EngineX-MetaX/enginex-c_series-vllm
9
1
Fork 1
Code Issues Pull Requests Actions Projects Releases Wiki Activity

[gpt-oss] Add gpt-oss bf16 support

Browse Source
This commit is contained in:
wangjing
2025-08-13 21:25:57 +08:00
parent 5d2e7edf78
commit 17ea2ec6aa
1232 changed files with 777 additions and 36 deletions
Download Patch File Download Diff File Expand all files Collapse all files

420
vllm/transformers_utils/processors/ovis.py Normal file
View File

@@ -0,0 +1,420 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# yapf: disable
# ruff: noqa: E501
# coding=utf-8
# adapted from https://github.com/AIDC-AI/Ovis/blob/35ab51a1a1e3542fa6db260a1084cefbc8f164bb/ovis/vllm/processing_ovis.py
# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import cached_property
from typing import Union
import PIL
import torch
from transformers import AutoProcessor, BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import (ProcessingKwargs, ProcessorMixin,
Unpack)
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from vllm.multimodal.image import convert_image_mode
__all__ = ['OvisProcessor']
IGNORE_ID = -100
class OvisProcessorKwargs(ProcessingKwargs, total=False): # type: ignore[call-arg]
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {
'max_partition':9,
'covering_threshold':0.9,
'convert_to_rgb':True,
'return_tensors':'pt'},
}
class OvisProcessor(ProcessorMixin):
r"""
Constructs a Ovis processor which wraps a Ovis image processor and a Qwen2 tokenizer into a single processor.
[`OvisProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
[`~OvisProcessor.__call__`] and [`~OvisProcessor.decode`] for more information.
Args:
image_processor ([`Qwen2VLImageProcessor`], *optional*):
The image processor is a required input.
tokenizer ([`Qwen2TokenizerFast`], *optional*):
The tokenizer is a required input.
chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
in a chat into a tokenizable string.
"""
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["chat_template", "image_pad_token", "image_segement_len"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor=None,
tokenizer=None,
chat_template=None,
image_pad_token=None,
image_segment_len=255,
**kwargs,
):
self.image_token = "<image>"
self.image_pad_token = image_pad_token
self.image_segment_len = image_segment_len
super().__init__(image_processor, tokenizer, chat_template=chat_template)
@cached_property
def extra_special_tokens(self):
image_pad_token_id = self.tokenizer.get_vocab()[self.image_pad_token]
extra_special_tokens = {
"image_token": -200,
"image_atom": -300,
"image_start": -301,
"image_prefix": -302,
"image_col_sep": -303,
"image_row_sep": -304,
"image_end": -305,
'image_pad': image_pad_token_id,
}
return extra_special_tokens
def __call__(
self,
images: ImageInput = None,
text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
**kwargs: Unpack[OvisProcessorKwargs],
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are supported.
text (`str`, `list[str]`, `list[list[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
- **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
- **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
- **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
- **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
"""
output_kwargs = self._merge_kwargs(
OvisProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
# Process all images first
image_features = {}
if images is not None:
processed_images = []
image_placeholders_list = []
grids = []
# Process each image
for image in images if isinstance(images, list) else [images]:
pixel_values, image_placeholders, grid = self.preprocess_image(
image=image, **output_kwargs["images_kwargs"]
)
processed_images.append(pixel_values)
image_placeholders_list.append(image_placeholders)
grids.append(grid)
# assign all processed images
if processed_images:
image_features["image_placeholders"] = image_placeholders_list
# Process text input
if text is not None:
if not isinstance(text, list):
text = [text]
tokenized_batched_text = self._tokenize_with_image_symbol(text)
image_token_id = self.get_token_value("image_token")
replaced_ids_list = []
idx = 0
for ids_tensor in tokenized_batched_text:
if image_token_id in ids_tensor and "image_placeholders" in image_features:
if idx < len(image_features["image_placeholders"]):
# Converts in list for ease of use
ids_list = ids_tensor.tolist()
new_ids = []
# replace placeholders
for i, token_id in enumerate(ids_list):
if token_id == image_token_id:
placeholder_ids = image_features["image_placeholders"][idx]
new_ids.extend(placeholder_ids)
idx += 1
else:
new_ids.append(token_id)
# Converts back to tensors
ids_tensor = torch.tensor(new_ids, dtype=torch.long)
else:
raise RuntimeError(
'Mismatch between the images you provided and the number of placeholder present in the text')
replaced_ids_list.append(ids_tensor)
if replaced_ids_list:
replaced_and_tokenized_ids = torch.stack(replaced_ids_list)
else:
replaced_and_tokenized_ids = torch.tensor([], dtype=torch.long)
# Create the output with text features
output = BatchFeature(
data={
"input_ids": replaced_and_tokenized_ids,
}
)
# Add image features if present
if image_features:
output["pixel_values"] = processed_images
output['grids'] = grids
return output
# If only images were provided
return BatchFeature(data=image_features)
def _tokenize_with_image_symbol(self, text_list: list[str]) -> torch.LongTensor:
batch_token_ids = []
for text in text_list:
