[CI] Drop ascend scheduler from test (#4613)

Drop ascend scheduler from test

- vLLM version: v0.11.2

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

.github/workflows/_e2e_test.yaml

@@ -91,7 +91,6 @@ jobs:
           pytest -sv tests/e2e/singlecard/test_completion_with_prompt_embeds.py
           pytest -sv tests/e2e/singlecard/test_aclgraph.py
           pytest -sv tests/e2e/singlecard/test_aclgraph_mem.py
-          pytest -sv tests/e2e/singlecard/test_ascend_scheduler.py
           pytest -sv tests/e2e/singlecard/test_bge_model.py
           pytest -sv tests/e2e/singlecard/test_camem.py
           pytest -sv tests/e2e/singlecard/test_embedding.py

tests/e2e/singlecard/test_ascend_scheduler.py

@@ -1,118 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import pytest
-from vllm import SamplingParams
-
-from tests.e2e.conftest import VllmRunner
-from tests.e2e.model_utils import check_outputs_equal
-
-MODEL = "Qwen/Qwen3-0.6B"
-
-
-@pytest.mark.parametrize("enforce_eager", [True, False])
-def test_concurrent_partial_prefill(enforce_eager):
-    with VllmRunner(MODEL,
-                    max_num_seqs=3,
-                    max_num_batched_tokens=8192,
-                    enforce_eager=enforce_eager,
-                    gpu_memory_utilization=0.7) as vllm_model:
-        outputs = vllm_model.model.generate(["Hello my name is Robert and I"] *
-                                            3)
-        assert len(outputs) == 3
-        for output in outputs:
-            assert len(output.outputs) == 1
-
-
-@pytest.mark.parametrize("enforce_eager", [True, False])
-def test_prefix_cache_stats_is_recorded(enforce_eager):
-    with VllmRunner(MODEL,
-                    max_num_seqs=3,
-                    max_num_batched_tokens=8192,
-                    enforce_eager=enforce_eager,
-                    gpu_memory_utilization=0.7) as vllm_model:
-        # 17 tokens will make sure first 16 tokens are cached in a block
-        input_tokens = {"prompt_token_ids": [101] * 129}
-        _ = vllm_model.model.generate([input_tokens])
-        outputs = vllm_model.model.generate([input_tokens])
-        assert outputs[0].num_cached_tokens == 128
-
-
-@pytest.mark.parametrize("max_tokens",
-                         [4])  # cannot align results when max_tokens > 4
-@pytest.mark.parametrize("chunked_prefill_token_size", [2048])
-def test_chunked_prefill_with_scheduler_dynamic_batch(
-        max_tokens: int, chunked_prefill_token_size: int) -> None:
-    example_prompts = [
-        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
-    ]
-    max_num_seqs = chunked_prefill_token_size
-    max_num_batched_tokens = chunked_prefill_token_size
-    with VllmRunner(MODEL,
-                    additional_config={
-                        'SLO_limits_for_dynamic_batch': 0,
-                    },
-                    max_num_seqs=max_num_seqs,
-                    max_num_batched_tokens=max_num_batched_tokens,
-                    max_model_len=2048,
-                    gpu_memory_utilization=0.7) as vllm_model:
-        dynamic_batch_output = vllm_model.generate_greedy(
-            example_prompts, max_tokens)
-
-    with VllmRunner(MODEL,
-                    additional_config={
-                        'SLO_limits_for_dynamic_batch': -1,
-                    },
-                    max_model_len=2048,
-                    gpu_memory_utilization=0.7) as vllm_model:
-        vllm_output = vllm_model.generate_greedy(example_prompts, max_tokens)
-
-    check_outputs_equal(
-        outputs_0_lst=vllm_output,
-        outputs_1_lst=dynamic_batch_output,
-        name_0="vllm_output",
-        name_1="chunked_prefill_output",
-    )
-
-
-def test_async_scheduling_eager() -> None:
-    prompts = [
-        "Hello, my name is",
-        "The president of the United States is",
-        "The capital of France is",
-        "The future of AI is",
-    ] * 10
-    sampling_params = SamplingParams(temperature=0.2,
-                                     max_tokens=10,
-                                     stop_token_ids=None)
-
-    with VllmRunner(
-            "Qwen/Qwen2.5-0.5B-Instruct",
-            max_model_len=4096,
-            max_num_seqs=50,
-            dtype="bfloat16",
-            gpu_memory_utilization=0.9,
-            async_scheduling=True,
-    ) as vllm_model:
-        vllm_model.generate(prompts, sampling_params=sampling_params)
-
-
-def test_async_scheduling_with_full_graph() -> None:
-    prompts = [
-        "Hello, my name is",
-        "The president of the United States is",
-        "The capital of France is",
-        "The future of AI is",
-    ] * 10
-    sampling_params = SamplingParams(temperature=0.2,
-                                     max_tokens=10,
-                                     stop_token_ids=None)
-
-    with VllmRunner("Qwen/Qwen3-8B",
-                    max_model_len=4096,
-                    max_num_seqs=50,
-                    dtype="bfloat16",
-                    gpu_memory_utilization=0.9,
-                    async_scheduling=True,
-                    compilation_config={"cudagraph_mode":
-                                        "FULL"}) as vllm_model:
-        vllm_model.generate(prompts, sampling_params=sampling_params)

tests/ut/quantization/test_w8a8_dynamic.py

@@ -33,13 +33,6 @@ class TestAscendW8A8FusedMoEMethod(TestBase):
             mock_get_ep_group.return_value = mock_ep_group
             mock_ascend_config = Mock()
 
