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Update qwen2_5_vl attention forward #908

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Merged
mgawarkiewicz-intel merged 1 commit into from
Feb 2, 2026
Merged

Update qwen2_5_vl attention forward #908

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40 changes: 22 additions & 18 deletions vllm_gaudi/models/qwen2_5_vl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,22 +77,25 @@ def forward(cls, q, k, v, mask, cu_seqlens, qwen2_5_vl, q_block_size=64):
Support long sequence at prompt phase
"""
q_len = q.size(-2)
lens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
if mask is not None or len(lens) == 1:
if not qwen2_5_vl or (qwen2_5_vl and q_len < 65536):
if qwen2_5_vl:
if q_len < 65536:
return FusedSDPA.apply(q, k, v, mask, 0.0, False, None, cls.softmax_mode)
else:
return AttentionLongSequence.forward(q, k, v, mask, q_block_size, cls.softmax_mode)
else:
q_chunks = torch.split(q, lens, dim=2)
k_chunks = torch.split(k, lens, dim=2)
v_chunks = torch.split(v, lens, dim=2)
outputs = []
for q_i, k_i, v_i in zip(q_chunks, k_chunks, v_chunks):
output_i = FusedSDPA.apply(q_i, k_i, v_i, None, 0.0, False, None, cls.softmax_mode)
outputs.append(output_i)
context_layer = torch.cat(outputs, dim=2)
return context_layer
lens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
if len(lens) == 1:
return FusedSDPA.apply(q, k, v, None, 0.0, False, None, cls.softmax_mode)
else:
q_chunks = torch.split(q, lens, dim=2)
k_chunks = torch.split(k, lens, dim=2)
v_chunks = torch.split(v, lens, dim=2)
outputs = []
for q_i, k_i, v_i in zip(q_chunks, k_chunks, v_chunks):
output_i = FusedSDPA.apply(q_i, k_i, v_i, None, 0.0, False, None, cls.softmax_mode)
outputs.append(output_i)
context_layer = torch.cat(outputs, dim=2)
return context_layer


def create_block_diagonal_attention_mask(indices):
Expand Down Expand Up @@ -237,13 +240,11 @@ def forward(
seqlens: Optional[list[int]] = None, # Only used for xFormers
attn_mask: Optional[torch.Tensor] = None, # Only used for HPU
) -> torch.Tensor:
mask_to_use = attn_mask if attn_mask is not None else cu_seqlens

x = x + self.attn(self.norm1(x),
cu_seqlens=cu_seqlens,
rotary_pos_emb_cos=rotary_pos_emb_cos,
rotary_pos_emb_sin=rotary_pos_emb_sin,
attn_mask=mask_to_use)
attn_mask=attn_mask)

x = x + self.mlp(self.norm2(x))
return x
Expand Down Expand Up @@ -363,7 +364,8 @@ def pre_attn(self, x: torch.Tensor, grid_thw: torch.Tensor):
)

def forward(self, hidden_states: torch.Tensor, rotary_pos_emb_cos: torch.Tensor, rotary_pos_emb_sin: torch.Tensor,
padding_attn_mask_window: torch.Tensor, padding_attn_mask_full: torch.Tensor) -> torch.Tensor:
padding_attn_mask_window: torch.Tensor, padding_attn_mask_full: torch.Tensor,
cu_seqlens: torch.Tensor) -> torch.Tensor:
hidden_states = hidden_states.unsqueeze(1)
for layer_num, blk in enumerate(self.blocks):
if layer_num in self.fullatt_block_indexes:
Expand All @@ -373,9 +375,10 @@ def forward(self, hidden_states: torch.Tensor, rotary_pos_emb_cos: torch.Tensor,

hidden_states = blk(
hidden_states,
cu_seqlens=padding_attn_mask_now,
cu_seqlens=cu_seqlens,
rotary_pos_emb_cos=rotary_pos_emb_cos,
rotary_pos_emb_sin=rotary_pos_emb_sin,
attn_mask=padding_attn_mask_now,
)

# For Qwen2.5-VL-3B, float16 will overflow at last block
Expand Down Expand Up @@ -443,7 +446,8 @@ def get_image_embeds(
rotary_pos_emb_cos=rot_pos_emb_cos,
rotary_pos_emb_sin=rot_pos_emb_sin,
padding_attn_mask_window=padding_attn_mask_window,
padding_attn_mask_full=padding_attn_mask_full)
padding_attn_mask_full=padding_attn_mask_full,
cu_seqlens=cu_seqlens)
htcore.mark_step()

# remove padding
Expand Down