerStats/src/serialize.jl        |  6 +++---
 opt/TokenizerStats/test/serialize_tests.jl | 22 +++++++++++++++++++++-
 3 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/opt/TokenizerStats/src/collect.jl b/opt/TokenizerStats/src/collect.jl
index 54e1b934..15ddf596 100644
--- a/opt/TokenizerStats/src/collect.jl
+++ b/opt/TokenizerStats/src/collect.jl
@@ -1,9 +1,9 @@
 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,UInt32}), 5),
+        ngrams=ntuple(i -> CountMap(Dict{NTuple{i,UInt32}, Int}()), 5),
     )
 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/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

From 2ad4d759c2c2e2710ac09718bb408ca8db9c3907 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 2 Jun 2025 15:37:53 -0400
Subject: [PATCH 46/54] feat: fix merge and refactor to reduce memory usage

---
 opt/TokenizerStats/src/cli.jl     | 9 ++++-----
 opt/TokenizerStats/submit_jobs.py | 2 +-
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index 60a83761..6e66e421 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -73,7 +73,7 @@ end
     end
     @add_arg_table! s["merge"] begin
         "--pattern"
-        default = r"usage.+?_rank_\d+\.jld2"
+        default = r"usage.+?_rank_\d+\.jld2$"
         arg_type = Regex
         "directory"
         help = "Directory to search for files to merge"
@@ -152,12 +152,11 @@ function merge_usage_stats(files::Vector{String}; output::String="merged.jld2",
                     merged[split]["out_of_vocab"] += other["out_of_vocab"]
                     merged[split]["samples"] += other["samples"]
                     merged[split]["out_of_vocab"] += other["out_of_vocab"]
-                    merged[split]["fertility"] = Dict(mergewith(+, merged[split]["fertility"], other["fertility"]))
-                    merged[split]["nunique"] = Dict(mergewith(+, merged[split]["nunique"], other["nunique"]))
+                    mergewith!(+, merged[split]["fertility"], other["fertility"])
+                    mergewith!(+, merged[split]["nunique"], other["nunique"])
                     for n in 1:length(merged[split]["ngrams"])
-                        a_ngram = merged[split]["ngrams"]["$n"]
                         b_ngram = compact_ngrams(other["ngrams"]["$n"])
-                        merged[split]["ngrams"]["$n"] = Dict(mergewith(+, a_ngram, b_ngram))
+                        mergewith!(+, merged[split]["ngrams"]["$n"], b_ngram)
                     end
                 else
                     merged[split]["out_of_vocab"] = other[split]["out_of_vocab"]
diff --git a/opt/TokenizerStats/submit_jobs.py b/opt/TokenizerStats/submit_jobs.py
index b7255ac6..8a0baeae 100755
--- a/opt/TokenizerStats/submit_jobs.py
+++ b/opt/TokenizerStats/submit_jobs.py
@@ -354,7 +354,7 @@ def usage_array(
     }
     wk._add_process(p)
     wk.add_process(
-        ["submit_tok_stats.sh", "merge", p.output, output],
+        ["submit_tok_stats.sh", "merge", p.output.with_suffix(""), output],
         inputs=[p.output],
         output=output,
         slurm=slurm,

From 693e99f4b06d228d3004b99d4048c929c5649d54 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 13 Jun 2025 09:50:56 -0400
Subject: [PATCH 47/54] add tracy profile option

---
 opt/TokenizerStats/activate             |  2 +-
 opt/TokenizerStats/main.jl              |  2 +-
 opt/TokenizerStats/src/cli.jl           | 71 +++++++++++++++----------
 opt/TokenizerStats/submit_precompile.sh |  2 +-
 opt/TokenizerStats/submit_tok_stats.sh  | 11 +++-
 5 files changed, 55 insertions(+), 33 deletions(-)

diff --git a/opt/TokenizerStats/activate b/opt/TokenizerStats/activate
index 6c072d92..94d901db 100755
--- a/opt/TokenizerStats/activate
+++ b/opt/TokenizerStats/activate
@@ -5,7 +5,7 @@ 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"
diff --git a/opt/TokenizerStats/main.jl b/opt/TokenizerStats/main.jl
index 70e9f21d..c079f1df 100755
--- a/opt/TokenizerStats/main.jl
+++ b/opt/TokenizerStats/main.jl
@@ -2,7 +2,7 @@
 using TokenizerStats: main
 using Tracy: wait_for_tracy
 
