mirror of
https://git.datalinker.icu/vllm-project/vllm.git
synced 2025-12-24 00:55:01 +08:00
610 lines
24 KiB
Python
610 lines
24 KiB
Python
from functools import cached_property
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from types import MethodType
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from typing import (Iterable, List, Literal, Mapping, Optional, Protocol, Set,
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Tuple, TypedDict, Union)
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import torch
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import torch.nn as nn
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from transformers import (BatchFeature, CLIPVisionConfig, LlavaConfig,
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PixtralVisionConfig, PretrainedConfig,
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ProcessorMixin, SiglipVisionConfig)
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from transformers.models.llava import LlavaProcessor
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from transformers.models.pixtral import PixtralProcessor
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from vllm.attention import AttentionMetadata
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from vllm.config import VllmConfig
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from vllm.inputs import InputContext
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import NestedTensors
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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MultiModalDataItems, ProcessorInputs,
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PromptReplacement)
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from vllm.sequence import IntermediateTensors
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from .clip import (CLIPVisionModel, dummy_image_for_clip,
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get_max_clip_image_tokens)
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from .interfaces import SupportsMultiModal, SupportsPP
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from .pixtral import (PixtralHFVisionModel, dummy_image_for_pixtral_hf,
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get_max_pixtral_hf_image_tokens,
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get_pixtral_hf_image_feature_size)
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from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
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get_max_siglip_image_tokens)
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from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
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maybe_prefix, merge_multimodal_embeddings)
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class LlavaImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: Union[torch.Tensor, List[torch.Tensor]]
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"""
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Shape: `(batch_size * num_images, num_channels, height, width)`
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Note that `height` or `width` may be different per batch and image,
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in which case the data is passed as a list instead of a batched tensor.
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"""
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class LlavaImageEmbeddingInputs(TypedDict):
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type: Literal["image_embeds"]
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data: torch.Tensor
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"""Shape: `(batch_size * num_images, image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageEmbeddingInputs]
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class LlavaMultiModalProjector(nn.Module):
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def __init__(self,
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vision_hidden_size: int,
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text_hidden_size: int,
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projector_hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = ""):
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super().__init__()
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self.linear_1 = ColumnParallelLinear(vision_hidden_size,
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text_hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.linear_1")
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self.act = get_act_fn(projector_hidden_act)
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self.linear_2 = RowParallelLinear(text_hidden_size,
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text_hidden_size,
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bias=True,
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quant_config=quant_config,
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prefix=f"{prefix}.linear_2")
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def forward(self, image_features: torch.Tensor) -> torch.Tensor:
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hidden_states, _ = self.linear_1(image_features)
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hidden_states = self.act(hidden_states)
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hidden_states, _ = self.linear_2(hidden_states)
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return hidden_states
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def get_max_llava_image_tokens(ctx: InputContext):
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hf_config = ctx.get_hf_config(LlavaConfig)
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vision_config = hf_config.vision_config
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if isinstance(vision_config, CLIPVisionConfig):
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num_image_tokens = get_max_clip_image_tokens(vision_config)
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elif isinstance(vision_config, SiglipVisionConfig):
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num_image_tokens = get_max_siglip_image_tokens(vision_config)
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elif isinstance(vision_config, PixtralVisionConfig):
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num_image_tokens = get_max_pixtral_hf_image_tokens(vision_config)
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else:
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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strategy = hf_config.vision_feature_select_strategy
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if strategy == "default":
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return num_image_tokens - 1
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elif strategy == "full":
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return num_image_tokens
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else:
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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class LlavaMultiModalProcessor(BaseMultiModalProcessor):
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def _patch_pixtral_processor(self, hf_processor: PixtralProcessor):
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if getattr(hf_processor, "__is_patched__", False):
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return # Already patched
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image_processor = hf_processor.image_processor # type: ignore
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orig_preprocess = image_processor.preprocess
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def preprocess(__self, *args, **kwargs):
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hf_inputs = orig_preprocess(*args, **kwargs)
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hf_inputs["is_pixtral"] = torch.tensor(True)
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return hf_inputs
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image_processor.preprocess = MethodType(preprocess, image_processor)
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hf_processor.__is_patched__ = True # type: ignore
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def _get_hf_processor(self) -> Union[LlavaProcessor, PixtralProcessor]:
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hf_processor = self.ctx.get_hf_processor(
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(LlavaProcessor, PixtralProcessor))
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if isinstance(hf_processor, PixtralProcessor):
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self._patch_pixtral_processor(hf_processor)
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return hf_processor
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def _get_prompt_replacements(
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self,
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mm_items: MultiModalDataItems,
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hf_inputs: BatchFeature,
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mm_processor_kwargs: Mapping[str, object],
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) -> list[PromptReplacement]:
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hf_config = self.ctx.get_hf_config(LlavaConfig)
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image_token_id = hf_config.image_token_index
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processor = self._get_hf_processor()
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if isinstance(processor, PixtralProcessor):
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image_token = processor.image_token
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image_break_token = processor.image_break_token
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image_end_token = processor.image_end_token
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vision_config = hf_config.vision_config
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assert isinstance(vision_config, PixtralVisionConfig)
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def get_replacement_pixtral(item_idx: int):
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image_size = mm_items.get_image_size(item_idx)
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(
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num_width_tokens,
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num_height_tokens,
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) = get_pixtral_hf_image_feature_size(
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vision_config,
