7fe0469e27
### What this PR does / why we need it?
1. For all parts of the current test module involving the millisecond
download model, add the `local_file_only` parameter to specify offline
mode; this ensures that CI will not fail due to network instability.
2. Install modelscope from a fixed commit until it next release
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
check if the env or arg `local_files_only` works
1) set the env:
```shell
export HF_HUB_OFFLINE=1
```
2) run the script
```python
from transformers import PretrainedConfig
import huggingface_hub
from modelscope.utils.hf_util import patch_hub
patch_hub()
model="Qwen/Qwen3-0.6B"
kwargs = {}
config_dict, _ = PretrainedConfig.get_config_dict(
model,
trust_remote_code=True,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
**kwargs,
)
print(config_dict)
```
it works well:
```shell
2026-03-06 06:40:12,546 - modelscope - WARNING - We can not confirm the cached file is for revision: master
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
{'architectures': ['Qwen3ForCausalLM'], 'attention_bias': False, 'attention_dropout': 0.0, 'bos_token_id': 151643, 'eos_token_id': 151645, 'head_dim': 128, 'hidden_act': 'silu', 'hidden_size': 1024, 'initializer_range': 0.02, 'intermediate_size': 3072, 'max_position_embeddings': 40960, 'max_window_layers': 28, 'model_type': 'qwen3', 'num_attention_heads': 16, 'num_hidden_layers': 28, 'num_key_value_heads': 8, 'rms_norm_eps': 1e-06, 'rope_scaling': None, 'rope_theta': 1000000, 'sliding_window': None, 'tie_word_embeddings': True, 'torch_dtype': 'bfloat16', 'transformers_version': '4.51.0', 'use_cache': True, 'use_sliding_window': False, 'vocab_size': 151936, '_commit_hash': None}
```
3) test the model repo does not cached locally when the env
`HF_HUB_OFFLINE`==True
```python
from transformers import PretrainedConfig
import huggingface_hub
from modelscope.utils.hf_util import patch_hub
patch_hub()
model="FireRedTeam/FireRed-OCR"
kwargs = {}
config_dict, _ = PretrainedConfig.get_config_dict(
model,
trust_remote_code=True,
local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
**kwargs,
)
print(config_dict)
```
and the result is as expected:
```shell
File "/workspace/demo.py", line 12, in <module>
config_dict, _ = PretrainedConfig.get_config_dict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/utils/hf_util/patcher.py", line 189, in patch_get_config_dict
model_dir = get_model_dir(pretrained_model_name_or_path,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/utils/hf_util/patcher.py", line 164, in get_model_dir
model_dir = snapshot_download(
^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/hub/snapshot_download.py", line 137, in snapshot_download
return _snapshot_download(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/python3.11.14/lib/python3.11/site-packages/modelscope/hub/snapshot_download.py", line 283, in _snapshot_download
raise ValueError(
ValueError: Cannot find the requested files in the cached path and outgoing traffic has been disabled. To enable look-ups and downloads online, set 'local_files_only' to False
```
- vLLM version: v0.16.0
- vLLM main:
15d76f74e2
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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#
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import pytest
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from modelscope import snapshot_download # type: ignore[import-untyped]
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import huggingface_hub
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from tests.e2e.conftest import HfRunner, VllmRunner
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from tests.e2e.utils import check_embeddings_close
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MODELS = [
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"Qwen/Qwen3-Embedding-0.6B", # lasttoken
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"intfloat/multilingual-e5-small" # mean_tokens
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]
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@pytest.mark.parametrize("model", MODELS)
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def test_embed_models_correctness(model: str):
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queries = ['What is the capital of China?', 'Explain gravity']
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model_name = snapshot_download(model, local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,)
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with VllmRunner(
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model_name,
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runner="pooling",
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max_model_len=None,
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cudagraph_capture_sizes=[4],
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) as vllm_runner:
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vllm_outputs = vllm_runner.embed(queries)
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with HfRunner(
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model_name,
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dtype="float32",
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is_sentence_transformer=True,
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) as hf_runner:
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hf_outputs = hf_runner.encode(queries)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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def test_bge_m3_correctness():
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queries = ['What is the capital of China?', 'Explain gravity']
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model_name = snapshot_download("BAAI/bge-m3", local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,)
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with VllmRunner(
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model_name,
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runner="pooling",
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cudagraph_capture_sizes=[4],
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) as vllm_aclgraph_runner:
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vllm_aclgraph_outputs = vllm_aclgraph_runner.embed(queries)
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with VllmRunner(
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model_name,
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runner="pooling",
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enforce_eager=True,
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) as vllm_runner:
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vllm_eager_outputs = vllm_runner.embed(queries)
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with HfRunner(
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model_name,
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dtype="float32",
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is_sentence_transformer=True,
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) as hf_runner:
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hf_outputs = hf_runner.encode(queries)
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check_embeddings_close(
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embeddings_0_lst=hf_outputs,
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embeddings_1_lst=vllm_eager_outputs,
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name_0="hf",
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name_1="vllm",
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tol=1e-2,
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)
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check_embeddings_close(
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embeddings_0_lst=vllm_eager_outputs,
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embeddings_1_lst=vllm_aclgraph_outputs,
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name_0="eager",
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name_1="aclgraph",
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tol=1e-2,
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)
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