feat: Custom masking utils for Gemma3 VLM

Signed-off-by: Balaram Buddharaju <[email protected]>

Author: brb-nv

tensorrt_llm/_torch/attention_backend/flashinfer.py

@@ -14,7 +14,7 @@
 
 from ..utils import get_global_attrs, get_model_extra_attrs
 from .interface import (AttentionBackend, AttentionMask, AttentionMetadata,
-                        PredefinedAttentionMask)
+                        CustomAttentionMask, PredefinedAttentionMask)
 
 try:
     check_cuda_arch()
@@ -366,6 +366,12 @@ def _plan_with_params(self, plan_params: PlanParams) -> PlanParams:
         is_causal = plan_params.attention_mask_type == AttentionMaskType.causal
 
         def prefill_plan():
+            # Setting `window_left` to -1 for custom attention mask is important.
+            # Else, FlashInfer proceeds to use SWA regardless of attention_mask_data.
+            if plan_params.attention_mask_data is not None:
+                window_left = -1
+            else:
+                window_left = plan_params.window_left
             prefill_wrapper.plan(
                 self.qo_indptr[:self.num_contexts + 1],
                 self.paged_kv_indptr_prefill[:self.num_contexts + 1],
@@ -377,9 +383,10 @@ def prefill_plan():
                 self.page_size,
                 causal=is_causal,
                 sm_scale=plan_params.sm_scale,
-                window_left=plan_params.window_left,
+                window_left=window_left,
                 q_data_type=plan_params.q_dtype,
                 kv_data_type=plan_params.kv_dtype,
+                custom_mask=plan_params.attention_mask_data,
             )
 
         if plan_params in self._plan_params_to_wrappers:
@@ -473,8 +480,14 @@ def forward(self,
                 *,
                 attention_window_size: Optional[int] = None,
                 attention_mask: AttentionMask = PredefinedAttentionMask.CAUSAL,
+                attention_mask_data: Optional[torch.Tensor] = None,
                 **kwargs) -> torch.Tensor:
-        if attention_mask == PredefinedAttentionMask.CAUSAL:
+        if attention_mask == CustomAttentionMask.CUSTOM:
+            assert attention_mask_data is not None, "attention_mask_data is required for custom attention mask."
+            attention_mask_type = int(AttentionMaskType.custom_mask)
+            attention_mask_data = attention_mask_data if attention_mask_data.ndim == 1 else attention_mask_data.flatten(
+            )
+        elif attention_mask == PredefinedAttentionMask.CAUSAL:
             attention_mask_type = int(AttentionMaskType.causal)
             attention_mask_data = None
         elif attention_mask == PredefinedAttentionMask.FULL:

tensorrt_llm/_torch/attention_backend/interface.py

@@ -500,8 +500,15 @@ class PredefinedAttentionMask(str, Enum):
     FULL = "full"
 
 
+class CustomAttentionMask(str, Enum):
+    """
+    Custom attention mask types
+    """
+    CUSTOM = "custom"
+
+
 # May extend to custom attention mask type
-AttentionMask = Union[PredefinedAttentionMask]
+AttentionMask = Union[PredefinedAttentionMask, CustomAttentionMask]
 
 
 class AttentionBackend(Generic[TMetadata]):

tensorrt_llm/_torch/models/modeling_gemma3.py

@@ -10,8 +10,9 @@
 from tensorrt_llm.functional import PositionEmbeddingType, RotaryScalingType
 from tensorrt_llm.mapping import Mapping
 
-from ..attention_backend import AttentionMetadata
-from ..attention_backend.interface import (PositionalEmbeddingParams,
+from ..attention_backend import AttentionMetadata, FlashInferAttentionMetadata
+from ..attention_backend.interface import (AttentionMask, CustomAttentionMask,
+                                           PositionalEmbeddingParams,
                                            PredefinedAttentionMask, RopeParams)
 from ..distributed import AllReduceParams
 from ..model_config import ModelConfig
@@ -101,14 +102,19 @@ def forward(
         position_ids: Optional[torch.IntTensor],
         hidden_states: torch.Tensor,
         attn_metadata: AttentionMetadata,
-        attention_mask: PredefinedAttentionMask = PredefinedAttentionMask.
-        CAUSAL,
+        attention_mask: AttentionMask = PredefinedAttentionMask.CAUSAL,
         mrope_config: Optional[dict] = None,
         all_reduce_params: Optional[AllReduceParams] = None,
         lora_params: Optional[dict] = None,
+        attention_mask_data: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
 
