Merge branch 'main' into support-llama3-eagle3-head-with-llama4-verifier

Author: rahul-tuli

docs/models/supported_models.md

@@ -677,7 +677,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ | ✅︎ |
 | `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ | ✅︎ |
 | `Idefics3ForConditionalGeneration` | Idefics3 | T + I | `HuggingFaceM4/Idefics3-8B-Llama3`, etc. | ✅︎ | | ✅︎ |
-| `InternS1ForConditionalGeneration` | Intern-S1 | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S1`, etc. | ✅︎ | ✅︎ | ✅︎ |
+| `InternS1ForConditionalGeneration` | Intern-S1 | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S1`, `internlm/Intern-S1-mini`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternVLChatModel` | InternVL 3.5, InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0 | T + I<sup>E+</sup> + (V<sup>E+</sup>) | `OpenGVLab/InternVL3_5-14B`, `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/Mono-InternVL-2B`, `OpenGVLab/InternVL2-4B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternVLForConditionalGeneration` | InternVL 3.0 (HF format) | T + I<sup>E+</sup> + V<sup>E+</sup> | `OpenGVLab/InternVL3-1B-hf`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `KeyeForConditionalGeneration` | Keye-VL-8B-Preview | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-8B-Preview` | ✅︎ | ✅︎ | ✅︎ |

examples/offline_inference/vision_language.py

@@ -576,7 +576,7 @@ def run_idefics3(questions: list[str], modality: str) -> ModelRequestData:
 
 # Intern-S1
 def run_interns1(questions: list[str], modality: str) -> ModelRequestData:
-    model_name = "internlm/Intern-S1"
+    model_name = "internlm/Intern-S1-mini"
 
     engine_args = EngineArgs(
         model=model_name,

examples/offline_inference/vision_language_multi_image.py

@@ -309,7 +309,7 @@ def load_idefics3(question: str, image_urls: list[str]) -> ModelRequestData:
 
 
 def load_interns1(question: str, image_urls: list[str]) -> ModelRequestData:
-    model_name = "internlm/Intern-S1"
+    model_name = "internlm/Intern-S1-mini"
 
     engine_args = EngineArgs(
         model=model_name,

tests/conftest.py

@@ -756,7 +756,7 @@ def __init__(
 
     def get_inputs(
         self,
-        prompts: Union[list[str], list[torch.Tensor], list[int]],
+        prompts: Union[list[str], list[torch.Tensor], list[list[int]]],
         images: Optional[PromptImageInput] = None,
         videos: Optional[PromptVideoInput] = None,
         audios: Optional[PromptAudioInput] = None,

tests/entrypoints/llm/test_generate.py

@@ -86,3 +86,16 @@ def test_max_model_len():
         # It can be less if generation finishes due to other reasons (e.g., EOS)
         # before reaching the absolute model length limit.
         assert num_total_tokens <= max_model_len
+
+
+def test_log_stats():
+    llm = LLM(
+        model=MODEL_NAME,
+        disable_log_stats=False,
+        gpu_memory_utilization=0.10,
+        enforce_eager=True,  # reduce test time
+    )
+    outputs = llm.generate(PROMPTS, sampling_params=None)
+
+    # disable_log_stats is False, every output should have metrics
+    assert all(output.metrics is not None for output in outputs)

tests/models/language/generation/test_hybrid.py

@@ -240,12 +240,12 @@ def test_distributed_correctness(
     num_logprobs: int,
 ) -> None:
     with vllm_runner(model, tensor_parallel_size=1,
-                     max_num_seqs=2) as vllm_model:
+                     max_num_seqs=MAX_NUM_SEQS) as vllm_model:
         vllm_outputs_tp_1 = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 
     with vllm_runner(model, tensor_parallel_size=2,
-                     max_num_seqs=2) as vllm_model:
+                     max_num_seqs=MAX_NUM_SEQS) as vllm_model:
         vllm_outputs_tp_2 = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 

vllm/model_executor/models/interns1.py

@@ -25,7 +25,7 @@
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
-                                    MultiModalKwargsItems, NestedTensors)
+                                    MultiModalKwargsItems)
 from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
                                    ImageSize, MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
@@ -39,7 +39,7 @@
 
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP)
-from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
+from .utils import (AutoWeightsLoader, WeightsMapper,
                     init_vllm_registered_model, maybe_prefix)
 
 
@@ -304,7 +304,7 @@ def _call_hf_processor(
         mm_data: Mapping[str, object],
         mm_kwargs: Mapping[str, object],
         tok_kwargs: Mapping[str, object],
-    ) -> Mapping[str, NestedTensors]:
+    ) -> BatchFeature:
         mm_data = dict(mm_data)
         videos = mm_data.pop("videos", [])
         images = mm_data.pop("images", [])
@@ -342,7 +342,7 @@ def _call_hf_processor(
                                         image_placeholder, 1)
 
             num_patches = [len(item) for item in image_pixel_values]
-            image_outputs: dict[str, NestedTensors] = {
+            image_outputs = {
                 "pixel_values": torch.concat(image_pixel_values),
                 "image_num_patches": torch.tensor(num_patches),
                 "image_token_id": torch.tensor(hf_processor.image_token_id),
@@ -370,7 +370,7 @@ def _call_hf_processor(
                                         video_placeholder, 1)
 
             num_frames = [len(item) for item in video_pixel_values]
-            video_outputs: dict[str, NestedTensors] = {
+            video_outputs = {
                 "pixel_values_videos": torch.concat(video_pixel_values),
                 "video_num_patches": torch.tensor(num_frames),
                 "video_token_id": torch.tensor(video_token_id),
@@ -382,16 +382,11 @@ def _call_hf_processor(
                         prompt)
         text_outputs = tokenizer(prompt, **tok_kwargs, return_tensors="pt")
 
