chore: using poetry for dependency management

.github/workflows/tests.yml

@@ -42,18 +42,20 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          cache: 'pip'
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
       - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install flake8 pytest
-          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        run: poetry install --no-interaction
       - name: Lint with flake8
         run: |
           # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          poetry run flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
           # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+          poetry run flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+      - name: black code formatting
+        run: poetry run black . --check
+      - name: isort linting
+        run: poetry run isort . --check-only --diff
       - name: Run Unit Tests
         run: |
           make unit-test

.gitignore

@@ -99,7 +99,7 @@ ipython_config.py
 #   This is especially recommended for binary packages to ensure reproducibility, and is more
 #   commonly ignored for libraries.
 #   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
-#poetry.lock
+poetry.lock
 
 # pdm
 #   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.

README.md

@@ -5,12 +5,10 @@ This codebase contains training scripts and analysis code for Sparse AutoEncoder
 
 ## Set Up
 
-```
-
-conda create --name mats_sae_training python=3.11 -y
-conda activate mats_sae_training
-pip install -r requirements.txt
+This project uses [Poetry](https://python-poetry.org/) for dependency management. Ensure Poetry is installed, then to install the dependencies, run:
 
+```
+poetry install
 ```
 
 ## Background

makefile

@@ -1,14 +1,19 @@
 format:
+	poetry run black .
+	poetry run isort .
 
 check-format:
+	poetry run flake8 .
+	poetry run black --check .
+	poetry run isort --check-only --diff .
 
 
 test:
 	make unit-test
 	make acceptance-test
 
 unit-test:
-	pytest -v --cov=sae_training/ --cov-report=term-missing --cov-branch tests/unit
+	poetry run pytest -v --cov=sae_training/ --cov-report=term-missing --cov-branch tests/unit
 
 acceptance-test:
-	pytest -v --cov=sae_training/ --cov-report=term-missing --cov-branch tests/acceptance
+	poetry run pytest -v --cov=sae_training/ --cov-report=term-missing --cov-branch tests/acceptance

pyproject.toml

@@ -0,0 +1,36 @@
+[tool.poetry]
+name = "mats_sae_training"
+version = "0.1.0"
+description = "Training Sparse Autoencoders (SAEs)"
+authors = ["Joseph Bloom"]
+readme = "README.md"
+packages = [{include = "sae_analysis"}, {include = "sae_training"}]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+transformer-lens = "^1.14.0"
+transformers = "^4.38.1"
+jupyter = "^1.0.0"
+plotly = "^5.19.0"
+plotly-express = "^0.4.1"
+nbformat = "^5.9.2"
+ipykernel = "^6.29.2"
+matplotlib = "^3.8.3"
+matplotlib-inline = "^0.1.6"
+eindex = {git = "https://github.com/callummcdougall/eindex.git"}
+
+
+[tool.poetry.group.dev.dependencies]
+black = "^24.2.0"
+pytest = "^8.0.2"
+pytest-cov = "^4.1.0"
+pre-commit = "^3.6.2"
+flake8 = "^7.0.0"
+isort = "^5.13.2"
+
+[tool.isort]
+profile = "black"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

requirements.txt

@@ -7,7 +7,8 @@ nbformat==5.9.2
 ipykernel==6.27.1
 matplotlib==3.8.2
 matplotlib-inline==0.1.6
-pylint==3.0.2
+flake8==7.0.0
+isort==5.13.2
 black==23.11.0
 pytest==7.4.3
 pytest-cov==4.1.0

sae_analysis/dashboard_runner.py

@@ -14,10 +14,10 @@
 import plotly
 import plotly.express as px
 import torch
+import wandb
 from torch.nn.functional import cosine_similarity
 from tqdm import tqdm
 
-import wandb
 from sae_analysis.visualizer.data_fns import get_feature_data
 from sae_training.utils import LMSparseAutoencoderSessionloader
 
@@ -148,9 +148,7 @@ def init_sae_session(self):
             self.activation_store,
         ) = LMSparseAutoencoderSessionloader.load_session_from_pretrained(self.sae_path)
 
-    def get_tokens(
-        self, n_batches_to_sample_from=2**12, n_prompts_to_select=4096 * 6
-    ):
+    def get_tokens(self, n_batches_to_sample_from=2**12, n_prompts_to_select=4096 * 6):
         """
