wehub-resource-sync
989 lines
34 KiB
Python
989 lines
34 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Inference-only Deepseek-OCR model compatible with HuggingFace weights."""
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import math
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Any, Literal
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import torch
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import torch.nn as nn
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from transformers import BatchFeature, CLIPVisionConfig
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions
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from vllm.inputs import MultiModalDataDict
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from vllm.model_executor.models.interfaces import (
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MultiModalEmbeddings,
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SupportsEncoderCudaGraph,
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SupportsLoRA,
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SupportsMultiModal,
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SupportsPP,
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)
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.utils import (
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AutoWeightsLoader,
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WeightsMapper,
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init_vllm_registered_model,
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maybe_prefix,
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)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (
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MultiModalFieldConfig,
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MultiModalKwargsItems,
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NestedTensors,
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)
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from vllm.multimodal.parse import (
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ImageEmbeddingItems,
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ImageProcessorItems,
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ImageSize,
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MultiModalDataItems,
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)
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from vllm.multimodal.processing import (
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BaseDummyInputsBuilder,
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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PromptReplacement,
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PromptUpdate,
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)
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from vllm.sampling_params import SamplingParams
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from vllm.sequence import IntermediateTensors
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from vllm.tokenizers import cached_tokenizer_from_config
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from vllm.transformers_utils.configs.deepseek_vl2 import DeepseekVLV2Config
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from vllm.transformers_utils.processors.deepseek_ocr import (
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BASE_SIZE,
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CROP_MODE,
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IMAGE_SIZE,
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DeepseekOCRProcessor,
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count_tiles,
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)
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from vllm.v1.sample.logits_processor import (
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AdapterLogitsProcessor,
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RequestLogitsProcessor,
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)
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from vllm.v1.worker.encoder_cudagraph_defs import (
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EncoderCudaGraphCaptureInputs,
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EncoderCudaGraphConfig,
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EncoderCudaGraphReplayBuffers,
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EncoderItemSpec,
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)
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from .deepencoder import DeepCLIPVisionTransformer, build_sam_vit_b
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from .deepseek_vl2 import MlpProjector
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# The image token id may be various
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_IMAGE_TOKEN = "<image>"
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class DeepseekOCRImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- b: Batch size
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- n: Number of images
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- p: Number of patches
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- base_size: Base size of the processor
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- image_size: Image size of the processor
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"""
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type: Literal["pixel_values"]
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data: Annotated[
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torch.Tensor,
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TensorShape("bn", 3, "base_size", "base_size", dynamic_dims={"bnp"}),
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]
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images_crop: Annotated[
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torch.Tensor,
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TensorShape("bnp", 3, "image_size", "image_size", dynamic_dims={"bnp"}),
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]
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images_spatial_crop: Annotated[torch.Tensor, TensorShape("bn", 2)]
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class NoRepeatNGramLogitsProcessor:
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def __init__(
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self,
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ngram_size: int,
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window_size: int,
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whitelist_token_ids: set[int] | None = None,
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):
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self.ngram_size = ngram_size
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self.window_size = window_size
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self.whitelist_token_ids = whitelist_token_ids or set()
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def __call__(
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self,
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output_ids: list[int],
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logits: torch.Tensor,
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) -> torch.Tensor:
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if len(output_ids) < self.ngram_size:
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return logits
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current_prefix = tuple(output_ids[-(self.ngram_size - 1) :])
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search_start = max(0, len(output_ids) - self.window_size)
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search_end = len(output_ids) - self.ngram_size + 1
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banned_tokens = set()
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for i in range(search_start, search_end):
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ngram = tuple(output_ids[i : i + self.ngram_size])
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if ngram[:-1] == current_prefix:
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banned_tokens.add(ngram[-1])
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banned_tokens = banned_tokens - self.whitelist_token_ids
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if banned_tokens:
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logits[list(banned_tokens)] = -float("inf")
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return logits
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class NGramPerReqLogitsProcessor(AdapterLogitsProcessor):
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"""Example of overriding the wrapper class `__init__()` in order to utilize
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info about the device type"""
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@classmethod
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def validate_params(cls, params: SamplingParams):
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ngram_size = params.extra_args and params.extra_args.get("ngram_size")
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window_size = params.extra_args and params.extra_args.get("window_size", 100)
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whitelist_token_ids = params.extra_args and params.extra_args.get(
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"whitelist_token_ids", None
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)
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# if ngram_size is not provided, skip validation because the processor
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# will not be used.
