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diffusion: reduce effort of supporting new model (#12982)

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This commit is contained in:
Mick
2025-11-10 21:20:33 +08:00
committed by GitHub
parent afee2843d5
commit 5639145fac
66 changed files with 667 additions and 2385 deletions
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4
python/sglang/multimodal_gen/README.md
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@@ -20,7 +20,7 @@ SGLang Diffusion has the following features:
uv pip install 'sglang[diffusion]' --prerelease=allow
```
For more installation methods (e.g. pypi, uv, docker), check the [docs](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install.md).
For more installation methods (e.g. pypi, uv, docker), check [install.md](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/install.md).
## Inference
@@ -61,7 +61,7 @@ sglang generate --model-path Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
--save-output
```
For more usage examples (e.g. OpenAI compatible API, server mode), check the [docs](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/cli.md).
For more usage examples (e.g. OpenAI compatible API, server mode), check [cli.md](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen/docs/cli.md).
## Contributing

4
python/sglang/multimodal_gen/configs/pipelines/__init__.py
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@@ -9,9 +9,6 @@ from sglang.multimodal_gen.configs.pipelines.hunyuan import (
FastHunyuanConfig,
HunyuanConfig,
)
from sglang.multimodal_gen.configs.pipelines.registry import (
get_pipeline_config_cls_from_name,
)
from sglang.multimodal_gen.configs.pipelines.stepvideo import StepVideoT2VConfig
from sglang.multimodal_gen.configs.pipelines.wan import (
SelfForcingWanT2V480PConfig,
@@ -33,5 +30,4 @@ __all__ = [
"WanI2V720PConfig",
"StepVideoT2VConfig",
"SelfForcingWanT2V480PConfig",
"get_pipeline_config_cls_from_name",
]

27
python/sglang/multimodal_gen/configs/pipelines/base.py
View File

@@ -5,7 +5,7 @@ import json
from collections.abc import Callable
from dataclasses import asdict, dataclass, field, fields
from enum import Enum
from typing import Any, cast
from typing import Any
import torch
from diffusers.image_processor import VaeImageProcessor
@@ -312,19 +312,6 @@ class PipelineConfig:
self.dit_config, args, f"{prefix_with_dot}dit_config", pop_args=True
)
@classmethod
def from_pretrained(cls, model_path: str) -> "PipelineConfig":
"""
use the pipeline class setting from model_path to match the pipeline config
"""
from sglang.multimodal_gen.configs.pipelines.registry import (
get_pipeline_config_cls_from_name,
)
pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))
@classmethod
def from_kwargs(
cls, kwargs: dict[str, Any], config_cli_prefix: str = ""
@@ -334,9 +321,7 @@ class PipelineConfig:
kwargs: dictionary of kwargs
config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
"""
from sglang.multimodal_gen.configs.pipelines.registry import (
get_pipeline_config_cls_from_name,
)
from sglang.multimodal_gen.registry import get_model_info
prefix_with_dot = (
f"{config_cli_prefix}." if (config_cli_prefix.strip() != "") else ""
@@ -352,17 +337,17 @@ class PipelineConfig:
raise ValueError("model_path is required in kwargs")
# 1. Get the pipeline config class from the registry
pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
model_info = get_model_info(model_path)
# 2. Instantiate PipelineConfig
if pipeline_config_cls is None:
if model_info is None:
logger.warning(
"Couldn't find pipeline config for %s. Using the default pipeline config.",
"Couldn't find model info for %s. Using the default pipeline config.",
model_path,
)
pipeline_config = cls()
else:
pipeline_config = pipeline_config_cls()
pipeline_config = model_info.pipeline_config_cls()
# 3. Load PipelineConfig from a json file or a PipelineConfig object if provided
if isinstance(pipeline_config_or_path, str):

10
python/sglang/multimodal_gen/configs/pipelines/qwen_image.py
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@@ -117,15 +117,7 @@ class QwenImagePipelineConfig(PipelineConfig):
width = 2 * (batch.width // (self.vae_config.arch_config.vae_scale_factor * 2))
num_channels_latents = self.dit_config.arch_config.in_channels // 4
# pack latents
# _pack_latents(latents, batch_size, num_channels_latents, height, width)
latents = latents.view(
batch_size, num_channels_latents, height // 2, 2, width // 2, 2
)
latents = latents.permute(0, 2, 4, 1, 3, 5)
latents = latents.reshape(
batch_size, (height // 2) * (width // 2), num_channels_latents * 4
)
return latents
return _pack_latents(latents, batch_size, num_channels_latents, height, width)
@staticmethod
def get_freqs_cis(img_shapes, txt_seq_lens, rotary_emb, device, dtype):

168
python/sglang/multimodal_gen/configs/pipelines/registry.py
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@@ -1,168 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""Registry for pipeline weight-specific configurations."""
import os
from collections.abc import Callable
from sglang.multimodal_gen.configs.pipelines.base import PipelineConfig
from sglang.multimodal_gen.configs.pipelines.flux import FluxPipelineConfig
from sglang.multimodal_gen.configs.pipelines.hunyuan import (
FastHunyuanConfig,
HunyuanConfig,
)
from sglang.multimodal_gen.configs.pipelines.qwen_image import (
QwenImageEditPipelineConfig,
QwenImagePipelineConfig,
)
from sglang.multimodal_gen.configs.pipelines.stepvideo import StepVideoT2VConfig
# isort: off
from sglang.multimodal_gen.configs.pipelines.wan import (
FastWan2_1_T2V_480P_Config,
FastWan2_2_TI2V_5B_Config,
Wan2_2_I2V_A14B_Config,
Wan2_2_T2V_A14B_Config,
Wan2_2_TI2V_5B_Config,
WanI2V480PConfig,
WanI2V720PConfig,
WanT2V480PConfig,
WanT2V720PConfig,
SelfForcingWanT2V480PConfig,
)
from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
verify_model_config_and_directory,
maybe_download_model_index,
)
# isort: on
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
# Registry maps specific model weights to their config classes
PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
"FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
"hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
"weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
"Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
"Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
"Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
"FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWan2_1_T2V_480P_Config,
"FastVideo/FastWan2.1-T2V-14B-480P-Diffusers": FastWan2_1_T2V_480P_Config,
"FastVideo/FastWan2.2-TI2V-5B-Diffusers": FastWan2_2_TI2V_5B_Config,
"FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
"FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
"wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
"Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
"Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
"Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
# Add other specific weight variants
"black-forest-labs/FLUX.1-dev": FluxPipelineConfig,
"Qwen/Qwen-Image": QwenImagePipelineConfig,
"Qwen/Qwen-Image-Edit": QwenImageEditPipelineConfig,
}
# For determining pipeline type from model ID
PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
"hunyuan": lambda id: "hunyuan" in id.lower(),
"wanpipeline": lambda id: "wanpipeline" in id.lower(),
"wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
"wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
"wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
"stepvideo": lambda id: "stepvideo" in id.lower(),
"qwenimage": lambda id: "qwen-image" in id.lower() and "edit" not in id.lower(),
"qwenimageedit": lambda id: "qwen-image-edit" in id.lower(),
# Add other pipeline architecture detectors
}
# Fallback configs when exact match isn't found but architecture is detected
PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
"hunyuan": HunyuanConfig, # Base Hunyuan config as fallback for any Hunyuan variant
"wanpipeline": WanT2V480PConfig, # Base Wan config as fallback for any Wan variant
"wanimagetovideo": WanI2V480PConfig,
"wandmdpipeline": FastWan2_1_T2V_480P_Config,
"wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
"stepvideo": StepVideoT2VConfig,
"qwenimage": QwenImagePipelineConfig,
"qwenimageedit": QwenImageEditPipelineConfig,
# Other fallbacks by architecture
}
def get_pipeline_config_cls_from_name(
pipeline_name_or_path: str,
) -> type[PipelineConfig]:
"""Get the appropriate configuration class for a given pipeline name or path.
This function implements a multi-step lookup process to find the most suitable
configuration class for a given pipeline. It follows this order:
1. Exact match in the PIPE_NAME_TO_CONFIG
2. Partial match in the PIPE_NAME_TO_CONFIG
3. Fallback to class name in the model_index.json
4. else raise an error
Args:
pipeline_name_or_path (str): The name or path of the pipeline. This can be:
- A registered model ID (e.g., "FastVideo/FastHunyuan-diffusers")
- A local path to a model directory
- A model ID that will be downloaded
Returns:
Type[PipelineConfig]: The configuration class that best matches the pipeline.
This will be one of:
- A specific weight configuration class if an exact match is found
- A fallback configuration class based on the pipeline architecture
- The base PipelineConfig class if no matches are found
Note:
- For local paths, the function will verify the model configuration
- For remote models, it will attempt to download the model index
- Warning messages are logged when falling back to less specific configurations
"""
pipeline_config_cls: type[PipelineConfig] | None = None
# First try exact match for specific weights
if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:
pipeline_config_cls = PIPE_NAME_TO_CONFIG[pipeline_name_or_path]
if pipeline_config_cls is None:
# Try partial matches (for local paths that might include the weight ID)
for registered_id, config_class in PIPE_NAME_TO_CONFIG.items():
if registered_id in pipeline_name_or_path:
pipeline_config_cls = config_class
break
# If no match, try to use the fallback config
if pipeline_config_cls is None:
if os.path.exists(pipeline_name_or_path):
config = verify_model_config_and_directory(pipeline_name_or_path)
else:
config = maybe_download_model_index(pipeline_name_or_path)
logger.warning(
"Trying to use the config from the model_index.json. sgl-diffusion may not correctly identify the optimal config for this model in this situation."
)
pipeline_name = config["_class_name"]
# Try to determine pipeline architecture for fallback
for pipeline_type, detector in PIPELINE_DETECTOR.items():
if detector(pipeline_name.lower()):
pipeline_config_cls = PIPELINE_FALLBACK_CONFIG.get(pipeline_type)
break
if pipeline_config_cls is not None:
logger.warning(
"No match found for pipeline %s, using fallback config %s.",
pipeline_name_or_path,
pipeline_config_cls,
)
if pipeline_config_cls is None:
raise ValueError(
f"No match found for pipeline {pipeline_name_or_path}, please check the pipeline name or path."
)
return pipeline_config_cls

12
python/sglang/multimodal_gen/configs/sample/base.py
View File

@@ -205,14 +205,12 @@ class SamplingParams:
@classmethod
def from_pretrained(cls, model_path: str, **kwargs) -> "SamplingParams":
from sglang.multimodal_gen.configs.sample.registry import (
get_sampling_param_cls_for_name,
)
from sglang.multimodal_gen.registry import get_model_info
sampling_cls = get_sampling_param_cls_for_name(model_path)
logger.debug(f"Using pretrained SamplingParam: {sampling_cls}")
if sampling_cls is not None:
sampling_params: SamplingParams = sampling_cls(**kwargs)
model_info = get_model_info(model_path)
logger.debug(f"Found model info: {model_info}")
if model_info is not None:
sampling_params: SamplingParams = model_info.sampling_param_cls(**kwargs)
else:
logger.warning(
"Couldn't find an optimal sampling param for %s. Using the default sampling param.",

122
python/sglang/multimodal_gen/configs/sample/registry.py
View File

@@ -1,122 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
import os
from collections.abc import Callable
from typing import Any
from sglang.multimodal_gen.configs.sample.flux import FluxSamplingParams
from sglang.multimodal_gen.configs.sample.hunyuan import (
FastHunyuanSamplingParam,
HunyuanSamplingParams,
)
from sglang.multimodal_gen.configs.sample.qwenimage import QwenImageSamplingParams
from sglang.multimodal_gen.configs.sample.stepvideo import StepVideoT2VSamplingParams
# isort: off
from sglang.multimodal_gen.configs.sample.wan import (
FastWanT2V480PConfig,
Wan2_1_Fun_1_3B_InP_SamplingParams,
Wan2_2_I2V_A14B_SamplingParam,
Wan2_2_T2V_A14B_SamplingParam,
Wan2_2_TI2V_5B_SamplingParam,
WanI2V_14B_480P_SamplingParam,
WanI2V_14B_720P_SamplingParam,
WanT2V_1_3B_SamplingParams,
WanT2V_14B_SamplingParams,
SelfForcingWanT2V480PConfig,
)
from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
maybe_download_model_index,
verify_model_config_and_directory,
)
# isort: on
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
# Registry maps specific model weights to their config classes
SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
"FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
"hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParams,
"FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParams,
# Wan2.1
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParams,
"Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParams,
"Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
"Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
"weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": Wan2_1_Fun_1_3B_InP_SamplingParams,
# Wan2.2
"Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
"FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
"Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
"Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
# FastWan2.1
"FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
# FastWan2.2
"FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
# Causal Self-Forcing Wan2.1
"wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
# Add other specific weight variants
"black-forest-labs/FLUX.1-dev": FluxSamplingParams,
"Qwen/Qwen-Image": QwenImageSamplingParams,
"Qwen/Qwen-Image-Edit": QwenImageSamplingParams,
}
# For determining pipeline type from model ID
SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
"hunyuan": lambda id: "hunyuan" in id.lower(),
"wanpipeline": lambda id: "wanpipeline" in id.lower(),
"wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
"stepvideo": lambda id: "stepvideo" in id.lower(),
# Add other pipeline architecture detectors
"flux": lambda id: "flux" in id.lower(),
}
# Fallback configs when exact match isn't found but architecture is detected
SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
"hunyuan": HunyuanSamplingParams, # Base Hunyuan config as fallback for any Hunyuan variant
"wanpipeline": WanT2V_1_3B_SamplingParams, # Base Wan config as fallback for any Wan variant
"wanimagetovideo": WanI2V_14B_480P_SamplingParam,
"stepvideo": StepVideoT2VSamplingParams,
# Other fallbacks by architecture
"flux": FluxSamplingParams,
}
def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
"""Get the appropriate sampling param for specific pretrained weights."""
if os.path.exists(pipeline_name_or_path):
config = verify_model_config_and_directory(pipeline_name_or_path)
logger.warning(
"sgl-diffusion may not correctly identify the optimal sampling param for this model, as the local directory may have been renamed."
)
else:
config = maybe_download_model_index(pipeline_name_or_path)
pipeline_name = config["_class_name"]
# First try exact match for specific weights
if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]
# Try partial matches (for local paths that might include the weight ID)
for registered_id, config_class in SAMPLING_PARAM_REGISTRY.items():
if registered_id in pipeline_name_or_path:
return config_class
# If no match, try to use the fallback config
fallback_config = None
# Try to determine pipeline architecture for fallback
for pipeline_type, detector in SAMPLING_PARAM_DETECTOR.items():
if detector(pipeline_name.lower()):
fallback_config = SAMPLING_FALLBACK_PARAM.get(pipeline_type)
break
logger.warning(
"No match found for pipeline %s, using fallback sampling param %s.",
pipeline_name_or_path,
fallback_config,
)
return fallback_config

