Add tests against Neel's anthropic paper comment implementation (#122)

.vscode/cspell.json

@@ -12,6 +12,7 @@
         "capturable",
         "categoricalwprobabilities",
         "circuitsvis",
+        "coeff",
         "colab",
         "cuda",
         "cudnn",

pyproject.toml

@@ -122,6 +122,7 @@
         addopts=[
             "--doctest-modules",
             "--jaxtyping-packages=sparse_autoencoder,beartype.beartype",
+            "-W ignore::beartype.roar.BeartypeDecorHintPep585DeprecationWarning",
             "-s",
         ]
 

sparse_autoencoder/autoencoder/components/tests/test_compare_neel_implementation.py

@@ -0,0 +1,203 @@
+"""Compare the SAE implementation to Neel's 1L Implementation.
+
+https://github.com/neelnanda-io/1L-Sparse-Autoencoder/blob/main/utils.py
+"""
+import torch
+from torch import nn
+
+from sparse_autoencoder.autoencoder.model import SparseAutoencoder
+
+
+class NeelAutoencoder(nn.Module):
+    """Neel's 1L autoencoder implementation."""
+
+    def __init__(
+        self,
+        d_hidden: int,
+        act_size: int,
+        l1_coeff: float,
+        dtype: torch.dtype = torch.float32,
+    ) -> None:
+        """Initialize the autoencoder."""
+        super().__init__()
+        self.b_dec = nn.Parameter(torch.zeros(act_size, dtype=dtype))
+        self.W_enc = nn.Parameter(
+            torch.nn.init.kaiming_uniform_(torch.empty(act_size, d_hidden, dtype=dtype))
+        )
+        self.b_enc = nn.Parameter(torch.zeros(d_hidden, dtype=dtype))
+        self.W_dec = nn.Parameter(
+            torch.nn.init.kaiming_uniform_(torch.empty(d_hidden, act_size, dtype=dtype))
+        )
+
+        self.W_dec.data[:] = self.W_dec / self.W_dec.norm(dim=-1, keepdim=True)
+
+        self.d_hidden = d_hidden
+        self.l1_coeff = l1_coeff
+
+    def forward(
+        self, x: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Forward pass."""
+        x_cent = x - self.b_dec
+        acts = nn.functional.relu(x_cent @ self.W_enc + self.b_enc)
+        x_reconstruct = acts @ self.W_dec + self.b_dec
+        l2_loss = (x_reconstruct.float() - x.float()).pow(2).sum(-1).mean(0)
+        l1_loss = self.l1_coeff * (acts.float().abs().sum())
+        loss = l2_loss + l1_loss
+        return loss, x_reconstruct, acts, l2_loss, l1_loss
+
+    def make_decoder_weights_and_grad_unit_norm(self) -> None:
+        """Make decoder weights and gradient unit norm."""
+        with torch.no_grad():
+            weight_dec_normed = self.W_dec / self.W_dec.norm(dim=-1, keepdim=True)
+            weight_dec_grad_proj = (self.W_dec.grad * weight_dec_normed).sum(
+                -1, keepdim=True
+            ) * weight_dec_normed
+            self.W_dec.grad -= weight_dec_grad_proj
+            # Bugfix(?)
+            self.W_dec.data = weight_dec_normed
+
+
+def test_biases_initialised_same_way() -> None:
+    """Test that the biases are initialised the same."""
+    n_input_features: int = 2
+    n_learned_features: int = 3
+    l1_coefficient: float = 0.01
+
+    torch.random.manual_seed(0)
+    autoencoder = SparseAutoencoder(
+        n_input_features=n_input_features,
+        n_learned_features=n_learned_features,
+    )
+
+    torch.random.manual_seed(0)
+    neel_autoencoder = NeelAutoencoder(
+        d_hidden=n_learned_features,
+        act_size=n_input_features,
+        l1_coeff=l1_coefficient,
+    )
+
+    assert torch.allclose(autoencoder.tied_bias, neel_autoencoder.b_dec)
+    # Note we can't compare weights as Neel's implementation uses rotated tensors and applies
+    # kaiming incorrectly (uses leaky relu version and incorrect fan strategy for the rotation
+    # used). Note also that the encoder bias is initialised to zero in Neel's implementation,
+    # whereas we use the standard PyTorch initialisation.
+
+
+def test_forward_pass_same_weights() -> None:
+    """Test a forward pass with the same weights."""
+    n_input_features: int = 12
+    n_learned_features: int = 48
+    l1_coefficient: float = 0.01
+
+    autoencoder = SparseAutoencoder(
+        n_input_features=n_input_features,
+        n_learned_features=n_learned_features,
+    )
+    neel_autoencoder = NeelAutoencoder(
+        d_hidden=n_learned_features,
+        act_size=n_input_features,
+        l1_coeff=l1_coefficient,
