412 lines
16 KiB
Python
412 lines
16 KiB
Python
from functools import cached_property
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from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
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TypedDict, 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 CLIPVisionConfig, LlavaConfig, SiglipVisionConfig
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from vllm.attention import AttentionMetadata
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from vllm.config import CacheConfig, MultiModalConfig
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from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import Sampler, SamplerOutput
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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.sequence import IntermediateTensors
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from vllm.utils import is_list_of
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from .clip import (CLIPVisionModel, dummy_image_for_clip,
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dummy_seq_data_for_clip, get_max_clip_image_tokens,
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input_processor_for_clip)
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from .interfaces import SupportsMultiModal, SupportsPP
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from .siglip import (SiglipVisionModel, dummy_image_for_siglip,
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dummy_seq_data_for_siglip, get_max_siglip_image_tokens,
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input_processor_for_siglip)
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from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
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merge_multimodal_embeddings)
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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_images, num_channels, height, width)`"""
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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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# 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 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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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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def dummy_data_for_llava(ctx: InputContext, seq_len: int,
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mm_counts: Mapping[str, int]):
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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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num_images = mm_counts["image"]
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image_feature_size = get_max_llava_image_tokens(ctx)
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if isinstance(vision_config, CLIPVisionConfig):
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seq_data = dummy_seq_data_for_clip(
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vision_config,
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seq_len,
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num_images,
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image_token_id=hf_config.image_token_index,
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image_feature_size_override=image_feature_size,
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)
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mm_data = dummy_image_for_clip(vision_config, num_images)
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return seq_data, mm_data
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elif isinstance(vision_config, SiglipVisionConfig):
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seq_data = dummy_seq_data_for_siglip(
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vision_config,
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seq_len,
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num_images,
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image_token_id=hf_config.image_token_index,
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image_feature_size_override=image_feature_size,
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)
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mm_data = dummy_image_for_siglip(vision_config, num_images)
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return seq_data, mm_data
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msg = f"Unsupported vision config: {type(vision_config)}"
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raise NotImplementedError(msg)
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def input_processor_for_llava(ctx: InputContext, llm_inputs: LLMInputs):
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multi_modal_data = llm_inputs.get("multi_modal_data")
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if multi_modal_data is None or "image" not in multi_modal_data:
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return llm_inputs
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model_config = ctx.model_config
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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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image_data = multi_modal_data["image"]
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if isinstance(image_data, Image.Image):
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image_feature_size = get_max_llava_image_tokens(ctx)
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elif is_list_of(image_data, Image.Image):
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image_feature_size = [get_max_llava_image_tokens(ctx)
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] * len(image_data)
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elif isinstance(image_data, torch.Tensor):
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num_images, image_feature_size, hidden_size = image_data.shape
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elif is_list_of(image_data, torch.Tensor):
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image_feature_size = [item.shape[1] for item in image_data]
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else:
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raise TypeError(f"Invalid image type: {type(image_data)}")
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if isinstance(vision_config, CLIPVisionConfig):
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return input_processor_for_clip(
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model_config,
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vision_config,
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llm_inputs,
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image_token_id=hf_config.image_token_index,
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image_feature_size_override=image_feature_size,
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)
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elif isinstance(vision_config, SiglipVisionConfig):
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return input_processor_for_siglip(
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model_config,
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vision_config,
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llm_inputs,
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image_token_id=hf_config.image_token_index,
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image_feature_size_override=image_feature_size,
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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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def _init_vision_tower(hf_config: LlavaConfig):
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vision_config = hf_config.vision_config
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# Initialize the vision tower only up to the required feature layer
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vision_feature_layer = hf_config.vision_feature_layer
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if vision_feature_layer < 0:
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num_hidden_layers = hf_config.vision_config.num_hidden_layers \
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+ vision_feature_layer + 1
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else:
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num_hidden_layers = vision_feature_layer + 1
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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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num_hidden_layers_override=num_hidden_layers,
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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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num_hidden_layers_override=num_hidden_layers,
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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_image_input_mapper()
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_llava_image_tokens)
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@INPUT_REGISTRY.register_dummy_data(dummy_data_for_llava)
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@INPUT_REGISTRY.register_input_processor(input_processor_for_llava)
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class LlavaForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
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def __init__(self,
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config: LlavaConfig,
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multimodal_config: MultiModalConfig,
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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.multimodal_config = multimodal_config
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# TODO: Optionally initializes this for supporting embeddings.
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self.vision_tower = _init_vision_tower(config)
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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.language_model = init_vllm_registered_model(
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config.text_config, cache_config, quant_config)
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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 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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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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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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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 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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intermediate_tensors: Optional[IntermediateTensors] = None,
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**kwargs: object,
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) -> Union[torch.Tensor, IntermediateTensors]:
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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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`"USER: <image>\\nWhat's the content of the image?\\nASSISTANT:"`.
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Tokenizer outputs:
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`[1, 3148, 1001, 29901, 29871, 32000, 29871, 13, 5618, 29915, 29879,
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278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566, 29901]`.
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To reserve space in KV cache, we have to insert placeholder tokens
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before they are inputted to the model, so the input processor prepends
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additional image tokens (denoted as `32000`), resulting in:
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`[1, 3148, 1001, 29901, 29871, 32000, ..., 32000, 29871, 13, 5618,
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29915, 29879, 278, 2793, 310, 278, 1967, 29973, 13, 22933, 9047, 13566,
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29901]`.
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We insert 575 tokens so that including the original image token in the
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input, there are a total of 576 (24 * 24) image tokens, which
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corresponds to the number of image tokens inputted to the language
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model, i.e. the number of image tokens outputted by the visual encoder.
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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 input image.
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See also:
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:class:`LlavaImageInputs`
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"""
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if intermediate_tensors is not None:
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input_ids = None
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inputs_embeds = None
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else:
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# always pass the input via `inputs_embeds`
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# to make sure the computation graph is consistent
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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.model.get_input_embeddings(
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input_ids)
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inputs_embeds = merge_multimodal_embeddings(
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input_ids, inputs_embeds, vision_embeddings,
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self.config.image_token_index)
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else:
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inputs_embeds = self.language_model.model.get_input_embeddings(
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input_ids)
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input_ids = None
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hidden_states = self.language_model.model(input_ids,
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positions,
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kv_caches,
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attn_metadata,
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intermediate_tensors,
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inputs_embeds=inputs_embeds)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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return self.language_model.compute_logits(hidden_states,
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sampling_metadata)
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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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return self.language_model.sample(logits, sampling_metadata)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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loader = AutoWeightsLoader(self)
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loader.load_weights(weights)
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