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if ngram_size is None:
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return None
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if not isinstance(ngram_size, int) or ngram_size <= 0:
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raise ValueError(
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f"`ngram_size` has to be a strictly positive integer, got {ngram_size}."
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)
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if not isinstance(window_size, int) or window_size <= 0:
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raise ValueError(
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"`window_size` has to be a strictly positive integer, "
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f"got {window_size}."
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)
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if whitelist_token_ids is not None and not isinstance(
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whitelist_token_ids, Iterable
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):
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raise ValueError(
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"`whitelist_token_ids` has to be a sequence of integers, "
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f"got {whitelist_token_ids}."
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)
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def is_argmax_invariant(self) -> bool:
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return False
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def new_req_logits_processor(
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self,
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params: SamplingParams,
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) -> RequestLogitsProcessor | None:
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ngram_size = params.extra_args and params.extra_args.get("ngram_size")
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window_size = params.extra_args and params.extra_args.get("window_size", 100)
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whitelist_token_ids = params.extra_args and params.extra_args.get(
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"whitelist_token_ids", None
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)
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if ngram_size is None:
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return None
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whitelist_token_ids = set(whitelist_token_ids) if whitelist_token_ids else None
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return NoRepeatNGramLogitsProcessor(
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ngram_size=ngram_size,
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window_size=window_size,
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whitelist_token_ids=whitelist_token_ids,
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)
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class DeepseekOCRProcessingInfo(BaseProcessingInfo):
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def get_hf_config(self):
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return self.ctx.get_hf_config(DeepseekVLV2Config)
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def get_hf_processor(self, **kwargs: object):
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v1_processor_config = dict(
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image_size=IMAGE_SIZE,
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base_size=BASE_SIZE,
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crop_mode=CROP_MODE,
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strategy="v1",
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)
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return self.ctx.get_hf_processor(
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DeepseekOCRProcessor,
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**{**v1_processor_config, **kwargs},
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)
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
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return {"image": None}
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def get_num_image_tokens(
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self, *, image_width: int, image_height: int, cropping: bool = True
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) -> int:
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image_size = IMAGE_SIZE
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base_size = BASE_SIZE
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patch_size = 16
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downsample_ratio = 4
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# Use the caller-supplied `cropping` flag so that callers that disable
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# crop mode for multi-image requests get a consistent token count.
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if cropping:
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if image_width <= IMAGE_SIZE and image_height <= IMAGE_SIZE:
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crop_ratio = [1, 1]
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else:
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# find the closest aspect ratio to the target
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crop_ratio = count_tiles(
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image_width, image_height, image_size=IMAGE_SIZE
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)
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num_width_tiles, num_height_tiles = crop_ratio
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else:
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num_width_tiles = num_height_tiles = 1
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h = w = math.ceil((base_size // patch_size) / downsample_ratio)
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h2 = w2 = math.ceil((image_size // patch_size) / downsample_ratio)
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global_views_tokens = h * (w + 1)
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if num_width_tiles > 1 or num_height_tiles > 1:
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local_views_tokens = (num_height_tiles * h2) * (num_width_tiles * w2 + 1)
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else:
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local_views_tokens = 0
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return global_views_tokens + local_views_tokens + 1
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def get_image_size_with_most_features(self) -> ImageSize:
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if IMAGE_SIZE == 1024 and BASE_SIZE == 1280:
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return ImageSize(width=1024 * 2, height=1024 * 2)
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return ImageSize(width=640 * 2, height=640 * 2)
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class DeepseekOCRDummyInputsBuilder(BaseDummyInputsBuilder[DeepseekOCRProcessingInfo]):
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def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
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num_images = mm_counts.get("image", 0)
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processor = self.info.get_hf_processor()
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image_token = processor.image_token
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return image_token * num_images
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def get_dummy_mm_data(
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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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mm_options: Mapping[str, BaseDummyOptions],
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) -> MultiModalDataDict:
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num_images = mm_counts.get("image", 0)
