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https://git.datalinker.icu/vllm-project/vllm.git
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346 lines
14 KiB
Python
346 lines
14 KiB
Python
from typing import Iterable, List, Literal, Optional, 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 LlavaConfig
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, VisionLanguageConfig
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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from vllm.model_executor.layers.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.clip import CLIPVisionModel
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from vllm.model_executor.models.llama import LlamaModel
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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.image import get_dummy_image_data
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from vllm.sequence import SamplerOutput
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from .interfaces import SupportsVision
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_KEYS_TO_MODIFY_MAPPING = {
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"language_model.lm_head": "lm_head",
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"language_model.model": "language_model",
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}
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# TODO(xwjiang): Run benchmark and decide if TP.
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class LlavaMultiModalProjector(nn.Module):
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def __init__(self, vision_hidden_size: int, text_hidden_size: int,
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projector_hidden_act: str):
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super().__init__()
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self.linear_1 = nn.Linear(vision_hidden_size,
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text_hidden_size,
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bias=True)
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self.act = get_act_fn(projector_hidden_act)
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self.linear_2 = nn.Linear(text_hidden_size,
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text_hidden_size,
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bias=True)
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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 merge_vision_embeddings(input_ids: torch.Tensor,
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inputs_embeds: torch.Tensor,
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vision_embeddings: torch.Tensor,
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image_token_id: int) -> torch.Tensor:
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"""In place merges in vision_embeddings with inputs_embeds."""
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mask = (input_ids == image_token_id)
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image_feature_size = vision_embeddings.shape[0] * vision_embeddings.shape[1]
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if mask.sum() != image_feature_size:
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raise ValueError(f"image_feature_size should be {image_feature_size}, "
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f"but found: {mask.sum()}")
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inputs_embeds[mask] = vision_embeddings.view(image_feature_size,
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vision_embeddings.shape[-1])
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return inputs_embeds
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class LlavaImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: torch.Tensor
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"""Shape: (batch_size, num_channels, height, width)"""
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class LlavaImageFeatureInputs(TypedDict):
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type: Literal["image_features"]
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data: torch.Tensor
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"""Shape: (batch_size, image_feature_size, hidden_size)"""
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LlavaImageInputs = Union[LlavaImagePixelInputs, LlavaImageFeatureInputs]
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@MULTIMODAL_REGISTRY.register_image_feature_input()
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@MULTIMODAL_REGISTRY.register_image_pixel_input()
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@MULTIMODAL_REGISTRY.register_dummy_data(get_dummy_image_data)
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class LlavaForConditionalGeneration(nn.Module, SupportsVision):
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def __init__(self,
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config: LlavaConfig,
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vlm_config: VisionLanguageConfig,
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cache_config: Optional[CacheConfig] = None,
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quant_config: Optional[QuantizationConfig] = None) -> None:
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super().__init__()
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self.config = config
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self.vlm_config = vlm_config
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if self.vlm_config.image_input_type == (
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VisionLanguageConfig.ImageInputType.PIXEL_VALUES):
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self.vision_tower = CLIPVisionModel(config.vision_config)
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else:
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self.vision_tower = None
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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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self.quant_config = quant_config
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self.language_model = LlamaModel(config.text_config, cache_config,
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quant_config)
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self.unpadded_vocab_size = config.text_config.vocab_size
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self.lm_head = ParallelLMHead(
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self.unpadded_vocab_size,
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config.text_config.hidden_size,
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org_num_embeddings=self.language_model.org_vocab_size)
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logit_scale = getattr(config, "logit_scale", 1.0)
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self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
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config.vocab_size, logit_scale)
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self.sampler = Sampler()
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def _validate_image_data(self, data: torch.Tensor) -> torch.Tensor:
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if list(data.shape[1:]) != list(self.vlm_config.image_input_shape[1:]):
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raise ValueError(
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f"The expected image tensor shape is batch dimension plus "
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f"{self.vlm_config.image_input_shape[1:]}. "
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f"You supplied {data.shape}. "
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f"If you are using vLLM's entrypoint, make sure your "
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f"supplied image input is consistent with "
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f"image_input_shape in engine args.")
