[CI] Upgrade trasnformers version (#6307)

Upgrade transformers to >=4.56.4

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

.github/workflows/_e2e_nightly_single_node.yaml

@@ -58,7 +58,7 @@ jobs:
     container:
       image: ${{ inputs.image }}
     env:
-      TRANSFORMERS_OFFLINE: 1
+      HF_HUB_OFFLINE: 1
       VLLM_USE_MODELSCOPE: True
     steps:
       - name: Check npu and CANN info

.github/workflows/_e2e_test.yaml

@@ -28,7 +28,7 @@ jobs:
       env:
         VLLM_LOGGING_LEVEL: ERROR
         VLLM_USE_MODELSCOPE: True
-        TRANSFORMERS_OFFLINE: 1
+        HF_HUB_OFFLINE: 1
     steps:
       - name: Check npu and CANN info
         run: |
@@ -137,7 +137,7 @@ jobs:
         VLLM_LOGGING_LEVEL: ERROR
         VLLM_USE_MODELSCOPE: True
         HCCL_BUFFSIZE: 1024
-        TRANSFORMERS_OFFLINE: 1
+        HF_HUB_OFFLINE: 1
     steps:
       - name: Check npu and CANN info
         run: |
@@ -245,7 +245,7 @@ jobs:
       env:
         VLLM_LOGGING_LEVEL: ERROR
         VLLM_USE_MODELSCOPE: True
-        TRANSFORMERS_OFFLINE: 1
+        HF_HUB_OFFLINE: 1
     steps:
       - name: Check npu and CANN info
         run: |
@@ -322,7 +322,7 @@ jobs:
       env:
         VLLM_LOGGING_LEVEL: ERROR
         VLLM_USE_MODELSCOPE: True
-        TRANSFORMERS_OFFLINE: 1
+        HF_HUB_OFFLINE: 1
     steps:
       - name: Check npu and CANN info
         run: |
@@ -380,7 +380,7 @@ jobs:
       env:
         VLLM_LOGGING_LEVEL: ERROR
         VLLM_USE_MODELSCOPE: True
-        TRANSFORMERS_OFFLINE: 1
+        HF_HUB_OFFLINE: 1
     steps:
       - name: Check npu and CANN info
         run: |

pyproject.toml

@@ -17,7 +17,7 @@ requires = [
     "psutil",
     "setuptools>=64",
     "setuptools-scm>=8",
-    "transformers<=4.57.1",
+    "transformers>=4.57.4",
     "torch-npu==2.9.0",
     "torch==2.9.0",
     "torchvision",

requirements.txt

@@ -32,6 +32,6 @@ numba
 torch-npu==2.9.0
 
 arctic-inference==0.1.1
-transformers>=4.57.3
+transformers>=4.57.4
 fastapi<0.124.0
 triton-ascend==3.2.0

tests/e2e/multicard/2-cards/test_ilama_lora_tp2.py

@@ -1,5 +1,4 @@
 import pytest
-from modelscope import snapshot_download  # type: ignore
 
 from tests.e2e.conftest import VllmRunner
 from tests.e2e.singlecard.test_ilama_lora import (EXPECTED_LORA_OUTPUT,
@@ -9,7 +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(
-            snapshot_download(MODEL_PATH),
+            MODEL_PATH,
             enable_lora=True,
             max_loras=4,
             dtype="half",

tests/e2e/multicard/2-cards/test_offline_inference_distributed.py

@@ -24,7 +24,6 @@ import os
 from unittest.mock import patch
 
 import pytest
-from modelscope import snapshot_download  # type: ignore
 from vllm import SamplingParams
 
 from tests.e2e.conftest import VllmRunner
@@ -77,7 +76,7 @@ def test_qwen3_w4a8_dynamic_tp2(model):
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download(model),
+            model,
             max_model_len=8192,
             dtype="auto",
             tensor_parallel_size=2,
@@ -96,7 +95,7 @@ def test_qwen3_moe_sp_tp2() -> None:
                                      top_k=50,
                                      top_p=0.9)
 