424
python/sglang/multimodal_gen/registry.py Normal file
View File

@@ -0,0 +1,424 @@
# SPDX-License-Identifier: Apache-2.0
"""
Central registry for multimodal models.
This module provides a centralized registry for multimodal models, including pipelines
and sampling parameters. It allows for easy registration and retrieval of model
information based on model paths or other identifiers.
"""
import dataclasses
import importlib
import os
import pkgutil
from typing import Any, Callable, Dict, List, Optional, Tuple, Type
from sglang.multimodal_gen.configs.pipelines import (
FastHunyuanConfig,
FluxPipelineConfig,
HunyuanConfig,
StepVideoT2VConfig,
WanI2V480PConfig,
WanI2V720PConfig,
WanT2V480PConfig,
WanT2V720PConfig,
)
from sglang.multimodal_gen.configs.pipelines.base import PipelineConfig
from sglang.multimodal_gen.configs.pipelines.qwen_image import (
QwenImageEditPipelineConfig,
QwenImagePipelineConfig,
)
from sglang.multimodal_gen.configs.pipelines.wan import (
FastWan2_1_T2V_480P_Config,
FastWan2_2_TI2V_5B_Config,
Wan2_2_I2V_A14B_Config,
Wan2_2_T2V_A14B_Config,
Wan2_2_TI2V_5B_Config,
)
from sglang.multimodal_gen.configs.sample.flux import FluxSamplingParams
from sglang.multimodal_gen.configs.sample.hunyuan import (
FastHunyuanSamplingParam,
HunyuanSamplingParams,
)
from sglang.multimodal_gen.configs.sample.qwenimage import QwenImageSamplingParams
from sglang.multimodal_gen.configs.sample.stepvideo import StepVideoT2VSamplingParams
from sglang.multimodal_gen.configs.sample.wan import (
FastWanT2V480PConfig,
Wan2_1_Fun_1_3B_InP_SamplingParams,
Wan2_2_I2V_A14B_SamplingParam,
Wan2_2_T2V_A14B_SamplingParam,
Wan2_2_TI2V_5B_SamplingParam,
WanI2V_14B_480P_SamplingParam,
WanI2V_14B_720P_SamplingParam,
WanT2V_1_3B_SamplingParams,
WanT2V_14B_SamplingParams,
)
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
maybe_download_model_index,
verify_model_config_and_directory,
)
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
# --- Part 1: Pipeline Discovery ---
_PIPELINE_REGISTRY: Dict[str, Type[ComposedPipelineBase]] = {}
def _discover_and_register_pipelines():
"""
Automatically discover and register all ComposedPipelineBase subclasses.
This function scans the 'sglang.multimodal_gen.runtime.architectures' package,
finds modules with an 'EntryClass' attribute, and maps the class's 'pipeline_name'
to the class itself in a global registry.
"""
if _PIPELINE_REGISTRY: # E-run only once
return
package_name = "sglang.multimodal_gen.runtime.architectures"
package = importlib.import_module(package_name)
for _, pipeline_type_str, ispkg in pkgutil.iter_modules(package.__path__):
if not ispkg:
continue
pipeline_type_package_name = f"{package_name}.{pipeline_type_str}"
pipeline_type_package = importlib.import_module(pipeline_type_package_name)
for _, arch, ispkg_arch in pkgutil.iter_modules(pipeline_type_package.__path__):
if not ispkg_arch:
continue
arch_package_name = f"{pipeline_type_package_name}.{arch}"
arch_package = importlib.import_module(arch_package_name)
for _, module_name, ispkg_module in pkgutil.walk_packages(
arch_package.__path__, arch_package.__name__ + "."
):
if not ispkg_module:
pipeline_module = importlib.import_module(module_name)
if hasattr(pipeline_module, "EntryClass"):
entry_cls = pipeline_module.EntryClass
if not isinstance(entry_cls, list):
entry_cls_list = [entry_cls]
else:
entry_cls_list = entry_cls
for cls in entry_cls_list:
if hasattr(cls, "pipeline_name"):
if cls.pipeline_name in _PIPELINE_REGISTRY:
logger.warning(
f"Duplicate pipeline name '{cls.pipeline_name}' found. Overwriting."
)
_PIPELINE_REGISTRY[cls.pipeline_name] = cls
# else:
# logger.warning(
# f"Pipeline class {cls.__name__} does not have a 'pipeline_name' attribute."
# )
# --- Part 2: Config Registration ---
@dataclasses.dataclass
class ConfigInfo:
"""Encapsulates all configuration information required to register a
diffusers model within this framework."""
sampling_param_cls: Any
pipeline_config_cls: Type[PipelineConfig]
# The central registry mapping a model name to its configuration information
_CONFIG_REGISTRY: Dict[str, ConfigInfo] = {}
# Mappings from Hugging Face model paths to our internal model names
_MODEL_PATH_TO_NAME: Dict[str, str] = {}
# Detectors to identify model families from paths or class names
_MODEL_NAME_DETECTORS: List[Tuple[str, Callable[[str], bool]]] = []
def register_configs(
model_name: str,
sampling_param_cls: Any,
pipeline_config_cls: Type[PipelineConfig],
model_path_to_name_mappings: Optional[Dict[str, str]] = None,
model_name_detectors: Optional[List[Tuple[str, Callable[[str], bool]]]] = None,
):
"""
Registers configuration classes for a new model family.
"""
if model_name in _CONFIG_REGISTRY:
logger.warning(
f"Config for model '{model_name}' is already registered and will be overwritten."
)
_CONFIG_REGISTRY[model_name] = ConfigInfo(
sampling_param_cls=sampling_param_cls,
pipeline_config_cls=pipeline_config_cls,
)
if model_path_to_name_mappings:
for path, name in model_path_to_name_mappings.items():
if path in _MODEL_PATH_TO_NAME:
logger.warning(
f"Model path '{path}' is already mapped to '{_MODEL_PATH_TO_NAME[path]}' and will be overwritten by '{name}'."
)
_MODEL_PATH_TO_NAME[path] = name
if model_name_detectors:
_MODEL_NAME_DETECTORS.extend(model_name_detectors)
def _get_config_info(model_path: str) -> Optional[ConfigInfo]:
"""
Gets the ConfigInfo for a given model path using mappings and detectors.
"""
# 1. Exact match
if model_path in _MODEL_PATH_TO_NAME:
model_name = _MODEL_PATH_TO_NAME[model_path]
return _CONFIG_REGISTRY.get(model_name)
# 2. Partial match
for registered_id, model_name in _MODEL_PATH_TO_NAME.items():
if registered_id in model_path:
return _CONFIG_REGISTRY.get(model_name)
# 3. Use detectors
if os.path.exists(model_path):
config = verify_model_config_and_directory(model_path)
else:
config = maybe_download_model_index(model_path)
pipeline_name = config.get("_class_name", "").lower()
for model_name, detector in _MODEL_NAME_DETECTORS:
if detector(model_path.lower()) or detector(pipeline_name):
return _CONFIG_REGISTRY.get(model_name)
return None
# --- Part 3: Main Resolver ---
@dataclasses.dataclass
class ModelInfo:
"""
Encapsulates all configuration information required to register a
diffusers model within this framework.
"""
pipeline_cls: Type[ComposedPipelineBase]
sampling_param_cls: Any
pipeline_config_cls: Type[PipelineConfig]
def get_model_info(model_path: str) -> Optional[ModelInfo]:
"""
Resolves all necessary classes (pipeline, sampling, config) for a given model path.
This function serves as the main entry point for model resolution. It performs two main tasks:
1. Dynamically resolves the pipeline class by reading 'model_index.json' and matching
'_class_name' against an auto-discovered registry of pipeline implementations.
2. Resolves the associated configuration classes (for sampling and pipeline) using a
manually registered mapping based on the model path.
"""
# 1. Discover all available pipeline classes and cache them
_discover_and_register_pipelines()
# 2. Get pipeline class from model's model_index.json
try:
if os.path.exists(model_path):
config = verify_model_config_and_directory(model_path)
else:
config = maybe_download_model_index(model_path)
except Exception as e:
logger.error(f"Could not read model config for '{model_path}': {e}")
return None
pipeline_class_name = config.get("_class_name")
if not pipeline_class_name:
logger.error(f"'_class_name' not found in model_index.json for '{model_path}'")
return None
pipeline_cls = _PIPELINE_REGISTRY.get(pipeline_class_name)
if not pipeline_cls:
logger.error(
f"Pipeline class '{pipeline_class_name}' specified in '{model_path}' is not a registered EntryClass in the framework. "
f"Available pipelines: {list(_PIPELINE_REGISTRY.keys())}"
)
return None
# 3. Get configuration classes (sampling, pipeline config)
config_info = _get_config_info(model_path)
if not config_info:
logger.warning(
f"No specific configuration registered for '{model_path}'. "
f"Falling back to default SamplingParams and PipelineConfig."
)
# Fallback to defaults if no specific config is found
from sglang.multimodal_gen.configs.sample.base import SamplingParams
config_info = ConfigInfo(
sampling_param_cls=SamplingParams, pipeline_config_cls=PipelineConfig
)
# 4. Combine and return the complete model info
return ModelInfo(
pipeline_cls=pipeline_cls,
sampling_param_cls=config_info.sampling_param_cls,
pipeline_config_cls=config_info.pipeline_config_cls,
)
# Registration of model configs
def _register_configs():
# Hunyuan
register_configs(
model_name="hunyuan",
sampling_param_cls=HunyuanSamplingParams,
pipeline_config_cls=HunyuanConfig,
model_path_to_name_mappings={
"hunyuanvideo-community/HunyuanVideo": "hunyuan",
},
model_name_detectors=[("hunyuan", lambda id: "hunyuan" in id.lower())],
)
register_configs(
model_name="fasthunyuan",
sampling_param_cls=FastHunyuanSamplingParam,
pipeline_config_cls=FastHunyuanConfig,
model_path_to_name_mappings={
"FastVideo/FastHunyuan-diffusers": "fasthunyuan",
},
)
# StepVideo
register_configs(
model_name="stepvideo",
sampling_param_cls=StepVideoT2VSamplingParams,
pipeline_config_cls=StepVideoT2VConfig,
model_path_to_name_mappings={
"FastVideo/stepvideo-t2v-diffusers": "stepvideo",
},
model_name_detectors=[("stepvideo", lambda id: "stepvideo" in id.lower())],
)
# Wan
register_configs(
model_name="wan-t2v-1.3b",
sampling_param_cls=WanT2V_1_3B_SamplingParams,
pipeline_config_cls=WanT2V480PConfig,
model_path_to_name_mappings={
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers": "wan-t2v-1.3b",
},
model_name_detectors=[("wanpipeline", lambda id: "wanpipeline" in id.lower())],
)
register_configs(
model_name="wan-t2v-14b",
sampling_param_cls=WanT2V_14B_SamplingParams,
pipeline_config_cls=WanT2V720PConfig,
model_path_to_name_mappings={
"Wan-AI/Wan2.1-T2V-14B-Diffusers": "wan-t2v-14b",
},
)
register_configs(
model_name="wan-i2v-14b-480p",
sampling_param_cls=WanI2V_14B_480P_SamplingParam,
pipeline_config_cls=WanI2V480PConfig,
model_path_to_name_mappings={
"Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": "wan-i2v-14b-480p",
},
model_name_detectors=[
("wanimagetovideo", lambda id: "wanimagetovideo" in id.lower())
],
)
register_configs(
model_name="wan-i2v-14b-720p",
sampling_param_cls=WanI2V_14B_720P_SamplingParam,
pipeline_config_cls=WanI2V720PConfig,
model_path_to_name_mappings={
"Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": "wan-i2v-14b-720p",
},
)
register_configs(
model_name="wan-fun-1.3b-inp",
sampling_param_cls=Wan2_1_Fun_1_3B_InP_SamplingParams,
pipeline_config_cls=WanI2V480PConfig,
model_path_to_name_mappings={
"weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": "wan-fun-1.3b-inp",
},
)
register_configs(
model_name="wan-ti2v-5b",
sampling_param_cls=Wan2_2_TI2V_5B_SamplingParam,
pipeline_config_cls=Wan2_2_TI2V_5B_Config,
model_path_to_name_mappings={
"Wan-AI/Wan2.2-TI2V-5B-Diffusers": "wan-ti2v-5b",
},
)
register_configs(
model_name="fastwan-ti2v-5b",
sampling_param_cls=Wan2_2_TI2V_5B_SamplingParam,
pipeline_config_cls=FastWan2_2_TI2V_5B_Config,
model_path_to_name_mappings={
"FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers": "fastwan-ti2v-5b",
"FastVideo/FastWan2.2-TI2V-5B-Diffusers": "fastwan-ti2v-5b",
},
)
register_configs(
model_name="wan-t2v-a14b",
sampling_param_cls=Wan2_2_T2V_A14B_SamplingParam,
pipeline_config_cls=Wan2_2_T2V_A14B_Config,
model_path_to_name_mappings={
"Wan-AI/Wan2.2-T2V-A14B-Diffusers": "wan-t2v-a14b",
},
)
register_configs(
model_name="wan-i2v-a14b",
sampling_param_cls=Wan2_2_I2V_A14B_SamplingParam,
pipeline_config_cls=Wan2_2_I2V_A14B_Config,
model_path_to_name_mappings={
"Wan-AI/Wan2.2-I2V-A14B-Diffusers": "wan-i2v-a14b",
},
)
register_configs(
model_name="fast-wan-t2v-1.3b",
sampling_param_cls=FastWanT2V480PConfig,
pipeline_config_cls=FastWan2_1_T2V_480P_Config,
model_path_to_name_mappings={
"FastVideo/FastWan2.1-T2V-1.3B-Diffusers": "fast-wan-t2v-1.3b",
},
)
# FLUX
register_configs(
model_name="flux",
sampling_param_cls=FluxSamplingParams,
pipeline_config_cls=FluxPipelineConfig,
model_path_to_name_mappings={
"black-forest-labs/FLUX.1-dev": "flux",
},
model_name_detectors=[("flux", lambda id: "flux" in id.lower())],
)
# Qwen-Image
register_configs(
model_name="qwen-image",
sampling_param_cls=QwenImageSamplingParams,
pipeline_config_cls=QwenImagePipelineConfig,
model_path_to_name_mappings={
"Qwen/Qwen-Image": "qwen-image",
},
)
register_configs(
model_name="qwen-image-edit",
sampling_param_cls=QwenImageSamplingParams,
pipeline_config_cls=QwenImageEditPipelineConfig,
model_path_to_name_mappings={
"Qwen/Qwen-Image-Edit": "qwen-image-edit",
},
)
_register_configs()

