From 18d7e8a6ac5b38cfd121ca67af51635db2643d18 Mon Sep 17 00:00:00 2001 From: Fengyuan Hu <127644049+HuFY-dev@users.noreply.github.com> Date: Fri, 29 Mar 2024 00:05:15 -0400 Subject: [PATCH 1/6] Resolved import error for PyTorch >= 2.2 --- sparse_autoencoder/optimizer/adam_with_reset.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/sparse_autoencoder/optimizer/adam_with_reset.py b/sparse_autoencoder/optimizer/adam_with_reset.py index 5a84c941..dd1921c7 100644 --- a/sparse_autoencoder/optimizer/adam_with_reset.py +++ b/sparse_autoencoder/optimizer/adam_with_reset.py @@ -2,16 +2,20 @@ This reset method is useful when resampling dead neurons during training. """ -from collections.abc import Iterator +from collections.abc import Iterable, Iterator +from typing import Any +from typing_extensions import TypeAlias from jaxtyping import Float, Int from torch import Tensor from torch.nn.parameter import Parameter from torch.optim import Adam -from torch.optim.optimizer import params_t from sparse_autoencoder.tensor_types import Axis +# params_t was renamed to ParamsT in PyTorch 2.2, which caused import errors +# Copied from PyTorch 2.2 with modifications for better style +ParamsT: TypeAlias = Iterable[Tensor] | Iterable[dict[str, Any]] class AdamWithReset(Adam): """Adam Optimizer with a reset method. @@ -35,7 +39,7 @@ class AdamWithReset(Adam): def __init__( # (extending existing implementation) self, - params: params_t, + params: ParamsT, lr: float | Float[Tensor, Axis.names(Axis.SINGLE_ITEM)] = 1e-3, betas: tuple[float, float] = (0.9, 0.999), eps: float = 1e-8, From 7cbbf82cff692a133906746c0738d23e9d998b9c Mon Sep 17 00:00:00 2001 From: hufy <127644049+HuFY-dev@users.noreply.github.com> Date: Fri, 29 Mar 2024 00:19:43 -0400 Subject: [PATCH 2/6] Fixed style issue. --- sparse_autoencoder/optimizer/adam_with_reset.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sparse_autoencoder/optimizer/adam_with_reset.py b/sparse_autoencoder/optimizer/adam_with_reset.py index dd1921c7..2dee9b15 100644 --- a/sparse_autoencoder/optimizer/adam_with_reset.py +++ b/sparse_autoencoder/optimizer/adam_with_reset.py @@ -4,15 +4,16 @@ """ from collections.abc import Iterable, Iterator from typing import Any -from typing_extensions import TypeAlias from jaxtyping import Float, Int from torch import Tensor from torch.nn.parameter import Parameter from torch.optim import Adam +from typing_extensions import TypeAlias from sparse_autoencoder.tensor_types import Axis + # params_t was renamed to ParamsT in PyTorch 2.2, which caused import errors # Copied from PyTorch 2.2 with modifications for better style ParamsT: TypeAlias = Iterable[Tensor] | Iterable[dict[str, Any]] From 0d936148dd70ee4a7b5c07eb52a343aa6a1475d1 Mon Sep 17 00:00:00 2001 From: hufy <127644049+HuFY-dev@users.noreply.github.com> Date: Fri, 29 Mar 2024 00:25:12 -0400 Subject: [PATCH 3/6] Fixed style issue. --- sparse_autoencoder/optimizer/adam_with_reset.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/sparse_autoencoder/optimizer/adam_with_reset.py b/sparse_autoencoder/optimizer/adam_with_reset.py index 2dee9b15..73fb88ab 100644 --- a/sparse_autoencoder/optimizer/adam_with_reset.py +++ b/sparse_autoencoder/optimizer/adam_with_reset.py @@ -2,6 +2,7 @@ This reset method is useful when resampling dead neurons during training. """ + from collections.abc import Iterable, Iterator from typing import Any @@ -18,6 +19,7 @@ # Copied from PyTorch 2.2 with modifications for better style ParamsT: TypeAlias = Iterable[Tensor] | Iterable[dict[str, Any]] + class AdamWithReset(Adam): """Adam Optimizer with a reset method. From cf79a57c84c65ae15196363d68b810f4cbdfa62c Mon Sep 17 00:00:00 2001 From: HuFY-dev <127644049+HuFY-dev@users.noreply.github.com> Date: Sun, 31 Mar 2024 20:51:43 -0400 Subject: [PATCH 4/6] Added new architecture. --- docs/content/demo.ipynb | 231 +- poetry.lock | 2851 +++++++++-------- .../autoencoder/components/linear_decoder.py | 164 + .../autoencoder/components/tanh_encoder.py | 229 ++ sparse_autoencoder/autoencoder/model.py | 49 +- sparse_autoencoder/train/sweep.py | 4 +- sparse_autoencoder/train/sweep_config.py | 8 + 7 files changed, 2129 insertions(+), 1407 deletions(-) create mode 100644 sparse_autoencoder/autoencoder/components/linear_decoder.py create mode 100644 sparse_autoencoder/autoencoder/components/tanh_encoder.py diff --git a/docs/content/demo.ipynb b/docs/content/demo.ipynb index edb7d54e..26a90dd3 100644 --- a/docs/content/demo.ipynb +++ b/docs/content/demo.