[diffusion] UX: suppress excessive logging from httpx and httpcore (#20452)

python/sglang/multimodal_gen/runtime/pipelines_core/schedule_batch.py

@@ -11,6 +11,7 @@ in a functional manner, reducing the need for explicit parameter passing.
 
 from __future__ import annotations
 
+import logging
 import os
 import pprint
 from copy import deepcopy
@@ -21,11 +22,11 @@ import PIL.Image
 import torch
 
 from sglang.multimodal_gen.configs.sample.sampling_params import SamplingParams
-from sglang.multimodal_gen.runtime.server_args import (
+from sglang.multimodal_gen.runtime.server_args import ServerArgs
-    ServerArgs,
+from sglang.multimodal_gen.runtime.utils.logging_utils import (
     _sanitize_for_logging,
+    init_logger,
 )
-from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 from sglang.multimodal_gen.runtime.utils.perf_logger import RequestMetrics
 from sglang.multimodal_gen.utils import align_to
 
@@ -288,20 +289,22 @@ class Req:
         else:
             target_width = -1
 
-        # sanitize prompts for info-level logging
+        if logger.isEnabledFor(logging.DEBUG):
-        sanitized_prompt = _sanitize_for_logging(self.prompt, key_hint="prompt")
+            display_prompt = self.prompt
-        sanitized_neg_prompt = _sanitize_for_logging(
+            display_neg_prompt = self.negative_prompt
-            self.negative_prompt, key_hint="negative_prompt"
+        else:
-        )
+            display_prompt = _sanitize_for_logging(self.prompt, key_hint="prompt")
+            display_neg_prompt = _sanitize_for_logging(
+                self.negative_prompt, key_hint="negative_prompt"
+            )
 
-        # Log sampling parameters
         debug_str = f"""Sampling params:
                        width: {target_width}
                       height: {target_height}
                   num_frames: {self.num_frames}
                          fps: {self.fps}
-                      prompt: {sanitized_prompt}
+                      prompt: {display_prompt}
-                  neg_prompt: {sanitized_neg_prompt}
+                  neg_prompt: {display_neg_prompt}
                         seed: {self.seed}
                  infer_steps: {self.num_inference_steps}
       num_outputs_per_prompt: {self.num_outputs_per_prompt}
@@ -313,7 +316,7 @@ class Req:
                  save_output: {self.save_output}
             output_file_path: {self.output_file_path()}
         """  # type: ignore[attr-defined]
-        logger.debug(debug_str)
+        logger.info(debug_str)
 
 
 @dataclass

python/sglang/multimodal_gen/runtime/server_args.py

@@ -6,7 +6,6 @@
 
 import argparse
 import dataclasses
-import inspect
 import json
 import math
 import os
@@ -37,6 +36,7 @@ from sglang.multimodal_gen.runtime.utils.common import (
     is_valid_ipv6_address,
 )
 from sglang.multimodal_gen.runtime.utils.logging_utils import (
+    _sanitize_for_logging,
     configure_logger,
     init_logger,
 )
@@ -49,119 +49,6 @@ from sglang.multimodal_gen.utils import (
 logger = init_logger(__name__)
 