12
python/sglang/multimodal_gen/runtime/architectures/basic/flux/flux.py
View File

@@ -1,14 +1,10 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""
Hunyuan video diffusion pipeline implementation.
This module contains an implementation of the Hunyuan video diffusion pipeline
using the modular pipeline architecture.
"""
from sglang.multimodal_gen.runtime.pipelines import ComposedPipelineBase, Req
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages import (
ConditioningStage,
DecodingStage,

4
python/sglang/multimodal_gen/runtime/architectures/basic/hunyuan/hunyuan_pipeline.py
View File

@@ -9,7 +9,9 @@ using the modular pipeline architecture.
"""
from sglang.multimodal_gen.runtime.pipelines import ComposedPipelineBase
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.stages import (
ConditioningStage,
DecodingStage,

15
python/sglang/multimodal_gen/runtime/architectures/basic/qwen_image/qwen_image.py
View File

@@ -1,17 +1,13 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""
Hunyuan video diffusion pipeline implementation.
This module contains an implementation of the Hunyuan video diffusion pipeline
using the modular pipeline architecture.
"""
from diffusers.image_processor import VaeImageProcessor
from sglang.multimodal_gen.runtime.pipelines import ComposedPipelineBase, Req
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages import (
ConditioningStage,
DecodingStage,
DenoisingStage,
ImageEncodingStage,
@@ -21,6 +17,9 @@ from sglang.multimodal_gen.runtime.pipelines.stages import (
TextEncodingStage,
TimestepPreparationStage,
)
from sglang.multimodal_gen.runtime.pipelines.stages.conditioning import (
ConditioningStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

7
python/sglang/multimodal_gen/runtime/architectures/basic/wan/wan_causal_dmd_pipeline.py
View File

@@ -7,7 +7,10 @@ Wan causal DMD pipeline implementation.
This module wires the causal DMD denoising stage into the modular pipeline.
"""
from sglang.multimodal_gen.runtime.pipelines import ComposedPipelineBase, LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.lora_pipeline import LoRAPipeline
# isort: off
from sglang.multimodal_gen.runtime.pipelines.stages import (
@@ -27,7 +30,7 @@ logger = init_logger(__name__)
class WanCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
pipeline_name = "WanPipeline"
pipeline_name = "WanCausalDMDPipeline"
_required_config_modules = [
"text_encoder",

5
python/sglang/multimodal_gen/runtime/architectures/basic/wan/wan_dmd_pipeline.py
View File

@@ -11,7 +11,10 @@ using the modular pipeline architecture.
from sglang.multimodal_gen.runtime.models.schedulers.scheduling_flow_match_euler_discrete import (
FlowMatchEulerDiscreteScheduler,
)
from sglang.multimodal_gen.runtime.pipelines import ComposedPipelineBase, LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.lora_pipeline import LoRAPipeline
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

2
python/sglang/multimodal_gen/runtime/architectures/basic/wan/wan_i2v_dmd_pipeline.py
View File

@@ -37,7 +37,7 @@ logger = init_logger(__name__)
class WanImageToVideoDmdPipeline(LoRAPipeline, ComposedPipelineBase):
pipeline_name = "WanCausalDMDPipeline"
pipeline_name = "WanImageToVideoDmdPipeline"
_required_config_modules = [
"text_encoder",

7
python/sglang/multimodal_gen/runtime/architectures/basic/wan/wan_pipeline.py
View File

@@ -11,7 +11,10 @@ using the modular pipeline architecture.
from sglang.multimodal_gen.runtime.models.schedulers.scheduling_flow_unipc_multistep import (
FlowUniPCMultistepScheduler,
)
from sglang.multimodal_gen.runtime.pipelines import ComposedPipelineBase, LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.lora_pipeline import LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines.stages import (
ConditioningStage,
DecodingStage,
@@ -32,7 +35,7 @@ class WanPipeline(LoRAPipeline, ComposedPipelineBase):
Wan video diffusion pipeline with LoRA support.
"""
pipeline_name = "WanImageToVideoPipeline"
pipeline_name = "WanPipeline"
_required_config_modules = [
"text_encoder",

1
python/sglang/multimodal_gen/runtime/architectures/preprocess/__init__.py
View File

@@ -1 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

433
python/sglang/multimodal_gen/runtime/architectures/preprocess/preprocess_pipeline_base.py
View File

@@ -1,433 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
import os
from typing import Any
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from sglang.multimodal_gen.dataset import getdataset
from sglang.multimodal_gen.dataset.dataloader.parquet_io import (
ParquetDatasetWriter,
records_to_table,
)
from sglang.multimodal_gen.dataset.preprocessing_datasets import PreprocessBatch
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.stages import TextEncodingStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
class BasePreprocessPipeline(ComposedPipelineBase):
"""Base class for preprocessing pipelines that handles common functionality."""
def create_pipeline_stages(self, server_args: ServerArgs):
"""Set up pipeline stages with proper dependency injection."""
self.add_stage(
stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
),
)
@torch.no_grad()
def forward(
self,
batch: Req,
server_args: ServerArgs,
args,
):
if not self.post_init_called:
self.post_init()
# Initialize class variables for data sharing
self.video_data: dict[str, Any] = {} # Store video metadata and paths
self.latent_data: dict[str, Any] = {} # Store latent tensors
self.preprocess_video_and_text(server_args, args)
def get_extra_features(
self, valid_data: dict[str, Any], server_args: ServerArgs
) -> dict[str, Any]:
"""Get additional features specific to the pipeline type. Override in subclasses."""
return {}
def get_pyarrow_schema(self) -> pa.Schema:
"""Return the PyArrow schema for this pipeline. Must be overridden."""
raise NotImplementedError
def get_schema_fields(self) -> list[str]:
"""Get the schema fields for the pipeline type."""
return [f.name for f in self.get_pyarrow_schema()]
def create_record_for_schema(
self, preprocess_batch: PreprocessBatch, schema: pa.Schema, strict: bool = False
) -> dict[str, Any]:
"""Create a record for the Parquet dataset using a generic schema-based approach.
Args:
preprocess_batch: The batch containing the data to extract
schema: PyArrow schema defining the expected fields
strict: If True, raises an exception when required fields are missing or unfilled
Returns:
Dictionary record matching the schema
Raises:
ValueError: If strict=True and required fields are missing or unfilled
"""
record = {}
unfilled_fields = []
for field in schema.names:
field_filled = False
if field.endswith("_bytes"):
# Handle binary tensor data - convert numpy array or tensor to bytes
tensor_name = field.replace("_bytes", "")
tensor_data = getattr(preprocess_batch, tensor_name, None)
if tensor_data is not None:
try:
if hasattr(tensor_data, "numpy"): # torch tensor
record[field] = tensor_data.cpu().numpy().tobytes()
field_filled = True
elif hasattr(tensor_data, "tobytes"): # numpy array
record[field] = tensor_data.tobytes()
field_filled = True
else:
raise ValueError(
f"Unsupported tensor type for field {field}: {type(tensor_data)}"
)
except Exception as e:
if strict:
raise ValueError(
f"Failed to convert tensor {tensor_name} to bytes: {e}"
) from e
record[field] = b"" # Empty bytes for missing data
else:
record[field] = b"" # Empty bytes for missing data
elif field.endswith("_shape"):
# Handle tensor shape info
tensor_name = field.replace("_shape", "")
tensor_data = getattr(preprocess_batch, tensor_name, None)
if tensor_data is not None and hasattr(tensor_data, "shape"):
record[field] = list(tensor_data.shape)
field_filled = True
else:
record[field] = []
elif field.endswith("_dtype"):
# Handle tensor dtype info
tensor_name = field.replace("_dtype", "")
tensor_data = getattr(preprocess_batch, tensor_name, None)
if tensor_data is not None and hasattr(tensor_data, "dtype"):
record[field] = str(tensor_data.dtype)
field_filled = True
else:
record[field] = "unknown"
elif field in ["width", "height", "num_frames"]:
# Handle integer metadata fields
value = getattr(preprocess_batch, field, None)
if value is not None:
try:
record[field] = int(value)
field_filled = True
except (ValueError, TypeError) as e:
if strict:
raise ValueError(
f"Failed to convert field {field} to int: {e}"
) from e
record[field] = 0
else:
record[field] = 0
elif field in ["duration_sec", "fps"]:
# Handle float metadata fields
# Map schema field names to batch attribute names
attr_name = "duration" if field == "duration_sec" else field
value = getattr(preprocess_batch, attr_name, None)
if value is not None:
try:
record[field] = float(value)
field_filled = True
except (ValueError, TypeError) as e:
if strict:
raise ValueError(
f"Failed to convert field {field} to float: {e}"
) from e
record[field] = 0.0
else:
record[field] = 0.0
else:
# Handle string fields (id, file_name, caption, media_type, etc.)
# Map common schema field names to batch attribute names
attr_name = field
if field == "caption":
attr_name = "text"
elif field == "file_name":
attr_name = "path"
elif field == "id":
# Generate ID from path if available
path_value = getattr(preprocess_batch, "path", None)
if path_value:
import os
record[field] = os.path.basename(path_value).split(".")[0]
field_filled = True
else:
record[field] = ""
continue
elif field == "media_type":
# Determine media type from path
path_value = getattr(preprocess_batch, "path", None)
if path_value:
record[field] = (
"video" if path_value.endswith(".mp4") else "image"
)
field_filled = True
else:
record[field] = ""
continue
value = getattr(preprocess_batch, attr_name, None)
if value is not None:
record[field] = str(value)
field_filled = True
else:
record[field] = ""
# Track unfilled fields
if not field_filled:
unfilled_fields.append(field)
# Handle strict mode
if strict and unfilled_fields:
raise ValueError(f"Required fields were not filled: {unfilled_fields}")
# Log unfilled fields as warning if not in strict mode
if unfilled_fields:
logger.warning(
"Some fields were not filled and got default values: %s",
unfilled_fields,
)
return record
def create_record(
self,
video_name: str,
vae_latent: np.ndarray,
text_embedding: np.ndarray,
valid_data: dict[str, Any],
idx: int,
extra_features: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""Create a record for the Parquet dataset."""
record = {
"id": video_name,
"vae_latent_bytes": vae_latent.tobytes(),
"vae_latent_shape": list(vae_latent.shape),
"vae_latent_dtype": str(vae_latent.dtype),
"text_embedding_bytes": text_embedding.tobytes(),
"text_embedding_shape": list(text_embedding.shape),
"text_embedding_dtype": str(text_embedding.dtype),
"file_name": video_name,
"caption": valid_data["text"][idx] if len(valid_data["text"]) > 0 else "",
"media_type": "video",
"width": (
valid_data["pixel_values"][idx].shape[-2]
if len(valid_data["pixel_values"]) > 0
else 0
),
"height": (
valid_data["pixel_values"][idx].shape[-1]
if len(valid_data["pixel_values"]) > 0
else 0
),
"num_frames": vae_latent.shape[1] if len(vae_latent.shape) > 1 else 0,
"duration_sec": (
float(valid_data["duration"][idx])
if len(valid_data["duration"]) > 0
else 0.0
),
"fps": float(valid_data["fps"][idx]) if len(valid_data["fps"]) > 0 else 0.0,
}
if extra_features:
record.update(extra_features)
return record
def preprocess_video_and_text(self, server_args: ServerArgs, args):
os.makedirs(args.output_dir, exist_ok=True)
# Create directory for combined data
combined_parquet_dir = os.path.join(args.output_dir, "combined_parquet_dataset")
os.makedirs(combined_parquet_dir, exist_ok=True)
local_rank = int(os.getenv("RANK", 0))
# Get how many samples have already been processed
start_idx = 0
for root, _, files in os.walk(combined_parquet_dir):
for file in files:
if file.endswith(".parquet"):
table = pq.read_table(os.path.join(root, file))
start_idx += table.num_rows
# Loading dataset
train_dataset = getdataset(args)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.preprocess_video_batch_size,
num_workers=args.dataloader_num_workers,
)
num_processed_samples = 0
# Add progress bar for video preprocessing
pbar = tqdm(
train_dataloader,
desc="Processing videos",
unit="batch",
disable=local_rank != 0,
)
for batch_idx, data in enumerate(pbar):
if data is None:
continue
with torch.inference_mode():
# Filter out invalid samples (those with all zeros)
valid_indices = []
for i, pixel_values in enumerate(data["pixel_values"]):
if not torch.all(pixel_values == 0): # Check if all values are zero
valid_indices.append(i)
num_processed_samples += len(valid_indices)
if not valid_indices:
continue
# Create new batch with only valid samples
valid_data = {
"pixel_values": torch.stack(
[data["pixel_values"][i] for i in valid_indices]
),
"text": [data["text"][i] for i in valid_indices],
"path": [data["path"][i] for i in valid_indices],
"fps": [data["fps"][i] for i in valid_indices],
"duration": [data["duration"][i] for i in valid_indices],
}
# VAE
with torch.autocast("cuda", dtype=torch.float32):
latents = (
self.get_module("vae")
.encode(valid_data["pixel_values"].to(get_local_torch_device()))
.mean
)
# Get extra features if needed
extra_features = self.get_extra_features(valid_data, server_args)
batch_captions = valid_data["text"]
batch = Req(
data_type="video",
prompt=batch_captions,
prompt_embeds=[],
prompt_attention_mask=[],
)
assert hasattr(self, "prompt_encoding_stage")
result_batch = self.prompt_encoding_stage(batch, server_args)
prompt_embeds, prompt_attention_mask = (
result_batch.prompt_embeds[0],
result_batch.prompt_attention_mask[0],
)
assert prompt_embeds.shape[0] == prompt_attention_mask.shape[0]
# Get sequence lengths from attention masks (number of 1s)
seq_lens = prompt_attention_mask.sum(dim=1)
non_padded_embeds = []
non_padded_masks = []
# Process each item in the batch
for i in range(prompt_embeds.size(0)):
seq_len = seq_lens[i].item()
# Slice the embeddings and masks to keep only non-padding parts
non_padded_embeds.append(prompt_embeds[i, :seq_len])
non_padded_masks.append(prompt_attention_mask[i, :seq_len])
# Update the tensors with non-padded versions
prompt_embeds = non_padded_embeds
prompt_attention_mask = non_padded_masks
# Prepare batch data for Parquet dataset
batch_data = []
# Add progress bar for saving outputs
save_pbar = tqdm(
enumerate(valid_data["path"]),
desc="Saving outputs",
unit="item",
leave=False,
)
for idx, video_path in save_pbar:
# Get the corresponding latent and info using video name
latent = latents[idx].cpu()
video_name = os.path.basename(video_path).split(".")[0]
# Convert tensors to numpy arrays
vae_latent = latent.cpu().numpy()
text_embedding = prompt_embeds[idx].cpu().numpy()
# Get extra features for this sample if needed
sample_extra_features = {}
if extra_features:
for key, value in extra_features.items():
if isinstance(value, torch.Tensor):
sample_extra_features[key] = value[idx].cpu().numpy()
else:
sample_extra_features[key] = value[idx]
# Create record for Parquet dataset
record = self.create_record(
video_name=video_name,
vae_latent=vae_latent,
text_embedding=text_embedding,
valid_data=valid_data,
idx=idx,
extra_features=sample_extra_features,
)
batch_data.append(record)
if batch_data:
write_pbar = tqdm(
total=1, desc="Writing to Parquet dataset", unit="batch"
)
table = records_to_table(batch_data, self.get_pyarrow_schema())
write_pbar.update(1)
write_pbar.close()
if not hasattr(self, "dataset_writer"):
self.dataset_writer = ParquetDatasetWriter(
out_dir=combined_parquet_dir,
samples_per_file=args.samples_per_file,
)
self.dataset_writer.append_table(table)
logger.info("Collected batch with %s samples", len(table))
if num_processed_samples >= args.flush_frequency:
written = self.dataset_writer.flush()
logger.info("Flushed %s samples to parquet", written)
num_processed_samples = 0

