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000000000..acb833913 --- /dev/null +++ b/test_ast_parity.py @@ -0,0 +1,86 @@ +import torch +from transformers import ASTForAudioClassification, ASTConfig +from transformer_lens import HookedTransformer, HookedTransformerConfig +from transformer_lens.model_bridge.supported_architectures.ast import ASTAdapter, ASTEmbed + +def test_ast_parity(): + print("Loading HuggingFace AST . . .") + # use tiny config to make the test run instantly locally + hf_config = ASTConfig( + hidden_size=12, + num_hidden_layers=2, + num_attention_heads=2, + intermediate_size=24, + patch_size=16, + ) + hf_model = ASTForAudioClassification(hf_config) + + print("Booting TransformerLens Bridge . . .") + tl_config_dict = ASTAdapter.get_config_map(hf_config) + # safety catch: remove 'is_multimodal' in case + tl_config_dict.pop("is_multimodal", None) + + # dynamically grab the true sequence length HF generated + actual_n_ctx = hf_model.state_dict()["audio_spectrogram_transformer.embeddings.position_embeddings"].shape[1] + tl_config_dict["n_ctx"] = actual_n_ctx + + tl_config = HookedTransformerConfig(**tl_config_dict) + + # 2. boot a blank HookedTransformer + tl_model = HookedTransformer(tl_config) + # 3. hot-swap: replace the standard text embedder with our AST patch extractor + tl_model.embed = ASTEmbed(tl_config) + + # 4. map and load the weights + mapped_weights = ASTAdapter.convert_weights(hf_model.state_dict(), tl_config) + tl_model.load_state_dict(mapped_weights, strict=False) + + # create dummy mel spectrogram + dummy_spectrogram = torch.randn(1, 1024, 128) + + print("Running forward passes . . .") + with torch.no_grad(): + hf_logits = hf_model(dummy_spectrogram).logits + + # == TL manual matrix forward pass == + # 1. extract patches & CLS tokens via custom ASTEmbed + resid = tl_model.embed(dummy_spectrogram) + + # 2. add positional embeddings (sliced to actual sequence length) + seq_len = resid.shape[1] + resid = resid + tl_model.pos_embed.W_pos[:seq_len, :] + + # 3. pass thru standard transformer blocks + for block in tl_model.blocks: + resid = block(resid) + + # 4. final LayerNorm + resid = tl_model.ln_final(resid) + + # 5. ViT: only unembed the CLS token (index 0) + cls_token_out = resid[:, 0, :] + dist_token_out = resid[:, 1, :] + pooled_out = (cls_token_out + dist_token_out) / 2.0 + + # AST: second LayerNorm applied ONLY to pooled token + classifier_ln_w = hf_model.state_dict()["classifier.layernorm.weight"] + classifier_ln_b = hf_model.state_dict()["classifier.layernorm.bias"] + + pooled_out = torch.nn.functional.layer_norm( + pooled_out, + normalized_shape=(tl_model.cfg.d_model,), + weight=classifier_ln_w, + bias=classifier_ln_b, + eps=tl_model.cfg.eps + ) + + tl_logits = tl_model.unembed(pooled_out) + + diff = (hf_logits - tl_logits).abs().max().item() + print(f"Max logit difference: {diff:.6e}") + + assert diff < 1e-4, "parity failed: tensors do not match" + print("Parity dub. adapter mapping math yes") + +if __name__ == "__main__": + test_ast_parity() \ No newline at end of file diff --git a/transformer_lens/model_bridge/supported_architectures/ast.py b/transformer_lens/model_bridge/supported_architectures/ast.py new file mode 100644 index 000000000..a82e1754c --- /dev/null +++ b/transformer_lens/model_bridge/supported_architectures/ast.py @@ -0,0 +1,171 @@ +import torch +import torch.nn as nn +from typing import Dict, Any +from transformers import ASTConfig + +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter +from transformer_lens.config.hooked_transformer_config import HookedTransformerConfig +from transformer_lens.hook_points import HookPoint + +class ASTEmbed(nn.Module): + # custom embedding layer for ViT/AST architectures + # extracts 16x16 patches using Conv2d, flattens into sequence + # prepends to the CLS token + + def __init__(self, cfg): + super().