-if haskey(ENV, "TRACYJL_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!"
diff --git a/opt/TokenizerStats/src/cli.jl b/opt/TokenizerStats/src/cli.jl
index 6e66e421..4d4e5404 100644
--- a/opt/TokenizerStats/src/cli.jl
+++ b/opt/TokenizerStats/src/cli.jl
@@ -135,42 +135,57 @@ function parse_splits(x::Vector{<:AbstractString})
 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"]
-                    mergewith!(+, merged[split]["fertility"], other["fertility"])
-                    mergewith!(+, merged[split]["nunique"], other["nunique"])
-                    for n in 1:length(merged[split]["ngrams"])
-                        b_ngram = compact_ngrams(other["ngrams"]["$n"])
-                        mergewith!(+, merged[split]["ngrams"]["$n"], 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/submit_precompile.sh b/opt/TokenizerStats/submit_precompile.sh
index 64f7ac77..d7701a40 100755
--- a/opt/TokenizerStats/submit_precompile.sh
+++ b/opt/TokenizerStats/submit_precompile.sh
@@ -15,7 +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 $@

From a95ed3d2007a7e7dbc35215916af8977979cd38c Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Fri, 13 Jun 2025 09:51:09 -0400
Subject: [PATCH 48/54] feat: transfer tokenizer stats archive to dataden

---
 opt/TokenizerStats/submit_archive.sh | 21 +++++++++++++++++----
 1 file changed, 17 insertions(+), 4 deletions(-)

diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
index a2c36cec..f319be7d 100644
--- a/opt/TokenizerStats/submit_archive.sh
+++ b/opt/TokenizerStats/submit_archive.sh
@@ -6,18 +6,21 @@
 set -x
 my_job_header
 
+# Locate directory with tokenizers stats
+STATS_DIR=$(realpath stats/)
+
 # Add tokenizers.json
-cp tokenizers.json stats/
+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  $stats_archive \
+tar -cavf  "$stats_archive" \
     --xz \
     --exclude="*.slurm" \
     --exclude="*.tmp" \
-    --exclude='*/.unmerged/*'
-    -C stats \
+    --exclude='*/.unmerged/*' \
+    -C "$STATS_DIR" \
     .
 mkdir -p ./archive
 ln -sf $stats_archive ./archive/ngram_tokenizer_stats.tar.xz
@@ -34,3 +37,13 @@ ln -sf $code_archive ./archive/TokenizerStats.tar.xz
 SMIRK_VERSION="v0.1.1"
 curl -L https://github.com/BattModels/smirk/archive/refs/tags/$(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

From 18640c039da9199bb8d83699773486af30421ea6 Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 1 Sep 2025 17:06:26 -0400
Subject: [PATCH 49/54] fix pre-commit and import error

---
 container/build.sh                                | 0
 electrolyte_fm/utils/cache.py                     | 6 +++++-
 opt/TokenizerStats/plots/plots.jl                 | 1 -
 opt/TokenizerStats/plots/src/figures/fe_models.jl | 1 -
 opt/TokenizerStats/plots/src/figures/info_loss.jl | 1 -
 opt/TokenizerStats/plots/src/figures/intrinsic.jl | 1 -
 opt/TokenizerStats/plots/src/plot_utils.jl        | 2 --
 opt/TokenizerStats/plots/src/prognostics.jl       | 1 -
 opt/TokenizerStats/python/helper/loader.py        | 1 -
 opt/TokenizerStats/test/cli_tests.jl              | 1 -
 test/test_dataset.py                              | 2 +-
 11 files changed, 6 insertions(+), 11 deletions(-)
 mode change 100644 => 100755 container/build.sh

diff --git a/container/build.sh b/container/build.sh
old mode 100644
new mode 100755
diff --git a/electrolyte_fm/utils/cache.py b/electrolyte_fm/utils/cache.py
index e7e2fb99..b8a753ee 100644
--- a/electrolyte_fm/utils/cache.py
+++ b/electrolyte_fm/utils/cache.py
@@ -51,7 +51,11 @@ def cached_download(url: str, path: Path, disable_ssl=False) -> Path:
         import ssl
         import urllib.request
 