247
python/sglang/multimodal_gen/runtime/architectures/preprocess/preprocess_pipeline_i2v.py
View File

@@ -1,247 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""
I2V Data Preprocessing pipeline implementation.
This module contains an implementation of the I2V Data Preprocessing pipeline
using the modular pipeline architecture.
"""
from typing import Any
import numpy as np
import torch
from PIL import Image
from sglang.multimodal_gen.dataset.dataloader.schema import pyarrow_schema_i2v
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
from sglang.multimodal_gen.runtime.pipelines.preprocess.preprocess_pipeline_base import (
BasePreprocessPipeline,
)
from sglang.multimodal_gen.runtime.pipelines.stages import (
ImageEncodingStage,
TextEncodingStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
class PreprocessPipeline_I2V(BasePreprocessPipeline):
"""I2V preprocessing pipeline implementation."""
_required_config_modules = [
"text_encoder",
"tokenizer",
"vae",
"image_encoder",
"image_processor",
]
def create_pipeline_stages(self, server_args: ServerArgs):
self.add_stage(
stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
),
)
self.add_stage(
stage_name="image_encoding_stage",
stage=ImageEncodingStage(
image_encoder=self.get_module("image_encoder"),
image_processor=self.get_module("image_processor"),
),
)
def get_pyarrow_schema(self):
"""Return the PyArrow schema for I2V pipeline."""
return pyarrow_schema_i2v
def get_extra_features(
self, valid_data: dict[str, Any], server_args: ServerArgs
) -> dict[str, Any]:
# TODO(will): move these to cpu at some point
self.get_module("image_encoder").to(get_local_torch_device())
self.get_module("vae").to(get_local_torch_device())
features = {}
"""Get CLIP features from the first frame of each video."""
first_frame = valid_data["pixel_values"][:, :, 0, :, :].permute(
0, 2, 3, 1
) # (B, C, T, H, W) -> (B, H, W, C)
_, _, num_frames, height, width = valid_data["pixel_values"].shape
# latent_height = height // self.get_module(
# "vae").spatial_compression_ratio
# latent_width = width // self.get_module("vae").spatial_compression_ratio
processed_images = []
# Frame has values between -1 and 1
for frame in first_frame:
frame = (frame + 1) * 127.5
frame_pil = Image.fromarray(frame.cpu().numpy().astype(np.uint8))
processed_img = self.get_module("image_processor")(
images=frame_pil, return_tensors="pt"
)
processed_images.append(processed_img)
# Get CLIP features
pixel_values = torch.cat(
[img["pixel_values"] for img in processed_images], dim=0
).to(get_local_torch_device())
with torch.no_grad():
image_inputs = {"pixel_values": pixel_values}
with set_forward_context(current_timestep=0, attn_metadata=None):
clip_features = self.get_module("image_encoder")(**image_inputs)
clip_features = clip_features.last_hidden_state
features["clip_feature"] = clip_features
"""Get VAE features from the first frame of each video"""
video_conditions = []
for frame in first_frame:
processed_img = frame.to(device="cpu", dtype=torch.float32)
processed_img = processed_img.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(2)
# (B, H, W, C) -> (B, C, 1, H, W)
video_condition = torch.cat(
[
processed_img,
processed_img.new_zeros(
processed_img.shape[0],
processed_img.shape[1],
num_frames - 1,
height,
width,
),
],
dim=2,
)
video_condition = video_condition.to(
device=get_local_torch_device(), dtype=torch.float32
)
video_conditions.append(video_condition)
video_conditions = torch.cat(video_conditions, dim=0)
with torch.autocast(device_type="cuda", dtype=torch.float32, enabled=True):
encoder_outputs = self.get_module("vae").encode(video_conditions)
latent_condition = encoder_outputs.mean
if (
hasattr(self.get_module("vae"), "shift_factor")
and self.get_module("vae").shift_factor is not None
):
if isinstance(self.get_module("vae").shift_factor, torch.Tensor):
latent_condition -= self.get_module("vae").shift_factor.to(
latent_condition.device, latent_condition.dtype
)
else:
latent_condition -= self.get_module("vae").shift_factor
if isinstance(self.get_module("vae").scaling_factor, torch.Tensor):
latent_condition = latent_condition * self.get_module(
"vae"
).scaling_factor.to(latent_condition.device, latent_condition.dtype)
else:
latent_condition = latent_condition * self.get_module("vae").scaling_factor
# mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height,
# latent_width)
# mask_lat_size[:, :, list(range(1, num_frames))] = 0
# first_frame_mask = mask_lat_size[:, :, 0:1]
# first_frame_mask = torch.repeat_interleave(
# first_frame_mask,
# dim=2,
# repeats=self.get_module("vae").temporal_compression_ratio)
# mask_lat_size = torch.concat(
# [first_frame_mask, mask_lat_size[:, :, 1:, :]], dim=2)
# mask_lat_size = mask_lat_size.view(
# batch_size, -1,
# self.get_module("vae").temporal_compression_ratio, latent_height,
# latent_width)
# mask_lat_size = mask_lat_size.transpose(1, 2)
# mask_lat_size = mask_lat_size.to(latent_condition.device)
# image_latent = torch.concat([mask_lat_size, latent_condition], dim=1)
features["first_frame_latent"] = latent_condition
return features
def create_record(
self,
video_name: str,
vae_latent: np.ndarray,
text_embedding: np.ndarray,
valid_data: dict[str, Any],
idx: int,
extra_features: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""Create a record for the Parquet dataset with CLIP features."""
record = super().create_record(
video_name=video_name,
vae_latent=vae_latent,
text_embedding=text_embedding,
valid_data=valid_data,
idx=idx,
extra_features=extra_features,
)
if extra_features and "clip_feature" in extra_features:
clip_feature = extra_features["clip_feature"]
record.update(
{
"clip_feature_bytes": clip_feature.tobytes(),
"clip_feature_shape": list(clip_feature.shape),
"clip_feature_dtype": str(clip_feature.dtype),
}
)
else:
record.update(
{
"clip_feature_bytes": b"",
"clip_feature_shape": [],
"clip_feature_dtype": "",
}
)
if extra_features and "first_frame_latent" in extra_features:
first_frame_latent = extra_features["first_frame_latent"]
record.update(
{
"first_frame_latent_bytes": first_frame_latent.tobytes(),
"first_frame_latent_shape": list(first_frame_latent.shape),
"first_frame_latent_dtype": str(first_frame_latent.dtype),
}
)
else:
record.update(
{
"first_frame_latent_bytes": b"",
"first_frame_latent_shape": [],
"first_frame_latent_dtype": "",
}
)
if extra_features and "pil_image" in extra_features:
pil_image = extra_features["pil_image"]
record.update(
{
"pil_image_bytes": pil_image.tobytes(),
"pil_image_shape": list(pil_image.shape),
"pil_image_dtype": str(pil_image.dtype),
}
)
else:
record.update(
{
"pil_image_bytes": b"",
"pil_image_shape": [],
"pil_image_dtype": "",
}
)
return record
EntryClass = PreprocessPipeline_I2V

355
python/sglang/multimodal_gen/runtime/architectures/preprocess/preprocess_pipeline_ode_trajectory.py
View File

@@ -1,355 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""
ODE Trajectory Data Preprocessing pipeline implementation.
This module contains an implementation of the ODE Trajectory Data Preprocessing pipeline
using the modular pipeline architecture.
Sec 4.3 of CausVid paper: https://arxiv.org/pdf/2412.07772
"""
import os
from collections.abc import Iterator
from typing import Any
import pyarrow as pa
import torch
from torch.utils.data import DataLoader
from torchdata.stateful_dataloader import StatefulDataLoader
from tqdm import tqdm
from sglang.multimodal_gen.configs.sample import SamplingParams
from sglang.multimodal_gen.dataset import gettextdataset
from sglang.multimodal_gen.dataset.dataloader.parquet_io import (
ParquetDatasetWriter,
records_to_table,
)
from sglang.multimodal_gen.dataset.dataloader.record_schema import (
ode_text_only_record_creator,
)
from sglang.multimodal_gen.dataset.dataloader.schema import (
pyarrow_schema_ode_trajectory_text_only,
)
from sglang.multimodal_gen.runtime.models.schedulers.scheduling_self_forcing_flow_match import (
SelfForcingFlowMatchScheduler,
)
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.preprocess.preprocess_pipeline_base import (
BasePreprocessPipeline,
)
from sglang.multimodal_gen.runtime.pipelines.stages import (
DecodingStage,
DenoisingStage,
InputValidationStage,
LatentPreparationStage,
TextEncodingStage,
TimestepPreparationStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.utils import save_decoded_latents_as_video, shallow_asdict
logger = init_logger(__name__)
class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
"""ODE Trajectory preprocessing pipeline implementation."""
_required_config_modules = [
"text_encoder",
"tokenizer",
"vae",
"transformer",
"scheduler",
]
preprocess_dataloader: StatefulDataLoader
preprocess_loader_iter: Iterator[dict[str, Any]]
pbar: Any
num_processed_samples: int
def get_pyarrow_schema(self) -> pa.Schema:
"""Return the PyArrow schema for ODE Trajectory pipeline."""
return pyarrow_schema_ode_trajectory_text_only
def create_pipeline_stages(self, server_args: ServerArgs):
"""Set up pipeline stages with proper dependency injection."""
assert server_args.pipeline_config.flow_shift == 5
self.modules["scheduler"] = SelfForcingFlowMatchScheduler(
shift=server_args.pipeline_config.flow_shift,
sigma_min=0.0,
extra_one_step=True,
)
self.modules["scheduler"].set_timesteps(
num_inference_steps=48, denoising_strength=1.0
)
self.add_stage(
stage_name="input_validation_stage", stage=InputValidationStage()
)
self.add_stage(
stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
),
)
self.add_stage(
stage_name="timestep_preparation_stage",
stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")),
)
self.add_stage(
stage_name="latent_preparation_stage",
stage=LatentPreparationStage(
scheduler=self.get_module("scheduler"),
transformer=self.get_module("transformer", None),
),
)
self.add_stage(
stage_name="denoising_stage",
stage=DenoisingStage(
transformer=self.get_module("transformer"),
scheduler=self.get_module("scheduler"),
pipeline=self,
),
)
self.add_stage(
stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"))
)
def preprocess_text_and_trajectory(self, server_args: ServerArgs, args):
"""Preprocess text-only data and generate trajectory information."""
for batch_idx, data in enumerate(self.pbar):
if data is None:
continue
with torch.inference_mode():
# For text-only processing, we only need text data
# Filter out samples without text
valid_indices = []
for i, text in enumerate(data["text"]):
if text and text.strip(): # Check if text is not empty
valid_indices.append(i)
self.num_processed_samples += len(valid_indices)
if not valid_indices:
continue
# Create new batch with only valid samples (text-only)
valid_data = {
"text": [data["text"][i] for i in valid_indices],
"path": [data["path"][i] for i in valid_indices],
}
# Add fps and duration if available in data
if "fps" in data:
valid_data["fps"] = [data["fps"][i] for i in valid_indices]
if "duration" in data:
valid_data["duration"] = [
data["duration"][i] for i in valid_indices
]
batch_captions = valid_data["text"]
# Encode text using the standalone TextEncodingStage API
prompt_embeds_list, prompt_masks_list = (
self.prompt_encoding_stage.encode_text(
batch_captions,
server_args,
encoder_index=[0],
return_attention_mask=True,
)
)
prompt_embeds = prompt_embeds_list[0]
prompt_attention_masks = prompt_masks_list[0]
assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
sampling_params = SamplingParams.from_pretrained(args.model_path)
# encode negative prompt for trajectory collection
if (
sampling_params.guidance_scale > 1
and sampling_params.negative_prompt is not None
):
negative_prompt_embeds_list, negative_prompt_masks_list = (
self.prompt_encoding_stage.encode_text(
sampling_params.negative_prompt,
server_args,
encoder_index=[0],
return_attention_mask=True,
)
)
negative_prompt_embed = negative_prompt_embeds_list[0][0]
negative_prompt_attention_mask = negative_prompt_masks_list[0][0]
else:
negative_prompt_embed = None
negative_prompt_attention_mask = None
trajectory_latents = []
trajectory_timesteps = []
trajectory_decoded = []
for i, (prompt_embed, prompt_attention_mask) in enumerate(
zip(prompt_embeds, prompt_attention_masks, strict=False)
):
prompt_embed = prompt_embed.unsqueeze(0)
prompt_attention_mask = prompt_attention_mask.unsqueeze(0)
# Collect the trajectory data (text-to-video generation)
batch = Req(
**shallow_asdict(sampling_params),
)
batch.prompt_embeds = [prompt_embed]
batch.prompt_attention_mask = [prompt_attention_mask]
batch.negative_prompt_embeds = [negative_prompt_embed]
batch.negative_attention_mask = [negative_prompt_attention_mask]
batch.num_inference_steps = 48
batch.return_trajectory_latents = True
# Enabling this will save the decoded trajectory videos.
# Used for debugging.
batch.return_trajectory_decoded = False
batch.height = args.max_height
batch.width = args.max_width
batch.fps = args.train_fps
batch.guidance_scale = 6.0
batch.do_classifier_free_guidance = True
result_batch = self.input_validation_stage(batch, server_args)
result_batch = self.timestep_preparation_stage(batch, server_args)
result_batch = self.latent_preparation_stage(
result_batch, server_args
)
result_batch = self.denoising_stage(result_batch, server_args)
result_batch = self.decoding_stage(result_batch, server_args)
trajectory_latents.append(result_batch.trajectory_latents.cpu())
trajectory_timesteps.append(result_batch.trajectory_timesteps.cpu())
trajectory_decoded.append(result_batch.trajectory_decoded)
# Prepare extra features for text-only processing
extra_features = {
"trajectory_latents": trajectory_latents,
"trajectory_timesteps": trajectory_timesteps,
}
if batch.return_trajectory_decoded:
for i, decoded_frames in enumerate(trajectory_decoded):
for j, decoded_frame in enumerate(decoded_frames):
save_decoded_latents_as_video(
decoded_frame,
f"decoded_videos/trajectory_decoded_{i}_{j}.mp4",
args.train_fps,
)
# Prepare batch data for Parquet dataset
batch_data: list[dict[str, Any]] = []
# Add progress bar for saving outputs
save_pbar = tqdm(
enumerate(valid_data["path"]),
desc="Saving outputs",
unit="item",
leave=False,
)
for idx, video_path in save_pbar:
video_name = os.path.basename(video_path).split(".")[0]
# Convert tensors to numpy arrays
text_embedding = prompt_embeds[idx].cpu().numpy()
# Get extra features for this sample
sample_extra_features = {}
if extra_features:
for key, value in extra_features.items():
if isinstance(value, torch.Tensor):
sample_extra_features[key] = value[idx].cpu().numpy()
else:
assert isinstance(value, list)
if isinstance(value[idx], torch.Tensor):
sample_extra_features[key] = (
value[idx].cpu().float().numpy()
)
else:
sample_extra_features[key] = value[idx]
# Create record for Parquet dataset (text-only ODE schema)
record: dict[str, Any] = ode_text_only_record_creator(
video_name=video_name,
text_embedding=text_embedding,
caption=valid_data["text"][idx],
trajectory_latents=sample_extra_features["trajectory_latents"],
trajectory_timesteps=sample_extra_features[
"trajectory_timesteps"
],
)
batch_data.append(record)
if batch_data:
write_pbar = tqdm(
total=1, desc="Writing to Parquet dataset", unit="batch"
)
table = records_to_table(batch_data, self.get_pyarrow_schema())
write_pbar.update(1)
write_pbar.close()
if not hasattr(self, "dataset_writer"):
self.dataset_writer = ParquetDatasetWriter(
out_dir=self.combined_parquet_dir,
samples_per_file=args.samples_per_file,
)
self.dataset_writer.append_table(table)
logger.info("Collected batch with %s samples", len(table))
if self.num_processed_samples >= args.flush_frequency:
written = self.dataset_writer.flush()
logger.info("Flushed %s samples to parquet", written)
self.num_processed_samples = 0
# Final flush for any remaining samples
if hasattr(self, "dataset_writer"):
written = self.dataset_writer.flush(write_remainder=True)
if written:
logger.info("Final flush wrote %s samples", written)
def forward(self, batch: Req, server_args: ServerArgs, args):
if not self.post_init_called:
self.post_init()
self.local_rank = int(os.getenv("RANK", 0))
os.makedirs(args.output_dir, exist_ok=True)
# Create directory for combined data
self.combined_parquet_dir = os.path.join(
args.output_dir, "combined_parquet_dataset"
)
os.makedirs(self.combined_parquet_dir, exist_ok=True)
# Loading dataset
train_dataset = gettextdataset(args)
self.preprocess_dataloader = DataLoader(
train_dataset,
batch_size=args.preprocess_video_batch_size,
num_workers=args.dataloader_num_workers,
)
self.preprocess_loader_iter = iter(self.preprocess_dataloader)
self.num_processed_samples = 0
# Add progress bar for video preprocessing
self.pbar = tqdm(
self.preprocess_loader_iter,
desc="Processing videos",
unit="batch",
disable=self.local_rank != 0,
)
# Initialize class variables for data sharing
self.video_data: dict[str, Any] = {} # Store video metadata and paths
self.latent_data: dict[str, Any] = {} # Store latent tensors
self.preprocess_text_and_trajectory(server_args, args)
EntryClass = PreprocessPipeline_ODE_Trajectory