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = '1'" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -38,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -62,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -146,19 +156,23 @@ " pre_download=Parameter(value=False), # Default to streaming the dataset\n", " ),\n", " autoencoder=AutoencoderHyperparameters(\n", - " expansion_factor=Parameter(value=expansion_factor)\n", + " expansion_factor=Parameter(value=expansion_factor),\n", + " type=Parameter(\"tanh_encoder\"),\n", " ),\n", " pipeline=PipelineHyperparameters(\n", " max_activations=Parameter(1_000_000_000),\n", " checkpoint_frequency=Parameter(100_000_000),\n", " validation_frequency=Parameter(100_000_000),\n", - " max_store_size=Parameter(1_000_000),\n", + " max_store_size=Parameter(100_000),\n", + " source_data_batch_size=Parameter(16),\n", + " train_batch_size=Parameter(8192)\n", " ),\n", " activation_resampler=ActivationResamplerHyperparameters(\n", " resample_interval=Parameter(200_000_000),\n", " n_activations_activity_collate=Parameter(100_000_000),\n", " threshold_is_dead_portion_fires=Parameter(1e-6),\n", " max_n_resamples=Parameter(4),\n", + " resample_dataset_size=Parameter(81920),\n", " ),\n", " ),\n", " method=Method.RANDOM,\n", @@ -184,9 +198,212 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], + "source": [ + "os.environ[\"TOKENIZERS_PARALLELISM\"]=\"false\"" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Create sweep with ID: 00f0cyw4\n", + "Sweep URL: https://wandb.ai/hufy-dev/sparse-autoencoder/sweeps/00f0cyw4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Agent Starting Run: dsm2zn3g with config:\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \tactivation_resampler: {'max_n_resamples': 4, 'n_activations_activity_collate': 100000000, 'resample_dataset_size': 409600, 'resample_interval': 200000000, 'threshold_is_dead_portion_fires': 1e-06}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \tautoencoder: {'expansion_factor': 4, 'type': 'tanh_encoder'}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \tloss: {'l1_coefficient': 0.02815741075080031}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \toptimizer: {'adam_beta_1': 0.9, 'adam_beta_2': 0.99, 'adam_weight_decay': 0, 'amsgrad': False, 'fused': False, 'lr': 0.0008266150398536362}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \tpipeline: {'checkpoint_frequency': 100000000, 'log_frequency': 100, 'max_activations': 1000000000, 'max_store_size': 500000, 'num_workers_data_loading': 0, 'source_data_batch_size': 8, 'train_batch_size': 4096, 'validation_frequency': 100000000, 'validation_n_activations': 8192}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \trandom_seed: 49\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \tsource_data: {'context_size': 256, 'dataset_column_name': 'input_ids', 'dataset_path': 'alancooney/sae-monology-pile-uncopyrighted-tokenizer-gpt2', 'pre_download': False, 'pre_tokenized': True}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: \tsource_model: {'cache_names': ['blocks.0.hook_mlp_out', 'blocks.1.hook_mlp_out', 'blocks.2.hook_mlp_out', 'blocks.3.hook_mlp_out', 'blocks.4.hook_mlp_out', 'blocks.5.hook_mlp_out', 'blocks.6.hook_mlp_out', 'blocks.7.hook_mlp_out', 'blocks.8.hook_mlp_out', 'blocks.9.hook_mlp_out', 'blocks.10.hook_mlp_out', 'blocks.11.hook_mlp_out'], 'dtype': 'float32', 'hook_dimension': 768, 'name': 'gpt2'}\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mhufy-dev\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "wandb version 0.16.5 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.16.2" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /home/hufy/sparse_autoencoder/docs/content/wandb/run-20240331_183640-dsm2zn3g" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run royal-sweep-1 to Weights & Biases (docs)
Sweep page: https://wandb.ai/hufy-dev/sparse-autoencoder/sweeps/00f0cyw4" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/hufy-dev/sparse-autoencoder" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View sweep at https://wandb.ai/hufy-dev/sparse-autoencoder/sweeps/00f0cyw4" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/hufy-dev/sparse-autoencoder/runs/dsm2zn3g" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded pretrained model gpt2 into HookedTransformer\n", + "Moving model to device: cuda\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b3246f01db11415ba8f4caa918c7c4b1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Resolving data files: 0%| | 0/64 [00:00here for more info. \n", + "\u001b[1;31mView Jupyter log for further details." + ] + } + ], "source": [ "train_gpt_small_mlp_layers()" ] @@ -221,7 +438,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.11.4" }, "vscode": { "interpreter": { diff --git a/poetry.lock b/poetry.lock index a80a2c7b..f0d07e45 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,13 +2,13 @@ [[package]] name = "accelerate" -version = "0.26.1" +version = "0.28.0" description = "Accelerate" optional = false python-versions = ">=3.8.0" files = [ - {file = "accelerate-0.26.1-py3-none-any.whl", hash = "sha256:04df826b84ac7bad8a0a8ab90e6aeacdecb1ea5a2d744d7e94f6735c29183227"}, - {file = "accelerate-0.26.1.tar.gz", hash = "sha256:bf63716b6bd9460d87da970cf4d833abb824ca0aa633be36b741e63a1b504f89"}, 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"jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [[package]] name = "zstandard" diff --git a/sparse_autoencoder/autoencoder/components/linear_decoder.py b/sparse_autoencoder/autoencoder/components/linear_decoder.py new file mode 100644 index 00000000..ff333929 --- /dev/null +++ b/sparse_autoencoder/autoencoder/components/linear_decoder.py @@ -0,0 +1,164 @@ +"""Linear decoder layer.""" +import math +from typing import final + +import einops +from jaxtyping import Float, Int64 +from pydantic import PositiveInt, validate_call +import torch +from torch import Tensor +from torch.nn import Module, Parameter, init + +from sparse_autoencoder.autoencoder.types import ResetOptimizerParameterDetails +from sparse_autoencoder.tensor_types import Axis +from sparse_autoencoder.utils.tensor_shape import shape_with_optional_dimensions + + +@final +class LinearDecoder(Module): + r"""Constrained unit norm linear decoder layer. + + Linear layer decoder, where the dictionary vectors (columns of the weight matrix) are NOT + constrained to have unit norm. + + $$ \begin{align*} + m &= \text{learned features dimension} \\ + n &= \text{input and output dimension} \\ + b &= \text{batch items dimension} \\ + f \in \mathbb{R}^{b \times m} &= \text{encoder output} \\ + W_d \in \mathbb{R}^{n \times m} &= \text{weight matrix} \\ + z \in \mathbb{R}^{b \times m} &= f W_d^T = \text{UnitNormDecoder output (pre-tied bias)} + \end{align*} $$ + + Motivation: + TODO + """ + + _learnt_features: int + """Number of learnt features (inputs to this layer).""" + + _decoded_features: int + """Number of decoded features (outputs from this layer).""" + + _n_components: int | None + + weight: Float[ + Parameter, + Axis.names(Axis.COMPONENT_OPTIONAL, Axis.INPUT_OUTPUT_FEATURE, Axis.LEARNT_FEATURE), + ] + """Weight parameter. + + Each column in the weights matrix acts as a dictionary vector, representing a single basis + element in the learned activation space. + """ + + @property + def reset_optimizer_parameter_details(self) -> list[ResetOptimizerParameterDetails]: + """Reset optimizer parameter details. + + Details of the parameters that should be reset in the optimizer, when resetting + dictionary vectors. + + Returns: + List of tuples of the form `(parameter, axis)`, where `parameter` is the parameter to + reset (e.g. encoder.weight), and `axis` is the axis of the parameter to reset. + """ + return [ResetOptimizerParameterDetails(parameter=self.weight, axis=-1)] + + @validate_call + def __init__( + self, + learnt_features: PositiveInt, + decoded_features: PositiveInt, + n_components: PositiveInt | None, + ) -> None: + """Initialize the constrained unit norm linear layer. + + Args: + learnt_features: Number of learnt features in the autoencoder. + decoded_features: Number of decoded (output) features in the autoencoder. + n_components: Number of source model components the SAE is trained on. + """ + super().__init__() + + self._learnt_features = learnt_features + self._decoded_features = decoded_features + self._n_components = n_components + + # Create the linear layer as per the standard PyTorch linear layer + self.weight = Parameter( + torch.empty( + shape_with_optional_dimensions(n_components, decoded_features, learnt_features), + ) + ) + self.reset_parameters() + + def update_dictionary_vectors( + self, + dictionary_vector_indices: Int64[ + Tensor, Axis.names(Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE_IDX) + ], + updated_weights: Float[ + Tensor, + Axis.names(Axis.COMPONENT_OPTIONAL, Axis.INPUT_OUTPUT_FEATURE, Axis.LEARNT_FEATURE_IDX), + ], + component_idx: int | None = None, + ) -> None: + """Update decoder dictionary vectors. + + Updates the dictionary vectors (rows in the weight matrix) with the given values. Typically + this is used when resampling neurons (dictionary vectors) that have died. + + Args: + dictionary_vector_indices: Indices of the dictionary vectors to update. + updated_weights: Updated weights for just these dictionary vectors. + component_idx: Component index to update. + + Raises: + ValueError: If `component_idx` is not specified when `n_components` is not None. + """ + if dictionary_vector_indices.numel() == 0: + return + + with torch.no_grad(): + if component_idx is None: + if self._n_components is not None: + error_message = "component_idx must be specified when n_components is not None" + raise ValueError(error_message) + + self.weight[:, dictionary_vector_indices] = updated_weights + else: + self.weight[component_idx, :, dictionary_vector_indices] = updated_weights + + def reset_parameters(self) -> None: + """Initialize or reset the parameters.""" + # Assumes we are using ReLU activation function (for e.g. leaky ReLU, the `a` parameter and + # `nonlinerity` must be changed. + init.kaiming_uniform_(self.weight, nonlinearity="relu") + + def forward( + self, x: Float[Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE)] + ) -> Float[Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL, Axis.INPUT_OUTPUT_FEATURE)]: + """Forward pass. + + Args: + x: Input tensor. + + Returns: + Output of the forward pass. + """ + return einops.einsum( + x, + self.weight, + f"{Axis.BATCH} ... {Axis.LEARNT_FEATURE}, \ + ... {Axis.INPUT_OUTPUT_FEATURE} {Axis.LEARNT_FEATURE} \ + -> {Axis.BATCH} ... {Axis.INPUT_OUTPUT_FEATURE}", + ) + + def extra_repr(self) -> str: + """String extra representation of the module.""" + return ( + f"learnt_features={self._learnt_features}, " + f"decoded_features={self._decoded_features}, " + f"n_components={self._n_components}" + ) diff --git a/sparse_autoencoder/autoencoder/components/tanh_encoder.py b/sparse_autoencoder/autoencoder/components/tanh_encoder.py new file mode 100644 index 00000000..0d9b6841 --- /dev/null +++ b/sparse_autoencoder/autoencoder/components/tanh_encoder.py @@ -0,0 +1,229 @@ +"""Linear encoder layer with tanh(ReLU()) activation.""" +import math +from typing import final + +import einops +from jaxtyping import Float, Int64 +from pydantic import PositiveInt, validate_call +import torch +from torch import Tensor +from torch.nn import Module, Parameter, ReLU, init, Tanh + +from sparse_autoencoder.autoencoder.types import ResetOptimizerParameterDetails +from sparse_autoencoder.tensor_types import Axis +from sparse_autoencoder.utils.tensor_shape import shape_with_optional_dimensions + +class TanhReLU(Module): + def __init__(self): + super(TanhReLU, self).__init__() + self.tanh = Tanh() + self.relu = ReLU() + + def forward(self, x): + return self.tanh(self.relu(x)) + +@final +class TanhEncoder(Module): + r"""Linear encoder layer. + + Linear encoder layer (essentially `nn.Linear`, with a ReLU activation function). Designed to be + used as the encoder in a sparse autoencoder (excluding any outer tied bias). + + $$ + \begin{align*} + m &= \text{learned features dimension} \\ + n &= \text{input and output dimension} \\ + b &= \text{batch items dimension} \\ + \overline{\mathbf{x}} \in \mathbb{R}^{b \times n} &= \text{input after tied bias} \\ + W_e \in \mathbb{R}^{m \times n} &= \text{weight matrix} \\ + b_e \in \mathbb{R}^{m} &= \text{bias vector} \\ + f &= \text{ReLU}(\overline{\mathbf{x}} W_e^T + b_e) = \text{LinearEncoder output} + \end{align*} + $$ + """ + + _learnt_features: int + """Number of learnt features (inputs to this layer).""" + + _input_features: int + """Number of input features from the source model.""" + + _n_components: int | None + + weight: Float[ + Parameter, + Axis.names(Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE, Axis.INPUT_OUTPUT_FEATURE), + ] + """Weight parameter. + + Each row in the weights matrix acts as a dictionary vector, representing a single basis + element in the learned activation space. + """ + + bias: Float[Parameter, Axis.names(Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE)] + """Bias parameter.""" + + @property + def reset_optimizer_parameter_details(self) -> list[ResetOptimizerParameterDetails]: + """Reset optimizer parameter details. + + Details of the parameters that should be reset in the optimizer, when resetting + dictionary vectors. + + Returns: + List of tuples of the form `(parameter, axis)`, where `parameter` is the parameter to + reset (e.g. encoder.weight), and `axis` is the axis of the parameter to reset. + """ + return [ + ResetOptimizerParameterDetails(parameter=self.weight, axis=-2), + ResetOptimizerParameterDetails(parameter=self.bias, axis=-1), + ] + + activation_function: TanhReLU + """Activation function.""" + + @validate_call + def __init__( + self, + input_features: PositiveInt, + learnt_features: PositiveInt, + n_components: PositiveInt | None, + ): + """Initialize the linear encoder layer. + + Args: + input_features: Number of input features to the autoencoder. + learnt_features: Number of learnt features in the autoencoder. + n_components: Number of source model components the SAE is trained on. + """ + super().__init__() + + self._learnt_features = learnt_features + self._input_features = input_features + self._n_components = n_components + + self.weight = Parameter( + torch.empty( + shape_with_optional_dimensions(n_components, learnt_features, input_features), + ) + ) + self.bias = Parameter( + torch.zeros(shape_with_optional_dimensions(n_components, learnt_features)) + ) + self.activation_function = TanhReLU() + + self.reset_parameters() + + def reset_parameters(self) -> None: + """Initialize or reset the parameters.""" + # Assumes we are using ReLU activation function (for e.g. leaky ReLU, the `a` parameter and + # `nonlinerity` must be changed. + init.kaiming_uniform_(self.weight, nonlinearity="relu") + + # Bias (approach from nn.Linear) + fan_in = self.weight.size(1) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + init.uniform_(self.bias, -bound, bound) + + def forward( + self, + x: Float[ + Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL, Axis.INPUT_OUTPUT_FEATURE) + ], + ) -> Float[Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE)]: + """Forward pass. + + Args: + x: Input tensor. + + Returns: + Output of the forward