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[Model] Remove unnecessary processor definition for Nemotron Parse (#37456)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-03-19 02:25:13 +08:00
committed by GitHub
parent f3732bd931
commit 7476d148db
3 changed files with 0 additions and 259 deletions
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12
vllm/model_executor/models/nemotron_parse.py
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@@ -55,7 +55,6 @@ from vllm.multimodal.processing import (
)
from vllm.renderers import TokenizeParams
from vllm.transformers_utils.configs.radio import RadioConfig
from vllm.transformers_utils.processors.nemotron_parse import NemotronParseProcessor
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from vllm.v1.attention.backend import AttentionType
@@ -367,17 +366,6 @@ class NemotronParsePixelInputs(TensorSchema):
class NemotronParseProcessingInfo(BaseProcessingInfo):
def get_hf_config(self):
return self.ctx.get_hf_config()
def get_hf_processor(self, **kwargs) -> NemotronParseProcessor:
return self.ctx.init_processor(
NemotronParseProcessor,
config=self.get_hf_config(),
tokenizer=self.get_tokenizer(),
**kwargs,
)
def get_default_tok_params(self) -> TokenizeParams:
return super().get_default_tok_params().with_kwargs(add_special_tokens=False)

2
vllm/transformers_utils/processors/__init__.py
View File

@@ -25,7 +25,6 @@ __all__ = [
"MistralCommonPixtralProcessor",
"MistralCommonVoxtralProcessor",
"NanoNemotronVLProcessor",
"NemotronParseProcessor",
"NemotronVLProcessor",
"LlamaNemotronVLEmbedProcessor",
"NVLMProcessor",
@@ -50,7 +49,6 @@ _CLASS_TO_MODULE: dict[str, str] = {
"MistralCommonPixtralProcessor": "vllm.transformers_utils.processors.pixtral",
"MistralCommonVoxtralProcessor": "vllm.transformers_utils.processors.voxtral",
"NanoNemotronVLProcessor": "vllm.transformers_utils.processors.nano_nemotron_vl",
"NemotronParseProcessor": "vllm.transformers_utils.processors.nemotron_parse",
"NemotronVLProcessor": "vllm.transformers_utils.processors.nemotron_vl",
"LlamaNemotronVLEmbedProcessor": "vllm.transformers_utils.processors.nemotron_vl",
"NVLMProcessor": "vllm.transformers_utils.processors.nvlm_d",