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image_width=image_size.width,
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image_height=image_size.height,
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)
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tokens = ([image_token] * num_width_tokens +
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[image_break_token]) * num_height_tokens
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tokens[-1] = image_end_token
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return "".join(tokens)
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=get_replacement_pixtral,
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),
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]
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max_image_tokens = get_max_llava_image_tokens(self.ctx)
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=[image_token_id] * max_image_tokens,
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)
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]
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def _get_dummy_mm_inputs(
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self,
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mm_counts: Mapping[str, int],
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) -> ProcessorInputs:
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hf_config = self.ctx.get_hf_config(LlavaConfig)
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vision_config = hf_config.vision_config
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num_images = mm_counts["image"]
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if isinstance(vision_config, CLIPVisionConfig):
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data = dummy_image_for_clip(vision_config, num_images)
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elif isinstance(vision_config, SiglipVisionConfig):
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data = dummy_image_for_siglip(vision_config, num_images)
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elif isinstance(vision_config, PixtralVisionConfig):
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data = dummy_image_for_pixtral_hf(vision_config, num_images)
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else:
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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hf_processor = self._get_hf_processor()
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image_token = hf_processor.image_token
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return ProcessorInputs(
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prompt_text=image_token * num_images,
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mm_data=data,
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mm_processor_kwargs={},
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)
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class LlavaLikeConfig(Protocol):
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vision_config: PretrainedConfig
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vision_feature_layer: Union[int, List[int]]
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def _get_num_hidden_layers(hf_config: LlavaLikeConfig) -> int:
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"""Determine the number of hidden layers to initialize up to in the
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visual encoder.
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Args:
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hf_config: Model config with vision feature layer(s).
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"""
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feature_layers = hf_config.vision_feature_layer
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num_hidden_layers = hf_config.vision_config.num_hidden_layers
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# If we have one feature layer, initialize up to that layer
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if isinstance(feature_layers, int):
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return _get_layer_index(feature_layers, num_hidden_layers)
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# If we have multiple feature layers, initialize up to the deepest one
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elif isinstance(feature_layers, (list, tuple)):
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return max(
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_get_layer_index(idx, num_hidden_layers) for idx in feature_layers)
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raise TypeError(f"vision_layer_feature type: {type(feature_layers)}"
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" is not supported")
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def _get_layer_index(feature_layer_index: int, num_hidden_layers: int) -> int:
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"""Given an signed vision feature layer, get the number of hidden layers
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needed to leverage it.
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Args:
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feature_layer_index: Index of a required layer in the visual encoder.
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num_hidden_layers: The total number of hidden layers in the visual
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encoder.
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"""
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if feature_layer_index < 0:
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return num_hidden_layers + feature_layer_index + 1
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return feature_layer_index + 1
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def init_vision_tower_for_llava(
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hf_config: LlavaLikeConfig,
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quant_config: Optional[QuantizationConfig],
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*,
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require_post_norm: Optional[bool] = None,
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prefix: str = "",
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):
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vision_config = hf_config.vision_config
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# Initialize the vision tower only up to the deepest required feature layer
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num_hidden_layers = _get_num_hidden_layers(hf_config)
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if isinstance(vision_config, CLIPVisionConfig):
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return CLIPVisionModel(
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vision_config,
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quant_config=quant_config,
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num_hidden_layers_override=num_hidden_layers,
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require_post_norm=require_post_norm,
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prefix=prefix,
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)
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elif isinstance(vision_config, SiglipVisionConfig):
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return SiglipVisionModel(
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vision_config,
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quant_config=quant_config,
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num_hidden_layers_override=num_hidden_layers,
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require_post_norm=require_post_norm,
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prefix=prefix,
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)
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elif isinstance(vision_config, PixtralVisionConfig):
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return PixtralHFVisionModel(
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vision_config,
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quant_config=quant_config,
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num_hidden_layers_override=num_hidden_layers,
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require_post_norm=require_post_norm,
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prefix=prefix,
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)
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)
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@MULTIMODAL_REGISTRY.register_processor(LlavaMultiModalProcessor)
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class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
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# BitandBytes specific attributes
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bitsandbytes_stacked_params_mapping = {
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# shard_name, weight_name, index
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"q_proj": ("qkv_proj", 0),
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"k_proj": ("qkv_proj", 1),
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"v_proj": ("qkv_proj", 2),
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"gate_proj": ("gate_up_proj", 0),
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"up_proj": ("gate_up_proj", 1),
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}
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = "") -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.multimodal_config = multimodal_config
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# NOTE: These are special cases for Pixtral-12B in the HF-format
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# https://huggingface.co/mistral-community/pixtral-12b/blob/main/config.json # noqa
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if (config.text_config.architectures is None
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and config.text_config.model_type == "mistral"):
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config.text_config.architectures = ["MistralForCausalLM"]
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if (config.projector_hidden_act is None
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and config.vision_config.hidden_act == "gelu"):
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config.projector_hidden_act = "gelu"
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# TODO: Optionally initializes this for supporting embeddings.
