Revert "[Kernel] Add cuda kernel for gpt_oss activation" (#22948)

vllm/model_executor/layers/activation.py

@@ -239,35 +239,6 @@ class GeluAndMul(CustomOp):
         return f'approximate={repr(self.approximate)}'
 
 
-@CustomOp.register("swigluoai_and_mul")
-class SwigluOAIAndMul(CustomOp):
-    # https://github.com/huggingface/transformers/blob/v4.55.0/src/transformers/models/gpt_oss/modeling_gpt_oss.py#L106-L110
-    def __init__(self, alpha: float = 1.702, limit: float = 7.0):
-        super().__init__()
-        self.alpha = alpha
-        self.limit = limit
-
-    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
-        """PyTorch-native implementation equivalent to forward()."""
-
-        gate, up = x[..., ::2], x[..., 1::2]
-        gate = gate.clamp(min=None, max=self.limit)
-        up = up.clamp(min=-self.limit, max=self.limit)
-        glu = gate * torch.sigmoid(gate * self.alpha)
-        gated_output = (up + 1) * glu
-        return gated_output
-
-    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
-        d = x.shape[-1] // 2
-        output_shape = (x.shape[:-1] + (d, ))
-        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
-        torch.ops._C.swigluoai_and_mul(out, x, self.alpha, self.limit)
-        return out
-
-    def extra_repr(self) -> str:
-        return f"alpha={repr(self.alpha)}, limit={repr(self.limit)}"
-
-
 @CustomOp.register("gelu_new")
 class NewGELU(CustomOp):
 
@@ -359,7 +330,6 @@ class ReLUSquaredActivation(CustomOp):
         return torch.square(F.relu(x))
 
     def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
-        #TODO : implement cuda kenrels
         return self.forward_native(x)
 
 
@@ -436,14 +406,9 @@ def get_act_fn(act_fn_name: str) -> nn.Module:
 
 
 _ACTIVATION_AND_MUL_REGISTRY = LazyDict({
-    "gelu":
-    lambda: GeluAndMul(),
-    "silu":
-    lambda: SiluAndMul(),
-    "geglu":
-    lambda: GeluAndMul(),
-    "swigluoai_and_mul":
-    lambda *args, **kwargs: SwigluOAIAndMul(*args, **kwargs),
+    "gelu": lambda: GeluAndMul(),
+    "silu": lambda: SiluAndMul(),
+    "geglu": lambda: GeluAndMul(),
 })