feat: Add Ascend NPU attention backend with NPU-specific FlashAttention, LayerNorm, and Rotary Embedding implementations.

vllm_npu/ops/layernorm.py

@@ -1,41 +1,36 @@
 """
 NPU-optimized layer normalization for Ascend.
 
-Provides RMS norm operations using ``torch_npu.npu_rms_norm`` and
-``torch_npu.npu_add_rms_norm``.
+Provides ``AscendRMSNorm`` — a proper ``RMSNorm`` subclass with
+``forward_oot()`` so that vLLM's ``CustomOp`` dispatch can route
+to NPU kernels automatically.
 """
 
+from typing import Optional, Tuple, Union
+
 import torch
+from vllm.model_executor.layers.layernorm import RMSNorm
 
 
-def rms_norm_npu(
-    out: torch.Tensor,
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    epsilon: float,
-) -> None:
-    """RMS norm using Ascend NPU fused kernel.
+class AscendRMSNorm(RMSNorm):
+    """RMSNorm using Ascend NPU fused kernels.
 
-    Writes the result into ``out`` in-place.
+    Uses ``torch_npu.npu_rms_norm`` for standalone normalization and
+    ``torch_npu.npu_add_rms_norm`` for fused residual-add + norm.
     """
-    import torch_npu  # noqa: F401
-    normed, _residual = torch_npu.npu_rms_norm(input, weight, epsilon)
-    out.copy_(normed)
 
+    def forward_oot(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        import torch_npu  # noqa: F401
 
-def fused_add_rms_norm_npu(
-    input: torch.Tensor,
-    residual: torch.Tensor,
-    weight: torch.Tensor,
-    epsilon: float,
-) -> None:
-    """Fused add + RMS norm using Ascend NPU kernel.
+        if residual is not None:
+            x, _, residual = torch_npu.npu_add_rms_norm(
+                x, residual, self.weight, self.variance_epsilon
+            )
+            return x, residual
 
-    Modifies ``input`` and ``residual`` in-place.
-    """
-    import torch_npu  # noqa: F401
-    normed, residual_out = torch_npu.npu_add_rms_norm(
-        input, residual, weight, epsilon
-    )
-    input.copy_(normed)
-    residual.copy_(residual_out)
+        x, _ = torch_npu.npu_rms_norm(x, self.weight, self.variance_epsilon)
+        return x

vllm_npu/ops/rotary_embedding.py

@@ -1,31 +1,77 @@
 """
 NPU-optimized rotary embedding for Ascend.
 
-Wraps ``torch_npu._npu_rotary_embedding`` for fused RoPE application.
+Provides ``AscendRotaryEmbedding`` — a proper ``RotaryEmbedding`` subclass
+with ``forward_oot()`` so that vLLM's ``CustomOp`` dispatch can route
+to the NPU fused kernel automatically.
 """
 
+from typing import Optional, Tuple
+
 import torch
+from vllm.model_executor.layers.rotary_embedding import RotaryEmbedding
 
 
-def rotary_embedding_npu(
-    positions: torch.Tensor,
-    query: torch.Tensor,
-    key: torch.Tensor,
-    head_size: int,
-    cos_sin_cache: torch.Tensor,
-    is_neox: bool,
-) -> None:
-    """Apply rotary position embedding using Ascend NPU fused kernel.
+class AscendRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding using Ascend NPU fused kernel.
 
-    Modifies ``query`` and ``key`` in-place.
+    Uses ``torch_npu._npu_rotary_embedding`` for in-place RoPE application.
     """
-    import torch_npu  # noqa: F401
 
-    if not query.is_contiguous():
-        query = query.contiguous()
-    if not key.is_contiguous():
-        key = key.contiguous()
+    def forward_oot(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        import torch_npu  # noqa: F401
 
-    torch_npu._npu_rotary_embedding(
-        positions, query, key, head_size, cos_sin_cache, is_neox
-    )
+        query_shape, key_shape = query.shape, key.shape
+
+        if self.cos_sin_cache.device != query.device:
+            self.cos_sin_cache = self.cos_sin_cache.to(query.device)
+        if self.cos_sin_cache.dtype != query.dtype:
+            self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
+
+        if offsets is not None:
+            raise NotImplementedError(
+                "Batched rotary embedding is currently not supported on NPU."
+            )
+
+        if self.rotary_dim < self.head_size:
+            # Partial rotary embedding: only rotate first rotary_dim dims
+            num_tokens = query.shape[0]
+            query = query.view(num_tokens, -1, self.head_size)
+            key = key.view(num_tokens, -1, self.head_size)
+
+            q_rot = query[..., :self.rotary_dim]
+            q_pass = query[..., self.rotary_dim:]
+            k_rot = key[..., :self.rotary_dim]
+            k_pass = key[..., self.rotary_dim:]
+
+            q_rot = q_rot.contiguous().view(num_tokens, -1)
+            k_rot = k_rot.contiguous().view(num_tokens, -1)
+
+            torch_npu._npu_rotary_embedding(
+                positions, q_rot, k_rot,
+                self.head_size, self.cos_sin_cache, self.is_neox_style,
+            )
+
+            q_rot = q_rot.view(num_tokens, -1, self.rotary_dim)
+            k_rot = k_rot.view(num_tokens, -1, self.rotary_dim)
+            q = torch.cat((q_rot, q_pass), dim=-1).reshape(query_shape)
+            k = torch.cat((k_rot, k_pass), dim=-1).reshape(key_shape)
+            return q, k
+        else:
+            # Full rotary embedding
+            # TODO: Remove the contiguous in the future.
+            query = query.contiguous().view(query.shape[0], -1)
+            key = key.contiguous().view(key.shape[0], -1)
+
+            torch_npu._npu_rotary_embedding(
+                positions, query, key,
+                self.head_size, self.cos_sin_cache, self.is_neox_style,
+            )
+
+            return query.view(query_shape), key.view(key_shape)