[VLM] Merged multi-modal processor for InternVL-based models (#12553)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Isotr0py <2037008807@qq.com> Co-authored-by: Isotr0py <2037008807@qq.com>
This commit is contained in:
Cyrus Leung
@@ -6,44 +6,190 @@
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# Copyright (c) 2024 NVIDIA
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# Licensed under Apache 2.0 License [see LICENSE for details]
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# --------------------------------------------------------
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from typing import Optional
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from typing import Mapping, Optional
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.inputs import INPUT_REGISTRY
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import MultiModalKwargs
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from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
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MultiModalDataItems)
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from vllm.multimodal.processing import (PromptReplacement,
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PromptReplacementDetails)
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from vllm.multimodal.profiling import ProcessorInputs
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from .intern_vit import InternVisionModel
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from .internvl import (InternVLChatModel, InternVLInputPipeline,
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get_max_internvl_image_tokens)
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from .internvl import (BaseInternVLProcessingInfo, BaseInternVLProcessor,
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InternVLChatModel, InternVLDummyInputsBuilder,
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InternVLMultiModalProcessor)
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IMG_START = '<|vision_start|>'
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IMG_END = '<|vision_end|>'
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IMG_CONTEXT = '<|vision_pad|>'
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IMG_PAD = "<|vision_pad|>"
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class NVLMInputPipeline(InternVLInputPipeline):
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class NVLMProcessor(BaseInternVLProcessor):
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@property
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def image_token_id(self) -> int:
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return self.tokenizer.get_vocab()[IMG_PAD]
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def get_image_repl_features(
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self,
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feature_size: int,
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num_patches: Optional[int],
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) -> str:
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if num_patches is None:
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raise NotImplementedError("Embedding inputs are not supported")
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tile_pos_identifiers = [f"<tile_{i}>" for i in range(1, num_patches)]
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if self.use_thumbnail and num_patches != 1:
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tile_pos_identifiers += ["<tile_global_thumbnail>"]
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def _create_image_prompt(self, feature_size: int, num_patches: int) -> str:
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tile_pos_identifiers = ([f"<tile_{i}>"
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for i in range(1, num_patches)] +
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["<tile_global_thumbnail>"])
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context_size = feature_size // num_patches
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features = "".join(identifier + IMG_PAD * context_size
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for identifier in tile_pos_identifiers)
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return '<Image>' + ''.join(
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tile_pos_identifier + self.img_context_token * context_size
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for tile_pos_identifier in tile_pos_identifiers) + '</Image>'
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# We include the start and end as well because "<Image><tile" is
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# tokenized as ["<Image", "><", "tile"], resulting in assertion error
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# when trying to find "<tile" as a subsequence of "<Image><tile"
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return "<Image>" + features + "</Image>"
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def get_image_repl_full(
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self,
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feature_size: int,
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num_patches: Optional[int],
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) -> str:
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return self.get_image_repl_features(feature_size, num_patches)
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input_pipeline = NVLMInputPipeline(IMG_START, IMG_END, IMG_CONTEXT)
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class NVLMProcessingInfo(BaseInternVLProcessingInfo):
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def get_hf_processor(
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self,
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*,
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max_dynamic_patch: Optional[int] = None,
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dynamic_image_size: Optional[bool] = None,
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) -> NVLMProcessor:
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return NVLMProcessor(
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self.get_hf_config(),
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self.get_tokenizer(),
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max_dynamic_patch=max_dynamic_patch,
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dynamic_image_size=dynamic_image_size,
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)
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def get_max_image_tokens(self) -> int:
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hf_processor = self.get_hf_processor()
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tokenizer = hf_processor.tokenizer
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max_num_patches = hf_processor.max_dynamic_patch
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# we need +1 here because max_dynamic_patch in config doesn't
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# include the thumbnail patch
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tile_pos_identifiers = [
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f"<tile_{i+1}>" for i in range(max_num_patches)
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]
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if hf_processor.use_thumbnail and max_num_patches != 1:
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tile_pos_identifiers += ["<tile_global_thumbnail>"]
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# "<Image><tile" is tokenized as ["<Image", "><", "tile"]
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# so we include <tile_1> in the start_str
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start_str = "<Image>" + tile_pos_identifiers.pop(0)
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end_str = "</Image>"
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start_token_len = len(tokenizer.encode(start_str))
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end_token_len = len(tokenizer.encode(end_str))
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tile_token_len = sum(
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len(tokenizer.encode(identifier))
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for identifier in tile_pos_identifiers)
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non_image_tokens_num = start_token_len + end_token_len + tile_token_len
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return super().get_max_image_tokens() + non_image_tokens_num
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@MULTIMODAL_REGISTRY.register_image_input_mapper(input_pipeline.input_mapper)
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@MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_internvl_image_tokens)
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@INPUT_REGISTRY.register_dummy_data(input_pipeline.dummy_data)
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@INPUT_REGISTRY.register_input_processor(input_pipeline.input_processor)
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class NVLMDummyInputsBuilder(InternVLDummyInputsBuilder[NVLMProcessingInfo]):
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def get_dummy_processor_inputs(
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self,
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seq_len: int,
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mm_counts: Mapping[str, int],
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) -> ProcessorInputs:
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target_width, target_height = \
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self.info.get_image_size_with_most_features()
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num_images = mm_counts.get("image", 0)
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mm_data = {
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"image":
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self._get_dummy_images(width=target_width,
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height=target_height,
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num_images=num_images)
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}
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return ProcessorInputs(
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# The newline is necessary to separate ">" of the current item
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# and "<" of the next item
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prompt_text="<image>\n" * num_images,
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mm_data=mm_data,
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)
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class NVLMMultiModalProcessor(InternVLMultiModalProcessor[NVLMProcessingInfo]):
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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_processor_mm_kwargs: Mapping[str, object],
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out_mm_kwargs: MultiModalKwargs,
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) -> list[PromptReplacement]:
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hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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if "image_num_patches" in out_mm_kwargs:
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image_num_patches = out_mm_kwargs["image_num_patches"]
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assert isinstance(image_num_patches, torch.Tensor)
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image_num_patches = image_num_patches.tolist()
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elif "image_embeds" in out_mm_kwargs:
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# TODO: Use image size information in dictionary embedding inputs
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# to compute num_patches (similar to Qwen2-VL)
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image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
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else:
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image_num_patches = []
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def get_replacement_nvlm(item_idx: int):
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems))
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if isinstance(images, ImageEmbeddingItems):
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feature_size = images.get_feature_size(item_idx)
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else:
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image_size = images.get_image_size(item_idx)
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feature_size = self.info.get_num_image_tokens(
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image_width=image_size.width,
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image_height=image_size.height,
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processor=hf_processor,
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)
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num_patches = image_num_patches[item_idx]
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if num_patches is not None:
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assert isinstance(num_patches, int)
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return PromptReplacementDetails(
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full=hf_processor.get_image_repl_full(feature_size,
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num_patches) + "\n",
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features=hf_processor.get_image_repl_features(
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feature_size, num_patches) + "\n",
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)
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# See note in dummy data regarding why we have the extra newline
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return [
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PromptReplacement(
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modality="image",
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target="<image>\n",
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replacement=get_replacement_nvlm,
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)
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]
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@MULTIMODAL_REGISTRY.register_processor(NVLMMultiModalProcessor,
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info=NVLMProcessingInfo,
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dummy_inputs=NVLMDummyInputsBuilder)
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class NVLM_D_Model(InternVLChatModel):
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def _init_mlp1(self, config: PretrainedConfig) -> nn.Sequential:
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