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[diffusion] chore: minor code cleanups and improve logging (#14916)

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This commit is contained in:
Mick
2025-12-12 18:48:07 +08:00
committed by GitHub
parent 94e1251131
commit 82e33170e1
9 changed files with 40 additions and 78 deletions
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2
python/sglang/multimodal_gen/runtime/entrypoints/http_server.py
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@@ -75,6 +75,6 @@ if __name__ == "__main__":
app,
host=server_args.host,
port=server_args.port,
log_config=None,
use_colors=True,
reload=False, # Set to True during development for auto-reloading
)

2
python/sglang/multimodal_gen/runtime/launch_server.py
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@@ -134,7 +134,7 @@ def launch_server(server_args: ServerArgs, launch_http_server: bool = True):
app = create_app(server_args)
uvicorn.run(
app,
log_config=None,
use_colors=True,
log_level=server_args.log_level,
host=server_args.host,
port=server_args.port,

18
python/sglang/multimodal_gen/runtime/layers/layernorm.py
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@@ -8,6 +8,7 @@ from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from sgl_kernel import fused_add_rmsnorm, rmsnorm
from sglang.multimodal_gen.runtime.layers.custom_op import CustomOp
from sglang.multimodal_gen.runtime.layers.triton_ops import (
@@ -15,22 +16,7 @@ from sglang.multimodal_gen.runtime.layers.triton_ops import (
norm_infer,
rms_norm_fn,
)
from sglang.multimodal_gen.runtime.utils.common import (
get_bool_env_var,
is_cpu,
is_cuda,
is_hip,
is_npu,
is_xpu,
)
_is_cuda = is_cuda()
_is_hip = is_hip()
_is_npu = is_npu()
_is_cpu = is_cpu()
_is_xpu = is_xpu()
from sgl_kernel import fused_add_rmsnorm, rmsnorm
from sglang.multimodal_gen.runtime.utils.common import get_bool_env_var
# Copied and adapted from sglang

73
python/sglang/multimodal_gen/runtime/loader/component_loader.py
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@@ -87,33 +87,6 @@ def _list_safetensors_files(model_path: str) -> list[str]:
return sorted(glob.glob(os.path.join(str(model_path), "*.safetensors")))
def load_native(library, component_module_path: str, server_args: ServerArgs):
if library == "transformers":
from transformers import AutoModel
config = get_hf_config(
component_module_path,
trust_remote_code=server_args.trust_remote_code,
revision=server_args.revision,
)
return AutoModel.from_pretrained(
component_module_path,
config=config,
trust_remote_code=server_args.trust_remote_code,
revision=server_args.revision,
)
elif library == "diffusers":
from diffusers import AutoModel
return AutoModel.from_pretrained(
component_module_path,
revision=server_args.revision,
trust_remote_code=server_args.trust_remote_code,
)
else:
raise ValueError(f"Unsupported library: {library}")
class ComponentLoader(ABC):
"""Base class for loading a specific type of model component."""
@@ -193,7 +166,30 @@ class ComponentLoader(ABC):
"""
Load the component using the native library (transformers/diffusers).
"""
return load_native(transformers_or_diffusers, component_model_path, server_args)
if transformers_or_diffusers == "transformers":
from transformers import AutoModel
config = get_hf_config(
component_model_path,
trust_remote_code=server_args.trust_remote_code,
revision=server_args.revision,
)
return AutoModel.from_pretrained(
component_model_path,
config=config,
trust_remote_code=server_args.trust_remote_code,
revision=server_args.revision,
)
elif transformers_or_diffusers == "diffusers":
from diffusers import AutoModel
return AutoModel.from_pretrained(
component_model_path,
revision=server_args.revision,
trust_remote_code=server_args.trust_remote_code,
)
else:
raise ValueError(f"Unsupported library: {transformers_or_diffusers}")
def load_customized(
self, component_model_path: str, server_args: ServerArgs, module_name: str
@@ -621,7 +617,6 @@ class TransformerLoader(ComponentLoader):
"Only diffusers format is supported."
)
logger.info("transformer cls_name: %s", cls_name)
if server_args.override_transformer_cls_name is not None:
cls_name = server_args.override_transformer_cls_name
logger.info("Overriding transformer cls_name to %s", cls_name)
@@ -660,16 +655,16 @@ class TransformerLoader(ComponentLoader):
), "Custom initialization weights must be a safetensors file"
safetensors_list = [custom_weights_path]
logger.info(
"Loading model from %s safetensors files: %s",
len(safetensors_list),
safetensors_list,
)
default_dtype = PRECISION_TO_TYPE[server_args.pipeline_config.dit_precision]
logger.info(
"Loading %s from %s safetensors files, default_dtype: %s",
cls_name,
len(safetensors_list),
default_dtype,
)
# Load the model using FSDP loader
logger.info("Loading %s, default_dtype: %s", cls_name, default_dtype)
assert server_args.hsdp_shard_dim is not None
model = maybe_load_fsdp_model(
model_cls=model_cls,
@@ -760,12 +755,6 @@ class PipelineComponentLoader:
Returns:
The loaded module
"""
logger.info(
"Loading %s using %s from %s",
module_name,
transformers_or_diffusers,
component_model_path,
)
# Get the appropriate loader for this module type
loader = ComponentLoader.for_module_type(module_name, transformers_or_diffusers)

