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[diffusion] refactor: remove PreprocessorConfig (#13248)

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This commit is contained in:
Mick
2025-11-20 12:53:26 +08:00
committed by GitHub
parent 21370ef7b5
commit 2e3a69ae05
2 changed files with 0 additions and 212 deletions
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203
python/sglang/multimodal_gen/configs/configs.py
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@@ -1,12 +1,8 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
import dataclasses
from enum import Enum
from typing import Any, Optional
from sglang.multimodal_gen.configs.utils import update_config_from_args
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.utils import FlexibleArgumentParser, StoreBoolean
logger = init_logger(__name__)
@@ -57,202 +53,3 @@ class VideoLoaderType(str, Enum):
def choices(cls) -> list[str]:
"""Get all available choices as strings for argparse."""
return [video_loader.value for video_loader in cls]
@dataclasses.dataclass
class PreprocessConfig:
"""Configuration for preprocessing operations."""
# Model and dataset configuration
model_path: str = ""
dataset_path: str = ""
dataset_type: DatasetType = DatasetType.HF
dataset_output_dir: str = "./output"
# Dataloader configuration
dataloader_num_workers: int = 1
preprocess_video_batch_size: int = 2
# Saver configuration
samples_per_file: int = 64
flush_frequency: int = 256
# Video processing parameters
video_loader_type: VideoLoaderType = VideoLoaderType.TORCHCODEC
max_height: int = 480
max_width: int = 848
num_frames: int = 163
video_length_tolerance_range: float = 2.0
train_fps: int = 30
speed_factor: float = 1.0
drop_short_ratio: float = 1.0
do_temporal_sample: bool = False
# Model configuration
training_cfg_rate: float = 0.0
# framework configuration
seed: int = 42
@staticmethod
def add_cli_args(
parser: FlexibleArgumentParser, prefix: str = "preprocess"
) -> FlexibleArgumentParser:
"""Add preprocessing configuration arguments to the parser."""
prefix_with_dot = f"{prefix}." if (prefix.strip() != "") else ""
preprocess_args = parser.add_argument_group("Preprocessing Arguments")
# Model & Dataset
preprocess_args.add_argument(
f"--{prefix_with_dot}model-path",
type=str,
default=PreprocessConfig.model_path,
help="Path to the model for preprocessing",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}dataset-path",
type=str,
default=PreprocessConfig.dataset_path,
help="Path to the dataset directory for preprocessing",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}dataset-type",
type=str,
choices=DatasetType.choices(),
default=PreprocessConfig.dataset_type.value,
help="Type of the dataset",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}dataset-output-dir",
type=str,
default=PreprocessConfig.dataset_output_dir,
help="The output directory where the dataset will be written.",
)
# Dataloader
preprocess_args.add_argument(
f"--{prefix_with_dot}dataloader-num-workers",
type=int,
default=PreprocessConfig.dataloader_num_workers,
help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}preprocess-video-batch-size",
type=int,
default=PreprocessConfig.preprocess_video_batch_size,
help="Batch size (per device) for the training dataloader.",
)
# Saver
preprocess_args.add_argument(
f"--{prefix_with_dot}samples-per-file",
type=int,
default=PreprocessConfig.samples_per_file,
help="Number of samples per output file",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}flush-frequency",
type=int,
default=PreprocessConfig.flush_frequency,
help="How often to save to parquet files",
)
# Video processing parameters
preprocess_args.add_argument(
f"--{prefix_with_dot}video-loader-type",
type=str,
choices=VideoLoaderType.choices(),
default=PreprocessConfig.video_loader_type.value,
help="Type of the video loader",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}max-height",
type=int,
default=PreprocessConfig.max_height,
help="Maximum height for video processing",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}max-width",
type=int,
default=PreprocessConfig.max_width,
help="Maximum width for video processing",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}num-frames",
type=int,
default=PreprocessConfig.num_frames,
help="Number of frames to process",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}video-length-tolerance-range",
type=float,
default=PreprocessConfig.video_length_tolerance_range,
help="Video length tolerance range",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}train-fps",
type=int,
default=PreprocessConfig.train_fps,
help="Training FPS",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}speed-factor",
type=float,
default=PreprocessConfig.speed_factor,
help="Speed factor for video processing",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}drop-short-ratio",
type=float,
default=PreprocessConfig.drop_short_ratio,
help="Ratio for dropping short videos",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}do-temporal-sample",
action=StoreBoolean,
default=PreprocessConfig.do_temporal_sample,
help="Whether to do temporal sampling",
)
# Model Training configuration
preprocess_args.add_argument(
f"--{prefix_with_dot}training-cfg-rate",
type=float,
default=PreprocessConfig.training_cfg_rate,
help="Training CFG rate",
)
preprocess_args.add_argument(
f"--{prefix_with_dot}seed",
type=int,
default=PreprocessConfig.seed,
help="Seed for random number generator",
)
return parser
@classmethod
def from_kwargs(cls, kwargs: dict[str, Any]) -> Optional["PreprocessConfig"]:
"""Create PreprocessConfig from keyword arguments."""
if "dataset_type" in kwargs and isinstance(kwargs["dataset_type"], str):
kwargs["dataset_type"] = DatasetType.from_string(kwargs["dataset_type"])
if "video_loader_type" in kwargs and isinstance(
kwargs["video_loader_type"], str
):
kwargs["video_loader_type"] = VideoLoaderType.from_string(
kwargs["video_loader_type"]
)
preprocess_config = cls()
if not update_config_from_args(
preprocess_config, kwargs, prefix="preprocess", pop_args=True
):
return None
return preprocess_config
def check_preprocess_config(self) -> None:
if self.dataset_path == "":
raise ValueError("dataset_path must be set for preprocess mode")
if self.samples_per_file <= 0:
raise ValueError("samples_per_file must be greater than 0")
if self.flush_frequency <= 0:
raise ValueError("flush_frequency must be greater than 0")

