[CI] Fix LoRA downloading issues and respect offline flag

python/pyproject_other.toml

@@ -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",

python/sglang/multimodal_gen/runtime/utils/hf_diffusers_utils.py

@@ -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 @@
     _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
     """
-
-    local_path = maybe_download_model(model_name_or_path, local_dir, download)
+    allow_patterns = ["*.json", "*.safetensors", "*.bin"]
+
+    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 _verify_model_complete(path: str) -> bool:
             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 _verify_model_complete(path: str) -> bool:
                 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

python/sglang/multimodal_gen/test/run_suite.py

@@ -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")

scripts/ci/ci_install_dependency.sh

@@ -96,6 +96,7 @@ fi
 # Clean up existing installations
 $PIP_UNINSTALL_CMD sgl-kernel sglang $PIP_UNINSTALL_SUFFIX || true
 $PIP_UNINSTALL_CMD flashinfer-python flashinfer-cubin flashinfer-jit-cache $PIP_UNINSTALL_SUFFIX || true
+$PIP_UNINSTALL_CMD opencv-python opencv-python-headless $PIP_UNINSTALL_SUFFIX || true
 
 # Install the main package
 EXTRAS="dev"