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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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image_features = kwargs.pop("image_features", None)
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expected_input_type = self.vlm_config.image_input_type
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ImageInputType = VisionLanguageConfig.ImageInputType
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if expected_input_type == ImageInputType.PIXEL_VALUES:
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if image_features is not None:
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raise ValueError(
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"Expected pixel values but got image features")
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if pixel_values is None:
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return None
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if not isinstance(pixel_values, torch.Tensor):
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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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return LlavaImagePixelInputs(
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type="pixel_values",
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data=self._validate_image_data(pixel_values),
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)
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if expected_input_type == ImageInputType.IMAGE_FEATURES:
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if pixel_values is not None:
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raise ValueError(
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"Expected image features but got pixel values")
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if image_features is None:
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return None
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if not isinstance(image_features, torch.Tensor):
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raise ValueError("Incorrect type of image features. "
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f"Got type: {type(image_features)}")
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return LlavaImageFeatureInputs(
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type="image_features",
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data=self._validate_image_data(image_features),
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)
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return None
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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(self, vision_tower: CLIPVisionModel,
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pixel_values: torch.Tensor) -> 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.to(vision_tower.device),
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self.config.vision_feature_layer)
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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"] == "pixel_values":
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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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else:
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image_features = image_input["data"]
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return self.multi_modal_projector(image_features)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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kv_caches: List[torch.Tensor],
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attn_metadata: AttentionMetadata,
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**kwargs: object,
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) -> SamplerOutput:
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"""Run forward pass for LLaVA-1.5.
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One key thing to understand is the `input_ids` already accounts for the
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positions of the to-be-inserted image embeddings.
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Concretely, consider a text prompt:
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"<image>\nUSER: What's the content of the image?\nASSISTANT:".
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Tokenizer outputs:
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[1, 32000, 29871, 13, 11889, 29901, 1724, 29915, 29879, 278,
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2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901].
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The to-be-inserted image has a size of 576 (24 * 24) along the context
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length dimension.
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`input_ids` is thus [1, 32000, ..., 32000, 29871, 13, 11889, 29901,
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1724, 29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933,
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9047, 13566, 29901].
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There will be 576 `32000` in the `input_ids`.
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(32000 is the token id for `<image>`.)
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This way, the `positions` and `attn_metadata` are consistent
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with the `input_ids`.
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This model has two modes of image inputs:
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`PIXEL_VALUES` and `IMAGE_FEATURES`.
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Args:
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input_ids: Flattened (concatenated) input_ids corresponding to a
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batch.
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pixel_values: The pixels in each input image.
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Expects a batch with shape `[1, 3, 336, 336]`.
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(Only applicable to `PIXEL_VALUES` mode)
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image_features: The image features for each input image outputted by
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the vision tower before passing to the multi-modal projector.
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Expects a batch with shape `[1, 576, 1024]`.
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(Only applicable to `IMAGE_FEATURES` mode)
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See also:
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Each input maps to huggingface implementation, as follows:
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- `pixel_values`: https://github.com/huggingface/transformers/blob/v4.41.1/src/transformers/models/llava/modeling_llava.py#L360
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- `image_features`: https://github.com/huggingface/transformers/blob/v4.41.1/src/transformers/models/llava/modeling_llava.py#L437
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"""
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image_input = self._parse_and_validate_image_input(**kwargs)
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if image_input is not None:
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vision_embeddings = self._process_image_input(image_input)
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inputs_embeds = self.language_model.get_input_embeddings(input_ids)
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inputs_embeds = merge_vision_embeddings(
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input_ids, inputs_embeds, vision_embeddings,
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self.vlm_config.image_token_id)
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input_ids = None
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else:
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inputs_embeds = None
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hidden_states = self.language_model(input_ids,
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positions,
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kv_caches,
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attn_metadata,
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inputs_embeds=inputs_embeds)
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return hidden_states
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def compute_logits(self, hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata) -> torch.Tensor:
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logits = self.logits_processor(self.lm_head.weight, hidden_states,
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sampling_metadata)
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return logits
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def sample(
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self,
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logits: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[SamplerOutput]:
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next_tokens = self.sampler(logits, sampling_metadata)
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return next_tokens
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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# only doing this for language model part for now.
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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# post_layernorm is not needed in CLIPVisionModel
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if "vision_model.post_layernorm" in name:
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continue
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for key_to_modify, new_key in _KEYS_TO_MODIFY_MAPPING.items():
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if key_to_modify in name:
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name = name.replace(key_to_modify, new_key)
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use_default_weight_loading = False
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if "vision" in name:
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if self.vision_tower is not None:
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# We only do sharding for language model and
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# not vision model for now.
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use_default_weight_loading = True
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else:
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for (param_name, weight_name,
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shard_id) in stacked_params_mapping:
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if weight_name not in name:
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continue
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param = params_dict[name.replace(weight_name, param_name)]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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use_default_weight_loading = True
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if use_default_weight_loading:
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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