pass. + """ + z = ( + einops.einsum( + x, + self.weight, + f"{Axis.BATCH} ... {Axis.INPUT_OUTPUT_FEATURE}, \ + ... {Axis.LEARNT_FEATURE} {Axis.INPUT_OUTPUT_FEATURE} \ + -> {Axis.BATCH} ... {Axis.LEARNT_FEATURE}", + ) + + self.bias + ) + + return self.activation_function(z) + + @final + def update_dictionary_vectors( + self, + dictionary_vector_indices: Int64[Tensor, Axis.names(Axis.LEARNT_FEATURE_IDX)], + updated_dictionary_weights: Float[ + Tensor, Axis.names(Axis.LEARNT_FEATURE_IDX, Axis.INPUT_OUTPUT_FEATURE) + ], + component_idx: int | None = None, + ) -> None: + """Update encoder dictionary vectors. + + Updates the dictionary vectors (columns in the weight matrix) with the given values. + + Args: + dictionary_vector_indices: Indices of the dictionary vectors to update. + updated_dictionary_weights: Updated weights for just these dictionary vectors. + component_idx: Component index to update. + + Raises: + ValueError: If there are multiple components and `component_idx` is not specified. + """ + if dictionary_vector_indices.numel() == 0: + return + + with torch.no_grad(): + if component_idx is None: + if self._n_components is not None: + error_message = "component_idx must be specified when n_components is not None" + raise ValueError(error_message) + + self.weight[dictionary_vector_indices] = updated_dictionary_weights + else: + self.weight[component_idx, dictionary_vector_indices] = updated_dictionary_weights + + @final + def update_bias( + self, + update_parameter_indices: Int64[ + Tensor, Axis.names(Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE_IDX) + ], + updated_bias_features: Float[ + Tensor, Axis.names(Axis.COMPONENT_OPTIONAL, Axis.LEARNT_FEATURE_IDX) + ], + component_idx: int | None = None, + ) -> None: + """Update encoder bias. + + Args: + update_parameter_indices: Indices of the bias features to update. + updated_bias_features: Updated bias features for just these indices. + component_idx: Component index to update. + + Raises: + ValueError: If there are multiple components and `component_idx` is not specified. + """ + if update_parameter_indices.numel() == 0: + return + + with torch.no_grad(): + if component_idx is None: + if self._n_components is not None: + error_message = "component_idx must be specified when n_components is not None" + raise ValueError(error_message) + + self.bias[update_parameter_indices] = updated_bias_features + else: + self.bias[component_idx, update_parameter_indices] = updated_bias_features + + def extra_repr(self) -> str: + """String extra representation of the module.""" + return ( + f"input_features={self._input_features}, " + f"learnt_features={self._learnt_features}, " + f"n_components={self._n_components}" + ) diff --git a/sparse_autoencoder/autoencoder/model.py b/sparse_autoencoder/autoencoder/model.py index 4084de93..d5bdb52e 100644 --- a/sparse_autoencoder/autoencoder/model.py +++ b/sparse_autoencoder/autoencoder/model.py @@ -21,6 +21,8 @@ from sparse_autoencoder.autoencoder.components.linear_encoder import LinearEncoder from sparse_autoencoder.autoencoder.components.tied_bias import TiedBias, TiedBiasPosition from sparse_autoencoder.autoencoder.components.unit_norm_decoder import UnitNormDecoder +from sparse_autoencoder.autoencoder.components.tanh_encoder import TanhEncoder +from sparse_autoencoder.autoencoder.components.linear_decoder import LinearDecoder from sparse_autoencoder.autoencoder.types import ResetOptimizerParameterDetails from sparse_autoencoder.tensor_types import Axis from sparse_autoencoder.utils.tensor_shape import shape_with_optional_dimensions @@ -48,6 +50,11 @@ class SparseAutoencoderConfig(BaseModel): If `None`, the SAE is assumed to be trained on just one component (in this case the model won't contain a component axis in any of the parameters). """ + + type: str = "unit_norm_decoder" + """ + TODO + """ class SparseAutoencoderState(BaseModel, arbitrary_types_allowed=True): @@ -103,10 +110,10 @@ class SparseAutoencoder(Module): pre_encoder_bias: TiedBias """Pre-Encoder Bias.""" - encoder: LinearEncoder + encoder: LinearEncoder | TanhEncoder """Encoder.""" - decoder: UnitNormDecoder + decoder: UnitNormDecoder | LinearDecoder """Decoder.""" post_decoder_bias: TiedBias @@ -149,17 +156,32 @@ def __init__( # Initialize the components self.pre_encoder_bias = TiedBias(self.tied_bias, TiedBiasPosition.PRE_ENCODER) - self.encoder = LinearEncoder( - input_features=config.n_input_features, - learnt_features=config.n_learned_features, - n_components=config.n_components, - ) + if config.type == "unit_norm_decoder": + self.encoder = LinearEncoder( + input_features=config.n_input_features, + learnt_features=config.n_learned_features, + n_components=config.n_components, + ) - self.decoder = UnitNormDecoder( - learnt_features=config.n_learned_features, - decoded_features=config.n_input_features, - n_components=config.n_components, - ) + self.decoder = UnitNormDecoder( + learnt_features=config.n_learned_features, + decoded_features=config.n_input_features, + n_components=config.n_components, + ) + elif