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35d483e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 | """Deterministic, dependency-light PyTorch training loop."""
from __future__ import annotations
import json
import os
import random
from collections.abc import Iterable, Mapping
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any
import torch
from torch import Tensor, nn
from turn_detection.models.common import TurnDetectionOutput
from turn_detection.training.losses import MultiTaskLossConfig, MultiTaskTurnLoss
from turn_detection.training.metrics import (
binary_classification_metrics,
threshold_at_max_fpr,
)
def seed_everything(seed: int, deterministic: bool = True) -> None:
"""Seed Python/PyTorch and select deterministic kernels when requested."""
random.seed(seed)
try:
import numpy as np
except ImportError:
np = None
if np is not None:
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
if deterministic:
torch.use_deterministic_algorithms(True, warn_only=True)
if torch.backends.cudnn.is_available():
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
@dataclass(frozen=True)
class TrainerConfig:
seed: int = 17
epochs: int = 20
learning_rate: float = 3e-4
weight_decay: float = 1e-3
gradient_accumulation_steps: int = 1
max_gradient_norm: float = 1.0
mixed_precision: bool = True
deterministic: bool = True
device: str = "auto"
early_stopping_patience: int = 5
min_improvement: float = 1e-4
false_positive_rate_budget: float = 0.02
selection_metric: str = "constrained_recall"
@classmethod
def from_mapping(cls, values: Mapping[str, Any]) -> TrainerConfig:
known = {field.name for field in cls.__dataclass_fields__.values()}
return cls(**{k: v for k, v in values.items() if k in known})
def _resolve_device(requested: str) -> torch.device:
if requested != "auto":
return torch.device(requested)
if torch.cuda.is_available():
return torch.device("cuda")
mps = getattr(torch.backends, "mps", None)
if mps is not None and mps.is_available():
return torch.device("mps")
return torch.device("cpu")
def _extract_batch(batch: Mapping[str, Any], device: torch.device) -> tuple[Tensor, ...]:
features = batch.get("log_mel", batch.get("input_features", batch.get("features")))
if features is None:
raise KeyError("batch needs log_mel, input_features, or features")
endpoint = batch.get("endpoint", batch.get("endpoint_bool", batch.get("label")))
if endpoint is None:
raise KeyError("batch needs endpoint, endpoint_bool, or label")
features = torch.as_tensor(features, dtype=torch.float32, device=device)
endpoint = torch.as_tensor(endpoint, dtype=torch.float32, device=device)
mask_value = batch.get("attention_mask", batch.get("frame_mask"))
if mask_value is None:
attention_mask = torch.ones(
(features.shape[0], features.shape[-1]), dtype=torch.bool, device=device
)
else:
attention_mask = torch.as_tensor(mask_value, dtype=torch.bool, device=device)
def optional(name: str) -> Tensor | None:
value = batch.get(name)
return None if value is None else torch.as_tensor(value, dtype=torch.float32, device=device)
return features, attention_mask, endpoint, optional("midfiller"), optional("endfiller")
class Trainer:
def __init__(
self,
model: nn.Module,
config: TrainerConfig | None = None,
loss_config: MultiTaskLossConfig | None = None,
output_dir: str | Path = "artifacts/run",
artifact_metadata: Mapping[str, Any] | None = None,
) -> None:
self.model = model
self.config = config or TrainerConfig()
self.loss = MultiTaskTurnLoss(loss_config)
self.output_dir = Path(output_dir)
self.artifact_metadata = dict(artifact_metadata or {})
self.device = _resolve_device(self.config.device)
self.history: list[dict[str, Any]] = []
def fit(self, train_loader: Iterable, validation_loader: Iterable) -> dict[str, Any]:
seed_everything(self.config.seed, self.config.deterministic)
self.output_dir.mkdir(parents=True, exist_ok=True)
self.model.to(self.device)
trainable = [parameter for parameter in self.model.parameters() if parameter.requires_grad]
if not trainable:
raise ValueError("model has no trainable parameters")
optimizer = torch.optim.AdamW(
trainable,
lr=self.config.learning_rate,
weight_decay=self.config.weight_decay,
)
scaler = torch.amp.GradScaler(
"cuda", enabled=self.config.mixed_precision and self.device.type == "cuda"
)
best_score = -float("inf")
selected_validation_loss = float("inf")
stale_epochs = 0
best_path = self.output_dir / "best.pt"
for epoch in range(1, self.config.epochs + 1):
train_dataset = getattr(train_loader, "dataset", None)
if hasattr(train_dataset, "set_epoch"):
train_dataset.set_epoch(epoch - 1)
train_metrics = self._train_epoch(train_loader, optimizer, scaler)
validation_metrics, labels, probabilities = self.evaluate(validation_loader)
operating_point = threshold_at_max_fpr(
labels,
probabilities,
self.config.false_positive_rate_budget,
)
record = {
"epoch": epoch,
"train": train_metrics,
"validation": validation_metrics,
"operating_point": operating_point,
}
self.history.append(record)
print(
json.dumps(
{
"epoch": epoch,
"train_loss": train_metrics["loss"],
"validation_loss": validation_metrics["loss"],
"validation_roc_auc": validation_metrics["roc_auc"],
"validation_average_precision": validation_metrics["average_precision"],
"operating_threshold": operating_point["threshold"],
"operating_fpr": operating_point["false_positive_rate"],
"operating_recall": operating_point["recall"],
},
allow_nan=False,
),
flush=True,
)
