[ROCm] [CI] Add new fusion test cases that are relevant to vLLM IR Ops (#34307)

Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>
Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Co-authored-by: vllmellm <vllm.ellm@embeddedllm.com>

.buildkite/test-amd.yaml

@@ -610,6 +610,8 @@ steps:
       --ignore=lora/test_qwen3moe_tp.py
   parallelism: 4
 
+##### .buildkite/test_areas/pytorch.yaml #####
+# corresponds to .buildkite/test_areas/pytorch.yaml
 - label: PyTorch Compilation Unit Tests # 15min
   timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental, amdproduction]
@@ -627,6 +629,20 @@ steps:
   # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
   - "find compile/ -maxdepth 1 -name 'test_*.py' -exec pytest -s -v {} \\\\;"
 
+# corresponds to .buildkite/test_areas/pytorch.yaml
+- label: PyTorch Compilation Passes Unit Tests
+  timeout_in_minutes: 20
+  mirror_hardwares: [amdexperimental, amdproduction]
+  agent_pool: mi325_1
+  source_file_dependencies:
+    - vllm/
+    - tests/compile/passes
+  commands:
+  # TODO: clean up this comment if not needed. It is used to 
+  # keep track of the tests changes during vLLM IR Ops refactoring.
+  # Use `find` to launch multiple instances of pytest.
+  - "find compile/passes -maxdepth 1 -name 'test_*.py' -exec pytest -s -v {} \\\\;"
+
 - label: PyTorch Fullgraph Smoke Test # 15min
   timeout_in_minutes: 30
   mirror_hardwares: [amdexperimental, amdproduction]
@@ -1211,41 +1227,6 @@ steps:
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
     - pytest -v -s tests/kernels/moe/test_cutedsl_moe.py
 
-- label: Blackwell Fusion and Compile Tests # 30 min
-  timeout_in_minutes: 40
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/v1/worker/
-  - vllm/v1/cudagraph_dispatcher.py
-  - vllm/compilation/
-  # can affect pattern matching
-  - vllm/model_executor/layers/layernorm.py
-  - vllm/model_executor/layers/activation.py
-  - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - tests/compile/passes/test_fusion_attn.py
-  - tests/compile/passes/test_silu_mul_quant_fusion.py
-  - tests/compile/passes/distributed/test_fusion_all_reduce.py
-  - tests/compile/fullgraph/test_full_graph.py
-  commands:
-    - nvidia-smi
-    - pytest -v -s tests/compile/passes/test_fusion_attn.py
-    - pytest -v -s tests/compile/passes/test_silu_mul_quant_fusion.py
-    # this runner has 2 GPUs available even though num_gpus=2 is not set
-    - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
-
-    # # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
-    # # Wrap with quotes to escape yaml
-    # - "pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
-    # Old E2E tests were removed in https://github.com/vllm-project/vllm/pull/33293
-    # in favor of new tests in fusions_e2e. We avoid replicating the new jobs in this file as it's deprecated.
-
-    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
-    - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
-
 - label: Blackwell GPT-OSS Eval
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
@@ -1371,7 +1352,6 @@ steps:
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - pytest -v -s compile/correctness_e2e/test_sequence_parallel.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
@@ -1601,16 +1581,16 @@ steps:
   commands:
     - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/passes/distributed/test_async_tp.py
     - pytest -v -s tests/compile/passes/distributed/test_sequence_parallelism.py
-    - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
+    # TODO: this test is not supported on ROCm, there are aiter kernels for this.
+    # - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
     #- pytest -v -s tests/compile/distributed/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
     # - "VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/distributed/test_fusions_e2e.py -k 'not Llama-4'"
     # Old E2E tests were removed in https://github.com/vllm-project/vllm/pull/33293
     # in favor of new tests in fusions_e2e. We avoid replicating the new jobs in this file as it's deprecated.
-
-    - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
     - HIP_VISIBLE_DEVICES=0,1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=allgather_reducescatter --disable-nccl-for-dp-synchronization
-    - pytest -v -s tests/v1/distributed/test_dbo.py
+    # this test is not supported on ROCm
+    # - pytest -v -s tests/v1/distributed/test_dbo.py
 
 ##### B200 test #####
 - label: Distributed Tests (B200) # optional
@@ -1721,6 +1701,93 @@ steps:
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040
 
