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79aa244678
Signed-off-by: wwl2755 <wangwenlong2755@gmail.com> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
1366 lines
50 KiB
Python
1366 lines
50 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, Optional, TypedDict, TypeVar, Union
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import numpy.typing as npt
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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
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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 (HasInnerState, IsHybrid,
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MultiModalEmbeddings,
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SupportsMultiModal)
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from vllm.model_executor.models.internvl import (calculate_internvl_targets,
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get_internvl_target_ratios)
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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 (flatten_bn,
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init_vllm_registered_model,
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maybe_prefix)
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
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MultiModalKwargs, MultiModalKwargsItems)
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from vllm.multimodal.parse import (ImageEmbeddingItems, ImageProcessorItems,
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ImageSize, MultiModalDataItems)
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from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo, PromptReplacement,
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PromptUpdate, PromptUpdateDetails)
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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 AnyTokenizer
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from vllm.utils.tensor_schema import TensorSchema, TensorShape
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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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class NanoNemotronVLImagePixelInputs(TypedDict):
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type: Literal["pixel_values"]
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pixel_values_flat: torch.Tensor
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"""
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Shape:
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`(batch_size * num_images * (1 + num_patches), num_channels, height, width)`
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"""
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num_patches: torch.Tensor
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"""Shape: `(batch_size * num_images)`"""
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class NanoNemotronVLImageEmbeddinInputs(TypedDict):
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type: Literal["image_embeds"]
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data: Union[torch.Tensor, list[torch.Tensor]]
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"""
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A tensor of shape `(num_images, total_image_feature_size, hidden_size)`
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or a list of tensors of shape `(total_image_feature_size, hidden_size)`
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`hidden_size` must match the hidden size of language model backbone.
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"""
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NanoNemotronVLImageInputs = Union[NanoNemotronVLImagePixelInputs,
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NanoNemotronVLImageEmbeddinInputs]
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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 images
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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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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[Union[torch.Tensor, list[torch.Tensor]],
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TensorShape("n", "f", "h")]
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NanoNemotronVLVideoInputs = Union[NanoNemotronVLVideoPixelInputs,
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NanoNemotronVLVideoEmbeddingInputs]
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def dynamic_preprocess(image,
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*,
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image_size=512,
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max_num_tiles=12,
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use_thumbnail=True,
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idx=0):
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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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# 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
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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),
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interpolation=T.InterpolationMode.BICUBIC)(img)
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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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# 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[0])
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return torch.stack(frames_tensors)
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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__(self, config: PretrainedConfig, tokenizer: AnyTokenizer,
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*args, **kwargs) -> 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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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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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: Optional[int],
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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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) 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(
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images, max_num_tiles)
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image_inputs = {
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"pixel_values_flat":
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torch.cat(pixel_values_lst),
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"image_num_patches":
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torch.tensor([len(item) for item in pixel_values_lst]),
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}
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for pixel_values in 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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text = [t.replace('<image>', image_repl.full, 1) for t in text]
