[qwen2-vl] fix FA2 inference

src/transformers/modeling_flash_attention_utils.py

@@ -508,6 +508,22 @@ def _flash_attention_forward(
         query_states, key_states, value_states, target_dtype
     )
 
+    # We will use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
+    # under two cases:
+    # Case 1. If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
+    # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
+    # Case 2. Some models pass directly pre-computed `cu_seqlens` so we don't need to infer it from position ids. It is safe to
+    # use `flash_attn_varlen_func` knowing we already have all necessary the kwargs. NOTE: it is user's responsibility
+    # to take care of flattenning `position_ids` if that's needed by the model. See #39121 for more information
+    is_fa2_with_position_ids = (
+        position_ids is not None
+        and query_states.shape[0] == 1
+        and (max_length_q is not None or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all()))
+    )
+    is_fa2_with_varlen_kwargs = all(
+        kwarg is not None for kwarg in (cu_seq_lens_q, cu_seq_lens_k, max_length_q, max_length_k)
+    )
+
     # Contains at least one padding token in the sequence
     if attention_mask is not None:
         batch_size = query_states.shape[0]
@@ -531,14 +547,7 @@ def _flash_attention_forward(
         )
         attn_output = _pad_input(attn_output_unpad, indices_q, batch_size, query_length)
 
-    # If position_ids is provided and check all examples do not contain only 1 sequence, If tensor in increasing
-    # then we probably have one sequence, otherwise it is packed. Additionally check we are in pre-fill/training stage.
-    # Use `flash_attn_varlen_func` to prevent cross-example attention and also allow padding free approach
-    elif (
-        position_ids is not None
-        and query_states.shape[0] == 1
-        and (max_length_q is not None or (query_length != 1 and not (torch.diff(position_ids, dim=-1) >= 0).all()))
-    ):
+    elif is_fa2_with_varlen_kwargs or is_fa2_with_position_ids:
         batch_size = query_states.size(0)
 
         if cu_seq_lens_q is None or cu_seq_lens_k is None:

src/transformers/models/glm4v/modeling_glm4v.py

@@ -279,14 +279,15 @@ def eager_attention_forward(
 class Glm4vVisionAttention(nn.Module):
     def __init__(self, config: Glm4vVisionConfig) -> None:
         super().__init__()
-        self.config = config
+        self.dim = config.hidden_size
         self.num_heads = config.num_heads
-        self.head_dim = config.hidden_size // self.num_heads
-        self.num_key_value_groups = 1
-        self.scale = self.head_dim**-0.5
-        self.attention_dropout = config.attention_dropout
+        self.head_dim = self.dim // self.num_heads
+        self.num_key_value_groups = 1  # needed for eager attention
         self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.attention_bias)
         self.proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+        self.scaling = self.head_dim**-0.5
+        self.config = config
+        self.attention_dropout = config.attention_dropout
         self.is_causal = False
 
     def forward(
@@ -295,23 +296,31 @@ def forward(
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: Optional[torch.Tensor] = None,
         position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
     ) -> torch.Tensor:
         seq_length = hidden_states.shape[0]
         query_states, key_states, value_states = (
             self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
         )
-
-        cos, sin = position_embeddings
+        if position_embeddings is None:
+            logger.warning_once(
+                "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
+                "through `rotary_pos_emb` (2D tensor of RoPE theta values), to using externally computed "
+                "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.54 `rotary_pos_emb` will be "
+                "removed and `position_embeddings` will be mandatory."
+            )
+            emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        else:
+            cos, sin = position_embeddings
         query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
 
         query_states = query_states.transpose(0, 1).unsqueeze(0)
         key_states = key_states.transpose(0, 1).unsqueeze(0)
         value_states = value_states.transpose(0, 1).unsqueeze(0)
-
-        attention_mask = torch.zeros([1, 1, seq_length, seq_length], device=query_states.device, dtype=torch.bool)
-        for i in range(1, len(cu_seqlens)):
-            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
+        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
 
         attention_interface: Callable = eager_attention_forward
         if self.config._attn_implementation != "eager":
@@ -322,13 +331,17 @@ def forward(
             query_states,
             key_states,
             value_states,
-            attention_mask,
+            attention_mask=attention_mask,
             dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scale,
-            is_causal=self.is_causal,
+            scaling=self.scaling,
+            cu_seq_lens_q=cu_seqlens,  # pass cu seq lens for FA2
+            cu_seq_lens_k=cu_seqlens,
+            max_length_q=max_seqlen,
+            max_length_k=max_seqlen,
+            is_causal=False,
             **kwargs,
         )
-        attn_output = attn_output.squeeze(0)
+
         attn_output = attn_output.reshape(seq_length, -1).contiguous()
         attn_output = self.proj(attn_output)
         return attn_output
@@ -348,13 +361,15 @@ def forward(
         cu_seqlens: torch.Tensor,
         rotary_pos_emb: Optional[torch.Tensor] = None,
         position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        attention_mask: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
         hidden_states = hidden_states + self.attn(
             self.norm1(hidden_states),
             cu_seqlens=cu_seqlens,
             rotary_pos_emb=rotary_pos_emb,
             position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
             **kwargs,
         )
         hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
@@ -452,6 +467,25 @@ def rot_pos_emb(self, grid_thw):
         rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
         return rotary_pos_emb, pos_ids
 
