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https://git.datalinker.icu/vllm-project/vllm.git
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1741 lines
63 KiB
Python
1741 lines
63 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# --------------------------------------------------------
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# Adapted from
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# https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/internvl.py
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# under Apache-2.0 License
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# LICENSE is in root directory.
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# --------------------------------------------------------
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import copy
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import warnings
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from abc import ABC, abstractmethod
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from collections.abc import Iterable, Mapping, Sequence
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from typing import Annotated, Any, Literal, TypeAlias, TypeVar
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import numpy.typing as npt
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import regex as re
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import torch
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import torch.nn as nn
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import torchvision.transforms as T
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from PIL import Image
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from transformers import BatchFeature, PretrainedConfig, TensorType
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from vllm.config import VllmConfig
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from vllm.config.multimodal import BaseDummyOptions, VideoDummyOptions
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from vllm.model_executor.layers.activation import ReLUSquaredActivation
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.interfaces import (
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HasInnerState,
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IsHybrid,
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MultiModalEmbeddings,
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SupportsMultiModal,
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SupportsMultiModalPruning,
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)
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from vllm.model_executor.models.internvl import (
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calculate_internvl_targets,
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get_internvl_target_ratios,
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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.nemotron_h import NemotronHForCausalLM
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from vllm.model_executor.models.radio import RadioModel
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from vllm.model_executor.models.utils import (
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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.evs import (
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compute_retained_tokens_count,
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compute_retention_mask,
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)
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from vllm.multimodal.inputs import (
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MultiModalDataDict,
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MultiModalFieldConfig,
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MultiModalKwargs,
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MultiModalKwargsItems,
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VideoItem,
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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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MultiModalDataParser,
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)
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from vllm.multimodal.processing import (
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BaseMultiModalProcessor,
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BaseProcessingInfo,
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PromptReplacement,
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PromptUpdate,
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PromptUpdateDetails,
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_seq2tokens,
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)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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from vllm.transformers_utils.configs.radio import RadioConfig
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from vllm.transformers_utils.tokenizer import (
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AnyTokenizer,
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cached_tokenizer_from_config,
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encode_tokens,
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)
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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from .utils import _merge_multimodal_embeddings
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# Configure PIL to handle large images without warnings
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# This prevents DecompressionBombWarning for legitimate large images
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Image.MAX_IMAGE_PIXELS = None # Disable the limit entirely
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# Alternative: Set a specific higher limit
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# Image.MAX_IMAGE_PIXELS = 300000000 # ~300M pixels
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IMG_START = "<img>"
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IMG_END = "</img>"
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IMG_CONTEXT = "<image>"
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# Profiling
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# MAX_FRAMES = 16
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DEFAULT_NUM_TILES = 12
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class NanoNemotronVLImagePixelInputs(TensorSchema):
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"""
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Dimensions:
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- bn: Batch size * number of images
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- bnp: Batch size * number of images * (1 + num_patches)
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- c: Number of channels (3)
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- h: Height of each image patch
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- w: Width of each image patch
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"""
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type: Literal["pixel_values"]
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pixel_values_flat: Annotated[torch.Tensor, TensorShape("bnp", 3, "h", "w")]
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num_patches: Annotated[torch.Tensor, TensorShape("bn")]
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class NanoNemotronVLImageEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- n: Number of images
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- f: Total image feature size
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- h: Hidden size (must match the hidden size of language model backbone)
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"""
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type: Literal["image_embeds"]
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data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]
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NanoNemotronVLImageInputs: TypeAlias = (
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NanoNemotronVLImagePixelInputs | NanoNemotronVLImageEmbeddingInputs
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)
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class NanoNemotronVLVideoPixelInputs(TensorSchema):
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"""
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Dimensions:
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- bvf: Batch size * number of videos * num_frames
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- bn: Batch size * number of videos
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- f: Number of frames
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- c: Number of channels (3)
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- h: Height of each video frame
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- w: Width of each video frame
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"""
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type: Literal["pixel_values_videos"]
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pixel_values_flat: Annotated[torch.Tensor, TensorShape("bvf", 3, "h", "w")]
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num_patches: Annotated[torch.Tensor, TensorShape("bn")]
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frames_indices: Annotated[torch.Tensor, TensorShape("bvf")]
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frame_duration_ms: Annotated[torch.Tensor, TensorShape("bn")]
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class NanoNemotronVLVideoEmbeddingInputs(TensorSchema):
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"""
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Dimensions:
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- n: Number of videos
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- f: Total video feature size
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- h: Hidden size (must match the hidden size of language model backbone)
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"""
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type: Literal["video_embeds"]
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data: Annotated[torch.Tensor | list[torch.Tensor], TensorShape("n", "f", "h")]
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NanoNemotronVLVideoInputs: TypeAlias = (
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NanoNemotronVLVideoPixelInputs | NanoNemotronVLVideoEmbeddingInputs
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)
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def dynamic_preprocess(
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image, *, image_size=512, max_num_tiles=12, use_thumbnail=True, idx=0
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):
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orig_width, orig_height = image.size
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target_ratios = get_internvl_target_ratios(1, max_num_tiles)
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blocks, target_width, target_height = calculate_internvl_targets(
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orig_width=orig_width,
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orig_height=orig_height,
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target_ratios=target_ratios,
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image_size=image_size,
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use_thumbnail=False,
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)
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# resize the image
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resized_img = image.resize((target_width, target_height))
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processed_images = []
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for i in range(blocks):
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box = (
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(i % (target_width // image_size)) * image_size,
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(i // (target_width // image_size)) * image_size,
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((i % (target_width // image_size)) + 1) * image_size,
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((i // (target_width // image_size)) + 1) * image_size,
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)
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# split the image
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split_img = resized_img.crop(box)
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processed_images.append(split_img)
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assert len(processed_images) == blocks
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if use_thumbnail and len(processed_images) != 1:
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thumbnail_img = image.resize((image_size, image_size))
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processed_images.append(thumbnail_img)
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processed_images = [
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img.convert("RGB") if img.mode != "RGB" else img for img in processed_images
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]
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processed_images = [
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T.Resize((image_size, image_size), interpolation=T.InterpolationMode.BICUBIC)(
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img
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)
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for img in processed_images
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]
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processed_images = [T.ToTensor()(img) for img in processed_images]
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return processed_images
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def image_to_pixel_values(
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image: Image.Image,
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*,
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input_size: int,
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max_num: int,
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use_thumbnail: bool,
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idx: int,
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) -> torch.Tensor:
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images = dynamic_preprocess(
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image,
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image_size=input_size,
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max_num_tiles=max_num,
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use_thumbnail=use_thumbnail,
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idx=idx,
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)
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pixel_values = torch.stack(images)
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return pixel_values
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def video_to_pixel_values(
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video: npt.NDArray,
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*,
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input_size: int,
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max_num_tiles: int = 1,
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use_thumbnail: bool,
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) -> torch.Tensor:
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assert max_num_tiles == 1, "Video modality always uses one tile"
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# Convert each frame to a single resized tile tensor consistent
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# with image path
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frames_tensors: list[torch.Tensor] = []
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for frame in video:
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pil_frame = dynamic_preprocess(
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Image.fromarray(frame, mode="RGB"),
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image_size=input_size,
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max_num_tiles=max_num_tiles,
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use_thumbnail=use_thumbnail,
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idx=0,
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)
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# dynamic_preprocess returns tensors already; take the single tile
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assert len(pil_frame) >= 1
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frames_tensors.append(pil_frame[-1])
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return torch.stack(frames_tensors)
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def input_conditioner(x, norm_mean, norm_std):
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return (x - norm_mean) / norm_std
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def calculate_timestamps(
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indices: list[int] | torch.Tensor,
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frame_duration_ms: int,
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):
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if not isinstance(indices, list):
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indices = indices.tolist()
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timestamps = [int(i) * frame_duration_ms / 1000.0 for i in indices]
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return timestamps
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class BaseNanoNemotronVLProcessor(ABC):
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"""
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This model doesn't define its own HF processor,
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so we implement our own one here.
