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Unverified Commit f8f9244a authored by Adarsh Shirawalmath's avatar Adarsh Shirawalmath Committed by GitHub
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[Bug Fix] Add partial rotary factor support for Phi-4 and upgrade to transformers v4.50.0 (#3984) 


Co-authored-by: default avatarChayenne <zhaochen20@outlook.com>
parent ecbfe58b
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python/pyproject.toml
View file @ f8f9244a
...@@ -35,7 +35,7 @@ runtime_common = [ ...@@ -35,7 +35,7 @@ runtime_common = [
"python-multipart", "python-multipart",
"pyzmq>=25.1.2", "pyzmq>=25.1.2",
"torchao>=0.7.0", "torchao>=0.7.0",
"transformers==4.48.3", "transformers==4.50.0",
"uvicorn", "uvicorn",
"uvloop", "uvloop",
"xgrammar==0.1.16", "xgrammar==0.1.16",
......
python/sglang/srt/configs/__init__.py
View file @ f8f9244a
...@@ -2,21 +2,12 @@ from sglang.srt.configs.chatglm import ChatGLMConfig ...@@ -2,21 +2,12 @@ from sglang.srt.configs.chatglm import ChatGLMConfig
from sglang.srt.configs.dbrx import DbrxConfig from sglang.srt.configs.dbrx import DbrxConfig
from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config from sglang.srt.configs.deepseekvl2 import DeepseekVL2Config
from sglang.srt.configs.exaone import ExaoneConfig from sglang.srt.configs.exaone import ExaoneConfig
from sglang.srt.configs.gemma3 import Gemma3Config, Gemma3TextConfig
from sglang.srt.configs.janus_pro import MultiModalityConfig from sglang.srt.configs.janus_pro import MultiModalityConfig
from sglang.srt.configs.qwen2_5_vl_config import (
Qwen2_5_VLConfig,
Qwen2_5_VLVisionConfig,
)
__all__ = [ __all__ = [
"ExaoneConfig", "ExaoneConfig",
"ChatGLMConfig", "ChatGLMConfig",
"DbrxConfig", "DbrxConfig",
"DeepseekVL2Config", "DeepseekVL2Config",
"Qwen2_5_VLConfig",
"Qwen2_5_VLVisionConfig",
"MultiModalityConfig", "MultiModalityConfig",
"Gemma3Config",
"Gemma3TextConfig",
] ]
python/sglang/srt/configs/gemma3.py deleted 100644 → 0
View file @ ecbfe58b
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/gemma3/modular_gemma3.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_gemma3.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
#
#
# 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.
import itertools
import logging
import math
import re
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
import PIL
import transformers
from torch import TensorType
from transformers import (
AutoImageProcessor,
AutoProcessor,
BatchFeature,
PretrainedConfig,
SiglipVisionConfig,
)
from transformers.image_processing_utils import BaseImageProcessor, get_size_dict
from transformers.image_transforms import (
convert_to_rgb,
resize,
to_channel_dimension_format,
)
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
infer_channel_dimension_format,
is_pil_image,
is_scaled_image,
is_valid_image,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from transformers.modeling_rope_utils import rope_config_validation
from transformers.processing_utils import (
ImagesKwargs,
ProcessingKwargs,
ProcessorMixin,
Unpack,
)
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
filter_out_non_signature_kwargs,
to_py_obj,
)
logger = logging.getLogger(__name__)
def is_valid_list_of_images(images: List):
return images and all(is_valid_image(image) for image in images)
# copied from transformer
def make_nested_list_of_images(
images: Union[List[ImageInput], ImageInput],
) -> ImageInput:
"""
Ensure that the output is a nested list of images.
Args:
images (`Union[List[ImageInput], ImageInput]`):
The input image.
Returns:
list: A list of list of images or a list of 4d array of images.
"""
# If it's a list of batches, it's already in the right format
if (
isinstance(images, (list, tuple))
and all(isinstance(images_i, (list, tuple)) for images_i in images)
and all(is_valid_list_of_images(images_i) for images_i in images)
):
return images
# If it's a list of images, it's a single batch, so convert it to a list of lists
if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
if is_pil_image(images[0]) or images[0].ndim == 3:
return [images]
if images[0].ndim == 4:
return [list(image) for image in images]
# If it's a single image, convert it to a list of lists
if is_valid_image(images):
if is_pil_image(images) or images.ndim == 3:
return [[images]]
if images.ndim == 4:
return [list(images)]
raise ValueError(
"Invalid input type. Must be a single image, a list of images, or a list of batches of images."
)
def rescale(
image: np.ndarray,
scale: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Rescale an image by a scale factor. image = image * scale.
Args:
image (`np.ndarray`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The rescaled image.
"""
return transformers.image_transforms.rescale(
image,
scale=scale,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def normalize(
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Normalize an image. image = (image - image_mean) / image_std.
Args:
image (`np.ndarray`):
Image to normalize.
mean (`float` or `Iterable[float]`):
Image mean to use for normalization.
std (`float` or `Iterable[float]`):
Image standard deviation to use for normalization.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The normalized image.
"""
return transformers.image_transforms.normalize(
image,
mean=mean,
std=std,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
class Gemma3ImagesKwargs(ImagesKwargs):
do_pan_and_scan: Optional[bool]
pan_and_scan_min_crop_size: Optional[int]
pan_and_scan_max_num_crops: Optional[int]
pan_and_scan_min_ratio_to_activate: Optional[float]
do_convert_rgb: Optional[bool]
class Gemma3ProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: Gemma3ImagesKwargs
_defaults = {
"text_kwargs": {
"padding": False,
},
"images_kwargs": {
"do_pan_and_scan": False,
"pan_and_scan_min_crop_size": 256,
"pan_and_scan_max_num_crops": 4,
"pan_and_scan_min_ratio_to_activate": 1.2,
},
}
class Gemma3Processor(ProcessorMixin):
attributes = ["image_processor", "tokenizer"]
valid_kwargs = ["chat_template", "image_seq_length"]
image_processor_class = "AutoImageProcessor"
tokenizer_class = "AutoTokenizer"
def __init__(
self,
image_processor,
tokenizer,
chat_template=None,
image_seq_length: int = 256,
**kwargs,
):
self.image_seq_length = image_seq_length
self.image_token_id = tokenizer.image_token_id
self.boi_token = tokenizer.boi_token
image_tokens_expanded = "".join([tokenizer.image_token] * image_seq_length)
self.full_image_sequence = (
f"\n\n{tokenizer.boi_token}{image_tokens_expanded}{tokenizer.eoi_token}\n\n"
)
super().__init__(
image_processor=image_processor,
tokenizer=tokenizer,
chat_template=chat_template,
**kwargs,
)
# TODO: if transformers is updated, the chat_template needs to be adjusted
self.tokenizer.add_bos_token = False
def __call__(
self,
images: ImageInput = None,
text: Union[
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
] = None,
videos=None,
audio=None,
**kwargs: Unpack[Gemma3ProcessorKwargs],
) -> BatchFeature:
if text is None and images is None:
raise ValueError("Provide at least one of `text` or `images`.")
