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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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125
tests/kernels/moe/modular_kernel_tools/make_feature_matrix.py
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@@ -10,14 +10,21 @@ import torch
from tqdm import tqdm
from vllm.config import VllmConfig, set_current_vllm_config
from vllm.model_executor.layers.fused_moe.config import (
FUSED_MOE_UNQUANTIZED_CONFIG)
from vllm.model_executor.layers.fused_moe.config import FUSED_MOE_UNQUANTIZED_CONFIG
from vllm.platforms import current_platform
from .common import (Config, RankTensors, WeightTensors, reference_moe_impl,
run_modular_kernel)
from .mk_objects import (MK_FUSED_EXPERT_TYPES,
MK_MULTI_GPU_PREPARE_FINALIZE_TYPES, MK_QUANT_CONFIGS)
from .common import (
Config,
RankTensors,
WeightTensors,
reference_moe_impl,
run_modular_kernel,
)
from .mk_objects import (
MK_FUSED_EXPERT_TYPES,
MK_MULTI_GPU_PREPARE_FINALIZE_TYPES,
MK_QUANT_CONFIGS,
)
from .parallel_utils import ProcessGroupInfo, parallel_launch_with_config
@@ -38,8 +45,9 @@ def rank_worker(
# sanity check
from vllm import envs
if config.fused_moe_chunk_size is not None:
assert (config.fused_moe_chunk_size == envs.VLLM_FUSED_MOE_CHUNK_SIZE)
assert config.fused_moe_chunk_size == envs.VLLM_FUSED_MOE_CHUNK_SIZE
# get weights to this device
weights.to_current_device()
@@ -60,8 +68,7 @@ def rank_worker(
rank_tensors = RankTensors.make(cfgx, pgi)
# modular kernel out
mk_out = run_modular_kernel(pgi, vllm_config, cfgx, weights,
rank_tensors)
mk_out = run_modular_kernel(pgi, vllm_config, cfgx, weights, rank_tensors)
with set_current_vllm_config(vllm_config):
ref_out = reference_moe_impl(cfgx, weights, rank_tensors)
@@ -70,28 +77,27 @@ def rank_worker(
def make_feature_matrix(csv_file_path: str):
from dataclasses import asdict
import pandas as pd
def add_to_results(config: Config,
success: Result,
results_df: Optional[pd.DataFrame] = None):
def add_to_results(
config: Config, success: Result, results_df: Optional[pd.DataFrame] = None
):
config_dict = asdict(config)
config_dict['prepare_finalize_type'] = config_dict[
'prepare_finalize_type'].__name__
config_dict['fused_experts_type'] = config_dict[
'fused_experts_type'].__name__
config_dict['per_tensor_act_quant'] = config.is_per_tensor_act_quant
quant_config_dict = config_dict['quant_config']
del config_dict['quant_config']
config_dict["prepare_finalize_type"] = config_dict[
"prepare_finalize_type"
].__name__
config_dict["fused_experts_type"] = config_dict["fused_experts_type"].__name__
config_dict["per_tensor_act_quant"] = config.is_per_tensor_act_quant
quant_config_dict = config_dict["quant_config"]
del config_dict["quant_config"]
if quant_config_dict is None:
quant_config = FUSED_MOE_UNQUANTIZED_CONFIG
quant_config_dict = asdict(quant_config)
config_dict |= quant_config_dict
result_dict = config_dict | {'success': success.name}
result_dict = config_dict | {"success": success.name}
result_df = pd.DataFrame([result_dict])
if results_df is None:
@@ -112,22 +118,26 @@ def make_feature_matrix(csv_file_path: str):
Q_TYPES = MK_QUANT_CONFIGS
combinations = list(
product(Ms, Ks, Ns, Es, TOPKs, DTYPEs, PF_TYPES, FE_TYPES, Q_TYPES))
product(Ms, Ks, Ns, Es, TOPKs, DTYPEs, PF_TYPES, FE_TYPES, Q_TYPES)
)
results_df: Optional[pd.DataFrame] = None
for m, k, n, e, topks, dtype, pf_type, experts_type, quant_config in tqdm(
combinations): #noqa: E501
config = Config(Ms=[m],
K=k,
N=n,
E=e,
topks=topks,
dtype=dtype,
prepare_finalize_type=pf_type,
fused_experts_type=experts_type,
quant_config=quant_config,
world_size=2,
fused_moe_chunk_size=None)
combinations
): # noqa: E501
config = Config(
Ms=[m],
K=k,
N=n,
E=e,
topks=topks,
dtype=dtype,
prepare_finalize_type=pf_type,
fused_experts_type=experts_type,
quant_config=quant_config,
world_size=2,
fused_moe_chunk_size=None,
)
success = None
if config.is_valid():
@@ -135,9 +145,14 @@ def make_feature_matrix(csv_file_path: str):
try:
weights: WeightTensors = WeightTensors.make(config)
vllm_config, env_dict = config.make_env_data()
parallel_launch_with_config(config.world_size, rank_worker,
vllm_config, env_dict, config,
weights)
parallel_launch_with_config(
config.world_size,
rank_worker,
vllm_config,
env_dict,
config,
weights,
)
success = Result.PASS
except Exception as _:
success = Result.FAIL
@@ -150,25 +165,33 @@ def make_feature_matrix(csv_file_path: str):
results_df.to_csv(f"{csv_file_path}")
if __name__ == '__main__':
if __name__ == "__main__":
import argparse
from pathlib import Path
parser = argparse.ArgumentParser(description=(
"Make ModularKernel feature matrix \n"
"Example : python3 -m tests.kernels.moe.modular_kernel_tools.make_feature_matrix " #noqa: E501
"-f ./feature_matrices/feature_matrix.csv"))
parser.add_argument("-f",
"--feature-matrix-csv-file-path",
type=str,
required=True,
help="File name to Generate a .csv file")
parser = argparse.ArgumentParser(
description=(
"Make ModularKernel feature matrix \n"
"Example : python3 -m tests.kernels.moe.modular_kernel_tools.make_feature_matrix " # noqa: E501
"-f ./feature_matrices/feature_matrix.csv"
)
)
parser.add_argument(
"-f",
"--feature-matrix-csv-file-path",
type=str,
required=True,
help="File name to Generate a .csv file",
)
args = parser.parse_args()
csv_path = args.feature_matrix_csv_file_path
assert csv_path.endswith(
'csv'), f"Need a file path ending with .csv, got {csv_path}"
assert Path(csv_path).parent.is_dir(
), f"Cannot find parent directory for {Path(csv_path).parent}"
assert csv_path.endswith("csv"), (
f"Need a file path ending with .csv, got {csv_path}"
)
assert Path(csv_path).parent.is_dir(), (
f"Cannot find parent directory for {Path(csv_path).parent}"
)
make_feature_matrix(args.feature_matrix_csv_file_path)