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The code to insert image tokens is based on:
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https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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tokenizer: AnyTokenizer,
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*args,
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max_num_tiles: int | None = None,
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**kwargs,
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) -> None:
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super().__init__()
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self.config = config
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self.tokenizer = tokenizer
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self.max_num_tiles = max_num_tiles or DEFAULT_NUM_TILES
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image_size: int = config.force_image_size
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patch_size: int = config.patch_size
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self.num_image_token = int(
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(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
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)
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self.image_size = image_size
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self.use_thumbnail: bool = config.use_thumbnail
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self.norm_mean = torch.Tensor(config.norm_mean).reshape(1, 3, 1, 1)
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self.norm_std = torch.Tensor(config.norm_std).reshape(1, 3, 1, 1)
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@property
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@abstractmethod
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def image_token_id(self) -> int:
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raise NotImplementedError
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@abstractmethod
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def get_image_repl(
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self,
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feature_size: int,
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num_patches: int | None,
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) -> PromptUpdateDetails[str]:
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raise NotImplementedError
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def get_num_image_tokens(
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self,
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*,
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image_width: int,
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image_height: int,
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max_num_tiles: int,
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) -> int:
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target_ratios = get_internvl_target_ratios(1, max_num_tiles)
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num_patches, _, _ = calculate_internvl_targets(
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orig_width=image_width,
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orig_height=image_height,
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target_ratios=target_ratios,
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image_size=self.image_size,
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use_thumbnail=self.use_thumbnail,
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)
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return num_patches * self.num_image_token
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def _images_to_pixel_values_lst(
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self,
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images: list[Image.Image],
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max_num_tiles: int,
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) -> list[torch.Tensor]:
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return [
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image_to_pixel_values(
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image,
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input_size=self.image_size,
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max_num=max_num_tiles,
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use_thumbnail=self.use_thumbnail,
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idx=idx,
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)
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for idx, image in enumerate(images)
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]
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def _preprocess_image(
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self,
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text: list[str],
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images: list[Image.Image],
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max_num_tiles: int,
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) -> tuple[list[str], dict[str, torch.Tensor]]:
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if len(images) == 0:
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image_inputs = {}
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else:
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pixel_values_lst = self._images_to_pixel_values_lst(images, max_num_tiles)
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image_inputs = {
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"pixel_values_flat": input_conditioner(
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torch.cat(pixel_values_lst), self.norm_mean, self.norm_std
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),
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"image_num_patches": torch.tensor(
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[len(item) for item in pixel_values_lst]
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),
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}
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assert len(text) == 1, (
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"hf_processor is called on the output of get_dummy_text, "
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"which should be a single string"
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)
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parts = [x for x in re.split(r"(<image>)", text[0]) if x]
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assert parts.count("<image>") == len(pixel_values_lst), (
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"the number of <image> tokens in the text should be the "
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"same as the number of images"
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)
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for i, pixel_values in enumerate(pixel_values_lst):
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num_patches = pixel_values.shape[0]
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feature_size = num_patches * self.num_image_token
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image_repl = self.get_image_repl(feature_size, num_patches)
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parts[i] = parts[i].replace("<image>", image_repl.full)
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text = ["".join(parts)]
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return text, image_inputs
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def _make_batch_input(self, input_item: Any | list[Any] | None = None):
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if input_item is None:
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input_item = []
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if not isinstance(input_item, list):
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input_item = [input_item]
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return input_item
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def __call__(
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self,
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text: str | list[str] | None = None,
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images: Image.Image | list[Image.Image] | None = None,
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return_tensors: str | TensorType | None = None,
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max_num_tiles: int | None = None,
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) -> BatchFeature:
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# Use default if not provided
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if max_num_tiles is None:
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max_num_tiles = self.max_num_tiles
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text, images = [self._make_batch_input(x) for x in (text, images)]
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text, image_inputs = self._preprocess_image(
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text=text,
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images=images,
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max_num_tiles=max_num_tiles,
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)
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text_inputs = self.tokenizer(text, add_special_tokens=False)
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combined_outputs = {**text_inputs, **image_inputs}
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return BatchFeature(combined_outputs, tensor_type=return_tensors)
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|
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class NanoNemotronVLProcessor(BaseNanoNemotronVLProcessor):
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"""
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HF Processor with extended video processing logic.
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Code for video processing is adapted from video example:
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https://huggingface.co/OpenGVLab/InternVL3-1B#inference-with-transformers
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"""
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def __init__(
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self,
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config: PretrainedConfig,
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tokenizer: AnyTokenizer,
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*,
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max_num_tiles: int | None = None,
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min_dynamic_patch: int | None = None,
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max_dynamic_patch: int | None = None,
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dynamic_image_size: bool | None = None,
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video_token: str | None = None,
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video_pruning_rate: float | None = None,
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) -> None:
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super().__init__(
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config=config,
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tokenizer=tokenizer,
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max_num_tiles=max_num_tiles,
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min_dynamic_patch=min_dynamic_patch,