 
-def _is_torch_tensor(obj: Any) -> tuple[bool, Any]:
-    """Return (is_tensor, torch_module_or_None) without importing torch at module import time."""
-    try:
-        import torch  # type: ignore
-
-        return isinstance(obj, torch.Tensor), torch
-    except Exception:
-        return False, None
-
-
-def _sanitize_for_logging(obj: Any, key_hint: str | None = None) -> Any:
-    """Recursively convert objects to JSON-serializable forms for concise logging.
-
-    Rules:
-    - Drop any field/dict key named 'param_names_mapping'.
-    - Render Enums using their value.
-    - Render torch.Tensor as a compact summary; if key name is 'scaling_factor', include stats.
-    - Dataclasses are expanded to dicts and sanitized recursively.
-    - Callables/functions are rendered as their qualified name.
-    - Redact sensitive fields like 'prompt' and 'negative_prompt' (only show length).
-    - Fallback to str(...) for unknown types.
-    """
-    # Handle simple types quickly
-    if obj is None or isinstance(obj, (str, int, float, bool)):
-        # redact sensitive prompt fields
-        if key_hint in ("prompt", "negative_prompt"):
-            if isinstance(obj, str):
-                return f"<redacted, len={len(obj)}>"
-        return obj
-
-    # Enum -> value for readability
-    if isinstance(obj, Enum):
-        return obj.value
-
-    # torch.Tensor handling (lazy import)
-    is_tensor, torch_mod = _is_torch_tensor(obj)
-    if is_tensor:
-        try:
-            ten = obj.detach().cpu()
-            if key_hint == "scaling_factor":
-                # Provide a compact, single-line summary for scaling_factor
-                stats = {
-                    "shape": list(ten.shape),
-                    "dtype": str(ten.dtype),
-                }
-                # Stats might fail for some dtypes; guard individually
-                try:
-                    stats["min"] = float(ten.min().item())
-                except Exception:
-                    pass
-                try:
-                    stats["max"] = float(ten.max().item())
-                except Exception:
-                    pass
-                try:
-                    stats["mean"] = float(ten.float().mean().item())
-                except Exception:
-                    pass
-                return {"tensor": "scaling_factor", **stats}
-            # Generic tensor summary
-            return {"tensor": True, "shape": list(ten.shape), "dtype": str(ten.dtype)}
-        except Exception:
-            return "<tensor>"
-
-    # Dataclasses -> dict
-    if dataclasses.is_dataclass(obj):
-        result: dict[str, Any] = {}
-        for f in dataclasses.fields(obj):
-            if not f.repr:
-                continue
-            name = f.name
-            if "names_mapping" in name:  # drop noisy mappings
-                continue
-            try:
-                value = getattr(obj, name)
-            except Exception:
-                continue
-            result[name] = _sanitize_for_logging(value, key_hint=name)
-        return result
-
-    # Dicts -> sanitize keys/values; drop 'param_names_mapping'
-    if isinstance(obj, dict):
-        result_dict: dict[str, Any] = {}
-        for k, v in obj.items():
-            try:
-                key_str = str(k)
-            except Exception:
-                key_str = "<key>"
-            if key_str == "param_names_mapping":
-                continue
-            result_dict[key_str] = _sanitize_for_logging(v, key_hint=key_str)
-        return result_dict
-
-    # Sequences/Sets -> list
-    if isinstance(obj, (list, tuple, set)):
-        return [_sanitize_for_logging(x, key_hint=key_hint) for x in obj]
-
-    # Functions / Callables -> qualified name
-    try:
-        if inspect.isroutine(obj) or inspect.isclass(obj):
-            module = getattr(obj, "__module__", "")
-            qn = getattr(obj, "__qualname__", getattr(obj, "__name__", "<callable>"))
-            return f"{module}.{qn}" if module else qn
-    except Exception:
-        pass
-
-    # Fallback: string representation
-    try:
-        return str(obj)
-    except Exception:
-        return "<unserializable>"
-
-
 class Backend(str, Enum):
     """
     Enumeration for different model backends.

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

@@ -6,12 +6,15 @@
 
 import argparse
 import contextlib
+import dataclasses
 import datetime
+import inspect
 import logging
 import os
 import sys
 import time
 from contextlib import contextmanager
+from enum import Enum
 from functools import lru_cache, partial
 from logging import Logger
 from types import MethodType
@@ -281,6 +284,107 @@ def init_logger(name: str) -> _SGLDiffusionLogger:
 logger = init_logger(__name__)
 