+        if attention_mask_data is not None:
+            assert isinstance(
+                attn_metadata, FlashInferAttentionMetadata
+            ), "Only FlashInfer backend supports custom attention mask currently."
+            assert attention_mask == CustomAttentionMask.CUSTOM
         return super().forward(position_ids=position_ids,
                                hidden_states=hidden_states,
                                attn_metadata=attn_metadata,
@@ -117,6 +123,7 @@ def forward(
                                all_reduce_params=all_reduce_params,
                                lora_params=lora_params,
                                attention_window_size=self.attention_window_size,
+                               attention_mask_data=attention_mask_data,
                                **kwargs)
 
     def apply_qk_norm(self, q, k):
@@ -214,6 +221,7 @@ def forward(
         hidden_states: torch.Tensor,
         attn_metadata: AttentionMetadata,
         residual: Optional[torch.Tensor] = None,
+        attention_mask_data: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
 
@@ -223,6 +231,9 @@ def forward(
             position_ids=position_ids,
             hidden_states=hidden_states,
             attn_metadata=attn_metadata,
+            attention_mask=CustomAttentionMask.CUSTOM if attention_mask_data
+            is not None else PredefinedAttentionMask.CAUSAL,
+            attention_mask_data=attention_mask_data,
             **kwargs,
         )
         hidden_states = self.post_attention_layernorm(hidden_states)
@@ -267,6 +278,8 @@ def forward(
         input_ids: Optional[torch.IntTensor] = None,
         position_ids: Optional[torch.IntTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
+        local_attention_mask_data: Optional[torch.Tensor] = None,
+        global_attention_mask_data: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
         if (input_ids is None) ^ (inputs_embeds is not None):
@@ -280,9 +293,13 @@ def forward(
         hidden_states = inputs_embeds.to(self.dtype)
 
         for decoder_layer in self.layers:
-            hidden_states = decoder_layer(position_ids=position_ids,
-                                          hidden_states=hidden_states,
-                                          attn_metadata=attn_metadata)
+            hidden_states = decoder_layer(
+                position_ids=position_ids,
+                hidden_states=hidden_states,
+                attn_metadata=attn_metadata,
+                attention_mask_data=local_attention_mask_data
+                if decoder_layer.self_attn.is_sliding else
+                global_attention_mask_data)
 
         hidden_states = self.norm(hidden_states)
         return hidden_states
@@ -301,21 +318,130 @@ def __init__(
                          hidden_size=model_config.pretrained_config.hidden_size,
                          vocab_size=model_config.pretrained_config.vocab_size)
 