         Get the tokens needed for dashboard generation.
         """

sae_analysis/visualizer/data_fns.py

@@ -1,13 +1,10 @@
 import gzip
-import json
 import os
 import pickle
 import time
-from collections import defaultdict
 from dataclasses import dataclass
-from functools import partial
 from pathlib import Path
-from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Dict, List, Literal, Optional, Tuple, Union
 
 import einops
 import numpy as np

sae_analysis/visualizer/html_fns.py

@@ -230,8 +230,8 @@ def generate_tables_html(
         ],
         [None, "+.2f", ".1%", None, "+.2f", "+.2f", None, "+.2f", "+.2f"],
     ):
-        fn = (
-            lambda m: str(mylist[int(m.group(1))])
+        fn = lambda m: (
+            str(mylist[int(m.group(1))])
             if myformat is None
             else format(mylist[int(m.group(1))], myformat)
         )

sae_analysis/visualizer/model_fns.py

@@ -1,11 +1,11 @@
-from transformer_lens import utils
-import torch
 import pprint
+from dataclasses import dataclass
+
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import tqdm.notebook as tqdm
-from dataclasses import dataclass
-
+from transformer_lens import utils
 
 DTYPES = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
 

sae_training/activations_store.py

@@ -206,9 +206,9 @@ def get_buffer(self, n_batches_in_buffer):
                     taking_subset_of_file = True
 
                 # Add it to the buffer
-                new_buffer[
-                    n_tokens_filled : n_tokens_filled + activations.shape[0]
-                ] = activations
+                new_buffer[n_tokens_filled : n_tokens_filled + activations.shape[0]] = (
+                    activations
+                )
 
                 # Update counters
                 n_tokens_filled += activations.shape[0]
@@ -235,9 +235,9 @@ def get_buffer(self, n_batches_in_buffer):
         for refill_batch_idx_start in refill_iterator:
             refill_batch_tokens = self.get_batch_tokens()
             refill_activations = self.get_activations(refill_batch_tokens)
-            new_buffer[
-                refill_batch_idx_start : refill_batch_idx_start + batch_size
-            ] = refill_activations
+            new_buffer[refill_batch_idx_start : refill_batch_idx_start + batch_size] = (
+                refill_activations
+            )
 
             # pbar.update(1)
 

sae_training/cache_activations_runner.py

@@ -2,8 +2,8 @@
 import os
 
 import torch
-from transformer_lens import HookedTransformer
 from tqdm import tqdm
+from transformer_lens import HookedTransformer
 
 from sae_training.activations_store import ActivationsStore
 from sae_training.config import CacheActivationsRunnerConfig

sae_training/config.py

@@ -3,7 +3,6 @@
 from typing import Optional
 
 import torch
-
 import wandb
 
 
@@ -22,9 +21,9 @@ class RunnerConfig(ABC):
     is_dataset_tokenized: bool = True
     context_size: int = 128
     use_cached_activations: bool = False
-    cached_activations_path: Optional[
-        str
-    ] = None  # Defaults to "activations/{dataset}/{model}/{full_hook_name}_{hook_point_head_index}"
+    cached_activations_path: Optional[str] = (
+        None  # Defaults to "activations/{dataset}/{model}/{full_hook_name}_{hook_point_head_index}"
+    )
 
     # SAE Parameters
     d_in: int = 512
@@ -61,7 +60,9 @@ class LanguageModelSAERunnerConfig(RunnerConfig):
     # Training Parameters
     l1_coefficient: float = 1e-3
     lr: float = 3e-4
-    lr_scheduler_name: str = "constantwithwarmup"  # constant, constantwithwarmup, linearwarmupdecay, cosineannealing, cosineannealingwarmup
+    lr_scheduler_name: str = (
+        "constantwithwarmup"  # constant, constantwithwarmup, linearwarmupdecay, cosineannealing, cosineannealingwarmup
+    )
     lr_warm_up_steps: int = 500
     train_batch_size: int = 4096
 

sae_training/evals.py

@@ -2,11 +2,11 @@
 
 import pandas as pd
 import torch
+import wandb
 from tqdm import tqdm
 from transformer_lens import HookedTransformer
 from transformer_lens.utils import get_act_name
 
-import wandb
 from sae_training.activations_store import ActivationsStore
 from sae_training.sparse_autoencoder import SparseAutoencoder
 