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max_image_size = self.info.get_image_size_with_most_features()
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return {
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"image": self._get_dummy_images(
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width=max_image_size.width,
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height=max_image_size.height,
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num_images=num_images,
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)
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}
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class DeepseekOCRMultiModalProcessor(
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BaseMultiModalProcessor[DeepseekOCRProcessingInfo]
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):
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def _call_hf_processor(
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self,
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prompt: str,
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mm_data: Mapping[str, object],
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mm_kwargs: Mapping[str, object],
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tok_kwargs: Mapping[str, object],
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) -> BatchFeature:
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if mm_data:
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processed_outputs = self.info.ctx.call_hf_processor(
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self.info.get_hf_processor(**mm_kwargs),
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dict(prompt=prompt, **mm_data),
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mm_kwargs,
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)
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else:
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tokenizer = self.info.get_tokenizer()
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processed_outputs = tokenizer(
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prompt, add_special_tokens=True, return_tensors="pt"
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)
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return processed_outputs
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def _get_mm_fields_config(
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self,
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hf_inputs: BatchFeature,
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hf_processor_mm_kwargs: Mapping[str, object],
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) -> Mapping[str, MultiModalFieldConfig]:
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images_spatial_crop = hf_inputs.get("images_spatial_crop", torch.empty((0, 2)))
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is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
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patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
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return dict(
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pixel_values=MultiModalFieldConfig.batched("image"),
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images_spatial_crop=MultiModalFieldConfig.batched("image"),
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images_crop=MultiModalFieldConfig.flat_from_sizes(
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"image", patches_per_image
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),
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)
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def _get_prompt_updates(
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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: MultiModalKwargsItems,
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) -> Sequence[PromptUpdate]:
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hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
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image_token_id = hf_processor.image_token_id
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assert isinstance(image_token_id, int)
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def get_replacement_deepseek_vl2(item_idx: int):
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images = mm_items.get_items(
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"image", (ImageEmbeddingItems, ImageProcessorItems)
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)
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if isinstance(images, ImageEmbeddingItems):
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num_image_tokens = images.get_feature_size(item_idx)
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else:
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size = images.get_image_size(item_idx)
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num_image_tokens = self.info.get_num_image_tokens(
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image_width=size.width,
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image_height=size.height,
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cropping=CROP_MODE,
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)
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return [image_token_id] * num_image_tokens
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return [
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PromptReplacement(
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modality="image",
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target=[image_token_id],
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replacement=get_replacement_deepseek_vl2,
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)
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]
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@MULTIMODAL_REGISTRY.register_processor(
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DeepseekOCRMultiModalProcessor,
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info=DeepseekOCRProcessingInfo,
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dummy_inputs=DeepseekOCRDummyInputsBuilder,
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)
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class DeepseekOCRForCausalLM(
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nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA, SupportsEncoderCudaGraph
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):
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_prefix={
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# map prefix for language backbone
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"model.embed_tokens.": "language_model.model.embed_tokens.",
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"model.layers.": "language_model.model.layers.",
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"model.norm.": "language_model.model.norm.",
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"lm_head.": "language_model.lm_head.",
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# remove "model." prefix for other components
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"model.": "",
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}
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)
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@classmethod
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def get_placeholder_str(cls, modality: str, i: int) -> str | None:
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if modality.startswith("image"):
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return "<image>"
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raise ValueError("Only image modality is supported")
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config: DeepseekVLV2Config = vllm_config.model_config.hf_config
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quant_config = vllm_config.quant_config
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multimodal_config = vllm_config.model_config.multimodal_config
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self.config = config
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self.model_config = vllm_config.model_config
