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

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Signed-off-by: tjtanaa <tunjian.tan@embeddedllm.com>
Signed-off-by: vllmellm <vllm.ellm@embeddedllm.com>
Co-authored-by: vllmellm <vllm.ellm@embeddedllm.com>
This commit is contained in:
TJian
2026-03-03 22:24:21 +08:00
committed by GitHub
parent ea463978bb
commit fb7fdc49c4
10 changed files with 217 additions and 61 deletions
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147
.buildkite/test-amd.yaml
View File

@@ -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)
#####################################################################################################################################

4
tests/compile/fusions_e2e/common.py
View File

@@ -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(

5
tests/compile/fusions_e2e/conftest.py
View File

@@ -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.

22
tests/compile/fusions_e2e/models.py
View File

@@ -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,

42
tests/compile/fusions_e2e/test_tp1_quant.py
View File

@@ -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,
)

3
tests/compile/fusions_e2e/test_tp2_ar_rms.py
View File

@@ -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(

3
tests/compile/fusions_e2e/test_tp2_async_tp.py
View File

@@ -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(

2
tests/compile/passes/distributed/test_sequence_parallelism.py
View File

@@ -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",

28
tests/compile/passes/test_silu_mul_quant_fusion.py
View File

@@ -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)]

22
vllm/compilation/passes/fusion/rocm_aiter_fusion.py
View File

@@ -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)