+##### .buildkite/test_areas/compile.yaml #####
+# Slowly setting up the tests so that it is also easier for the 
+# CI team to review and upstream to the pipelinev2.
+# The following tests are important for vLLM IR Ops refactoring,
+# which affects fusion passes on ROCm. So we have to 
+# enable them as as soon as possible.
+
+## TODO: Enable the test in this group
+# # corresponds to .buildkite/test_areas/compile.yaml
+# - label: Fusion and Compile Unit Tests (2xMI325 GPUs)
+#   timeout_in_minutes: 20
+#   working_dir: "/vllm-workspace/"
+#   mirror_hardwares: [amdexperimental, amdproduction, tj]
+#   agent_pool: mi325_1 # changed to 1 GPU until the fusion all reduce is enabled then only revert back to 2 GPUs
+#   source_file_dependencies:
+#   - csrc/quantization/fp4/
+#   - vllm/model_executor/layers/quantization/
+#   - vllm/model_executor/layers/layernorm.py
+#   - vllm/model_executor/layers/activation.py
+#   - vllm/model_executor/layers/attention/attention.py
+#   - vllm/v1/attention/backends/flashinfer.py
+#   - vllm/compilation/ # TODO(luka) limit to vllm/compilation/passes
+#   - tests/compile/test_fusion_attn.py
+#   - tests/compile/test_silu_mul_quant_fusion.py
+#   - tests/compile/distributed/test_fusion_all_reduce.py
+#   - tests/compile/fullgraph/test_full_graph.py
+#   commands:
+#     - rocm-smi
+#     # we run all backend tests on ROCm
+#     # These two tests are covered in "PyTorch Compilation Passes Unit Tests"
+#     # - "pytest -v -s tests/compile/passes/test_fusion_attn.py"
+#     # - "pytest -v -s tests/compile/passes/test_silu_mul_quant_fusion.py"
+#     # TODO: this test is not supported on ROCm, there are aiter kernels for this.
+#     # - pytest -v -s tests/compile/passes/distributed/test_fusion_all_reduce.py
+#     # TODO: find out more details
+#     # - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
+
+# corresponds to .buildkite/test_areas/compile.yaml
+- label: Fusion E2E Quick (MI325)
+  timeout_in_minutes: 15
+  working_dir: "/vllm-workspace/"
+  mirror_hardwares: [amdexperimental, amdproduction]
+  agent_pool: mi325_1
+  num_devices: 1
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/model_executor/
+    - vllm/v1/attention/
+    - vllm/compilation/
+    - tests/compile/fusions_e2e/
+  commands:
+    - rocm-smi
+    # Run all models and attn backends but only Inductor partition and native custom ops
+    - "pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k 'inductor_partition and not +rms_norm and not +quant_fp8'"
+    # Different from CUDA, Qwen requires +rms_norm and +quant_fp8 as rms+quant fusion is only supported on AITER
+    - "pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k 'inductor_partition and +rms_norm and +quant_fp8 and qwen3'"
+
+# corresponds to .buildkite/test_areas/compile.yaml
+- label: Fusion E2E Config Sweep (MI325)
+  timeout_in_minutes: 30
+  working_dir: "/vllm-workspace/"
+  mirror_hardwares: [amdexperimental, amdproduction]
+  agent_pool: mi325_1
+  num_devices: 1
+  source_file_dependencies:
+    - csrc/quantization/
+    - vllm/compilation/
+    # can affect pattern matching
+    - vllm/model_executor/layers/layernorm.py
+    - vllm/model_executor/layers/activation.py
+    - vllm/model_executor/layers/attention/attention.py
+    - vllm/model_executor/layers/quantization/input_quant_fp8.py
+    - tests/compile/fusions_e2e/
+  commands:
+    - rocm-smi
+    # Run just llama3 (fp8) for all config combinations
+    - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
+
+## There are no ops on ROCm for these tests.
+## The test still passes but the logs are not useful.
+## fused ops just call torch.ops.symm_mem which 
+## exists in ROCm even though they don't work
+# - label: AsyncTP Correctness Tests  (2xMI325 GPUs)
+# - label: Fusion E2E TP2 Quick (MI325)
+# - label: Fusion E2E TP2 AsyncTP Config Sweep (MI325)
+# - label: Fusion E2E TP2 (MI325)
+# - label: Sequence Parallel Correctness Tests (2xMI325 GPUs)
 
 
 #####################################################################################################################################

tests/compile/fusions_e2e/common.py

@@ -13,6 +13,7 @@ from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
 class Matches(NamedTuple):
     # simple pointwise
+    aiter_rms_quant_fusion: int = 0
     rms_quant_fusion: int = 0
     act_quant_fusion: int = 0
     norm_rope_fusion: int = 0
@@ -82,6 +83,9 @@ INDUCTOR_GRAPH_PARTITION = [
 ]
 