# print(f"processing, text:{text}")
output_kwargs = self._merge_kwargs(
Gemma3ProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if isinstance(text, str):
text = [text]
elif not isinstance(text, list) and not isinstance(text[0], str):
raise ValueError(
"Invalid input text. Please provide a string, or a list of strings"
)
image_inputs = {}
if images is not None:
batched_images = make_nested_list_of_images(images)
image_inputs = self.image_processor(
batched_images, **output_kwargs["images_kwargs"]
)
# Create empty text to be replaced with placeholders
if not text:
text = [
" ".join([self.boi_token] * len(images))
for images in batched_images
]
if len(batched_images) != len(text):
raise ValueError(
f"Received inconsistently sized batches of images ({len(batched_images)}) and text ({len(text)})."
)
# Replace image tokens by the full expanded sequence
batch_num_crops = to_py_obj(image_inputs.pop("num_crops"))
text_with_crops = text
for batch_idx, (prompt, images, num_crops) in enumerate(
zip(text, batched_images, batch_num_crops)
):
image_indexes = [m.start() for m in re.finditer(self.boi_token, prompt)]
if len(images) != len(image_indexes):
raise ValueError(
f"Prompt contained {len(image_indexes)} image tokens but received {len(images)} images."
)
# Insert additional image tokens for Pan-and-Scan crops
for num, idx in reversed(list(zip(num_crops, image_indexes))):
if num:
formatted_image_text = (
f"Here is the original image {self.boi_token} and here are some crops to help you see better "
+ " ".join([self.boi_token] * num)
)
prompt = (
prompt[:idx]
+ formatted_image_text
+ prompt[idx + len(self.boi_token) :]
)
text_with_crops[batch_idx] = prompt
# Expand placeholder image tokens to the full image token sequence
text = [
prompt.replace(self.boi_token, self.full_image_sequence)
for prompt in text
]
return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
text_inputs = self.tokenizer(
text=text, **output_kwargs["text_kwargs"], return_tensors="np"
)
# print(f"processing, text_inputs:{text_inputs}")
# Add token type ids manually, as tokenizer can't do arbitrary position token types
array_ids = np.array(text_inputs["input_ids"])
mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
mm_token_type_ids[array_ids == self.image_token_id] = 1
text_inputs = {
k: v.tolist() for k, v in text_inputs.items()
} # in case user requested list inputs
text_inputs["token_type_ids"] = mm_token_type_ids.tolist()
return BatchFeature(
data={**text_inputs, **image_inputs}, tensor_type=return_tensors
)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Gemma
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Gemma
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
@property
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names + ["token_type_ids"]
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
class Gemma3ImageProcessor(BaseImageProcessor):
r"""
Constructs a SigLIP image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):
Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image by the specified mean and standard deviation. Can be overridden by
`do_normalize` in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
do_pan_and_scan (`bool`, *optional*):
Whether to apply `pan_and_scan` to images.
pan_and_scan_min_crop_size (`int`, *optional*):
Minimum size of each crop in pan and scan.
pan_and_scan_max_num_crops (`int`, *optional*):
Maximum number of crops per image in pan and scan.
pan_and_scan_min_ratio_to_activate (`float`, *optional*):
Minimum aspect ratio to activate pan and scan.
"""
model_input_names = ["pixel_values", "num_crops"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BILINEAR,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = None,
do_pan_and_scan: bool = None,
pan_and_scan_min_crop_size: int = None,
pan_and_scan_max_num_crops: int = None,
pan_and_scan_min_ratio_to_activate: float = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
size = size if size is not None else {"height": 224, "width": 224}
size = get_size_dict(size, default_to_square=True)
image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean
self.image_std = image_std
self.do_convert_rgb = do_convert_rgb
self.do_pan_and_scan = do_pan_and_scan
self.pan_and_scan_min_crop_size = pan_and_scan_min_crop_size
self.pan_and_scan_max_num_crops = pan_and_scan_max_num_crops
self.pan_and_scan_min_ratio_to_activate = pan_and_scan_min_ratio_to_activate
def pan_and_scan(
self,
image: np.ndarray,
pan_and_scan_min_crop_size: int,
pan_and_scan_max_num_crops: int,
pan_and_scan_min_ratio_to_activate: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Pan and Scan and image, by cropping into smaller images when the aspect ratio exceeds
minumum allowed ratio.
Args:
image (`np.ndarray`):
Image to resize.
pan_and_scan_min_crop_size (`int`, *optional*):
Minimum size of each crop in pan and scan.
pan_and_scan_max_num_crops (`int`, *optional*):
Maximum number of crops per image in pan and scan.
pan_and_scan_min_ratio_to_activate (`float`, *optional*):
Minimum aspect ratio to activate pan and scan.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format of the image. If not provided, it will be the same as the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format of the input image. If not provided, it will be inferred.