10
python/sglang/multimodal_gen/runtime/models/schedulers/scheduling_flow_unipc_multistep.py
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@@ -278,10 +278,6 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
return sample
# Copied from diffusers.schedulers.scheduling_flow_match_euler_discrete.FlowMatchEulerDiscreteScheduler._sigma_to_t
def _sigma_to_t(self, sigma):
return sigma * self.config.num_train_timesteps
def _sigma_to_alpha_sigma_t(self, sigma) -> tuple[Any, Any]:
return 1 - sigma, sigma
@@ -324,9 +320,6 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
"Passing `timesteps` is deprecated and has no effect as model output conversion is now handled via an internal counter `self.step_index`",
)
sigma = self.sigmas[self.step_index]
alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
if self.predict_x0:
if self.config.prediction_type == "flow_prediction":
sigma_t = self.sigmas[self.step_index]
@@ -846,8 +839,5 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
noisy_samples = alpha_t * original_samples + sigma_t * noise
return noisy_samples
def __len__(self):
return self.config.num_train_timesteps
EntryClass = FlowUniPCMultistepScheduler

1
python/sglang/multimodal_gen/runtime/pipelines_core/composed_pipeline_base.py
View File

@@ -319,7 +319,6 @@ class ComposedPipelineBase(ABC):
transformers_or_diffusers=transformers_or_diffusers,
server_args=server_args,
)
logger.info("Loaded module %s from %s", module_name, component_model_path)
if module_name in components:
logger.warning("Overwriting module %s", module_name)

6
python/sglang/multimodal_gen/runtime/utils/perf_logger.py
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@@ -1,7 +1,6 @@
# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo
import dataclasses
import json
import logging
import os
import subprocess
import sys
@@ -223,10 +222,9 @@ class PerformanceLogger:
os.makedirs(os.path.dirname(abs_path), exist_ok=True)
with open(abs_path, "w", encoding="utf-8") as f:
json.dump(report, f, indent=2)
print(f"[Performance] Metrics dumped to: {abs_path}")
logger.info(f"[Performance] Metrics dumped to: {abs_path}")
except IOError as e:
print(f"[Performance] Failed to dump metrics to {abs_path}: {e}")
logging.getLogger(__name__).error(f"Dump failed: {e}")
logger.error(f"[Performance] Failed to dump metrics to {abs_path}: {e}")
@classmethod
def log_request_summary(

2
python/sglang/multimodal_gen/test/run_suite.py
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@@ -69,7 +69,7 @@ def run_pytest(files):
base_cmd = [sys.executable, "-m", "pytest", "-s", "-v", "--log-cli-level=INFO"]
max_retries = 2
max_retries = 4
# retry if the perf assertion failed, for {max_retries} times
for i in range(max_retries + 1):
cmd = list(base_cmd)

4
python/sglang/multimodal_gen/test/server/perf_baselines.json
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@@ -461,7 +461,7 @@
"stages_ms": {
"InputValidationStage": 23,
"ImageEncodingStage": 1485.0,
"ImageVAEEncodingStage": 350.0,
"ImageVAEEncodingStage": 400.0,
"ConditioningStage": 0.13,
"TimestepPreparationStage": 13.78,
"LatentPreparationStage": 15.0,
@@ -1203,7 +1203,7 @@
"48": 3721.72,
"49": 3715.61
},
"expected_e2e_ms": 198187.89,
"expected_e2e_ms": 220000,
"expected_avg_denoise_ms": 3751.95,
"expected_median_denoise_ms": 3724.06
}