-        ctx = ssl.create_default_context() if not disable_ssl else ssl._create_unverified_context()
+        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})
diff --git a/opt/TokenizerStats/plots/plots.jl b/opt/TokenizerStats/plots/plots.jl
index cdaa84ff..5b97d2f6 100644
--- a/opt/TokenizerStats/plots/plots.jl
+++ b/opt/TokenizerStats/plots/plots.jl
@@ -175,4 +175,3 @@ with_theme(SmirkPaperPlots.theme()) do
         end
     end
 end
-
diff --git a/opt/TokenizerStats/plots/src/figures/fe_models.jl b/opt/TokenizerStats/plots/src/figures/fe_models.jl
index 76ab19ae..3a6f8830 100644
--- a/opt/TokenizerStats/plots/src/figures/fe_models.jl
+++ b/opt/TokenizerStats/plots/src/figures/fe_models.jl
@@ -204,4 +204,3 @@ function val_ci_dodge(models; dodge::Union{AbstractVector,Nothing}=nothing, scal
     )
     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 4ae27369..46b98f5b 100644
--- a/opt/TokenizerStats/plots/src/figures/info_loss.jl
+++ b/opt/TokenizerStats/plots/src/figures/info_loss.jl
@@ -578,4 +578,3 @@ function figure_ngram_metrics(tok_info, df_loss, df_info; ngram=5)
 
     return f
 end
-
diff --git a/opt/TokenizerStats/plots/src/figures/intrinsic.jl b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
index 192d9c35..b9e1be08 100644
--- a/opt/TokenizerStats/plots/src/figures/intrinsic.jl
+++ b/opt/TokenizerStats/plots/src/figures/intrinsic.jl
@@ -144,4 +144,3 @@ function Makie.plot!(plt::VSpan)
     poly!(plt, p; Makie.shared_attributes(plt, Poly)...)
     return plt
 end
-
diff --git a/opt/TokenizerStats/plots/src/plot_utils.jl b/opt/TokenizerStats/plots/src/plot_utils.jl
index 46fd4a25..2b853de7 100644
--- a/opt/TokenizerStats/plots/src/plot_utils.jl
+++ b/opt/TokenizerStats/plots/src/plot_utils.jl
@@ -319,5 +319,3 @@ function barploterrors!(ax, x, y, dodge; std=nothing, colormap, colorrange=nothi
 
     return h
 end
-
-
diff --git a/opt/TokenizerStats/plots/src/prognostics.jl b/opt/TokenizerStats/plots/src/prognostics.jl
index ebe95e92..3b625006 100644
--- a/opt/TokenizerStats/plots/src/prognostics.jl
+++ b/opt/TokenizerStats/plots/src/prognostics.jl
@@ -84,4 +84,3 @@ function Base.show(io::IO, mime::MIME"text/plain", stt::SpearmanTTest)
     println(io, "t:       $(round(stt.t; sigdigits=3))")
     println(io, "p-value: $(round(pvalue(stt); sigdigits=3))")
 end
-
diff --git a/opt/TokenizerStats/python/helper/loader.py b/opt/TokenizerStats/python/helper/loader.py
index 3fffcbf4..9433c799 100644
--- a/opt/TokenizerStats/python/helper/loader.py
+++ b/opt/TokenizerStats/python/helper/loader.py
@@ -1,4 +1,3 @@
-from itertools import islice
 from pathlib import Path
 from dataclasses import dataclass
 from datasets import load_dataset
diff --git a/opt/TokenizerStats/test/cli_tests.jl b/opt/TokenizerStats/test/cli_tests.jl
index c15cc797..7b7fcc8c 100644
--- a/opt/TokenizerStats/test/cli_tests.jl
+++ b/opt/TokenizerStats/test/cli_tests.jl
@@ -11,4 +11,3 @@ end
     @test Set(parse_splits("val,train")) == Set(["val", "train"])
     @test Set(parse_splits("all")) == Set(["train", "val", "test"])
 end
-
diff --git a/test/test_dataset.py b/test/test_dataset.py
index 79c00566..20798d0a 100644
--- a/test/test_dataset.py
+++ b/test/test_dataset.py
@@ -10,10 +10,10 @@
     RobertaDataSet,
     tmQMDataModule,
 )
-from electrolyte_fm.data_modules.molnet_dataset import strip_unk_tokens
 from electrolyte_fm.data_modules.utils import (
     MolEncoding,
     encode_molecules,
+    strip_unk_tokens,
 )
 from electrolyte_fm.utils.tokenizer import load_tokenizer
 