"""
height, width = get_image_size(image)
# Square or landscape image.
if width >= height:
# Only apply PaS if the image is sufficiently exaggerated
if width / height < pan_and_scan_min_ratio_to_activate:
return []
# Select ideal number of crops close to the image aspect ratio and such that crop_size > min_crop_size.
num_crops_w = int(
math.floor(width / height + 0.5)
) # Half round up rounding.
num_crops_w = min(
int(math.floor(width / pan_and_scan_min_crop_size)), num_crops_w
)
# Make sure the number of crops is in range [2, pan_and_scan_max_num_crops].
num_crops_w = max(2, num_crops_w)
num_crops_w = min(pan_and_scan_max_num_crops, num_crops_w)
num_crops_h = 1
# Portrait image.
else:
# Only apply PaS if the image is sufficiently exaggerated
if height / width < pan_and_scan_min_ratio_to_activate:
return []
# Select ideal number of crops close to the image aspect ratio and such that crop_size > min_crop_size.
num_crops_h = int(math.floor(height / width + 0.5))
num_crops_h = min(
int(math.floor(height / pan_and_scan_min_crop_size)), num_crops_h
)
# Make sure the number of crops is in range [2, pan_and_scan_max_num_crops].
num_crops_h = max(2, num_crops_h)
num_crops_h = min(pan_and_scan_max_num_crops, num_crops_h)
num_crops_w = 1
crop_size_w = int(math.ceil(width / num_crops_w))
crop_size_h = int(math.ceil(height / num_crops_h))
# Don't apply PaS if crop size is too small.
if min(crop_size_w, crop_size_h) < pan_and_scan_min_crop_size:
return []
crop_positions_w = [crop_size_w * i for i in range(num_crops_w)]
crop_positions_h = [crop_size_h * i for i in range(num_crops_h)]
if input_data_format == ChannelDimension.LAST:
image_crops = [
image[pos_h : pos_h + crop_size_h, pos_w : pos_w + crop_size_w]
for pos_h, pos_w in itertools.product(
crop_positions_h, crop_positions_w
)
]
else:
image_crops = [
image[:, pos_h : pos_h + crop_size_h, pos_w : pos_w + crop_size_w]
for pos_h, pos_w in itertools.product(
crop_positions_h, crop_positions_w
)
]
return image_crops
def _process_images_for_pan_and_scan(
self,
images: List[np.ndarray],
do_pan_and_scan: bool,
pan_and_scan_min_crop_size: int,
pan_and_scan_max_num_crops: int,
pan_and_scan_min_ratio_to_activate: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
pas_images_list = []
num_crops = []
for image in images:
pas_images = self.pan_and_scan(
image=image,
pan_and_scan_min_crop_size=pan_and_scan_min_crop_size,
pan_and_scan_max_num_crops=pan_and_scan_max_num_crops,
pan_and_scan_min_ratio_to_activate=pan_and_scan_min_ratio_to_activate,
data_format=data_format,
input_data_format=input_data_format,
)
pas_images_list.extend([image] + pas_images)
num_crops.append(len(pas_images))
return pas_images_list, num_crops
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
do_convert_rgb: bool = None,
do_pan_and_scan: bool = None,
pan_and_scan_min_crop_size: int = None,
pan_and_scan_max_num_crops: int = None,
pan_and_scan_min_ratio_to_activate: float = None,
) -> PIL.Image.Image:
"""
Preprocess an image or batch of images.
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
do_pan_and_scan (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to apply `pan_and_scan` to images.
pan_and_scan_min_crop_size (`int`, *optional*, defaults to `self.pan_and_scan_min_crop_size`):
Minimum size of each crop in pan and scan.
pan_and_scan_max_num_crops (`int`, *optional*, defaults to `self.pan_and_scan_max_num_crops`):
Maximum number of crops per image in pan and scan.
pan_and_scan_min_ratio_to_activate (`float`, *optional*, defaults to `self.pan_and_scan_min_ratio_to_activate`):
Minimum aspect ratio to activate pan and scan.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size, param_name="size", default_to_square=False)
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = (
rescale_factor if rescale_factor is not None else self.rescale_factor
)
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = (
do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
)
do_pan_and_scan = (
do_pan_and_scan if do_pan_and_scan is not None else self.do_pan_and_scan
)
pan_and_scan_min_crop_size = (
pan_and_scan_min_crop_size
if pan_and_scan_min_crop_size is not None
else self.pan_and_scan_min_crop_size
)
pan_and_scan_max_num_crops = (
pan_and_scan_max_num_crops
if pan_and_scan_max_num_crops is not None
else self.pan_and_scan_max_num_crops
)
pan_and_scan_min_ratio_to_activate = (
pan_and_scan_min_ratio_to_activate
if pan_and_scan_min_ratio_to_activate is not None
else self.pan_and_scan_min_ratio_to_activate
)
images_list = make_nested_list_of_images(images)
if not valid_images(images_list[0]):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
if do_convert_rgb:
images_list = [
[convert_to_rgb(image) for image in images] for images in images_list
]
# All transformations expect numpy arrays.
images_list = [
[to_numpy_array(image) for image in images] for images in images_list
]
if do_rescale and is_scaled_image(images_list[0][0]):
logger.warning(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images_list[0][0])
if do_pan_and_scan:
images_list_and_num_crops = [
self._process_images_for_pan_and_scan(
images=images,
do_pan_and_scan=do_pan_and_scan,
pan_and_scan_min_crop_size=pan_and_scan_min_crop_size,
pan_and_scan_max_num_crops=pan_and_scan_max_num_crops,
pan_and_scan_min_ratio_to_activate=pan_and_scan_min_ratio_to_activate,
data_format=data_format,
input_data_format=input_data_format,
)
for images in images_list
]
images_list = [images for images, _ in images_list_and_num_crops]
num_crops = [num_crops for _, num_crops in images_list_and_num_crops]
else:
num_crops = [[0] for images in images_list]
processed_images = []
for images in images_list:
for image in images:
if do_resize:
height, width = size["height"], size["width"]
image = resize(
image=image,
size=(height, width),
resample=resample,
input_data_format=input_data_format,
)
if do_rescale:
image = rescale(
image=image,
scale=rescale_factor,
input_data_format=input_data_format,
)
if do_normalize:
image = normalize(
image=image,
mean=image_mean,
std=image_std,
input_data_format=input_data_format,
)
image = to_channel_dimension_format(
image, data_format, input_channel_dim=input_data_format
)
processed_images.append(image)
data = {"pixel_values": processed_images, "num_crops": num_crops}
return BatchFeature(data=data, tensor_type=return_tensors)
class Gemma3TextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma3TextModel`]. It is used to instantiate an Gemma3Text
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Gemma3Text-7B.
e.g. [google/gemma3_text-7b](https://huggingface.co/google/gemma3_text-7b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 262208):
Vocabulary size of the Gemma3Text model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Gemma3TextModel`]
hidden_size (`int`, *optional*, defaults to 2304):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 9216):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 26):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
max_position_embeddings (`int`, *optional*, defaults to 131072):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
eos_token_id (`int`, *optional*, defaults to 1):
End of stream token id.
bos_token_id (`int`, *optional*, defaults to 2):
Beginning of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `True`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
query_pre_attn_scalar (`float`, *optional*, defaults to 256):
Scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma3Text, every other layer uses sliding window attention. This is the
size of the sliding window.
final_logit_softcapping (`float`, *optional*):
Scaling factor when applying tanh softcapping on the logits.
attn_logit_softcapping (`float`, *optional*):
Scaling factor when applying tanh softcapping on the attention scores.
cache_implementation (`str`, *optional*, defaults to `"hybrid"`): the cache type to be used with `generate`.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings used in gloabl attention. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
rope_local_base_freq (float, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings for local attention.
sliding_window_pattern (`int`, *optional*, defaults to 6):
Pattern for the sliding window attention.
