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Fix FlexAttention cross-attention masks for dynamic text lengths #107

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Fix FlexAttention cross-attention masks for dynamic text lengths #107

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7 changes: 6 additions & 1 deletion wan_va/modules/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,6 +95,7 @@ def half(x):
def init_mask(
latent_shape,
action_shape,
text_sequence_length,
padded_length,
chunk_size,
window_size,
Expand Down Expand Up @@ -133,7 +134,9 @@ def init_mask(
)
FlexAttnFunc.attention_mask = block_mask

text_seq_ids = torch.arange(B)[:, None].expand(-1, 512).flatten()
text_seq_ids = torch.arange(B)[:, None].expand(
-1, text_sequence_length
).flatten()
mask_mod_cross = FlexAttnFunc._get_cross_mask_mod(seq_ids.long().to(device), text_seq_ids.long().to(device))
block_mask_cross = FlexAttnFunc.compiled_create_block_mask(
mask_mod_cross, 1, 1, len(seq_ids), len(text_seq_ids), device=device, _compile=True
Expand Down Expand Up @@ -712,6 +715,7 @@ def forward_train(self, input_dict):
latent_hidden_states = self._input_embed(latent_dict['noisy_latents'], input_type='latent').flatten(0, 1)[None]
action_hidden_states = self._input_embed(action_dict['noisy_latents'], input_type='action').flatten(0, 1)[None]
text_hidden_states = self._input_embed(latent_dict["text_emb"], input_type='text')
text_sequence_length = text_hidden_states.shape[1]

text_hidden_states = text_hidden_states.flatten(0, 1)[None]

Expand Down Expand Up @@ -764,6 +768,7 @@ def forward_train(self, input_dict):

FlexAttnFunc.init_mask(latent_dict['noisy_latents'].shape,
action_dict['noisy_latents'].shape,
text_sequence_length,
padded_length,
input_dict["chunk_size"],
window_size=input_dict['window_size'],
Expand Down