mirror of
https://github.com/wassname/vllm.git
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461 lines
19 KiB
Python
461 lines
19 KiB
Python
from typing import (Dict, Iterable, List, Literal, Optional, Tuple, TypedDict,
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Union)
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import torch
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import torch.nn as nn
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from PIL import Image
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from transformers import LlavaNextConfig
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from transformers.models.llava_next.modeling_llava_next import (
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get_anyres_image_grid_shape, unpad_image)
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from typing_extensions import NotRequired
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, ModelConfig, VisionLanguageConfig
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from vllm.logger import init_logger
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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, MultiModalData
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from vllm.multimodal.image import ImagePixelData, get_dummy_image_data
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from vllm.sequence import SamplerOutput, SequenceData
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from .interfaces import SupportsVision
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from .llava import LlavaMultiModalProjector, merge_vision_embeddings
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logger = init_logger(__name__)
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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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class LlavaNextImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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data: torch.Tensor
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"""Shape: (batch_size, 1 + num_patches, num_channels, height, width)"""
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image_sizes: NotRequired[torch.Tensor]
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"""Shape: (batch_size, 2)"""
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class LlavaNextImageFeatureInputs(TypedDict):
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type: Literal["image_features"]
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data: torch.Tensor
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"""Shape: (batch_size, 1 + num_patches, image_feature_size, hidden_size)"""
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image_sizes: NotRequired[torch.Tensor]
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"""Shape: (batch_size, 2)"""
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LlavaNextImageInputs = Union[LlavaNextImagePixelInputs,
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LlavaNextImageFeatureInputs]
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def _get_dummy_image_data(
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seq_len: int,
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model_config: ModelConfig,
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vlm_config: VisionLanguageConfig,
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) -> Tuple[SequenceData, MultiModalData]:
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seq_data, fake_mm_data = get_dummy_image_data(seq_len, model_config,
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vlm_config)
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config_input_type = vlm_config.image_input_type
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ImageInputType = VisionLanguageConfig.ImageInputType
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if config_input_type == ImageInputType.PIXEL_VALUES:
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_, c, h, w = vlm_config.image_input_shape
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mode = {1: "L", 3: "RGB"}[c]
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fake_mm_data = ImagePixelData(Image.new(mode, (w, h), color=0))
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return seq_data, fake_mm_data
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def _image_pixel_processor(
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data: ImagePixelData,
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model_config: ModelConfig,
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vlm_config: VisionLanguageConfig,
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) -> Dict[str, torch.Tensor]:
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image = data.image
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if isinstance(image, torch.Tensor):
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pixel_values = image.to(model_config.dtype)
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batch_size, _, _, h, w = pixel_values.shape
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image_sizes = torch.tensor([(w, h) for _ in range(batch_size)])
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return {"pixel_values": pixel_values, "image_sizes": image_sizes}
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# Temporary patch before dynamic number of image tokens is supported
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_, _, h, w = vlm_config.image_input_shape
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if (w, h) != (image.width, image.height):
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logger.warning(
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"Dynamic image shape is currently not supported. "
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"Resizing input image to (%d, %d).", w, h)
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data.image = image.resize((w, h))
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return MULTIMODAL_REGISTRY._get_plugin_for_data_type(ImagePixelData) \
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._default_input_processor(data, model_config, vlm_config)
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@MULTIMODAL_REGISTRY.register_image_pixel_input(_image_pixel_processor)
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@MULTIMODAL_REGISTRY.register_dummy_data(_get_dummy_image_data)
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class LlavaNextForConditionalGeneration(nn.Module, SupportsVision):
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supports_vision = True
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def __init__(self,
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config: LlavaNextConfig,
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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=config.vision_config)
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else:
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raise TypeError("Image features are not supported by LLaVA-NeXT")
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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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self.image_newline = nn.Parameter(
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torch.empty(config.text_config.hidden_size))
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def _validate_image_pixels(self, data: torch.Tensor) -> torch.Tensor:
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_, num_channels, _, _ = self.vlm_config.image_input_shape
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# Note that this is different from that of vLLM vision_language_config
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# since the image is resized by the HuggingFace preprocessor
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height = width = self.config.vision_config.image_size
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if list(data.shape[2:]) != [num_channels, height, width]:
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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"num_patches plus {[num_channels, height, width]}. "
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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 _validate_image_sizes(self, data: torch.Tensor) -> torch.Tensor:
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if list(data.shape[1:]) != [2]:
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raise ValueError(
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f"The expected image sizes shape is batch dimension plus "
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f"{[2]}. You supplied {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[LlavaNextImageInputs]:
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pixel_values = kwargs.pop("pixel_values", None)
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image_sizes = kwargs.pop("image_sizes", 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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if not isinstance(image_sizes, torch.Tensor):
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raise ValueError("Incorrect type of image sizes. "
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f"Got type: {type(image_sizes)}")
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return LlavaNextImagePixelInputs(
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type="pixel_values",
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data=self._validate_image_pixels(pixel_values),
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image_sizes=self._validate_image_sizes(image_sizes),
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)
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assert expected_input_type != ImageInputType.IMAGE_FEATURES, (
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"Failed to validate this at initialization time")
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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 _merge_image_patch_embeddings(self, image_size: torch.Tensor,
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patch_embeddings: torch.Tensor, *,
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strategy: str) -> torch.Tensor:
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# Based on: https://github.com/haotian-liu/LLaVA/blob/main/llava/model/llava_arch.py
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if strategy == "flat":
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return patch_embeddings.flatten(0, 1)
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if strategy.startswith("spatial"):
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orig_width, orig_height = image_size
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height = width = self.config.vision_config.image_size \
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// self.config.vision_config.patch_size
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base_patch_embeds = patch_embeddings[0]
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if height * width != base_patch_embeds.shape[0]:
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raise ValueError(
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"The number of patches is not consistent with the "
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"image size.")