```python
>>> from transformers import Gemma3TextModel, Gemma3TextConfig
>>> # Initializing a Gemma3Text gemma3_text-7b style configuration
>>> configuration = Gemma3TextConfig()
>>> # Initializing a model from the gemma3_text-7b style configuration
>>> model = Gemma3TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
rope_local_base_freq (float, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings for local attention.
sliding_window_pattern (`int`, *optional*, defaults to 6):
Pattern for the sliding window attention.
"""
model_type = "gemma3_text"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=262_208,
hidden_size=2304,
intermediate_size=9216,
num_hidden_layers=26,
num_attention_heads=8,
num_key_value_heads=4,
head_dim=256,
hidden_activation="gelu_pytorch_tanh",
max_position_embeddings=131_072,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=0,
eos_token_id=1,
bos_token_id=2,
tie_word_embeddings=True,
rope_theta=1_000_000.0,
attention_bias=False,
attention_dropout=0.0,
query_pre_attn_scalar=256,
sliding_window=4096,
final_logit_softcapping=None,
attn_logit_softcapping=None,
cache_implementation="hybrid",
rope_scaling=None,
rope_local_base_freq=10_000.0,
sliding_window_pattern=6,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim = head_dim
self.num_key_value_heads = num_key_value_heads
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.hidden_activation = hidden_activation
self.query_pre_attn_scalar = query_pre_attn_scalar
self.sliding_window = sliding_window
self.final_logit_softcapping = final_logit_softcapping
self.attn_logit_softcapping = attn_logit_softcapping
self.cache_implementation = cache_implementation
self.rope_local_base_freq = rope_local_base_freq
# For configuring HybridCache to work with 5:1 attention pattern
self.sliding_window_pattern = sliding_window_pattern
self.rope_scaling = rope_scaling
rope_config_validation(self)
class Gemma3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma3ForConditionalGeneration`]. It is used to instantiate an
Gemma3ForConditionalGeneration according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the PaliGemma-2B.
e.g. [google/gemma-3-4b](https://huggingface.co/google/gemma-3-4b)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`Union[Gemma3TextConfig, dict]`, *optional*):
The config object of the text backbone.
vision_config (`Union[AutoConfig, dict]`, *optional*):
Custom vision config or dict.
mm_tokens_per_image (`int`, *optional*, defaults to 256):
The number of tokens per image embedding.
boi_token_index (`int`, *optional*, defaults to 255999):
The begin-of-image token index to wrap the image prompt.
eoi_token_index (`int`, *optional*, defaults to 256000):
The end-of-image token index to wrap the image prompt.
image_token_index (`int`, *optional*, defaults to 262144):
The image token index to encode the image prompt.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
Example:
```python
>>> from transformers import Gemma3ForConditionalGeneration, Gemma3Config, SiglipVisionConfig, Gemma3TextConfig
>>> # Initializing a Siglip-like vision config
>>> vision_config = SiglipVisionConfig()
>>> # Initializing a Gemma3 Text config
>>> text_config = Gemma3TextConfig()
>>> # Initializing a Gemma3 gemma-3-4b style configuration
>>> configuration = Gemma3Config(vision_config, text_config)
>>> # Initializing a model from the gemma-3-4b style configuration
>>> model = Gemma3TextConfig(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gemma3"
sub_configs = {
"text_config": Gemma3TextConfig,
"vision_config": SiglipVisionConfig,
}
def __init__(
self,
text_config: Optional[Gemma3TextConfig] = None,
vision_config: Optional[SiglipVisionConfig] = None,
mm_tokens_per_image: int = 256,
boi_token_index: int = 255_999,
eoi_token_index: int = 256_000,
image_token_index: int = 262_144,
initializer_range: float = 0.02,
**kwargs,
):
if text_config is None:
text_config = Gemma3TextConfig()
# logger.info(
# "text_config is None, using default Gemma3TextConfig config."
# )
elif isinstance(text_config, dict):
text_config = Gemma3TextConfig(**text_config)
if isinstance(vision_config, dict):
vision_config = SiglipVisionConfig(**vision_config)
elif isinstance(vision_config, SiglipVisionConfig):
pass
else:
# logger.info(
# "vision_config is None or incompatible with Gemma3VisionConfig initialization. Gemma3 will be limited "
# "to text tasks."
# )
# logger.info(f"vision_config: {vision_config}")
vision_config = SiglipVisionConfig()
self.text_config = text_config
self.vision_config = vision_config
self.mm_tokens_per_image = mm_tokens_per_image
self.boi_token_index = boi_token_index
self.eoi_token_index = eoi_token_index
self.image_token_index = image_token_index
self.initializer_range = initializer_range
super().__init__(**kwargs)
AutoProcessor.register(
config_class=Gemma3Config, processor_class=Gemma3Processor, exist_ok=True
)
AutoImageProcessor.register(
config_class=Gemma3Config,
image_processor_class=None,
slow_image_processor_class=Gemma3ImageProcessor,
fast_image_processor_class=None,
exist_ok=True,
)
python/sglang/srt/configs/qwen2_5_vl_config.py deleted 100644 → 0
View file @ ecbfe58b
# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team.