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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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# add extra video token for video processing
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self.video_token = video_token
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self.video_pruning_rate = video_pruning_rate
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# Pre-tokenize special tokens for video processing
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# to avoid repeated tokenization
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self._img_start_token_ids = encode_tokens(
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tokenizer, IMG_START, add_special_tokens=False
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)
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self._img_end_token_ids = encode_tokens(
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tokenizer, IMG_END, add_special_tokens=False
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)
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self._img_context_token_ids = encode_tokens(
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tokenizer, IMG_CONTEXT, add_special_tokens=False
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)
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@property
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def supports_video(self) -> bool:
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return self.video_token_id is not None
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@property
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def video_token_id(self) -> int | None:
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if self.video_token is None:
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return None
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return self.tokenizer.get_vocab().get(self.video_token, None)
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@property
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def image_token_id(self) -> int:
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return self.tokenizer.convert_tokens_to_ids(IMG_CONTEXT)
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def _videos_to_pixel_values_lst(
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self,
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videos: list[npt.NDArray],
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max_num_tiles: int,
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dynamic_image_size: bool | None = None,
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) -> list[torch.Tensor]:
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return [
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video_to_pixel_values(
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video,
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input_size=self.image_size,
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max_num_tiles=max_num_tiles,
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use_thumbnail=self.use_thumbnail,
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)
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for video in videos
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]
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def _preprocess_video(
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self,
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text: list[str],
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videos: list[tuple[npt.NDArray, dict[str, Any]]],
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max_num_tiles: int,
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dynamic_image_size: bool | None = None,
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):
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if len(videos) == 0 or not self.supports_video:
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video_inputs = {}
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else:
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videos_lst = [v[0] for v in videos]
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video_metadata_lst = [v[1] for v in videos]
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pixel_values_lst_video = self._videos_to_pixel_values_lst(
|
|
videos_lst,
|
|
max_num_tiles=max_num_tiles,
|
|
dynamic_image_size=dynamic_image_size,
|
|
)
|
|
|
|
# We use frame duration in milliseconds (as integer) to ensure
|
|
# we have consistent timestamps calculation. At preprocessing
|
|
# fps parameter is given in fp32, while at inference it is bf16
|
|
# which leads to inaccurate timestamp calculation and causes
|
|
# timestamp values to differ.In rare cases this causes
|
|
# mismatching number of output tokens for tokenized frame prefixes
|
|
frame_duration_ms_lst = [
|
|
int(1000.0 / metadata["fps"]) for metadata in video_metadata_lst
|
|
]
|
|
frames_indices_lst = [
|
|
metadata["frames_indices"] for metadata in video_metadata_lst
|
|
]
|
|
|
|
video_inputs = {
|
|
"pixel_values_flat_video": input_conditioner(
|
|
torch.cat(pixel_values_lst_video), self.norm_mean, self.norm_std
|
|
),
|
|
"video_num_patches": torch.tensor(
|
|
[len(item) for item in pixel_values_lst_video]
|
|
),
|
|
"frames_indices": frames_indices_lst,
|
|
"frame_duration_ms": torch.tensor(frame_duration_ms_lst),
|
|
}
|
|
|
|
image_size: int = self.config.force_image_size
|
|
patch_size: int = self.config.patch_size
|
|
downsample_ratio = self.config.downsample_ratio
|
|
tokens_in_single_frame = int(
|
|
(image_size * image_size // patch_size**2) * (downsample_ratio**2)
|
|
)
|
|
|
|
for pixel_values, video_metadata, frames_indices, frame_duration_ms in zip(
|
|
pixel_values_lst_video,
|
|
video_metadata_lst,
|
|
frames_indices_lst,
|
|
frame_duration_ms_lst,
|
|
):
|
|
num_frames = pixel_values.shape[0]
|
|
|
|
if (
|
|
self.video_pruning_rate is not None
|
|
and self.video_pruning_rate > 0.0
|
|
):
|
|
# Start of EVS-specific code
|
|
num_tokens = compute_retained_tokens_count(
|
|
tokens_per_frame=tokens_in_single_frame,
|
|
num_frames=num_frames,
|
|
q=self.video_pruning_rate,
|
|
)
|
|
|
|
# Here we just need placeholders that won't actually be replaced -
|
|
# we just need to make sure the total number of tokens is correct
|
|
# assign all tokens to the first frame
|
|
tokens_per_frame = [num_tokens] + [0] * (num_frames - 1)
|
|
|
|
# End of EVS-specific code
|
|
else:
|
|
tokens_per_frame = [tokens_in_single_frame] * num_frames
|
|
|
|
video_repl = self.get_video_repl(
|
|
tokens_per_frame=tokens_per_frame,
|
|
frames_indices=frames_indices,
|
|
frame_duration_ms=frame_duration_ms,
|
|
tokenizer=self.tokenizer,
|
|
img_start_token_ids=self._img_start_token_ids,
|
|
img_end_token_ids=self._img_end_token_ids,
|
|
img_context_token_ids=self._img_context_token_ids,
|
|
)
|
|
|
|
# video_repl.full is a list of token IDs
|
|
# Convert token IDs back to text for the HF processor flow
|
|
video_repl_text = self.tokenizer.decode(
|
|
video_repl.full, skip_special_tokens=False
|
|
)
|
|
text = [t.replace("<video>", video_repl_text, 1) for t in text]
|
|
return text, video_inputs
|
|
|
|
def __call__(
|
|
self,
|
|
text: str | list[str] | None = None,
|
|
images: Image.Image | list[Image.Image] | None = None,
|
|
videos: list[tuple[npt.NDArray, dict[str, Any]]] | None = None,
|
|
return_tensors: str | TensorType | None = None,
|
|
max_num_tiles: int | None = None,
|
|
dynamic_image_size: bool | None = None,
|
|
) -> BatchFeature:
|
|
# Use default if not provided
|
|
if max_num_tiles is None:
|
|
max_num_tiles = self.max_num_tiles
|
|
|
|
text, images, videos = [
|
|
self._make_batch_input(x) for x in (text, images, videos)
|
|
]
|
|
|
|
text, image_inputs = self._preprocess_image(
|
|
text=text,
|
|
images=images,
|
|
max_num_tiles=max_num_tiles,
|
|
)
|
|
|
|
text, video_inputs = self._preprocess_video(
|
|
text=text,
|
|
videos=videos,
|
|
max_num_tiles=1,
|
|
dynamic_image_size=dynamic_image_size,
|
|
)
|
|
|
|
text_inputs = self.tokenizer(text, add_special_tokens=False)
|
|
|
|
combined_outputs = {**text_inputs, **image_inputs, **video_inputs}
|
|
|
|
return BatchFeature(combined_outputs, tensor_type=return_tensors)
|
|
|
|
def get_image_repl(
|
|
self,
|
|
feature_size: int,
|
|
num_patches: int | None,
|
|
) -> PromptUpdateDetails[str]:
|
|
repl_features = IMG_CONTEXT * feature_size
|
|
repl_full = IMG_START + repl_features + IMG_END
|
|
|
|
return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
|
|
|
|
@classmethod
|
|
def get_video_repl(
|
|
cls,
|
|
*,
|
|
tokens_per_frame: list[int],
|
|
frames_indices: list[int],
|
|
frame_duration_ms: int,
|
|
tokenizer: AnyTokenizer,
|
|
img_start_token_ids: list[int],
|
|
img_end_token_ids: list[int],
|
|
img_context_token_ids: list[int],
|
|
) -> PromptUpdateDetails[list[int]]:
|
|
"""
|
|
Build prompt replacement for a video.
|
|
The replacement returned is not actually used to replace the placeholder
|
|
tokens - it's just used to make sure we allocate the correct number
|
|
of tokens.
|
|
Actual replacement is done in embed_multimodal of
|
|
NemotronH_Nano_VL_V2
|
|
(specifically in _process_video_input -> _create_final_video_embeddings).
|
|
There, we create the final embeddings with text embeddings for indicator tokens
|
|
and video embeddings for video tokens.
|
|
This is a single function that handles all cases - non EVS, EVS dummy, EVS real.
|
|
The differentiation is done via tokens_per_frame parameter.
|
|
- non EVS case - constant value same value across all frames
|
|
- EVS dummy - Doesn't matter how tokens are distributed between frames - just
|
|
make sure the total number of tokens is correct.
|
|
- EVS real (called from get_real_video_repl_for_evs) - different value per frame
|
|
Args:
|
|
tokens_per_frame (list[int]): number of tokens per frame
|
|
frames_indices (list[int]): frame indices
|
|
frame_duration_ms (int): duration of each frame in milliseconds
|
|
tokenizer (AnyTokenizer): tokenizer to use for tokenizing frame separators
|
|
img_start_token_ids (list[int]): pre-tokenized IMG_START tokens
|
|
img_end_token_ids (list[int]): pre-tokenized IMG_END tokens
|
|
img_context_token_ids (list[int]): pre-tokenized IMG_CONTEXT tokens
|
|
"""
|
|
# TODO: Add support of frame_duration_ms to be None
|
|
# At preprocessing step we should allow absent / metadata without
|
|
# frames_indices field.
|
|
timestamps_enabled = frame_duration_ms is not None
|
|
|
|
if timestamps_enabled:
|
|
timestamps = calculate_timestamps(frames_indices, frame_duration_ms)
|
|
|
|
assert len(timestamps) == len(tokens_per_frame), (
|
|
"timestamps and tokens_per_frame must have the same length"
|
|
)
|
|
frame_separators = [
|
|
f"Frame {i + 1} sampled at {timestamp:.2f} seconds: "
|
|
for i, timestamp in enumerate(timestamps)
|
|
]
|
|
else:
|
|
frame_separators = [
|
|
f"Frame {i + 1}: " for i, _ in enumerate(tokens_per_frame)
|
|
]
|
|
|
|
# Tokenize frame separator independently
|
|
frame_separators_tokenized = [
|
|
_seq2tokens(tokenizer, sep) for sep in frame_separators
|
|
]
|
|
|
|
# Tokenize each component independently to avoid tokenizer merging tokens
|
|
# across boundaries. This ensures consistent tokenization regardless of
|
|
# num_tokens_per_frame values.
|
|
all_token_ids = []
|
|
for i, num_tokens in enumerate(tokens_per_frame):
|
|
frame_sep_token_ids = frame_separators_tokenized[i]
|
|
all_token_ids.extend(frame_sep_token_ids)
|
|
|
|
# Add pre-tokenized special tokens
|
|
all_token_ids.extend(img_start_token_ids)
|
|
all_token_ids.extend(img_context_token_ids * num_tokens)
|
|
all_token_ids.extend(img_end_token_ids)
|
|
|
|
return PromptUpdateDetails.from_seq(all_token_ids)
|
|
|
|
|
|
class BaseNanoNemotronVLProcessingInfo(BaseProcessingInfo):
|
|
"""Basic image-only ProcessingInfo for InternVL-style models."""
