[V1] Override `mm_counts` for dummy data creation (#15703)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/decoder_only/vision_language/test_models.py

@@ -385,18 +385,7 @@ VLM_TEST_SETTINGS = {
     ),
     "minicpmo_26": VLMTestInfo(
         models=["openbmb/MiniCPM-o-2_6"],
-        test_type=(VLMTestType.IMAGE),
-        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
-        max_model_len=4096,
-        max_num_seqs=2,
-        get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
-        hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
-        patch_hf_runner=model_utils.minicpmo_26_patch_hf_runner,
-    ),
-    "minicpmo_26_multi_image": VLMTestInfo(
-        models=["openbmb/MiniCPM-o-2_6"],
-        test_type=(VLMTestType.MULTI_IMAGE),
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
         prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
         img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
         max_model_len=4096,
@@ -404,22 +393,10 @@ VLM_TEST_SETTINGS = {
         get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
         hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
         patch_hf_runner=model_utils.minicpmo_26_patch_hf_runner,
-        marks=[large_gpu_mark(min_gb=32)],
     ),
     "minicpmv_26": VLMTestInfo(
         models=["openbmb/MiniCPM-V-2_6"],
-        test_type=(VLMTestType.IMAGE),
-        prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
-        img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
-        max_model_len=4096,
-        max_num_seqs=2,
-        get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
-        hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
-        patch_hf_runner=model_utils.minicpmv_26_patch_hf_runner,
-    ),
-    "minicpmv_26_multi_image": VLMTestInfo(
-        models=["openbmb/MiniCPM-V-2_6"],
-        test_type=(VLMTestType.MULTI_IMAGE),
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
         prompt_formatter=lambda img_prompt: f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{img_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",  # noqa: E501
         img_idx_to_prompt=lambda idx: "(<image>./</image>)\n",
         max_model_len=4096,
@@ -427,7 +404,6 @@ VLM_TEST_SETTINGS = {
         get_stop_token_ids=lambda tok: tok.convert_tokens_to_ids(['<|im_end|>', '<|endoftext|>']),  # noqa: E501
         hf_output_post_proc=model_utils.minicpmv_trunc_hf_output,
         patch_hf_runner=model_utils.minicpmv_26_patch_hf_runner,
-        marks=[large_gpu_mark(min_gb=32)],
     ),
     "molmo": VLMTestInfo(
         models=["allenai/Molmo-7B-D-0924"],

vllm/model_executor/models/llava_next_video.py

@@ -71,7 +71,8 @@ class LlavaNextVideoProcessingInfo(BaseProcessingInfo):
         max_video_tokens = self.get_num_video_tokens(
             image_width=target_width,
             image_height=target_height,
-            num_frames=self.get_num_frames_with_most_features(seq_len),
+            num_frames=self.get_num_frames_with_most_features(
+                seq_len, mm_counts),
         )
 
         return {"video": max_video_tokens}
@@ -130,9 +131,12 @@ class LlavaNextVideoProcessingInfo(BaseProcessingInfo):
 
         return num_frames
 
-    def get_num_frames_with_most_features(self, seq_len: int) -> int:
-        mm_config = self.ctx.get_mm_config()
-        max_videos = mm_config.get_limit_per_prompt("video")
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_videos = mm_counts.get("video", 0)
 
         max_total_frames = self._get_max_video_frames(seq_len)
 
@@ -155,7 +159,7 @@ class LlavaNextVideoDummyInputsBuilder(
         target_width, target_height = \
             self.info.get_image_size_with_most_features()
         target_num_frames = \
-            self.info.get_num_frames_with_most_features(seq_len)
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
 
         mm_data = {
             "video":

vllm/model_executor/models/llava_onevision.py

@@ -108,7 +108,7 @@ class LlavaOnevisionProcessingInfo(LlavaNextProcessingInfo):
     ) -> Mapping[str, int]:
         return {
             "image": self.get_max_image_tokens(),
-            "video": self.get_max_video_tokens(seq_len),
+            "video": self.get_max_video_tokens(seq_len, mm_counts),
         }
 
     # Based on: https://github.com/huggingface/text-generation-inference/blob/v3.0.1/server/text_generation_server/models/vlm_causal_lm.py#L86
@@ -202,10 +202,13 @@ class LlavaOnevisionProcessingInfo(LlavaNextProcessingInfo):
 
         return num_frames
 
-    def get_num_frames_with_most_features(self, seq_len: int) -> int:
-        mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.get_limit_per_prompt("video")
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
 