26
python/sglang/multimodal_gen/runtime/architectures/preprocess/preprocess_pipeline_t2v.py
View File

@@ -1,26 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""
T2V Data Preprocessing pipeline implementation.
This module contains an implementation of the T2V Data Preprocessing pipeline
using the modular pipeline architecture.
"""
from sglang.multimodal_gen.dataset.dataloader.schema import pyarrow_schema_t2v
from sglang.multimodal_gen.runtime.pipelines.preprocess.preprocess_pipeline_base import (
BasePreprocessPipeline,
)
class PreprocessPipeline_T2V(BasePreprocessPipeline):
"""T2V preprocessing pipeline implementation."""
_required_config_modules = ["text_encoder", "tokenizer", "vae"]
def get_pyarrow_schema(self):
"""Return the PyArrow schema for T2V pipeline."""
return pyarrow_schema_t2v
EntryClass = PreprocessPipeline_T2V

200
python/sglang/multimodal_gen/runtime/architectures/preprocess/preprocess_pipeline_text.py
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@@ -1,200 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
"""
Text-only Data Preprocessing pipeline implementation.
This module contains an implementation of the Text-only Data Preprocessing pipeline
using the modular pipeline architecture, based on the ODE Trajectory preprocessing.
"""
import os
from collections.abc import Iterator
from typing import Any
import torch
from torch.utils.data import DataLoader
from torchdata.stateful_dataloader import StatefulDataLoader
from tqdm import tqdm
from sglang.multimodal_gen.dataset import gettextdataset
from sglang.multimodal_gen.dataset.dataloader.parquet_io import (
ParquetDatasetWriter,
records_to_table,
)
from sglang.multimodal_gen.dataset.dataloader.record_schema import (
text_only_record_creator,
)
from sglang.multimodal_gen.dataset.dataloader.schema import pyarrow_schema_text_only
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.preprocess.preprocess_pipeline_base import (
BasePreprocessPipeline,
)
from sglang.multimodal_gen.runtime.pipelines.stages import TextEncodingStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
class PreprocessPipeline_Text(BasePreprocessPipeline):
"""Text-only preprocessing pipeline implementation."""
_required_config_modules = ["text_encoder", "tokenizer"]
preprocess_dataloader: StatefulDataLoader
preprocess_loader_iter: Iterator[dict[str, Any]]
pbar: Any
num_processed_samples: int = 0
def get_pyarrow_schema(self):
"""Return the PyArrow schema for text-only pipeline."""
return pyarrow_schema_text_only
def create_pipeline_stages(self, server_args: ServerArgs):
"""Set up pipeline stages with proper dependency injection."""
self.add_stage(
stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
),
)
def preprocess_text_only(self, server_args: ServerArgs, args):
"""Preprocess text-only data."""
for batch_idx, data in enumerate(self.pbar):
if data is None:
continue
with torch.inference_mode():
# For text-only processing, we only need text data
# Filter out samples without text
valid_indices = []
for i, text in enumerate(data["text"]):
if text and text.strip(): # Check if text is not empty
valid_indices.append(i)
self.num_processed_samples += len(valid_indices)
if not valid_indices:
continue
# Create new batch with only valid samples (text-only)
valid_data = {
"text": [data["text"][i] for i in valid_indices],
"path": [data["path"][i] for i in valid_indices],
}
batch_captions = valid_data["text"]
# Encode text using the standalone TextEncodingStage API
prompt_embeds_list, prompt_masks_list = (
self.prompt_encoding_stage.encode_text(
batch_captions,
server_args,
encoder_index=[0],
return_attention_mask=True,
)
)
prompt_embeds = prompt_embeds_list[0]
prompt_attention_masks = prompt_masks_list[0]
assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
logger.info("===== prompt_embeds: %s", prompt_embeds.shape)
logger.info(
"===== prompt_attention_masks: %s", prompt_attention_masks.shape
)
# Prepare batch data for Parquet dataset
batch_data = []
# Add progress bar for saving outputs
save_pbar = tqdm(
enumerate(valid_data["path"]),
desc="Saving outputs",
unit="item",
leave=False,
)
for idx, text_path in save_pbar:
text_name = os.path.basename(text_path).split(".")[0]
# Convert tensors to numpy arrays
text_embedding = prompt_embeds[idx].cpu().numpy()
# Create record for Parquet dataset (text-only schema)
record = text_only_record_creator(
text_name=text_name,
text_embedding=text_embedding,
caption=valid_data["text"][idx],
)
batch_data.append(record)
if batch_data:
write_pbar = tqdm(
total=1, desc="Writing to Parquet dataset", unit="batch"
)
table = records_to_table(batch_data, pyarrow_schema_text_only)
write_pbar.update(1)
write_pbar.close()
if not hasattr(self, "dataset_writer"):
self.dataset_writer = ParquetDatasetWriter(
out_dir=self.combined_parquet_dir,
samples_per_file=args.samples_per_file,
)
self.dataset_writer.append_table(table)
logger.info("Collected batch with %s samples", len(table))
if self.num_processed_samples >= args.flush_frequency:
written = self.dataset_writer.flush()
logger.info("Flushed %s samples to parquet", written)
self.num_processed_samples = 0
# Final flush for any remaining samples
if hasattr(self, "dataset_writer"):
written = self.dataset_writer.flush(write_remainder=True)
if written:
logger.info("Final flush wrote %s samples", written)
# Text-only record creation moved to sglang.multimodal_gen.dataset.dataloader.record_schema
def forward(self, batch: Req, server_args: ServerArgs, args):
if not self.post_init_called:
self.post_init()
self.local_rank = int(os.getenv("RANK", 0))
os.makedirs(args.output_dir, exist_ok=True)
# Create directory for combined data
self.combined_parquet_dir = os.path.join(
args.output_dir, "combined_parquet_dataset"
)
os.makedirs(self.combined_parquet_dir, exist_ok=True)
# Loading text dataset
train_dataset = gettextdataset(args)
self.preprocess_dataloader = DataLoader(
train_dataset,
batch_size=args.preprocess_video_batch_size,
num_workers=args.dataloader_num_workers,
)
self.preprocess_loader_iter = iter(self.preprocess_dataloader)
self.num_processed_samples = 0
# Add progress bar for text preprocessing
self.pbar = tqdm(
self.preprocess_loader_iter,
desc="Processing text",
unit="batch",
disable=self.local_rank != 0,
)
# Initialize class variables for data sharing
self.text_data: dict[str, Any] = {} # Store text metadata and paths
self.preprocess_text_only(server_args, args)
EntryClass = PreprocessPipeline_Text

134
python/sglang/multimodal_gen/runtime/architectures/preprocess/preprocess_stages.py
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@@ -1,134 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
import random
from collections.abc import Callable
from typing import cast
import numpy as np
import torch
import torchvision
from einops import rearrange
from torchvision import transforms
from sglang.multimodal_gen.configs.configs import VideoLoaderType
from sglang.multimodal_gen.dataset.transform import (
CenterCropResizeVideo,
TemporalRandomCrop,
)
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import (
PreprocessBatch,
Req,
)
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs, WorkloadType
class VideoTransformStage(PipelineStage):
"""
Crop a video in temporal dimension.
"""
def __init__(
self,
train_fps: int,
num_frames: int,
max_height: int,
max_width: int,
do_temporal_sample: bool,
) -> None:
self.train_fps = train_fps
self.num_frames = num_frames
if do_temporal_sample:
self.temporal_sample_fn: Callable | None = TemporalRandomCrop(num_frames)
else:
self.temporal_sample_fn = None
self.video_transform = transforms.Compose(
[
CenterCropResizeVideo((max_height, max_width)),
]
)
def forward(self, batch: Req, server_args: ServerArgs) -> Req:
batch = cast(PreprocessBatch, batch)
assert isinstance(batch.fps, list)
assert isinstance(batch.num_frames, list)
if batch.data_type != "video":
return batch
if len(batch.video_loader) == 0:
raise ValueError("Video loader is not set")
video_pixel_batch = []
for i in range(len(batch.video_loader)):
frame_interval = batch.fps[i] / self.train_fps
start_frame_idx = 0
frame_indices = np.arange(
start_frame_idx, batch.num_frames[i], frame_interval
).astype(int)
if len(frame_indices) > self.num_frames:
if self.temporal_sample_fn is not None:
begin_index, end_index = self.temporal_sample_fn(len(frame_indices))
frame_indices = frame_indices[begin_index:end_index]
else:
frame_indices = frame_indices[: self.num_frames]
if (
server_args.preprocess_config.video_loader_type
== VideoLoaderType.TORCHCODEC
):
video = batch.video_loader[i].get_frames_at(frame_indices).data
elif (
server_args.preprocess_config.video_loader_type
== VideoLoaderType.TORCHVISION
):
video, _, _ = torchvision.io.read_video(
batch.video_loader[i], output_format="TCHW"
)
video = video[frame_indices]
else:
raise ValueError(
f"Invalid video loader type: {server_args.preprocess_config.video_loader_type}"
)
video = self.video_transform(video)
video_pixel_batch.append(video)
video_pixel_values = torch.stack(video_pixel_batch)
video_pixel_values = rearrange(video_pixel_values, "b t c h w -> b c t h w")
video_pixel_values = video_pixel_values.to(torch.uint8)
if server_args.workload_type == WorkloadType.I2V:
batch.pil_image = video_pixel_values[:, :, 0, :, :]
video_pixel_values = video_pixel_values.float() / 255.0
batch.latents = video_pixel_values
batch.num_frames = [video_pixel_values.shape[2]] * len(batch.video_loader)
batch.height = [video_pixel_values.shape[3]] * len(batch.video_loader)
batch.width = [video_pixel_values.shape[4]] * len(batch.video_loader)
return cast(Req, batch)
class TextTransformStage(PipelineStage):
"""
Process text data according to the cfg rate.
"""
def __init__(self, cfg_uncondition_drop_rate: float, seed: int) -> None:
self.cfg_rate = cfg_uncondition_drop_rate
self.rng = random.Random(seed)
def forward(self, batch: Req, server_args: ServerArgs) -> Req:
batch = cast(PreprocessBatch, batch)
prompts = []
for prompt in batch.prompt:
if not isinstance(prompt, list):
prompt = [prompt]
prompt = self.rng.choice(prompt)
prompt = prompt if self.rng.random() > self.cfg_rate else ""
prompts.append(prompt)
batch.prompt = prompts
return cast(Req, batch)

