[Bugfix] Fix platform-specific routing in CustomOp implementations (#24444)

Signed-off-by: Konrad Zawora <kzawora@habana.ai>

vllm/model_executor/layers/activation.py

@@ -454,7 +454,7 @@ class XIELU(CustomOp):
         )
         return result.view(original_shape)
 
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
+    def forward_native(self, input: torch.Tensor) -> torch.Tensor:
         if self._xielu_cuda_obj is not None and input.is_cuda:
             if not torch._dynamo.is_compiling():
                 return self._xielu_cuda_fn(input)
@@ -464,6 +464,9 @@ class XIELU(CustomOp):
                 )
         return self._xielu_python(input)
 
+    def forward_cuda(self, input: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(input)
+
 
 class ScaledActivation(nn.Module):
     """An activation function with post-scale parameters.

vllm/model_executor/layers/fused_moe/layer.py

@@ -1593,7 +1593,7 @@ class FusedMoE(CustomOp):
         else:
             return tensor_model_parallel_all_reduce(final_hidden_states)
 
-    def forward(
+    def forward_native(
         self,
         hidden_states: torch.Tensor,
         router_logits: torch.Tensor,
@@ -1627,6 +1627,13 @@ class FusedMoE(CustomOp):
             return (shared_output[..., :og_hidden_states],
                     fused_output[..., :og_hidden_states])
 
+    def forward_cuda(
+        self,
+        hidden_states: torch.Tensor,
+        router_logits: torch.Tensor,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        return self.forward_native(hidden_states, router_logits)
+
     def forward_impl_chunked(
         self,
         full_hidden_states: torch.Tensor,

vllm/model_executor/layers/rotary_embedding/deepseek_scaling_rope.py

@@ -88,7 +88,7 @@ class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
         cache = torch.cat((cos, sin), dim=-1)
         return cache
 
-    def forward(
+    def forward_native(
         self,
         positions: torch.Tensor,
         query: torch.Tensor,
@@ -129,3 +129,12 @@ class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
             query = query_rot
             key = key_rot
         return query, key
+
+    def forward_cuda(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        return self.forward_native(positions, query, key, offsets)

vllm/model_executor/layers/rotary_embedding/dual_chunk_rope.py

@@ -111,7 +111,7 @@ class DualChunkRotaryEmbedding(CustomOp):
                                              device=self.device)
         return q_cache, qc_cache, k_cache, qc_no_clamp_cache, q_inter_cache
 
-    def forward(
+    def forward_native(
         self,
         positions: torch.Tensor,
         query: torch.Tensor,
@@ -161,6 +161,15 @@ class DualChunkRotaryEmbedding(CustomOp):
                           dim=-1)
         return query, key
 
+    def forward_cuda(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return self.forward_native(positions, query, key, offsets)
+
     def _apply_rotary_embedding(self, cos_sin, hidden_rot, hidden_pass):
         cos, sin = cos_sin.chunk(2, dim=-1)
         if self.is_neox_style:

vllm/model_executor/layers/rotary_embedding/ernie45_vl_rope.py

@@ -12,7 +12,7 @@ from .mrope import MRotaryEmbedding
 class Ernie4_5_VLRotaryEmbedding(MRotaryEmbedding):
     """3D rotary positional embedding. 3D is t:time h:height w:width"""
 
-    def forward(
+    def forward_native(  # type: ignore[override]
         self,
         positions: torch.Tensor,
         query: torch.Tensor,
@@ -70,3 +70,11 @@ class Ernie4_5_VLRotaryEmbedding(MRotaryEmbedding):
                                             self.is_neox_style)
         key = torch.cat((key_rot, key_pass), dim=-1).reshape(key_shape)
         return query, key
+
+    def forward_cuda(  # type: ignore[override]
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        return self.forward_native(positions, query, key)
\ No newline at end of file

vllm/model_executor/layers/rotary_embedding/llama4_vision_rope.py

@@ -53,7 +53,7 @@ class Llama4VisionRotaryEmbedding(RotaryEmbedding):
             torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1))
         return cache
 
-    def forward(
+    def forward_native(  # type: ignore[override]
         self,
         query: torch.Tensor,
         key: Optional[torch.Tensor] = None,
@@ -72,3 +72,10 @@ class Llama4VisionRotaryEmbedding(RotaryEmbedding):
         query_out = torch.view_as_real(query_ * freqs_ci).flatten(3)
         key_out = torch.view_as_real(key_ * freqs_ci).flatten(3)
         return query_out.type_as(query), key_out.type_as(key)
+
+    def forward_cuda(  # type: ignore[override]
+        self,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
+        return self.forward_native(query, key)

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -8,7 +8,6 @@ import numpy as np
 import torch
 from transformers import PretrainedConfig
 
-from vllm.platforms import current_platform
 from vllm.triton_utils import tl, triton
 
 from .base import RotaryEmbedding
@@ -202,28 +201,6 @@ class MRotaryEmbedding(RotaryEmbedding):
         if self.mrope_section:
             assert sum(self.mrope_section) == rotary_dim // 2
 
-        self.use_triton = current_platform.is_cuda_alike()
-
-    def forward(
-        self,
-        positions: torch.Tensor,
-        query: torch.Tensor,
-        key: Optional[torch.Tensor] = None,
-    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
-        """MRope forward.
-
-        Args:
-            positions:
-                [num_tokens,] (text only) or
-                [3, num_tokens] (T/H/W positions with multimodal inputs)
-            query: [num_tokens, num_heads * head_size]
-            key: [num_tokens, num_kv_heads * head_size]
-        """
-        if self.use_triton:
-            return self.forward_cuda(positions, query, key)
-        else:
-            return self.forward_native(positions, query, key)
-
     def forward_native(
         self,
         positions: torch.Tensor,

vllm/model_executor/layers/vocab_parallel_embedding.py

@@ -399,7 +399,7 @@ class VocabParallelEmbedding(CustomOp):
         param[:loaded_weight.shape[0]].data.copy_(loaded_weight)
         param[loaded_weight.shape[0]:].data.fill_(0)
 
-    def forward(self, input_):
+    def forward_native(self, input_):
         if self.tp_size > 1:
             # Build the mask.
             masked_input, input_mask = get_masked_input_and_mask(
@@ -420,6 +420,9 @@ class VocabParallelEmbedding(CustomOp):
         output = tensor_model_parallel_all_reduce(output_parallel)
         return output
 
+    def forward_cuda(self, input_):
+        return self.forward_native(input_)
+
     def extra_repr(self) -> str:
         s = f"num_embeddings={self.num_embeddings_per_partition}"
         s += f", embedding_dim={self.embedding_dim}"