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self.multimodal_config = multimodal_config
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self.vision_config = config.vision_config
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self.projector_config = config.projector_config
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self.text_config = config.text_config
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model_config = vllm_config.model_config
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tokenizer = cached_tokenizer_from_config(model_config)
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self.image_token_id = tokenizer.vocab[_IMAGE_TOKEN]
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with self._mark_tower_model(vllm_config, "image"):
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self.sam_model = build_sam_vit_b()
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clip_vision_config = CLIPVisionConfig(
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hidden_size=1024,
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intermediate_size=4096,
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num_attention_heads=16,
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num_hidden_layers=24,
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image_size=224,
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patch_size=14,
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projection_dim=512,
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layer_norm_eps=1e-5,
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)
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self.vision_model = DeepCLIPVisionTransformer(
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config=clip_vision_config,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "vision_model"),
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)
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self.projector = MlpProjector(self.projector_config)
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self.tile_tag = config.tile_tag
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self.global_view_pos = config.global_view_pos
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# special token for image token sequence format
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n_embed = self.projector_config.n_embed
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embed_std = 1 / torch.sqrt(torch.tensor(n_embed, dtype=torch.float32))
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if self.tile_tag == "2D":
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# <|view_separator|>, <|\n|>
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self.image_newline = nn.Parameter(torch.randn(n_embed) * embed_std)
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# This is a typo in original implementation
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self.view_seperator = nn.Parameter(torch.randn(n_embed) * embed_std)
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else:
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raise ValueError(
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f"Only 2D tile_tag is supported currently, got: {self.tile_tag}"
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)
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with self._mark_language_model(vllm_config):
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self.language_model = init_vllm_registered_model(
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vllm_config=vllm_config,
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hf_config=self.text_config,
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prefix=maybe_prefix(prefix, "language_model"),
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)
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self.make_empty_intermediate_tensors = (
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self.language_model.make_empty_intermediate_tensors
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)
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def _parse_and_validate_image_input(
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self, **kwargs: object
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) -> DeepseekOCRImagePixelInputs | None:
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pixel_values = kwargs.pop("pixel_values", None)
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images_spatial_crop = kwargs.pop("images_spatial_crop", None)
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images_crop = kwargs.pop("images_crop", None)
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if pixel_values is None or torch.sum(pixel_values).item() == 0:
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return None
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# Use actual tensor spatial dim instead of hardcoded
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# vision_config.image_size (1024). The vision encoders (SAM & CLIP)
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# support arbitrary resolutions via pos-encoding interpolation,
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# so Tiny/Small/Base/Large variants all work with the same weights.
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base_size = pixel_values.shape[-1]
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image_size = images_crop.shape[-1] if images_crop is not None else base_size
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return DeepseekOCRImagePixelInputs(
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type="pixel_values",
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data=pixel_values,
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images_crop=images_crop,
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images_spatial_crop=images_spatial_crop,
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resolve_bindings={
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"base_size": base_size,
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"image_size": image_size,
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},
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)
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def _encode_global_features(self, image_tensor: torch.Tensor) -> torch.Tensor:
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global_features_1 = self.sam_model(image_tensor)
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global_features_2 = self.vision_model(image_tensor, global_features_1)
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features = torch.cat(
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(
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global_features_2[:, 1:],
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global_features_1.flatten(2).permute(0, 2, 1),
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),
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dim=-1,
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)
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features = self.projector(features)
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_, hw, dim = features.shape
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side = int(hw**0.5)
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features = features.view(side, side, dim)
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newline = self.image_newline[None, None, :].expand(side, 1, dim)
|
|
features = torch.cat([features, newline], dim=1)
|
|
return features.view(-1, dim)
|
|
|
|
def _encode_local_features(
|
|
self, patches: torch.Tensor, crop_shape: torch.Tensor
|
|
) -> torch.Tensor | None:
|
|
if torch.sum(patches).item() == 0:
|
|
return None
|
|
|
|
local_features_1 = self.sam_model(patches)
|
|
local_features_2 = self.vision_model(patches, local_features_1)
|
|
features = torch.cat(
|
|
(
|
|
local_features_2[:, 1:],
|
|
local_features_1.flatten(2).permute(0, 2, 1),
|
|
),
|
|
dim=-1,
|
|
)
|
|
features = self.projector(features)
|
|
|
|
return self._assemble_patch_grid(features, crop_shape)
|
|
|
|
def _assemble_patch_grid(
|
|
self, features: torch.Tensor, crop_shape: torch.Tensor
|
|
) -> torch.Tensor:
|
|
"""Assemble projected patches into a 2-D tile grid with newline columns."""