+    def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor:
+        # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen`
+        # NOTE: the created attention masl only approximates the ragged FA2 attention by
+        # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between
+        # blocks. Though it will not be a 100% match for FA2's `varlen` path
+        if self.config._attn_implementation == "flash_attention_2":
+            return None
+
+        seq_length = inputs_tensor.shape[0]
+        attention_mask = torch.full(
+            [1, 1, seq_length, seq_length],
+            torch.finfo(inputs_tensor.dtype).min,
+            device=inputs_tensor.device,
+            dtype=inputs_tensor.dtype,
+        )
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0
+        return attention_mask
+
     def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
         """
         Args:
@@ -481,14 +515,15 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.
         cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
         seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
         hidden_states = self.embeddings(hidden_states, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1])
+        attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens=cu_seqlens)
 
         for blk in self.blocks:
-            if self.gradient_checkpointing and self.training:
-                hidden_states = self._gradient_checkpointing_func(
-                    blk.__call__, hidden_states, cu_seqlens, None, position_embeddings
-                )
-            else:
-                hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings)
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                position_embeddings=position_embeddings,
+                attention_mask=attention_mask,
+            )
 
         hidden_states = self.post_layernorm(hidden_states)
 

src/transformers/models/glm4v/modular_glm4v.py

@@ -50,8 +50,8 @@
     Qwen2_5_VLPreTrainedModel,
     Qwen2_5_VLRotaryEmbedding,
     Qwen2_5_VLTextModel,
+    Qwen2_5_VLVisionAttention,
     Qwen2_5_VLVisionBlock,
-    apply_rotary_pos_emb_vision,
 )
 from ..qwen2_5_vl.processing_qwen2_5_vl import (
     Qwen2_5_VLProcessor,
@@ -505,62 +505,12 @@ def forward(self, embeddings, lengths, image_shapes, h_coords, w_coords) -> torc
         return embeddings
 
 
-class Glm4vVisionAttention(nn.Module):
+class Glm4vVisionAttention(Qwen2_5_VLVisionAttention):
     def __init__(self, config: Glm4vVisionConfig) -> None:
         super().__init__()
-        self.config = config
-        self.num_heads = config.num_heads
-        self.head_dim = config.hidden_size // self.num_heads
-        self.num_key_value_groups = 1
-        self.scale = self.head_dim**-0.5
         self.attention_dropout = config.attention_dropout
         self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.attention_bias)
         self.proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
-        self.is_causal = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        cu_seqlens: torch.Tensor,
-        rotary_pos_emb: Optional[torch.Tensor] = None,
-        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
-        **kwargs: Unpack[FlashAttentionKwargs],
-    ) -> torch.Tensor:
-        seq_length = hidden_states.shape[0]
-        query_states, key_states, value_states = (
-            self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
-        )
-
-        cos, sin = position_embeddings
-        query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
-
-        query_states = query_states.transpose(0, 1).unsqueeze(0)
-        key_states = key_states.transpose(0, 1).unsqueeze(0)
-        value_states = value_states.transpose(0, 1).unsqueeze(0)
-
-        attention_mask = torch.zeros([1, 1, seq_length, seq_length], device=query_states.device, dtype=torch.bool)
-        for i in range(1, len(cu_seqlens)):
-            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
-
-        attention_interface: Callable = eager_attention_forward
-        if self.config._attn_implementation != "eager":
-            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
-
-        attn_output, _ = attention_interface(
-            self,
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            dropout=0.0 if not self.training else self.attention_dropout,
-            scaling=self.scale,
-            is_causal=self.is_causal,
-            **kwargs,
-        )
-        attn_output = attn_output.squeeze(0)
-        attn_output = attn_output.reshape(seq_length, -1).contiguous()
-        attn_output = self.proj(attn_output)
-        return attn_output
 
 
 class Glm4vVisionBlock(Qwen2_5_VLVisionBlock):
@@ -653,6 +603,25 @@ def rot_pos_emb(self, grid_thw):
         rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
         return rotary_pos_emb, pos_ids
 
+    def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor:
+        # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen`
+        # NOTE: the created attention masl only approximates the ragged FA2 attention by
+        # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between
+        # blocks. Though it will not be a 100% match for FA2's `varlen` path
+        if self.config._attn_implementation == "flash_attention_2":
+            return None
+
+        seq_length = inputs_tensor.shape[0]
+        attention_mask = torch.full(
+            [1, 1, seq_length, seq_length],
+            torch.finfo(inputs_tensor.dtype).min,
+            device=inputs_tensor.device,
+            dtype=inputs_tensor.dtype,
+        )
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0
+        return attention_mask
+
     def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
         """
         Args:
@@ -682,14 +651,15 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.
         cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
         seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
         hidden_states = self.embeddings(hidden_states, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1])
+        attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens=cu_seqlens)
 
         for blk in self.blocks:
-            if self.gradient_checkpointing and self.training:
-                hidden_states = self._gradient_checkpointing_func(
-                    blk.__call__, hidden_states, cu_seqlens, None, position_embeddings
-                )
-            else:
-                hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings)
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                position_embeddings=position_embeddings,
+                attention_mask=attention_mask,
+            )
 
         hidden_states = self.post_layernorm(hidden_states)