147
python/sglang/multimodal_gen/runtime/architectures/preprocess/v1_preprocess.py
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@@ -1,147 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
import argparse
import os
from typing import Any
from sglang.multimodal_gen import PipelineConfig
from sglang.multimodal_gen.configs.models.vaes import WanVAEConfig
from sglang.multimodal_gen.runtime.architectures.preprocess.preprocess_pipeline_i2v import (
PreprocessPipeline_I2V,
)
from sglang.multimodal_gen.runtime.architectures.preprocess.preprocess_pipeline_ode_trajectory import (
PreprocessPipeline_ODE_Trajectory,
)
from sglang.multimodal_gen.runtime.architectures.preprocess.preprocess_pipeline_t2v import (
PreprocessPipeline_T2V,
)
from sglang.multimodal_gen.runtime.architectures.preprocess.preprocess_pipeline_text import (
PreprocessPipeline_Text,
)
from sglang.multimodal_gen.runtime.distributed import (
get_world_size,
maybe_init_distributed_environment_and_model_parallel,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import maybe_download_model
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
def main(args) -> None:
args.model_path = maybe_download_model(args.model_path)
maybe_init_distributed_environment_and_model_parallel(1, 1)
num_gpus = int(os.environ["WORLD_SIZE"])
assert num_gpus == 1, "Only support 1 GPU"
pipeline_config = PipelineConfig.from_pretrained(args.model_path)
kwargs: dict[str, Any] = {}
if args.preprocess_task == "text_only":
kwargs = {
"text_encoder_cpu_offload": False,
}
else:
# Full config for video/image processing
kwargs = {
"vae_precision": "fp32",
"vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
}
pipeline_config.update_config_from_dict(kwargs)
server_args = ServerArgs(
model_path=args.model_path,
num_gpus=get_world_size(),
dit_cpu_offload=False,
vae_cpu_offload=False,
text_encoder_cpu_offload=False,
pipeline_config=pipeline_config,
)
if args.preprocess_task == "t2v":
PreprocessPipeline = PreprocessPipeline_T2V
elif args.preprocess_task == "i2v":
PreprocessPipeline = PreprocessPipeline_I2V
elif args.preprocess_task == "text_only":
PreprocessPipeline = PreprocessPipeline_Text
elif args.preprocess_task == "ode_trajectory":
assert args.flow_shift is not None, "flow_shift is required for ode_trajectory"
server_args.pipeline_config.flow_shift = args.flow_shift
PreprocessPipeline = PreprocessPipeline_ODE_Trajectory
else:
raise ValueError(
f"Invalid preprocess task: {args.preprocess_task}. "
f"Valid options: t2v, i2v, ode_trajectory, text_only"
)
logger.info(
"Preprocess task: %s using %s",
args.preprocess_task,
PreprocessPipeline.__name__,
)
pipeline = PreprocessPipeline(args.model_path, server_args)
pipeline.forward(batch=None, server_args=server_args, args=args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# dataset & dataloader
parser.add_argument("--model_path", type=str, default="data/mochi")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--data_merge_path", type=str, required=True)
parser.add_argument("--num_frames", type=int, default=163)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=1,
help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
)
parser.add_argument(
"--preprocess_video_batch_size",
type=int,
default=2,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument("--samples_per_file", type=int, default=64)
parser.add_argument(
"--flush_frequency",
type=int,
default=256,
help="how often to save to parquet files",
)
parser.add_argument(
"--num_latent_t", type=int, default=28, help="Number of latent timesteps."
)
parser.add_argument("--max_height", type=int, default=480)
parser.add_argument("--max_width", type=int, default=848)
parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
parser.add_argument("--group_frame", action="store_true") # TODO
parser.add_argument("--group_resolution", action="store_true") # TODO
parser.add_argument("--flow_shift", type=float, default=None)
parser.add_argument(
"--preprocess_task",
type=str,
default="t2v",
choices=["t2v", "i2v", "text_only", "ode_trajectory"],
help="Type of preprocessing task to run",
)
parser.add_argument("--train_fps", type=int, default=30)
parser.add_argument("--use_image_num", type=int, default=0)
parser.add_argument("--text_max_length", type=int, default=256)
parser.add_argument("--speed_factor", type=float, default=1.0)
parser.add_argument("--drop_short_ratio", type=float, default=1.0)
parser.add_argument("--do_temporal_sample", default=False, action="store_true")
# text encoder & vae & diffusion model
parser.add_argument("--text_encoder_name", type=str, default="google/t5-v1_1-xxl")
parser.add_argument("--cache_dir", type=str, default="./cache_dir")
parser.add_argument("--training_cfg_rate", type=float, default=0.0)
parser.add_argument(
"--output_dir",
type=str,
default=None,
help="The output directory where the model predictions and checkpoints will be written.",
)
args = parser.parse_args()
main(args)

26
python/sglang/multimodal_gen/runtime/architectures/preprocess/v1_preprocessing_new.py
View File

@@ -1,26 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
from sglang.multimodal_gen.runtime.distributed import (
maybe_init_distributed_environment_and_model_parallel,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.runtime.workflow.workflow_base import WorkflowBase
from sglang.multimodal_gen.utils import FlexibleArgumentParser
logger = init_logger(__name__)
def main(server_args: ServerArgs) -> None:
maybe_init_distributed_environment_and_model_parallel(1, 1)
preprocess_workflow_cls = WorkflowBase.get_workflow_cls(server_args)
preprocess_workflow = preprocess_workflow_cls(server_args)
preprocess_workflow.run()
if __name__ == "__main__":
parser = FlexibleArgumentParser()
parser = ServerArgs.add_cli_args(parser)
args = parser.parse_args()
server_args = ServerArgs.from_cli_args(args)
main(server_args)

1
python/sglang/multimodal_gen/runtime/architectures/preprocess/wan/__init__.py
View File

@@ -1 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo

118
python/sglang/multimodal_gen/runtime/architectures/preprocess/wan/wan_preprocess_pipelines.py
View File

@@ -1,118 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.preprocess.preprocess_stages import (
TextTransformStage,
VideoTransformStage,
)
from sglang.multimodal_gen.runtime.pipelines.stages import (
EncodingStage,
ImageEncodingStage,
TextEncodingStage,
)
from sglang.multimodal_gen.runtime.pipelines.stages.image_encoding import (
ImageVAEEncodingStage,
)
from sglang.multimodal_gen.runtime.server_args import ServerArgs
class PreprocessPipelineI2V(ComposedPipelineBase):
_required_config_modules = [
"image_encoder",
"image_processor",
"text_encoder",
"tokenizer",
"vae",
]
def create_pipeline_stages(self, server_args: ServerArgs):
assert server_args.preprocess_config is not None
self.add_stage(
stage_name="text_transform_stage",
stage=TextTransformStage(
cfg_uncondition_drop_rate=server_args.preprocess_config.training_cfg_rate,
seed=server_args.preprocess_config.seed,
),
)
self.add_stage(
stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
),
)
self.add_stage(
stage_name="video_transform_stage",
stage=VideoTransformStage(
train_fps=server_args.preprocess_config.train_fps,
num_frames=server_args.preprocess_config.num_frames,
max_height=server_args.preprocess_config.max_height,
max_width=server_args.preprocess_config.max_width,
do_temporal_sample=server_args.preprocess_config.do_temporal_sample,
),
)
if (
self.get_module("image_encoder") is not None
and self.get_module("image_processor") is not None
):
self.add_stage(
stage_name="image_encoding_stage",
stage=ImageEncodingStage(
image_encoder=self.get_module("image_encoder"),
image_processor=self.get_module("image_processor"),
),
)
self.add_stage(
stage_name="image_vae_encoding_stage",
stage=ImageVAEEncodingStage(
vae=self.get_module("vae"),
),
)
self.add_stage(
stage_name="video_encoding_stage",
stage=EncodingStage(
vae=self.get_module("vae"),
),
)
class PreprocessPipelineT2V(ComposedPipelineBase):
_required_config_modules = ["text_encoder", "tokenizer", "vae"]
def create_pipeline_stages(self, server_args: ServerArgs):
assert server_args.preprocess_config is not None
self.add_stage(
stage_name="text_transform_stage",
stage=TextTransformStage(
cfg_uncondition_drop_rate=server_args.preprocess_config.training_cfg_rate,
seed=server_args.preprocess_config.seed,
),
)
self.add_stage(
stage_name="prompt_encoding_stage",
stage=TextEncodingStage(
text_encoders=[self.get_module("text_encoder")],
tokenizers=[self.get_module("tokenizer")],
),
)
self.add_stage(
stage_name="video_transform_stage",
stage=VideoTransformStage(
train_fps=server_args.preprocess_config.train_fps,
num_frames=server_args.preprocess_config.num_frames,
max_height=server_args.preprocess_config.max_height,
max_width=server_args.preprocess_config.max_width,
do_temporal_sample=server_args.preprocess_config.do_temporal_sample,
),
)
self.add_stage(
stage_name="video_encoding_stage",
stage=EncodingStage(
vae=self.get_module("vae"),
),
)
EntryClass = [PreprocessPipelineI2V, PreprocessPipelineT2V]

4
python/sglang/multimodal_gen/runtime/entrypoints/diffusion_generator.py
View File

@@ -21,6 +21,9 @@ import torch
import torchvision
from einops import rearrange
from sglang.multimodal_gen.runtime.pipelines import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import OutputBatch
# Suppress verbose logging from imageio, which is triggered when saving images.
logging.getLogger("imageio").setLevel(logging.WARNING)
logging.getLogger("imageio_ffmpeg").setLevel(logging.WARNING)
@@ -32,7 +35,6 @@ from sglang.multimodal_gen.configs.sample.base import DataType, SamplingParams
from sglang.multimodal_gen.runtime.entrypoints.utils import prepare_request
from sglang.multimodal_gen.runtime.launch_server import launch_server
from sglang.multimodal_gen.runtime.managers.schedulerbase import SchedulerBase
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import OutputBatch, Req
from sglang.multimodal_gen.runtime.server_args import PortArgs, ServerArgs
from sglang.multimodal_gen.runtime.sync_scheduler_client import sync_scheduler_client
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

2
python/sglang/multimodal_gen/runtime/entrypoints/openai/image_api.py
View File

@@ -24,7 +24,7 @@ from sglang.multimodal_gen.runtime.entrypoints.openai.utils import (
post_process_sample,
)
from sglang.multimodal_gen.runtime.entrypoints.utils import prepare_request
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.scheduler_client import scheduler_client
from sglang.multimodal_gen.runtime.server_args import get_global_server_args
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

2
python/sglang/multimodal_gen/runtime/entrypoints/openai/video_api.py
View File

@@ -34,7 +34,7 @@ from sglang.multimodal_gen.runtime.entrypoints.openai.utils import (
post_process_sample,
)
from sglang.multimodal_gen.runtime.entrypoints.utils import prepare_request
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.server_args import get_global_server_args
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

2
python/sglang/multimodal_gen/runtime/entrypoints/utils.py
View File

@@ -16,7 +16,7 @@ logging.getLogger("imageio").setLevel(logging.WARNING)
logging.getLogger("imageio_ffmpeg").setLevel(logging.WARNING)
from sglang.multimodal_gen.configs.sample.base import DataType, SamplingParams
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.utils import shallow_asdict

1
python/sglang/multimodal_gen/runtime/layers/attention/layer.py
View File

@@ -364,7 +364,6 @@ class USPAttention(nn.Module):
), "USPAttention does not support replicated_qkv."
forward_context: ForwardContext = get_forward_context()
ctx_attn_metadata = forward_context.attn_metadata
if get_sequence_parallel_world_size() == 1:
# No sequence parallelism, just run local attention.
out = self.attn_impl.forward(q, k, v, ctx_attn_metadata)

6
python/sglang/multimodal_gen/runtime/layers/visual_embedding.py
View File

@@ -101,7 +101,11 @@ class TimestepEmbedder(nn.Module):
t, self.frequency_embedding_size, self.max_period, dtype=self.freq_dtype
).to(self.mlp.fc_in.weight.dtype)
if timestep_seq_len is not None:
t_freq = t_freq.unflatten(0, (1, timestep_seq_len))
assert (
t_freq.shape[0] % timestep_seq_len == 0
), "timestep length is not divisible by timestep_seq_len"
batch_size = t_freq.shape[0] // timestep_seq_len
t_freq = t_freq.unflatten(0, (batch_size, timestep_seq_len))
# t_freq = t_freq.to(self.mlp.fc_in.weight.dtype)
t_emb = self.mlp(t_freq)
return t_emb

4
python/sglang/multimodal_gen/runtime/managers/gpu_worker.py
View File

@@ -16,8 +16,8 @@ from sglang.multimodal_gen.runtime.distributed.parallel_state import (
get_cfg_group,
get_tp_group,
)
from sglang.multimodal_gen.runtime.pipelines import build_pipeline
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import OutputBatch, Req
from sglang.multimodal_gen.runtime.pipelines import Req, build_pipeline
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import OutputBatch
from sglang.multimodal_gen.runtime.server_args import PortArgs, ServerArgs
from sglang.multimodal_gen.runtime.utils.common import set_cuda_arch
from sglang.multimodal_gen.runtime.utils.logging_utils import (

8
python/sglang/multimodal_gen/runtime/managers/scheduler.py
View File

@@ -6,7 +6,7 @@ from typing import Any
import zmq
from sglang.multimodal_gen.runtime.managers.gpu_worker import GPUWorker
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import OutputBatch
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import OutputBatch
from sglang.multimodal_gen.runtime.server_args import (
PortArgs,
ServerArgs,
@@ -42,9 +42,11 @@ class Scheduler:
# Inter-process Communication
self.context = zmq.Context(io_threads=2)
endpoint = server_args.scheduler_endpoint()
logger.info(f"Scheduler listening at endpoint: {endpoint}")
if gpu_id == 0:
self.receiver = get_zmq_socket(self.context, zmq.REP, endpoint, True)
self.receiver, actual_endpoint = get_zmq_socket(
self.context, zmq.REP, endpoint, True
)
logger.info(f"Scheduler bind at endpoint: {actual_endpoint}")
else:
self.receiver = None