|
|
_, hw, dim = features.shape
|
|
patch_side = int(hw**0.5)
|
|
width_tiles = int(crop_shape[0].item())
|
|
height_tiles = int(crop_shape[1].item())
|
|
|
|
features = (
|
|
features.view(height_tiles, width_tiles, patch_side, patch_side, dim)
|
|
.permute(0, 2, 1, 3, 4)
|
|
.reshape(height_tiles * patch_side, width_tiles * patch_side, dim)
|
|
)
|
|
newline = self.image_newline[None, None, :].expand(
|
|
height_tiles * patch_side, 1, dim
|
|
)
|
|
features = torch.cat([features, newline], dim=1)
|
|
return features.view(-1, dim)
|
|
|
|
def _pixel_values_to_embedding(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
images_crop: torch.Tensor,
|
|
images_spatial_crop: torch.Tensor,
|
|
) -> NestedTensors:
|
|
images_in_this_batch = []
|
|
|
|
is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
|
|
patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
|
|
images_crop = images_crop.split(patches_per_image.tolist())
|
|
for jdx in range(images_spatial_crop.size(0)):
|
|
patches = images_crop[jdx]
|
|
image_ori = pixel_values[[jdx]]
|
|
crop_shape = images_spatial_crop[jdx]
|
|
|
|
global_features = self._encode_global_features(image_ori)
|
|
local_features = self._encode_local_features(patches, crop_shape)
|
|
|
|
if local_features is not None:
|
|
combined = torch.cat(
|
|
[local_features, global_features, self.view_seperator[None, :]],
|
|
dim=0,
|
|
)
|
|
else:
|
|
combined = torch.cat(
|
|
[global_features, self.view_seperator[None, :]], dim=0
|
|
)
|
|
|
|
images_in_this_batch.append(combined)
|
|
|
|
return images_in_this_batch
|
|
|
|
def _process_image_input(
|
|
self, image_input: DeepseekOCRImagePixelInputs
|
|
) -> torch.Tensor:
|
|
pixel_values = image_input.data
|
|
images_crop = image_input.images_crop
|
|
images_spatial_crop = image_input.images_spatial_crop.to(dtype=torch.long)
|
|
|
|
vision_features = self._pixel_values_to_embedding(
|
|
pixel_values=pixel_values,
|
|
images_crop=images_crop,
|
|
images_spatial_crop=images_spatial_crop,
|
|
)
|
|
|
|
return vision_features
|
|
|
|
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
|
|
image_input = self._parse_and_validate_image_input(**kwargs)
|
|
if image_input is None:
|
|
return None
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
return vision_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor | None,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs: object,
|
|
):
|
|
if intermediate_tensors is not None:
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.language_model(
|
|
input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def compute_logits(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
) -> torch.Tensor | None:
|
|
return self.language_model.compute_logits(hidden_states)
|
|
|
|
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
|
loader = AutoWeightsLoader(self)
|
|
autoloaded_weights = loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
|
return autoloaded_weights
|
|
|
|
def get_mm_mapping(self) -> MultiModelKeys:
|
|
"""
|
|
Get the module prefix in multimodal models
|
|
"""
|
|
return MultiModelKeys.from_string_field(
|
|
language_model="language_model",
|
|
connector="projector",
|
|
tower_model=["sam_model", "vision_model"],
|
|
)
|
|
|
|
# -- Fixed spatial constants (computed from BASE_SIZE / IMAGE_SIZE) --
|
|
|
|
@property
|
|
def image_side(self) -> int:
|
|
"""Number of output grid cells per spatial dim for a global image."""
|
|
return math.ceil((BASE_SIZE // 16) / 4) # 16
|
|
|
|
@property
|
|
def global_image_output_token(self) -> int:
|
|
"""Tokens per global image (grid + one newline per row)."""
|
|
return self.image_side * (self.image_side + 1) # 272
|
|
|
|
@property
|
|
def patch_side(self) -> int:
|
|
"""Number of output grid cells per spatial dim for a local patch."""
|
|
return math.ceil((IMAGE_SIZE // 16) / 4) # 10
|
|
|
|
@property
|
|
def single_patch_output_token(self) -> int:
|
|
"""Tokens per local patch (square grid, no newlines)."""
|
|
return self.patch_side * self.patch_side # 100
|
|
|
|
# -- SupportsEncoderCudaGraph protocol methods --
|
|
|
|
def _get_num_input_output_tokens(
|
|
self,
|
|
image_spatial_crop: torch.Tensor | None = None,
|
|
) -> tuple[int, int, int, int]:
|
|
"""
|
|
Return (num_input_tokens, num_output_tokens, global_output_token,
|
|
local_output_token) for a single image described by
|
|
``image_spatial_crop``.