# All rights reserved.
#
# 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.
"""Qwen2VL model configuration"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from transformers import (
AutoImageProcessor,
AutoProcessor,
BaseImageProcessor,
BatchFeature,
PretrainedConfig,
ProcessorMixin,
TensorType,
)
from transformers.image_transforms import (
convert_to_rgb,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from transformers.image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
VideoInput,
get_image_size,
infer_channel_dimension_format,
is_pil_image,
is_valid_image,
make_list_of_images,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
)
from transformers.modeling_rope_utils import rope_config_validation
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
from transformers.processing_utils import ProcessingKwargs, Unpack, VideosKwargs
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def is_valid_list_of_images(images: List):
return images and all(is_valid_image(image) for image in images)
class Qwen2_5_VLVisionConfig(PretrainedConfig):
model_type = "qwen2_5_vl"
base_config_key = "vision_config"
def __init__(
self,
depth=32,
hidden_size=3584,
hidden_act="silu",
intermediate_size=3420,
num_heads=16,
in_channels=3,
patch_size=14,
spatial_merge_size=2,
temporal_patch_size=2,
tokens_per_second=4,
window_size=112,
out_hidden_size=3584,
fullatt_block_indexes=[7, 15, 23, 31],
**kwargs,
):
super().__init__(**kwargs)
self.depth = depth
self.hidden_size = hidden_size
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.num_heads = num_heads
self.in_channels = in_channels
self.patch_size = patch_size
self.spatial_merge_size = spatial_merge_size
self.temporal_patch_size = temporal_patch_size
self.tokens_per_second = tokens_per_second
self.window_size = window_size
self.fullatt_block_indexes = fullatt_block_indexes
self.out_hidden_size = out_hidden_size
class Qwen2_5_VLConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen2_5_VLModel`]. It is used to instantiate a
Qwen2-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen2-VL-7B-Instruct [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 152064):
Vocabulary size of the Qwen2_5_VL model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2_5_VLModel`]
hidden_size (`int`, *optional*, defaults to 8192):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 29568):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 80):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 64):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
use_sliding_window (`bool`, *optional*, defaults to `False`):
Whether to use sliding window attention.
sliding_window (`int`, *optional*, defaults to 4096):
Sliding window attention (SWA) window size. If not specified, will default to `4096`.
max_window_layers (`int`, *optional*, defaults to 80):
The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
vision_config (`Dict`, *optional*):
The config for the visual encoder initialization.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
```python
>>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig
>>> # Initializing a Qwen2_5_VL style configuration
>>> configuration = Qwen2_5_VLConfig()
>>> # Initializing a model from the Qwen2-VL-7B style configuration
>>> model = Qwen2_5_VLForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen2_5_vl"
sub_configs = {"vision_config": Qwen2_5_VLVisionConfig}
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen2_5_VL`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
def __init__(
self,
vocab_size=152064,
hidden_size=8192,
intermediate_size=29568,
num_hidden_layers=80,
num_attention_heads=64,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-05,
use_cache=True,
tie_word_embeddings=False,
rope_theta=1000000.0,
use_sliding_window=False,
sliding_window=4096,
max_window_layers=80,
attention_dropout=0.0,
vision_config=None,
rope_scaling=None,
**kwargs,
):
if isinstance(vision_config, dict):
self.vision_config = self.sub_configs["vision_config"](**vision_config)
elif vision_config is None:
self.vision_config = self.sub_configs["vision_config"]()
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.attention_dropout = attention_dropout
self.rope_scaling = rope_scaling
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
# and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations
# one can set it to "linear"/"dynamic" etc. to have scaled RoPE
# TODO: @raushan update config in the hub
if self.rope_scaling is not None and "type" in self.rope_scaling:
if self.rope_scaling["type"] == "mrope":
self.rope_scaling["type"] = "default"
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self, ignore_keys={"mrope_section"})
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
# FIXME: workaround of obsolete transformers version
class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
fps: Union[List[float], float]
class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
_defaults = {
"text_kwargs": {
"padding": False,
},
"videos_kwargs": {"fps": 2.0},
}
class Qwen2_5_VLProcessor(ProcessorMixin):
r"""
Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
[`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
[`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.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_processor_class = "AutoImageProcessor"
tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
def __init__(
self, image_processor=None, tokenizer=None, chat_template=None, **kwargs
):
self.image_token = (
"<|image_pad|>"
if not hasattr(tokenizer, "image_token")
else tokenizer.image_token
)
self.video_token = (
"<|video_pad|>"
if not hasattr(tokenizer, "video_token")
else tokenizer.video_token
)
super().__init__(image_processor, tokenizer, chat_template=chat_template)
def __call__(
self,
images: ImageInput = None,
text: Union[
TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
] = None,
videos: VideoInput = None,
**kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
) -> 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(
Qwen2_5_VLProcessorKwargs,
tokenizer_init_kwargs=self.tokenizer.init_kwargs,
**kwargs,
)
if images is not None:
image_inputs = self.image_processor(
images=images, videos=None, **output_kwargs["images_kwargs"]
)
image_grid_thw = image_inputs["image_grid_thw"]
else:
image_inputs = {}
image_grid_thw = None
if videos is not None:
videos_inputs = self.image_processor(
images=None, videos=videos, **output_kwargs["images_kwargs"]
)
video_grid_thw = videos_inputs["video_grid_thw"]
fps = output_kwargs["videos_kwargs"].pop("fps", 2.0)
if isinstance(fps, (int, float)):
second_per_grid_ts = [
self.image_processor.temporal_patch_size / fps
] * len(video_grid_thw)
elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
second_per_grid_ts = [
self.image_processor.temporal_patch_size / tmp for tmp in fps
]
else:
raise ValueError(
f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
)
videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
else:
videos_inputs = {}
video_grid_thw = None
if not isinstance(text, list):
text = [text]
if image_grid_thw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.image_token in text[i]:
text[i] = text[i].replace(
self.image_token,
"<|placeholder|>"
* (image_grid_thw[index].prod() // merge_length),
1,
)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.image_token)
if video_grid_thw is not None:
merge_length = self.image_processor.merge_size**2
index = 0
for i in range(len(text)):
while self.video_token in text[i]:
text[i] = text[i].replace(
self.video_token,
"<|placeholder|>"
* (video_grid_thw[index].prod() // merge_length),
1,
)
index += 1
text[i] = text[i].replace("<|placeholder|>", self.video_token)
text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs})
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"]
class Qwen2_5_VLImageProcessor(BaseImageProcessor):
r"""
Constructs a Qwen2.5-VL image processor that dynamically resizes images based on the original images.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
min_pixels (`int`, *optional*, defaults to `56 * 56`):
The min pixels of the image to resize the image.
max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
The max pixels of the image to resize the image.
patch_size (`int`, *optional*, defaults to 14):
The spacial patch size of the vision encoder.
temporal_patch_size (`int`, *optional*, defaults to 2):
The temporal patch size of the vision encoder.
merge_size (`int`, *optional*, defaults to 2):
The merge size of the vision encoder to llm encoder.
