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88e15cd | 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 | from __future__ import annotations
from pathlib import Path
import torch
from rich.console import Console
from .config import RLConfig
from .io import write_json
from .schemas import RewardRecord
from .training_common import (
evaluate_loader,
make_loader,
make_model,
move_batch,
optimizer_parameters,
seed_everything,
)
console = Console()
def contextual_bandit_loss(
logits: torch.Tensor,
rewards: torch.Tensor,
estimator: str = "reinforce",
rollouts_per_prompt: int = 8,
entropy_coefficient: float = 0.01,
normalize_advantage: bool = False,
) -> tuple[torch.Tensor, dict[str, float]]:
distribution = torch.distributions.Categorical(logits=logits)
entropy = distribution.entropy().mean()
probabilities = distribution.probs
if estimator == "expected_reward":
objective = (probabilities * rewards).sum(dim=-1).mean()
policy_loss = -objective
sampled_reward = objective.detach()
elif estimator == "reinforce":
if rollouts_per_prompt < 1:
raise ValueError("rollouts_per_prompt must be at least 1")
actions = distribution.sample((rollouts_per_prompt,))
expanded_rewards = rewards.unsqueeze(0).expand(rollouts_per_prompt, -1, -1)
sampled = expanded_rewards.gather(2, actions.unsqueeze(-1)).squeeze(-1)
baseline = (probabilities.detach() * rewards).sum(dim=-1).unsqueeze(0)
advantage = sampled - baseline
if normalize_advantage and advantage.numel() > 1:
advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-6)
log_prob = distribution.log_prob(actions)
policy_loss = -(log_prob * advantage.detach()).mean()
sampled_reward = sampled.mean().detach()
else:
raise ValueError(f"Unknown estimator: {estimator}")
loss = policy_loss - entropy_coefficient * entropy
reward_spread = rewards.max(dim=-1).values - rewards.min(dim=-1).values
metrics = {
"loss": float(loss.detach()),
"sampled_reward": float(sampled_reward),
"entropy": float(entropy.detach()),
"zero_spread_fraction": float((reward_spread.abs() < 1e-8).float().mean()),
}
return loss, metrics
def train_rl(
records: list[RewardRecord],
config: RLConfig,
output_dir: str | Path,
checkpoint: str | None = None,
) -> dict:
seed_everything(config.training.seed)
worker_ids = records[0].worker_ids
model = make_model(config.model, worker_ids, checkpoint)
train_loader = make_loader(
records, model, "train", config.training.batch_size, shuffle=True
)
try:
validation_loader = make_loader(
records, model, "validation", config.training.batch_size, shuffle=False
)
except ValueError:
validation_loader = None
optimizer = torch.optim.AdamW(
optimizer_parameters(model),
lr=config.training.learning_rate,
weight_decay=config.training.weight_decay,
)
trainable, total = model.trainable_parameter_counts()
console.print(
f"RL device={model.device_ref}; trainable={trainable:,}/{total:,} "
f"({100 * trainable / total:.4f}%)"
)
history: list[dict] = []
global_step = 0
optimizer.zero_grad(set_to_none=True)
for epoch in range(config.training.epochs):
model.train()
epoch_metrics: list[dict[str, float]] = []
for step, batch in enumerate(train_loader):
inputs, rewards = move_batch(batch, model.device_ref)
logits = model(**inputs)
loss, metrics = contextual_bandit_loss(
logits,
rewards,
estimator=config.training.estimator,
rollouts_per_prompt=config.training.rollouts_per_prompt,
entropy_coefficient=config.training.entropy_coefficient,
normalize_advantage=config.training.normalize_advantage,
)
(loss / config.training.gradient_accumulation_steps).backward()
epoch_metrics.append(metrics)
should_step = (
(step + 1) % config.training.gradient_accumulation_steps == 0
or step + 1 == len(train_loader)
)
if should_step:
torch.nn.utils.clip_grad_norm_(
list(optimizer_parameters(model)), config.training.max_grad_norm
)
optimizer.step()
optimizer.zero_grad(set_to_none=True)
global_step += 1
if global_step % config.training.log_every == 0:
console.print(f"RL step={global_step} {metrics}")
averaged = {
key: sum(item[key] for item in epoch_metrics) / max(1, len(epoch_metrics))
for key in epoch_metrics[0]
}
epoch_report = {"epoch": epoch + 1, "train": averaged}
if validation_loader is not None:
epoch_report["validation"] = evaluate_loader(model, validation_loader)
history.append(epoch_report)
console.print(epoch_report)
output = Path(output_dir)
model.save_checkpoint(
output,
metadata={"stage": "rl", "global_step": global_step, "history": history},
)
report = {
"stage": "rl",
"checkpoint": str(output),
"worker_ids": worker_ids,
"global_step": global_step,
"history": history,
}
write_json(output / "training_report.json", report)
return report
|