[BugFix][v0.18.0][cherry-pick] Fix embedding prefix caching for APC (#7894)

## What this PR does / why we need it?
pick-from:https://github.com/vllm-project/vllm-ascend/pull/7452
### Problem
Embedding models produce inconsistent outputs when prefix caching is
enabled vs disabled.

### Root Cause
The attention router condition was too broad:
- All `model_runner_type == "pooling"` → `_forward_encoder_attention()`
→ uses `npu_fusion_attention`
- **But `npu_fusion_attention` does NOT support prefix caching**
- Result: Numerical mismatch when KV cache is managed by prefix caching

### Solution
Refine the router condition to check causality:

**Before**: 
```
if attn_metadata.model_runner_type == "pooling":
    → npu_fusion_attention (no prefix caching support)
```

**After**: 
```
if attn_metadata.model_runner_type == "pooling" and not attn_metadata.causal:
    → npu_fusion_attention (for true encoders)
else:
    → npu_fused_infer_attention_score (prefix caching support)
```
### Changes Made

1. **Fixed router condition** (`vllm_ascend/attention/attention_v1.py`
L968)
   - Added `and not attn_metadata.causal` check
   - Effect: Non-causal embeddings now use correct operator

2. **Simplified encoder attention**
(`vllm_ascend/attention/attention_v1.py` L864-877)
   - Removed redundant causal branch (encoders never use causal mask)
   - Reduced from 34 lines to 14 lines

3. **Added test** (`tests/e2e/singlecard/pooling/test_embedding.py`)
- Validates embedding outputs with/without prefix caching are consistent
  
## Does this PR introduce _any_ user-facing change?

### Functional Changes
✅ **Yes** - Bug fix: Embedding models now produce consistent outputs
with prefix caching

### API Changes
❌ **No** - All public APIs unchanged

### Configuration Changes
❌ **No** - No new configuration required

### Backward Compatibility
✅ **Fully compatible** - Only fixes incorrect behavior

## How was this patch tested?
### New Test
Added `test_embed_models_using_prefix_caching_correctness()`:
- Tests: `Qwen3-Embedding-0.6B`
- Validates numerical consistency between runs with/without prefix
caching
- Uses long sequences to activate prefix caching
- Tolerance: 1e-2
- vLLM version: v0.18.0

Signed-off-by: underfituu <hzhucong@163.com>

tests/e2e/singlecard/pooling/test_embedding.py

@@ -57,6 +57,32 @@ def test_embed_models_correctness(model: str):
         tol=1e-2,
     )
 
+def test_causal_embed_models_using_prefix_caching_correctness():
+    # This test is to verify the correctness of prefix caching for embedding models. 
+    # We compare the outputs of vLLM with and without prefix caching enabled, and check if they are close enough.
+    # We set the input query to be very long to make sure prefix caching is triggered.
+    queries = ['What is the capital of China?' * 256, 'Explain gravity']
+
+    model_name = snapshot_download("Qwen/Qwen3-Embedding-0.6B", local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,)
+    with VllmRunner(
+            model_name,
+            runner="pooling",
+            max_model_len=None,
+            cudagraph_capture_sizes=[4],
+            enable_prefix_caching=True,
+    ) as vllm_runner_using_caching:
+        vllm_outputs_without_caching = vllm_runner_using_caching.embed(queries)
+        vllm_outputs_with_caching = vllm_runner_using_caching.embed(queries)
+
+
+    check_embeddings_close(
+        embeddings_0_lst=vllm_outputs_without_caching,
+        embeddings_1_lst=vllm_outputs_with_caching,
+        name_0="without_caching",
+        name_1="with_caching",
+        tol=1e-2,
+    )
+
 
 def test_bge_m3_correctness():
     queries = ['What is the capital of China?', 'Explain gravity']

vllm_ascend/attention/attention_v1.py

@@ -867,23 +867,6 @@ class AscendAttentionBackendImpl(AttentionImpl):
         attn_metadata: AscendMetadata,
         _: torch.Tensor,
     ) -> torch.Tensor:
-        assert attn_metadata is not None
-
-        if attn_metadata.causal:
-            # use sparse_mode 3 in causal scenario
-            return torch_npu.npu_fusion_attention(
-                query=query,
-                key=key,
-                value=value,
-                head_num=self.num_heads,
-                input_layout="TND",
-                scale=self.scale,
-                sparse_mode=3,
-                atten_mask=attn_metadata.attn_mask,
-                actual_seq_qlen=attn_metadata.actual_seq_lengths_q,
-                actual_seq_kvlen=attn_metadata.actual_seq_lengths_q,
-            )[0]
-        else:
         # use default sparse_mode 0 in normal scenario, which means no mask works on it
         return torch_npu.npu_fusion_attention(
             query=query,
@@ -985,7 +968,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 query, key, value, kv_cache, attn_metadata, output
             )
         # pooling model branch
-        if attn_metadata.model_runner_type == "pooling":
+        if attn_metadata.model_runner_type == "pooling" and not attn_metadata.causal:
             attn_output = self._forward_encoder_attention(query, key, value, attn_metadata, output)
             output[:num_tokens] = attn_output[:num_tokens]
             return output