chenchenghao/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[diffusion] CI: fix LoRA downloading issues and respect offline flag (#15813)

Browse Source
This commit is contained in:
Prozac614
2025-12-30 11:39:27 +08:00
committed by GitHub
parent 1e45320198
commit f253f43c9d
4 changed files with 136 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
python/pyproject_other.toml
View File

@@ -105,7 +105,7 @@ test = [
]
diffusion = [
"diffusers @ git+https://github.com/huggingface/diffusers.git@6290fdfda40610ce7b99920146853614ba529c6e",
"opencv-python==4.10.0.84",
"opencv-python-headless==4.10.0.84",
"imageio==2.36.0",
"imageio-ffmpeg==0.5.1",
"PyYAML==6.0.1",

154
python/sglang/multimodal_gen/runtime/utils/hf_diffusers_utils.py
View File

@@ -21,6 +21,7 @@
import contextlib
import json
import os
import time
from functools import reduce
from pathlib import Path
from typing import Any, Optional, cast
@@ -29,6 +30,13 @@ from diffusers.loaders.lora_base import (
_best_guess_weight_name, # watch out for potetential removal from diffusers
)
from huggingface_hub import snapshot_download
from huggingface_hub.errors import (
LocalEntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
)
from requests.exceptions import ConnectionError as RequestsConnectionError
from requests.exceptions import RequestException
from transformers import AutoConfig, PretrainedConfig
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
@@ -208,15 +216,20 @@ def maybe_download_lora(
Returns:
Local path to the model
"""
allow_patterns = ["*.json", "*.safetensors", "*.bin"]
local_path = maybe_download_model(model_name_or_path, local_dir, download)
local_path = maybe_download_model(
model_name_or_path,
local_dir,
download,
is_lora=True,
allow_patterns=allow_patterns,
)
# return directly if local_path is a file
if os.path.isfile(local_path):
return local_path
weight_name = _best_guess_weight_name(
model_name_or_path, file_extension=".safetensors"
)
weight_name = _best_guess_weight_name(local_path, file_extension=".safetensors")
return os.path.join(local_path, weight_name)
@@ -349,7 +362,11 @@ def maybe_download_model_index(model_name_or_path: str) -> dict[str, Any]:
def maybe_download_model(
model_name_or_path: str, local_dir: str | None = None, download: bool = True
model_name_or_path: str,
local_dir: str | None = None,
download: bool = True,
is_lora: bool = False,
allow_patterns: list[str] | None = None,
) -> str:
"""
Check if the model path is a Hugging Face Hub model ID and download it if needed.
@@ -358,6 +375,7 @@ def maybe_download_model(
model_name_or_path: Local path or Hugging Face Hub model ID
local_dir: Local directory to save the model
download: Whether to download the model from Hugging Face Hub
is_lora: If True, skip model completeness verification (LoRA models don't have transformer/vae directories)
Returns:
Local path to the model
@@ -374,9 +392,9 @@ def maybe_download_model(
and os.path.exists(vae_dir)
)
# If the path exists locally, verify it's complete
# 1. Local path check: if path exists locally, verify it's complete (skip for LoRA)
if os.path.exists(model_name_or_path):
if _verify_model_complete(model_name_or_path):
if is_lora or _verify_model_complete(model_name_or_path):
logger.info("Model already exists locally and is complete")
return model_name_or_path
else:
@@ -386,34 +404,118 @@ def maybe_download_model(
model_name_or_path,
)
# Otherwise, assume it's a HF Hub model ID and try to download it
# 2. Cache-first strategy (Fast Path)
# Try to read from HF cache without network access
try:
logger.info(
"Downloading model snapshot from HF Hub for %s...", model_name_or_path
"Checking for cached model in HF Hub cache for %s...", model_name_or_path
)
with get_lock(model_name_or_path).acquire(poll_interval=2):
local_path = snapshot_download(
repo_id=model_name_or_path,
ignore_patterns=["*.onnx", "*.msgpack"],
local_dir=local_dir,
local_path = snapshot_download(
repo_id=model_name_or_path,
