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a15535e | 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 | """SFT warm-start trainer for both roles.
Run on a Colab T4/A100 GPU. Reads `warmstart/data/repair_pairs.jsonl` (or
`drift_pairs.jsonl`), wraps in TRL SFTTrainer with Unsloth's 4-bit Qwen2.5
loader, and saves a LoRA adapter.
Usage:
python -m forgeenv.training.sft_warmstart \\
--role repair_agent \\
--data warmstart/data/repair_pairs.jsonl \\
--output_dir artifacts/checkpoints/repair_agent_sft \\
--base_model unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit \\
--max_steps 200
"""
from __future__ import annotations
import argparse
import json
import os
from pathlib import Path
from typing import Optional
def _load_jsonl(path: str) -> list[dict]:
rows: list[dict] = []
with open(path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if line:
rows.append(json.loads(line))
return rows
def _format_chat(rows: list[dict]) -> list[dict]:
"""Flatten messages -> a single `text` field for SFT."""
out: list[dict] = []
for row in rows:
msgs = row["messages"]
text_parts = []
for m in msgs:
text_parts.append(f"<|im_start|>{m['role']}\n{m['content']}<|im_end|>")
out.append({"text": "\n".join(text_parts)})
return out
def run_sft(
role: str,
data_path: str,
output_dir: str,
base_model: str = "unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit",
max_steps: int = 200,
batch_size: int = 2,
learning_rate: float = 2e-4,
lora_r: int = 16,
seed: int = 0,
use_unsloth: Optional[bool] = None,
) -> None:
"""Run SFT. Imports unsloth/trl lazily so this module is importable on
machines without a GPU."""
rows = _load_jsonl(data_path)
formatted = _format_chat(rows)
print(f"[forgeenv.sft] Loaded {len(formatted)} rows for role={role}")
if use_unsloth is None:
use_unsloth = os.environ.get("FORGEENV_USE_UNSLOTH", "1") == "1"
if use_unsloth:
from unsloth import FastLanguageModel
from datasets import Dataset
from trl import SFTConfig, SFTTrainer
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=base_model,
max_seq_length=4096,
dtype=None,
load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(
model,
r=lora_r,
lora_alpha=lora_r * 2,
lora_dropout=0.0,
bias="none",
target_modules=[
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",
],
use_gradient_checkpointing="unsloth",
random_state=seed,
)
dataset = Dataset.from_list(formatted)
sft_config = SFTConfig(
output_dir=output_dir,
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=4,
warmup_steps=10,
max_steps=max_steps,
learning_rate=learning_rate,
logging_steps=10,
optim="adamw_8bit",
weight_decay=0.01,
lr_scheduler_type="linear",
seed=seed,
save_steps=max(50, max_steps // 4),
save_total_limit=2,
report_to="none",
dataset_text_field="text",
max_seq_length=4096,
)
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
args=sft_config,
)
trainer.train()
Path(output_dir).mkdir(parents=True, exist_ok=True)
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print(f"[forgeenv.sft] Saved adapter to {output_dir}")
return
# CPU/dry-run fallback: just dump the formatted dataset to disk so we
# can verify the pipeline shape locally.
Path(output_dir).mkdir(parents=True, exist_ok=True)
out_file = Path(output_dir) / "formatted_dataset.jsonl"
with out_file.open("w", encoding="utf-8") as f:
for row in formatted:
f.write(json.dumps(row) + "\n")
print(f"[forgeenv.sft] (dry run) wrote {len(formatted)} rows to {out_file}")
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--role", choices=["repair_agent", "drift_generator"], required=True
)
parser.add_argument("--data", required=True, help="Path to JSONL warm-start file")
parser.add_argument("--output_dir", required=True)
parser.add_argument(
"--base_model", default="unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit"
)
parser.add_argument("--max_steps", type=int, default=200)
parser.add_argument("--batch_size", type=int, default=2)
parser.add_argument("--learning_rate", type=float, default=2e-4)
parser.add_argument("--lora_r", type=int, default=16)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--dry_run", action="store_true")
return parser.parse_args()
if __name__ == "__main__":
args = _parse_args()
run_sft(
role=args.role,
data_path=args.data,
output_dir=args.output_dir,
base_model=args.base_model,
max_steps=args.max_steps,
batch_size=args.batch_size,
learning_rate=args.learning_rate,
lora_r=args.lora_r,
seed=args.seed,
use_unsloth=not args.dry_run,
)
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