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_total_frames = self._get_max_video_frames(seq_len -
@@ -215,13 +218,18 @@ class LlavaOnevisionProcessingInfo(LlavaNextProcessingInfo):
 
         return max(max_frames_per_video, 1)
 
-    def get_max_video_tokens(self, seq_len: int) -> int:
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
         target_width, target_height = self.get_image_size_with_most_features()
 
         return self.get_num_video_tokens(
             image_width=target_width,
             image_height=target_height,
-            num_frames=self.get_num_frames_with_most_features(seq_len),
+            num_frames=self.get_num_frames_with_most_features(
+                seq_len, mm_counts),
         )
 
 
@@ -243,7 +251,8 @@ class LlavaOnevisionDummyInputsBuilder(
         target_width, target_height = \
             self.info.get_image_size_with_most_features()
         target_num_frames = \
-            self.info.get_num_frames_with_most_features(seq_len)
+            self.info.get_num_frames_with_most_features(seq_len,
+                                                        mm_counts)
 
         mm_data = {
             "image":

vllm/model_executor/models/minicpmo.py

@@ -43,7 +43,8 @@ from vllm.multimodal.parse import (AudioItem, AudioProcessorItems,
 from vllm.multimodal.processing import PromptReplacement, PromptUpdate
 from vllm.multimodal.profiling import ProcessorInputs
 
-from .minicpmv import (MiniCPMV2_6, MiniCPMVDummyInputsBuilder,
+from .minicpmv import (_MAX_FRAMES_PER_VIDEO, MiniCPMV2_6,
+                       MiniCPMVDummyInputsBuilder,
                        MiniCPMVMultiModalDataParser,
                        MiniCPMVMultiModalProcessor, MiniCPMVProcessingInfo,
                        _minicpmv_field_config)
@@ -203,8 +204,8 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
         return 30
 
     def get_max_audio_tokens(self) -> int:
-        return self.get_max_audio_tokens_per_chunk(
-        ) * self.get_max_audio_chunks_with_most_features()
+        num_chunks = self.get_max_audio_chunks_with_most_features()
+        return self.get_max_audio_tokens_per_chunk() * num_chunks
 
     def get_audio_len_by_num_chunks(self, num_chunks: int) -> int:
         sampling_rate = self.get_default_audio_sampling_rate()
@@ -212,21 +213,24 @@ class MiniCPMOProcessingInfo(MiniCPMVProcessingInfo):
         num_tokens_per_chunk = self.get_max_audio_tokens_per_chunk() - 2
         return int(num_chunks * sampling_rate / num_tokens_per_chunk) + 1
 
-    def get_num_frames_with_most_features(self, seq_len: int) -> int:
-        mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.get_limit_per_prompt("video")
-        max_audios = mm_config.get_limit_per_prompt("audio")
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
+        max_audios = mm_counts.get("audio", 0)
 
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_audio_tokens = self.get_max_audio_tokens() * max_audios
         max_total_frames = self.get_max_video_frames(seq_len -
                                                      max_image_tokens -
                                                      max_audio_tokens)
+        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
+                                   _MAX_FRAMES_PER_VIDEO)
 
-        num_frames = max(max_total_frames // max(max_videos, 1), 1)
-
-        return num_frames
+        return max(max_frames_per_video, 1)
 
 
 class MiniCPMODummyInputsBuilder(

vllm/model_executor/models/minicpmv.py

@@ -69,6 +69,9 @@ from .utils import (AutoWeightsLoader, flatten_bn, maybe_prefix,
                     merge_multimodal_embeddings)
 from .vision import scatter_patch_features, select_patch_features
 
+# For profile run
+_MAX_FRAMES_PER_VIDEO = 16
+
 
 class MiniCPMVImagePixelInputs(TypedDict):
     type: Literal["pixel_values"]
@@ -369,7 +372,8 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
     ) -> Mapping[str, int]:
         mm_max_tokens = {"image": self.get_max_image_tokens()}
         if self.get_model_version() == (2, 6):
-            mm_max_tokens["video"] = self.get_max_video_tokens(seq_len)
+            mm_max_tokens["video"] = self.get_max_video_tokens(
+                seq_len, mm_counts)
 
         return mm_max_tokens
 
@@ -432,9 +436,14 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
             use_image_id=False,
         )
 
-    def get_max_video_tokens(self, seq_len: int) -> int:
-        return self.get_max_video_frame_tokens(
-        ) * self.get_num_frames_with_most_features(seq_len)
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        num_frames = self.get_num_frames_with_most_features(seq_len, mm_counts)
+        num_video_tokens_total = self.get_max_video_frame_tokens() * num_frames
+        return num_video_tokens_total
 
     def get_video_max_slice_num(self) -> int:
         return 1
@@ -449,18 +458,21 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
         num_frames = max_tokens // num_frame_tokens
         return num_frames
 