+    def get_context_mask(
+        self,
+        image_token_mask: torch.BoolTensor,
+        effective_sliding_window: Optional[int] = None,
+    ):
+        """
+        Returns an attention mask such that text tokens attend to each other in causal fashion while image
+        tokens attend in causal fashion as well as to all other image tokens in a bidirectional manner.
+        Args:
+            image_token_mask: A boolean tensor of shape (sequence_length,) where True indicates an image token.
+            effective_sliding_window: The effective sliding window size for the attention mask. Default is None, which means no sliding window.
+            For Gemma3, this is the sliding window size from config (e.g. 512 for 1B model).
+        Returns:
+            A boolean attention mask of shape (sequence_length, sequence_length).
+        """
+        device = image_token_mask.device
+        sequence_length = len(image_token_mask)
+        if effective_sliding_window is None or effective_sliding_window >= sequence_length:
+            causal_mask = torch.arange(
+                sequence_length, device=device).unsqueeze(0) <= torch.arange(
+                    sequence_length, device=device).unsqueeze(1)
+        else:
+            attention_mask_1 = torch.arange(
+                sequence_length, device=device).unsqueeze(0) <= torch.arange(
+                    sequence_length, device=device).unsqueeze(1)
+            attention_mask_2 = torch.arange(
+                sequence_length, device=device).unsqueeze(0) > torch.arange(
+                    sequence_length,
+                    device=device).unsqueeze(1) - effective_sliding_window
+            causal_mask = attention_mask_1 & attention_mask_2
+
+        # Apply a bidirectional mask for image tokens.
+        token_type_ids = torch.zeros(sequence_length,
+                                     dtype=torch.int32,
+                                     device=device)
+        # 1 for image tokens, 0 for text tokens.
+        token_type_ids[image_token_mask] = 1
+        token_type_mask = token_type_ids.unsqueeze(
+            0) == token_type_ids.unsqueeze(1)
+        # If text token, do not change anything.
+        token_type_mask[token_type_ids == 0] = False
+        causal_mask = causal_mask.masked_fill(token_type_mask, True)
+        return causal_mask
+
+    # ASSUMPTIONS:
+    # 1) Chunked prefill is disabled to avoid chunking image tokens as they need bidirectional attention.
+    # 2) KV cache reuse is disabled to avoid partially matched image tokens (entire image must be reused to get things correct).
+    def get_flashinfer_attention_mask(
+            self,
+            image_token_mask: torch.BoolTensor,
+            attn_metadata: AttentionMetadata,
+            effective_sliding_window: Optional[int] = None) -> torch.Tensor:
+        """
+        This is specifically needed for context phase requests. Currently, we don't create custom mask for generation requests because FlashInfer backend
+        doesn't use it anyway and there's nothing special we need to do for generation requests.
+        - This function will only be called for a batch when there's at least one context request in the batch with image tokens.
+        - In context phase, each sample's input_ids may have a mix of image tokens and text tokens where tokens corresponding to an image
+        appear as a contiguous blob. Example: torch.IntTensor([2, 3, 4, 5, img_idx, img_idx, img_idx, ..., img_idx, 100])
+        - While the text tokens attend to other tokens in a causal fashion, image tokens attend to others in a causal fashion and well as
+        attend to other image tokens in a bidirectional manner. Hence, the need for custom masking.
+        Args:
+            image_token_mask: A boolean tensor of shape (len(input_ids),) where True indicates an image token. This corresponds to concatenated
+            list of tokens for all samples in the batch.
+            attn_metadata: The attention metadata for the batch.
+            effective_sliding_window: The effective sliding window size for the attention mask. Default is None, which means no sliding window.
+            For Gemma3, this is the sliding window size from config (e.g. 512 for 1B model).
+        Returns:
+            A flattened boolean mask of shape (sum(q_len[i] * k_len[i] for i in range(batch_size)).
+        """
+
+        assert isinstance(
+            attn_metadata, FlashInferAttentionMetadata
+        ), "Only FlashInfer backend supports custom mask currently."
+        num_contexts = attn_metadata.num_contexts
+        assert num_contexts > 0, "There should be at least one context request in the batch for custom mask."
+
+        qo_indptr = attn_metadata.qo_indptr[:num_contexts + 1]
+        cached_token_lens = attn_metadata.cached_token_lens[:num_contexts]
+        assert (cached_token_lens == 0).all(
+        ), "cached_token_lens should be 0 for context requests since chunked prefill and kv cache reuse must be disabled."
+
+        # Create masks for context requests.
+        context_mask_list = []
+        for i in range(num_contexts):
+            mask_i = self.get_context_mask(
+                image_token_mask=image_token_mask[qo_indptr[i]:qo_indptr[i +
+                                                                         1]],
+                effective_sliding_window=effective_sliding_window,
+            )
+            context_mask_list.append(mask_i.flatten())
+        return torch.cat(context_mask_list, dim=0).contiguous()
+
     def forward(
         self,
         attn_metadata: AttentionMetadata,
         input_ids: torch.IntTensor = None,
         position_ids: Optional[torch.IntTensor] = None,
         inputs_embeds: Optional[torch.FloatTensor] = None,
         return_context_logits: bool = False,
+        image_token_mask: Optional[torch.Tensor] = None,
         **kwargs,
     ) -> torch.Tensor:
 
+        local_attention_mask_data = None
+        global_attention_mask_data = None
+        if image_token_mask is not None:
+            global_attention_mask_data = self.get_flashinfer_attention_mask(
+                image_token_mask=image_token_mask,
+                attn_metadata=attn_metadata,
+                effective_sliding_window=None,
+            )
+            local_attention_mask_data = self.get_flashinfer_attention_mask(
+                image_token_mask=image_token_mask,
+                attn_metadata=attn_metadata,
+                effective_sliding_window=self.config.sliding_window,
+            )
+
         output = self.model(
             input_ids=input_ids,
             attn_metadata=attn_metadata,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
+            local_attention_mask_data=local_attention_mask_data,
+            global_attention_mask_data=global_attention_mask_data,
         )
 
         return self.logits_processor.forward(

tensorrt_llm/_torch/models/modeling_gemma3vl.py

@@ -101,12 +101,54 @@ def __call__(
         input_ids = preprocess_outputs[0]["mm_processor_kwargs"]["input_ids"]
         mm_features = self._process(pixel_values)
         multimodal_data = {}
-        multimodal_data["multimodal_embedding"] = mm_features
+        multimodal_data["multimodal_embedding"] = mm_features.squeeze(dim=0)
         return input_ids[0].to(torch.int32).tolist(), {
             "multimodal_data": multimodal_data
         }
 