__init__() + self.cfg = cfg + # AST uses a 16x16 patch size by default + patch_size = (16, 16) + + # the Conv2d layer acts as the patch extractor + self.patch_embeddings = nn.Conv2d( + in_channels=1, + out_channels=cfg.d_model, + kernel_size=patch_size, + stride=patch_size + ) + + # learnable CLS token prepended to sequence + self.cls_token = nn.Parameter(torch.zeros(1, 1, cfg.d_model)) + self.distillation_token = nn.Parameter(torch.zeros(1, 1, cfg.d_model)) + + # standard TransformerLens hook point + self.hook_embed = HookPoint() + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # input shape: [batch, channels (1), freq, time] + batch_size = x.shape[0] + + # mimic HF's internals: 1. add channel dim [batch, 1, time, freq], 2. transposes to image format [batch, 1, freq, time] + x = x.unsqueeze(1).transpose(2, 3) + + # 1. extract patches -> [batch, d_model, grid_h, grid_w] + x = self.patch_embeddings(x) + + # 2. flatten spatial dimensions -> [batch, d_model, num_patches] + x = x.flatten(2) + + # 3. transpose to standard sequence format -> [batch, num_patches, d_model] + x = x.transpose(1, 2) + + # 4. expand and prepend CLS token -> [batch, 1 + num_patches, d_model] + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + dist_tokens = self.distillation_token.expand(batch_size, -1, -1) + + # concat cls, then distillation, then patches + x = torch.cat((cls_tokens, dist_tokens, x), dim=1) + + return self.hook_embed(x) + +class ASTAdapter(ArchitectureAdapter): + # adapter for Audio Spectrogram Transformer (AST) + # takes audio input as image, same core architecture as ViT-base + + @classmethod + def get_config_map(cls, hf_config: ASTConfig) -> Dict[str, Any]: + # maps huggingface AST config to HookedTransformerConfig + return { + "d_model": hf_config.hidden_size, + "n_layers": hf_config.num_hidden_layers, + "n_heads": hf_config.num_attention_heads, + "d_mlp": hf_config.intermediate_size, + "d_vocab": hf_config.num_labels, + "n_ctx": hf_config.max_length, + "eps": hf_config.layer_norm_eps, + "act_fn": "gelu", + "d_head": hf_config.hidden_size // hf_config.num_attention_heads, + } + + @classmethod + def convert_weights(cls, hf_state_dict: Dict[str, torch.Tensor], tl_config: HookedTransformerConfig) -> Dict[str, torch.Tensor]: + # translate the huggingface weight keys to TransformerLens + state_dict = {} + + # 1. convolutional patch embedding + if "audio_spectrogram_transformer.embeddings.patch_embeddings.projection.weight" in hf_state_dict: + state_dict["embed.patch_embeddings.weight"] = hf_state_dict["audio_spectrogram_transformer.embeddings.patch_embeddings.projection.weight"] + state_dict["embed.patch_embeddings.bias"] = hf_state_dict["audio_spectrogram_transformer.embeddings.patch_embeddings.projection.bias"] + + # 2. positional embeddings & CLS token + if "audio_spectrogram_transformer.embeddings.position_embeddings" in hf_state_dict: + state_dict["pos_embed.W_pos"] = hf_state_dict["audio_spectrogram_transformer.embeddings.position_embeddings"].squeeze(0) + + if "audio_spectrogram_transformer.embeddings.cls_token" in hf_state_dict: + state_dict["embed.cls_token"] = hf_state_dict["audio_spectrogram_transformer.embeddings.cls_token"] + + if "audio_spectrogram_transformer.embeddings.distillation_token" in hf_state_dict: + state_dict["embed.distillation_token"] = hf_state_dict["audio_spectrogram_transformer.embeddings.distillation_token"] + + # 3. dense transformer blocks + for i in range(tl_config.n_layers): + hf_prefix = f"audio_spectrogram_transformer.layers.