 
+def _is_torch_tensor(obj: Any) -> tuple[bool, Any]:
+    """Return (is_tensor, torch_module_or_None) without importing torch at module import time."""
+    try:
+        import torch  # type: ignore
+
+        return isinstance(obj, torch.Tensor), torch
+    except Exception:
+        return False, None
+
+
+def _sanitize_for_logging(obj: Any, key_hint: str | None = None) -> Any:
+    """Recursively convert objects to JSON-serializable forms for concise logging.
+
+    Rules:
+    - Drop any field/dict key named 'param_names_mapping'.
+    - Render Enums using their value.
+    - Render torch.Tensor as a compact summary; if key name is 'scaling_factor', include stats.
+    - Dataclasses are expanded to dicts and sanitized recursively.
+    - Callables/functions are rendered as their qualified name.
+    - Redact sensitive fields like 'prompt' and 'negative_prompt' (only show length).
+    - Fallback to str(...) for unknown types.
+    """
+    if obj is None or isinstance(obj, (str, int, float, bool)):
+        if key_hint in ("prompt", "negative_prompt"):
+            if isinstance(obj, str):
+                return f"<redacted, len={len(obj)}>"
+        return obj
+
+    if isinstance(obj, Enum):
+        return obj.value
+
+    is_tensor, torch_mod = _is_torch_tensor(obj)
+    if is_tensor:
+        try:
+            ten = obj.detach().cpu()
+            if key_hint == "scaling_factor":
+                stats = {
+                    "shape": list(ten.shape),
+                    "dtype": str(ten.dtype),
+                }
+                try:
+                    stats["min"] = float(ten.min().item())
+                except Exception:
+                    pass
+                try:
+                    stats["max"] = float(ten.max().item())
+                except Exception:
+                    pass
+                try:
+                    stats["mean"] = float(ten.float().mean().item())
+                except Exception:
+                    pass
+                return {"tensor": "scaling_factor", **stats}
+            return {"tensor": True, "shape": list(ten.shape), "dtype": str(ten.dtype)}
+        except Exception:
+            return "<tensor>"
+
+    if dataclasses.is_dataclass(obj):
+        result: dict[str, Any] = {}
+        for f in dataclasses.fields(obj):
+            if not f.repr:
+                continue
+            name = f.name
+            if "names_mapping" in name:
+                continue
+            try:
+                value = getattr(obj, name)
+            except Exception:
+                continue
+            result[name] = _sanitize_for_logging(value, key_hint=name)
+        return result
+
+    if isinstance(obj, dict):
+        result_dict: dict[str, Any] = {}
+        for k, v in obj.items():
+            try:
+                key_str = str(k)
+            except Exception:
+                key_str = "<key>"
+            if key_str == "param_names_mapping":
+                continue
+            result_dict[key_str] = _sanitize_for_logging(v, key_hint=key_str)
+        return result_dict
+
+    if isinstance(obj, (list, tuple, set)):
+        return [_sanitize_for_logging(x, key_hint=key_hint) for x in obj]
+
+    try:
+        if inspect.isroutine(obj) or inspect.isclass(obj):
+            module = getattr(obj, "__module__", "")
+            qn = getattr(obj, "__qualname__", getattr(obj, "__name__", "<callable>"))
+            return f"{module}.{qn}" if module else qn
+    except Exception:
+        pass
+
+    try:
+        return str(obj)
+    except Exception:
+        return "<unserializable>"
+
+
 def _trace_calls(log_path, root_dir, frame, event, arg=None):
     if event in ["call", "return"]:
         # Extract the filename, line number, function name, and the code object
@@ -403,6 +507,8 @@ def globally_suppress_loggers():
         "python_multipart.multipart",
         "filelock",
         "urllib3",
+        "httpx",
+        "httpcore",
     ]
 
     for name in target_names:
@@ -457,7 +563,7 @@ def log_generation_timer(
             "Processing prompt %d/%d: %s",
             request_idx,
             total_requests,
-            prompt[:100],
+            _sanitize_for_logging(prompt, key_hint="prompt"),
         )
 
     timer = GenerationTimer()