|
|
|
|
@abstractmethod
|
|
def get_hf_processor(
|
|
self,
|
|
**kwargs: object,
|
|
) -> BaseNanoNemotronVLProcessor:
|
|
raise NotImplementedError
|
|
|
|
def get_supported_mm_limits(self) -> Mapping[str, int | None]:
|
|
return {"image": None}
|
|
|
|
def get_num_image_tokens(
|
|
self,
|
|
*,
|
|
image_width: int,
|
|
image_height: int,
|
|
max_num_tiles: int,
|
|
processor: BaseNanoNemotronVLProcessor | None,
|
|
) -> int:
|
|
if processor is None:
|
|
processor = self.get_hf_processor()
|
|
|
|
return processor.get_num_image_tokens(
|
|
image_width=image_width,
|
|
image_height=image_height,
|
|
max_num_tiles=max_num_tiles,
|
|
)
|
|
|
|
def get_image_size_with_most_features(self, max_num_tiles: int) -> ImageSize:
|
|
processor = self.get_hf_processor()
|
|
|
|
base_size = processor.image_size
|
|
target_ratios = get_internvl_target_ratios(1, max_num_tiles)
|
|
|
|
largest_feature_size, largest_feature_pinpoint = 0, None
|
|
for wr, hr in target_ratios:
|
|
width, height = base_size * wr, base_size * hr
|
|
|
|
feat_size = self.get_num_image_tokens(
|
|
image_width=width,
|
|
image_height=height,
|
|
max_num_tiles=max_num_tiles,
|
|
processor=processor,
|
|
)
|
|
if feat_size > largest_feature_size:
|
|
largest_feature_size = feat_size
|
|
largest_feature_pinpoint = ImageSize(width=width, height=height)
|
|
|
|
if largest_feature_size == 0 or largest_feature_pinpoint is None:
|
|
raise ValueError("Cannot have a largest feature size of 0!")
|
|
|
|
return largest_feature_pinpoint
|
|
|
|
def get_max_image_tokens(self) -> int:
|
|
processor = self.get_hf_processor()
|
|
# Use default max_num_tiles for max tokens calculation
|
|
max_num_tiles = processor.max_num_tiles
|
|
target_width, target_height = self.get_image_size_with_most_features(
|
|
max_num_tiles
|
|
)
|
|
|
|
return self.get_num_image_tokens(
|
|
image_width=target_width,
|
|
image_height=target_height,
|
|
max_num_tiles=max_num_tiles,
|
|
processor=processor,
|
|
)
|
|
|
|
|
|
_I = TypeVar("_I", bound=BaseNanoNemotronVLProcessingInfo)
|
|
|
|
|
|
class NanoNemotronVLProcessingInfo(BaseNanoNemotronVLProcessingInfo):
|
|
"""ProcessingInfo extended for video processing"""
|
|
|
|
@property
|
|
def supports_video(self):
|
|
return self.get_hf_processor().supports_video
|
|
|
|
def get_supported_mm_limits(self):
|
|
video_limit = {"video": None} if self.supports_video else {}
|
|
return {**super().get_supported_mm_limits(), **video_limit}
|
|
|
|
def get_video_token(self) -> str | None:
|
|
return IMG_CONTEXT
|
|
|
|
def get_video_pruning_rate(self) -> float | None:
|
|
return self.ctx.get_mm_config().video_pruning_rate
|
|
|
|
def get_num_frames_with_most_features(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
) -> int:
|
|
max_images = mm_counts.get("image", 0)
|
|
max_videos = mm_counts.get("video", 0)
|
|
|
|
processor = self.get_hf_processor() # we get the CustomProcessor here
|
|
|
|
max_image_tokens = self.get_max_image_tokens() * max_images
|
|
max_total_frames = (seq_len - max_image_tokens) // processor.num_image_token
|
|
max_frames_per_video = max_total_frames // max(max_videos, 1)
|
|
return max(max_frames_per_video, 1)
|
|
|
|
def get_hf_processor(self, **kwargs: object) -> NanoNemotronVLProcessor:
|
|
return self.ctx.init_processor(
|
|
NanoNemotronVLProcessor,
|
|
config=self.get_hf_config(),
|
|
tokenizer=self.get_tokenizer(),
|
|
video_token=self.get_video_token(),
|
|
video_pruning_rate=self.get_video_pruning_rate(),
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class NanoNemotronBaseVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
|
|
"""Basic image-only MultiModalProcessor for InternVL-style models."""
|
|
|
|
def _get_mm_fields_config(
|
|
self,
|
|
hf_inputs: BatchFeature,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
) -> Mapping[str, MultiModalFieldConfig]:
|
|
image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
|
|
|
|
return dict(
|
|
pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
|
|
"image", image_num_patches
|
|
),
|
|
image_num_patches=MultiModalFieldConfig.batched("image"),
|
|
image_embeds=MultiModalFieldConfig.batched("image"),
|
|
)
|
|
|
|
def _get_prompt_updates(
|
|
self,
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
out_mm_kwargs: MultiModalKwargs,
|
|
) -> Sequence[PromptUpdate]:
|
|
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
|
|
|
|
if "image_num_patches" in out_mm_kwargs:
|
|
image_num_patches = out_mm_kwargs["image_num_patches"]
|
|
assert isinstance(image_num_patches, torch.Tensor)
|
|
image_num_patches = image_num_patches.tolist()
|
|
elif "image_embeds" in out_mm_kwargs:
|
|
# to compute num_patches (similar to Qwen2-VL)
|
|
image_num_patches = [None] * len(out_mm_kwargs["image_embeds"])
|
|
else:
|
|
image_num_patches = []
|
|
|
|
def get_replacement_custom(item_idx: int):
|
|
images = mm_items.get_items(
|
|
"image", (ImageEmbeddingItems, ImageProcessorItems)
|
|
)
|
|
|
|
if isinstance(images, ImageEmbeddingItems):
|
|
feature_size = images.get_feature_size(item_idx)
|
|
else:
|
|
image_size = images.get_image_size(item_idx)
|
|
# Extract max_num_tiles from kwargs, default to 12
|
|
max_num_tiles = hf_processor_mm_kwargs.get(
|
|
"max_num_tiles", hf_processor.max_num_tiles
|
|
)
|
|
feature_size = self.info.get_num_image_tokens(
|
|
image_width=image_size.width,
|
|
image_height=image_size.height,
|
|
max_num_tiles=max_num_tiles,
|
|
processor=hf_processor,
|
|
)
|
|
|
|
num_patches = None
|
|
local_image_num_patches = image_num_patches
|
|
if isinstance(local_image_num_patches, torch.Tensor):
|
|
local_image_num_patches = local_image_num_patches.tolist()
|
|
if isinstance(local_image_num_patches, (list, tuple)) and item_idx < len(
|
|
local_image_num_patches
|
|
):
|
|
num_patches = int(local_image_num_patches[item_idx])
|
|
|
|
return hf_processor.get_image_repl(feature_size, num_patches)
|
|
|
|
return [
|
|
PromptReplacement(
|
|
modality="image",
|
|
target="<image>",
|
|
replacement=get_replacement_custom,
|
|
)
|
|
]
|
|
|
|
|
|
class NanoNemotronVLMultiModalProcessor(
|
|
NanoNemotronBaseVLMultiModalProcessor[NanoNemotronVLProcessingInfo]
|
|
):
|
|
"""MultiModalProcessor extended for video support"""
|
|
|
|
def _get_data_parser(self) -> MultiModalDataParser:
|
|
return MultiModalDataParser(video_needs_metadata=True)
|
|
|
|
def _get_mm_fields_config(
|
|
self,
|
|
hf_inputs: BatchFeature,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
) -> Mapping[str, MultiModalFieldConfig]:
|
|
image_fields = super()._get_mm_fields_config(hf_inputs, hf_processor_mm_kwargs)
|
|
if self.info.supports_video:
|
|
video_num_patches = hf_inputs.get("video_num_patches", torch.empty(0))
|
|
|
|
video_fields = dict(
|
|
pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
|
|
"video", video_num_patches
|
|
),
|
|
video_num_patches=MultiModalFieldConfig.batched("video"),
|
|
frames_indices=MultiModalFieldConfig.batched("video"),
|
|