{i}." + tl_prefix = f"blocks.{i}." + + # LayerNorm 1 + state_dict[f"{tl_prefix}ln1.w"] = hf_state_dict[f"{hf_prefix}layernorm_before.weight"] + state_dict[f"{tl_prefix}ln1.b"] = hf_state_dict[f"{hf_prefix}layernorm_before.bias"] + + state_dict[f"{tl_prefix}attn.W_Q"] = cls._reshape_weight( + hf_state_dict[f"{hf_prefix}attention.q_proj.weight"], tl_config + ) + state_dict[f"{tl_prefix}attn.b_Q"] = cls._reshape_bias( + hf_state_dict[f"{hf_prefix}attention.q_proj.bias"], tl_config + ) + + state_dict[f"{tl_prefix}attn.W_K"] = cls._reshape_weight( + hf_state_dict[f"{hf_prefix}attention.k_proj.weight"], tl_config + ) + state_dict[f"{tl_prefix}attn.b_K"] = cls._reshape_bias( + hf_state_dict[f"{hf_prefix}attention.k_proj.bias"], tl_config + ) + + state_dict[f"{tl_prefix}attn.W_V"] = cls._reshape_weight( + hf_state_dict[f"{hf_prefix}attention.v_proj.weight"], tl_config + ) + state_dict[f"{tl_prefix}attn.b_V"] = cls._reshape_bias( + hf_state_dict[f"{hf_prefix}attention.v_proj.bias"], tl_config + ) + + # attention output + state_dict[f"{tl_prefix}attn.W_O"] = cls._reshape_weight( + hf_state_dict[f"{hf_prefix}attention.o_proj.weight"], tl_config, is_output=True + ) + state_dict[f"{tl_prefix}attn.b_O"] = hf_state_dict[f"{hf_prefix}attention.o_proj.bias"] + + # LayerNorm 2 + state_dict[f"{tl_prefix}ln2.w"] = hf_state_dict[f"{hf_prefix}layernorm_after.weight"] + state_dict[f"{tl_prefix}ln2.b"] = hf_state_dict[f"{hf_prefix}layernorm_after.bias"] + + # MLP + state_dict[f"{tl_prefix}mlp.W_in"] = hf_state_dict[f"{hf_prefix}mlp.fc1.weight"].T + state_dict[f"{tl_prefix}mlp.b_in"] = hf_state_dict[f"{hf_prefix}mlp.fc1.bias"] + + state_dict[f"{tl_prefix}mlp.W_out"] = hf_state_dict[f"{hf_prefix}mlp.fc2.weight"].T + state_dict[f"{tl_prefix}mlp.b_out"] = hf_state_dict[f"{hf_prefix}mlp.fc2.bias"] + + # 4. final LayerNorm & Classifier Unembedding + if "audio_spectrogram_transformer.layernorm.weight" in hf_state_dict: + state_dict["ln_final.w"] = hf_state_dict["audio_spectrogram_transformer.layernorm.weight"] + state_dict["ln_final.b"] = hf_state_dict["audio_spectrogram_transformer.layernorm.bias"] + + if "classifier.dense.weight" in hf_state_dict: + state_dict["unembed.W_U"] = hf_state_dict["classifier.dense.weight"].T + state_dict["unembed.b_U"] = hf_state_dict["classifier.dense.bias"] + + return state_dict + + @staticmethod + def _reshape_weight(weight: torch.Tensor, cfg: HookedTransformerConfig, is_output: bool = False) -> torch.Tensor: + # reshapes dense HF attention weights into [n_heads, d_model, d_head] for TL + if is_output: + # W_O requires distinct shaping: [n_heads, d_head, d_model] + return weight.view(cfg.d_model, cfg.n_heads, cfg.d_head).permute(1, 2, 0) + else: + # Q, K, V + return weight.view(cfg.n_heads, cfg.d_head, cfg.d_model).transpose(1, 2) + + @staticmethod + def _reshape_bias(bias: torch.Tensor, cfg: HookedTransformerConfig) -> torch.Tensor: + # reshapes HF attention biases into [n_heads, d_head] for TL + return bias.view(cfg.n_heads, cfg.d_head) \ No newline at end of file From 4fc469e31786952d7eeacd805c30c0dceb66e469 Mon Sep 17 00:00:00 2001 From: dlyanreal Date: Sat, 4 Jul 2026 20:16:01 -0500 Subject: [PATCH 2/4] fix(ast): correct ASTEmbed for overlapping patch stride of 10 --- .../test_ast_adapter.py | 35 +++++++++++++------ .../supported_architectures/ast.py | 9 +++-- 2 files changed, 31 insertions(+), 13 deletions(-) rename test_ast_parity.py => tests/unit/model_bridge/supported_architectures/test_ast_adapter.py (68%) diff --git a/test_ast_parity.py b/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py similarity index 68% rename from test_ast_parity.py rename to tests/unit/model_bridge/supported_architectures/test_ast_adapter.py index acb833913..a6edc8b86 100644 --- a/test_ast_parity.py +++ b/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py @@ -35,29 +35,41 @@ def test_ast_parity(): mapped_weights = ASTAdapter.convert_weights(hf_model.state_dict(), tl_config) tl_model.load_state_dict(mapped_weights, strict=False) + # lock both models to disable dropout for increased reproducibility + hf_model.eval() + tl_model.eval() + # create dummy mel spectrogram dummy_spectrogram = torch.randn(1, 1024, 128) - print("Running forward passes . . .") + print("\n--- INITIATING LAYER BY LAYER AUTOPSY ---") with torch.no_grad(): + + # 0. HF forward pass with hidden states exposed + hf_outputs = hf_model.audio_spectrogram_transformer(dummy_spectrogram, output_hidden_states=True) + hf_hidden_states = hf_outputs.hidden_states hf_logits = hf_model(dummy_spectrogram).logits # == TL manual matrix forward pass == - # 1. extract patches & CLS tokens via custom ASTEmbed + # STEP 1. embeddings parity resid = tl_model.embed(dummy_spectrogram) - - # 2. add positional embeddings (sliced to actual sequence length) seq_len = resid.shape[1] resid = resid + tl_model.pos_embed.W_pos[:seq_len, :] - # 3. pass thru standard transformer blocks - for block in tl_model.blocks: + diff_embed = (hf_hidden_states[0] - resid).abs().max().item() + print(f"1. Embeddings Max Diff: {diff_embed:.6e}") + + # STEP 2. transformer blocks parity + for i, block in enumerate(tl_model.blocks): resid = block(resid) + # compare against the corresponding HF hidden state (i+1 because index 0 is embeddings) + diff_block = (hf_hidden_states[i+1] - resid).abs().max().item() + print(f"2. Block {i} Max Diff: {diff_block:.6e}") - # 4. final LayerNorm + # STEP 3: final LayerNorm resid = tl_model.ln_final(resid) - # 5. ViT: only unembed the CLS token (index 0) + # STEP 4: pooling and classifier cls_token_out = resid[:, 0, :] dist_token_out = resid[:, 1, :] pooled_out = (cls_token_out + dist_token_out) / 2.0 @@ -76,10 +88,11 @@ def test_ast_parity(): tl_logits = tl_model.unembed(pooled_out) - diff = (hf_logits - tl_logits).abs().max().item() - print(f"Max logit difference: {diff:.6e}") + diff_final = (hf_logits - tl_logits).abs().max().item() + print(f"3. Final Logits Max Diff: {diff_final:.6e}") + print("-----------------------------------------\n") - assert diff < 1e-4, "parity failed: tensors do not match" + assert diff_final < 1e-4, "parity failed: tensors do not match" print("Parity dub. adapter mapping math yes") if __name__ == "__main__": diff --git a/transformer_lens/model_bridge/supported_architectures/ast.py b/transformer_lens/model_bridge/supported_architectures/ast.py index a82e1754c..0f00fd265 100644 --- a/transformer_lens/model_bridge/supported_architectures/ast.py +++ b/transformer_lens/model_bridge/supported_architectures/ast.py @@ -15,15 +15,17 @@ class ASTEmbed(nn.Module): def __init__(self, cfg): super().