validation_loss = float(validation_metrics["loss"])
if self.config.selection_metric == "validation_loss":
selection_score = -validation_loss
elif self.config.selection_metric == "average_precision":
selection_score = float(validation_metrics["average_precision"] or 0.0)
elif self.config.selection_metric == "constrained_recall":
selection_score = float(operating_point["recall"] or 0.0)
else:
raise ValueError(
"selection_metric must be constrained_recall, average_precision, "
"or validation_loss"
)
record["selection_metric"] = self.config.selection_metric
record["selection_score"] = selection_score
improved = selection_score > best_score + self.config.min_improvement
if improved:
best_score = selection_score
selected_validation_loss = validation_loss
stale_epochs = 0
self._save_checkpoint(
best_path,
epoch,
optimizer,
threshold=float(operating_point["threshold"]),
metrics=record,
)
else:
stale_epochs += 1
self._write_history()
if stale_epochs >= self.config.early_stopping_patience:
break
checkpoint = torch.load(best_path, map_location=self.device, weights_only=False)
self.model.load_state_dict(checkpoint["model_state"])
return {
"checkpoint": str(best_path),
"best_validation_loss": selected_validation_loss,
"best_selection_metric": self.config.selection_metric,
"best_selection_score": best_score,
"threshold": checkpoint["threshold"],
"epochs_completed": len(self.history),
"history": self.history,
}
def _train_epoch(
self, loader: Iterable, optimizer: torch.optim.Optimizer, scaler: Any
) -> dict[str, float]:
self.model.train()
optimizer.zero_grad(set_to_none=True)
loss_totals = {name: 0.0 for name in ("total", "endpoint", "midfiller", "endfiller")}
batches = 0
accumulation = max(1, self.config.gradient_accumulation_steps)
for batch_index, batch in enumerate(loader, start=1):
features, mask, endpoint, mid, end = _extract_batch(batch, self.device)
autocast_enabled = self.config.mixed_precision and self.device.type == "cuda"
with torch.autocast(device_type=self.device.type, enabled=autocast_enabled):
output: TurnDetectionOutput = self.model(features, mask)
losses = self.loss(output, endpoint, mid, end)
scaled_loss = losses["total"] / accumulation
scaler.scale(scaled_loss).backward()
if batch_index % accumulation == 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.config.max_gradient_norm
)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
for name in loss_totals:
loss_totals[name] += float(losses[name].detach().cpu())
batches += 1
if batches == 0:
raise ValueError("training loader produced no batches")
# Flush a partial accumulation window.
if batches % accumulation:
scaler.unscale_(optimizer)
correction = accumulation / (batches % accumulation)
for parameter in self.model.parameters():
if parameter.grad is not None:
parameter.grad.mul_(correction)
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.config.max_gradient_norm)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
return {
"loss": loss_totals["total"] / batches,
"endpoint_loss": loss_totals["endpoint"] / batches,
"midfiller_loss": loss_totals["midfiller"] / batches,
"endfiller_loss": loss_totals["endfiller"] / batches,
"batches": float(batches),
}
@torch.no_grad()
def evaluate(self, loader: Iterable) -> tuple[dict[str, Any], list[int], list[float]]:
self.model.eval()
loss_totals = {name: 0.0 for name in ("total", "endpoint", "midfiller", "endfiller")}
batches = 0
labels: list[int] = []
probabilities: list[float] = []
for batch in loader:
features, mask, endpoint, mid, end = _extract_batch(batch, self.device)
output: TurnDetectionOutput = self.model(features, mask)
losses = self.loss(output, endpoint, mid, end)
for name in loss_totals:
loss_totals[name] += float(losses[name].cpu())
batches += 1
labels.extend(int(value) for value in endpoint.detach().cpu().tolist())
probabilities.extend(
float(value) for value in torch.sigmoid(output.endpoint_logits).cpu().tolist()
)
if batches == 0:
raise ValueError("validation loader produced no batches")
metrics = binary_classification_metrics(labels, probabilities, threshold=0.5)
metrics["loss"] = loss_totals["total"] / batches
metrics["endpoint_loss"] = loss_totals["endpoint"] / batches
metrics["midfiller_loss"] = loss_totals["midfiller"] / batches
metrics["endfiller_loss"] = loss_totals["endfiller"] / batches
return metrics, labels, probabilities
def _save_checkpoint(
self,
path: Path,
epoch: int,
optimizer: torch.optim.Optimizer,
threshold: float,
metrics: Mapping[str, Any],
) -> None:
model_config = (
self.model.model_config()
if hasattr(self.model, "model_config")
else self.artifact_metadata.get("model_config")
)
if not isinstance(model_config, Mapping):
raise ValueError("model must expose model_config() for a self-describing checkpoint")
payload = {
"format_version": 1,
"epoch": epoch,
"model_config": dict(model_config),
"model_state": self.model.state_dict(),
"optimizer_state": optimizer.state_dict(),
"trainer_config": asdict(self.config),
"threshold": threshold,
"metrics": dict(metrics),
"metadata": self.artifact_metadata,
}
temporary = path.with_suffix(path.suffix + ".tmp")
torch.save(payload, temporary)
os.replace(temporary, path)
def _write_history(self) -> None:
path = self.output_dir / "history.json"
temporary = path.with_suffix(".json.tmp")
temporary.write_text(json.dumps(self.history, indent=2, allow_nan=False), encoding="utf-8")
os.replace(temporary, path)
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