-    def get_num_frames_with_most_features(self, seq_len: int) -> int:
-        mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.get_limit_per_prompt("video")
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
 
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_total_frames = self.get_max_video_frames(seq_len -
                                                      max_image_tokens)
+        max_frames_per_video = min(max_total_frames // max(max_videos, 1),
+                                   _MAX_FRAMES_PER_VIDEO)
 
-        num_frames = max(max_total_frames // max(max_videos, 1), 1)
-
-        return num_frames
+        return max(max_frames_per_video, 1)
 
 
 _I = TypeVar("_I",
@@ -483,7 +495,7 @@ class MiniCPMVDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
         video_width, video_height = \
             self.info.get_video_frame_size_with_most_features()
         num_video_frames = \
-            self.info.get_num_frames_with_most_features(seq_len)
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
 
         mm_data = {
             "image":

vllm/model_executor/models/qwen2_vl.py

@@ -806,7 +806,7 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
         max_pixels: Optional[int] = None,
         size: Optional[dict[str, int]] = None,
         **kwargs: object,
-    ):
+    ) -> Qwen2VLImageProcessor:
         return cached_image_processor_from_config(
             self.ctx.model_config,
             **self._get_image_processor_kwargs(min_pixels=min_pixels,
@@ -825,7 +825,7 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
     ) -> Mapping[str, int]:
         return {
             "image": self.get_max_image_tokens(),
-            "video": self.get_max_video_tokens(seq_len),
+            "video": self.get_max_video_tokens(seq_len, mm_counts),
         }
 
     def _get_vision_info(
@@ -941,10 +941,13 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
 
         return num_frames
 
-    def get_num_frames_with_most_features(self, seq_len: int) -> int:
-        mm_config = self.ctx.get_mm_config()
-        max_images = mm_config.get_limit_per_prompt("image")
-        max_videos = mm_config.get_limit_per_prompt("video")
+    def get_num_frames_with_most_features(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
+        max_images = mm_counts.get("image", 0)
+        max_videos = mm_counts.get("video", 0)
 
         max_image_tokens = self.get_max_image_tokens() * max_images
         max_total_frames = self._get_max_video_frames(seq_len -
@@ -954,13 +957,18 @@ class Qwen2VLProcessingInfo(BaseProcessingInfo):
 
         return max(max_frames_per_video, 1)
 
-    def get_max_video_tokens(self, seq_len: int) -> int:
+    def get_max_video_tokens(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> int:
         target_width, target_height = self.get_image_size_with_most_features()
 
         return self.get_num_video_tokens(
             image_width=target_width,
             image_height=target_height,
-            num_frames=self.get_num_frames_with_most_features(seq_len),
+            num_frames=self.get_num_frames_with_most_features(
+                seq_len, mm_counts),
             image_processor=None,
         )
 
@@ -982,7 +990,7 @@ class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]):
         target_width, target_height = \
             self.info.get_image_size_with_most_features()
         target_num_frames = \
-            self.info.get_num_frames_with_most_features(seq_len)
+            self.info.get_num_frames_with_most_features(seq_len, mm_counts)
 
         mm_data = {
             "image":

vllm/multimodal/profiling.py

@@ -3,7 +3,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import Mapping
 from dataclasses import dataclass, field
-from typing import Generic, NamedTuple, TypeVar, cast
+from typing import Generic, NamedTuple, Optional, TypeVar, cast
 
 import numpy as np
 import numpy.typing as npt
@@ -160,17 +160,19 @@ class MultiModalProfiler(Generic[_I]):
     def get_and_validate_mm_inputs(
         self,
         seq_len: int,
+        mm_counts: Optional[Mapping[str, int]] = None,
     ) -> tuple[MultiModalInputs, Mapping[str, int]]:
-        mm_counts = self.get_mm_limits()
+        if mm_counts is None:
+            mm_counts = self.get_mm_limits()
 
         info = self.processing_info
         mm_max_tokens_per_item = info.get_mm_max_tokens_per_item(
             seq_len, mm_counts)
 
-        if mm_counts.keys() != mm_max_tokens_per_item.keys():
+        if mm_counts.keys() - mm_max_tokens_per_item.keys():
             raise AssertionError(
                 "The keys returned by `get_supported_mm_limits` "
-                f"({set(mm_counts.keys())}) should be the same as those "
+                f"({set(mm_counts.keys())}) should be a subset of those "
                 "returned by `get_mm_max_tokens_per_item` "
                 f"({set(mm_max_tokens_per_item.keys())})")
 