config.type == "tanh_encoder": + self.encoder = TanhEncoder( + input_features=config.n_input_features, + learnt_features=config.n_learned_features, + n_components=config.n_components, + ) + + self.decoder = LinearDecoder( + learnt_features=config.n_learned_features, + decoded_features=config.n_input_features, + n_components=config.n_components, + ) + else: + raise ValueError(f"Unknown SAE type: {config.type}") self.post_decoder_bias = TiedBias(self.tied_bias, TiedBiasPosition.POST_DECODER) @@ -217,7 +239,8 @@ def post_backwards_hook(self) -> None: This can be used to e.g. constrain weights to unit norm. """ - self.decoder.constrain_weights_unit_norm() + if self.config.type == "unit_norm_decoder": + self.decoder.constrain_weights_unit_norm() @staticmethod @validate_call diff --git a/sparse_autoencoder/train/sweep.py b/sparse_autoencoder/train/sweep.py index f67ba505..b116bacb 100644 --- a/sparse_autoencoder/train/sweep.py +++ b/sparse_autoencoder/train/sweep.py @@ -56,6 +56,7 @@ def setup_autoencoder( """ autoencoder_input_dim: int = hyperparameters["source_model"]["hook_dimension"] expansion_factor = hyperparameters["autoencoder"]["expansion_factor"] + type = hyperparameters["autoencoder"]["type"] config = LitSparseAutoencoderConfig( n_input_features=autoencoder_input_dim, @@ -72,9 +73,10 @@ def setup_autoencoder( resample_threshold_is_dead_portion_fires=hyperparameters["activation_resampler"][ "threshold_is_dead_portion_fires" ], + type=type, ) - return LitSparseAutoencoder(config) + return LitSparseAutoencoder(config).cuda() def setup_source_data(hyperparameters: RuntimeHyperparameters) -> SourceDataset: diff --git a/sparse_autoencoder/train/sweep_config.py b/sparse_autoencoder/train/sweep_config.py index 1967495c..e62e5bf7 100644 --- a/sparse_autoencoder/train/sweep_config.py +++ b/sparse_autoencoder/train/sweep_config.py @@ -82,12 +82,20 @@ class AutoencoderHyperparameters(NestedParameter): Size of the learned features relative to the input features. A good expansion factor to start with is typically 2-4. """ + + type: Parameter[str] = field(default=Parameter("unit_norm_decoder")) + """Type of the autoencoder + + Default is unit_norm_decoder + """ class AutoencoderRuntimeHyperparameters(TypedDict): """Autoencoder runtime hyperparameters.""" expansion_factor: int + + type: str @dataclass(frozen=True) From 2660f57fabc816587a2f488e2b30e60fe15af920 Mon Sep 17 00:00:00 2001 From: Fengyuan Hu <127644049+HuFY-dev@users.noreply.github.com> Date: Mon, 1 Apr 2024 19:14:52 -0400 Subject: [PATCH 5/6] Configured for computing cluster --- docs/content/demo.ipynb | 6 +- poetry.lock | 99 ++++++++++---------- pyproject.toml | 4 +- sparse_autoencoder/train/sweep.py | 2 +- sparse_autoencoder/training_runs/modified.py | 90 ++++++++++++++++++ 5 files changed, 144 insertions(+), 57 deletions(-) create mode 100644 sparse_autoencoder/training_runs/modified.py diff --git a/docs/content/demo.ipynb b/docs/content/demo.ipynb index 26a90dd3..ea374c77 100644 --- a/docs/content/demo.ipynb +++ b/docs/content/demo.ipynb @@ -424,7 +424,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -438,7 +438,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.9" }, "vscode": { "interpreter": { @@ -447,5 +447,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/poetry.lock b/poetry.lock index f0d07e45..935d59a8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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["cffi (>=1.11)"] [metadata] lock-version = "2.0" python-versions = ">=3.10, <3.12" -content-hash = "6e644eea19cb89a2fa277b57b2e5e931427160735d16c857f82f91d820923f86" +content-hash = "16d8093029616d15c938d8ed03973b39f463e1dee6d338edc66611bb9b86e4ec" diff --git a/pyproject.toml b/pyproject.toml index 39dbad87..9e0a5d0c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,8 +17,8 @@ python=">=3.10, <3.12" strenum=">=0.4.15" tokenizers=">=0.15.1" - torch=">=2.1.2" - torchmetrics="^1.3.0.post0" + torch="2.1.2" + torchmetrics="1.3.0.post0" transformers=">=4.37.2" wandb=">=0.16.2" zstandard=">=0.22.0" diff --git a/sparse_autoencoder/train/sweep.py b/sparse_autoencoder/train/sweep.py index b116bacb..1ad1b033 100644 --- a/sparse_autoencoder/train/sweep.py +++ b/sparse_autoencoder/train/sweep.py @@ -76,7 +76,7 @@ def setup_autoencoder( type=type, ) - return LitSparseAutoencoder(config).cuda() + return LitSparseAutoencoder(config) def setup_source_data(hyperparameters: RuntimeHyperparameters) -> SourceDataset: diff --git a/sparse_autoencoder/training_runs/modified.py b/sparse_autoencoder/training_runs/modified.py new file mode 100644 index 00000000..ff2ab06f --- /dev/null +++ b/sparse_autoencoder/training_runs/modified.py @@ -0,0 +1,90 @@ +"""Run an sweep on all layers of GPT2 Small. + +Command: + +```bash +git clone https://github.com/ai-safety-foundation/sparse_autoencoder.git && cd sparse_autoencoder && +poetry env use python3.11 && poetry install && +poetry