275 lines
9.0 KiB
Python
275 lines
9.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from abc import ABC
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from collections.abc import Mapping
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from dataclasses import dataclass, field
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from typing import Generic, NamedTuple, Optional, TypeVar, cast
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import numpy as np
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import numpy.typing as npt
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from PIL import Image
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import vllm.envs as envs
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from vllm.logger import init_logger
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from .inputs import (MultiModalDataDict, MultiModalEncDecInputs,
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MultiModalInputs, MultiModalKwargs,
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MultiModalPlaceholderDict)
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from .processing import (BaseMultiModalProcessor, BaseProcessingInfo,
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EncDecMultiModalProcessor)
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logger = init_logger(__name__)
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@dataclass
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class ProcessorInputs:
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"""
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Represents the keyword arguments to
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:meth:`vllm.multimodal.processing.BaseMultiModalProcessor.apply`.
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"""
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prompt_text: str
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mm_data: MultiModalDataDict
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hf_processor_mm_kwargs: Mapping[str, object] = field(default_factory=dict)
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class DummyEncoderData(NamedTuple):
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"""Dummy data used for profiling."""
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prompt_token_ids: list[int]
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class DummyDecoderData(NamedTuple):
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"""Dummy data used for profiling."""
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prompt_token_ids: list[int]
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multi_modal_data: MultiModalKwargs
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multi_modal_placeholders: MultiModalPlaceholderDict
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_I = TypeVar("_I", bound=BaseProcessingInfo)
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class BaseDummyInputsBuilder(ABC, Generic[_I]):
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"""
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Abstract base class that constructs the dummy data to profile
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multi-modal models.
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"""
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def __init__(self, info: _I) -> None:
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super().__init__()
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self.info = info
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# TODO: @abstractmethod after transition
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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"""
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Build the text input corresponding to :code:`mm_counts`.
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"""
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if (type(self).get_dummy_processor_inputs ==
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BaseDummyInputsBuilder.get_dummy_processor_inputs):
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raise NotImplementedError
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logger.warning_once("`get_dummy_processor_inputs` has been split up "
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"into `get_dummy_text` and `get_dummy_mm_data`. "
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"These two methods will be marked as abstract "
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"in an upcoming release.")
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seq_len = self.info.ctx.model_config.max_model_len
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return self.get_dummy_processor_inputs(seq_len, mm_counts).prompt_text
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# TODO: @abstractmethod after transition
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def get_dummy_mm_data(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> MultiModalDataDict:
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"""
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Build the multimodal input which, after processing, results in
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the maximum possible number of placeholder tokens.
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"""
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raise NotImplementedError
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def get_dummy_processor_inputs(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> ProcessorInputs:
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"""
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Build the input which, after processing, results in
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the maximum possible number of placeholder tokens.
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"""
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dummy_text = self.get_dummy_text(mm_counts)
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dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts)
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return ProcessorInputs(prompt_text=dummy_text, mm_data=dummy_mm_data)
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def _get_dummy_audios(
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self,
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*,
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length: int,
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num_audios: int,
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) -> list[npt.NDArray]:
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if num_audios == 0:
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return []
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audio = np.zeros((length, ))
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return [audio] * num_audios
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def _get_dummy_images(
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self,
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*,
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width: int,
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height: int,
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num_images: int,
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) -> list[Image.Image]:
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if num_images == 0:
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return []
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image = Image.new("RGB", (width, height), color=255)
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return [image] * num_images
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def _get_dummy_videos(
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self,
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*,
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width: int,
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height: int,
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num_frames: int,
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num_videos: int,
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) -> list[npt.NDArray]:
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if num_videos == 0:
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return []
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video = np.full((num_frames, width, height, 3), 255)
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return [video] * num_videos
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class MultiModalProfiler(Generic[_I]):
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"""
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Contains code for running memory profiling for multi-modal models.