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return text, image_inputs
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def _make_batch_input(self,
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input_item: Optional[Union[Any, list[Any]]] = 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: Optional[Union[str, list[str]]] = None,
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images: Optional[Union[Image.Image, list[Image.Image]]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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max_num_tiles: Optional[int] = 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 = 12
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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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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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min_dynamic_patch: Optional[int] = None,
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max_dynamic_patch: Optional[int] = None,
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dynamic_image_size: Optional[bool] = None,
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video_token: Optional[str] = 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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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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@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) -> Optional[int]:
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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: Optional[bool] = 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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) 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[npt.NDArray],
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max_num_tiles: int,
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dynamic_image_size: Optional[bool] = 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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pixel_values_lst_video = self._videos_to_pixel_values_lst(
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videos,
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max_num_tiles=max_num_tiles,
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dynamic_image_size=dynamic_image_size,
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)
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video_inputs = {
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"pixel_values_flat_video":
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torch.cat(pixel_values_lst_video),
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"video_num_patches":
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torch.tensor([len(item) for item in pixel_values_lst_video]),
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}
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for pixel_values in pixel_values_lst_video:
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num_patches = pixel_values.shape[0]
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video_repl = self.get_video_repl(self.num_image_token,
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num_patches, self.video_token)
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text = [t.replace('<video>', video_repl.full, 1) for t in text]
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return text, video_inputs
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def __call__(
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self,
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text: Optional[Union[str, list[str]]] = None,
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images: Optional[Union[Image.Image, list[Image.Image]]] = None,
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videos: Optional[Union[npt.NDArray, list[npt.NDArray]]] = None,
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return_tensors: Optional[Union[str, TensorType]] = None,
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max_num_tiles: Optional[int] = None,
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dynamic_image_size: Optional[bool] = 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 = 12
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text, images, videos = [
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self._make_batch_input(x) for x in (text, images, videos)
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]
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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, video_inputs = self._preprocess_video(
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text=text,
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videos=videos,
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max_num_tiles=max_num_tiles,
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dynamic_image_size=dynamic_image_size,
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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, **video_inputs}
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return BatchFeature(combined_outputs, tensor_type=return_tensors)
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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: Optional[int],
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) -> PromptUpdateDetails[str]:
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repl_features = IMG_CONTEXT * feature_size
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repl_full = IMG_START + repl_features + IMG_END
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return PromptUpdateDetails.select_text(repl_full, IMG_CONTEXT)
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|
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def get_video_repl(
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self,
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feature_size: int,
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num_patches: Optional[int] = None,
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video_context_token: str = IMG_CONTEXT,
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) -> PromptUpdateDetails[str]:
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repl_features = video_context_token * self.num_image_token
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repl_features_with_sep = IMG_START + repl_features + IMG_END
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# num_patches is equal to num_frames
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repl_full = ''.join([
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f'Frame{i+1}: {repl_features_with_sep}' for i in range(num_patches)
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])
|
|
|
|
return PromptUpdateDetails.select_text(repl_full, video_context_token)
|
|
|
|
|
|
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, Optional[int]]:
|
|
return {"image": None}
|
|
|
|
def get_num_image_tokens(
|
|
self,
|
|
*,
|
|
image_width: int,
|
|
image_height: int,
|
|
max_num_tiles: int,
|
|
processor: Optional[BaseNanoNemotronVLProcessor],