text_chunks = [self.tokenizer(chunk, add_special_tokens=False).input_ids for chunk in
text.split(self.image_token)]
token_ids = []
num_chuck = len(text_chunks)
for i, chunk in enumerate(text_chunks):
token_ids.extend(chunk)
if i < num_chuck - 1:
token_ids.append(self.get_token_value("image_token"))
batch_token_ids.append(token_ids)
return torch.tensor(batch_token_ids, dtype=torch.long)
def get_image_size(self):
size = self.image_processor.size
if 'shortest_edge' in size:
width = height = size['shortest_edge']
elif "height" in size and "width" in size:
width = size['width']
height = size['height']
else:
raise ValueError( "Can't parse image size from image_processor config.")
return height, width
def get_token_value(self, tok):
return self.extra_special_tokens[tok]
def construct_image_indicators(self, grid):
image_placeholders = [self.get_token_value('image_start'),
self.get_token_value('image_atom'),
self.get_token_value('image_prefix')]
if grid[0] * grid[1] > 1:
for r in range(grid[0]):
for c in range(grid[1]):
image_placeholders.append(self.get_token_value('image_atom') )
if c < grid[1] - 1:
image_placeholders.append(self.get_token_value('image_col_sep'))
if r < grid[0] - 1:
image_placeholders.append(self.get_token_value('image_row_sep'))
image_placeholders.append(self.get_token_value('image_end'))
return image_placeholders
def construct_image_placeholders(self, grid):
image_placeholders = self.construct_image_indicators(grid)
image_atom_token_id = self.get_token_value('image_atom')
# Extract the padding token ID from tokenizer
image_padding_token_id = self.get_token_value('image_pad')
# Create a new list with padding tokens inserted
padded_placeholder_tokens = []
for token in image_placeholders:
padded_placeholder_tokens.append(image_padding_token_id)
if token == image_atom_token_id:
padded_placeholder_tokens.extend([image_padding_token_id] * self.image_segment_len)
return padded_placeholder_tokens
def preprocess_image(self, image: PIL.Image.Image, max_partition, covering_threshold, convert_to_rgb, return_tensors):
def _preprocess(img: PIL.Image.Image, side):
# first resize and preprocess
w, h = img.size
if w == h:
new_width = new_height = side
elif w > h:
new_width = side
new_height = int(h / w * new_width)
else:
new_height = side
new_width = int(w / h * new_height)
new_size = dict(height=new_height, width=new_width)
pixel_values = self.image_processor.preprocess(img, size=new_size, return_tensors=return_tensors)['pixel_values']
# then pad to square
square_values = torch.zeros([1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device)
new_height, new_width = pixel_values.shape[2:]
if new_height == new_width:
square_values[:, :, :, :] = pixel_values
elif new_height > new_width:
from_index = (side - new_width) // 2
square_values[:, :, :, from_index:from_index + new_width] = pixel_values
else:
from_index = (side - new_height) // 2
square_values[:, :, from_index:from_index + new_height, :] = pixel_values
return square_values
def _partition(img, grid) -> list[tuple[int, int, int, int]]:
w, h = img.size
row_height = h // grid[0]
col_width = w // grid[1]
partition = []
for row in range(grid[0]):
for col in range(grid[1]):
left = col * col_width
upper = row * row_height
right = w if col == grid[1] - 1 else (col + 1) * col_width
lower = h if row == grid[0] - 1 else (row + 1) * row_height
partition.append((left, upper, right, lower))
return partition
def _covering_area(left, upper, right, lower, side):
w = right - left
h = lower - upper
w, h = max(w, h), min(w, h)
if w > side:
h = h / w * side
w = side
return w * h
def _get_best_grid(img, side):
img_area = img.size[0] * img.size[1]
candidate_grids = []
for i in range(1, max_partition + 1):
for j in range(1, max_partition + 1):
if i * j <= max_partition:
candidate_grids.append((i, j))
all_grids = []
good_grids = []
for grid in candidate_grids:
partition = _partition(img, grid)
covering_ratio = sum([_covering_area(*p, side) for p in partition]) / img_area
assert covering_ratio <= 1.0
all_grids.append((grid, covering_ratio))
if covering_ratio > covering_threshold:
good_grids.append((grid, covering_ratio))
if len(good_grids) > 0:
# pick the good partition with minimum #sub_images and break the tie using covering_ratio
return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0]
else:
# pick the partition with maximum covering_ratio and break the tie using #sub_images
return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
if convert_to_rgb:
image = convert_image_mode(image, 'RGB')
sides = self.get_image_size()
if sides[0] != sides[1]:
raise ValueError('get_image_size() returns non-square size')
side = sides[0]
grid = _get_best_grid(image, side)
partition = _partition(image, grid)
crops = [image.crop(p) for p in partition]
if len(crops) > 1:
crops.insert(0, image)
pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0)
image_placeholders = self.construct_image_placeholders(grid)
return pixel_values, image_placeholders, grid
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def post_process_image_text_to_text(self, generated_outputs):
"""
Post-process the output of the model to decode the text.
Args:
generated_outputs (`torch.Tensor` or `np.ndarray`):
The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
or `(sequence_length,)`.
Returns:
`list[str]`: The decoded text.
"""
return self.tokenizer.batch_decode(
generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
return names_from_processor + ["second_per_grid_ts"]
AutoProcessor.register("OvisProcessor", OvisProcessor)