|
|
"""
|
|
is_tiled = False
|
|
if image_spatial_crop is not None:
|
|
is_tiled = image_spatial_crop[0] > 1 or image_spatial_crop[1] > 1
|
|
|
|
# Compute input size:
|
|
global_input_side = BASE_SIZE // 16 # 64
|
|
local_input_side = IMAGE_SIZE // 16 # 40
|
|
num_input_tokens = global_input_side**2
|
|
|
|
if is_tiled:
|
|
num_patches = image_spatial_crop.prod(dim=-1)
|
|
num_input_tokens += num_patches * (local_input_side**2)
|
|
|
|
global_output_token = self.global_image_output_token
|
|
num_output_tokens = global_output_token
|
|
|
|
local_output_token = 0
|
|
if is_tiled:
|
|
local_output_token = num_patches * self.single_patch_output_token
|
|
num_output_tokens += local_output_token
|
|
|
|
return (
|
|
num_input_tokens,
|
|
num_output_tokens,
|
|
global_output_token,
|
|
local_output_token,
|
|
)
|
|
|
|
def get_encoder_cudagraph_config(self):
|
|
return EncoderCudaGraphConfig(
|
|
modalities=["image"],
|
|
buffer_keys=["pixel_values"],
|
|
out_hidden_size=self.projector_config.n_embed,
|
|
enable_dual_path_graph=True,
|
|
global_token_per_image=self.global_image_output_token,
|
|
local_token_per_patch=self.single_patch_output_token,
|
|
)
|
|
|
|
def get_encoder_cudagraph_budget_range(
|
|
self,
|
|
vllm_config,
|
|
) -> tuple[int, int]:
|
|
# Min budget: at least one global image with newline tokens (without patches).
|
|
min_budget = self.global_image_output_token
|
|
max_budget = min(
|
|
vllm_config.scheduler_config.max_num_batched_tokens,
|
|
self.model_config.max_model_len,
|
|
)
|
|
return (min_budget, max_budget)
|
|
|
|
def get_encoder_cudagraph_item_specs(
|
|
self,
|
|
mm_kwargs: dict[str, Any],
|
|
) -> list[EncoderItemSpec]:
|
|
item_specs = []
|
|
for image_spatial_crop in mm_kwargs["images_spatial_crop"]:
|
|
(
|
|
num_input_tokens,
|
|
num_output_tokens,
|
|
global_output_token,
|
|
local_output_token,
|
|
) = self._get_num_input_output_tokens(image_spatial_crop)
|
|
item_specs.append(
|
|
EncoderItemSpec(
|
|
input_size=num_input_tokens,
|
|
output_tokens=num_output_tokens,
|
|
global_output_tokens=global_output_token,
|
|
local_output_tokens=local_output_token,
|
|
)
|
|
)
|
|
return item_specs
|
|
|
|
def select_encoder_cudagraph_items(
|
|
self,
|
|
mm_kwargs: dict[str, Any],
|
|
indices: list[int],
|
|
) -> dict[str, Any]:
|
|
pixel_values = mm_kwargs["pixel_values"]
|
|
images_crop = mm_kwargs["images_crop"]
|
|
images_spatial_crop = mm_kwargs["images_spatial_crop"]
|
|
|
|
if len(indices) == 0:
|
|
return {
|
|
"pixel_values": pixel_values[:0],
|
|
"images_crop": images_crop[:0],
|
|
"images_spatial_crop": images_spatial_crop[:0],
|
|
}
|
|
|
|
is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
|
|
patches_per_image = torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
|
|
cum_patches = [0]
|
|
for num_patches in patches_per_image:
|
|
cum_patches.append(cum_patches[-1] + int(num_patches))
|
|
|
|
selected_pv = pixel_values[indices]
|
|
selected_ic = torch.cat(
|
|
[images_crop[cum_patches[i] : cum_patches[i + 1]] for i in indices]
|
|
)
|
|
selected_sp = images_spatial_crop[indices]
|
|
|
|
return {
|
|
"pixel_values": selected_pv,
|
|
"images_crop": selected_ic,
|
|
"images_spatial_crop": selected_sp,
|
|
}
|
|
|
|
def prepare_encoder_cudagraph_capture_inputs(
|
|
self,
|
|
token_budget: int,
|
|
max_batch_size: int,
|
|