+    )
+
+    # Set the same weights
+    autoencoder.encoder.weight.data = neel_autoencoder.W_enc.data.T
+    autoencoder.decoder.weight.data = neel_autoencoder.W_dec.data.T
+    autoencoder.tied_bias.data = neel_autoencoder.b_dec.data
+    autoencoder.encoder.bias.data = neel_autoencoder.b_enc.data
+
+    # Create some test data
+    test_batch = torch.randn(4, n_input_features)
+    learned, hidden = autoencoder.forward(test_batch)
+    _loss, x_reconstruct, acts, _l2_loss, _l1_loss = neel_autoencoder.forward(test_batch)
+
+    assert torch.allclose(learned, acts)
+    assert torch.allclose(hidden, x_reconstruct)
+
+
+def test_unit_norm_weights() -> None:
+    """Test that the decoder weights are unit normalized in the same way."""
+    n_input_features: int = 2
+    n_learned_features: int = 4
+    l1_coefficient: float = 0.01
+
+    autoencoder = SparseAutoencoder(
+        n_input_features=n_input_features,
+        n_learned_features=n_learned_features,
+    )
+    neel_autoencoder = NeelAutoencoder(
+        d_hidden=n_learned_features,
+        act_size=n_input_features,
+        l1_coeff=l1_coefficient,
+    )
+    pre_unit_norm_weights = autoencoder.decoder.weight.clone()
+    pre_unit_norm_neel_weights = neel_autoencoder.W_dec.clone()
+
+    # Set the same decoder weights
+    decoder_weights = torch.rand_like(autoencoder.decoder.weight)
+    autoencoder.decoder._weight.data = decoder_weights  # noqa: SLF001 # type: ignore
+    neel_autoencoder.W_dec.data = decoder_weights.T
+
+    # Do a forward & backward pass so we have gradients
+    test_batch = torch.randn(4, n_input_features)
+    _learned, decoded = autoencoder.forward(test_batch)
+    decoded.sum().backward()
+    decoded = neel_autoencoder.forward(test_batch)[1]
+    decoded.sum().backward()
+
+    # Apply the unit norm
+    autoencoder.decoder.constrain_weights_unit_norm()
+    neel_autoencoder.make_decoder_weights_and_grad_unit_norm()
+
+    # Check the decoder weights are the same with both models
+    assert torch.allclose(autoencoder.decoder.weight, neel_autoencoder.W_dec.T)
+
+    # Check the trivial case that the weights haven't just stayed the same as before the unit norm
+    assert not torch.allclose(autoencoder.decoder.weight, pre_unit_norm_weights)
+    assert not torch.allclose(neel_autoencoder.W_dec, pre_unit_norm_neel_weights)
+
+
+def test_unit_norm_weights_grad() -> None:
+    """Test that the decoder weights are unit normalized in the same way."""
+    torch.random.manual_seed(42)
+    n_input_features: int = 2
+    n_learned_features: int = 4
+    l1_coefficient: float = 0.01
+
+    autoencoder = SparseAutoencoder(
+        n_input_features=n_input_features,
+        n_learned_features=n_learned_features,
+    )
+    neel_autoencoder = NeelAutoencoder(
+        d_hidden=n_learned_features,
+        act_size=n_input_features,
+        l1_coeff=l1_coefficient,
+    )
+
+    # Set the same decoder weights
+    decoder_weights = torch.rand_like(autoencoder.decoder.weight)
+    autoencoder.decoder._weight.data = decoder_weights  # noqa: SLF001 # type: ignore
+    neel_autoencoder.W_dec.data = decoder_weights.T
+    autoencoder.decoder._weight.grad = torch.zeros_like(autoencoder.decoder.weight)  # noqa: SLF001 # type: ignore
+    neel_autoencoder.W_dec.grad = torch.zeros_like(neel_autoencoder.W_dec)
+
+    # Set the same tied bias weights
+    neel_autoencoder.b_dec.data = autoencoder.tied_bias.data
+    neel_autoencoder.b_enc.data = autoencoder.encoder.bias.data
+    neel_autoencoder.W_enc.data = autoencoder.encoder.weight.data.T
+
+    # Do a forward & backward pass so we have gradients
+    test_batch = torch.randn(4, n_input_features)
+    _learned, decoded = autoencoder.forward(test_batch)
+    _loss = decoded.sum().backward()
+    neel_decoded = neel_autoencoder.forward(test_batch)[1]
+    _loss_neel = neel_decoded.sum().backward()
+
+    # Apply the unit norm
+    autoencoder.decoder.constrain_weights_unit_norm()
+    neel_autoencoder.make_decoder_weights_and_grad_unit_norm()
+
+    # Check the gradient weights are the same
+    assert autoencoder.decoder.weight.grad is not None
+    assert neel_autoencoder.W_dec.grad is not None
+    assert torch.allclose(autoencoder.decoder.weight.grad, neel_autoencoder.W_dec.grad.T, rtol=1e-4)