__init__() self.cfg = cfg - # AST uses a 16x16 patch size by default + # AST uses a 16x16 patch size patch_size = (16, 16) + # AST uses overlapping patches with stride of 10, this frequenly causes problems if not known + stride = (10, 10) # the Conv2d layer acts as the patch extractor self.patch_embeddings = nn.Conv2d( in_channels=1, out_channels=cfg.d_model, kernel_size=patch_size, - stride=patch_size + stride=stride ) # learnable CLS token prepended to sequence @@ -75,6 +77,9 @@ def get_config_map(cls, hf_config: ASTConfig) -> Dict[str, Any]: "eps": hf_config.layer_norm_eps, "act_fn": "gelu", "d_head": hf_config.hidden_size // hf_config.num_attention_heads, + + # tell TL this is Image/Audio model not Text model + "attention_dir": "bidirectional", } @classmethod From 0c2e23b40e10aa96f0cdfa3a81923e45e99f3b5a Mon Sep 17 00:00:00 2001 From: dlyanreal Date: Sat, 4 Jul 2026 20:24:22 -0500 Subject: [PATCH 3/4] chore: resolve format check w/ import sorting --- .../model_bridge/supported_architectures/test_ast_adapter.py | 3 ++- transformer_lens/model_bridge/supported_architectures/ast.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py b/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py index a6edc8b86..45a9e3436 100644 --- a/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py @@ -1,5 +1,6 @@ import torch -from transformers import ASTForAudioClassification, ASTConfig +from transformers import ASTConfig, ASTForAudioClassification + from transformer_lens import HookedTransformer, HookedTransformerConfig from transformer_lens.model_bridge.supported_architectures.ast import ASTAdapter, ASTEmbed diff --git a/transformer_lens/model_bridge/supported_architectures/ast.py b/transformer_lens/model_bridge/supported_architectures/ast.py index 0f00fd265..067189fd2 100644 --- a/transformer_lens/model_bridge/supported_architectures/ast.py +++ b/transformer_lens/model_bridge/supported_architectures/ast.py @@ -1,11 +1,12 @@ +from typing import Any, Dict + import torch import torch.nn as nn -from typing import Dict, Any from transformers import ASTConfig -from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter from transformer_lens.config.hooked_transformer_config import HookedTransformerConfig from transformer_lens.hook_points import HookPoint +from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter class ASTEmbed(nn.Module): # custom embedding layer for ViT/AST architectures From bc7afb1bcbc7c5dcfaa96c693cb70ee0f06cadd8 Mon Sep 17 00:00:00 2001 From: dlyanreal Date: Sat, 4 Jul 2026 20:34:53 -0500 Subject: [PATCH 4/4] style: run make format for import compliance --- .../test_ast_adapter.py | 28 ++++++--- .../supported_architectures/ast.py | 63 ++++++++++++------- 2 files changed, 58 insertions(+), 33 deletions(-) diff --git a/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py b/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py index 45a9e3436..978220a40 100644 --- a/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py +++ b/tests/unit/model_bridge/supported_architectures/test_ast_adapter.py @@ -2,7 +2,11 @@ from transformers import ASTConfig, ASTForAudioClassification from transformer_lens import HookedTransformer, HookedTransformerConfig -from transformer_lens.model_bridge.supported_architectures.ast import ASTAdapter, ASTEmbed +from transformer_lens.model_bridge.supported_architectures.ast import ( + ASTAdapter, + ASTEmbed, +) + def test_ast_parity(): print("Loading HuggingFace AST . . .") @@ -22,7 +26,9 @@ def test_ast_parity(): tl_config_dict.pop("is_multimodal", None) # dynamically grab the true sequence length HF generated - actual_n_ctx = hf_model.state_dict()["audio_spectrogram_transformer.embeddings.position_embeddings"].shape[1] + actual_n_ctx = hf_model.state_dict()[ + "audio_spectrogram_transformer.embeddings.position_embeddings" + ].shape[1] tl_config_dict["n_ctx"] = actual_n_ctx tl_config = HookedTransformerConfig(**tl_config_dict) @@ -45,9 +51,10 @@ def test_ast_parity(): print("\n--- INITIATING LAYER BY LAYER AUTOPSY ---") with torch.no_grad(): - # 0. HF forward pass with hidden states exposed - hf_outputs = hf_model.audio_spectrogram_transformer(dummy_spectrogram, output_hidden_states=True) + hf_outputs = hf_model.audio_spectrogram_transformer( + dummy_spectrogram, output_hidden_states=True + ) hf_hidden_states = hf_outputs.hidden_states hf_logits = hf_model(dummy_spectrogram).logits @@ -60,13 +67,13 @@ def test_ast_parity(): diff_embed = (hf_hidden_states[0] - resid).abs().max().item() print(f"1. Embeddings Max Diff: {diff_embed:.6e}") - # STEP 2. transformer blocks parity + # STEP 2. transformer blocks parity for i, block in enumerate(tl_model.blocks): resid = block(resid) # compare against the corresponding HF hidden state (i+1 because index 0 is embeddings) - diff_block = (hf_hidden_states[i+1] - resid).abs().max().item() + diff_block = (hf_hidden_states[i + 1] - resid).abs().max().item() print(f"2. Block {i} Max Diff: {diff_block:.6e}") - + # STEP 3: final LayerNorm resid = tl_model.ln_final(resid) @@ -84,11 +91,11 @@ def test_ast_parity(): normalized_shape=(tl_model.cfg.d_model,), weight=classifier_ln_w, bias=classifier_ln_b, - eps=tl_model.cfg.eps + eps=tl_model.cfg.eps, ) tl_logits = tl_model.unembed(pooled_out) - + diff_final = (hf_logits - tl_logits).abs().max().item() print(f"3. Final Logits Max Diff: {diff_final:.6e}") print("-----------------------------------------\n") @@ -96,5 +103,6 @@ def test_ast_parity(): assert diff_final < 1e-4, "parity failed: tensors do not match" print("Parity dub. adapter mapping math yes") + if __name__ == "__main__": - test_ast_parity() \ No newline at end of file + test_ast_parity() diff --git a/transformer_lens/model_bridge/supported_architectures/ast.py b/transformer_lens/model_bridge/supported_architectures/ast.py index 067189fd2..0daea95c1 100644 --- a/transformer_lens/model_bridge/supported_architectures/ast.py +++ b/transformer_lens/model_bridge/supported_architectures/ast.py @@ -8,6 +8,7 @@ from transformer_lens.hook_points import HookPoint from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter + class ASTEmbed(nn.Module): # custom embedding layer for ViT/AST architectures # extracts 16x16 patches using Conv2d, flattens into sequence @@ -23,10 +24,7 @@ def __init__(self, cfg): # the Conv2d layer acts as the patch extractor self.patch_embeddings = nn.Conv2d( - in_channels=1, - out_channels=cfg.d_model, - kernel_size=patch_size, - stride=stride + in_channels=1, out_channels=cfg.d_model, kernel_size=patch_size, stride=stride ) # learnable CLS token prepended to sequence @@ -35,7 +33,7 @@ def __init__(self, cfg): # standard TransformerLens hook point self.hook_embed = HookPoint() - + def forward(self, x: torch.Tensor) -> torch.Tensor: # input shape: [batch, channels (1), freq, time] batch_size = x.shape[0] @@ -61,6 +59,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return self.hook_embed(x) + class ASTAdapter(ArchitectureAdapter): # adapter for Audio Spectrogram Transformer (AST) # takes audio input as image, same core architecture as ViT-base @@ -78,30 +77,44 @@ def get_config_map(cls, hf_config: ASTConfig) -> Dict[str, Any]: "eps": hf_config.layer_norm_eps, "act_fn": "gelu", "d_head": hf_config.hidden_size // hf_config.num_attention_heads, - # tell TL this is Image/Audio model not Text model "attention_dir": "bidirectional", } - + @classmethod - def convert_weights(cls, hf_state_dict: Dict[str, torch.Tensor], tl_config: HookedTransformerConfig) -> Dict[str, torch.Tensor]: + def convert_weights( + cls, hf_state_dict: Dict[str, torch.Tensor], tl_config: HookedTransformerConfig + ) -> Dict[str, torch.Tensor]: # translate the huggingface weight keys to TransformerLens state_dict = {} # 