 FUSION_LOG_PATTERNS: dict[str, re.Pattern] = {
+    "aiter_rms_quant_fusion": re.compile(
+        r"RocmAiterRMSNormQuantFusionPass Replaced (\d+) patterns"
+    ),
     "rms_quant_fusion": re.compile(r"rms_quant_fusion.py:\d+] Replaced (\d+) patterns"),
     "act_quant_fusion": re.compile(r"act_quant_fusion.py:\d+] Replaced (\d+) patterns"),
     "norm_rope_fusion": re.compile(

tests/compile/fusions_e2e/conftest.py

@@ -63,9 +63,14 @@ def run_e2e_fusion_test(monkeypatch, caplog_mp_spawn):
         compilation_config: dict,
         matches_check: list[str],
         use_deepgemm: bool = False,
+        use_aiter: bool = False,
         tp_size: int = 1,
     ):
         monkeypatch.setenv("VLLM_USE_DEEP_GEMM", "1" if use_deepgemm else "0")
+        monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1" if use_aiter else "0")
+        from vllm._aiter_ops import rocm_aiter_ops
+
+        rocm_aiter_ops.refresh_env_variables()
 
         # Disable, compile cache to make sure custom passes run.
         # Otherwise, we can't verify fusion happened through the logs.

tests/compile/fusions_e2e/models.py

@@ -2,6 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import pytest
 
+from vllm._aiter_ops import is_aiter_found_and_supported
+from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer
 from vllm.v1.attention.backends.registry import AttentionBackendEnum
 
@@ -24,6 +26,24 @@ TRITON_ATTN = pytest.param(
     AttentionBackendCase(backend=AttentionBackendEnum.TRITON_ATTN), id="TRITON_ATTN"
 )
 
+ROCM_ATTN = pytest.param(
+    AttentionBackendCase(backend=AttentionBackendEnum.ROCM_ATTN),
+    id="ROCM_ATTN",
+    marks=pytest.mark.skipif(
+        not current_platform.is_rocm(),
+        reason="ROCm attention only for AMD",
+    ),
+)
+
+ROCM_AITER_UNIFIED_ATTN = pytest.param(
+    AttentionBackendCase(backend=AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN),
+    id="ROCM_AITER_UNIFIED_ATTN",
+    marks=pytest.mark.skipif(
+        not is_aiter_found_and_supported(),
+        reason="ROCM_AITER_UNIFIED_ATTN only for AMD when AITER is installed",
+    ),
+)
+
 # Models
 llama3_8b = ModelFusionInfo(
     model_name="meta-llama/Llama-3.1-8B-Instruct",
@@ -49,7 +69,6 @@ llama3_8b_fp8 = ModelFusionInfo(
 llama3_8b_fp4 = ModelFusionInfo(
     model_name="nvidia/Llama-3.1-8B-Instruct-FP4",
     matches=lambda n_layers: Matches(
-        rms_quant_fusion=0,
         act_quant_fusion=n_layers,
         attn_quant_fusion=n_layers,
         ar_rms_fusion=n_layers * 2 + 1,
@@ -79,7 +98,6 @@ llama4_scout_fp4 = ModelFusionInfo(
     model_name="nvidia/Llama-4-Scout-17B-16E-Instruct-NVFP4",
     hf_overrides=lambda n_layers: {"text_config": {"num_hidden_layers": n_layers}},
     matches=lambda n_layers: Matches(
-        rms_quant_fusion=0,
         attn_quant_fusion=n_layers,
         ar_rms_fusion=n_layers * 2,
         sequence_parallel=n_layers * 2,

tests/compile/fusions_e2e/test_tp1_quant.py

@@ -5,6 +5,7 @@ from collections.abc import Callable
 import pytest
 
 from vllm.config import PassConfig
+from vllm.platforms import current_platform
 from vllm.utils.flashinfer import is_flashinfer_fp8_blockscale_gemm_supported
 
 from .common import (
@@ -16,6 +17,8 @@ from .common import (
 )
 from .models import (
     FLASHINFER_ATTN,
+    ROCM_AITER_UNIFIED_ATTN,
+    ROCM_ATTN,
     TRITON_ATTN,
     llama3_8b_fp4,
     llama3_8b_fp8,
@@ -29,12 +32,33 @@ from .models import (
     "model_name, matches_fn, model_kwargs, hf_overrides, use_deepgemm",
     [
         (*llama3_8b_fp8, False),
-        (*llama4_scout_fp8, False),
         (*qwen3_a3b_fp8, False),
-        (*qwen3_a3b_fp8, True),
+        pytest.param(
+            *llama4_scout_fp8,
+            False,
+            marks=pytest.mark.skipif(
+                not current_platform.is_cuda(),
+                reason="Llama4 Scout FP8 only supported on CUDA",
+            ),
+        ),
+        pytest.param(
+            *qwen3_a3b_fp8,
+            True,
+            marks=pytest.mark.skipif(
+                not current_platform.is_cuda(), reason="DeepGemm only supported on CUDA"
+            ),
+        ),
+    ],
+)
+@pytest.mark.parametrize(
+    "attn_backend",
+    [
+        TRITON_ATTN,
+        FLASHINFER_ATTN,
+        ROCM_ATTN,
+        ROCM_AITER_UNIFIED_ATTN,
     ],
 )
-@pytest.mark.parametrize("attn_backend", [TRITON_ATTN, FLASHINFER_ATTN])
 @pytest.mark.parametrize("n_layers", [6])
 @pytest.mark.parametrize("custom_ops", custom_ops_combos("quant_fp8", "rms_norm"))
 @pytest.mark.parametrize("inductor_graph_partition", INDUCTOR_GRAPH_PARTITION)
@@ -81,6 +105,8 @@ def test_tp1_fp8_fusions(
         ),
     )
 
+    use_aiter = current_platform.is_rocm() and ("qwen" in model_name.lower())
+
     matches_check = [
         "rms_quant_fusion",
         "act_quant_fusion",
@@ -88,6 +114,15 @@ def test_tp1_fp8_fusions(
         "attn_quant_fusion",
     ]
 
+    if use_aiter:
+        matches_check[0] = "aiter_rms_quant_fusion"
+
+        matches = matches._replace(aiter_rms_quant_fusion=matches.rms_quant_fusion)
+        # TODO: enable the `norm_rope_fusion` test,
+        # On ROCm norm_rope_fusion is only supported without
+        # enabling AITER.
+        matches_check.remove("norm_rope_fusion")
+
     run_e2e_fusion_test(
         model_name,
         matches,
@@ -96,6 +131,7 @@ def test_tp1_fp8_fusions(
         compilation_config,
         matches_check,
         use_deepgemm=use_deepgemm,
+        use_aiter=use_aiter,
     )
 
 

tests/compile/fusions_e2e/test_tp2_ar_rms.py

@@ -5,6 +5,7 @@ from collections.abc import Callable
 import pytest
 
 from vllm.config import PassConfig
+from vllm.platforms import current_platform
 
 from ...utils import multi_gpu_test
 from .common import (
@@ -26,6 +27,8 @@ from .models import (
     qwen3_a3b_fp8,
 )
 
+pytestmark = pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
+
 
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize(

tests/compile/fusions_e2e/test_tp2_async_tp.py

@@ -5,6 +5,7 @@ from collections.abc import Callable
 import pytest
 
 from vllm.config import PassConfig
+from vllm.platforms import current_platform
 
 from ...utils import multi_gpu_test
 from .common import (
@@ -23,6 +24,8 @@ from .models import (
     qwen3_a3b,
 )
 
+pytestmark = pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
+
 
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize(

tests/compile/passes/distributed/test_sequence_parallelism.py

@@ -36,6 +36,8 @@ from vllm.platforms import current_platform
 from vllm.utils.system_utils import update_environment_variables
 from vllm.utils.torch_utils import set_random_seed
 
+pytestmark = pytest.mark.skipif(not current_platform.is_cuda(), reason="Only test CUDA")
+
 FP8_DTYPE = current_platform.fp8_dtype()
 prompts = [
     "Hello, my name is",

tests/compile/passes/test_silu_mul_quant_fusion.py

@@ -182,8 +182,24 @@ TEST_KERNELS = ROCM_KERNELS if current_platform.is_rocm() else CUDA_KERNELS
     "model_class, enable_quant_fp8_custom_op, force_kernel",
     list(itertools.product([TestSiluMulFp8QuantModel], [True, False], TEST_KERNELS))
     + [
-        (TestSiluMulNvfp4QuantModel, False, None),
-        (TestSiluMulGroupFp8QuantModel, False, None),
+        pytest.param(
+            TestSiluMulNvfp4QuantModel,
+            False,
+            None,
+            marks=pytest.mark.skipif(
+                not current_platform.is_cuda(), reason="CUDA only"
+            ),
+        ),
+        # GroupFP8Quant fusion only works with AITER on ROCm.
+        # and the enable_quant_fp8_custom_op must be True.
+        pytest.param(
+            TestSiluMulGroupFp8QuantModel,
+            True,
+            None,
+            marks=pytest.mark.skipif(
+                not current_platform.is_rocm(), reason="ROCm only"
+            ),
+        ),
     ],
 )
 @pytest.mark.skipif(
@@ -201,6 +217,7 @@ def test_fusion_silu_and_mul_quant(
     enable_silu_mul_custom_op: bool,
     enable_quant_fp8_custom_op: bool,
     force_kernel: FP8ScaledMMLinearKernel | None,
+    monkeypatch: pytest.MonkeyPatch,
 ):
     if model_class is TestSiluMulNvfp4QuantModel and not is_nvfp4_supported():
         pytest.skip("NVFP4 is not supported on this GPU.")
@@ -227,13 +244,16 @@ def test_fusion_silu_and_mul_quant(
         ),
     )
 