@@ -193,8 +195,12 @@ class MultiModalProfiler(Generic[_I]):
                 "tokens.")
         return mm_inputs, total_placeholders_by_modality
 
-    def get_encoder_dummy_data(self, seq_len: int) -> DummyEncoderData:
-        mm_inputs, _ = self.get_and_validate_mm_inputs(seq_len)
+    def get_encoder_dummy_data(
+        self,
+        seq_len: int,
+        mm_counts: Optional[Mapping[str, int]] = None,
+    ) -> DummyEncoderData:
+        mm_inputs, _ = self.get_and_validate_mm_inputs(seq_len, mm_counts)
         mm_inputs = cast(MultiModalEncDecInputs, mm_inputs)
 
         # For encoder-decoder models, use encoder prompt token ids instead of
@@ -207,9 +213,15 @@ class MultiModalProfiler(Generic[_I]):
 
         return DummyEncoderData(encoder_prompt_token_ids)
 
-    def get_decoder_dummy_data(self, seq_len: int) -> DummyDecoderData:
-        (mm_inputs, total_placeholders_by_modality
-         ) = self.get_and_validate_mm_inputs(seq_len)
+    def get_decoder_dummy_data(
+        self,
+        seq_len: int,
+        mm_counts: Optional[Mapping[str, int]] = None,
+    ) -> DummyDecoderData:
+        (
+            mm_inputs,
+            total_placeholders_by_modality,
+        ) = self.get_and_validate_mm_inputs(seq_len, mm_counts)
 
         prompt_token_ids = mm_inputs["prompt_token_ids"]
         total_len = len(prompt_token_ids)

vllm/multimodal/registry.py

@@ -458,6 +458,7 @@ class MultiModalRegistry:
         self,
         model_config: "ModelConfig",
         seq_len: int,
+        mm_counts: Optional[Mapping[str, int]] = None,
     ) -> DummyDecoderData:
         """
         Create dummy data for profiling the memory usage of a model.
@@ -466,7 +467,7 @@ class MultiModalRegistry:
         """
         processor = self.create_processor(model_config, disable_cache=True)
         profiler = MultiModalProfiler(processor)
-        dummy_data = profiler.get_decoder_dummy_data(seq_len)
+        dummy_data = profiler.get_decoder_dummy_data(seq_len, mm_counts)
 
         # Having more tokens is over-conservative but otherwise fine
         token_ids = dummy_data.prompt_token_ids
@@ -481,6 +482,7 @@ class MultiModalRegistry:
         self,
         model_config: "ModelConfig",
         seq_len: int,
+        mm_counts: Optional[Mapping[str, int]] = None,
     ) -> DummyEncoderData:
         """
         Create dummy data for profiling the memory usage of a model.
@@ -489,7 +491,7 @@ class MultiModalRegistry:
         """
         processor = self.create_processor(model_config, disable_cache=True)
         profiler = MultiModalProfiler(processor)
-        dummy_data = profiler.get_encoder_dummy_data(seq_len)
+        dummy_data = profiler.get_encoder_dummy_data(seq_len, mm_counts)
 
         # Having more tokens is over-conservative but otherwise fine
         token_ids = dummy_data.prompt_token_ids

vllm/v1/worker/gpu_model_runner.py

@@ -1470,19 +1470,13 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 encoder_budget, max_num_mm_items, dummy_data_modality)
 
             # Create dummy batch of multimodal inputs.
-            dummy_request_data = self.mm_registry.get_decoder_dummy_data(
+            dummy_mm_kwargs = self.mm_registry.get_decoder_dummy_data(
                 model_config=self.model_config,
                 seq_len=self.max_num_tokens,
-            )
-            dummy_mm_data = dummy_request_data.multi_modal_data
-
-            # Dummy data definition may contain multiple multimodal items
-            # (e.g, multiple images) for a single request, therefore here we
-            # always replicate first item by max_num_mm_items times since in V1
-            # they are scheduled to be processed separately.
-            dummy_mm_item = dummy_mm_data.get_item(
-                modality=dummy_data_modality, item_index=0)
-            dummy_mm_kwargs = MultiModalKwargs.from_items([dummy_mm_item])
+                mm_counts={
+                    dummy_data_modality: 1
+                },
+            ).multi_modal_data
 
             batched_dummy_mm_inputs = MultiModalKwargs.batch(
                 [dummy_mm_kwargs] * max_num_mm_items)