1. convolutional patch embedding - if "audio_spectrogram_transformer.embeddings.patch_embeddings.projection.weight" in hf_state_dict: - state_dict["embed.patch_embeddings.weight"] = hf_state_dict["audio_spectrogram_transformer.embeddings.patch_embeddings.projection.weight"] - state_dict["embed.patch_embeddings.bias"] = hf_state_dict["audio_spectrogram_transformer.embeddings.patch_embeddings.projection.bias"] - + if ( + "audio_spectrogram_transformer.embeddings.patch_embeddings.projection.weight" + in hf_state_dict + ): + state_dict["embed.patch_embeddings.weight"] = hf_state_dict[ + "audio_spectrogram_transformer.embeddings.patch_embeddings.projection.weight" + ] + state_dict["embed.patch_embeddings.bias"] = hf_state_dict[ + "audio_spectrogram_transformer.embeddings.patch_embeddings.projection.bias" + ] + # 2. positional embeddings & CLS token if "audio_spectrogram_transformer.embeddings.position_embeddings" in hf_state_dict: - state_dict["pos_embed.W_pos"] = hf_state_dict["audio_spectrogram_transformer.embeddings.position_embeddings"].squeeze(0) + state_dict["pos_embed.W_pos"] = hf_state_dict[ + "audio_spectrogram_transformer.embeddings.position_embeddings" + ].squeeze(0) if "audio_spectrogram_transformer.embeddings.cls_token" in hf_state_dict: - state_dict["embed.cls_token"] = hf_state_dict["audio_spectrogram_transformer.embeddings.cls_token"] - + state_dict["embed.cls_token"] = hf_state_dict[ + "audio_spectrogram_transformer.embeddings.cls_token" + ] + if "audio_spectrogram_transformer.embeddings.distillation_token" in hf_state_dict: - state_dict["embed.distillation_token"] = hf_state_dict["audio_spectrogram_transformer.embeddings.distillation_token"] + state_dict["embed.distillation_token"] = hf_state_dict[ + "audio_spectrogram_transformer.embeddings.distillation_token" + ] # 3. dense transformer blocks for i in range(tl_config.n_layers): @@ -149,20 +162,24 @@ def convert_weights(cls, hf_state_dict: Dict[str, torch.Tensor], tl_config: Hook state_dict[f"{tl_prefix}mlp.W_out"] = hf_state_dict[f"{hf_prefix}mlp.fc2.weight"].T state_dict[f"{tl_prefix}mlp.b_out"] = hf_state_dict[f"{hf_prefix}mlp.fc2.bias"] - + # 4. final LayerNorm & Classifier Unembedding if "audio_spectrogram_transformer.layernorm.weight" in hf_state_dict: - state_dict["ln_final.w"] = hf_state_dict["audio_spectrogram_transformer.layernorm.weight"] + state_dict["ln_final.w"] = hf_state_dict[ + "audio_spectrogram_transformer.layernorm.weight" + ] state_dict["ln_final.b"] = hf_state_dict["audio_spectrogram_transformer.layernorm.bias"] if "classifier.dense.weight" in hf_state_dict: state_dict["unembed.W_U"] = hf_state_dict["classifier.dense.weight"].T state_dict["unembed.b_U"] = hf_state_dict["classifier.dense.bias"] - + return state_dict - + @staticmethod - def _reshape_weight(weight: torch.Tensor, cfg: HookedTransformerConfig, is_output: bool = False) -> torch.Tensor: + def _reshape_weight( + weight: torch.Tensor, cfg: HookedTransformerConfig, is_output: bool = False + ) -> torch.Tensor: # reshapes dense HF attention weights into [n_heads, d_model, d_head] for TL if is_output: # W_O requires distinct shaping: [n_heads, d_head, d_model] @@ -170,8 +187,8 @@ def _reshape_weight(weight: torch.Tensor, cfg: HookedTransformerConfig, is_outpu else: # Q, K, V return weight.view(cfg.n_heads, cfg.d_head, cfg.d_model).transpose(1, 2) - + @staticmethod def _reshape_bias(bias: torch.Tensor, cfg: HookedTransformerConfig) -> torch.Tensor: # reshapes HF attention biases into [n_heads, d_head] for TL - return bias.view(cfg.n_heads, cfg.d_head) \ No newline at end of file + return bias.view(cfg.n_heads, cfg.d_head)