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ok Merge branch 'master' of github.com:modelscope/modelscope into release/1.36

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班扬
2026-04-20 11:15:05 +08:00
parent 4a54747bb0 55af6ce8e4
commit 0d745602af
9 changed files with 75 additions and 70 deletions
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107
docker/Dockerfile.ascend
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@@ -1,64 +1,51 @@
FROM {base_image}
# Use bash so that `source` and other bash builtins work in all following RUN steps.
SHELL ["/bin/bash", "-c"]
ENV PIP_DISABLE_PIP_VERSION_CHECK=1
ENV PIP_DEFAULT_TIMEOUT=300
ENV PIP_RETRIES=10
ENV TRANSFORMERS_VERBOSITY=error
ENV TRANSFORMERS_NO_ADVISORY_WARNINGS=1
RUN rm -f /etc/apt/apt.conf.d/docker-clean && \
find /etc/apt/apt.conf.d -maxdepth 1 -type f | xargs -r grep -l "APT::Update::Post-Invoke\|docker-clean" | xargs -r rm -f && \
apt-get update -y && \
DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
gcc g++ cmake ninja-build libnuma-dev libgl1 libglib2.0-0 libsm6 libxext6 libxrender1 \
wget git curl jq vim build-essential ca-certificates && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN pip config set global.index-url https://mirrors.aliyun.com/pypi/simple && \
pip config set install.trusted-host mirrors.aliyun.com
# Prepare required system dependencies
RUN apt-get update -y && \
apt-get install -y --no-install-recommends gcc g++ cmake libnuma-dev wget git curl jq vim build-essential && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/* && \
pip install --upgrade pip setuptools packaging && \
pip cache purge
{extra_content}
# Prepare repositories with low update frequency
RUN ARCH=$(uname -m) && \
# Set extra pip index for x86_64 platform
echo "[LOG INFO] Detected architecture: $ARCH" && \
if [ "$ARCH" = "x86_64" ]; then \
pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/"; \
fi && \
# Clone libs
git clone --depth 1 --branch v0.11.0 https://github.com/vllm-project/vllm && \
git clone --depth 1 --branch v0.11.0rc1 https://github.com/vllm-project/vllm-ascend.git && \
git clone https://gitcode.com/Ascend/MindSpeed.git && \
cd MindSpeed && git checkout f2b0977e && cd .. && \
git clone --depth 1 --branch core_v0.12.1 https://github.com/NVIDIA/Megatron-LM.git
# Reuse the vllm-ascend base image and only add the extra repos we need.
# --depth 1 keeps the image smaller; branch/tag names work with shallow clone.
RUN git clone --depth 1 --branch v0.15.3 https://github.com/NVIDIA/Megatron-LM.git /Megatron-LM && \
git clone --depth 1 --branch core_r0.15.3 https://gitcode.com/Ascend/MindSpeed.git /MindSpeed && \
GIT_LFS_SKIP_SMUDGE=1 git clone --depth 1 -b {swift_branch} --single-branch https://github.com/modelscope/ms-swift.git /ms-swift && \
git clone --depth 1 https://github.com/modelscope/mcore-bridge.git /mcore-bridge
# Install repositories with low update frequency
RUN ARCH=$(uname -m) && \
# Export and source env
if [ "$ARCH" = "aarch64" ]; then \
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.3.RC1/aarch64-linux/devlib/linux/aarch64:$LD_LIBRARY_PATH; \
elif [ "$ARCH" = "x86_64" ]; then \
export LD_LIBRARY_PATH=/usr/local/Ascend/ascend-toolkit/8.3.RC1/x86_64-linux/devlib/linux/x86_64/:$LD_LIBRARY_PATH; \
fi && \
source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
source /usr/local/Ascend/nnal/atb/set_env.sh && \
# Install torch & torch_npu & torchvision
pip install torch==2.7.1 torch_npu==2.7.1 torchvision==0.22.1 && \
# Install vllm
cd vllm && VLLM_TARGET_DEVICE=empty pip install -v -e . && cd .. && \
# Install vllm-ascend
cd vllm-ascend && pip install -v -e . && cd .. && \
# Install MindSpeed & Megatron
pip install -e MindSpeed && \
# Clear extra files
rm -rf /tmp/* /var/tmp/* && \
RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
if [ -f /usr/local/Ascend/nnal/atb/set_env.sh ]; then source /usr/local/Ascend/nnal/atb/set_env.sh; fi && \