3
python/sglang/multimodal_gen/runtime/managers/schedulerbase.py
View File

@@ -6,7 +6,8 @@ from typing import TypeVar
import zmq
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import OutputBatch, Req
from sglang.multimodal_gen.runtime.pipelines import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import OutputBatch
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.utils import init_logger

9
python/sglang/multimodal_gen/runtime/models/dits/wanvideo.py
View File

@@ -349,7 +349,6 @@ class WanTransformerBlock(nn.Module):
hidden_states = hidden_states.squeeze(1)
bs, seq_length, _ = hidden_states.shape
orig_dtype = hidden_states.dtype
if temb.dim() == 4:
# temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
@@ -368,12 +367,12 @@ class WanTransformerBlock(nn.Module):
shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
e.chunk(6, dim=1)
)
assert shift_msa.dtype == torch.float32
# 1. Self-attention
norm_hidden_states = (
self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa
).to(orig_dtype)
norm1 = self.norm1(hidden_states.float())
norm_hidden_states = (norm1 * (1 + scale_msa) + shift_msa).to(orig_dtype)
query, _ = self.to_q(norm_hidden_states)
key, _ = self.to_k(norm_hidden_states)
value, _ = self.to_v(norm_hidden_states)
@@ -720,6 +719,7 @@ class WanTransformer3DModel(CachableDiT):
encoder_hidden_states_image = None
batch_size, num_channels, num_frames, height, width = hidden_states.shape
p_t, p_h, p_w = self.patch_size
post_patch_num_frames = num_frames // p_t
post_patch_height = height // p_h
@@ -744,7 +744,6 @@ class WanTransformer3DModel(CachableDiT):
hidden_states = self.patch_embedding(hidden_states)
hidden_states = hidden_states.flatten(2).transpose(1, 2)
# timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
if timestep.dim() == 2:
# ti2v

41
python/sglang/multimodal_gen/runtime/pipelines/__init__.py
View File

@@ -9,15 +9,12 @@ This package contains diffusion pipelines for generating videos and images.
from typing import cast
from sglang.multimodal_gen.registry import get_model_info
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.lora_pipeline import LoRAPipeline
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.pipeline_registry import (
PipelineType,
get_pipeline_registry,
)
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (
maybe_download_model,
@@ -36,7 +33,6 @@ class PipelineWithLoRA(LoRAPipeline, ComposedPipelineBase):
def build_pipeline(
server_args: ServerArgs,
pipeline_type: PipelineType | str = PipelineType.BASIC,
) -> PipelineWithLoRA:
"""
Only works with valid hf diffusers configs. (model_index.json)
@@ -45,37 +41,12 @@ def build_pipeline(
2. verify the model config and directory
3. based on the config, determine the pipeline class
"""
# Get pipeline type
model_path = server_args.model_path
model_path = maybe_download_model(model_path)
# server_args.downloaded_model_path = model_path
logger.info("Model path: %s", model_path)
model_info = get_model_info(model_path)
if model_info is None:
raise ValueError(f"Unsupported model: {model_path}")
config = verify_model_config_and_directory(model_path)
pipeline_name = config.get("_class_name")
if pipeline_name is None:
raise ValueError(
"Model config does not contain a _class_name attribute. "
"Only diffusers format is supported."
)
# Get the appropriate pipeline registry based on pipeline_type
logger.info(
"Building pipeline of type: %s",
(
pipeline_type.value
if isinstance(pipeline_type, PipelineType)
else pipeline_type
),
)
pipeline_registry = get_pipeline_registry(pipeline_type)
if isinstance(pipeline_type, str):
pipeline_type = PipelineType.from_string(pipeline_type)
pipeline_cls = pipeline_registry.resolve_pipeline_cls(
pipeline_name, pipeline_type, server_args.workload_type
)
pipeline_cls = model_info.pipeline_cls
# instantiate the pipelines
pipeline = pipeline_cls(model_path, server_args)

2
python/sglang/multimodal_gen/runtime/pipelines/composed_pipeline_base.py
View File

@@ -22,7 +22,7 @@ from sglang.multimodal_gen.runtime.loader.component_loader import (
from sglang.multimodal_gen.runtime.pipelines.executors.pipeline_executor import (
PipelineExecutor,
)
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages import PipelineStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.hf_diffusers_utils import (

2
python/sglang/multimodal_gen/runtime/pipelines/executors/pipeline_executor.py
View File

@@ -8,7 +8,7 @@ import time
from abc import ABC, abstractmethod
from typing import List
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages import PipelineStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

2
python/sglang/multimodal_gen/runtime/pipelines/executors/sync_executor.py
View File

@@ -11,7 +11,7 @@ from sglang.multimodal_gen.runtime.pipelines.executors.pipeline_executor import
Timer,
logger,
)
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages import PipelineStage
from sglang.multimodal_gen.runtime.server_args import ServerArgs

239
python/sglang/multimodal_gen/runtime/pipelines/pipeline_registry.py
View File

@@ -1,239 +0,0 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
# SPDX-License-Identifier: Apache-2.0
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/models/registry.py
# and https://github.com/sgl-project/sglang/blob/v0.4.3/python/sglang/srt/models/registry.py
import dataclasses
import importlib
import pkgutil
from collections.abc import Set
from dataclasses import dataclass
from enum import Enum
from functools import lru_cache
from sglang.multimodal_gen.runtime.pipelines.composed_pipeline_base import (
ComposedPipelineBase,
)
from sglang.multimodal_gen.runtime.pipelines.lora_pipeline import LoRAPipeline
from sglang.multimodal_gen.runtime.server_args import WorkloadType
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
logger = init_logger(__name__)
_PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME: dict[WorkloadType, str] = {
WorkloadType.I2V: "PreprocessPipelineI2V",
WorkloadType.T2V: "PreprocessPipelineT2V",
}
class PipelineType(str, Enum):
"""
Enumeration for different pipeline types.
Inherits from str to allow string comparison for backward compatibility.
"""
BASIC = "basic"
PREPROCESS = "preprocess"
@classmethod
def from_string(cls, value: str) -> "PipelineType":
"""Convert string to PipelineType enum."""
try:
return cls(value.lower())
except ValueError:
raise ValueError(
f"Invalid pipeline type: {value}. Must be one of: {', '.join([t.value for t in cls])}"
) from None
@classmethod
def choices(cls) -> list[str]:
"""Get all available choices as strings."""
return [pipeline_type.value for pipeline_type in cls]
@dataclass
class _PipelineRegistry:
# Keyed by pipeline_type -> architecture -> pipeline_name
# pipelines[pipeline_type][architecture][pipeline_name] = pipeline_cls
pipelines: dict[str, dict[str, type[ComposedPipelineBase] | None]] = (
dataclasses.field(default_factory=dict)
)
def get_supported_archs(
self, pipeline_name_in_config: str, pipeline_type: PipelineType
) -> Set[str]:
"""Get supported architectures, optionally filtered by pipeline type and workload type."""
return set(self.pipelines[pipeline_type.value].keys())
def _load_preprocess_pipeline_cls(
self, workload_type: WorkloadType
) -> type[ComposedPipelineBase] | None:
pipeline_name = _PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME[workload_type]
return self.pipelines[PipelineType.PREPROCESS.value][pipeline_name]
def _try_load_pipeline_cls(
self,
pipeline_name_in_config: str,
pipeline_type: PipelineType,
workload_type: WorkloadType,
) -> type[ComposedPipelineBase] | type[LoRAPipeline] | None:
"""Try to load a pipeline class for the given architecture, pipeline type, and workload type."""
if pipeline_type.value not in self.pipelines:
return None
try:
if pipeline_type == PipelineType.PREPROCESS:
return self._load_preprocess_pipeline_cls(workload_type)
elif pipeline_type == PipelineType.BASIC:
return self.pipelines[pipeline_type.value][pipeline_name_in_config]
else:
raise ValueError(f"Invalid pipeline type: {pipeline_type.value}")
except KeyError as e:
logger.error(
f"Please check if the ComposedPipeline class has been defined associated with {pipeline_type.value}.{pipeline_name_in_config}"
)
raise e
return None
def resolve_pipeline_cls(
self,
pipeline_name_in_config: str,
pipeline_type: PipelineType,
workload_type: WorkloadType,
) -> type[ComposedPipelineBase] | type[LoRAPipeline]:
"""Resolve pipeline class based on pipeline name in the config, pipeline type, and workload type."""
if not pipeline_name_in_config:
logger.warning("No pipeline architecture is specified")
pipeline_cls = self._try_load_pipeline_cls(
pipeline_name_in_config, pipeline_type, workload_type
)
if pipeline_cls is not None:
return pipeline_cls
supported_archs = self.get_supported_archs(
pipeline_name_in_config, pipeline_type
)
raise ValueError(
f"Pipeline architecture '{pipeline_name_in_config}' is not supported for pipeline type '{pipeline_type.value}' "
f"and workload type '{workload_type.value}'. "
f"Supported architectures: {supported_archs}"
)
@lru_cache
def import_pipeline_classes(
pipeline_types: list[PipelineType] | PipelineType | None = None,
) -> dict[str, dict[str, type[ComposedPipelineBase] | None]]:
"""
Import pipeline classes based on the pipeline type and workload type.
Args:
pipeline_types: The pipeline types to load (basic, preprocess).
If None, loads all types.
Returns:
A three-level nested dictionary:
{pipeline_type: {architecture_name: {pipeline_name: pipeline_cls}}}
e.g., {"basic": {"wan": {"WanPipeline": WanPipeline}}}
"""
type_to_pipeline_dict: dict[str, dict[str, type[ComposedPipelineBase] | None]] = {}
package_name: str = "sglang.multimodal_gen.runtime.architectures"
# Determine which pipeline types to scan
if isinstance(pipeline_types, list):
pipeline_types_to_scan = [
pipeline_type.value for pipeline_type in pipeline_types
]
elif isinstance(pipeline_types, PipelineType):
pipeline_types_to_scan = [pipeline_types.value]
else:
pipeline_types_to_scan = [pt.value for pt in PipelineType]
logger.info("Loading pipelines for types: %s", pipeline_types_to_scan)
for pipeline_type_str in pipeline_types_to_scan:
# Try to load from pipeline-type-specific directory first
pipeline_type_package_name = f"{package_name}.{pipeline_type_str}"
pipeline_dict: dict[str, type[ComposedPipelineBase] | None] = {}
try:
pipeline_type_package = importlib.import_module(pipeline_type_package_name)
logger.debug("Successfully imported %s", pipeline_type_package_name)
for _, arch, ispkg in pkgutil.iter_modules(pipeline_type_package.__path__):
arch_package_name = f"{pipeline_type_package_name}.{arch}"
if ispkg:
arch_package = importlib.import_module(arch_package_name)
for _, module_name, ispkg in pkgutil.walk_packages(
arch_package.__path__, arch_package_name + "."
):
if not ispkg:
pipeline_module = importlib.import_module(module_name)
if hasattr(pipeline_module, "EntryClass"):
entry_cls_list = pipeline_module.EntryClass
if not isinstance(entry_cls_list, list):
entry_cls_list = [entry_cls_list]
if isinstance(pipeline_module.EntryClass, list):
pipeline_names = [
pipeline.__name__
for pipeline in pipeline_module.EntryClass
]
else:
pipeline_names = [
pipeline_module.EntryClass.__name__
]
for entry_cls, pipeline_name in zip(
entry_cls_list, pipeline_names
):
assert (
pipeline_name not in pipeline_dict
), f"Duplicated pipeline implementation for {pipeline_name} in {pipeline_type_str}.{arch_package_name}"
assert hasattr(
entry_cls, "pipeline_name"
), f"{entry_cls}"
pipeline_dict[pipeline_name] = entry_cls
type_to_pipeline_dict[pipeline_type_str] = pipeline_dict
except ImportError as e:
raise ImportError(
f"Could not import {pipeline_type_package_name} when importing pipeline classes: {e}"
) from None
# Log summary
total_pipelines = sum(
len(pipeline_dict) for pipeline_dict in type_to_pipeline_dict.values()
)
logger.info(
"Loaded %d pipeline classes across %d types",
total_pipelines,
len(pipeline_types_to_scan),
)
return type_to_pipeline_dict
def get_pipeline_registry(
pipeline_type: PipelineType | str | None = None,
) -> _PipelineRegistry:
"""
Get a pipeline registry for the specified mode, pipeline type, and workload type.
Args:
pipeline_type: Pipeline type to load. If None and mode is provided, will be derived from mode.
Returns:
A pipeline registry instance.
"""
if isinstance(pipeline_type, str):
pipeline_type = PipelineType.from_string(pipeline_type)
pipeline_classes = import_pipeline_classes(pipeline_type)
return _PipelineRegistry(pipeline_classes)

0
python/sglang/multimodal_gen/runtime/pipelines/pipeline_batch_info.py → python/sglang/multimodal_gen/runtime/pipelines/schedule_batch.py
View File

2
python/sglang/multimodal_gen/runtime/pipelines/stages/base.py
View File

@@ -16,7 +16,7 @@ from enum import Enum, auto
import torch
import sglang.multimodal_gen.envs as envs
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.validators import VerificationResult
from sglang.multimodal_gen.runtime.server_args import ServerArgs, get_global_server_args
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

2
python/sglang/multimodal_gen/runtime/pipelines/stages/causal_denoising.py
View File

@@ -5,7 +5,7 @@ import torch # type: ignore
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
from sglang.multimodal_gen.runtime.models.utils import pred_noise_to_pred_video
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.denoising import DenoisingStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators as V,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/conditioning.py
View File

@@ -7,7 +7,7 @@ Conditioning stage for diffusion pipelines.
import torch
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators as V,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/decoding.py
View File

@@ -17,7 +17,7 @@ from sglang.multimodal_gen.configs.pipelines.qwen_image import (
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.loader.component_loader import VAELoader
from sglang.multimodal_gen.runtime.models.vaes.common import ParallelTiledVAE
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import OutputBatch, Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import OutputBatch, Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import (
PipelineStage,
StageParallelismType,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/denoising.py
View File

@@ -44,7 +44,7 @@ from sglang.multimodal_gen.runtime.layers.attention.STA_configuration import (
)
from sglang.multimodal_gen.runtime.loader.component_loader import TransformerLoader
from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import (
PipelineStage,
StageParallelismType,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/denoising_dmd.py
View File

