[Misc] Fix metrics, weight update lock, request logging (#2543)

python/sglang/srt/utils.py

@@ -14,6 +14,7 @@
 """Common utilities."""
 
 import base64
+import dataclasses
 import ipaddress
 import itertools
 import json
@@ -1238,49 +1239,37 @@ def cuda_device_count_stateless() -> int:
     return _cuda_device_count_stateless(os.environ.get("CUDA_VISIBLE_DEVICES", None))
 
 
-def should_use_tensor_core(
-    kv_cache_dtype: torch.dtype,
-    num_attention_heads: int,
-    num_kv_heads: int,
-) -> bool:
-    """
-    Determine whether to use tensor cores for attention computation.
-
-    Args:
-        kv_cache_dtype: Data type of the KV cache
-        num_attention_heads: Number of attention heads
-        num_kv_heads: Number of key/value heads
-
-    Returns:
-        bool: Whether to use tensor cores
-    """
-    # Try to use environment variable first
-    env_override = os.environ.get("SGLANG_FLASHINFER_USE_TENSOR_CORE")
-    if env_override is not None:
-        return env_override.lower() == "true"
-
-    # Try to use _grouped_size_compiled_for_decode_kernels if available
-    # This is for flashinfer <=0.1.6. Otherwise, there is an accuracy bug
-    try:
-        from flashinfer.decode import _grouped_size_compiled_for_decode_kernels
-
-        if not _grouped_size_compiled_for_decode_kernels(
-            num_attention_heads,
-            num_kv_heads,
-        ):
-            return True
+def dataclass_to_string_truncated(data, max_length=2048):
+    if isinstance(data, str):
+        if len(data) > max_length:
+            half_length = max_length // 2
+            return f'"{data[:half_length]} ... {data[-half_length:]}"'
         else:
-            return False
-    except (ImportError, AttributeError):
-        pass
-
-    # Calculate GQA group size
-    gqa_group_size = num_attention_heads // num_kv_heads
-
-    # Determine based on dtype and GQA group size
-    if kv_cache_dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
-        return True
-    elif kv_cache_dtype in (torch.float16, torch.half, torch.bfloat16):
-        return gqa_group_size > 4
+            return f'"{data}"'
+    elif isinstance(data, (list, tuple)):
+        if len(data) > max_length:
+            half_length = max_length // 2
+            return str(data[:half_length]) + " ... " + str(data[-half_length:])
+        else:
+            return str(data)
+    elif isinstance(data, dict):
+        return (
+            "{"
+            + ", ".join(
+                f"{k}: {dataclass_to_string_truncated(v, max_length)}"
+                for k, v in data.items()
+            )
+            + "}"
+        )
+    elif dataclasses.is_dataclass(data):
+        fields = dataclasses.fields(data)
+        return (
+            f"{data.__class__.__name__}("
+            + ", ".join(
+                f"{f.name}={dataclass_to_string_truncated(getattr(data, f.name), max_length)}"
+                for f in fields
+            )
+            + ")"
+        )
     else:
-        return False
+        return str(data)