"""
model_input_names = [
"pixel_values",
"image_grid_thw",
"pixel_values_videos",
"video_grid_thw",
"second_per_grid_ts",
]
def __init__(
self,
do_resize: bool = True,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = True,
min_pixels: int = 56 * 56,
max_pixels: int = 28 * 28 * 1280,
patch_size: int = 14,
temporal_patch_size: int = 2,
merge_size: int = 2,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.do_resize = do_resize
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
self.min_pixels = min_pixels
self.max_pixels = max_pixels
self.patch_size = patch_size
self.temporal_patch_size = temporal_patch_size
self.merge_size = merge_size
self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}
self.do_convert_rgb = do_convert_rgb
def rescale(
self,
image: np.ndarray,
scale: float,
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Rescale an image by a scale factor. image = image * scale.
Args:
image (`np.ndarray`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The rescaled image.
"""
return rescale(
image,
scale=scale,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def normalize(
self,
image: np.ndarray,
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
data_format: Optional[Union[str, ChannelDimension]] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
**kwargs,
) -> np.ndarray:
"""
Normalize an image. image = (image - image_mean) / image_std.
Args:
image (`np.ndarray`):
Image to normalize.
mean (`float` or `Iterable[float]`):
Image mean to use for normalization.
std (`float` or `Iterable[float]`):
Image standard deviation to use for normalization.
data_format (`str` or `ChannelDimension`, *optional*):
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
Returns:
`np.ndarray`: The normalized image.
"""
return normalize(
image,
mean=mean,
std=std,
data_format=data_format,
input_data_format=input_data_format,
**kwargs,
)
def _preprocess(
self,
images: Union[ImageInput, VideoInput],
do_resize: bool = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
Args:
images (`ImageInput`):
Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
vision_info (`List[Dict]`, *optional*):
Optional list of dictionaries containing additional information about vision inputs.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Scale factor to use if rescaling the image.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
images = make_list_of_images(images)
if do_convert_rgb:
images = [convert_to_rgb(image) for image in images]
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
height, width = get_image_size(images[0], channel_dim=input_data_format)
resized_height, resized_width = height, width
processed_images = []
for image in images:
if do_resize:
resized_height, resized_width = smart_resize(
height,
width,
factor=self.patch_size * self.merge_size,
min_pixels=self.min_pixels,
max_pixels=self.max_pixels,
)
image = resize(
image,
size=(resized_height, resized_width),
resample=resample,
input_data_format=input_data_format,
)
if do_rescale:
image = self.rescale(
image, scale=rescale_factor, input_data_format=input_data_format
)
if do_normalize:
image = self.normalize(
image=image,
mean=image_mean,
std=image_std,
input_data_format=input_data_format,
)
image = to_channel_dimension_format(
image, data_format, input_channel_dim=input_data_format
)
processed_images.append(image)
patches = np.array(processed_images)
if data_format == ChannelDimension.LAST:
patches = patches.transpose(0, 3, 1, 2)
if patches.shape[0] % self.temporal_patch_size != 0:
repeats = np.repeat(
patches[-1][np.newaxis], self.temporal_patch_size - 1, axis=0
)
patches = np.concatenate([patches, repeats], axis=0)
channel = patches.shape[1]
grid_t = patches.shape[0] // self.temporal_patch_size
grid_h, grid_w = (
resized_height // self.patch_size,
resized_width // self.patch_size,
)
patches = patches.reshape(
grid_t,
self.temporal_patch_size,
channel,
grid_h // self.merge_size,
self.merge_size,
self.patch_size,
grid_w // self.merge_size,
self.merge_size,
self.patch_size,
)
patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
flatten_patches = patches.reshape(
grid_t * grid_h * grid_w,
channel * self.temporal_patch_size * self.patch_size * self.patch_size,
)
return flatten_patches, (grid_t, grid_h, grid_w)
def preprocess(
self,
images: ImageInput,
videos: VideoInput = None,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None,
do_convert_rgb: bool = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
):
"""
Args:
images (`ImageInput`):
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
videos (`VideoInput`):
Video to preprocess. Expects a single or batch of videos with pixel values ranging from 0 to 255. If
passing in videos with pixel values between 0 and 1, set `do_rescale=False`.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resample`):
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`.
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
Whether to convert the image to RGB.
return_tensors (`str` or `TensorType`, *optional*):
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = (
rescale_factor if rescale_factor is not None else self.rescale_factor
)
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
image_mean = image_mean if image_mean is not None else self.image_mean
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = (
do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
)
def make_flat_list_of_images(
images: Union[List[ImageInput], ImageInput],
) -> ImageInput:
"""
Ensure that the output is a flat list of images. If the input is a single image, it is converted to a list of length 1.
If the input is a nested list of images, it is converted to a flat list of images.
Args:
images (`Union[List[ImageInput], ImageInput]`):
The input image.
Returns:
list: A list of images or a 4d array of images.
"""
# If the input is a nested list of images, we flatten it
if (
isinstance(images, (list, tuple))
and all(isinstance(images_i, (list, tuple)) for images_i in images)
and all(is_valid_list_of_images(images_i) for images_i in images)
):
return [img for img_list in images for img in img_list]
if isinstance(images, (list, tuple)) and is_valid_list_of_images(images):
if is_pil_image(images[0]) or images[0].ndim == 3:
return images
if images[0].ndim == 4:
return [img for img_list in images for img in img_list]
if is_valid_image(images):
if is_pil_image(images) or images.ndim == 3:
return [images]
if images.ndim == 4:
return list(images)
raise ValueError(f"Could not make a flat list of images from {images}")
def make_batched_videos(videos) -> VideoInput:
"""
Ensure that the input is a list of videos.
Args:
videos (`VideoInput`):
Video or videos to turn into a list of videos.
Returns:
list: A list of videos.
