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5dd1bb4 9e64e71 5dd1bb4 9e64e71 5dd1bb4 9e64e71 5dd1bb4 9e64e71 5dd1bb4 9e64e71 5dd1bb4 9e64e71 5dd1bb4 | 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 | """Notebook-oriented helpers for GRPO training orchestration."""
from __future__ import annotations
import random
from typing import Any
def _precision_kwargs(precision: str) -> dict[str, bool]:
"""Map precision string to TRL config kwargs."""
if precision == "fp16":
return {"fp16": True, "bf16": False}
if precision == "bf16":
return {"fp16": False, "bf16": True}
if precision == "fp32":
return {"fp16": False, "bf16": False}
# "auto" — let TRL/transformers decide
return {}
def sample_random_baseline(
prompts: list[str],
*,
step_budget: int,
seed: int,
) -> list[dict[str, Any]]:
"""Generate simple random-action transcripts for baseline comparison."""
rng = random.Random(seed)
action_types = ["DESCRIBE", "SAMPLE", "QUERY", "ANSWER"]
transcripts: list[dict[str, Any]] = []
for prompt in prompts:
step_count = max(1, min(step_budget, 5))
lines = []
for _ in range(step_count):
action = rng.choice(action_types)
argument = "table_1" if action != "QUERY" else "SELECT 1"
lines.append(f"{action}: {argument}")
transcripts.append(
{
"prompt": prompt,
"completion": "\n".join(lines),
"content": "\n".join(lines),
"metadata": {"policy": "random", "step_count": step_count},
}
)
return transcripts
def build_trainer(
*,
model: Any,
tokenizer: Any,
prompts: list[str],
config: Any,
trl_grpo_config_cls: type,
grpo_trainer_cls: type,
reward_funcs: list[Any],
environment_factory: type | None = None,
callbacks: list[Any] | None = None,
) -> Any:
"""Build a GRPO trainer instance using notebook config objects."""
extra_kwargs: dict[str, Any] = {}
if getattr(config, "gradient_checkpointing", False):
extra_kwargs["gradient_checkpointing"] = True
trainer_config = trl_grpo_config_cls(
output_dir=config.output_dir,
learning_rate=config.learning_rate,
per_device_train_batch_size=config.per_device_train_batch_size,
gradient_accumulation_steps=config.gradient_accumulation_steps,
num_train_epochs=config.num_train_epochs,
logging_steps=config.logging_steps,
max_completion_length=config.max_new_tokens,
num_generations=config.num_generations,
generation_batch_size=config.num_generations,
beta=getattr(config, "beta", 0.04),
**_precision_kwargs(getattr(config, "precision", "auto")),
**extra_kwargs,
remove_unused_columns=False,
log_completions=True,
num_completions_to_print=1,
chat_template_kwargs={
"enable_thinking": getattr(config, "enable_thinking", False),
},
)
trainer_kwargs: dict[str, Any] = {
"model": model,
"processing_class": tokenizer,
"args": trainer_config,
"train_dataset": prompts,
"reward_funcs": reward_funcs,
}
if environment_factory is not None:
configure = getattr(environment_factory, "configure", None)
if not callable(configure):
configure = getattr(environment_factory, "_configure", None)
if callable(configure):
configure(
questions_path=config.questions_path,
db_dir=config.db_dir,
step_budget=config.step_budget,
)
trainer_kwargs["environment_factory"] = environment_factory
if callbacks is not None:
trainer_kwargs["callbacks"] = callbacks
return grpo_trainer_cls(
**trainer_kwargs,
)
def run_training_with_metrics(trainer: Any) -> tuple[Any, list[int], list[float]]:
"""Run trainer.train() and extract plotting-friendly step/reward vectors."""
train_output = trainer.train()
log_history: list[dict[str, Any]] = []
if hasattr(trainer, "state") and hasattr(trainer.state, "log_history"):
maybe_history = trainer.state.log_history
if isinstance(maybe_history, list):
log_history = maybe_history
steps: list[int] = []
rewards: list[float] = []
for item in log_history:
if not isinstance(item, dict):
continue
if "step" not in item or "reward" not in item:
continue
steps.append(int(item["step"]))
rewards.append(float(item["reward"]))
return train_output, steps, rewards
def format_oom_guidance(error: Exception) -> str:
"""Return actionable guidance when training hits OOM."""
return (
f"Training failed with OOM: {error}. "
"Try reducing per_device_train_batch_size or num_generations."
)
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