File size: 7,674 Bytes
e1a9145 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | from __future__ import annotations
import contextvars
import os
import time
from collections import deque
from contextlib import asynccontextmanager, contextmanager
from dataclasses import dataclass, field
from functools import wraps
from typing import Any
PROFILING_ENABLED = os.environ.get("GRADIO_PROFILING", "").strip() in ("1", "true")
@dataclass
class RequestTrace:
event_id: str | None = None
fn_name: str | None = None
session_hash: str | None = None
timestamp: float = field(default_factory=time.time)
queue_wait_ms: float = 0.0
preprocess_ms: float = 0.0
fn_call_ms: float = 0.0
postprocess_ms: float = 0.0
streaming_diff_ms: float = 0.0
total_ms: float = 0.0
n_iterations: int = 0
upload_ms: float = 0.0
preprocess_move_to_cache_ms: float = 0.0
preprocess_format_image_ms: float = 0.0
postprocess_save_img_array_to_cache_ms: float = 0.0
preprocess_audio_from_file_ms: float = 0.0
postprocess_save_audio_to_cache_ms: float = 0.0
preprocess_video_ms: float = 0.0
postprocess_video_convert_video_to_playable_mp4_ms: float = 0.0
postprocess_update_state_in_config_ms: float = 0.0
postprocess_move_to_cache_ms: float = 0.0
postprocess_video_ms: float = 0.0
postprocess_save_pil_to_cache_ms: float = 0.0
postprocess_save_bytes_to_cache_ms: float = 0.0
save_file_to_cache_ms: float = 0.0
def set_phase(self, name: str, duration_ms: float):
attr = f"{name}_ms"
if hasattr(self, attr):
# Accumulate across generator iterations
setattr(self, attr, getattr(self, attr) + duration_ms)
if name == "total":
self.n_iterations += 1
def to_dict(self) -> dict[str, Any]:
return {
"event_id": self.event_id,
"fn_name": self.fn_name,
"session_hash": self.session_hash,
"timestamp": self.timestamp,
"queue_wait_ms": self.queue_wait_ms,
"preprocess_ms": self.preprocess_ms,
"fn_call_ms": self.fn_call_ms,
"postprocess_ms": self.postprocess_ms,
"streaming_diff_ms": self.streaming_diff_ms,
"total_ms": self.total_ms,
"n_iterations": self.n_iterations,
"preprocess_move_to_cache_ms": self.preprocess_move_to_cache_ms,
"preprocess_format_image_ms": self.preprocess_format_image_ms,
"postprocess_save_img_array_to_cache_ms": self.postprocess_save_img_array_to_cache_ms,
"preprocess_audio_from_file_ms": self.preprocess_audio_from_file_ms,
"postprocess_save_audio_to_cache_ms": self.postprocess_save_audio_to_cache_ms,
"preprocess_video_ms": self.preprocess_video_ms,
"postprocess_video_convert_video_to_playable_mp4_ms": self.postprocess_video_convert_video_to_playable_mp4_ms,
"postprocess_update_state_in_config_ms": self.postprocess_update_state_in_config_ms,
"postprocess_move_to_cache_ms": self.postprocess_move_to_cache_ms,
"postprocess_video_ms": self.postprocess_video_ms,
"postprocess_save_pil_to_cache_ms": self.postprocess_save_pil_to_cache_ms,
"postprocess_save_bytes_to_cache_ms": self.postprocess_save_bytes_to_cache_ms,
"save_file_to_cache_ms": self.save_file_to_cache_ms,
}
_current_trace: contextvars.ContextVar[RequestTrace | None] = contextvars.ContextVar(
"_current_trace", default=None
)
def get_current_trace() -> RequestTrace | None:
return _current_trace.get()
def set_current_trace(trace: RequestTrace) -> contextvars.Token:
return _current_trace.set(trace)
@asynccontextmanager
async def trace_phase(name: str):
"""Async context manager that records timing for a named phase into the current trace."""
trace = _current_trace.get()
if trace is None:
yield
return
start = time.monotonic()
try:
yield
finally:
duration_ms = (time.monotonic() - start) * 1000
trace.set_phase(name, duration_ms)
@contextmanager
def trace_phase_sync(name: str):
"""Context manager that records timing for a named phase into the current trace."""
trace = _current_trace.get()
if trace is None:
yield
return
start = time.monotonic()
try:
yield
finally:
duration_ms = (time.monotonic() - start) * 1000
trace.set_phase(name, duration_ms)
def traced(phase):
if not PROFILING_ENABLED:
return lambda f: f
def _factory(f):
@wraps(f)
async def wrapper(*args, **kwargs):
async with trace_phase(phase):
return await f(*args, **kwargs)
return wrapper
return _factory
def traced_sync(phase):
if not PROFILING_ENABLED:
return lambda f: f
def _factory(f):
@wraps(f)
def wrapper(*args, **kwargs):
with trace_phase_sync(phase):
return f(*args, **kwargs)
return wrapper
return _factory
class TraceCollector:
def __init__(self, maxlen: int = 100_000):
self._traces: deque[RequestTrace] = deque(maxlen=maxlen)
def add(self, trace: RequestTrace):
self._traces.append(trace)
def get_all(self, last_n: int | None = None) -> list[dict[str, Any]]:
traces = list(self._traces)
if last_n is not None:
traces = traces[-last_n:]
return [t.to_dict() for t in traces]
def get_summary(self) -> dict[str, Any]:
if not self._traces:
return {"count": 0, "phases": {}}
import numpy as np
prediction_traces = [
t for t in self._traces if t.fn_name != "gradio_file_upload"
]
upload_traces = [t for t in self._traces if t.fn_name == "gradio_file_upload"]
phases = [
"queue_wait",
"preprocess",
"fn_call",
"postprocess",
"streaming_diff",
"total",
]
def _percentiles(arr):
return {
"p50": float(np.percentile(arr, 50)),
"p90": float(np.percentile(arr, 90)),
"p95": float(np.percentile(arr, 95)),
"p99": float(np.percentile(arr, 99)),
"mean": float(np.mean(arr)),
"min": float(np.min(arr)),
"max": float(np.max(arr)),
}
result: dict[str, Any] = {
"count": len(prediction_traces),
"phases": {},
}
for phase in phases:
values = [getattr(t, f"{phase}_ms") for t in prediction_traces]
if values:
result["phases"][phase] = _percentiles(np.array(values))
else:
result["phases"][phase] = {
"p50": 0.0,
"p90": 0.0,
"p95": 0.0,
"p99": 0.0,
"mean": 0.0,
"min": 0.0,
"max": 0.0,
}
if upload_traces:
upload_values = [t.upload_ms for t in upload_traces]
result["upload"] = {
"count": len(upload_traces),
**_percentiles(np.array(upload_values)),
}
return result
def clear(self):
self._traces.clear()
# Global collector instance
collector = TraceCollector()
if not PROFILING_ENABLED:
# Replace with no-ops for zero overhead
@asynccontextmanager
async def trace_phase(name: str): # noqa: ARG001
yield
@contextmanager
def trace_phase_sync(name: str): # noqa: ARG001
yield
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