[Model][Pixtral] Use memory_efficient_attention for PixtralHFVision (v…

…llm-project#9520)

vllm/model_executor/models/pixtral.py

@@ -13,8 +13,7 @@
 from transformers.models.pixtral.image_processing_pixtral import (
     _num_image_tokens)
 from transformers.models.pixtral.modeling_pixtral import (
-    PixtralRotaryEmbedding, apply_rotary_pos_emb,
-    generate_block_attention_mask, position_ids_in_meshgrid)
+    PixtralRotaryEmbedding, apply_rotary_pos_emb, position_ids_in_meshgrid)
 from xformers.ops.fmha import memory_efficient_attention
 from xformers.ops.fmha.attn_bias import BlockDiagonalMask
 
@@ -813,48 +812,30 @@ def __init__(self, config: PixtralVisionConfig):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
+        attention_mask: BlockDiagonalMask,
         position_embeddings: torch.Tensor,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
-        """Input shape: Batch x Time x Channel"""
+        batch, patches, _ = hidden_states.size()
 
-        batch_size, patches, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(batch_size, patches, self.n_heads,
-                                         self.head_dim).transpose(1, 2)
-        key_states = key_states.view(batch_size, patches, self.n_heads,
-                                     self.head_dim).transpose(1, 2)
-        value_states = value_states.view(batch_size, patches, self.n_heads,
-                                         self.head_dim).transpose(1, 2)
+        q = self.q_proj(hidden_states)
+        k = self.k_proj(hidden_states)
+        v = self.v_proj(hidden_states)
 
+        # Transpose q and k to apply HF's Rotary Position Embedding
+        q = q.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
+        k = k.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
         cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb(query_states,
-                                                        key_states,
-                                                        cos,
-                                                        sin,
-                                                        unsqueeze_dim=0)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(
-            2, 3)) * self.scale
-
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
+        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)
 
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights,
-                                             dim=-1,
-                                             dtype=torch.float32).to(
-                                                 query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
+        # Transpose q and k back for attention
+        q = q.transpose(1, 2).contiguous()
+        k = k.transpose(1, 2).contiguous()
+        v = v.reshape(batch, patches, self.n_heads, self.head_dim)
 
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(batch_size, patches, -1)
+        out = memory_efficient_attention(q, k, v, attn_bias=attention_mask)
+        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
 
-        return self.o_proj(attn_output)
+        return self.o_proj(out)
 
 
 class PixtralHFTransformerBlock(nn.Module):
@@ -869,7 +850,7 @@ def __init__(self, config: PixtralVisionConfig):
     def forward(
         self,
         hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor,
+        attention_mask: BlockDiagonalMask,
         position_embeddings: torch.Tensor,
     ) -> torch.Tensor:
         r = self.attention.forward(self.attention_norm(hidden_states),
@@ -892,7 +873,7 @@ def __init__(self, config: PixtralVisionConfig):
     def forward(
         self,
         x: torch.Tensor,
-        attention_mask: torch.Tensor,
+        attention_mask: BlockDiagonalMask,
         position_embeddings: torch.Tensor,
     ) -> torch.Tensor:
         for layer in self.layers:
@@ -953,9 +934,8 @@ def forward(
 
         position_embedding = self.patch_positional_embedding(
             patch_embeds, position_ids)
-        attention_mask = generate_block_attention_mask(
-            [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
-            patch_embeds)
+        attention_mask = BlockDiagonalMask.from_seqlens(
+            [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
         out = self.transformer(patch_embeds, attention_mask,
                                position_embedding)