-    with set_current_vllm_config(config):
+    with set_current_vllm_config(config), monkeypatch.context() as m:
         fusion_passes = [ActivationQuantFusionPass(config)]
-        if IS_AITER_FOUND:
+        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,
             )
 
+            m.setenv("VLLM_ROCM_USE_AITER", "1")
+            rocm_aiter_ops.refresh_env_variables()
             fusion_passes += [RocmAiterSiluMulFp8GroupQuantFusionPass(config)]
 
         passes = [NoOpEliminationPass(config), *fusion_passes, PostCleanupPass(config)]

vllm/compilation/passes/fusion/rocm_aiter_fusion.py

@@ -5,7 +5,6 @@ import torch
 import torch._inductor.pattern_matcher as pm
 from torch import fx
 from torch._inductor.pattern_matcher import PatternMatcherPass
-from torch._ops import OpOverload
 
 import vllm.model_executor.layers.quantization.utils.fp8_utils  # noqa: F401
 from vllm._aiter_ops import rocm_aiter_ops
@@ -15,6 +14,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     GroupShape,
     QuantKey,
     ScaleDesc,
+    kFp8Dynamic128Sym,
 )
 from vllm.platforms import current_platform
 
@@ -312,7 +312,9 @@ class RocmAiterRMSNormQuantFusionPass(VllmPatternMatcherPass):
     @VllmInductorPass.time_and_log
     def __call__(self, graph: fx.Graph) -> None:
         self.matched_count = self.patterns.apply(graph)
-        logger.debug("Replaced %s patterns", self.matched_count)
+        logger.debug(
+            "%s Replaced %s patterns", self.__class__.__name__, self.matched_count
+        )
 
     def uuid(self) -> str:
         fusion_patterns = [
@@ -332,9 +334,11 @@ class AiterSiluMulFp8GroupQuantPattern(ActivationQuantPattern):
 
     FUSED_SILU_MUL_QUANT_OP = rocm_aiter_ops.get_act_mul_fused_fp8_group_quant_op()
 
-    def __init__(self, quant_op: OpOverload) -> None:
+    def __init__(self) -> None:
         self.silu_and_mul_matcher = MatcherSiluAndMul()
-        self.quant_op = quant_op
+        self.quant_matcher = MatcherQuantFP8(
+            quant_key=kFp8Dynamic128Sym, match_rocm_aiter=True
+        )
 
     def get_inputs(self) -> list[torch.Tensor]:
         return [
@@ -346,7 +350,7 @@ class AiterSiluMulFp8GroupQuantPattern(ActivationQuantPattern):
             input: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             at1 = self.silu_and_mul_matcher(input)
-            at2 = self.quant_op(at1, 128)
+            at2 = self.quant_matcher(at1)
             return at2[0], at2[1]
 
         def replacement(
@@ -370,11 +374,6 @@ class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmPatternMatcherPass):
     https://github.com/pytorch/pytorch/pull/139321#issuecomment-2452354980
     """
 
-    AITER_GROUP_FP8_QUANT_OP = rocm_aiter_ops.get_group_quant_op()
-    TRITON_GROUP_FP8_QUANT_OP = torch.ops.vllm.triton_per_token_group_quant_fp8.default
-
-    QUANT_OPS = [AITER_GROUP_FP8_QUANT_OP, TRITON_GROUP_FP8_QUANT_OP]
-
     @enable_fake_mode
     def __init__(self, config: VllmConfig) -> None:
         super().__init__(config)
@@ -383,8 +382,7 @@ class RocmAiterSiluMulFp8GroupQuantFusionPass(VllmPatternMatcherPass):
             pass_name="rocm_aiter_silu_mul_fp8_group_quant_fusion_pass"
         )
 
-        for quant_op in self.QUANT_OPS:
-            AiterSiluMulFp8GroupQuantPattern(quant_op).register(self.patterns)
+        AiterSiluMulFp8GroupQuantPattern().register(self.patterns)
 
         self.dump_patterns(config, self.patterns)