ignore_patterns=["*.onnx", "*.msgpack"],
local_dir=local_dir,
local_files_only=True,
resume_download=True,
max_workers=8,
etag_timeout=60,
)
if is_lora or _verify_model_complete(local_path):
logger.info("Found complete model in cache at %s", local_path)
return str(local_path)
else:
# Model found in cache but incomplete
if not download:
raise ValueError(
f"Model {model_name_or_path} found in cache but is incomplete and download=False."
)
logger.info(
"Model found in cache but incomplete, will download from HF Hub"
)
# Verify downloaded model is complete
if not _verify_model_complete(local_path):
logger.warning(
"Downloaded model at %s is incomplete, retrying with force_download=True",
local_path,
except LocalEntryNotFoundError:
if not download:
raise ValueError(
f"Model {model_name_or_path} not found in local cache and download=False."
)
logger.info("Model not found in cache, will download from HF Hub")
except Exception as e:
logger.warning(
"Unexpected error while checking cache for %s: %s, will attempt download",
model_name_or_path,
e,
)
if not download:
raise ValueError(
f"Error checking cache for {model_name_or_path} and download=False: {e}"
) from e
# 3. Download strategy (with retry mechanism)
MAX_RETRIES = 5
for attempt in range(MAX_RETRIES):
try:
logger.info(
"Downloading model snapshot from HF Hub for %s (attempt %d/%d)...",
model_name_or_path,
attempt + 1,
MAX_RETRIES,
)
with get_lock(model_name_or_path).acquire(poll_interval=2):
local_path = snapshot_download(
repo_id=model_name_or_path,
ignore_patterns=["*.onnx", "*.msgpack"],
allow_patterns=allow_patterns,
local_dir=local_dir,
force_download=True,
resume_download=True,
max_workers=8,
etag_timeout=120,
)
logger.info("Downloaded model to %s", local_path)
return str(local_path)
except Exception as e:
raise ValueError(
f"Could not find model at {model_name_or_path} and failed to download from HF Hub: {e}"
) from e
# Verify downloaded model is complete (skip for LoRA)
if not is_lora and not _verify_model_complete(local_path):
logger.warning(
"Downloaded model at %s is incomplete, retrying with force_download=True",
local_path,
)
with get_lock(model_name_or_path).acquire(poll_interval=2):
local_path = snapshot_download(
repo_id=model_name_or_path,
ignore_patterns=["*.onnx", "*.msgpack"],
local_dir=local_dir,
resume_download=True,
max_workers=8,
etag_timeout=60,
force_download=True,
)
if not _verify_model_complete(local_path):
raise ValueError(
f"Downloaded model at {local_path} is still incomplete after forced re-download. "
"The model repository may be missing required components (model_index.json, transformer/, or vae/)."
)
logger.info("Downloaded model to %s", local_path)
return str(local_path)
except (RepositoryNotFoundError, RevisionNotFoundError) as e:
raise ValueError(
f"Model or revision not found at {model_name_or_path}. "
f"Please check the model ID or ensure you have access to the repository. Error: {e}"
) from e
except (RequestException, RequestsConnectionError) as e:
if attempt == MAX_RETRIES - 1:
raise ValueError(
f"Could not find model at {model_name_or_path} and failed to download from HF Hub "
f"after {MAX_RETRIES} attempts due to network error: {e}"
) from e
wait_time = 2**attempt
logger.warning(
"Download failed (attempt %d/%d) due to network error: %s. "
"Retrying in %d seconds...",
attempt + 1,
MAX_RETRIES,
e,
wait_time,
)
time.sleep(wait_time)
except Exception as e:
raise ValueError(
f"Could not find model at {model_name_or_path} and failed to download from HF Hub: {e}"
) from e

7
python/sglang/multimodal_gen/test/run_suite.py
View File

@@ -198,7 +198,12 @@ def run_pytest(files, filter_expr=None):
is_flaky_ci_assertion = "SafetensorError" in full_output
if not (is_perf_assertion or is_flaky_ci_assertion):
is_oom_error = (
"out of memory" in full_output.lower()
or "oom killer" in full_output.lower()
)
if not (is_perf_assertion or is_flaky_ci_assertion or is_oom_error):
return returncode
logger.info(f"Max retry exceeded")