frame_duration_ms=MultiModalFieldConfig.batched("video"),
|
|
)
|
|
else:
|
|
video_fields = {}
|
|
|
|
return image_fields | video_fields
|
|
|
|
def _get_prompt_updates(
|
|
self,
|
|
mm_items: MultiModalDataItems,
|
|
hf_processor_mm_kwargs: Mapping[str, object],
|
|
out_mm_kwargs: MultiModalKwargsItems,
|
|
) -> Sequence[PromptUpdate]:
|
|
prompt_repl = super()._get_prompt_updates(
|
|
mm_items=mm_items,
|
|
hf_processor_mm_kwargs=hf_processor_mm_kwargs,
|
|
out_mm_kwargs=out_mm_kwargs,
|
|
)
|
|
|
|
hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
|
|
|
|
out_mm_data = out_mm_kwargs.get_data()
|
|
if "video_num_patches" in out_mm_data:
|
|
video_num_patches = out_mm_data["video_num_patches"]
|
|
assert isinstance(video_num_patches, torch.Tensor)
|
|
video_num_patches = video_num_patches.tolist()
|
|
else:
|
|
video_num_patches = []
|
|
|
|
def get_video_replacement_internvl(item_idx: int):
|
|
feature_size = hf_processor.num_image_token
|
|
video, metadata = mm_items["video"][item_idx]
|
|
num_patches = video_num_patches[item_idx]
|
|
if num_patches is not None:
|
|
assert isinstance(num_patches, int)
|
|
|
|
video_pruning_rate = self.info.ctx.get_mm_config().video_pruning_rate
|
|
if video_pruning_rate is not None and video_pruning_rate > 0.0:
|
|
# Start of EVS-specific code
|
|
num_tokens = compute_retained_tokens_count(
|
|
tokens_per_frame=feature_size,
|
|
num_frames=num_patches,
|
|
q=video_pruning_rate,
|
|
)
|
|
# Here we just need placeholders that won't actually be replaced -
|
|
# we just need to make sure the total number of tokens is correct
|
|
# assign all tokens to the first frame
|
|
tokens_per_frame = [num_tokens] + [0] * (num_patches - 1)
|
|
|
|
# End of EVS-specific code
|
|
else:
|
|
tokens_per_frame = [feature_size] * num_patches
|
|
|
|
frame_duration_ms = int(1000 / metadata["fps"])
|
|
return hf_processor.get_video_repl(
|
|
tokens_per_frame=tokens_per_frame,
|
|
frames_indices=metadata["frames_indices"],
|
|
frame_duration_ms=frame_duration_ms,
|
|
tokenizer=hf_processor.tokenizer,
|
|
img_start_token_ids=hf_processor._img_start_token_ids,
|
|
img_end_token_ids=hf_processor._img_end_token_ids,
|
|
img_context_token_ids=hf_processor._img_context_token_ids,
|
|
)
|
|
|
|
if self.info.supports_video:
|
|
prompt_repl = [
|
|
*prompt_repl,
|
|
PromptReplacement(
|
|
modality="video",
|
|
target="<video>",
|
|
replacement=get_video_replacement_internvl,
|
|
),
|
|
]
|
|
|
|
return prompt_repl
|
|
|
|
|
|
class NanoNemotronVLDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
|
|
"""Basic image-only DummyInputsBuilder for InternVL-style models."""
|
|
|
|
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
|
num_images = mm_counts.get("image", 0)
|
|
|
|
return "<image>" * num_images
|
|
|
|
def get_dummy_mm_data(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
mm_options: Mapping[str, BaseDummyOptions] | None = None,
|
|
) -> MultiModalDataDict:
|
|
# Use default max_num_tiles for dummy data generation
|
|
max_num_tiles = 12
|
|
target_width, target_height = self.info.get_image_size_with_most_features(
|
|
max_num_tiles
|
|
)
|
|
num_images = mm_counts.get("image", 0)
|
|
|
|
image_overrides = mm_options.get("image") if mm_options else None
|
|
|
|
return {
|
|
"image": self._get_dummy_images(
|
|
width=target_width,
|
|
height=target_height,
|
|
num_images=num_images,
|
|
overrides=image_overrides,
|
|
)
|
|
}
|
|
|
|
|
|
class NanoNemotronVLDummyInputsBuilder(
|
|
NanoNemotronVLDummyInputsBuilder[NanoNemotronVLProcessingInfo]
|
|
):
|
|
"""DummyInputsBuilder extended for video support"""
|
|
|
|
def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
|
|
num_videos = mm_counts.get("video", 0)
|
|
|
|
return super().get_dummy_text(mm_counts) + "<video>" * num_videos
|
|
|
|
def _get_dummy_videos(
|
|
self,
|
|
*,
|
|
width: int,
|
|
height: int,
|
|
num_frames: int,
|
|
num_videos: int,
|
|
overrides: VideoDummyOptions | None = None,
|
|
) -> list[VideoItem]:
|
|
video = super()._get_dummy_videos(
|
|
width=width,
|
|
height=height,
|
|
num_frames=num_frames,
|
|
num_videos=1,
|
|
overrides=overrides,
|
|
)[0]
|
|
video_items = []
|
|
for _ in range(num_videos):
|
|
video_metadata = {
|
|
"total_num_frames": num_frames,
|
|
"fps": 2,
|
|
"duration": num_frames / 2.0,
|
|
"video_backend": "opencv_dynamic",
|
|
"frames_indices": [i for i in range(num_frames)],
|
|
"do_sample_frames": False,
|
|
}
|
|
video_item = (video.copy(), video_metadata)
|
|
video_items.append(video_item)
|
|
|
|
return video_items
|
|
|
|
def get_dummy_mm_data(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
mm_options: Mapping[str, BaseDummyOptions] | None = None,
|
|
) -> MultiModalDataDict:
|
|
dummy_image = super().get_dummy_mm_data(
|
|
seq_len=seq_len, mm_counts=mm_counts, mm_options=mm_options
|
|
)
|
|
if self.info.supports_video:
|
|
config = self.info.get_hf_config()
|
|
image_size: int = config.force_image_size
|
|
target_num_frames = self.info.get_num_frames_with_most_features(
|
|
seq_len, mm_counts
|
|
)
|
|
num_videos = mm_counts.get("video", 0)
|
|
video_overrides = mm_options.get("video") if mm_options else None
|
|
dummy_video = {
|
|
"video": self._get_dummy_videos(
|
|
width=image_size,
|
|
height=image_size,
|
|
num_frames=target_num_frames,
|
|
num_videos=num_videos,
|
|
overrides=video_overrides,
|
|
)
|
|
}
|
|
else:
|
|
dummy_video = {}
|
|
return {**dummy_image, **dummy_video}
|
|
|
|
|
|
@MULTIMODAL_REGISTRY.register_processor(
|
|
NanoNemotronVLMultiModalProcessor,
|
|
info=NanoNemotronVLProcessingInfo,
|
|
dummy_inputs=NanoNemotronVLDummyInputsBuilder,
|
|
)
|
|
class NemotronH_Nano_VL_V2(
|
|
nn.Module, HasInnerState, IsHybrid, SupportsMultiModal, SupportsMultiModalPruning
|
|
):
|
|
merge_by_field_config = True
|
|
|
|
@classmethod
|
|
def get_placeholder_str(cls, modality: str, i: int) -> str | None:
|
|
if modality.startswith("image"):
|
|
return "<image>"
|
|
if modality.startswith("video"):
|
|
return "<video>"
|
|
return None
|
|
|
|
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
|
super().__init__()
|
|
config = vllm_config.model_config.hf_config
|
|
multimodal_config = vllm_config.model_config.multimodal_config
|
|
image_size = config.force_image_size
|
|
patch_size = config.patch_size
|
|
self.patch_size = patch_size
|
|
self.template = config.template
|
|
self.num_image_token = int(
|
|
(image_size // patch_size) ** 2 * (config.downsample_ratio**2)
|
|
)
|
|
self.downsample_ratio = config.downsample_ratio
|
|
self.ps_version = config.ps_version
|
|
self.image_tag_type = config.image_tag_type
|
|
self.video_pruning_rate = multimodal_config.video_pruning_rate
|
|
self.language_model = init_vllm_registered_model(
|
|
vllm_config=vllm_config,
|
|
hf_config=config.text_config,
|
|
prefix=maybe_prefix(prefix, "language_model"),
|
|
)
|
|
self.vision_model = self.get_vit_model_from_radio_config(config).to(
|
|
self.language_model.config.dtype
|
|
)