245
vllm/transformers_utils/processors/nemotron_parse.py
View File

@@ -1,245 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
#
# Adapted from https://github.com/amalad/vllm/blob/nemotron_parse/vllm/model_executor/models/nemotron_parse.py
# that's based on https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1/blob/main/hf_nemotron_parse_modeling.py
from typing import TypeVar
import numpy as np
import torch
from PIL import Image
from timm.data.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
from torchvision import transforms as T
from transformers import BatchFeature, PretrainedConfig, TensorType
from vllm.tokenizers import TokenizerLike
_T = TypeVar("_T")
DEFAULT_FINAL_IMAGE_SIZE = (2048, 1648)
class NemotronParseImageProcessor:
"""
NemotronParse Image Processor
"""
def __init__(
self,
final_size: tuple = DEFAULT_FINAL_IMAGE_SIZE,
**kwargs,
):
# Ensure final_size is properly formatted
if isinstance(final_size, (list, tuple)) and len(final_size) >= 2:
self.final_size = (int(final_size[0]), int(final_size[1]))
elif isinstance(final_size, (int, float)):
self.final_size = (int(final_size), int(final_size))
else:
self.final_size = DEFAULT_FINAL_IMAGE_SIZE # Default fallback
self.norm_mean = torch.Tensor(OPENAI_CLIP_MEAN).reshape(1, 3, 1, 1)
self.norm_std = torch.Tensor(OPENAI_CLIP_STD).reshape(1, 3, 1, 1)
# Create transforms
self._create_transforms()
def _create_transforms(self):
"""Create transform objects."""
try:
import albumentations as A
except ImportError as err:
raise ImportError(
"The package `albumentations` is required to use "
"NemotronParse model. Please install it with `pip install "
"albumentations`."
) from err
# Ensure final_size is a tuple of integers
if isinstance(self.final_size, (list, tuple)):
self.target_height, self.target_width = (
int(self.final_size[0]),
int(self.final_size[1]),
)
else:
self.target_height = self.target_width = int(self.final_size)
import cv2
self.transform = A.Compose(
[
A.PadIfNeeded(
min_height=self.target_height,
min_width=self.target_width,
border_mode=cv2.BORDER_CONSTANT,
fill=[255, 255, 255],
p=1.0,
),
]
)
self.torch_transform = T.Compose(
[
T.ToTensor(),
]
)
def _resize_with_aspect_ratio(self, image: np.ndarray) -> np.ndarray:
"""Resize image maintaining aspect ratio (exact replica of original
LongestMaxSizeHW)."""
height, width = image.shape[:2]
max_size_height = self.target_height
max_size_width = self.target_width
# Original LongestMaxSizeHW algorithm from custom_augmentations.py
aspect_ratio = width / height
new_height = height
new_width = width
# If height too big then scale image down
if height > max_size_height:
new_height = max_size_height
new_width = int(new_height * aspect_ratio)
# If width too big, scale image down further
if new_width > max_size_width:
new_width = max_size_width
new_height = int(new_width / aspect_ratio)
# Use cv2.INTER_LINEAR like the original
import cv2
return cv2.resize(
image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
)
def _pad_to_size(self, image: np.ndarray) -> np.ndarray:
"""Pad image to target size with white padding (matches A.PadIfNeeded
behavior)."""
h, w = image.shape[:2]
min_height, min_width = self.target_height, self.target_width
# Only pad if image is smaller than target (matches A.PadIfNeeded logic)
pad_h = max(0, min_height - h)
pad_w = max(0, min_width - w)
if pad_h == 0 and pad_w == 0:
return image
# A.PadIfNeeded pads to bottom-right with constant value
if len(image.shape) == 3:
# Color image - pad bottom and right with white (255, 255, 255)
padded = np.pad(
image,
((0, pad_h), (0, pad_w), (0, 0)),
mode="constant",
constant_values=255,
)
else:
# Grayscale image - pad with white (255)
padded = np.pad(
image, ((0, pad_h), (0, pad_w)), mode="constant", constant_values=255
)
return padded
def preprocess(
self,
images: Image.Image | list[Image.Image],
**kwargs,
) -> dict[str, torch.Tensor]:
"""
Preprocess an image or batch of images for the NemotronParse model.
Args:
images: Input image(s)
"""
# Ensure images is a list
if not isinstance(images, list):
images = [images]
# Convert PIL images to numpy arrays if needed
processed_images = []
for image in images:
if isinstance(image, Image.Image):
image = np.asarray(image)
processed_images.append(image)
# Apply NemotronParse-specific transforms
pixel_values = []
for image in processed_images:
# Manual resize with aspect ratio preservation
# (replaces LongestMaxSizeHW)
processed_image = self._resize_with_aspect_ratio(image)
# Apply remaining albumentations transforms if available
if self.transform is not None:
transformed = self.transform(image=processed_image)
processed_image = transformed["image"]
else:
# Fallback: just pad to target size
processed_image = self._pad_to_size(processed_image)
# Convert to tensor
pixel_values_tensor = self.torch_transform(processed_image)
# Handle grayscale images
if pixel_values_tensor.shape[0] == 1:
pixel_values_tensor = pixel_values_tensor.expand(3, -1, -1)
pixel_values.append(pixel_values_tensor)
# Stack into batch
pixel_values = torch.stack(pixel_values)
# Normalize pixel values
normalized_values = (pixel_values - self.norm_mean) / self.norm_std
return {"pixel_values": normalized_values}
def __call__(
self, images: Image.Image | list[Image.Image], **kwargs
) -> dict[str, torch.Tensor]:
return self.preprocess(images, **kwargs)
class NemotronParseProcessor:
"""
NemotronParse Processor
"""
def __init__(
self,
config: PretrainedConfig,
tokenizer: TokenizerLike,
**kwargs,
) -> None:
super().__init__()
self.config = config
self.tokenizer = tokenizer
self.image_processor = NemotronParseImageProcessor(final_size=config.image_size)
def _make_batch_input(self, input_item: _T | list[_T] | None = None) -> list[_T]:
if input_item is None:
input_item = []
if not isinstance(input_item, list):
input_item = [input_item]
return input_item
def __call__(
self,
text: str | list[str] | None = None,
images: Image.Image | list[Image.Image] | None = None,
return_tensors: str | TensorType | None = None,
**kwargs,
) -> BatchFeature:
text = self._make_batch_input(text)
images = self._make_batch_input(images)
image_inputs = {} if len(images) == 0 else self.image_processor(images)
text_inputs = self.tokenizer(text, add_special_tokens=False, **kwargs)
combined_outputs = BatchFeature(
data={**text_inputs, **image_inputs},
tensor_type=return_tensors,
)
return combined_outputs