Use Conv1d for TDNN (huggingface#25728)

* use conv for tdnn

* run make fixup

* update TDNN

* add PEFT LoRA check

* propagate tdnn warnings to others

* add missing imports

* update TDNN in wav2vec2_bert

* add missing imports

src/transformers/models/data2vec/modeling_data2vec_audio.py

@@ -35,7 +35,13 @@
     XVectorOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from ...utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_peft_available,
+    logging,
+)
 from .configuration_data2vec_audio import Data2VecAudioConfig
 
 
@@ -1342,16 +1348,21 @@ def __init__(self, config, layer_id=0):
         self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
         self.activation = nn.ReLU()
 
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if is_peft_available():
+            from peft.tuners.lora import LoraLayer
+
+            if isinstance(self.kernel, LoraLayer):
+                warnings.warn(
+                    "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
+                    "You should exclude TDNNLayer from LoRA's target modules.",
+                )
+
+        # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
+        hidden_states = hidden_states.transpose(1, 2)
+        weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
+        hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
         hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
 
         hidden_states = self.activation(hidden_states)
         return hidden_states

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -41,6 +41,7 @@
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_peft_available,
     logging,
     replace_return_docstrings,
 )
@@ -1796,16 +1797,21 @@ def __init__(self, config, layer_id=0):
         self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
         self.activation = nn.ReLU()
 
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if is_peft_available():
+            from peft.tuners.lora import LoraLayer
+
+            if isinstance(self.kernel, LoraLayer):
+                warnings.warn(
+                    "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
+                    "You should exclude TDNNLayer from LoRA's target modules.",
+                )
+
+        # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
+        hidden_states = hidden_states.transpose(1, 2)
+        weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
+        hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
         hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
 
         hidden_states = self.activation(hidden_states)
         return hidden_states

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -44,6 +44,7 @@
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     cached_file,
+    is_peft_available,
     is_safetensors_available,
     logging,
     replace_return_docstrings,
@@ -2287,16 +2288,21 @@ def __init__(self, config, layer_id=0):
         self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
         self.activation = nn.ReLU()
 
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if is_peft_available():
+            from peft.tuners.lora import LoraLayer
+
+            if isinstance(self.kernel, LoraLayer):
+                warnings.warn(
+                    "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
+                    "You should exclude TDNNLayer from LoRA's target modules.",
+                )
+
+        # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
+        hidden_states = hidden_states.transpose(1, 2)
+        weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
+        hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
         hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
 
         hidden_states = self.activation(hidden_states)
         return hidden_states

src/transformers/models/wav2vec2_bert/modeling_wav2vec2_bert.py

@@ -15,6 +15,7 @@
 """ PyTorch Wav2Vec2-BERT model."""
 
 import math
+import warnings
 from typing import Optional, Tuple, Union
 
 import numpy as np
@@ -39,6 +40,7 @@
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_peft_available,
     logging,
 )
 from .configuration_wav2vec2_bert import Wav2Vec2BertConfig
@@ -1516,16 +1518,21 @@ def __init__(self, config, layer_id=0):
         self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
         self.activation = nn.ReLU()
 
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if is_peft_available():
+            from peft.tuners.lora import LoraLayer
+
+            if isinstance(self.kernel, LoraLayer):
+                warnings.warn(
+                    "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
+                    "You should exclude TDNNLayer from LoRA's target modules.",
+                )
+
+        # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
+        hidden_states = hidden_states.transpose(1, 2)
+        weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
+        hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
         hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
 
         hidden_states = self.activation(hidden_states)
         return hidden_states

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -15,6 +15,7 @@
 """ PyTorch Wav2Vec2-Conformer model."""
 
 import math
+import warnings
 from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 
@@ -40,6 +41,7 @@
     add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
+    is_peft_available,
     logging,
     replace_return_docstrings,
 )
@@ -1948,16 +1950,21 @@ def __init__(self, config, layer_id=0):
         self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
         self.activation = nn.ReLU()
 
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if is_peft_available():
+            from peft.tuners.lora import LoraLayer
+
+            if isinstance(self.kernel, LoraLayer):
+                warnings.warn(
+                    "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
+                    "You should exclude TDNNLayer from LoRA's target modules.",
+                )
+
+        # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
+        hidden_states = hidden_states.transpose(1, 2)
+        weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
+        hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
         hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
 
         hidden_states = self.activation(hidden_states)
         return hidden_states

src/transformers/models/wavlm/modeling_wavlm.py

@@ -36,7 +36,13 @@
     XVectorOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
+from ...utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_peft_available,
+    logging,
+)
 from .configuration_wavlm import WavLMConfig
 
 
@@ -1674,16 +1680,21 @@ def __init__(self, config, layer_id=0):
         self.kernel = nn.Linear(self.in_conv_dim * self.kernel_size, self.out_conv_dim)
         self.activation = nn.ReLU()
 
-    def forward(self, hidden_states):
-        hidden_states = hidden_states.unsqueeze(1)
-        hidden_states = nn.functional.unfold(
-            hidden_states,
-            (self.kernel_size, self.in_conv_dim),
-            stride=(1, self.in_conv_dim),
-            dilation=(self.dilation, 1),
-        )
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if is_peft_available():
+            from peft.tuners.lora import LoraLayer
+
+            if isinstance(self.kernel, LoraLayer):
+                warnings.warn(
+                    "Detected LoRA on TDNNLayer. LoRA weights won't be applied due to optimization. "
+                    "You should exclude TDNNLayer from LoRA's target modules.",
+                )
+
+        # for backward compatibility, we keep nn.Linear but call F.conv1d for speed up
+        hidden_states = hidden_states.transpose(1, 2)
+        weight = self.kernel.weight.view(self.out_conv_dim, self.kernel_size, self.in_conv_dim).transpose(1, 2)
+        hidden_states = nn.functional.conv1d(hidden_states, weight, self.kernel.bias, dilation=self.dilation)
         hidden_states = hidden_states.transpose(1, 2)
-        hidden_states = self.kernel(hidden_states)
 
         hidden_states = self.activation(hidden_states)
         return hidden_states