cd /MindSpeed && pip install --no-cache-dir -e . && \
cd /mcore-bridge && pip install --no-cache-dir -e . && \
pip cache purge
ENV PYTHONPATH ${PYTHONPATH}:/Megatron-LM
ARG INSTALL_MS_DEPS={install_ms_deps}
ENV MEGATRON_LM_PATH=/Megatron-LM
ENV PYTHONPATH=/Megatron-LM:${PYTHONPATH}
# install dependencies
COPY requirements /var/modelscope
RUN pip uninstall ms-swift modelscope -y && pip --no-cache-dir install pip==23.* -U && \
RUN pip uninstall ms-swift modelscope -y && pip install --no-cache-dir pip==23.* -U && \
if [ "$INSTALL_MS_DEPS" = "True" ]; then \
pip --no-cache-dir install omegaconf==2.0.6 && \
pip install --no-cache-dir omegaconf==2.0.6 && \
pip install 'editdistance==0.8.1' && \
pip install --no-cache-dir 'cython<=0.29.36' versioneer 'numpy<2.0' && \
pip install --no-cache-dir -r /var/modelscope/framework.txt && \
@@ -68,7 +55,7 @@ if [ "$INSTALL_MS_DEPS" = "True" ]; then \
pip install --no-cache-dir https://modelscope.oss-cn-beijing.aliyuncs.com/packages/imageio_ffmpeg-0.4.9-py3-none-any.whl --no-dependencies --force && \
pip install --no-cache-dir 'scipy<1.13.0' && \
pip install --no-cache-dir funtextprocessing typeguard==2.13.3 scikit-learn && \
pip install --no-cache-dir decord>=0.6.0 mpi4py paint_ldm ipykernel fasttext && \
pip install --no-cache-dir 'decord>=0.6.0' mpi4py paint_ldm ipykernel fasttext && \
pip install --no-cache-dir 'blobfile>=1.0.5' && \
pip uninstall MinDAEC -y && \
pip install https://modelscope.oss-cn-beijing.aliyuncs.com/releases/dependencies/MinDAEC-0.0.2-py3-none-any.whl && \
@@ -81,17 +68,22 @@ fi
ARG CUR_TIME={cur_time}
RUN echo $CUR_TIME
RUN pip install --no-cache-dir -U funasr scikit-learn && \
pip install --no-cache-dir -U qwen_vl_utils qwen_omni_utils librosa timm transformers accelerate peft trl safetensors && \
cd /tmp && GIT_LFS_SKIP_SMUDGE=1 git clone -b {swift_branch} --single-branch https://github.com/modelscope/ms-swift.git && \
cd ms-swift && pip install .[llm] && \
pip install .[eval] && pip install evalscope -U --no-dependencies && pip install ms-agent -U --no-dependencies && \
cd / && rm -fr /tmp/ms-swift && pip cache purge; \
cd /tmp && GIT_LFS_SKIP_SMUDGE=1 git clone -b {modelscope_branch} --single-branch https://github.com/modelscope/modelscope.git && \
RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh && \
if [ -f /usr/local/Ascend/nnal/atb/set_env.sh ]; then source /usr/local/Ascend/nnal/atb/set_env.sh; fi && \
pip install --no-cache-dir -U funasr scikit-learn && \
pip install --no-cache-dir -U qwen_vl_utils qwen_omni_utils librosa 'timm>=0.9.0' transformers accelerate peft trl safetensors && \
cd /ms-swift && pip install --no-cache-dir -e '.[llm]' && \
pip install --no-cache-dir -e '.[eval]' && pip install evalscope -U --no-dependencies && pip install ms-agent -U --no-dependencies && \
cd /tmp && GIT_LFS_SKIP_SMUDGE=1 git clone -b {modelscope_branch} --single-branch https://github.com/modelscope/modelscope.git && \
cd modelscope && pip install . -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html && \
cd / && rm -fr /tmp/modelscope && pip cache purge; \
pip install --no-cache-dir transformers diffusers timm>=0.9.0 && pip cache purge; \
pip install --no-cache-dir omegaconf==2.3.0 && pip cache purge;
cd / && rm -fr /tmp/modelscope && \
pip install --no-cache-dir diffusers && \
pip install --no-cache-dir omegaconf==2.3.0 && \
pip cache purge
RUN echo 'source /usr/local/Ascend/ascend-toolkit/set_env.sh' >> /root/.bashrc && \
echo '[ -f /usr/local/Ascend/nnal/atb/set_env.sh ] && source /usr/local/Ascend/nnal/atb/set_env.sh' >> /root/.bashrc && \
echo 'set +H' >> /root/.bashrc
ENV SETUPTOOLS_USE_DISTUTILS=stdlib
ENV VLLM_USE_MODELSCOPE=True
@@ -100,5 +92,6 @@ ENV MODELSCOPE_CACHE=/mnt/workspace/.cache/modelscope/hub
# Show install results
RUN pip list
WORKDIR /workspace
SHELL ["/bin/bash", "-c"]
CMD ["/bin/bash"]