From ebfa2e7fae77663e3fc12dbaa31832ed22aecb9f Mon Sep 17 00:00:00 2001
From: Alexius Wadell <awadell@gmail.com>
Date: Mon, 1 Sep 2025 17:14:35 -0400
Subject: [PATCH 50/54] merge updates from main

---
 container/training.def | 41 ++++++++++++++++++++++-------------------
 submit/dgx.j2          |  6 ++++--
 2 files changed, 26 insertions(+), 21 deletions(-)

diff --git a/container/training.def b/container/training.def
index 8d57a45d..224ac1fd 100644
--- a/container/training.def
+++ b/container/training.def
@@ -3,36 +3,39 @@ From: nvcr.io/nvidia/pytorch:{{ NGC_VERSION }}
 
 %arguments
     NGC_VERSION=25.01-py3
-    POETRY_VERSION=1.8.3
     PROJECT_DIR=/mist
 
 %files
     pyproject.toml {{ PROJECT_DIR }}/pyproject.toml
-    poetry.lock {{ PROJECT_DIR }}/poetry.lock
+    uv.lock {{ PROJECT_DIR }}/uv.lock
     README.md {{ PROJECT_DIR }}/README.md
 
 %post
-    apt-get update
-    apt-get install -y python3-venv cargo
-
-    # Install Poetry
-    export POETRY_HOME=/opt/poetry
-    export POETRY_VERSION={{ POETRY_VERSION }}
-    export POETRY_VIRTUALENVS_IN_PROJECT=1
-    export POETRY_VIRTUALENVS_CREATE=1 \
-    export POETRY_NO_INTERACTION=1
-    export POETRY_CACHE_DIR=/tmp/poetry_cache
-    curl -sSL https://install.python-poetry.org | python3 -
+    # Install cargo
+    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --profile=minimal
+    source $HOME/.cargo/env
+
+    # Install UV
+    export UV_UNMANAGED_INSTALL=/opt/uv
+    curl -LsSf https://astral.sh/uv/install.sh | sh
 
     # Setup SSH
     export SSH_AUTH_SOCK={{ SSH_AUTH_SOCK }}
     mkdir -p -m 0600 ~/.ssh
-    ssh-keyscan -H github.com >> ~/.ssh/known_hosts
+    ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts
+    ssh git@github.com || echo "success"
 
-    # Install project
-    $POETRY_HOME/bin/poetry install --compile --no-root --all-extras --directory {{ PROJECT_DIR }}
-    rm -rf $POETRY_CACHE_DIR
+    # Install dependencies
+    $UV_UNMANAGED_INSTALL/uv sync \
+        --directory {{ PROJECT_DIR }} \
+        --frozen \
+        --no-install-project \
+        --link-mode=copy \
+        --compile-bytecode \
+        --no-cache
 