|
|
|
|
# Construct the vision projection.
|
|
vit_hidden_size = config.vit_hidden_size
|
|
vision_projection_hidden_size = config.projector_hidden_size
|
|
llm_hidden_size = config.text_config.hidden_size
|
|
|
|
self.mlp1 = nn.Sequential(
|
|
RMSNorm(
|
|
hidden_size=vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
|
|
eps=1e-5,
|
|
),
|
|
nn.Linear(
|
|
vit_hidden_size * int(1 / self.downsample_ratio) ** 2,
|
|
vision_projection_hidden_size,
|
|
bias=False,
|
|
),
|
|
ReLUSquaredActivation(),
|
|
nn.Linear(vision_projection_hidden_size, llm_hidden_size, bias=False),
|
|
)
|
|
self.mlp1 = self.mlp1.to(self.language_model.config.dtype)
|
|
|
|
self.config = config
|
|
self.model_config = vllm_config.model_config
|
|
|
|
# Pre-tokenize special tokens for video processing
|
|
# to avoid repeated tokenization
|
|
tokenizer = cached_tokenizer_from_config(vllm_config.model_config)
|
|
self._img_start_token_ids = encode_tokens(
|
|
tokenizer, IMG_START, add_special_tokens=False
|
|
)
|
|
self._img_end_token_ids = encode_tokens(
|
|
tokenizer, IMG_END, add_special_tokens=False
|
|
)
|
|
self._img_context_token_ids = encode_tokens(
|
|
tokenizer, IMG_CONTEXT, add_special_tokens=False
|
|
)
|
|
|
|
def pixel_shuffle(self, x, scale_factor=0.5):
|
|
n, w, h, c = x.size()
|
|
# N, W, H, C --> N, W, H * scale, C // scale
|
|
x = x.view(
|
|
n,
|
|
w,
|
|
int(h * scale_factor),
|
|
int(c / scale_factor),
|
|
)
|
|
# N, W, H * scale, C // scale --> N, H * scale, W, C // scale
|
|
x = x.permute(0, 2, 1, 3).contiguous()
|
|
# N, H * scale, W, C // scale -->
|
|
# N, H * scale, W * scale, C // (scale ** 2)
|
|
x = x.view(
|
|
n,
|
|
int(h * scale_factor),
|
|
int(w * scale_factor),
|
|
int(c / (scale_factor * scale_factor)),
|
|
)
|
|
if self.ps_version == "v1":
|
|
warnings.warn(
|
|
"In ps_version 'v1', the height and width have not "
|
|
"been swapped back, which results in a transposed image.",
|
|
stacklevel=2,
|
|
)
|
|
else:
|
|
x = x.permute(0, 2, 1, 3).contiguous()
|
|
return x
|
|
|
|
def extract_feature(self, pixel_values):
|
|
# Process images in a micro-batch of at most 128 frames per call
|
|
# This is done on purpose to ensure peak GPU ram usage of huge batch
|
|
# (namely for really long videos with EVS ON) won't cause any problems
|
|
# as we don't support chunked prefill for video media
|
|
micro_batch_size = 128
|
|
n = pixel_values.shape[0]
|
|
vit_embeds_list = []
|
|
for i in range(0, n, micro_batch_size):
|
|
vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
|
|
vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
|
|
h = w = int(vit_embeds.shape[1] ** 0.5)
|
|
vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
|
|
vit_embeds = self.pixel_shuffle(
|
|
vit_embeds, scale_factor=self.downsample_ratio
|
|
)
|
|
vit_embeds = vit_embeds.reshape(
|
|
vit_embeds.shape[0], -1, vit_embeds.shape[-1]
|
|
)
|
|
vit_embeds = self.mlp1(vit_embeds)
|
|
vit_embeds_list.append(vit_embeds)
|
|
|
|
vit_embeds = torch.cat(vit_embeds_list, dim=0)
|
|
return vit_embeds
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object
|
|
) -> NanoNemotronVLImageInputs | None:
|
|
pixel_values_flat = kwargs.pop("pixel_values_flat", None)
|
|
image_num_patches = kwargs.pop("image_num_patches", None)
|
|
image_embeds = kwargs.pop("image_embeds", None)
|
|
|
|
if pixel_values_flat is None and image_embeds is None:
|
|
return None
|
|
|
|
if image_embeds is not None:
|
|
return NanoNemotronVLImageEmbeddingInputs(
|
|
type="image_embeds",
|
|
data=image_embeds,
|
|
)
|
|
|
|
if pixel_values_flat is not None:
|
|
return NanoNemotronVLImagePixelInputs(
|
|
type="pixel_values",
|
|
pixel_values_flat=pixel_values_flat,
|
|
num_patches=image_num_patches,
|
|
)
|
|
|
|
raise AssertionError("This line should be unreachable.")
|
|
|
|
def _process_image_input(
|
|
self, image_input: NanoNemotronVLImageInputs
|
|
) -> tuple[torch.Tensor, ...]:
|
|
if image_input["type"] == "image_embeds":
|
|
return image_input["data"]
|
|
|
|
assert self.vision_model is not None
|
|
|
|
image_embeds = self.extract_feature(image_input["pixel_values_flat"])
|
|
num_patches = image_input["num_patches"]
|
|
|
|
# Only one image in the current batch
|
|
if len(num_patches) == 1:
|
|
return (image_embeds.view(-1, self.config.text_config.hidden_size),)
|
|
|
|
# NOTE: Image embeddings are split into separate tensors for each image
|
|
# by the size of each embedding.
|
|
feature_size = image_embeds.shape[1]
|
|
image_embeds = image_embeds.view(-1, self.config.text_config.hidden_size)
|
|
image_feature_sizes = [
|
|
num_patches * feature_size for num_patches in num_patches
|
|
]
|
|
return image_embeds.split(image_feature_sizes)
|
|
|
|
def _process_video_input(
|
|
self, video_input: NanoNemotronVLVideoPixelInputs
|
|
) -> tuple[torch.Tensor, ...]:
|
|
"""Process video input and create final embeddings with video content
|
|
and indicator tokens."""
|
|
# Get video embeddings using the same processing as images
|
|
video_embeddings = self._process_image_input(video_input)
|
|
|
|
final_video_embeddings: tuple[torch.Tensor, ...] = ()
|
|
|
|
image_rows = image_cols = self.config.force_image_size
|
|
downsample_ratio = self.config.downsample_ratio
|
|
patch_size = self.config.patch_size
|
|
rows = int(image_rows * downsample_ratio // patch_size)
|
|
cols = int(image_cols * downsample_ratio // patch_size)
|
|
video_pruning_rate = self.video_pruning_rate
|
|
video_num_frames = video_input["num_patches"].tolist()
|
|
video_frames_indices = video_input["frames_indices"].split(video_num_frames)