@@ -23,7 +23,7 @@ from sglang.multimodal_gen.runtime.models.schedulers.scheduling_flow_match_euler
FlowMatchEulerDiscreteScheduler,
)
from sglang.multimodal_gen.runtime.models.utils import pred_noise_to_pred_video
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages import DenoisingStage
from sglang.multimodal_gen.runtime.pipelines.stages.denoising import (
st_attn_available,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/encoding.py
View File

@@ -9,7 +9,7 @@ import torch
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.models.vaes.common import ParallelTiledVAE
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
V, # Import validators

2
python/sglang/multimodal_gen/runtime/pipelines/stages/image_encoding.py
View File

@@ -25,7 +25,7 @@ from sglang.multimodal_gen.runtime.models.vision_utils import (
pil_to_numpy,
resize,
)
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators as V,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/input_validation.py
View File

@@ -14,7 +14,7 @@ from sglang.multimodal_gen.configs.pipelines.qwen_image import (
QwenImageEditPipelineConfig,
)
from sglang.multimodal_gen.runtime.models.vision_utils import load_image, load_video
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/latent_preparation.py
View File

@@ -7,7 +7,7 @@ Latent preparation stage for diffusion pipelines.
from diffusers.utils.torch_utils import randn_tensor
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators as V,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/stepvideo_encoding.py
View File

@@ -5,7 +5,7 @@
import torch
from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators as V,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/text_encoding.py
View File

@@ -13,7 +13,7 @@ from sglang.multimodal_gen.configs.models.encoders import BaseEncoderOutput
from sglang.multimodal_gen.configs.pipelines import FluxPipelineConfig
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.managers.forward_context import set_forward_context
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import PipelineStage
from sglang.multimodal_gen.runtime.pipelines.stages.validators import (
StageValidators as V,

2
python/sglang/multimodal_gen/runtime/pipelines/stages/timestep_preparation.py
View File

@@ -18,7 +18,7 @@ from sglang.multimodal_gen.configs.pipelines.qwen_image import (
QwenImagePipelineConfig,
)
from sglang.multimodal_gen.runtime.distributed import get_local_torch_device
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.pipelines.stages.base import (
PipelineStage,
StageParallelismType,

2
python/sglang/multimodal_gen/runtime/scheduler_client.py
View File

@@ -5,7 +5,7 @@ import asyncio
import zmq
import zmq.asyncio
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

44
python/sglang/multimodal_gen/runtime/server_args.py
View File

@@ -335,9 +335,12 @@ class ServerArgs:
return self.host is None or self.port is None
def __post_init__(self):
self.scheduler_port = self.settle_port(self.scheduler_port)
# Add randomization to avoid race condition when multiple servers start simultaneously
initial_scheduler_port = self.scheduler_port + random.randint(0, 100)
self.scheduler_port = self.settle_port(initial_scheduler_port)
# TODO: remove hard code
self.master_port = self.settle_port(self.master_port or 30005, 37)
initial_master_port = (self.master_port or 30005) + random.randint(0, 100)
self.master_port = self.settle_port(initial_master_port, 37)
if self.moba_config_path:
try:
with open(self.moba_config_path) as f:
@@ -646,14 +649,45 @@ class ServerArgs:
scheduler_host = self.host or "localhost"
return f"tcp://{scheduler_host}:{self.scheduler_port}"
def settle_port(self, port: int, port_inc: int = 42) -> int:
while True:
def settle_port(
self, port: int, port_inc: int = 42, max_attempts: int = 100
) -> int:
"""
Find an available port with retry logic.
Args:
port: Initial port to check
port_inc: Port increment for each attempt
max_attempts: Maximum number of attempts to find an available port
Returns:
An available port number
Raises:
RuntimeError: If no available port is found after max_attempts
"""
attempts = 0
original_port = port
while attempts < max_attempts:
if is_port_available(port):
if attempts > 0:
logger.info(
f"Port {original_port} was unavailable, using port {port} instead"
)
return port
attempts += 1
if port < 60000:
port += port_inc
else:
port -= port_inc + 1
# Wrap around with randomization to avoid collision
port = 5000 + random.randint(0, 1000)
raise RuntimeError(
f"Failed to find available port after {max_attempts} attempts "
f"(started from port {original_port})"
)
def post_init_serve(self):
"""

2
python/sglang/multimodal_gen/runtime/sync_scheduler_client.py
View File

@@ -2,7 +2,7 @@
import zmq
from sglang.multimodal_gen.runtime.pipelines.pipeline_batch_info import Req
from sglang.multimodal_gen.runtime.pipelines.schedule_batch import Req
from sglang.multimodal_gen.runtime.server_args import ServerArgs
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger

82
python/sglang/multimodal_gen/runtime/utils/common.py
View File

@@ -126,8 +126,26 @@ def is_port_available(port):
def get_zmq_socket(
context: zmq.Context, socket_type: zmq.SocketType, endpoint: str, bind: bool
) -> zmq.Socket:
context: zmq.Context,
socket_type: zmq.SocketType,
endpoint: str,
bind: bool,
max_bind_retries: int = 10,
) -> tuple[zmq.Socket, str]:
"""
Create and configure a ZMQ socket.
Args:
context: ZMQ context
socket_type: Type of ZMQ socket
endpoint: Endpoint string (e.g., "tcp://localhost:5555")
bind: Whether to bind (True) or connect (False)
max_bind_retries: Maximum number of retries if bind fails due to address already in use
Returns:
A tuple of (socket, actual_endpoint). The actual_endpoint may differ from the
requested endpoint if bind retry was needed.
"""
mem = psutil.virtual_memory()
total_mem = mem.total / 1024**3
available_mem = mem.available / 1024**3
@@ -165,11 +183,67 @@ def get_zmq_socket(
raise ValueError(f"Unsupported socket type: {socket_type}")
if bind:
socket.bind(endpoint)
# Parse port from endpoint for retry logic
import re
port_match = re.search(r":(\d+)$", endpoint)
if port_match and max_bind_retries > 1:
original_port = int(port_match.group(1))
last_exception = None
for attempt in range(max_bind_retries):
try:
current_endpoint = endpoint
if attempt > 0:
# Try next port (increment by 42 to match settle_port logic)
current_port = original_port + attempt * 42
current_endpoint = re.sub(
r":(\d+)$", f":{current_port}", endpoint
)
logger.info(
f"ZMQ bind failed for port {original_port + (attempt - 1) * 42}, "
f"retrying with port {current_port} (attempt {attempt + 1}/{max_bind_retries})"
)
socket.bind(current_endpoint)
if attempt > 0:
logger.warning(
f"Successfully bound ZMQ socket to {current_endpoint} after {attempt + 1} attempts. "
f"Original port {original_port} was unavailable."
)
return socket, current_endpoint
except zmq.ZMQError as e:
last_exception = e
if e.errno == zmq.EADDRINUSE and attempt < max_bind_retries - 1:
# Address already in use, try next port
continue
elif attempt == max_bind_retries - 1:
# Last attempt failed
logger.error(
f"Failed to bind ZMQ socket after {max_bind_retries} attempts. "
f"Original endpoint: {endpoint}, Last tried port: {original_port + attempt * 42}"
)
raise
else:
# Different error, raise immediately
raise
# Should not reach here, but just in case
if last_exception:
raise last_exception
else:
# No retry logic needed (either no port in endpoint or max_bind_retries == 1)
socket.bind(endpoint)
return socket, endpoint
else:
socket.connect(endpoint)
return socket, endpoint
return socket
return socket, endpoint
# https://pytorch.org/docs/stable/notes/hip.html#checking-for-hip

2
python/sglang/multimodal_gen/runtime/utils/logging_utils.py
View File

@@ -34,7 +34,7 @@ _warned_main_process = False
_FORMAT = (
f"{SGL_DIFFUSION_LOGGING_PREFIX}%(levelname)s %(asctime)s "
"[%(filename)s:%(lineno)d] %(message)s"
"[%(filename)s: %(lineno)d] %(message)s"
)
# _FORMAT = "[%(asctime)s] %(message)s"

5
python/sglang/multimodal_gen/runtime/workflow/preprocess/preprocess_workflow.py
View File

@@ -15,7 +15,6 @@ from sglang.multimodal_gen.dataset.dataloader.schema import (
pyarrow_schema_t2v,
)
from sglang.multimodal_gen.runtime.distributed.parallel_state import get_world_rank
from sglang.multimodal_gen.runtime.pipelines.pipeline_registry import PipelineType
from sglang.multimodal_gen.runtime.server_args import ServerArgs, WorkloadType
from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
from sglang.multimodal_gen.runtime.workflow.preprocess.components import (
@@ -32,9 +31,7 @@ logger = init_logger(__name__)
class PreprocessWorkflow(WorkflowBase):
def register_pipelines(self) -> None:
self.add_pipeline_config(
"preprocess_pipeline", (PipelineType.PREPROCESS, self.server_args)
)
self.add_pipeline_config("preprocess_pipeline", self.server_args)
def register_components(self) -> None:
assert self.server_args.preprocess_config is not None

6
python/sglang/multimodal_gen/test/cli/test_generate_common.py
View File

@@ -47,7 +47,8 @@ class TestGenerate(TestCLIBase):
command = [
"sglang",
"generate",
"--prompt='A curious raccoon'",
"--prompt",
"A curious raccoon",
"--output-path=outputs",
f"--model-path={self.model_path}",
"--save-output",
@@ -84,7 +85,8 @@ class TestWanGenerate(TestGenerate):
command = [
"sglang",
"generate",
"--prompt='A curious raccoon'",
"--prompt",
"A curious raccoon",
"--output-path=outputs",
f"--model-path={self.model_path}",
"--save-output",

5
python/sglang/multimodal_gen/test/cli/test_generate_t2i_perf.py
View File

@@ -48,7 +48,8 @@ class TestQwenImageEdit(TestGenerateBase):
"generate",
"--text-encoder-cpu-offload",
"--pin-cpu-memory",
f"--prompt='{self.prompt}'",
f"--prompt",
f"{self.prompt}",
"--save-output",
"--log-level=debug",
f"--width={self.width}",
@@ -58,7 +59,7 @@ class TestQwenImageEdit(TestGenerateBase):
def test_single_gpu(self):
self._run_test(
name=f"{self.model_name()}, single gpu",
name=f"{self.model_name()}_single_gpu",
args=None,
model_path=self.model_path,
test_key="test_single_gpu",

19
python/sglang/multimodal_gen/test/cli/test_generate_t2v_perf.py
View File

@@ -17,7 +17,7 @@ class TestFastWan2_1_T2V(TestGenerateBase):
"test_single_gpu": 13.0,
"test_cfg_parallel": 15.0,
"test_usp": 15.0,
"test_mixed": 15.0,
"test_mixed": 15.0 * 1.05,
}
# disabled for vsa
@@ -44,22 +44,31 @@ class TestWan2_1_T2V(TestGenerateBase):
thresholds = {
"test_single_gpu": 76.0 * 1.05,
"test_cfg_parallel": 46.5 * 1.05,
"test_usp": 22.5,
"test_mixed": 26.5,
"test_usp": 39.8 * 1.05,
"test_mixed": 37.3 * 1.05,
}
def test_mixed(self):
pass
def test_cfg_parallel(self):
pass
class TestWan2_2_T2V(TestGenerateBase):
model_path = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"
extra_args = []
data_type: DataType = DataType.VIDEO
thresholds = {
"test_single_gpu": 865,
"test_single_gpu": 904.3 * 1.05,
"test_cfg_parallel": 446,
"test_usp": 124,
"test_usp": 316 * 1.05,
"test_mixed": 159,
}
def test_single_gpu(self):
pass
def test_mixed(self):
pass

7
python/sglang/multimodal_gen/test/cli/test_generate_ti2v_perf.py
View File

@@ -15,7 +15,8 @@ class TestGenerateTI2VBase(TestGenerateBase):
cls.base_command = [
"sglang",
"generate",
f'--prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline\'s intricate details and the refreshing atmosphere of the seaside."',
"--prompt",
"Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.",
"--image-path",
"https://github.com/Wan-Video/Wan2.2/blob/990af50de458c19590c245151197326e208d7191/examples/i2v_input.JPG?raw=true",
"--save-output",
@@ -36,14 +37,14 @@ class TestGenerateTI2VBase(TestGenerateBase):
class TestWan2_1_I2V_14B_480P(TestGenerateTI2VBase):
model_path = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
thresholds = {
"test_usp": 530.5 * 1.05,
"test_usp": 557.9 * 1.05,
}
class TestWan2_1_I2V_14B_720P(TestGenerateTI2VBase):
model_path = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
thresholds = {
"test_usp": 530.5 * 1.05,
"test_usp": 558.4 * 1.05,
}

14
python/sglang/multimodal_gen/test/test_utils.py
View File

@@ -19,7 +19,7 @@ logger = init_logger(__name__)
def run_command(command) -> Optional[float]:
"""Runs a command and returns the execution time and status."""
print(f"Running command: {' '.join(command)}")
print(f"Running command: {shlex.join(command)}")
duration = None
with subprocess.Popen(
@@ -105,7 +105,8 @@ class TestCLIBase(unittest.TestCase):
"generate",
"--text-encoder-cpu-offload",
"--pin-cpu-memory",
"--prompt='A curious raccoon'",
"--prompt",
"A curious raccoon",
"--save-output",
"--log-level=debug",
f"--width={width}",
@@ -124,7 +125,7 @@ class TestCLIBase(unittest.TestCase):
self.base_command
+ [f"--model-path={model_path}"]
+ shlex.split(args or "")
+ [f"--output-file-name={name}"]
+ ["--output-file-name", f"{name}"]
+ self.extra_args
)
duration = run_command(command)
@@ -155,7 +156,8 @@ class TestGenerateBase(TestCLIBase):
"generate",
# "--text-encoder-cpu-offload",
# "--pin-cpu-memory",
f"--prompt='{prompt}'",
f"--prompt",
f"{prompt}",
"--save-output",
"--log-level=debug",
f"--width={width}",
@@ -237,7 +239,7 @@ class TestGenerateBase(TestCLIBase):
def test_single_gpu(self):
"""single gpu"""
self._run_test(
name=f"{self.model_name()}_single gpu",
name=f"{self.model_name()}_single_gpu",
args=None,
model_path=self.model_path,
test_key="test_single_gpu",
@@ -248,7 +250,7 @@ class TestGenerateBase(TestCLIBase):
if self.data_type == DataType.IMAGE:
return
self._run_test(
name=f"{self.model_name()}_cfg parallel",
name=f"{self.model_name()}_cfg_parallel",
args="--num-gpus 2 --enable-cfg-parallel",
model_path=self.model_path,
test_key="test_cfg_parallel",