 
+def get_gemma3_causal_mask(
+    input_ids: torch.Tensor,
+    image_token_index: int,
+    sliding_window: Optional[int] = None,
+):
+    print("[get_gemma3_causal_mask] input_ids: ", input_ids)
+    assert input_ids.ndim == 1, "input_ids should be a 1D tensor."
+    # Get token type ids. 0 corresponds to text tokens, 1 corresponds to image tokens.
+    token_type_ids = torch.zeros_like(input_ids, device=input_ids.device)
+    image_token_mask = (input_ids == image_token_index).to(
+        device=input_ids.device, dtype=torch.bool)
+    token_type_ids[image_token_mask] = 1
+
+    sequence_length = input_ids.shape[-1]
+    # TODO: Use causal when sliding_window is larger than sequence_length.
+    if sliding_window is None:
+        causal_mask = torch.arange(
+            sequence_length,
+            device=input_ids.device).unsqueeze(0) <= torch.arange(
+                sequence_length, device=input_ids.device).unsqueeze(1)
+    else:
+        attention_mask_1 = torch.arange(
+            sequence_length,
+            device=input_ids.device).unsqueeze(0) <= torch.arange(
+                sequence_length, device=input_ids.device).unsqueeze(1)
+        attention_mask_2 = torch.arange(
+            sequence_length,
+            device=input_ids.device).unsqueeze(0) > torch.arange(
+                sequence_length,
+                device=input_ids.device).unsqueeze(1) - sliding_window
+        causal_mask = attention_mask_1 & attention_mask_2
+
+    # Apply a bidirectional mask for image tokens.
+    if token_type_ids is not None:
+        token_type_mask = token_type_ids.unsqueeze(
+            0) == token_type_ids.unsqueeze(1)
+        # If text token, do not change anything.
+        token_type_mask[token_type_ids == 0] = False
+        causal_mask = causal_mask.masked_fill(token_type_mask, True)
+    return causal_mask
+
+
 @register_auto_model("Gemma3ForConditionalGeneration")
 @register_input_processor(Gemma3InputProcessor, model_type="gemma3")
 class Gemma3Model(PreTrainedModel):
@@ -129,6 +171,7 @@ def __init__(self, model_config: ModelConfig[PretrainedConfig], *args,
 
         self.model_config = model_config
         self.vocab_size = config.text_config.vocab_size
+        self.sliding_window = config.text_config.sliding_window
         self.model_dtype = getattr(config.text_config, "torch_dtype",
                                    torch.float16)
         logger.info(f"[Gemma3Model::__init__]{self.dtype=} {self.model_dtype=}")
@@ -172,14 +215,26 @@ def forward(
             mm_embed
         ) == num_context_requests, "Number of multimodal features (if provided) should be equal to number of context requests"
 
+        mm_token_ids = torch.tensor([self.image_token_index
+                                     ]).to(input_ids.device)
+        mm_token_mask = None
+        if len(mm_embed) > 0:
+            # Get token type ids. 0 corresponds to text tokens, 1 corresponds to image tokens.
+            mm_token_mask = torch.isin(input_ids, mm_token_ids)
         input_ids, inputs_embeds = fuse_input_embeds(
             embedding_layer=self.llm.model.embed_tokens,
             input_ids=input_ids,
             mm_embeds=mm_embed,
             mm_token_ids=torch.tensor([self.image_token_index
                                        ]).to(input_ids.device))
-        logits = self.llm.forward(attn_metadata, input_ids, position_ids,
-                                  inputs_embeds, return_context_logits)
+        logits = self.llm.forward(
+            attn_metadata=attn_metadata,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            return_context_logits=return_context_logits,
+            image_token_mask=mm_token_mask,
+        )
         return logits