sparse_autoencoder/loss/tests/test_neel_loss.py

@@ -0,0 +1,144 @@
+"""Tests against Neel's Autoencoder Loss.
+
+Compare module output against Neel's implementation at
+https://github.com/neelnanda-io/1L-Sparse-Autoencoder/blob/main/utils.py .
+"""
+from typing import TypedDict
+
+import pytest
+import torch
+
+from sparse_autoencoder.loss.decoded_activations_l2 import L2ReconstructionLoss
+from sparse_autoencoder.loss.learned_activations_l1 import LearnedActivationsL1Loss
+from sparse_autoencoder.loss.reducer import LossReducer
+from sparse_autoencoder.tensor_types import (
+    InputOutputActivationBatch,
+    ItemTensor,
+    LearnedActivationBatch,
+)
+
+
+def neel_loss(
+    source_activations: InputOutputActivationBatch,
+    learned_activations: LearnedActivationBatch,
+    decoded_activations: InputOutputActivationBatch,
+    l1_coefficient: float,
+) -> tuple[ItemTensor, ItemTensor, ItemTensor]:
+    """Neel's loss function."""
+    l2_loss = (decoded_activations.float() - source_activations.float()).pow(2).sum(-1).mean(0)
+    l1_loss = l1_coefficient * (learned_activations.float().abs().sum())
+    loss = l2_loss + l1_loss
+    return l1_loss, l2_loss, loss
+
+
+def lib_loss(
+    source_activations: InputOutputActivationBatch,
+    learned_activations: LearnedActivationBatch,
+    decoded_activations: InputOutputActivationBatch,
+    l1_coefficient: float,
+) -> tuple[ItemTensor, ItemTensor, ItemTensor]:
+    """This library's loss function."""
+    l1_loss_fn = LearnedActivationsL1Loss(
+        l1_coefficient=float(l1_coefficient),
+    )
+    l2_loss_fn = L2ReconstructionLoss()
+
+    loss_fn = LossReducer(l1_loss_fn, l2_loss_fn)
+
+    l1_loss = l1_loss_fn.forward(source_activations, learned_activations, decoded_activations)
+    l2_loss = l2_loss_fn.forward(source_activations, learned_activations, decoded_activations)
+    total_loss = loss_fn.forward(source_activations, learned_activations, decoded_activations)
+    return l1_loss.sum(), l2_loss.sum(), total_loss.sum()
+
+
+class MockActivations(TypedDict):
+    """Mock activations."""
+
+    source_activations: InputOutputActivationBatch
+    learned_activations: LearnedActivationBatch
+    decoded_activations: InputOutputActivationBatch
+
+
+@pytest.fixture()
+def mock_activations() -> MockActivations:
+    """Create mock activations.
+
+    Returns:
+        Tuple of source activations, learned activations, and decoded activations.
+    """
+    source_activations = torch.rand(10, 20)
+    learned_activations = torch.rand(10, 50)
+    decoded_activations = torch.rand(10, 20)
+    return {
+        "source_activations": source_activations,
+        "learned_activations": learned_activations,
+        "decoded_activations": decoded_activations,
+    }
+
+
