[BugFix] Fix num_pcp_pads Assignment Issues (#5273)

### What this PR does / why we need it?
The variable `self.num_pcp_pads` was incorrectly truncated during
assignment, causing errors in certain scenarios such as PD
disaggregated. This issue has now been resolved.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?

Co-author by: QiuChunshuo <qiuchunshuo@huawei.com>

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: daishixun <dsxsteven@sina.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>

tests/e2e/multicard/long_sequence/test_basic.py

@@ -135,3 +135,137 @@ def test_models_pcp_dcp_piece_wise():
                     block_size=128,
                     quantization="ascend") as runner:
         runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_basic():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_full_graph():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=False,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128,
+                    compilation_config={
+                        "cudagraph_mode": "FULL_DECODE_ONLY",
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+                    }) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_pcp_piece_wise():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=False,
+                    max_model_len=1024,
+                    tensor_parallel_size=2,
+                    prefill_context_parallel_size=2,
+                    decode_context_parallel_size=1,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_dcp_basic():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=True,
+                    max_model_len=1024,
+                    tensor_parallel_size=4,
+                    prefill_context_parallel_size=1,
+                    decode_context_parallel_size=2,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_dcp_full_graph():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=False,
+                    max_model_len=1024,
+                    tensor_parallel_size=4,
+                    prefill_context_parallel_size=1,
+                    decode_context_parallel_size=2,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128,
+                    compilation_config={
+                        "cudagraph_mode": "FULL_DECODE_ONLY",
+                        "cudagraph_capture_sizes": [4, 8, 24, 48, 60]
+                    }) as runner:
+        runner.model.generate(prompts, sampling_params)
+
+
+@pytest.mark.skipif(vllm_version_is('0.12.0'),
+                    reason="0.12.0 is not supported for context sequence.")
+def test_dcp_piece_wise():
+    prompts = [
+        "The capital of France is", "Hello, my name is Tom, I am",
+        "The president of United States is", "AI future is"
+    ]
+    model = "deepseek-ai/DeepSeek-V2-Lite-Chat"
+    sampling_params = SamplingParams(max_tokens=32, temperature=0.0)
+    with VllmRunner(model,
+                    enforce_eager=False,
+                    max_model_len=1024,
+                    tensor_parallel_size=4,
+                    prefill_context_parallel_size=1,
+                    decode_context_parallel_size=2,
+                    max_num_batched_tokens=1024,
+                    enable_expert_parallel=True,
+                    block_size=128) as runner:
+        runner.model.generate(prompts, sampling_params)

vllm_ascend/worker/model_runner_v1.py

@@ -575,10 +575,10 @@ class NPUModelRunner(GPUModelRunner):
                 tokens)
             num_scheduled_tokens = np.array(tokens, dtype=np.int32)
             total_num_scheduled_tokens = sum(num_scheduled_tokens[:num_reqs])
-            total_num_pcp_pads = torch.sum(self.num_pcp_pads).item()
+            total_num_pcp_pads = torch.sum(self.num_pcp_pads[:num_reqs]).item()
         else:
             position_pcp, pcp_unpad_mask = None, None
-            self.num_pcp_pads = self.num_pcp_pads[:num_reqs]
+            self.num_pcp_pads[:num_reqs] = 0
 
         max_num_scheduled_tokens = max(tokens)
         if not scheduler_output.scheduled_spec_decode_tokens:
@@ -3050,7 +3050,6 @@ class NPUModelRunner(GPUModelRunner):
 
     def _update_tokens_for_pcp(self, tokens):
         num_reqs = self.input_batch.num_reqs
-        self.num_pcp_pads = self.num_pcp_pads[:num_reqs]
         tokens = np.array(tokens, dtype=np.int32)
         num_decode_reqs = (np.array(tokens) <= self.decode_threshold).sum()
         num_decode_tokens = sum(tokens[:num_decode_reqs])
@@ -3059,7 +3058,8 @@ class NPUModelRunner(GPUModelRunner):
             (2 * self.pcp_size)).astype(np.int32) * (2 * self.pcp_size)
         num_padded_scheduled_tokens[:num_decode_reqs] = (
             tokens[:num_decode_reqs] * self.pcp_size)
-        self.num_pcp_pads = torch.tensor(num_padded_scheduled_tokens - tokens)
+        self.num_pcp_pads[:num_reqs] = torch.tensor(
+            num_padded_scheduled_tokens - tokens)
         cu_padded_tokens, pcp_padded_arange = \
             self._get_cumsum_and_arange(num_padded_scheduled_tokens)
         unpad_mask = torch.from_numpy(