max_frames_per_batch: int,
|
|
device: torch.device,
|
|
dtype: torch.dtype,
|
|
path: str = "default",
|
|
):
|
|
assert path in ("global", "local")
|
|
|
|
if path == "global":
|
|
max_num_images = token_budget // self.global_image_output_token
|
|
max_batch_size = min(max_batch_size, max_num_images)
|
|
dummy_pixel_values = torch.randn(
|
|
max_batch_size,
|
|
3,
|
|
BASE_SIZE,
|
|
BASE_SIZE,
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
values = {"pixel_values": dummy_pixel_values}
|
|
else:
|
|
max_num_patches = token_budget // self.single_patch_output_token
|
|
dummy_images_crop = torch.randn(
|
|
max_num_patches,
|
|
3,
|
|
IMAGE_SIZE,
|
|
IMAGE_SIZE,
|
|
device=device,
|
|
dtype=dtype,
|
|
)
|
|
values = {"images_crop": dummy_images_crop}
|
|
|
|
return EncoderCudaGraphCaptureInputs(values=values)
|
|
|
|
def prepare_encoder_cudagraph_replay_buffers(
|
|
self,
|
|
mm_kwargs: dict[str, Any],
|
|
max_batch_size: int,
|
|
max_frames_per_batch: int,
|
|
path: str = "default",
|
|
):
|
|
assert path in ("global", "local")
|
|
|
|
if path == "global":
|
|
values = {"pixel_values": mm_kwargs["pixel_values"]}
|
|
else:
|
|
values = {"images_crop": mm_kwargs["images_crop"]}
|
|
|
|
return EncoderCudaGraphReplayBuffers(values=values)
|
|
|
|
def _batched_encoder_forward_global_path(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Encode batched global images with newline tokens inserted.
|
|
Output shape: ``[B * 272, n_embed]``.
|
|
"""
|
|
bsz = pixel_values.shape[0]
|
|
global_features_1 = self.sam_model(pixel_values)
|
|
global_features_2 = self.vision_model(pixel_values, global_features_1)
|
|
features = torch.cat(
|
|
(
|
|
global_features_2[:, 1:],
|
|
global_features_1.flatten(2).permute(0, 2, 1),
|
|
),
|
|
dim=-1,
|
|
)
|
|
features = self.projector(features)
|
|
side = self.image_side
|
|
dim = features.shape[-1]
|
|
features = features.view(bsz, side, side, dim)
|
|
newline = self.image_newline.view(1, 1, 1, dim).expand(bsz, side, 1, dim)
|
|
features = torch.cat([features, newline], dim=2)
|
|
return features.view(-1, dim)
|
|
|
|
def _batched_encoder_forward_local_path(
|
|
self,
|
|
images_crop: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Encode local patches without newline insertion (newlines are added later
|
|
in ``postprocess_encoder_output`` via ``_assemble_patch_grid``).
|
|
Output shape: ``[P * 100, n_embed]``.
|
|
"""
|
|
features_1 = self.sam_model(images_crop)
|
|
features_2 = self.vision_model(images_crop, features_1)
|
|
features = torch.cat(
|
|
(
|
|
features_2[:, 1:],
|
|
features_1.flatten(2).permute(0, 2, 1),
|
|
),
|
|
dim=-1,
|
|
)
|
|
features = self.projector(features)
|
|
return features.view(-1, features.shape[-1])
|
|
|
|
def encoder_cudagraph_forward(
|
|
self,
|
|
values: dict[str, torch.Tensor],
|
|
path: str = "default",
|
|
) -> torch.Tensor:
|
|
assert path in ("global", "local")
|
|
|
|
if path == "global":
|
|
pixel_values = values["pixel_values"]
|
|
return self._batched_encoder_forward_global_path(pixel_values)
|
|
else:
|
|
images_crop = values["images_crop"]
|
|
return self._batched_encoder_forward_local_path(images_crop)
|
|
|
|
def encoder_eager_forward(
|
|
self,
|
|
mm_kwargs: dict[str, Any],
|
|
path: str = "default",
|
|
) -> torch.Tensor:
|
|
"""Eager encoder forward with optional per-path execution.
