Revert "feat(moe): Add is_act_and_mul=False support for Triton MoE kernels" (#31978)

vllm/model_executor/layers/fused_moe/modular_kernel.py

@@ -4,7 +4,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Callable
 from dataclasses import dataclass
 from enum import Enum
-from math import prod, sqrt
+from math import prod
 from typing import final
 
 import torch
@@ -575,35 +575,14 @@ class FusedMoEPermuteExpertsUnpermute(ABC):
     def activation(
         self, activation: str, output: torch.Tensor, input: torch.Tensor
     ) -> None:
-        # Fused activations (SwiGLU-style): output is half the size of input
+        assert output.size(-1) * 2 == input.size(-1)
         if activation == "silu":
-            assert output.size(-1) * 2 == input.size(-1)
             torch.ops._C.silu_and_mul(output, input)
         elif activation == "gelu":
-            assert output.size(-1) * 2 == input.size(-1)
             torch.ops._C.gelu_and_mul(output, input)
         elif activation == "swigluoai":
             # alpha = 1.702, limit = 7.0
-            assert output.size(-1) * 2 == input.size(-1)
             torch.ops._C.swigluoai_and_mul(output, input)
-        # Non-fused activations (is_act_and_mul=False): output same size as input
-        elif activation == "silu_no_mul":
-            assert output.size(-1) == input.size(-1)
-            # Use out= argument to avoid intermediate tensor
-            torch.sigmoid(input, out=output)
-            output.mul_(input)
-        elif activation == "gelu_no_mul":
-            assert output.size(-1) == input.size(-1)
-            # GELU(x) = 0.5 * x * (1 + erf(x / sqrt(2)))
-            # Use out= and in-place ops to avoid intermediate tensors
-            output.copy_(input).div_(sqrt(2))
-            torch.erf(output, out=output)
-            output.add_(1).mul_(input).mul_(0.5)
-        elif activation == "relu2_no_mul":
-            assert output.size(-1) == input.size(-1)
-            # ReLU²: clamp has out=, then in-place square
-            torch.clamp(input, min=0, out=output)
-            output.square_()
         else:
             raise ValueError(f"Unsupported FusedMoe activation: {activation}")