9
python/sglang/multimodal_gen/runtime/server_args.py
View File

@@ -15,7 +15,6 @@ from dataclasses import field
from enum import Enum
from typing import Any, Optional
from sglang.multimodal_gen.configs.configs import PreprocessConfig
from sglang.multimodal_gen.configs.pipelines import FluxPipelineConfig
from sglang.multimodal_gen.configs.pipelines.base import PipelineConfig, STA_Mode
from sglang.multimodal_gen.configs.pipelines.qwen_image import (
@@ -251,7 +250,6 @@ class ServerArgs:
dist_timeout: int | None = None # timeout for torch.distributed
pipeline_config: PipelineConfig = field(default_factory=PipelineConfig, repr=False)
preprocess_config: PreprocessConfig | None = None
# LoRA parameters
# (Wenxuan) prefer to keep it here instead of in pipeline config to not make it complicated.
@@ -626,9 +624,6 @@ class ServerArgs:
# Add pipeline configuration arguments
PipelineConfig.add_cli_args(parser)
# Add preprocessing configuration arguments
PreprocessConfig.add_cli_args(parser)
# Logging
parser.add_argument(
"--log-level",
@@ -734,9 +729,6 @@ class ServerArgs:
pipeline_config = PipelineConfig.from_kwargs(kwargs)
logger.debug(f"Using PipelineConfig: {type(pipeline_config)}")
server_args_kwargs["pipeline_config"] = pipeline_config
elif attr == "preprocess_config":
preprocess_config = PreprocessConfig.from_kwargs(kwargs)
server_args_kwargs["preprocess_config"] = preprocess_config
elif attr in kwargs:
server_args_kwargs[attr] = kwargs[attr]
@@ -772,7 +764,6 @@ class ServerArgs:
kwargs["workload_type"] = WorkloadType.from_string(kwargs["workload_type"])
kwargs["pipeline_config"] = PipelineConfig.from_kwargs(kwargs)
kwargs["preprocess_config"] = PreprocessConfig.from_kwargs(kwargs)
return cls(**kwargs)
@staticmethod