|
|
) -> 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 = 12
|
|
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) -> Optional[str]:
|
|
return IMG_CONTEXT
|
|
|
|
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)
|
|
|
|
max_frames_per_video = min(max_frames_per_video, MAX_FRAMES)
|
|
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(),
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class NanoNemotronBaseVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
|
|
"""Basic image-only MultiModalProcessor for InternVL-style models."""
|
|
|
|
def _call_hf_processor(
|
|
self,
|
|
prompt: str,
|
|
mm_data: Mapping[str, object],
|
|
mm_kwargs: Mapping[str, object],
|
|
tok_kwargs: Mapping[str, object],
|
|
) -> BatchFeature:
|
|
processed_outputs = super()._call_hf_processor(
|
|
prompt=prompt,
|
|
mm_data=mm_data,
|
|
mm_kwargs=mm_kwargs,
|
|
tok_kwargs=tok_kwargs,
|
|
)
|
|
|
|
hf_processor = self.info.get_hf_processor(**mm_kwargs)
|
|
image_token_id = hf_processor.image_token_id
|
|
|
|
# Since there may be extra tokens in the feature placeholders,
|
|
# we need to pass the image token ID to the model to select the
|
|
# tokens to merge from the vision encoder outputs
|
|
processed_outputs["image_token_id"] = torch.tensor(image_token_id)
|
|
|
|
return processed_outputs
|
|
|
|
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))
|
|
num_images = len(image_num_patches)
|
|
|
|
return dict(
|
|
pixel_values_flat=MultiModalFieldConfig.flat_from_sizes(
|
|
"image", image_num_patches),
|
|
image_num_patches=MultiModalFieldConfig.batched("image"),
|
|
image_embeds=MultiModalFieldConfig.batched("image"),
|
|
image_token_id=MultiModalFieldConfig.shared("image", num_images),
|
|
)
|
|
|
|
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", 12)
|
|
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 _call_hf_processor(
|
|
self,
|
|
prompt: str,
|
|
mm_data: Mapping[str, object],
|
|
mm_kwargs: Mapping[str, object],
|
|
tok_kwargs: Mapping[str, object],
|
|
) -> BatchFeature:
|
|
processed_outputs = super()._call_hf_processor(prompt, mm_data,
|
|
mm_kwargs, tok_kwargs)
|
|
|
|
hf_processor = self.info.get_hf_processor(**mm_kwargs)
|
|
if self.info.supports_video and (
|
|
video_token_id := hf_processor.video_token_id) is not None:
|
|
processed_outputs["video_token_id"] = torch.tensor(video_token_id)
|
|
return processed_outputs
|
|
|
|
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))
|
|
num_videos = len(video_num_patches)
|
|
video_fields = dict(
|
|
pixel_values_flat_video=MultiModalFieldConfig.flat_from_sizes(
|
|
"video", video_num_patches),
|
|
video_num_patches=MultiModalFieldConfig.batched("video"),
|
|
video_token_id=MultiModalFieldConfig.shared(
|
|
"video", num_videos))
|
|
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
|
|
num_patches = video_num_patches[item_idx]
|
|
if num_patches is not None:
|
|
assert isinstance(num_patches, int)
|
|
|
|
return hf_processor.get_video_repl(
|
|
feature_size,
|
|
num_patches,
|
|
video_context_token=hf_processor.video_token)
|
|
|
|
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: Optional[Mapping[str, BaseDummyOptions]] = 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_mm_data(
|
|
self,
|
|
seq_len: int,
|
|
mm_counts: Mapping[str, int],
|
|
mm_options: Optional[Mapping[str, BaseDummyOptions]] = 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):
|
|
|
|
@classmethod
|
|
def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
|
|
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
|
|
|
|
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.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.torch_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.torch_dtype)
|
|
|
|
self.img_context_token_id = None
|
|
self.video_context_token_id = None
|
|
self.config = config
|
|
|
|
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):
|
|
vit_embeds = self.vision_model(pixel_values)
|
|
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)
|
|
return vit_embeds
|
|
|
|
def _parse_and_validate_image_input(
|
|
self, **kwargs: object) -> Optional[NanoNemotronVLImageInputs]:
|
|
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:
|
|
if not isinstance(image_embeds, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image embeddings. "
|
|
f"Got type: {type(image_embeds)}")
|
|
|
|
return NanoNemotronVLImageEmbeddinInputs(
|
|
type="image_embeds",
|
|
data=flatten_bn(image_embeds),
|
|
)
|
|
|
|
image_token_id = kwargs["image_token_id"]
|
|
assert isinstance(image_token_id, torch.Tensor)
|
|
self.img_context_token_id = image_token_id.flatten().unique().item()
|
|
|
|
if pixel_values_flat is not None:
|
|
if not isinstance(pixel_values_flat, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values_flat)}")
|
|
|
|
if not isinstance(image_num_patches, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image_num_patches. "
|
|
f"Got type: {type(image_num_patches)}")
|
|
|
|
pixel_values_flat = flatten_bn(pixel_values_flat, concat=True)
|
|
image_num_patches = flatten_bn(image_num_patches, concat=True)
|
|
|
|
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) -> 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 _parse_and_validate_video_input(
|
|
self,
|
|
**kwargs: object) -> Optional[NanoNemotronVLVideoPixelInputs]:
|
|
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)
|
|
|
|
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=flatten_bn(video_embeds),
|
|
)
|
|
|
|
video_token_id = kwargs["video_token_id"]
|
|
assert isinstance(video_token_id, torch.Tensor)
|
|
self.video_context_token_id = video_token_id.flatten().unique().item()
|
|
|
|
if pixel_values_flat_video is not None:
|
|
if not isinstance(pixel_values_flat_video, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of pixel values. "
|
|
f"Got type: {type(pixel_values_flat_video)}")
|
|
|
|
if not isinstance(video_num_patches, (torch.Tensor, list)):
|
|
raise ValueError("Incorrect type of image_num_patches. "
|
|
f"Got type: {type(video_num_patches)}")
|
|
|
|
pixel_values_flat_video = flatten_bn(pixel_values_flat_video,
|
|
concat=True)
|
|
video_num_patches = flatten_bn(video_num_patches, concat=True)
|
|
expected_h = expected_w = self.config.force_image_size
|
|
resolve_bindings = {"h": expected_h, "w": expected_w}
|
|
|
|
return NanoNemotronVLVideoPixelInputs(
|
|
type="pixel_values_videos",
|
|
pixel_values_flat=pixel_values_flat_video,
|
|
num_patches=video_num_patches,
|
|
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 get_multimodal_embeddings(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"]
|
|
vision_embeddings = self._process_image_input(image_input)
|
|
multimodal_embeddings += vision_embeddings
|
|
if modality == "videos":
|
|
video_input = modalities["videos"]
|
|
video_embeddings = self._process_image_input(video_input)
|
|
multimodal_embeddings += 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: Optional[IntermediateTensors] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
**kwargs: object,
|
|
) -> Union[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,
|
|
) -> Optional[torch.Tensor]:
|
|
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)
|