Merge branch 'main' into fix-gpt-oss-with-speculative-decoding-handle-multiple-channels

Author: astralord

tests/models/multimodal/generation/test_common.py

@@ -222,8 +222,7 @@
         vllm_runner_kwargs={
             "model_impl": "transformers",
         },
-        # FIXME: Investigate mrope issue
-        marks=[large_gpu_mark(min_gb=32), pytest.mark.skip(reason="Mrope issue")],
+        marks=[large_gpu_mark(min_gb=32)],
     ),
     #### Extended model tests
     "aria": VLMTestInfo(

vllm/model_executor/models/transformers.py

@@ -79,7 +79,6 @@
     AutoWeightsLoader,
     PPMissingLayer,
     WeightsMapper,
-    flatten_bn,
     make_empty_intermediate_tensors_factory,
     maybe_prefix,
 )
@@ -347,54 +346,37 @@ def _get_prompt_updates(
 
     def _get_mm_fields_config(
         self,
-        hf_inputs,
-        hf_processor_mm_kwargs,
-        num_image_patches: torch.Tensor = None,
-    ):
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
         # HF Processors always return a mask but vLLM doesn't need it
         hf_inputs.pop("attention_mask", None)
+        num_image_patches = hf_inputs.get("num_image_patches")
         mm_fields = {
             key: MultiModalFieldConfig.flat_from_sizes("image", num_image_patches)
             for key in hf_inputs
         }
         mm_fields["image_embeds"] = MultiModalFieldConfig.flat_from_sizes(
             "image", num_image_patches
         )
+
+        # Keep these as batched, as they always have batch size as first dim
+        mm_fields["image_grid_thw"] = MultiModalFieldConfig.batched("image")
+        mm_fields["video_grid_thw"] = MultiModalFieldConfig.batched("image")
         mm_fields["num_image_patches"] = MultiModalFieldConfig.batched("image")
         return mm_fields
 
-    def _apply_hf_processor_text_mm(
+    def _get_hf_mm_data(
         self,
-        prompt_text: str,
         mm_items: MultiModalDataItems,
-        hf_processor_mm_kwargs: Mapping[str, object],
-        tokenization_kwargs: Mapping[str, object],
-    ) -> tuple[list[int], BatchFeature, bool]:
+    ) -> tuple[Mapping[str, object], Mapping[str, object]]:
         """
-        Apply the HF processor on the prompt text and multi-modal data
-        together.
-
-        In addition, return whether prompt replacements have been applied.
+        In contrast to the base class, this method always adds
+        `return_mm_token_type_ids` to the processor data
         """
-        processor_data, passthrough_data = self._get_hf_mm_data(mm_items)
+        processor_data, passthrough_data = super()._get_hf_mm_data(mm_items)
         processor_data["return_mm_token_type_ids"] = True
-
-        processed_data = self._call_hf_processor(
-            prompt=prompt_text,
-            mm_data=processor_data,
-            mm_kwargs=hf_processor_mm_kwargs,
-            tok_kwargs=tokenization_kwargs,
-        )
-        processed_data.update(passthrough_data)
-
-        (prompt_ids,) = processed_data.pop("input_ids").tolist()
-        mm_token_type_ids = (
-            processed_data.pop("mm_token_type_ids")
-            if "mm_token_type_ids" in processed_data
-            else processed_data.pop("token_type_ids")
-        )  # for gemma3 only
-
-        return prompt_ids, processed_data, mm_token_type_ids
+        return processor_data, passthrough_data
 
     def apply(
         self,
@@ -421,18 +403,28 @@ def apply(
             # into string
             prompt = hf_processor.decode(prompt)
 
-        (prompt_ids, processed_data, mm_token_type_ids) = (
-            self._apply_hf_processor_text_mm(
-                prompt_text=prompt,
-                mm_items=mm_items,
-                hf_processor_mm_kwargs=hf_processor_mm_kwargs,
-                tokenization_kwargs=tokenization_kwargs,
-            )
+        # Bypass cached processor and always apply to the full set of mm inputs
+        # NOTE: we can't just set caching=False because base class method
+        # transforms outputs to `MultiModalKwargs` which is not going to
+        # work for Transformers. We have a lot of logic tied to
+        # `mm_tokens_per_modality` below
+        prompt_ids, processed_data, _ = self._apply_hf_processor_text_mm(
+            prompt_text=prompt,
+            mm_items=mm_items,
+            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+            tokenization_kwargs=tokenization_kwargs,
         )
 
-        # HF processor will return `mm_token_type_ids` from which
-        # we can infer mm_placeholders. Until then hardcode to make code run
-        # Below tested on Llava. Prompts and `mm_token_type_ids` are always bs=1
+        # For gemma3 we check `token_type_ids` as the key
+        token_type_key = (
+            "mm_token_type_ids"
+            if "mm_token_type_ids" in processed_data
+            else "token_type_ids"
+        )
+        mm_token_type_ids = processed_data.pop(token_type_key)
+
+        # We can infer vLLM style placeholder from token type ids, if we split
+        # it for each input `mm_data`.
         mm_positions = torch.where(mm_token_type_ids == 1)[1]
         images = mm_items.get_items("image", ImageProcessorItems)
         multimodal_config = self.info.ctx.model_config.multimodal_config
@@ -462,17 +454,12 @@ def apply(
             ]
             mm_placeholders = {"image": ranges}
 
-        num_image_patches = (
-            torch.tensor(mm_tokens_per_modality["num_image_patches"])
-            if "num_image_patches" in mm_tokens_per_modality
-            else None
+        processed_data["num_image_patches"] = torch.tensor(
+            mm_tokens_per_modality["num_image_patches"]
         )
-        processed_data["num_image_patches"] = num_image_patches
         mm_kwargs = MultiModalKwargsItems.from_hf_inputs(
             processed_data,
-            self._get_mm_fields_config(
-                processed_data, hf_processor_mm_kwargs, num_image_patches
-            ),
+            self._get_mm_fields_config(processed_data, hf_processor_mm_kwargs),
         )
 
         # Use overrides if provided; fallback to data-dependent hashing.
@@ -531,8 +518,6 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
                 self.ignore_unexpected_suffixes.append(".bias")
 
         # Set correct attn and init on "meta" to delay allocating GPU tensors
-        # TODO: @raushan, use the public `model.set_attn_implementation()`
-        # method once its checks are fixed in Transformers.
         self.text_config._attn_implementation = "vllm"
         with init_on_device_without_buffers("meta"):
             self.model: PreTrainedModel = AutoModel.from_config(
@@ -844,17 +829,6 @@ def compute_logits(
         return logits
 
 
-def flatten_and_concat(x: list[torch.Tensor]) -> torch.Tensor:
-    """Flatten until a list of tensors can be concatenated then do concat"""
-
-    def _can_concat(x: list[torch.Tensor]):
-        return len(set(map(lambda _x: _x.shape[1:], x))) == 1
-
-    if _can_concat(x):
-        return torch.concat(x)
-    return flatten_and_concat(flatten_bn(x))
-
-
 @MULTIMODAL_REGISTRY.register_processor(
     MultiModalProcessor,
     info=MultiModalProcessingInfo,
@@ -935,9 +909,6 @@ def get_multimodal_embeddings(self, **kwargs):
             vision_embeddings = self.model.get_image_features(pixel_values, **kwargs)
 
             if isinstance(vision_embeddings, torch.Tensor):
-                if isinstance(num_image_patches, list):
-                    num_image_patches = torch.cat(num_image_patches)
-
                 if vision_embeddings.ndim == 2:
                     vision_embeddings = vision_embeddings.unsqueeze(0)