[bugfix](cp) replace None with zeros/inf tensor to avoid TypeError (#5837)

### What this PR does / why we need it?
When there is no kv cache in some devices, the `_compute_prefill_context
func` will return `None`, which is unexecpted. This PR replaces None
with full zeros/-inf tensors to avoid TypeError.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
```bash
pytest tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py -k test_models_chunked_prefill_with_empty_kvcache
```

- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef

---------

Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>

tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py

@@ -21,10 +21,16 @@ import random
 import string
 from unittest.mock import patch
 
+import pytest
 from vllm import SamplingParams
 
 from tests.e2e.conftest import VllmRunner
 
+MODELS = [
+    "vllm-ascend/Qwen3-30B-A3B-W8A8",
+    "vllm-ascend/DeepSeek-V2-Lite-W8A8",
+]
+
 
 def generate_prompts(input_len, batchsize):
     prompts = [
@@ -41,7 +47,9 @@ def generate_prompts(input_len, batchsize):
         "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
         "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
     })
-def test_models_chunked_prefill_mixed_length_prompts_including_1_token():
+@pytest.mark.parametrize("model", MODELS)
+def test_models_chunked_prefill_mixed_length_prompts_including_1_token(
+        model: str):
     TEST_ROPE_PARAMETERS = {
         "rope_theta": 1000000,
         "rope_type": "yarn",
@@ -55,7 +63,6 @@ def test_models_chunked_prefill_mixed_length_prompts_including_1_token():
     ]
     sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
 
-    model = "vllm-ascend/Qwen3-30B-A3B-W8A8"
     with VllmRunner(
             model,
             enforce_eager=True,
@@ -71,3 +78,45 @@ def test_models_chunked_prefill_mixed_length_prompts_including_1_token():
             hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
     ) as runner:
         runner.model.generate(prompts, sampling_params)
+
+
+@patch.dict(
+    os.environ, {
+        "HCCL_BUFFSIZE": "768",
+        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1",
+        "VLLM_ALLOW_LONG_MAX_MODEL_LEN": "1"
+    })
+@pytest.mark.parametrize("model", MODELS)
+def test_models_chunked_prefill_with_empty_kvcache(model: str):
+    TEST_ROPE_PARAMETERS = {
+        "rope_theta": 1000000,
+        "rope_type": "yarn",
+        "factor": 4,
+        "original_max_position_embeddings": 32768
+    }
+    # Note(qcs): we use chunk_size=50, kv_cache_interleave_size=128
+    # to simulate certain edge cases.
+    prompts = [
+        generate_prompts(128, 1)[0],
+        generate_prompts(1, 1)[0],
+        generate_prompts(130, 1)[0],
+        generate_prompts(51, 1)[0],
+        generate_prompts(129, 1)[0],
+    ]
+    sampling_params = SamplingParams(max_tokens=1, temperature=0.0)
+
+    with VllmRunner(
+            model,
+            enforce_eager=True,
+            max_num_seqs=2,
+            tensor_parallel_size=2,
+            prefill_context_parallel_size=2,
+            decode_context_parallel_size=1,
+            enable_expert_parallel=True,
+            long_prefill_token_threshold=50,
+            block_size=128,
+            cp_kv_cache_interleave_size=128,
+            quantization="ascend",
+            hf_overrides={"rope_parameters": TEST_ROPE_PARAMETERS},
+    ) as runner:
+        runner.model.generate(prompts, sampling_params)

vllm_ascend/attention/context_parallel/attention_cp.py

@@ -636,30 +636,37 @@ class AscendAttentionCPImpl(AscendAttentionBackendImpl):
         else:
             num_heads = self.num_heads
 
-        prefix_chunk_output, prefix_chunk_lse = None, None
+        if total_toks == 0:
-        if total_toks > 0:
+            return (torch.full((query.size(0), num_heads, self.head_size),
-            prefix_chunk_output, prefix_chunk_lse = torch.ops.npu.npu_fused_infer_attention_score(
+                               fill_value=0,
-                query,
+                               dtype=query.dtype,
-                key,
+                               device=query.device),
-                value,
+                    torch.full((query.size(0), num_heads, 1),
-                num_heads=num_heads,
+                               fill_value=-torch.inf,
-                num_key_value_heads=self.num_kv_heads,
+                               dtype=torch.float32,
-                input_layout="TND",
+                               device=query.device))
-                atten_mask=None,
+
-                scale=self.scale,
+        prefix_chunk_output, prefix_chunk_lse = torch.ops.npu.npu_fused_infer_attention_score(
-                sparse_mode=0,
+            query,
-                antiquant_mode=0,
+            key,
-                antiquant_scale=None,
+            value,
-                softmax_lse_flag=True,
+            num_heads=num_heads,
-                actual_seq_lengths_kv=prefill_metadata.chunked_context.
+            num_key_value_heads=self.num_kv_heads,
-                actual_seq_lengths_kv,
+            input_layout="TND",
-                actual_seq_lengths=attn_metadata.prefill.chunked_context.
+            atten_mask=None,
-                actual_chunk_seq_lengths)
+            scale=self.scale,
-            batch_chunk_seq_mask = attn_metadata.prefill.chunked_context.batch_chunk_seq_mask
+            sparse_mode=0,
-            lse_mask = batch_chunk_seq_mask[:, None,
+            antiquant_mode=0,
-                                            None].expand_as(prefix_chunk_lse)
+            antiquant_scale=None,
-            prefix_chunk_lse = torch.where(lse_mask, -torch.inf,
+            softmax_lse_flag=True,
-                                           prefix_chunk_lse)
+            actual_seq_lengths_kv=prefill_metadata.chunked_context.
+            actual_seq_lengths_kv,
+            actual_seq_lengths=attn_metadata.prefill.chunked_context.
+            actual_chunk_seq_lengths)
+        batch_chunk_seq_mask = attn_metadata.prefill.chunked_context.batch_chunk_seq_mask
+        lse_mask = batch_chunk_seq_mask[:, None,
+                                        None].expand_as(prefix_chunk_lse)
+        prefix_chunk_lse = torch.where(lse_mask, -torch.inf, prefix_chunk_lse)
 
         return prefix_chunk_output, prefix_chunk_lse