[Bugfix] Fix chunk prefill bug for long_sequence feature (#5444)

### What this PR does / why we need it?
Fix chunk prefill bug for long_sequence feature

When there are two requests with chunk prefill enabled in the
long-sequence scenario, if one request has only 1 token during
scheduling, it will be identified as a decode request and trigger an
error. This PR fixes the issue.
Closes: https://github.com/vllm-project/vllm-ascend/issues/5445

- vLLM version: release/v0.13.0
- vLLM main:
81786c8774
---------
Signed-off-by: LookAround <lixushi@huawei.com>

tests/e2e/multicard/long_sequence/test_chunked_prefill.py

@@ -0,0 +1,78 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/e2e/multicard/test_qwen3_moe.py`.
+"""
+
+import os
+import random
+import string
+from unittest.mock import patch
+
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+
+
+def generate_prompts(input_len, batchsize):
+    prompts = [
+        " ".join([
+            f"{random.choice(string.ascii_letters)}" for _ in range(input_len)
+        ]) for _ in range(batchsize)
+    ]
+    return prompts
+
+
+@patch.dict(
+    os.environ, {
+        "HCCL_BUFFSIZE": "768",
+        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
+        "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
+    })
+def test_models_chunked_prefill_mixed_length_prompts_including_1_token():
+    TEST_ROPE_PARAMETERS = {
+        "rope_theta": 1000000,
+        "rope_type": "yarn",
+        "factor": 4,
+        "original_max_position_embeddings": 32768
+    }
+    prompts = [
+        generate_prompts(128 * 1024, 1)[0],
+        generate_prompts(1, 1)[0],
+        generate_prompts(9104, 1)[0],
+    ]
+    sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
+
+    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
+    with VllmRunner(
+            model,
+            enforce_eager=True,
+            max_num_seqs=2,
+            max_num_batched_tokens=131000,
+            max_model_len=132000,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=1,
+            enable_expert_parallel=True,
+            block_size=128,
+            quantization="ascend",
+            hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)

vllm_ascend/worker/pcp_utils.py

@@ -463,13 +463,11 @@ class PCPManager:
                                             ]
 
         for i, req_id in enumerate(input_batch.req_ids):
-            num_scheduled_tokens = scheduler_output.num_scheduled_tokens[
-                req_id]
-            is_prefill = input_batch.num_computed_tokens_cpu[
-                i] < input_batch.num_prompt_tokens[i]
+            num_scheduled_token = scheduler_output.num_scheduled_tokens[req_id]
+            is_prefill = num_scheduled_token > self.decode_threshold
             if is_prefill:
                 num_cp_padded_scheduled_tokens = cdiv(
-                    num_scheduled_tokens,
+                    num_scheduled_token,
                     2 * self.pcp_world_size) * (2 * self.pcp_world_size)
                 chunk_size = num_cp_padded_scheduled_tokens // (
                     2 * self.pcp_world_size)