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Remove head_mask from Ultravox and Swin (#30764)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-12-16 16:02:41 +00:00
committed by GitHub
parent af506fd76a
commit 0b0acc758e
2 changed files with 16 additions and 17 deletions
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16
vllm/model_executor/models/swin.py
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@@ -102,7 +102,6 @@ class SwinSelfAttention(nn.Module):
self,
hidden_states: torch.Tensor,
attention_mask: torch.FloatTensor | None = None,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
) -> tuple[torch.Tensor, ...]:
batch_size, dim, num_channels = hidden_states.shape
@@ -201,12 +200,9 @@ class SwinAttention(nn.Module):
self,
hidden_states: torch.Tensor,
attention_mask: torch.FloatTensor | None = None,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
) -> tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states, attention_mask, head_mask, output_attentions
)
self_outputs = self.self(hidden_states, attention_mask, output_attentions)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:]
return outputs
@@ -339,18 +335,14 @@ class SwinStage(nn.Module):
self,
hidden_states: torch.Tensor,
input_dimensions: tuple[int, int],
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
always_partition: bool | None = False,
) -> tuple[torch.Tensor]:
height, width = input_dimensions
for i, layer_module in enumerate(self.blocks):
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
input_dimensions,
layer_head_mask,
output_attentions,
always_partition,
)
@@ -425,17 +417,13 @@ class SwinEncoder(nn.Module):
self,
hidden_states: torch.Tensor,
input_dimensions: tuple[int, int],
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = False,
always_partition: bool | None = False,
) -> tuple[torch.Tensor]:
for i, layer_module in enumerate(self.layers):
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
input_dimensions,
layer_head_mask,
output_attentions,
always_partition,
)
@@ -473,7 +461,6 @@ class SwinModel(nn.Module):
def forward(
self,
pixel_values: torch.FloatTensor | None = None,
head_mask: torch.FloatTensor | None = None,
output_attentions: bool | None = None,
) -> tuple[torch.Tensor]:
embedding_output, input_dimensions = self.embeddings(pixel_values)
@@ -481,7 +468,6 @@ class SwinModel(nn.Module):
encoder_outputs = self.encoder(
embedding_output,
input_dimensions,
head_mask=head_mask,
output_attentions=output_attentions,
)