[Feat] QWen-1M context support[2/2]: Update block sparse attention backend (#5949)

python/sglang/srt/hf_transformers_utils.py

@@ -14,10 +14,11 @@
 """Utilities for Huggingface Transformers."""
 
 import contextlib
+import json
 import os
 import warnings
 from pathlib import Path
-from typing import Dict, Optional, Type, Union
+from typing import Any, Dict, Optional, Type, Union
 
 import torch
 from huggingface_hub import snapshot_download
@@ -62,11 +63,17 @@ for name, cls in _CONFIG_REGISTRY.items():
         AutoConfig.register(name, cls)
 
 
-def download_from_hf(model_path: str):
+def download_from_hf(
+    model_path: str,
+    allow_patterns: Optional[Union[str, list]] = None,
+):
     if os.path.exists(model_path):
         return model_path
 
-    return snapshot_download(model_path, allow_patterns=["*.json", "*.bin", "*.model"])
+    if not allow_patterns:
+        allow_patterns = ["*.json", "*.bin", "*.model"]
+
+    return snapshot_download(model_path, allow_patterns=allow_patterns)
 
 
 def get_hf_text_config(config: PretrainedConfig):
@@ -171,6 +178,26 @@ def get_generation_config(
         return None
 
 
+# Qwen-1M related
+def get_sparse_attention_config(
+    model: str,
+    sparse_attention_config_filename: str = "sparse_attention_config.json",
+) -> Dict[str, Any]:
+    is_local = os.path.isdir(model)
+    if not is_local:
+        # Download the config files.
+        model = download_from_hf(model, allow_patterns=["*.json"])
+
+    config_file = os.path.join(model, sparse_attention_config_filename)
+    if not os.path.exists(config_file):
+        return {}
+
+    # Load the sparse attention config.
+    with open(config_file) as f:
+        config = json.load(f)
+    return config
+
+
 # Models don't use the same configuration key for determining the maximum
 # context length.  Store them here so we can sanely check them.
 # NOTE: The ordering here is important. Some models have two of these and we