[CI/UT] Unify model usage via ModelScope in CI (#1207)

### What this PR does / why we need it? Unify Model Usage via ModelScope ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed Signed-off-by: hfadzxy <starmoon_zhang@163.com>
2025-07-04 10:52:17 +08:00
parent a5f33590d3
commit 4e910186de
9 changed files with 17 additions and 26 deletions
--- a/.github/workflows/accuracy_test.yaml
+++ b/.github/workflows/accuracy_test.yaml
@@ -125,10 +125,9 @@ jobs:
    container:
      image: m.daocloud.io/quay.io/ascend/cann:8.1.rc1-910b-ubuntu22.04-py3.10
      env:
-        HF_ENDPOINT: https://hf-mirror.com
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        DATASET_SOURCE: ModelScope
        VLLM_USE_MODELSCOPE: True
+        USE_MODELSCOPE_HUB: 1
        # 1. If version specified (work_dispatch), do specified branch accuracy test
        # 2. If no version (labeled PR), do accuracy test by default ref:
        # The branch, tag or SHA to checkout. When checking out the repository that
--- a/.github/workflows/nightly_benchmarks.yaml
+++ b/.github/workflows/nightly_benchmarks.yaml
@@ -69,8 +69,7 @@ jobs:
        --device /dev/devmm_svm
        --device /dev/hisi_hdc
      env:
-        HF_ENDPOINT: https://hf-mirror.com
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        VLLM_USE_MODELSCOPE: True
        ES_OM_DOMAIN: ${{ secrets.ES_OM_DOMAIN }}
        ES_OM_AUTHORIZATION: ${{ secrets.ES_OM_AUTHORIZATION }}
        VLLM_USE_V1: ${{ matrix.vllm_use_v1 }}
--- a/.github/workflows/vllm_ascend_test.yaml
+++ b/.github/workflows/vllm_ascend_test.yaml
@@ -209,6 +209,7 @@ jobs:
      image: m.daocloud.io/quay.io/ascend/cann:8.1.rc1-910b-ubuntu22.04-py3.10
      env:
        VLLM_LOGGING_LEVEL: ERROR
+        VLLM_USE_MODELSCOPE: True
    steps:
      - name: Check npu and CANN info
        run: |
@@ -257,9 +258,7 @@ jobs:
          VLLM_USE_MODELSCOPE: True
        run: |
          pytest -sv tests/e2e/singlecard/test_offline_inference.py
-          # TODO: switch hf to modelscope
-          VLLM_USE_MODELSCOPE=False HF_ENDPOINT=https://hf-mirror.com \
-            pytest -sv tests/e2e/singlecard/test_ilama_lora.py
+          pytest -sv tests/e2e/singlecard/test_ilama_lora.py
          pytest -sv tests/e2e/singlecard/test_guided_decoding.py
          pytest -sv tests/e2e/singlecard/test_camem.py
          pytest -sv tests/e2e/singlecard/test_embedding.py
@@ -277,9 +276,7 @@ jobs:
          VLLM_USE_MODELSCOPE: True
        run: |
          pytest -sv tests/e2e/singlecard/test_offline_inference.py
-          # TODO: switch hf to modelscope
-          VLLM_USE_MODELSCOPE=False HF_ENDPOINT=https://hf-mirror.com \
-            pytest -sv tests/e2e/singlecard/test_ilama_lora.py
+          pytest -sv tests/e2e/singlecard/test_ilama_lora.py
          pytest -sv tests/e2e/singlecard/test_guided_decoding.py
          pytest -sv tests/e2e/singlecard/test_camem.py
          pytest -sv tests/e2e/singlecard/test_prompt_embedding.py
@@ -357,9 +354,7 @@ jobs:
          VLLM_WORKER_MULTIPROC_METHOD: spawn
          VLLM_USE_MODELSCOPE: True
        run: |
-          # TODO: switch hf to modelscope
-          VLLM_USE_MODELSCOPE=False HF_ENDPOINT=https://hf-mirror.com \
-            pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
+          pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
          # Fixme: run VLLM_USE_MODELSCOPE=True pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py will raise error.
          # To avoid oom, we need to run the test in a single process.
          pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_DeepSeek_multistream_moe
@@ -380,9 +375,7 @@ jobs:
          VLLM_USE_V1: 0
          VLLM_USE_MODELSCOPE: True
        run: |
-          # TODO: switch hf to modelscope
-          VLLM_USE_MODELSCOPE=False HF_ENDPOINT=https://hf-mirror.com \
-            pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
+          pytest -sv tests/e2e/multicard/test_ilama_lora_tp2.py
          # Fixme: run VLLM_USE_MODELSCOPE=True pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py will raise error.
          # To avoid oom, we need to run the test in a single process.
          pytest -sv tests/e2e/multicard/test_offline_inference_distributed.py::test_models_distributed_QwQ
--- a/.github/workflows/vllm_ascend_test_long_term.yaml
+++ b/.github/workflows/vllm_ascend_test_long_term.yaml
@@ -50,9 +50,8 @@ jobs:
      # TODO(yikun): Remove m.daocloud.io prefix when infra proxy ready
      image: m.daocloud.io/quay.io/ascend/cann:8.1.rc1-910b-ubuntu22.04-py3.10
      env:
-        HF_ENDPOINT: https://hf-mirror.com
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
        VLLM_LOGGING_LEVEL: ERROR
+        VLLM_USE_MODELSCOPE: True
    steps:
      - name: Check npu and CANN info
        run: |
--- a/.github/workflows/vllm_ascend_test_pd.yaml
+++ b/.github/workflows/vllm_ascend_test_pd.yaml
@@ -64,8 +64,7 @@ jobs:
        --device /dev/devmm_svm
        --device /dev/hisi_hdc
      env:
-        HF_ENDPOINT: https://hf-mirror.com
-        HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        VLLM_USE_MODELSCOPE: True
    steps:
      - name: Check npu and CANN info
        run: |
--- a/benchmarks/scripts/run-performance-benchmarks.sh
+++ b/benchmarks/scripts/run-performance-benchmarks.sh
@@ -295,7 +295,7 @@ main() {
  export VLLM_LOG_LEVEL="WARNING"
  