+def test_l1_loss_the_same(mock_activations: MockActivations) -> None:
+    """Test that the L1 loss is the same."""
+    l1_coefficient: float = 0.01
+
+    neel_l1_loss = neel_loss(
+        source_activations=mock_activations["source_activations"],
+        learned_activations=mock_activations["learned_activations"],
+        decoded_activations=mock_activations["decoded_activations"],
+        l1_coefficient=l1_coefficient,
+    )[0]
+
+    lib_l1_loss = lib_loss(
+        source_activations=mock_activations["source_activations"],
+        learned_activations=mock_activations["learned_activations"],
+        decoded_activations=mock_activations["decoded_activations"],
+        l1_coefficient=l1_coefficient,
+    )[0].sum()
+
+    assert torch.allclose(neel_l1_loss, lib_l1_loss)
+
+
+def test_l2_loss_the_same(mock_activations: MockActivations) -> None:
+    """Test that the L2 loss is the same."""
+    l1_coefficient: float = 0.01
+
+    neel_l2_loss = neel_loss(
+        source_activations=mock_activations["source_activations"],
+        learned_activations=mock_activations["learned_activations"],
+        decoded_activations=mock_activations["decoded_activations"],
+        l1_coefficient=l1_coefficient,
+    )[1]
+
+    lib_l2_loss = lib_loss(
+        source_activations=mock_activations["source_activations"],
+        learned_activations=mock_activations["learned_activations"],
+        decoded_activations=mock_activations["decoded_activations"],
+        l1_coefficient=l1_coefficient,
+    )[1].sum()
+
+    # Fix for the fact that Neel's L2 loss is summed across the features dimension and then averaged
+    # across the batch. By contrast for l1 it is summed across both features and batch dimensions.
+    neel_l2_loss_fixed = neel_l2_loss * len(mock_activations["source_activations"])
+
+    assert torch.allclose(neel_l2_loss_fixed, lib_l2_loss)
+
+
+@pytest.mark.skip("We believe Neel's L2 approach is different to the original paper.")
+def test_total_loss_the_same(mock_activations: MockActivations) -> None:
+    """Test that the total loss is the same."""
+    l1_coefficient: float = 0.01
+
+    neel_total_loss = neel_loss(
+        source_activations=mock_activations["source_activations"],
+        learned_activations=mock_activations["learned_activations"],
+        decoded_activations=mock_activations["decoded_activations"],
+        l1_coefficient=l1_coefficient,
+    )[2].sum()
+
+    lib_total_loss = lib_loss(
+        source_activations=mock_activations["source_activations"],
+        learned_activations=mock_activations["learned_activations"],
+        decoded_activations=mock_activations["decoded_activations"],
+        l1_coefficient=l1_coefficient,
+    )[2].sum()
+
+    assert torch.allclose(neel_total_loss, lib_total_loss)