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"""
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def __init__(
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self,
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processor: BaseMultiModalProcessor[_I],
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) -> None:
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super().__init__()
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self.processor = processor
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@property
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def processing_info(self) -> BaseProcessingInfo:
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return self.processor.info
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@property
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def dummy_inputs(self) -> BaseDummyInputsBuilder[_I]:
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return self.processor.dummy_inputs
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def get_mm_limits(self) -> Mapping[str, int]:
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return self.processing_info.get_allowed_mm_limits()
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def _get_dummy_mm_inputs(
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self,
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seq_len: int,
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mm_counts: Optional[Mapping[str, int]] = None,
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) -> MultiModalInputs:
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if mm_counts is None:
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mm_counts = self.get_mm_limits()
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factory = self.dummy_inputs
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processor_inputs = factory.get_dummy_processor_inputs(
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seq_len, mm_counts)
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return self.processor.apply(
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prompt=processor_inputs.prompt_text,
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mm_data=processor_inputs.mm_data,
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hf_processor_mm_kwargs=processor_inputs.hf_processor_mm_kwargs,
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)
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def _get_mm_num_tokens(
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self,
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mm_inputs: MultiModalInputs,
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) -> Mapping[str, int]:
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placeholders_by_modality = mm_inputs["mm_placeholders"]
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return {
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modality: sum(item.get_num_embeds() for item in placeholders)
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for modality, placeholders in placeholders_by_modality.items()
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}
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def get_encoder_dummy_data(
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self,
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seq_len: int,
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mm_counts: Optional[Mapping[str, int]] = None,
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) -> DummyEncoderData:
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mm_inputs = self._get_dummy_mm_inputs(seq_len, mm_counts)
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mm_inputs = cast(MultiModalEncDecInputs, mm_inputs)
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# For encoder-decoder models, use encoder prompt token ids instead of
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# decoder prompt to construct dummy seq_data for encoder profiling.
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encoder_prompt_token_ids = mm_inputs["encoder_prompt_token_ids"]
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total_len = len(encoder_prompt_token_ids)
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processor = cast(EncDecMultiModalProcessor, self.processor)
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if processor.pad_dummy_encoder_prompt:
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num_tokens_to_pad = max(total_len, seq_len) - total_len
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encoder_prompt_token_ids.extend([0] * num_tokens_to_pad)
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# NOTE: Whisper allows total_len > seq_len.
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elif total_len > seq_len and not envs.VLLM_USE_V1:
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# `max_num_batched_tokens` is defined by `SchedulerConfig`
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logger.warning_once(
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"The encoder sequence length used for profiling ("
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f"max_num_batched_tokens / max_num_seqs = {seq_len}) "
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" is too short "
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"to hold the multi-modal embeddings in the worst case "
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f"({total_len} tokens in total, out of which "
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f"{self._get_mm_num_tokens(mm_inputs)} are reserved for "
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"multi-modal embeddings). This may cause certain "
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"multi-modal inputs to fail during inference, even when "
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"the input text is short. To avoid this, you should "
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"increase `max_model_len`, reduce `max_num_seqs`, "
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"and/or reduce `mm_counts`.")
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return DummyEncoderData(encoder_prompt_token_ids)
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def get_decoder_dummy_data(
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self,
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seq_len: int,
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mm_counts: Optional[Mapping[str, int]] = None,
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) -> DummyDecoderData:
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mm_inputs = self._get_dummy_mm_inputs(seq_len, mm_counts)
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prompt_token_ids = mm_inputs["prompt_token_ids"]
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total_len = len(prompt_token_ids)
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# V0 does not support chunked prefill.
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if total_len > seq_len and not envs.VLLM_USE_V1:
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# `max_num_batched_tokens` is defined by `SchedulerConfig`
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logger.warning_once(
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"The sequence length used for profiling ("
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f"max_num_batched_tokens / max_num_seqs = {seq_len}) "
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"is too short "
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"to hold the multi-modal embeddings in the worst case "
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f"({total_len} tokens in total, out of which "
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f"{self._get_mm_num_tokens(mm_inputs)} are reserved for "
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"multi-modal embeddings). This may cause certain "
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"multi-modal inputs to fail during inference, even when "
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"the input text is short. To avoid this, you should "
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"increase `max_model_len`, reduce `max_num_seqs`, "
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"and/or reduce `mm_counts`.")
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if total_len < seq_len:
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prompt_token_ids.extend([0] * (seq_len - total_len))
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return DummyDecoderData(
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prompt_token_ids=prompt_token_ids,
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multi_modal_data=mm_inputs["mm_kwargs"],
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multi_modal_placeholders=mm_inputs["mm_placeholders"],
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)
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def get_mm_max_tokens(
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self,
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seq_len: int,
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mm_counts: Optional[Mapping[str, int]] = None,
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) -> Mapping[str, int]:
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mm_inputs = self._get_dummy_mm_inputs(seq_len, mm_counts)
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return self._get_mm_num_tokens(mm_inputs)
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