[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>

vllm_ascend/attention/attention_v1.py

@@ -867,34 +867,17 @@ 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,
-                key=key,
-                value=value,
-                head_num=self.num_heads,
-                input_layout="TND",
-                scale=self.scale,
-                actual_seq_qlen=attn_metadata.actual_seq_lengths_q,
-                actual_seq_kvlen=attn_metadata.actual_seq_lengths_q,
-            )[0]
+        # use default sparse_mode 0 in normal scenario, which means no mask works on it
+        return torch_npu.npu_fusion_attention(
+            query=query,
+            key=key,
+            value=value,
+            head_num=self.num_heads,
+            input_layout="TND",
+            scale=self.scale,
+            actual_seq_qlen=attn_metadata.actual_seq_lengths_q,
+            actual_seq_kvlen=attn_metadata.actual_seq_lengths_q,
+        )[0]
 
     def reshape_and_cache(
         self,
@@ -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