run python sparse_autoencoder/training_runs/gpt2.py +``` +""" +import os + +from sparse_autoencoder import ( + ActivationResamplerHyperparameters, + AutoencoderHyperparameters, + Hyperparameters, + LossHyperparameters, + Method, + OptimizerHyperparameters, + Parameter, + PipelineHyperparameters, + SourceDataHyperparameters, + SourceModelHyperparameters, + SweepConfig, + sweep, +) + + +os.environ["TOKENIZERS_PARALLELISM"] = "false" + + +def train_gpt_small_mlp_layers( + expansion_factor: int = 4, + n_layers: int = 12, +) -> None: + """Run a new sweep experiment on GPT 2 Small's MLP layers. + + Args: + expansion_factor: Expansion factor for the autoencoder. + n_layers: Number of layers to train on. Max is 12. + + """ + sweep_config = SweepConfig( + parameters=Hyperparameters( + loss=LossHyperparameters( + l1_coefficient=Parameter(max=0.03, min=0.008), + ), + optimizer=OptimizerHyperparameters( + lr=Parameter(max=0.001, min=0.00001), + ), + source_model=SourceModelHyperparameters( + name=Parameter("gpt2"), + cache_names=Parameter( + [f"blocks.{layer}.hook_mlp_out" for layer in range(n_layers)] + ), + hook_dimension=Parameter(768), + ), + source_data=SourceDataHyperparameters( + dataset_path=Parameter("alancooney/sae-monology-pile-uncopyrighted-tokenizer-gpt2"), + context_size=Parameter(256), + pre_tokenized=Parameter(value=True), + pre_download=Parameter(value=False), # Default to streaming the dataset + ), + autoencoder=AutoencoderHyperparameters( + expansion_factor=Parameter(value=expansion_factor), + type=Parameter("tanh_encoder"), + ), + pipeline=PipelineHyperparameters( + max_activations=Parameter(1_000_000_000), + checkpoint_frequency=Parameter(100_000_000), + validation_frequency=Parameter(100_000_000), + max_store_size=Parameter(100_000), + source_data_batch_size=Parameter(16), + train_batch_size=Parameter(8192) + ), + activation_resampler=ActivationResamplerHyperparameters( + resample_interval=Parameter(200_000_000), + n_activations_activity_collate=Parameter(100_000_000), + threshold_is_dead_portion_fires=Parameter(1e-6), + max_n_resamples=Parameter(4), + resample_dataset_size=Parameter(100_000), + ), + ), + method=Method.RANDOM, + ) + + sweep(sweep_config=sweep_config) + +if __name__ == "__main__": + train_gpt_small_mlp_layers() From e1943c30536389e8f775fdcd92fa4799dc474e92 Mon Sep 17 00:00:00 2001 From: HuFY-dev <127644049+HuFY-dev@users.noreply.github.com> Date: Mon, 1 Apr 2024 19:51:00 -0400 Subject: [PATCH 6/6] Added normalized l2 loss. --- sparse_autoencoder/autoencoder/lightning.py | 4 ++- .../metrics/loss/l2_reconstruction_loss.py | 26 +++++++++++++++++-- sparse_autoencoder/metrics/loss/sae_loss.py | 6 +++++ sparse_autoencoder/train/sweep.py | 4 +-- sparse_autoencoder/train/sweep_config.py | 10 +++++++ 5 files changed, 45 insertions(+), 5 deletions(-) diff --git a/sparse_autoencoder/autoencoder/lightning.py b/sparse_autoencoder/autoencoder/lightning.py index 24329217..4d6de377 100644 --- a/sparse_autoencoder/autoencoder/lightning.py +++ b/sparse_autoencoder/autoencoder/lightning.py @@ -46,6 +46,8 @@ class LitSparseAutoencoderConfig(SparseAutoencoderConfig): resample_loss_dataset_size: PositiveInt = 819200 resample_threshold_is_dead_portion_fires: NonNegativeFloat = 0.0 + + normalize_by_input_norm: bool = False def model_post_init(self, __context: Any) -> None: # noqa: ANN401 """Model post init validation. @@ -91,7 +93,7 @@ def __init__( # Create the loss & metrics self.loss_fn = SparseAutoencoderLoss( - num_components, config.l1_coefficient, keep_batch_dim=True + num_components, config.l1_coefficient, keep_batch_dim=True, config.normalize_by_input_norm ) self.train_metrics = MetricCollection( diff --git a/sparse_autoencoder/metrics/loss/l2_reconstruction_loss.py b/sparse_autoencoder/metrics/loss/l2_reconstruction_loss.py index 10996bc2..604db54f 100644 --- a/sparse_autoencoder/metrics/loss/l2_reconstruction_loss.py +++ b/sparse_autoencoder/metrics/loss/l2_reconstruction_loss.py @@ -17,8 +17,13 @@ class L2ReconstructionLoss(Metric): and it's corresponding decoded vector. The original paper found that models trained with some loss functions such as cross-entropy loss generally prefer to represent features polysemantically, whereas models trained with L2 may achieve the same loss for both - polysemantic and monosemantic representations of true features. - + polysemantic and monosemantic representations of true features. + + You have the option to set L2 reconstruction loss to normalize the input activations before + calculating the loss. This can be useful because the input vectors can vary in magnitude and + normalizing them can help to ensure that the loss is not dominated by the magnitude of the + activations. + Example: >>> import torch >>> loss = L2ReconstructionLoss(num_components=1) @@ -53,6 +58,7 @@ class L2ReconstructionLoss(Metric): # Settings _num_components: int _keep_batch_dim: bool + _normalize_by_input_norm: bool @property def keep_batch_dim(self) -> bool: @@ -97,11 +103,13 @@ def __init__( num_components: PositiveInt = 1, *, keep_batch_dim: bool = False, + normalize_by_input_norm: bool = False, ) -> None: """Initialise the L2 reconstruction loss.""" super().