fix: revert to _npu_reshape_and_cache (contiguous) and _npu_flash_attention

vllm_npu/attention/attention_v1.py

@@ -322,21 +322,24 @@ class AscendAttentionBackendImpl(AttentionImpl):
         Matches Huawei vllm-ascend: splits kv_cache[0]/[1] and writes via
         slot_mapping indices.
         """
+        import torch_npu  # noqa: F401
+
         if kv_cache.numel() > 0:
             if self._key_cache is None:
                 self._key_cache, self._value_cache = kv_cache[0], kv_cache[1]
 
-            slots = attn_metadata.slot_mapping
-            key_to_cache = key[:attn_metadata.num_actual_tokens]
-            val_to_cache = value[:attn_metadata.num_actual_tokens]
+            # Ensure contiguous tensors for the NPU op
+            key = key.contiguous()
+            value = value.contiguous()
+            slots = attn_metadata.slot_mapping.long() # indices must be long
 
-            # Use pure-PyTorch indexing (ATB reshape_and_cache may fail
-            # depending on environment; this is functionally identical)
-            block_size = self._key_cache.shape[1]
-            block_idx = slots // block_size
-            block_offset = slots % block_size
-            self._key_cache[block_idx, block_offset] = key_to_cache
-            self._value_cache[block_idx, block_offset] = val_to_cache
+            torch_npu._npu_reshape_and_cache(
+                key,
+                value,
+                self._key_cache,
+                self._value_cache,
+                slots,
+            )
 
         return key, value
 
@@ -450,28 +453,33 @@ class AscendAttentionBackendImpl(AttentionImpl):
         output: torch.Tensor,
         num_tokens: int,
     ) -> torch.Tensor:
-        """Prefill attention without KV cache (self-attention)."""
+        """Prefill attention without KV cache (self-attention) using _npu_flash_attention."""
         import torch_npu  # noqa: F401
 
-        cum_seq_len = attn_metadata.query_start_loc[1:].tolist()
-
-        attn_out = torch_npu.npu_fusion_attention(
-            query[:num_tokens],
-            key[:num_tokens],
-            value[:num_tokens],
-            head_num=self.num_heads,
-            input_layout="TND",
-            scale=self.scale,
-            sparse_mode=0,
-            atten_mask=attn_metadata.attn_mask,
-            pre_tockens=2147483647,
-            next_tockens=0,
-            actual_seq_qlen=cum_seq_len,
-            actual_seq_kvlen=cum_seq_len,
+        # Huawei uses _npu_flash_attention for prefill
+        # Ensure contiguous inputs
+        query = query.contiguous()
+        key = key.contiguous()
+        value = value.contiguous()
+        
+        # mask needs to be contiguous and cast to expected format if needed
+        # but _npu_flash_attention handles generic mask? 
+        # Huawei code: mask = attn_metadata.attn_mask... 
+        # We'll pass it as is, assuming AscendMetadataBuilder created it correctly.
+        
+        torch_npu._npu_flash_attention(
+            query=query,
+            key=key,
+            value=value,
+            mask=attn_metadata.attn_mask,
+            seq_len=attn_metadata.seq_lens,
+            scale_value=self.scale,
+            num_heads=self.num_heads,
+            num_kv_heads=self.num_kv_heads,
+            out=output
         )
 
-        output[:num_tokens] = attn_out[0]
-        return output
+        return output[:num_tokens]
 
     # -----------------------------------------------------------------
     # Chunked prefill — mixed prefill+decode via npu_fusion_attention