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[V0 Deprecation] Remove V0-only methods in multi-modal registry (#25362)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: yewentao256 <zhyanwentao@126.com>
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@ -209,7 +209,6 @@ def batch_make_video_embeddings(
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return visual(pixel_values_on_device,
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grid_thw=video_grid_thw_on_device).cpu()
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# V1 Test: this calls a V0 internal.
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video_embeds = torch.concat(llm.apply_model(get_image_embeds))
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# split into original batches
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@ -12,8 +12,7 @@ from vllm.transformers_utils.tokenizer import (AnyTokenizer,
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cached_tokenizer_from_config)
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from vllm.utils import ClassRegistry
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from .cache import (BaseMultiModalProcessorCache,
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processor_only_cache_from_config)
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from .cache import BaseMultiModalProcessorCache
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from .processing import BaseMultiModalProcessor, BaseProcessingInfo
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from .profiling import (BaseDummyInputsBuilder, DummyDecoderData,
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DummyEncoderData, MultiModalProfiler)
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@ -176,35 +175,6 @@ class MultiModalRegistry:
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if mm_limits[key] > 0
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}
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# TODO: Remove once V0 is gone
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def get_max_tokens_by_modality(
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self,
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model_config: "ModelConfig",
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) -> Mapping[str, int]:
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"""
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Get the maximum number of tokens from each modality
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for profiling the memory usage of a model.
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"""
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cache = processor_only_cache_from_config(model_config, self)
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mm_limits = self.get_mm_limits_per_prompt(model_config, cache=cache)
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max_tokens_per_item = self.get_max_tokens_per_item_by_modality(
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model_config,
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cache=cache,
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)
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return {
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key: mm_limits[key] * max_tokens_per_mm_item
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for key, max_tokens_per_mm_item in max_tokens_per_item.items()
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}
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# TODO: Remove once V0 is gone
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def get_max_multimodal_tokens(self, model_config: "ModelConfig") -> int:
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"""
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Get the maximum number of multi-modal tokens
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for profiling the memory usage of a model.
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"""
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return sum(self.get_max_tokens_by_modality(model_config).values())
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def get_mm_limits_per_prompt(
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self,
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model_config: "ModelConfig",
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