-    with VllmRunner(snapshot_download("Qwen/Qwen3-30B-A3B"),
+    with VllmRunner("Qwen/Qwen3-30B-A3B",
                     dtype="auto",
                     tensor_parallel_size=2,
                     distributed_executor_backend="mp",
@@ -119,7 +118,7 @@ def test_deepseek_w4a8_accuracy_tp2(model):
         '逍遙而至地去 accrued', '平行于我udo madreHelen', 'ysteepaolis backwards Kj'
     ]
     sampling_params = SamplingParams(max_tokens=5, temperature=0.0)
-    with VllmRunner(snapshot_download(model),
+    with VllmRunner(model,
                     dtype="auto",
                     tensor_parallel_size=2,
                     cudagraph_capture_sizes=[1, 2, 4, 8],
@@ -152,7 +151,7 @@ def test_qwen3_moe_fc2_tp2() -> None:
                                      top_k=50,
                                      top_p=0.9)
 
-    with VllmRunner(snapshot_download("Qwen/Qwen3-30B-A3B"),
+    with VllmRunner("Qwen/Qwen3-30B-A3B",
                     dtype="auto",
                     tensor_parallel_size=2,
                     distributed_executor_backend="mp",
@@ -173,7 +172,7 @@ def test_qwen3_moe_fc2_oshard_tp2() -> None:
                                      top_p=0.9)
 
     with VllmRunner(
-            snapshot_download("Qwen/Qwen3-30B-A3B"),
+            "Qwen/Qwen3-30B-A3B",
             dtype="auto",
             tensor_parallel_size=2,
             distributed_executor_backend="mp",
@@ -193,7 +192,7 @@ def test_deepseek_v2_lite_fc1_tp2() -> None:
                                      temperature=0.0,
                                      top_k=50,
                                      top_p=0.9)
-    with VllmRunner(snapshot_download("vllm-ascend/DeepSeek-V2-Lite-W8A8"),
+    with VllmRunner("vllm-ascend/DeepSeek-V2-Lite-W8A8",
                     dtype="auto",
                     tensor_parallel_size=2,
                     distributed_executor_backend="mp",
@@ -212,7 +211,7 @@ def test_qwen3_dense_fc1_tp2(model):
     max_tokens = 5
 
     with VllmRunner(
-            snapshot_download(model),
+            model,
             max_model_len=8192,
             dtype="auto",
             tensor_parallel_size=2,
@@ -231,7 +230,7 @@ def test_qwen3_dense_prefetch_mlp_weight_tp2(model):
     max_tokens = 5
 
     with VllmRunner(
-            snapshot_download(model),
+            model,
             max_model_len=8192,
             dtype="auto",
             tensor_parallel_size=2,
@@ -277,7 +276,7 @@ def test_qwen3_w4a4_distributed_tp2(model):
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download(model),
+            model,
             tensor_parallel_size=2,
             cudagraph_capture_sizes=[1, 2, 4, 8],
             quantization="ascend",

tests/e2e/multicard/2-cards/test_quantization.py

@@ -16,7 +16,6 @@
 # This file is a part of the vllm-ascend project.
 # Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
 #
-from modelscope import snapshot_download  # type: ignore
 import pytest
 
 from tests.e2e.conftest import VllmRunner
@@ -28,7 +27,7 @@ def test_qwen2_5_w8a8_external_quantized_tp2():
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download("neuralmagic/Qwen2.5-3B-quantized.w8a8"),
+            "neuralmagic/Qwen2.5-3B-quantized.w8a8",
             tensor_parallel_size=2,
             cudagraph_capture_sizes=[1, 2, 4, 8],
             max_model_len=4096,
@@ -52,8 +51,7 @@ def test_qwen3_moe_w8a8_dynamic_llm_compressor():
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download(
-                "vllm-ascend/Qwen3-30B-A3B-Instruct-2507-quantized.w8a8"),
+            "vllm-ascend/Qwen3-30B-A3B-Instruct-2507-quantized.w8a8",
             tensor_parallel_size=2,
             max_model_len=4096,
             gpu_memory_utilization=0.8,

tests/e2e/multicard/2-cards/test_qwen3_moe.py

@@ -22,7 +22,6 @@ from unittest.mock import patch
 
 import openai
 import pytest
-from modelscope import snapshot_download  # type: ignore
 from vllm.utils.network_utils import get_open_port
 
 from tests.e2e.conftest import RemoteOpenAIServer, VllmRunner
@@ -50,7 +49,7 @@ def test_qwen3_moe_w8a8_distributed_tp2():
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download("vllm-ascend/Qwen3-30B-A3B-W8A8"),
+            "vllm-ascend/Qwen3-30B-A3B-W8A8",
             max_model_len=8192,
             tensor_parallel_size=2,
             cudagraph_capture_sizes=[1, 2, 4, 8],

tests/e2e/multicard/4-cards/test_qwen3_next.py

@@ -19,8 +19,6 @@
 import os
 from unittest.mock import patch
 
-from modelscope import snapshot_download  # type: ignore
-
 from tests.e2e.conftest import VllmRunner
 