|
|
|
|
``path="default"``: full forward (global + local + assembly).
|
|
``path="global"``: global-only batched forward with newlines.
|
|
``path="local"``: local-only batched forward without newlines.
|
|
"""
|
|
if path == "default":
|
|
# Original eager implementation: process each image one by one
|
|
# (with both global and local paths) and concatenate results.
|
|
image_input = DeepseekOCRImagePixelInputs(
|
|
type="pixel_values",
|
|
data=mm_kwargs["pixel_values"],
|
|
images_crop=mm_kwargs["images_crop"],
|
|
images_spatial_crop=mm_kwargs["images_spatial_crop"],
|
|
)
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
return torch.cat(vision_embeddings, dim=0)
|
|
|
|
assert path in ("global", "local")
|
|
if path == "global":
|
|
pixel_values = mm_kwargs["pixel_values"]
|
|
return self._batched_encoder_forward_global_path(pixel_values)
|
|
else:
|
|
images_crop = mm_kwargs["images_crop"]
|
|
return self._batched_encoder_forward_local_path(images_crop)
|
|
|
|
def postprocess_encoder_output(
|
|
self,
|
|
output: torch.Tensor,
|
|
indices: list[int],
|
|
per_item_out_tokens: list[int],
|
|
dest: dict[int, torch.Tensor] | list[torch.Tensor | None],
|
|
clone: bool = False,
|
|
batch_mm_kwargs: dict[str, Any] | None = None,
|
|
local_output: torch.Tensor | None = None,
|
|
) -> None:
|
|
"""
|
|
Assemble per-image embeddings from global and local encoder outputs.
|
|
|
|
``output`` contains global-image features with newlines already
|
|
inserted (from CUDA graph replay or eager fallback):
|
|
``[B * 272, n_embed]``.
|
|
|
|
``local_output`` contains local-patch features without
|
|
newlines (from CUDA graph replay or eager fallback):
|
|
``[P * 100, n_embed]``. May be ``None`` if no patches in batch.
|
|
|
|
This method:
|
|
1. Splits ``output`` into per-image global portions.
|
|
2. Splits ``local_output`` into per-image patch groups.
|
|
3. For each image: assembles patch grid with newlines via
|
|
``_assemble_patch_grid``, then concatenates
|
|
``[local_tiled, global, view_seperator]``.
|
|
"""
|
|
bsz = len(indices)
|
|
n_embed = output.shape[-1]
|
|
|
|
images_spatial_crop = batch_mm_kwargs["images_spatial_crop"]
|
|
is_tiled = (images_spatial_crop[:, 0] > 1) | (images_spatial_crop[:, 1] > 1)
|
|
num_patches = [
|
|
int(np) for np in torch.where(is_tiled, images_spatial_crop.prod(dim=-1), 0)
|
|
]
|
|
total_patches = sum(num_patches)
|
|
|
|
global_part = output[: bsz * self.global_image_output_token].reshape(
|
|
bsz, self.global_image_output_token, n_embed
|
|
)
|
|
|
|
# Split local output into per-patch groups.
|
|
local_flat = None
|
|
if total_patches > 0 and local_output is not None:
|
|
local_flat = local_output[: total_patches * self.single_patch_output_token]
|
|
local_flat = local_flat.reshape(
|
|
total_patches, self.single_patch_output_token, n_embed
|
|
)
|
|
|
|
cur_patch = 0
|
|
for i, idx in enumerate(indices):
|
|
num_patch = num_patches[i]
|
|
single_image_output: list[torch.Tensor] = []
|
|
|
|
# 1. Process local patches: assemble tile grid, add 1 newline per row.
|
|
if num_patch > 0 and local_flat is not None:
|
|
patches = local_flat[cur_patch : cur_patch + num_patch]
|
|
cur_patch += num_patch
|
|
single_image_output.append(
|
|
self._assemble_patch_grid(patches, images_spatial_crop[i])
|
|
)
|
|
|
|
# 2. Global image: newlines already inserted.
|
|
single_image_output.append(global_part[i])
|
|
|
|
# 3. Add view separator for each image.
|
|
single_image_output.append(self.view_seperator[None, :])
|
|
|
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# 4. Save final outputs for each image.
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dest[idx] = torch.cat(single_image_output, dim=0)
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