|
|
# Calculate video feature dimensions (number of frames and
|
|
# their feature size (AKA tokens per frame))
|
|
# TODO: Maybe this can be optimized to avoid the loop?
|
|
for i, single_video_embeddings in enumerate(video_embeddings):
|
|
num_frames = video_num_frames[i]
|
|
frames_indices = video_frames_indices[i].tolist()
|
|
frame_duration_ms = video_input["frame_duration_ms"][i].item()
|
|
assert single_video_embeddings.shape[0] % num_frames == 0
|
|
|
|
if video_pruning_rate is not None and video_pruning_rate > 0.0:
|
|
# Start of EVS-specific code
|
|
retention_mask = compute_retention_mask(
|
|
single_video_embeddings,
|
|
video_size_thw=(num_frames, rows, cols),
|
|
spatial_merge_size=1,
|
|
q=video_pruning_rate,
|
|
)
|
|
|
|
# apply retention mask
|
|
single_video_embeddings = single_video_embeddings[retention_mask]
|
|
|
|
# calculate the actual number of retained tokens per frame
|
|
retention_mask_thw = retention_mask.reshape(num_frames, rows, cols)
|
|
num_tokens_per_frame = (
|
|
retention_mask_thw.sum(dim=(1, 2)).long().tolist()
|
|
)
|
|
# End of EVS-specific code
|
|
else:
|
|
feature_size = single_video_embeddings.shape[0] // num_frames
|
|
num_tokens_per_frame = [feature_size] * num_frames
|
|
|
|
final_video_embeddings += (
|
|
self._create_final_video_embeddings(
|
|
single_video_embeddings,
|
|
num_tokens_per_frame,
|
|
frames_indices,
|
|
frame_duration_ms,
|
|
),
|
|
)
|
|
|
|
return final_video_embeddings
|
|
|
|
def _create_final_video_embeddings(
|
|
self,
|
|
video_embeddings: torch.Tensor,
|
|
num_tokens_per_frame: list[int],
|
|
frames_indices: list[int],
|
|
frame_duration_ms: int,
|
|
) -> torch.Tensor:
|
|
"""Create final embeddings that combine video embeddings with
|
|
text embeddings of indicator tokens.
|
|
|
|
These final embeddings contain:
|
|
- Actual video embeddings in positions corresponding to video content
|
|
- Text embeddings for indicator tokens (<img>, </img>, and
|
|
frame separation text) in their respective positions
|
|
|
|
These embeddings will replace the placeholder embeddings to create
|
|
input_embeds for the LLM.
|
|
"""
|
|
device = video_embeddings.device
|
|
tokenizer = cached_tokenizer_from_config(self.model_config)
|
|
|
|
# Generate video replacement token IDs using get_video_repl
|
|
# This tokenizes each frame separator independently, then uses pre-tokenized
|
|
# special tokens to ensure consistent tokenization regardless of
|
|
# num_tokens_per_frame values.
|
|
video_repl = NanoNemotronVLProcessor.get_video_repl(
|
|
tokens_per_frame=num_tokens_per_frame,
|
|
frames_indices=frames_indices,
|
|
frame_duration_ms=frame_duration_ms,
|
|
tokenizer=tokenizer,
|
|
img_start_token_ids=self._img_start_token_ids,
|
|
img_end_token_ids=self._img_end_token_ids,
|
|
img_context_token_ids=self._img_context_token_ids,
|
|
)
|
|
|
|
# video_repl.full is a list of token IDs
|
|
repl_token_ids = torch.tensor(video_repl.full, device=device)
|
|
|
|
# Get embedding token IDs for image context (use pre-tokenized version)
|
|
embed_token_ids = torch.tensor(self._img_context_token_ids, device=device)
|
|
|
|
# Create mask for video embedding positions
|
|
is_video_embed = torch.isin(repl_token_ids, embed_token_ids)
|
|
|
|
# Create final video embeddings, merging text embeddings for indicator
|
|
# tokens with video embeddings
|
|
text_embeddings = self.get_language_model().embed_input_ids(repl_token_ids)
|
|
final_video_embeddings = _merge_multimodal_embeddings(
|
|
inputs_embeds=text_embeddings,
|
|
multimodal_embeddings=video_embeddings,
|
|
is_multimodal=is_video_embed,
|
|
)
|
|
|
|
return final_video_embeddings
|
|
|
|
def _parse_and_validate_video_input(
|
|
self, **kwargs: object
|
|
) -> NanoNemotronVLVideoPixelInputs | None:
|
|
pixel_values_flat_video = kwargs.pop("pixel_values_flat_video", None)
|
|
video_num_patches = kwargs.pop("video_num_patches", None)
|
|
video_embeds = kwargs.pop("video_embeds", None)
|
|
frames_indices = kwargs.pop("frames_indices", None)
|
|
frame_duration_ms = kwargs.pop("frame_duration_ms", None)
|
|
|
|
if pixel_values_flat_video is None and video_embeds is None:
|
|
return None
|
|
|
|
if video_embeds is not None:
|
|
return NanoNemotronVLVideoEmbeddingInputs(
|
|
type="video_embeds",
|
|
data=video_embeds,
|
|
)
|
|
|
|
if pixel_values_flat_video is not None:
|
|
if torch.is_tensor(frames_indices):
|
|
frames_indices = frames_indices.flatten()
|
|
else:
|
|
frames_indices = torch.cat([f.flatten() for f in frames_indices], dim=0)
|
|
|
|
frame_duration_ms = frame_duration_ms.flatten()
|
|
expected_h = expected_w = self.config.force_image_size
|
|
num_frames = video_num_patches[0].item()
|
|
resolve_bindings = {"h": expected_h, "w": expected_w, "f": num_frames}
|
|
|
|
return NanoNemotronVLVideoPixelInputs(
|
|
type="pixel_values_videos",
|
|
pixel_values_flat=pixel_values_flat_video,
|
|
num_patches=video_num_patches,
|
|
frames_indices=frames_indices,
|
|
frame_duration_ms=frame_duration_ms,
|
|
resolve_bindings=resolve_bindings,
|
|
)
|
|
|
|
raise AssertionError("This line should be unreachable.")
|
|
|
|
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
|
|
modalities = {}
|
|
# Preserve the order of modalities if there are multiple of them
|
|
# from the order of kwargs.
|
|
for input_key in kwargs:
|
|
if (
|
|
input_key in ("pixel_values_flat", "image_embeds")
|
|
and "images" not in modalities
|
|
):
|
|
modalities["images"] = self._parse_and_validate_image_input(**kwargs)
|
|
if input_key in ("pixel_values_flat_video",) and "videos" not in modalities:
|
|
modalities["videos"] = self._parse_and_validate_video_input(**kwargs)
|
|
|
|
return modalities
|
|
|
|
def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
|
|
# Validate the multimodal input keyword arguments
|
|
modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
|
|
if modalities is None:
|
|
return []
|
|
|
|
# # The result multimodal_embeddings is tuple of tensors, with each
|
|
# tensor correspoending to a multimodal data item (image or video).
|
|
multimodal_embeddings: tuple[torch.Tensor, ...] = ()
|
|
|
|
# NOTE: It is important to iterate over the keys in this dictionary
|
|
# to preserve the order of the modalities.
|
|
for modality in modalities:
|
|
if modality == "images":
|
|
image_input = modalities["images"]
|
|
image_embeddings = self._process_image_input(image_input)
|
|
multimodal_embeddings += tuple(image_embeddings)
|
|
if modality == "videos":
|
|
video_input = modalities["videos"]
|
|
video_embeddings = self._process_video_input(video_input)
|
|
multimodal_embeddings += tuple(video_embeddings)
|
|
|
|
return multimodal_embeddings
|
|
|
|
def get_language_model(self) -> torch.nn.Module:
|
|
return self.language_model
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**kwargs: object,
|
|
) -> torch.Tensor | IntermediateTensors:
|
|
if intermediate_tensors is not None:
|
|
input_ids = None
|
|
inputs_embeds = None
|
|
|
|
hidden_states = self.language_model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
**kwargs,
|
|
)
|
|
|
|
return hidden_states
|
|
|
|
def get_mm_mapping(self) -> MultiModelKeys:
|
|
"""
|
|
Get the module prefix in multimodal models
|
|
"""
|
|
return MultiModelKeys.from_string_field(
|
|
language_model="language_model",
|
|
connector="mlp1",
|
|
tower_model="vision_model",
|
|
)
|
|
|
|
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]]):
|
|
adapter_dict = dict(self.mlp1.named_parameters())
|
|
|
|
def is_llm(name: str) -> bool:
|
|
return name.startswith("language_model")
|
|
|
|
def is_adapter_weights(weight: tuple[str, torch.Tensor]):
|
|
return weight[0].startswith("mlp1")
|
|
|
|
def is_vision_weights(name: str) -> bool:
|
|
return name.startswith("vision_model.radio_model.")