  # set env
-  export HF_ENDPOINT="https://hf-mirror.com"
+  export VLLM_USE_MODELSCOPE=True

  # prepare for benchmarking
  cd benchmarks || exit 1
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -25,7 +25,7 @@ from typing import Any, List, Optional, Tuple, TypeVar, Union
 import numpy as np
 import pytest
 import torch
-from huggingface_hub import snapshot_download
+from modelscope import snapshot_download  # type: ignore[import-untyped]
 from PIL import Image
 from torch import nn
 from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
@@ -387,7 +387,7 @@ def example_prompts() -> list[str]:

@pytest.fixture(scope="session")
 def ilama_lora_files():
-    return snapshot_download(repo_id="jeeejeee/ilama-text2sql-spider")
+    return snapshot_download(repo_id="vllm-ascend/ilama-text2sql-spider")


 class HfRunner:
--- a/tests/e2e/multicard/test_ilama_lora_tp2.py
+++ b/tests/e2e/multicard/test_ilama_lora_tp2.py
@@ -1,4 +1,5 @@
 import pytest
+from modelscope import snapshot_download  # type: ignore

 from tests.conftest import VllmRunner
 from tests.e2e.singlecard.test_ilama_lora import (EXPECTED_LORA_OUTPUT,
@@ -7,7 +8,7 @@ from tests.e2e.singlecard.test_ilama_lora import (EXPECTED_LORA_OUTPUT,

@pytest.mark.parametrize("distributed_executor_backend", ["mp"])
 def test_ilama_lora_tp2(distributed_executor_backend, ilama_lora_files):
-    with VllmRunner(model_name=MODEL_PATH,
+    with VllmRunner(snapshot_download(MODEL_PATH),
                    enable_lora=True,
                    max_loras=4,
                    max_model_len=1024,
--- a/tests/e2e/singlecard/test_ilama_lora.py
+++ b/tests/e2e/singlecard/test_ilama_lora.py
@@ -1,10 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 import vllm
+from modelscope import snapshot_download  # type: ignore
 from vllm.lora.request import LoRARequest

 from tests.conftest import VllmRunner

-MODEL_PATH = "ArthurZ/ilama-3.2-1B"
+MODEL_PATH = "vllm-ascend/ilama-3.2-1B"

 PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:"""  # noqa: E501

@@ -44,7 +45,7 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:


 def test_ilama_lora(ilama_lora_files):
-    with VllmRunner(model_name=MODEL_PATH,
+    with VllmRunner(snapshot_download(MODEL_PATH),
                    enable_lora=True,
                    max_loras=4,
                    max_model_len=1024,