-        combined_outputs = dict(
-            **text_outputs,
-            **image_outputs,
-            **video_outputs,
-        )
-        return BatchFeature(combined_outputs)
+        return BatchFeature({**text_outputs, **image_outputs, **video_outputs})
 
     def _get_mm_fields_config(
         self,
-        hf_inputs: Mapping[str, NestedTensors],
+        hf_inputs: BatchFeature,
         hf_processor_mm_kwargs: Mapping[str, object],
     ) -> Mapping[str, MultiModalFieldConfig]:
 
@@ -487,6 +482,7 @@ def get_replacement_interns1_video(item_idx: int):
     dummy_inputs=InternS1DummyInputsBuilder)
 class InternS1ForConditionalGeneration(nn.Module, SupportsMultiModal,
                                        SupportsPP, SupportsLoRA):
+    merge_by_field_config = True
 
     # To ensure correct weight loading and mapping.
     hf_to_vllm_mapper = WeightsMapper(
@@ -561,7 +557,7 @@ def _init_vision_model(
             prefix=prefix,
         )
 
-    def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
+    def _init_mlp1(self, config: PretrainedConfig) -> nn.Module:
         return InternS1MultiModalProjector(config)
 
     def pixel_shuffle(self, x, scale_factor=0.5):
@@ -599,31 +595,16 @@ def _parse_and_validate_image_input(
             return None
 
         if image_embeds is not None:
-            if not isinstance(image_embeds, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of image embeddings. "
-                                 f"Got type: {type(image_embeds)}")
-
             return InternS1ImageEmbeddingInputs(
                 type="image_embeds",
-                data=flatten_bn(image_embeds),
+                data=image_embeds,
             )
 
         image_token_id = kwargs["image_token_id"]
         assert isinstance(image_token_id, torch.Tensor)
         self.img_context_token_id = image_token_id.flatten().unique().item()
 
         if pixel_values is not None:
-            if not isinstance(pixel_values, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of pixel values. "
-                                 f"Got type: {type(pixel_values)}")
-
-            if not isinstance(image_num_patches, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of image_num_patches. "
-                                 f"Got type: {type(image_num_patches)}")
-
-            pixel_values = flatten_bn(pixel_values, concat=True)
-            image_num_patches = flatten_bn(image_num_patches, concat=True)
-
             h, w = self.config.vision_config.image_size
             return InternS1ImagePixelInputs(
                 type="pixel_values",
@@ -638,7 +619,7 @@ def _parse_and_validate_image_input(
         raise AssertionError("This line should be unreachable.")
 
     def _parse_and_validate_video_input(
-            self, **kwargs: object) -> Optional[InternS1VideoPixelInputs]:
+            self, **kwargs: object) -> Optional[InternS1VideoInputs]:
         pixel_values_flat_video = kwargs.pop("pixel_values_videos", None)
         video_num_patches = kwargs.pop("video_num_patches", None)
         video_embeds = kwargs.pop("video_embeds", None)
@@ -647,32 +628,16 @@ def _parse_and_validate_video_input(
             return None
 
         if video_embeds is not None:
-            if not isinstance(video_embeds, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of video embeddings. "
-                                 f"Got type: {type(video_embeds)}")
-
-            return InternS1ImageEmbeddingInputs(
+            return InternS1VideoEmbeddingInputs(
                 type="video_embeds",
-                data=flatten_bn(video_embeds),
+                data=video_embeds,
             )
 
         video_token_id = kwargs["video_token_id"]
         assert isinstance(video_token_id, torch.Tensor)
         self.video_context_token_id = video_token_id.flatten().unique().item()
 
         if pixel_values_flat_video is not None:
-            if not isinstance(pixel_values_flat_video, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of pixel values. "
-                                 f"Got type: {type(pixel_values_flat_video)}")
-
-            if not isinstance(video_num_patches, (torch.Tensor, list)):
-                raise ValueError("Incorrect type of image_num_patches. "
-                                 f"Got type: {type(video_num_patches)}")
-
-            pixel_values_flat_video = flatten_bn(pixel_values_flat_video,
-                                                 concat=True)
-            video_num_patches = flatten_bn(video_num_patches, concat=True)
-
             h, w = self.config.vision_config.image_size
             return InternS1VideoPixelInputs(
                 type="pixel_values_videos",
@@ -686,11 +651,12 @@ def _parse_and_validate_video_input(
 
         raise AssertionError("This line should be unreachable.")
 
-    def _process_image_input(
+    def _process_vision_input(
         self,
-        image_input: Union[InternS1ImageInputs, InternS1VideoPixelInputs],
+        image_input: Union[InternS1ImageInputs, InternS1VideoInputs],
     ) -> tuple[torch.Tensor, ...]:
-        if image_input["type"] == "image_embeds":
+        if (image_input["type"] == "image_embeds"
+                or image_input["type"] == "video_embeds"):
             return image_input["data"]
 
         assert self.vision_tower is not None
@@ -753,11 +719,11 @@ def get_multimodal_embeddings(self,
         for modality in modalities:
             if modality == "images":
                 image_input = modalities["images"]
-                vision_embeddings = self._process_image_input(image_input)
+                vision_embeddings = self._process_vision_input(image_input)
                 multimodal_embeddings += vision_embeddings
             if modality == "videos":
                 video_input = modalities["videos"]
-                video_embeddings = self._process_image_input(video_input)
+                video_embeddings = self._process_vision_input(video_input)
                 multimodal_embeddings += video_embeddings
 
         return multimodal_embeddings