-            # 创建一个具有具体属性的 Mock 对象来表示 ascend_scheduler_config
-            mock_ascend_scheduler_config = Mock()
-            mock_ascend_scheduler_config.enabled = False
-            mock_ascend_scheduler_config.max_num_batched_tokens = 1024
-            mock_ascend_scheduler_config.max_model_len = 2048
-            mock_ascend_config.ascend_scheduler_config = mock_ascend_scheduler_config
-
             mock_ascend_config.torchair_graph_config = Mock(enabled=False)
             mock_ascend_config.enable_chunked_prefill = False
             mock_get_ascend_config.return_value = mock_ascend_config

tests/ut/test_ascend_config.py

@@ -56,9 +56,6 @@ class TestAscendConfig(TestBase):
         self.assertTrue(torchair_graph_config.enable_frozen_parameter)
         self.assertFalse(torchair_graph_config.enable_kv_nz)
 
-        ascend_scheduler_config = ascend_config.ascend_scheduler_config
-        self.assertFalse(ascend_scheduler_config.enabled)
-
     @_clean_up_ascend_config
     def test_init_ascend_config_with_additional_config(self):
         test_vllm_config = VllmConfig()
@@ -74,9 +71,6 @@ class TestAscendConfig(TestBase):
                 "enable_kv_nz": True
             },
             "multistream_overlap_shared_expert": True,
-            "ascend_scheduler_config": {
-                "enabled": True
-            },
             "expert_map_path": "test_expert_map_path",
             "refresh": True,
         }
@@ -94,9 +88,6 @@ class TestAscendConfig(TestBase):
         self.assertTrue(torchair_graph_config.enable_frozen_parameter)
         self.assertTrue(torchair_graph_config.enable_kv_nz)
 
-        ascend_scheduler_config = ascend_config.ascend_scheduler_config
-        self.assertTrue(ascend_scheduler_config.enabled)
-
     @_clean_up_ascend_config
     def test_init_ascend_config_with_refresh(self):
         test_vllm_config = VllmConfig()

tests/ut/test_platform.py

@@ -522,31 +522,6 @@ class TestNPUPlatform(TestBase):
         self.platform.check_and_update_config(vllm_config)
         self.assertEqual(vllm_config.compilation_config.custom_ops, [])
 
-    @patch('vllm_ascend.utils.get_ascend_device_type',
-           return_value=AscendDeviceType._910_93)
-    @patch("vllm_ascend.ascend_config.check_ascend_config")
-    @patch("vllm_ascend.ascend_config.init_ascend_config")
-    @patch(
-        "vllm_ascend.core.recompute_schedule_config.RecomputeSchedulerConfig.initialize_from_config"
-    )
-    def test_check_and_update_config_ascend_scheduler_config(
-            self, mock_init_recompute, mock_init_ascend, mock_check_ascend,
-            mock_soc_version):
-        mock_ascend_config = TestNPUPlatform.mock_vllm_ascend_config()
-        mock_ascend_config.ascend_scheduler_config.enabled = True
-        mock_init_ascend.return_value = mock_ascend_config
-        vllm_config = TestNPUPlatform.mock_vllm_config()
-        vllm_config.parallel_config.tensor_parallel_size = 1
-        mock_init_recompute.return_value = MagicMock()
-
-        with patch("vllm_ascend.core.schedule_config.AscendSchedulerConfig"
-                   ) as mock_scheduler:
-            from vllm_ascend import platform
-
-            importlib.reload(platform)
-            self.platform.check_and_update_config(vllm_config)
-            mock_scheduler.initialize_from_config.assert_called_once()
-
     @patch('vllm_ascend.platform.get_ascend_config')
     def test_get_attn_backend_cls_use_v1_and_mla(self, mock_get_ascend_config):
         mock_config = MagicMock()

tests/ut/test_utils.py

@@ -253,12 +253,10 @@ class TestUtils(TestBase):
         model_path = os.path.join(os.path.dirname(__file__), "fake_weight")
         test_model_config = ModelConfig(model=model_path, enforce_eager=True)
         test_parallel_config = ParallelConfig()
-        ascend_config = {"ascend_scheduler_config": {"enabled": False}}
         test_vllm_config = VllmConfig(
             model_config=test_model_config,
             compilation_config=test_compilation_config,
-            parallel_config=test_parallel_config,
+            parallel_config=test_parallel_config)
-            additional_config=ascend_config)
         utils.update_aclgraph_sizes(test_vllm_config)
         os.environ['HCCL_OP_EXPANSION_MODE'] = 'AIV'
         utils.update_aclgraph_sizes(test_vllm_config)

tests/ut/torchair/models/test_torchair_deepseek_v2.py

@@ -235,8 +235,6 @@ def test_torchair_deepseek_v2_mlp(mock_distributed, base_config):
                                 hidden_act="silu",
                                 quant_config=None)
     assert isinstance(mlp.act_fn, TorchairDeepseekV2SiluAndMul)
-    ascend_config = MagicMock()
-    ascend_config._ASCEND_CONFIG.ascend_scheduler_config.enabled = False
     with patch(
             "vllm_ascend.torchair.models.torchair_deepseek_v2.QuantizationConfig"
     ) as mock_quant_config: