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

tests/kernels/core/test_activation.py

@@ -11,7 +11,7 @@ from tests.kernels.utils import opcheck
 from vllm.model_executor.layers.activation import (FastGELU, FatreluAndMul,
                                                    GeluAndMul, MulAndSilu,
                                                    NewGELU, QuickGELU,
-                                                   SiluAndMul, SwigluOAIAndMul)
+                                                   SiluAndMul)
 from vllm.platforms import current_platform
 
 DTYPES = [torch.half, torch.bfloat16, torch.float]
@@ -25,15 +25,7 @@ CUDA_DEVICES = [
 
 @pytest.mark.parametrize(
     "activation",
-    [
-        "silu_and_mul",
-        "mul_and_silu",
-        "gelu",
-        "gelu_tanh",
-        "fatrelu",
-        "swigluoai_and_mul",
-    ],
-)
+    ["silu_and_mul", "mul_and_silu", "gelu", "gelu_tanh", "fatrelu"])
 @pytest.mark.parametrize("num_tokens", NUM_TOKENS)
 @pytest.mark.parametrize("d", D)
 @pytest.mark.parametrize("dtype", DTYPES)
@@ -67,43 +59,18 @@ def test_act_and_mul(
         threshold = random.uniform(0, 1)
         layer = FatreluAndMul(threshold)
         fn = torch.ops._C.fatrelu_and_mul
-    elif activation == "swigluoai_and_mul":
-        layer = SwigluOAIAndMul()
-        fn = torch.ops._C.swigluoai_and_mul
     out = layer(x)
     ref_out = layer.forward_native(x)
-    if activation == "swigluoai_and_mul":
-
-        rtol = {
-            #For fp16, change the relative tolerance from 1e-3 to 2e-3
-            torch.float16:
-            2e-3,
-            torch.bfloat16:
-            2e-2,
-            torch.float:
-            1.3e-6
-        }
-
-        def _get_rtol(output) -> float:
-            return rtol[output.dtype]
-
-        torch.testing.assert_close(out,
-                                   ref_out,
-                                   atol=get_default_atol(out),
-                                   rtol=_get_rtol(out))
-    else:
-        # The SiluAndMul, MulAndSilu, GELU and FatReLU implementations are
-        # equivalent to the native PyTorch implementations, so we can do exact
-        # comparison.
-        torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
+    # The SiluAndMul, MulAndSilu, GELU and FatReLU implementations are
+    # equivalent to the native PyTorch implementations, so we can do exact
+    # comparison.
+    torch.testing.assert_close(out, ref_out, atol=0.0, rtol=0.0)
 
     d = x.shape[-1] // 2
     output_shape = (x.shape[:-1] + (d, ))
     out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
     if activation == "fatrelu":
         opcheck(fn, (out, x, threshold))
-    elif activation == "swigluoai_and_mul":
-        opcheck(fn, (out, x, layer.alpha, layer.limit))
     else:
         opcheck(fn, (out, x))