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if patch_embeddings.shape[0] > 1:
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other_patch_embeds = patch_embeddings[1:]
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# image_aspect_ratio == "anyres"
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num_patch_width, num_patch_height = get_anyres_image_grid_shape(
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(orig_width, orig_height),
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self.config.image_grid_pinpoints,
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self.config.vision_config.image_size,
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)
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other_patch_embeds = other_patch_embeds \
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.view(num_patch_width, num_patch_height, height, width, -1)
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if "unpad" in strategy:
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other_patch_embeds = other_patch_embeds \
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.permute(4, 0, 2, 1, 3).contiguous() \
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.flatten(1, 2).flatten(2, 3)
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other_patch_embeds = unpad_image(other_patch_embeds,
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image_size)
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other_patch_embeds = torch.cat((
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other_patch_embeds,
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self.image_newline[:, None, None] \
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.expand(*other_patch_embeds.shape[:-1], 1) \
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.to(other_patch_embeds.device),
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), dim=-1)
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other_patch_embeds = other_patch_embeds \
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.flatten(1, 2).transpose(0, 1)
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else:
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other_patch_embeds = other_patch_embeds \
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.permute(0, 2, 1, 3, 4).contiguous() \
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.flatten(0, 3)
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merged_patch_embeddings = torch.cat(
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(base_patch_embeds, other_patch_embeds), dim=0)
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else:
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if "unpad" in strategy:
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merged_patch_embeddings = torch.cat(
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(base_patch_embeds,
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self.image_newline[None] \
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.to(base_patch_embeds.device)
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), dim=0)
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else:
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merged_patch_embeddings = base_patch_embeds
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return merged_patch_embeddings
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raise ValueError(f"Unexpected patch merge strategy: {strategy}")
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def _process_image_pixels(
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self, inputs: LlavaNextImagePixelInputs) -> 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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b, num_patches, c, h, w = pixel_values.shape
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stacked_pixel_values = pixel_values.view(b * num_patches, c, h, w)
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stacked_image_features = self._image_pixels_to_features(
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self.vision_tower, stacked_pixel_values)
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return stacked_image_features.view(b, num_patches,
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*stacked_image_features.shape[-2:])
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def _process_image_input(
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self, image_input: LlavaNextImageInputs) -> 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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patch_embeddings = self.multi_modal_projector(image_features)
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image_sizes = image_input.get("image_sizes")
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if image_sizes is None:
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batch_size = image_input["data"].shape[0]
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vision_config = self.config.vision_config
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default_width = default_height = vision_config.image_size
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image_sizes = torch.as_tensor([[default_width, default_height]
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for _ in range(batch_size)])
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merged_patch_embeddings = [
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self._merge_image_patch_embeddings(image_sizes[i],
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patch_features,
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strategy="spatial_unpad")
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for i, patch_features in enumerate(patch_embeddings)
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]
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return torch.stack(merged_patch_embeddings, dim=0)
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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-NeXT.
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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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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 grid patch for each input image.
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Expects a batch with shape `[1, num_patches, 3, 336, 336]`.
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image_sizes: The original `(width, height)` for each input image.
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Expects a batch with shape `[1, 2]`.
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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_next/modeling_llava_next.py#L690
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- `image_sizes`: https://github.com/huggingface/transformers/blob/v4.41.1/src/transformers/models/llava_next/modeling_llava_next.py#L691
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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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