__init__() self._num_components = num_components self.keep_batch_dim = keep_batch_dim + self._normalize_by_input_norm = normalize_by_input_norm self.add_state( "num_activation_vectors", default=torch.tensor(0, dtype=torch.int64), @@ -119,6 +127,16 @@ def calculate_mse( ) -> Float[Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL)]: """Calculate the MSE.""" return (decoded_activations - source_activations).pow(2).mean(dim=-1) + + @staticmethod + def normalize_input( + activations: Float[ + Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL, Axis.INPUT_OUTPUT_FEATURE) + ], + ) -> Float[Tensor, Axis.names(Axis.BATCH, Axis.COMPONENT_OPTIONAL, Axis.INPUT_OUTPUT_FEATURE)]: + """Normalize the input activations.""" + activation_norm = activations.norm(dim=-1, keepdim=True) + return activations / activation_norm def update( self, @@ -146,6 +164,10 @@ def update( source_activations: The source activations from the autoencoder. **kwargs: Ignored keyword arguments (to allow use with other metrics in a collection). """ + if self._normalize_by_input_norm: + decoded_activations = self.normalize_input(decoded_activations) + source_activations = self.normalize_input(source_activations) + mse = self.calculate_mse(decoded_activations, source_activations) if self.keep_batch_dim: diff --git a/sparse_autoencoder/metrics/loss/sae_loss.py b/sparse_autoencoder/metrics/loss/sae_loss.py index 486bb925..284b8e19 100644 --- a/sparse_autoencoder/metrics/loss/sae_loss.py +++ b/sparse_autoencoder/metrics/loss/sae_loss.py @@ -29,6 +29,7 @@ class SparseAutoencoderLoss(Metric): # Settings _num_components: int _keep_batch_dim: bool + _normalize_by_input_norm: bool _l1_coefficient: float @property @@ -88,11 +89,13 @@ def __init__( l1_coefficient: PositiveFloat = 0.001, *, keep_batch_dim: bool = False, + normalize_by_input_norm: bool = False, ): """Initialise the metric.""" super().__init__() self._num_components = num_components self.keep_batch_dim = keep_batch_dim + self._normalize_by_input_norm = normalize_by_input_norm self._l1_coefficient = l1_coefficient # Add the state @@ -117,6 +120,9 @@ def update( ) -> None: """Update the metric.""" absolute_loss = L1AbsoluteLoss.calculate_abs_sum(learned_activations) + if self._normalize_by_input_norm: + source_activations = L2ReconstructionLoss.normalize_input(source_activations) + decoded_activations = L2ReconstructionLoss.normalize_input(decoded_activations) mse = L2ReconstructionLoss.calculate_mse(decoded_activations, source_activations) if self.keep_batch_dim: diff --git a/sparse_autoencoder/train/sweep.py b/sparse_autoencoder/train/sweep.py index 1ad1b033..6bac113d 100644 --- a/sparse_autoencoder/train/sweep.py +++ b/sparse_autoencoder/train/sweep.py @@ -56,7 +56,6 @@ def setup_autoencoder( """ autoencoder_input_dim: int = hyperparameters["source_model"]["hook_dimension"] expansion_factor = hyperparameters["autoencoder"]["expansion_factor"] - type = hyperparameters["autoencoder"]["type"] config = LitSparseAutoencoderConfig( n_input_features=autoencoder_input_dim, @@ -64,6 +63,7 @@ def setup_autoencoder( n_components=len(hyperparameters["source_model"]["cache_names"]), component_names=hyperparameters["source_model"]["cache_names"], l1_coefficient=hyperparameters["loss"]["l1_coefficient"], + normalize_by_input_norm=hyperparameters["loss"]["normalize_by_input_norm"], resample_interval=hyperparameters["activation_resampler"]["resample_interval"], max_n_resamples=hyperparameters["activation_resampler"]["max_n_resamples"], resample_dead_neurons_dataset_size=hyperparameters["activation_resampler"][ @@ -73,7 +73,7 @@ def setup_autoencoder( resample_threshold_is_dead_portion_fires=hyperparameters["activation_resampler"][ "threshold_is_dead_portion_fires" ], - type=type, + type=hyperparameters["autoencoder"]["type"], ) return LitSparseAutoencoder(config) diff --git a/sparse_autoencoder/train/sweep_config.py b/sparse_autoencoder/train/sweep_config.py index e62e5bf7..8e8e450c 100644 --- a/sparse_autoencoder/train/sweep_config.py +++ b/sparse_autoencoder/train/sweep_config.py @@ -111,11 +111,21 @@ class LossHyperparameters(NestedParameter): starting point for the L1 coefficient is 1e-3. """ + normalize_by_input_norm: Parameter[bool] = field(default=Parameter(value=False)) + """Normalize by input norm. + + Whether to normalize the input and source activations before calculating the L2 loss. This can + be useful because the input vectors can vary in magnitude and normalizing them can help to + ensure that the loss is not dominated by activations of high magnitudes (often uninterpretable + activations from the <|endoftext|> token). + """ class LossRuntimeHyperparameters(TypedDict): """Loss runtime hyperparameters.""" l1_coefficient: float + + normalize_by_input_norm: bool @dataclass(frozen=True)