biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

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

Browse Source 
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
Download Patch File Download Diff File Expand all files Collapse all files

95
tests/kernels/moe/modular_kernel_tools/cli_args.py
View File

@@ -9,18 +9,19 @@ import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
from .common import Config
from .mk_objects import (MK_ALL_PREPARE_FINALIZE_TYPES, MK_FUSED_EXPERT_TYPES,
MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES)
from .mk_objects import (
MK_ALL_PREPARE_FINALIZE_TYPES,
MK_FUSED_EXPERT_TYPES,
MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES,
)
def make_config_arg_parser(description: str):
def to_pf_class_type(s: str) -> mk.FusedMoEPrepareAndFinalize:
for pf in MK_ALL_PREPARE_FINALIZE_TYPES:
if pf.__name__ == s:
return pf
raise ValueError(
f"Cannot find a PrepareFinalize type that matches {s}")
raise ValueError(f"Cannot find a PrepareFinalize type that matches {s}")
def to_experts_class_type(s: str) -> mk.FusedMoEPermuteExpertsUnpermute:
for fe in MK_FUSED_EXPERT_TYPES:
@@ -45,15 +46,18 @@ def make_config_arg_parser(description: str):
"--pf-type",
type=to_pf_class_type,
required=True,
help=("Choose a PrepareFinalize Type : "
f"{[x.__name__ for x in MK_ALL_PREPARE_FINALIZE_TYPES]}"),
help=(
"Choose a PrepareFinalize Type : "
f"{[x.__name__ for x in MK_ALL_PREPARE_FINALIZE_TYPES]}"
),
)
parser.add_argument(
"--experts-type",
type=to_experts_class_type,
required=True,
help=(f"Choose a FusedExpert type : "
f"{[x.__name__ for x in MK_FUSED_EXPERT_TYPES]}"),
help=(
f"Choose a FusedExpert type : {[x.__name__ for x in MK_FUSED_EXPERT_TYPES]}"
),
)
parser.add_argument(
"-m",
@@ -74,66 +78,65 @@ def make_config_arg_parser(description: str):
default=1024,
help="N dimension of the first fused-moe matmul",
)
parser.add_argument("--num-experts",
type=int,
default=32,
help="Global num experts")
parser.add_argument("--topk",
nargs="+",
type=int,
default=[4, 1],
help="num topk")
parser.add_argument(
"--num-experts", type=int, default=32, help="Global num experts"
)
parser.add_argument("--topk", nargs="+", type=int, default=[4, 1], help="num topk")
parser.add_argument(
"--fused-moe-chunk-size",
type=int,
help="Fused moe chunk size used for the non-batched fused experts impl."
help="Fused moe chunk size used for the non-batched fused experts impl.",
)
# Quant args
parser.add_argument("--quant-dtype",
type=to_quant_torch_dtype,
help="Quant datatype")
parser.add_argument("--per-token-quantized-activations",
action='store_true',
help=("The input activations must be per-token "
"quantized"))
parser.add_argument("--per-channel-quantized-weights",
action="store_true",
help="The weights must be per-channel quantized.")
parser.add_argument("--block-shape",
nargs="+",
type=int,
help="Quantization block shape")
parser.add_argument(
"--quant-dtype", type=to_quant_torch_dtype, help="Quant datatype"
)
parser.add_argument(
"--per-token-quantized-activations",
action="store_true",
help=("The input activations must be per-token quantized"),
)
parser.add_argument(
"--per-channel-quantized-weights",
action="store_true",
help="The weights must be per-channel quantized.",
)
parser.add_argument(
"--block-shape", nargs="+", type=int, help="Quantization block shape"
)
# Torch trace profile generation args
parser.add_argument("--torch-trace-dir-path",
type=str,
default=None,
help="Get torch trace for single execution")
parser.add_argument(
"--torch-trace-dir-path",
type=str,
default=None,
help="Get torch trace for single execution",
)
return parser
def _validate_args(args: argparse.Namespace):
if args.quant_dtype is not None:
assert args.quant_dtype == torch.float8_e4m3fn
if args.block_shape is not None:
assert len(args.block_shape) == 2, (
f"block shape must have 2 elements. got {args.block_shape}")
f"block shape must have 2 elements. got {args.block_shape}"
)
if args.experts_type in MK_SINGLE_GPU_PREPARE_FINALIZE_TYPES:
assert args.world_size == 1, (
"Single GPU objects need world size set to 1")
assert args.world_size == 1, "Single GPU objects need world size set to 1"
if args.torch_trace_dir_path is not None:
from pathlib import Path
assert Path(args.torch_trace_dir_path).is_dir(), (
f"Please create {args.torch_trace_dir_path}")
f"Please create {args.torch_trace_dir_path}"
)
def make_config(args: argparse.Namespace) -> Config:
_validate_args(args)
quant_config = None
@@ -142,7 +145,8 @@ def make_config(args: argparse.Namespace) -> Config:
quant_dtype=args.quant_dtype,
per_act_token_quant=args.per_token_quantized_activations,
per_out_ch_quant=args.per_channel_quantized_weights,
block_shape=args.block_shape)
block_shape=args.block_shape,
)
return Config(
Ms=args.m,
@@ -156,4 +160,5 @@ def make_config(args: argparse.Namespace) -> Config:
fused_experts_type=args.experts_type,
fused_moe_chunk_size=args.fused_moe_chunk_size,
world_size=args.world_size,
torch_trace_dir_path=args.torch_trace_dir_path)
torch_trace_dir_path=args.torch_trace_dir_path,
)