 
@@ -65,7 +63,7 @@ def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
     ]
     max_tokens = 5
     with VllmRunner(
-            snapshot_download("vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8"),
+            "vllm-ascend/Qwen3-Next-80B-A3B-Instruct-W8A8",
             max_model_len=4096,
             tensor_parallel_size=4,
             gpu_memory_utilization=0.4,

tests/e2e/nightly/multi_node/scripts/run.sh

@@ -18,7 +18,7 @@ export VLLM_LOGGING_LEVEL="INFO"
 # Reduce glog verbosity for mooncake
 export GLOG_minloglevel=1
 # Set transformers to offline mode to avoid downloading models during tests
-export TRANSFORMERS_OFFLINE="1"
+export HF_HUB_OFFLINE="1"
 
 # Function to print section headers
 print_section() {

tests/e2e/singlecard/test_aclgraph_mem.py

@@ -21,7 +21,6 @@ from unittest.mock import patch
 
 import pytest
 import torch
-from modelscope import snapshot_download  # type: ignore
 from vllm import SamplingParams
 
 from tests.e2e.conftest import VllmRunner
@@ -66,11 +65,11 @@ def test_aclgraph_mem_use(model: str, max_tokens: int) -> None:
         sampling_params = SamplingParams(max_tokens=max_tokens,
                                          temperature=0.0)
         if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
-            vllm_model = VllmRunner(snapshot_download(model),
+            vllm_model = VllmRunner(model,
                                     max_model_len=1024,
                                     quantization="ascend")
         else:
-            vllm_model = VllmRunner(snapshot_download(model))
+            vllm_model = VllmRunner(model)
         _ = vllm_model.generate(prompts, sampling_params)
 
     assert capture_called.value == 1, "capture_model was not called during test"

tests/e2e/singlecard/test_ilama_lora.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 import vllm
-from modelscope import snapshot_download  # type: ignore
 from vllm.lora.request import LoRARequest
 
 from tests.e2e.conftest import VllmRunner
@@ -46,7 +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(
-            snapshot_download(MODEL_PATH),
+            MODEL_PATH,
             enable_lora=True,
             dtype="half",
             max_loras=4,

tests/e2e/singlecard/test_models.py

@@ -20,7 +20,6 @@
 import os
 
 import pytest
-from modelscope import snapshot_download  # type: ignore
 from vllm import SamplingParams
 from vllm.assets.audio import AudioAsset
 
@@ -46,7 +45,7 @@ def test_minicpm(model) -> None:
     ]
     max_tokens = 5
 
-    with VllmRunner(snapshot_download(model),
+    with VllmRunner(model,
                     max_model_len=512,
                     gpu_memory_utilization=0.7) as runner:
         runner.generate_greedy(example_prompts, max_tokens)
@@ -61,7 +60,7 @@ def test_whisper(model) -> None:
                                      max_tokens=10,
                                      stop_token_ids=None)
 
-    with VllmRunner(snapshot_download(model),
+    with VllmRunner(model,
                     max_model_len=448,
                     max_num_seqs=5,
                     dtype="bfloat16",

tests/e2e/singlecard/test_quantization.py

@@ -15,8 +15,6 @@
 # limitations under the License.
 # This file is a part of the vllm-ascend project.
 #
-from modelscope import snapshot_download  # type: ignore[import-untyped]
-
 from tests.e2e.conftest import VllmRunner
 from tests.e2e.model_utils import check_outputs_equal
 
@@ -33,7 +31,7 @@ def test_qwen3_w8a8_quant():
                             )]
 
     with VllmRunner(
-            snapshot_download("vllm-ascend/Qwen3-0.6B-W8A8"),
+            "vllm-ascend/Qwen3-0.6B-W8A8",
             max_model_len=8192,
             gpu_memory_utilization=0.7,
             cudagraph_capture_sizes=[1, 2, 4, 8],
@@ -62,7 +60,7 @@ def test_qwen3_dense_w8a16():
                             )]
 
     with VllmRunner(
-            snapshot_download("vllm-ascend/Qwen3-0.6B-W8A16"),
+            "vllm-ascend/Qwen3-0.6B-W8A16",
             max_model_len=8192,
             enforce_eager=False,
             gpu_memory_utilization=0.7,