4
docker/build_image.py
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@@ -463,8 +463,8 @@ class AscendImageBuilder(StableGPUImageBuilder):
def init_args(self, args) -> Any:
if not args.base_image:
# other vision search for: https://hub.docker.com/r/ascendai/cann/tags
args.base_image = 'swr.cn-south-1.myhuaweicloud.com/ascendhub/cann:8.3.rc1-a3-ubuntu22.04-py3.11'
# Reuse the prebuilt vllm-ascend image to avoid rebuilding its stack.
args.base_image = 'quay.io/ascend/vllm-ascend:v0.14.0rc1-a3'
return super().init_args(args)
def generate_dockerfile(self) -> str:

2
examples/pytorch/llm_agent/_common.py
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@@ -12,7 +12,7 @@ import numpy as np
#
import torch
from matplotlib.figure import Figure
from swift import LoRAConfig, Swift
from swift.tuners import LoRAConfig, Swift
from tensorboard.backend.event_processing.event_accumulator import \
EventAccumulator
from torch import Tensor

2
modelscope/models/multi_modal/efficient_diffusion_tuning/efficient_stable_diffusion.py
View File

@@ -35,7 +35,7 @@ attention.deprecate = lambda *arg, **kwargs: None
__tuner_MAP__ = {'lora': LoRATuner, 'control_lora': ControlLoRATuner}
if is_swift_available():
from swift import AdapterConfig, LoRAConfig, PromptConfig, Swift
from swift.tuners import AdapterConfig, LoRAConfig, PromptConfig, Swift
@MODELS.register_module(

5
modelscope/pipelines/builder.py
View File

@@ -281,7 +281,10 @@ def external_engine_for_llm_checker(model: Union[str, List[str], Model,
llm_framework = kwargs.get('llm_framework', '')
if llm_framework == 'swift':
from swift.llm import get_model_info_meta
try:
from swift.model import get_model_info_meta
except ImportError:
from swift.llm import get_model_info_meta
# check if swift supports
if os.path.exists(model):
model_id = get_model_id_from_cache(model)

7
modelscope/pipelines/nlp/llm_pipeline.py
View File

@@ -217,8 +217,11 @@ class LLMPipeline(Pipeline, PipelineStreamingOutputMixin):
tokenizer_class) if tokenizer is None else tokenizer
def _init_swift(self, model_id, device) -> None:
from swift.llm import prepare_model_template
from swift.llm import InferArguments, get_model_info_meta
try:
from swift.pipelines import prepare_model_template
from swift.arguments import InferArguments
except ImportError:
from swift.llm import prepare_model_template, InferArguments
def format_messages(messages: Dict[str, List[Dict[str, str]]],
tokenizer: PreTrainedTokenizer,

10
modelscope/preprocessors/templates/utils.py
View File

@@ -390,7 +390,10 @@ def load_video_internvl(video_io: BytesIO, bound=None, num_segments=32):
def draw_plot(img_dir: str, bbox: List[int], bbox_type: str, output_file: str):
from PIL import Image, ImageDraw
from swift.llm.template.template import Template
try:
from swift.template import Template
except ImportError:
from swift.llm.template.template import Template
image = Image.open(img_dir)
objects = [{'bbox': bbox, 'bbox_type': bbox_type, 'image': 0}]
@@ -465,7 +468,10 @@ def load_audio_qwen(audio_io: BytesIO, sampling_rate: int):
def load_video_qwen2(video_path: str):
from swift.llm.template.template import get_env_args
try:
from swift.utils import get_env_args
except ImportError:
from swift.llm.template.template import get_env_args
import torchvision
from torchvision import io, transforms
from qwen_vl_utils.vision_process import (round_by_factor, FPS, FRAME_FACTOR, FPS_MIN_FRAMES, FPS_MAX_FRAMES,

2
modelscope/trainers/hooks/swift/swift_hook.py
View File

@@ -52,7 +52,7 @@ class SwiftCheckpointProcessor(CheckpointProcessor):
raise ValueError(
'Please install swift by `pip install ms-swift` to use SwiftHook.'
)
from swift import SwiftModel
from swift.tuners import SwiftModel
if isinstance(model, SwiftModel):
_swift_output_dir = output_dir + SwiftCheckpointProcessor.SWIFT_SAVE_SUFFIX
model.save_pretrained(

6
tests/trainers/test_finetune_vision_efficient_tuning_swift.py
View File

@@ -44,7 +44,7 @@ class TestVisionEfficientTuningSwiftTrainer(unittest.TestCase):
exists('transformers<5.0'),
'Skip test because transformers version is too high.')
def test_vision_efficient_tuning_swift_lora_train(self):
from swift import LoRAConfig
from swift.tuners import LoRAConfig
model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-lora'
def cfg_modify_fn(cfg):
@@ -86,7 +86,7 @@ class TestVisionEfficientTuningSwiftTrainer(unittest.TestCase):
exists('transformers<5.0'),
'Skip test because transformers version is too high.')
def test_vision_efficient_tuning_swift_adapter_train(self):
from swift import AdapterConfig
from swift.tuners import AdapterConfig
model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-adapter'
def cfg_modify_fn(cfg):
@@ -126,7 +126,7 @@ class TestVisionEfficientTuningSwiftTrainer(unittest.TestCase):
exists('transformers<5.0'),
'Skip test because transformers version is too high.')
def test_vision_efficient_tuning_swift_prompt_train(self):
from swift import PromptConfig
from swift.tuners import PromptConfig
model_id = 'damo/cv_vitb16_classification_vision-efficient-tuning-prompt'
def cfg_modify_fn(cfg):