|
|
|
|
# Separate weights by component
|
|
llm_weights = []
|
|
vision_weights = []
|
|
|
|
for name, w in weights:
|
|
if is_llm(name):
|
|
# Strip 'language_model.' prefix for LLM weights
|
|
llm_weights.append((".".join(name.split(".")[1:]), w))
|
|
elif is_adapter_weights((name, w)):
|
|
# Load vision-language adapter weights directly
|
|
trimmed_name = ".".join(name.split(".")[1:])
|
|
param = adapter_dict[trimmed_name]
|
|
with torch.no_grad():
|
|
default_weight_loader(param, w)
|
|
elif is_vision_weights(name):
|
|
# Convert: vision_model.radio_model.* → radio_model.*
|
|
hf_key = name[len("vision_model.") :] # Remove "vision_model." prefix
|
|
vision_weights.append((hf_key, w))
|
|
|
|
self.language_model.load_weights(llm_weights)
|
|
self.vision_model.load_weights(vision_weights)
|
|
|
|
def print_architecture(self, detailed: bool = True, save_to_file: str = None):
|
|
"""
|
|
Print model architecture with parameter names, shapes, and sizes.
|
|
|
|
Args:
|
|
detailed: If True, show detailed parameter breakdown
|
|
save_to_file: If provided, save output to this file path
|
|
"""
|
|
import sys
|
|
from io import StringIO
|
|
|
|
# Capture output if saving to file
|
|
original_stdout = sys.stdout
|
|
if save_to_file:
|
|
sys.stdout = StringIO()
|
|
|
|
try:
|
|
print("=" * 100)
|
|
print("NemotronH_Nano_VL_V2 Model Architecture")
|
|
print("=" * 100)
|
|
|
|
total_params = 0
|
|
param_groups = {
|
|
"language_model": [],
|
|
"vision_model": [],
|
|
"mlp1": [],
|
|
"other": [],
|
|
}
|
|
|
|
for name, param in self.named_parameters():
|
|
param_size = param.numel()
|
|
total_params += param_size
|
|
|
|
# Group parameters by main component
|
|
if name.startswith("language_model"):
|
|
param_groups["language_model"].append(
|
|
(name, param.shape, param_size, param.dtype)
|
|
)
|
|
elif name.startswith("vision_model"):
|
|
param_groups["vision_model"].append(
|
|
(name, param.shape, param_size, param.dtype)
|
|
)
|
|
elif name.startswith("mlp1"):
|
|
param_groups["mlp1"].append(
|
|
(name, param.shape, param_size, param.dtype)
|
|
)
|
|
else:
|
|
param_groups["other"].append(
|
|
(name, param.shape, param_size, param.dtype)
|
|
)
|
|
|
|
if detailed:
|
|
print(
|
|
f"{name:<70} | Shape: {str(param.shape):<25} | "
|
|
f"Size: {param_size:>12,} | Dtype: {param.dtype}"
|
|
)
|
|
|
|
print("=" * 100)
|
|
print("Summary by Component:")
|
|
print("-" * 60)
|
|
|
|
for component, params in param_groups.items():
|
|
if params: # Only show components that have parameters
|
|
component_total = sum(size for _, _, size, _ in params)
|
|
percentage = (
|
|
(component_total / total_params) * 100
|
|
if total_params > 0
|
|
else 0
|
|
)
|
|
print(
|
|
f"{component:<20} | Parameters: {len(params):>4} | "
|
|
f"Total Size: {component_total:>15,} | "
|
|
f"{percentage:>6.2f}%"
|
|
)
|
|
|
|
print("-" * 60)
|
|
print(f"{'Total Parameters':<20} | {total_params:>15,}")
|
|
|
|
# Estimate memory usage (assuming bfloat16 = 2 bytes per parameter)
|
|
memory_mb = total_params * 2 / (1024**2)
|
|
memory_gb = memory_mb / 1024
|
|
print(f"{'Est. Memory (MB)':<20} | {memory_mb:>15.2f}")
|
|
print(f"{'Est. Memory (GB)':<20} | {memory_gb:>15.2f}")
|
|
print("=" * 100)
|
|
|
|
# Save to file if requested
|
|
if save_to_file:
|
|
output = sys.stdout.getvalue()
|
|
sys.stdout = original_stdout
|
|
with open(save_to_file, "w") as f:
|
|
f.write(output)
|
|
print(f"Architecture saved to: {save_to_file}")
|
|
print(output) # Also print to console
|
|
|
|
finally:
|
|
if save_to_file and sys.stdout != original_stdout:
|
|
sys.stdout = original_stdout
|
|
|
|
def get_model_info(self):
|
|
"""
|
|
Get basic model information as a dictionary.
|
|
"""
|
|
total_params = sum(p.numel() for p in self.parameters())
|
|
|
|
component_info = {}
|
|
for name, param in self.named_parameters():
|
|
component = name.split(".")[0]
|
|
if component not in component_info:
|
|
component_info[component] = {"params": 0, "size": 0}
|
|
component_info[component]["params"] += 1
|
|
component_info[component]["size"] += param.numel()
|
|
|
|
return {
|
|
"model_name": "NemotronH_Nano_VL_V2",
|
|
"total_parameters": total_params,
|
|
"memory_estimate_mb": total_params * 2 / (1024**2), # bfloat16
|
|
"components": component_info,
|
|
"config": {
|
|
"image_size": getattr(self.config, "force_image_size", None),
|
|
"patch_size": getattr(self.config, "patch_size", None),
|
|
"num_image_token": self.num_image_token,
|
|
"downsample_ratio": self.downsample_ratio,
|
|
},
|
|
}
|
|
|
|
def get_vit_model_from_radio_config(self, hf_config):
|
|
hf_config_vision = hf_config.vision_config
|
|
model_name = hf_config_vision.args.get("model")
|
|
if model_name is None:
|
|
raise ValueError(f"Unsupported vit model type: {model_name}")
|
|
|
|
preferred_resolution = getattr(hf_config_vision, "preferred_resolution", None)
|
|
image_size = preferred_resolution[0] if preferred_resolution else 224
|
|
patch_size = getattr(hf_config_vision, "patch_size", 16)
|
|
|
|
radio_config = RadioConfig(
|
|
model_name=model_name,
|
|
image_size=image_size,
|
|
patch_size=patch_size,
|
|
norm_mean=hf_config.norm_mean,
|
|
norm_std=hf_config.norm_std,
|
|
reg_tokens=(
|
|
hf_config_vision.args.get("register_multiple")
|
|
if hasattr(hf_config_vision, "args")
|
|
and isinstance(hf_config_vision.args, dict)
|
|
else None
|
|
),
|
|
)
|
|
|
|
return RadioModel(config=radio_config)
|
|
|
|
def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
|
|
return self.language_model.mamba_cache.copy_inputs_before_cuda_graphs(
|
|
input_buffers, **kwargs
|
|
)
|
|
|
|
def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
|
|
return self.language_model.mamba_cache.get_seqlen_agnostic_capture_inputs(
|
|
batch_size
|
|
)
|
|
|
|
@classmethod
|
|
def get_mamba_state_shape_from_config(cls, vllm_config: "VllmConfig"):
|
|
text_config = vllm_config.model_config.hf_config.text_config
|
|
temp_vllm_config = copy.deepcopy(vllm_config)
|
|
temp_vllm_config.model_config.hf_config = text_config
|
|
return NemotronHForCausalLM.get_mamba_state_shape_from_config(temp_vllm_config)
|
|
|
|
@classmethod
|
|
def get_mamba_state_dtype_from_config(cls, vllm_config: "VllmConfig"):
|
|
text_config = vllm_config.model_config.hf_config.text_config
|
|
temp_vllm_config = copy.deepcopy(vllm_config)
|
|
temp_vllm_config.model_config.hf_config = text_config
|
|
return NemotronHForCausalLM.get_mamba_state_dtype_from_config(temp_vllm_config)
|