[ROCm][CI] Fix TestSiluMulGroupFp8QuantModel after W8A8 block linear refactor (#39799)

Signed-off-by: Andreas Karatzas <akaratza@amd.com>

tests/compile/passes/test_silu_mul_quant_fusion.py

@@ -10,7 +10,7 @@ import vllm.envs as envs
 from tests.compile.backend import TestBackend
 from tests.kernels.quantization.nvfp4_utils import quant_nvfp4_tensor
 from tests.utils import TestFP8Layer
-from vllm._aiter_ops import IS_AITER_FOUND
+from vllm._aiter_ops import IS_AITER_FOUND, rocm_aiter_ops
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
 from vllm.compilation.passes.fusion.act_quant_fusion import (
     FUSED_OPS,
@@ -157,12 +157,13 @@ class TestSiluMulGroupFp8QuantModel(torch.nn.Module):
             activation_quant_key=self.act_quant_key,
             input_dtype=dtype,
         )
-        self.w = torch.rand(hidden_size, hidden_size).to(dtype=FP8_DTYPE).t()
 
-        scale_hidden_size = (hidden_size + 128 - 1) // 128
-        self.wscale = torch.rand(
-            (scale_hidden_size, scale_hidden_size), dtype=torch.float32
-        )
+        if not current_platform.is_fp8_fnuz():
+            kernel = self.w8a8_block_fp8_linear.kernel
+            orig_quant = kernel.quant_fp8
+            kernel.quant_fp8 = lambda *a, use_triton=False, **kw: orig_quant(
+                *a, use_triton=True, **kw
+            )
 
         self.enable_silu_mul_custom_op = self.silu_and_mul.enabled()
 
@@ -174,6 +175,9 @@ class TestSiluMulGroupFp8QuantModel(torch.nn.Module):
     def ops_in_model_before(self):
         return [
             SILU_MUL_OP if self.enable_silu_mul_custom_op else torch.ops.aten.mul,
+            rocm_aiter_ops.get_group_quant_op()
+            if current_platform.is_fp8_fnuz()
+            else torch.ops.vllm.triton_per_token_group_quant_fp8.default,
         ]
 
     def ops_in_model_after(self):
@@ -324,7 +328,6 @@ def test_fusion_silu_and_mul_quant(
     with set_current_vllm_config(config), monkeypatch.context() as m:
         fusion_passes = [ActivationQuantFusionPass(config)]
         if IS_AITER_FOUND and model_class is TestSiluMulGroupFp8QuantModel:
-            from vllm._aiter_ops import rocm_aiter_ops
             from vllm.compilation.passes.fusion.rocm_aiter_fusion import (
                 RocmAiterSiluMulFp8GroupQuantFusionPass,
             )
@@ -352,10 +355,16 @@ def test_fusion_silu_and_mul_quant(
             atol, rtol = 1e-3, 1e-3
         elif isinstance(model, TestSiluMulNvfp4QuantModel):
             atol, rtol = 1e-1, 1e-1
-        elif isinstance(
-            model, (TestSiluMulGroupFp8QuantModel, TestSiluMulBlockQuantModel)
-        ):
+        elif isinstance(model, TestSiluMulGroupFp8QuantModel):
             atol, rtol = 5e-2, 5e-2
+        elif isinstance(model, TestSiluMulBlockQuantModel):
+            if current_platform.is_rocm():
+                atol, rtol = 1e-3, 1e-3
+            else:
+                # CUDA fused kernel computes silu*mul in fp32 while the reference
+                # goes through bf16/fp16 storage, so group maxima (and thus scales)
+                # can shift by one FP8-e4m3 code (~1/8 relative step).
+                atol, rtol = 5e-2, 5e-2
 
         torch.testing.assert_close(
             result[0].to(dtype=dtype), result2[0].to(dtype=dtype), atol=atol, rtol=rtol