@@ -27,7 +27,10 @@ def run_evals(
 
     # Get Reconstruction Score
     losses_df = recons_loss_batched(
-        sparse_autoencoder, model, activation_store, n_batches = 10,
+        sparse_autoencoder,
+        model,
+        activation_store,
+        n_batches=10,
     )
 
     recons_score = losses_df["score"].mean()

sae_training/geom_median/src/geom_median/numpy/main.py

@@ -1,8 +1,8 @@
 import numpy as np
 
+from . import utils
 from .weiszfeld_array import geometric_median_array, geometric_median_per_component
 from .weiszfeld_list_of_array import geometric_median_list_of_array
-from . import utils
 
 
 def compute_geometric_median(

sae_training/geom_median/src/geom_median/numpy/utils.py

@@ -1,4 +1,5 @@
 from itertools import zip_longest
+
 import numpy as np
 
 

sae_training/geom_median/src/geom_median/numpy/weiszfeld_array.py

@@ -1,6 +1,7 @@
-import numpy as np
 from types import SimpleNamespace
 
+import numpy as np
+
 
 def geometric_median_array(points, weights, eps=1e-6, maxiter=100, ftol=1e-20):
     """
@@ -36,9 +37,11 @@ def geometric_median_array(points, weights, eps=1e-6, maxiter=100, ftol=1e-20):
 
     return SimpleNamespace(
         median=median,
-        termination="function value converged within tolerance"
-        if early_termination
-        else "maximum iterations reached",
+        termination=(
+            "function value converged within tolerance"
+            if early_termination
+            else "maximum iterations reached"
+        ),
         logs=logs,
     )
 

sae_training/geom_median/src/geom_median/numpy/weiszfeld_list_of_array.py

@@ -1,6 +1,7 @@
-import numpy as np
 from types import SimpleNamespace
 
+import numpy as np
+
 
 def geometric_median_list_of_array(points, weights, eps=1e-6, maxiter=100, ftol=1e-20):
     """
@@ -38,9 +39,11 @@ def geometric_median_list_of_array(points, weights, eps=1e-6, maxiter=100, ftol=
 
     return SimpleNamespace(
         median=median,
-        termination="function value converged within tolerance"
-        if early_termination
-        else "maximum iterations reached",
+        termination=(
+            "function value converged within tolerance"
+            if early_termination
+            else "maximum iterations reached"
+        ),
         logs=logs,
     )
 

sae_training/geom_median/src/geom_median/torch/main.py

@@ -1,8 +1,8 @@
 import torch
 
+from . import utils
 from .weiszfeld_array import geometric_median_array, geometric_median_per_component
 from .weiszfeld_list_of_array import geometric_median_list_of_array
-from . import utils
 
 
 def compute_geometric_median(

sae_training/geom_median/src/geom_median/torch/utils.py

@@ -1,4 +1,5 @@
 from itertools import zip_longest
+
 import torch
 
 

sae_training/geom_median/src/geom_median/torch/weiszfeld_array.py

@@ -48,9 +48,11 @@ def geometric_median_array(points, weights, eps=1e-6, maxiter=100, ftol=1e-20):
     return SimpleNamespace(
         median=median,
         new_weights=new_weights,
-        termination="function value converged within tolerance"
-        if early_termination
-        else "maximum iterations reached",
+        termination=(
+            "function value converged within tolerance"
+            if early_termination
+            else "maximum iterations reached"
+        ),
         logs=logs,
     )
 

sae_training/geom_median/src/geom_median/torch/weiszfeld_list_of_array.py

@@ -1,6 +1,7 @@
+from types import SimpleNamespace
+
 import numpy as np
 import torch
-from types import SimpleNamespace
 
 
 def geometric_median_list_of_array(points, weights, eps=1e-6, maxiter=100, ftol=1e-20):
@@ -43,9 +44,11 @@ def geometric_median_list_of_array(points, weights, eps=1e-6, maxiter=100, ftol=
     return SimpleNamespace(
         median=median,
         new_weights=new_weights,
-        termination="function value converged within tolerance"
-        if early_termination
-        else "maximum iterations reached",
+        termination=(
+            "function value converged within tolerance"
+            if early_termination
+            else "maximum iterations reached"
+        ),
         logs=logs,
     )