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self.vision_tower = init_vision_tower_for_llava(
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config,
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quant_config,
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require_post_norm=False,
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prefix=maybe_prefix(prefix, "vision_tower"))
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self.multi_modal_projector = LlavaMultiModalProjector(
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vision_hidden_size=config.vision_config.hidden_size,
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text_hidden_size=config.text_config.hidden_size,
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projector_hidden_act=config.projector_hidden_act,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "multi_modal_projector"))
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=config.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors)
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@cached_property
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def sampler(self):
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if hasattr(self.language_model, "sampler"):
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return self.language_model.sampler
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return get_sampler()
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def _validate_pixel_values(self, data: torch.Tensor) -> torch.Tensor:
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h = w = self.config.vision_config.image_size
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expected_dims = (3, h, w)
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actual_dims = tuple(data.shape[1:])
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if actual_dims != expected_dims:
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expected_expr = ("batch_size", *map(str, expected_dims))
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raise ValueError(
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f"The expected shape of pixel values is {expected_expr}. "
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f"You supplied {tuple(data.shape)}.")
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return data
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def _parse_and_validate_image_input(
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self, **kwargs: object) -> Optional[LlavaImageInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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is_pixtral = kwargs.pop("is_pixtral", torch.tensor([False]))
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image_embeds = kwargs.pop("image_embeds", None)
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if pixel_values is None and image_embeds is None:
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return None
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if pixel_values is not None:
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if not isinstance(pixel_values, (torch.Tensor, list)):
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raise ValueError("Incorrect type of pixel values. "
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f"Got type: {type(pixel_values)}")
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assert isinstance(is_pixtral, torch.Tensor)
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if is_pixtral.any():
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images = pixel_values
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def flatten_to_3d_tensors(item):
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if isinstance(item, torch.Tensor):
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if item.dim() >= 3:
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return [t for t in item.view(-1, *item.shape[-3:])]
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else:
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raise ValueError(
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f"Unexpected tensor dimension: {item.dim()}")
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elif isinstance(item, list):
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return [
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t for subitem in item
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for t in flatten_to_3d_tensors(subitem)
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]
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else:
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raise ValueError(f"Unexpected type: {type(item)}")
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# Restructure the batched images into a list of lists of images
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images = flatten_to_3d_tensors(pixel_values)
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return LlavaImagePixelInputs(
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type="pixel_values",
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data=images,
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)
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return LlavaImagePixelInputs(
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type="pixel_values",
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data=self._validate_pixel_values(
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flatten_bn(pixel_values, concat=True)),
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)
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if image_embeds is not None:
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if not isinstance(image_embeds, (torch.Tensor, list)):
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raise ValueError("Incorrect type of image embeddings. "
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f"Got type: {type(image_embeds)}")
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return LlavaImageEmbeddingInputs(
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type="image_embeds",
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data=flatten_bn(image_embeds, concat=True),
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)
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raise AssertionError("This line should be unreachable.")