"""
if (
isinstance(videos, (list, tuple))
and isinstance(videos[0], (list, tuple))
and is_valid_image(videos[0][0])
):
# case 1: nested batch of videos so we flatten it
if not is_pil_image(videos[0][0]) and videos[0][0].ndim == 4:
videos = [
[video for batch_list in batched_videos for video in batch_list]
for batched_videos in videos
]
# case 2: list of videos represented as list of video frames
return videos
elif isinstance(videos, (list, tuple)) and is_valid_image(videos[0]):
if is_pil_image(videos[0]) or videos[0].ndim == 3:
return [videos]
elif videos[0].ndim == 4:
return [list(video) for video in videos]
elif is_valid_image(videos):
if is_pil_image(videos) or videos.ndim == 3:
return [[videos]]
elif videos.ndim == 4:
return [list(videos)]
raise ValueError(f"Could not make batched video from {videos}")
if images is not None:
images = make_flat_list_of_images(images)
if videos is not None:
videos = make_batched_videos(videos)
if images is not None and not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
if images is not None:
pixel_values, vision_grid_thws = [], []
for image in images:
patches, image_grid_thw = self._preprocess(
image,
do_resize=do_resize,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values.extend(patches)
vision_grid_thws.append(image_grid_thw)
pixel_values = np.array(pixel_values)
vision_grid_thws = np.array(vision_grid_thws)
data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}
if videos is not None:
pixel_values, vision_grid_thws = [], []
for images in videos:
patches, video_grid_thw = self._preprocess(
images,
do_resize=do_resize,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
data_format=data_format,
do_convert_rgb=do_convert_rgb,
input_data_format=input_data_format,
)
pixel_values.extend(patches)
vision_grid_thws.append(video_grid_thw)
pixel_values = np.array(pixel_values)
vision_grid_thws = np.array(vision_grid_thws)
data = {
"pixel_values_videos": pixel_values,
"video_grid_thw": vision_grid_thws,
}
return BatchFeature(data=data, tensor_type=return_tensors)
AutoImageProcessor.register(Qwen2_5_VLConfig, None, Qwen2_5_VLImageProcessor, None)
AutoProcessor.register(Qwen2_5_VLConfig, Qwen2_5_VLProcessor)
python/sglang/srt/hf_transformers_utils.py
View file @ f8f9244a
...@@ -35,10 +35,7 @@ from sglang.srt.configs import ( ...@@ -35,10 +35,7 @@ from sglang.srt.configs import (
DbrxConfig, DbrxConfig,
DeepseekVL2Config, DeepseekVL2Config,
ExaoneConfig, ExaoneConfig,
Gemma3Config,
Gemma3TextConfig,
MultiModalityConfig, MultiModalityConfig,
Qwen2_5_VLConfig,
) )
from sglang.srt.connector import create_remote_connector from sglang.srt.connector import create_remote_connector
from sglang.srt.utils import is_remote_url from sglang.srt.utils import is_remote_url
...@@ -47,11 +44,8 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = { ...@@ -47,11 +44,8 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = {
ChatGLMConfig.model_type: ChatGLMConfig, ChatGLMConfig.model_type: ChatGLMConfig,
DbrxConfig.model_type: DbrxConfig, DbrxConfig.model_type: DbrxConfig,
ExaoneConfig.model_type: ExaoneConfig, ExaoneConfig.model_type: ExaoneConfig,
Qwen2_5_VLConfig.model_type: Qwen2_5_VLConfig,
DeepseekVL2Config.model_type: DeepseekVL2Config, DeepseekVL2Config.model_type: DeepseekVL2Config,
MultiModalityConfig.model_type: MultiModalityConfig, MultiModalityConfig.model_type: MultiModalityConfig,
Gemma3Config.model_type: Gemma3Config,
Gemma3TextConfig.model_type: Gemma3TextConfig,
} }
for name, cls in _CONFIG_REGISTRY.items(): for name, cls in _CONFIG_REGISTRY.items():
...@@ -223,11 +217,26 @@ def get_processor( ...@@ -223,11 +217,26 @@ def get_processor(
tokenizer_revision: Optional[str] = None, tokenizer_revision: Optional[str] = None,
**kwargs, **kwargs,
): ):
# pop 'revision' from kwargs if present.
revision = kwargs.pop("revision", tokenizer_revision)
config = AutoConfig.from_pretrained(
tokenizer_name,
trust_remote_code=trust_remote_code,
revision=revision,
**kwargs,
)
# fix: for Qwen2-VL model, inject default 'size' if not provided.
if config.model_type in {"qwen2_vl"}:
if "size" not in kwargs:
kwargs["size"] = {"shortest_edge": 3136, "longest_edge": 1003520}
processor = AutoProcessor.from_pretrained( processor = AutoProcessor.from_pretrained(
tokenizer_name, tokenizer_name,
*args, *args,
trust_remote_code=trust_remote_code, trust_remote_code=trust_remote_code,
tokenizer_revision=tokenizer_revision, revision=revision,
**kwargs, **kwargs,
) )
......
python/sglang/srt/layers/rotary_embedding.py
View file @ f8f9244a
...@@ -441,16 +441,12 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module): ...@@ -441,16 +441,12 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
): ):
super().__init__() super().__init__()
if rotary_dim != head_size:
raise ValueError(
f"`Phi3LongRoPEScaledRotaryEmbedding` does not support \
rotary_dim != head_size ({rotary_dim}!={head_size})."
)
if is_neox_style is False: if is_neox_style is False:
raise ValueError( raise ValueError(
"`Phi3LongRoPEScaledRotaryEmbedding` only supports neox_style." "`Phi3LongRoPEScaledRotaryEmbedding` only supports neox_style."
