[Test] Clean up duplicate test for ascend scheduler (#1819)

There are some duplicate tests for ascend scheduler. This PR remove them
to make the test clear.

After this PR. the singlecard e2e cost time is reduced from 47min to
46min.

- vLLM version: v0.9.2
- vLLM main:
vllm-project/vllm@1eb2b9c

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

Author: wangxiyuan

tests/e2e/singlecard/core/__init__.py renamed to tests/e2e/__init__.py

@@ -15,14 +15,17 @@
 from vllm.v1.request import Request, RequestStatus
 from vllm.v1.structured_output import StructuredOutputManager
 
+from tests.e2e.conftest import VllmRunner
+from tests.e2e.model_utils import check_outputs_equal
 from vllm_ascend.core.scheduler import AscendScheduler
 from vllm_ascend.utils import vllm_version_is
 
 EOS_TOKEN_ID = 50256
+MODEL = "Qwen/Qwen3-0.6B"
 
 
 def create_scheduler(
-    model: str = "Qwen/Qwen2.5-0.5B-Instruct",
+    model: str = MODEL,
     max_num_seqs: int = 16,
     max_num_batched_tokens: int = 8192,
     enable_prefix_caching: Optional[bool] = None,
@@ -733,3 +736,83 @@ def test_memory_leak():
 
     # Confirm no memory leak.
     assert_scheduler_empty(scheduler)
+
+
+def test_concurrent_partial_prefill():
+    with VllmRunner(MODEL,
+                    additional_config={
+                        'ascend_scheduler_config': {
+                            'enabled': True,
+                        },
+                    },
+                    max_num_seqs=3,
+                    max_num_batched_tokens=200,
+                    enforce_eager=True,
+                    max_model_len=2048,
+                    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
+
+
+def test_prefix_cache_stats_is_recorded():
+    with VllmRunner(MODEL,
+                    additional_config={
+                        'ascend_scheduler_config': {
+                            'enabled': True,
+                        },
+                    },
+                    max_num_seqs=3,
+                    max_num_batched_tokens=200,
+                    enforce_eager=True,
+                    max_model_len=2048,
+                    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", [16])
+def test_chunked_prefill_with_ascend_scheduler(
+        example_prompts, max_tokens: int,
+        chunked_prefill_token_size: int) -> None:
+    max_num_seqs = chunked_prefill_token_size
+    max_num_batched_tokens = chunked_prefill_token_size
+    with VllmRunner(MODEL,
+                    additional_config={
+                        'ascend_scheduler_config': {
+                            'enabled': True,
+                            'enable_chunked_prefill': True,
+                        },
+                    },
+                    max_num_seqs=max_num_seqs,
+                    max_num_batched_tokens=max_num_batched_tokens,
+                    enforce_eager=True,
+                    max_model_len=2048,
+                    gpu_memory_utilization=0.7) as vllm_model:
+        chunked_prefill_output = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+    with VllmRunner(MODEL,
+                    additional_config={
+                        'ascend_scheduler_config': {
+                            'enabled': True,
+                        },
+                    },
+                    enforce_eager=True,
+                    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=chunked_prefill_output,
+        name_0="vllm_output",
+        name_1="chunked_prefill_output",
+    )