[Perf]Optimize rotary_emb implementation to use Triton operator for improved inference performance

Signed-off-by: cynthieye <[email protected]>
Co-authored-by: MagnetoWang <[email protected]>

Author: cynthieye

vllm/model_executor/layers/rotary_embedding.py

@@ -46,20 +46,12 @@ def _rotate_gptj(x: torch.Tensor) -> torch.Tensor:
     return x.flatten(-2)
 
 
-def _apply_rotary_emb(
+def _apply_rotary_emb_torch(
     x: torch.Tensor,
     cos: torch.Tensor,
     sin: torch.Tensor,
     is_neox_style: bool,
 ) -> torch.Tensor:
-    """
-    Args:
-        x: [num_tokens, num_heads, head_size]
-        cos: [num_tokens, head_size // 2]
-        sin: [num_tokens, head_size // 2]
-        is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
-            positional embeddings.
-    """
     cos = cos.unsqueeze(-2).to(x.dtype)
     sin = sin.unsqueeze(-2).to(x.dtype)
     if is_neox_style:
@@ -75,6 +67,26 @@ def _apply_rotary_emb(
         return torch.stack((o1, o2), dim=-1).flatten(-2)
 
 
+def _apply_rotary_emb(x: torch.Tensor,
+                      cos: torch.Tensor,
+                      sin: torch.Tensor,
+                      is_neox_style: bool) -> torch.Tensor:
+    """
+    Args:
+        x: [num_tokens, num_heads, head_size]
+        cos: [num_tokens, head_size // 2]
+        sin: [num_tokens, head_size // 2]
+        is_neox_style: Whether to use the Neox-style or GPT-J-style rotary
+            positional embeddings.
+    """
+    if current_platform.is_cuda_alike():
+        from vllm_flash_attn.layers.rotary import apply_rotary_emb
+        return apply_rotary_emb(x.unsqueeze(0), cos, sin,
+                                not is_neox_style).squeeze(0)
+    else:
+        return _apply_rotary_emb_torch(x, cos, sin, is_neox_style)
+
+
 @CustomOp.register("rotary_embedding")
 class RotaryEmbedding(CustomOp):
     """Original rotary positional embedding."""
@@ -141,14 +153,14 @@ def forward_native(
         query = query.view(num_tokens, -1, self.head_size)
         query_rot = query[..., :self.rotary_dim]
         query_pass = query[..., self.rotary_dim:]
-        query_rot = _apply_rotary_emb(query_rot, cos, sin, self.is_neox_style)
+        query_rot = _apply_rotary_emb_torch(query_rot, cos, sin, self.is_neox_style)
         query = torch.cat((query_rot, query_pass), dim=-1).reshape(query_shape)
 
         key_shape = key.shape
         key = key.view(num_tokens, -1, self.head_size)
         key_rot = key[..., :self.rotary_dim]
         key_pass = key[..., self.rotary_dim:]
-        key_rot = _apply_rotary_emb(key_rot, cos, sin, self.is_neox_style)
+        key_rot = _apply_rotary_emb_torch(key_rot, cos, sin, self.is_neox_style)
         key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
         return query, key
 
@@ -309,9 +321,9 @@ def _apply_rotary_emb_neuron(
         key = key.view(num_tokens, -1, self.head_size)
 
         if self.rotary_dim == self.head_size:
-            query = _apply_rotary_emb(query, cos, sin, self.is_neox_style)
+            query = _apply_rotary_emb_torch(query, cos, sin, self.is_neox_style)
             query = query.reshape(query_shape)
-            key = _apply_rotary_emb(key, cos, sin, self.is_neox_style)
+            key = _apply_rotary_emb_torch(key, cos, sin, self.is_neox_style)
             key = key.reshape(key_shape)
         else:
             head_size = query.shape[-1]