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def _select_image_features(self, image_features: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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# Copied from https://github.com/huggingface/transformers/blob/39c3c0a72af6fbda5614dde02ff236069bb79827/src/transformers/models/llava/modeling_llava.py#L421 # noqa
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if strategy == "default":
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return image_features[:, 1:]
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elif strategy == "full":
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return image_features
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raise ValueError(f"Unexpected select feature strategy: {strategy}")
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def _image_pixels_to_features(
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self,
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vision_tower: Union[CLIPVisionModel, SiglipVisionModel,
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PixtralHFVisionModel],
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pixel_values: torch.Tensor,
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) -> torch.Tensor:
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# NOTE: we skip the step to select the vision feature layer since
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# this is already done inside the vision tower
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image_features = vision_tower(pixel_values)
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return self._select_image_features(
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image_features,
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strategy=self.config.vision_feature_select_strategy,
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)
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def _process_image_pixels(self,
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inputs: LlavaImagePixelInputs) -> torch.Tensor:
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assert self.vision_tower is not None
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pixel_values = inputs["data"]
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return self._image_pixels_to_features(self.vision_tower, pixel_values)
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def _process_image_input(self,
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image_input: LlavaImageInputs) -> torch.Tensor:
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if image_input["type"] == "image_embeds":
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return image_input["data"]
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assert self.vision_tower is not None
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image_features = self._process_image_pixels(image_input)
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return self.multi_modal_projector(image_features)
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def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
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image_input = self._parse_and_validate_image_input(**kwargs)
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if image_input is None:
|
|
return None
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
return vision_embeddings
|
|
|
|
def get_input_embeddings(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
multimodal_embeddings: Optional[NestedTensors] = None,
|
|
) -> torch.Tensor:
|
|
inputs_embeds = self.language_model.get_input_embeddings(input_ids)
|
|
if multimodal_embeddings is not None:
|
|
inputs_embeds = merge_multimodal_embeddings(
|
|
input_ids, inputs_embeds, multimodal_embeddings,
|
|
self.config.image_token_index)
|
|
return inputs_embeds
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
kv_caches: List[torch.Tensor],
|
|
attn_metadata: AttentionMetadata,
|
|
intermediate_tensors: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[torch.Tensor, IntermediateTensors]:
|
|
"""Run forward pass for LLaVA-1.5.
|
|
|
|
One key thing to understand is the `input_ids` already accounts for the
|
|
positions of the to-be-inserted image embeddings.
|
|
|
|
Concretely, consider a text prompt:
|
|
`"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.
|
|
|
|
Tokenizer outputs:
|
|
`[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
|
|
278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.
|
|
|
|
To reserve space in KV cache, we have to insert placeholder tokens
|
|
before they are inputted to the model, so the input processor prepends
|
|
additional image tokens (denoted as `32000`), resulting in:
|
|
`[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
|
|
29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
|
|
29901]`.
|
|
|
|
We insert 575 tokens so that including the original image token in the
|
|
input, there are a total of 576 (24 * 24) image tokens, which
|
|
corresponds to the number of image tokens inputted to the language
|
|
model, i.e. the number of image tokens outputted by the visual encoder.
|
|
|
|
This way, the `positions` and `attn_metadata` are consistent
|
|
with the `input_ids`.
|
|
|
|
Args:
|
|
input_ids: Flattened (concatenated) input_ids corresponding to a
|
|
batch.
|
|
pixel_values: The pixels in each input image.
|
|
|
|
See also:
|
|
:class:`LlavaImageInputs`
|
|
"""
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
# NOTE: In v1, inputs_embeds is always generated at model runner, this
|
|
# condition is for v0 compatibility.
|
|
elif inputs_embeds is None:
|
|
vision_embeddings = self.get_multimodal_embeddings(**kwargs)
|
|
inputs_embeds = self.get_input_embeddings(input_ids,
|
|
vision_embeddings)
|
|
input_ids = None
|
|
|
|
hidden_states = self.language_model.model(input_ids,
|
|
positions,
|
|
kv_caches,
|
|
attn_metadata,
|
|
intermediate_tensors,
|
|
inputs_embeds=inputs_embeds)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[torch.Tensor]:
|
|
return self.language_model.compute_logits(hidden_states,
|
|
sampling_metadata)
|
|
|
|
def sample(
|
|
self,
|
|
logits: torch.Tensor,
|
|
sampling_metadata: SamplingMetadata,
|
|
) -> Optional[SamplerOutput]:
|
|
return self.language_model.sample(logits, sampling_metadata)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str,
|
|
torch.Tensor]]) -> Set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
return loader.load_weights(weights)
|
|
|
|
|
|
class MantisMultiModalProcessor(LlavaMultiModalProcessor):
|
|
|
|
def _get_hf_processor(self) -> ProcessorMixin:
|
|
try:
|
|
from mantis.models.mllava import MLlavaProcessor
|
|
except ModuleNotFoundError as exc:
|
|
raise ModuleNotFoundError(
|
|
"You need to `pip install "
|
|
"git+https://github.com/TIGER-AI-Lab/Mantis.git` "
|
|
"to use this model") from exc
|
|
|
|
processor = MLlavaProcessor.from_pretrained(
|
|
self.ctx.model_config.tokenizer)
|
|
assert isinstance(processor, ProcessorMixin)
|
|
return processor
|
|
|
|
|
|
# To use this model, please use
|
|
# `--hf_overrides '{"architectures": ["MantisForConditionalGeneration"]}'`
|
|
@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)
|
|
@MULTIMODAL_REGISTRY.register_processor(MantisMultiModalProcessor)
|
|
class MantisForConditionalGeneration(LlavaForConditionalGeneration):
|
|
pass
|