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804ee23 | 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 | from __future__ import annotations
import os
import time
from contextlib import contextmanager
from contextvars import ContextVar, Token
from dataclasses import dataclass
from multiprocessing import Queue
from typing import Iterator
import torch
from loguru import logger
INFERENCE_STAGE_NAMES = (
"FM",
"latent_encoder",
"patch_encoder",
"LLM",
"latent_decoder",
"speaker_encoder",
"vocoder",
)
_INFERENCE_STAGE_NAME_MAP = {
name.lower(): name for name in INFERENCE_STAGE_NAMES
}
_CURRENT_INFERENCE_PROFILER: ContextVar[InferenceProfiler | None] = ContextVar(
"current_inference_profiler",
default=None,
)
def normalize_inference_stage_name(name: str) -> str:
canonical = _INFERENCE_STAGE_NAME_MAP.get(name.strip().lower())
if canonical is None:
raise ValueError(
f"Unsupported inference stage '{name}'. "
f"Expected one of: {', '.join(INFERENCE_STAGE_NAMES)}."
)
return canonical
@dataclass(slots=True)
class InferenceStageStat:
seconds: float = 0.0
count: int = 0
@dataclass(frozen=True, slots=True)
class ProfileEvent:
stage: str
seconds: float
count: int
pid: int
class DataProfiler:
def __init__(self, queue: Queue | None = None):
self._queue = queue
self._pid = os.getpid()
@property
def enabled(self) -> bool:
return self._queue is not None
@contextmanager
def measure(self, stage: str, *, count: int = 1) -> Iterator[None]:
if self._queue is None:
yield
return
start = time.perf_counter()
try:
yield
finally:
self._queue.put(
ProfileEvent(
stage=stage,
seconds=time.perf_counter() - start,
count=int(count),
pid=self._pid,
)
)
def child(self) -> DataProfiler:
return DataProfiler(self._queue)
def ensure_data_profiler(profiler: DataProfiler | None) -> DataProfiler:
return DataProfiler() if profiler is None else profiler
class InferenceProfiler:
def __init__(self, device: torch.device):
self._device = device
self._stats = {
stage: InferenceStageStat() for stage in INFERENCE_STAGE_NAMES
}
def _sync(self) -> None:
if self._device.type == "cuda":
torch.cuda.synchronize(self._device)
@contextmanager
def measure(self, stage: str, *, count: int = 1) -> Iterator[None]:
stage = normalize_inference_stage_name(stage)
self._sync()
start = time.perf_counter()
try:
yield
finally:
self._sync()
stat = self._stats[stage]
stat.seconds += time.perf_counter() - start
stat.count += int(count)
def summary(
self,
*,
duration_seconds: float | None = None,
) -> dict[str, dict[str, float | int]]:
summary: dict[str, dict[str, float | int]] = {}
for stage in INFERENCE_STAGE_NAMES:
stat = self._stats[stage]
payload: dict[str, float | int] = {
"seconds": stat.seconds,
"count": stat.count,
}
if duration_seconds is not None:
payload["rtf"] = (
stat.seconds / duration_seconds
if duration_seconds > 0
else float("inf")
)
summary[stage] = payload
return summary
@contextmanager
def inference_profiling(
*,
enabled: bool,
device: torch.device,
) -> Iterator[InferenceProfiler | None]:
profiler = InferenceProfiler(device) if enabled else None
with activate_inference_profiler(profiler):
yield profiler
@contextmanager
def activate_inference_profiler(
profiler: InferenceProfiler | None,
) -> Iterator[InferenceProfiler | None]:
if profiler is None:
yield None
return
token: Token[InferenceProfiler | None] = _CURRENT_INFERENCE_PROFILER.set(profiler)
try:
yield profiler
finally:
_CURRENT_INFERENCE_PROFILER.reset(token)
@contextmanager
def measure_inference(stage: str, *, count: int = 1) -> Iterator[None]:
profiler = _CURRENT_INFERENCE_PROFILER.get()
if profiler is None:
yield
return
with profiler.measure(stage, count=count):
yield
def log_inference_profile(
*,
request_id: str,
profiling: dict[str, dict[str, float | int]],
duration_seconds: float,
) -> None:
active_stages = [
stage
for stage in INFERENCE_STAGE_NAMES
if int(profiling[stage]["count"]) > 0
]
if not active_stages:
logger.info(
"Inference profiling summary: request_id={} no_profiled_stages duration_seconds={:.3f}",
request_id,
duration_seconds,
)
return
for stage in active_stages:
stats = profiling[stage]
logger.info(
"Inference profiling: request_id={} stage={} seconds={:.4f} count={} rtf={:.4f}",
request_id,
stage,
float(stats["seconds"]),
int(stats["count"]),
float(stats["rtf"]),
)
__all__ = [
"DataProfiler",
"ProfileEvent",
"INFERENCE_STAGE_NAMES",
"activate_inference_profiler",
"ensure_data_profiler",
"InferenceProfiler",
"InferenceStageStat",
"inference_profiling",
"log_inference_profile",
"measure_inference",
"normalize_inference_stage_name",
]
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