 %runscript
-    source {{ PROJECT_DIR }}/.venv/bin/activate
-    exec $@
+    /opt/uv/uv run --project {{ PROJECT_DIR }} $@
+
+%environment
+    export TRITON_LIBCUDA_PATH=/usr/local/cuda/compat/lib.real/libcuda.so
diff --git a/submit/dgx.j2 b/submit/dgx.j2
index 0c88d7d3..b4cd52a3 100644
--- a/submit/dgx.j2
+++ b/submit/dgx.j2
@@ -49,6 +49,7 @@ cat > ${PL_CONFIG}<<EOF
 EOF
 
 sbcast -fp ${PL_CONFIG} ${PL_CONFIG}
+cat ${PL_CONFIG}
 
 {%- if requeue %}
 # Look for a requeue checkpoint
@@ -61,9 +62,10 @@ fi
 {%- endif %}
 
 srun --mpi=pmix \
-$(which apptainer) run \
---bind /lustre/fs0,${TMPDIR:-/tmp} \
+$(which apptainer) run  \
+--bind /lustre/fs0,$TMPDIR \
 --nv \
+--env-file $ENV_FILE \
 {{ container }} \
 ${PWD}/submit/set_node_rank \
 {%- if nsys is defined %}

From 634160effa26a58ca4f77660c0e5efe107ce7dbc Mon Sep 17 00:00:00 2001
From: Anoushka Bhutani <abhutani@umich.edu>
Date: Mon, 20 Oct 2025 22:43:26 -0400
Subject: [PATCH 51/54] fix: pre-commit

---
 opt/TokenizerStats/check_ambi_smiles.py | 38 -------------------------
 1 file changed, 38 deletions(-)

diff --git a/opt/TokenizerStats/check_ambi_smiles.py b/opt/TokenizerStats/check_ambi_smiles.py
index 92661a36..e258b77b 100755
--- a/opt/TokenizerStats/check_ambi_smiles.py
+++ b/opt/TokenizerStats/check_ambi_smiles.py
@@ -141,44 +141,6 @@ def process_smiles(
     typer.echo(f"Results written incrementally to: {output_file}")
 
 
-@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}")
-
-
 @app.command("stats")
 def tabulate_ambiguities(
     output_file: str,

From 3fcd19df65285d89135bf2e65cf7ddf5b51aa2f9 Mon Sep 17 00:00:00 2001
From: Anoushka Bhutani <abhutani@umich.edu>
Date: Mon, 20 Oct 2025 22:47:33 -0400
Subject: [PATCH 52/54] mark shebang executable

---
 opt/TokenizerStats/submit_archive.sh | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 mode change 100644 => 100755 opt/TokenizerStats/submit_archive.sh

diff --git a/opt/TokenizerStats/submit_archive.sh b/opt/TokenizerStats/submit_archive.sh
old mode 100644
new mode 100755

From a9db85a815a65b6c30e9fc2a3e005e852070871d Mon Sep 17 00:00:00 2001
From: Anoushka Bhutani <abhutani@umich.edu>
Date: Mon, 20 Oct 2025 22:51:44 -0400
Subject: [PATCH 53/54] formatting

---
 electrolyte_fm/data_modules/utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py
index d8a39014..576befc2 100644
--- a/electrolyte_fm/data_modules/utils.py
+++ b/electrolyte_fm/data_modules/utils.py
@@ -3,7 +3,6 @@
 from enum import Enum
 import random
 from asyncio import Semaphore
-from enum import Enum
 from typing import Optional, TypeVar
 
 from datasets import Dataset, DatasetDict, IterableDatasetDict

From f13512c23bdb7e7d802d62ad7d19862b86bf40c5 Mon Sep 17 00:00:00 2001
From: Anoushka Bhutani <abhutani@umich.edu>
Date: Mon, 20 Oct 2025 23:32:03 -0400
Subject: [PATCH 54/54] formatting

---
 electrolyte_fm/data_modules/utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/electrolyte_fm/data_modules/utils.py b/electrolyte_fm/data_modules/utils.py
index 904c98fd..0dc3132a 100644
--- a/electrolyte_fm/data_modules/utils.py
+++ b/electrolyte_fm/data_modules/utils.py
@@ -8,7 +8,6 @@
 from rdkit import Chem
 from datasets import Dataset, DatasetDict, IterableDatasetDict
 from datasets.distributed import split_dataset_by_node
-from rdkit import Chem
 from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles
 from sklearn.model_selection import GroupShuffleSplit