) )
self.rotary_dim = rotary_dim
self.head_size = head_size self.head_size = head_size
self.max_position_embeddings = max_position_embeddings self.max_position_embeddings = max_position_embeddings
self.original_max_position_embeddings = original_max_position_embeddings self.original_max_position_embeddings = original_max_position_embeddings
...@@ -499,8 +495,8 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module): ...@@ -499,8 +495,8 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
* ( * (
self.base self.base
** ( ** (
torch.arange(0, self.head_size, 2, dtype=torch.float) torch.arange(0, self.rotary_dim, 2, dtype=torch.float)
/ self.head_size / self.rotary_dim
) )
) )
) )
...@@ -549,8 +545,15 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module): ...@@ -549,8 +545,15 @@ class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
cos = cos.repeat(1, 2).unsqueeze(-2) cos = cos.repeat(1, 2).unsqueeze(-2)
sin = sin.repeat(1, 2).unsqueeze(-2) sin = sin.repeat(1, 2).unsqueeze(-2)
query = query * cos + _rotate_neox(query) * sin query_rot = query[..., : self.rotary_dim]
key = key * cos + _rotate_neox(key) * sin query_pass = query[..., self.rotary_dim :]
query_rot = query_rot * cos + _rotate_neox(query_rot) * sin
query = torch.cat((query_rot, query_pass), dim=-1)
key_rot = key[..., : self.rotary_dim]
key_pass = key[..., self.rotary_dim :]
key_rot = key_rot * cos + _rotate_neox(key_rot) * sin
key = torch.cat((key_rot, key_pass), dim=-1)
return query.flatten(-2), key.flatten(-2) return query.flatten(-2), key.flatten(-2)
......
python/sglang/srt/models/gemma3_causal.py
View file @ f8f9244a
...@@ -21,11 +21,11 @@ from torch import nn ...@@ -21,11 +21,11 @@ from torch import nn
from transformers import ( from transformers import (
ROPE_INIT_FUNCTIONS, ROPE_INIT_FUNCTIONS,
AutoModel, AutoModel,
Gemma3TextConfig,
PretrainedConfig, PretrainedConfig,
PreTrainedModel, PreTrainedModel,
) )
from sglang.srt.configs.gemma3 import Gemma3TextConfig
from sglang.srt.distributed import get_tensor_model_parallel_world_size from sglang.srt.distributed import get_tensor_model_parallel_world_size
from sglang.srt.layers.activation import GeluAndMul from sglang.srt.layers.activation import GeluAndMul
from sglang.srt.layers.layernorm import Gemma3RMSNorm from sglang.srt.layers.layernorm import Gemma3RMSNorm
......
python/sglang/srt/models/gemma3_mm.py
View file @ f8f9244a
...@@ -21,9 +21,15 @@ from typing import Dict, Iterable, List, Optional, Set, Tuple, TypedDict ...@@ -21,9 +21,15 @@ from typing import Dict, Iterable, List, Optional, Set, Tuple, TypedDict
import torch import torch
from torch import nn from torch import nn
from transformers import AutoModel, PreTrainedModel from transformers import (
AutoModel,
BatchFeature,
Gemma3Config,
Gemma3Processor,
PreTrainedModel,
)
from transformers.models.gemma3.processing_gemma3 import Gemma3ProcessorKwargs
from sglang.srt.configs import Gemma3Config
from sglang.srt.hf_transformers_utils import get_processor from sglang.srt.hf_transformers_utils import get_processor
from sglang.srt.layers.layernorm import Gemma3RMSNorm from sglang.srt.layers.layernorm import Gemma3RMSNorm
from sglang.srt.layers.logits_processor import LogitsProcessor from sglang.srt.layers.logits_processor import LogitsProcessor
......
python/sglang/srt/models/llama.py
View file @ f8f9244a
...@@ -129,6 +129,8 @@ class LlamaAttention(nn.Module): ...@@ -129,6 +129,8 @@ class LlamaAttention(nn.Module):
self.head_dim = getattr( self.head_dim = getattr(
config, "head_dim", self.hidden_size // self.total_num_heads config, "head_dim", self.hidden_size // self.total_num_heads
) )
partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
self.rotary_dim = int(partial_rotary_factor * self.head_dim)
self.q_size = self.num_heads * self.head_dim self.q_size = self.num_heads * self.head_dim
self.kv_size = self.num_kv_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim
self.scaling = self.head_dim**-0.5 self.scaling = self.head_dim**-0.5
...@@ -154,7 +156,7 @@ class LlamaAttention(nn.Module): ...@@ -154,7 +156,7 @@ class LlamaAttention(nn.Module):
self.rotary_emb = get_rope( self.rotary_emb = get_rope(
self.head_dim, self.head_dim,
rotary_dim=self.head_dim, rotary_dim=self.rotary_dim,
max_position=max_position_embeddings, max_position=max_position_embeddings,
base=rope_theta, base=rope_theta,
rope_scaling=rope_scaling, rope_scaling=rope_scaling,
......
python/sglang/srt/models/qwen2_5_vl.py
View file @ f8f9244a
...@@ -34,8 +34,15 @@ from einops import rearrange ...@@ -34,8 +34,15 @@ from einops import rearrange
from transformers import AutoModel, Qwen2VLConfig from transformers import AutoModel, Qwen2VLConfig
from transformers.activations import ACT2FN from transformers.activations import ACT2FN
from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm from transformers.models.qwen2.modeling_qwen2 import Qwen2RMSNorm
from transformers.models.qwen2_5_vl import Qwen2_5_VLProcessor
from transformers.models.qwen2_5_vl.configuration_qwen2_5_vl import (
Qwen2_5_VLConfig,
Qwen2_5_VLVisionConfig,
)
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
Qwen2_5_VLForConditionalGeneration,
)
from sglang.srt.configs import Qwen2_5_VLConfig, Qwen2_5_VLVisionConfig
from sglang.srt.distributed import ( from sglang.srt.distributed import (
get_tensor_model_parallel_rank, get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size, get_tensor_model_parallel_world_size,
...@@ -714,4 +721,3 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module): ...@@ -714,4 +721,3 @@ class Qwen2_5_VLForConditionalGeneration(nn.Module):
EntryClass = [Qwen2_5_VLForConditionalGeneration] EntryClass = [Qwen2_5_VLForConditionalGeneration]
AutoModel.register(Qwen2_5_VLConfig, Qwen2_5_VLForConditionalGeneration)
scripts/ci_install_dependency.sh
View file @ f8f9244a
...@@ -20,7 +20,7 @@ pip install flashinfer_python==0.2.3 --find-links ${FLASHINFER_REPO} --force-rei ...@@ -20,7 +20,7 @@ pip install flashinfer_python==0.2.3 --find-links ${FLASHINFER_REPO} --force-rei
pip install torch_memory_saver --force-reinstall pip install torch_memory_saver --force-reinstall
pip install transformers==4.48.3 sentence_transformers accelerate==1.4.0 peft pandas datasets pip install transformers==4.50.0 sentence_transformers accelerate==1.4.0 peft pandas datasets
# For compling xgrammar kernels # For compling xgrammar kernels
pip install cuda-python nvidia-cuda-nvrtc-cu12 pip install cuda-python nvidia-cuda-nvrtc-cu12
......
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