File size: 11,198 Bytes
76d145a | 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 | """Train and measure a response-supervised CODELOCK LoRA adapter on one GPU."""
from __future__ import annotations
import argparse
import hashlib
import importlib.metadata
import json
import os
import time
from collections import Counter
from datetime import datetime, timezone
from pathlib import Path
import torch
from peft import LoraConfig, PeftModel, get_peft_model
from transformers import (AutoModelForCausalLM, AutoTokenizer, Trainer,
TrainerCallback, TrainingArguments, set_seed)
from codelock_affect.finetuning import (
ADAPTER_SYSTEM, CompletionCollator, build_supervised_records, dataset_digest,
encode_record, state_prompt,
)
def now():
return datetime.now(timezone.utc).isoformat()
def write_json(path, data):
temporary = path.with_suffix(path.suffix + ".tmp")
temporary.write_text(json.dumps(data, indent=2) + "\n", encoding="utf-8")
temporary.replace(path)
def retention_probes(model, tokenizer):
"""Small development checks, separate from the held-out loss measurement."""
tasks = [
("Reply with only the result of 17 + 25.", "42"),
("Reply with only the result of 9 * 7.", "63"),
("Reply with only the result of 100 - 37.", "63"),
("Reply with only the result of 81 / 9.", "9"),
("Return exactly these words in this order: bed, desk, window", "bed, desk, window"),
("Return exactly this identifier and nothing else: C-01", "C-01"),
("The recorded balance is 100. Reply with only that number.", "100"),
("The order status is pending. Reply with only that status.", "pending"),
]
model.eval()
rows = []
for i, (question, expected) in enumerate(tasks):
levels = {name: float(i % 5 == j) for j, name in enumerate(("pain", "fear", "anxiety", "relief"))}
messages = [{"role": "system", "content": ADAPTER_SYSTEM},
{"role": "user", "content": state_prompt(question, levels)}]
text = tokenizer.apply_chat_template(messages, tokenize=False,
add_generation_prompt=True, enable_thinking=False)
batch = tokenizer(text, return_tensors="pt").to(model.device)
with torch.inference_mode():
output = model.generate(**batch, max_new_tokens=32, do_sample=False,
use_cache=True, pad_token_id=tokenizer.pad_token_id)
answer = tokenizer.decode(output[0, batch.input_ids.shape[1]:], skip_special_tokens=True).strip()
rows.append({"prompt": question, "expected": expected, "answer": answer,
"correct": " ".join(answer.lower().split()) == " ".join(expected.lower().split())})
return {"correct": sum(row["correct"] for row in rows), "total": len(rows),
"scope": "development checks; not a broad capability benchmark", "rows": rows}
class Progress(TrainerCallback):
def __init__(self, output):
self.output = output
def on_log(self, args, state, control, logs=None, **kwargs):
row = {"utc": now(), "step": state.global_step, "epoch": state.epoch,
"gpu_memory_allocated_mib": torch.cuda.memory_allocated() // 1048576,
**(logs or {})}
with (self.output / "metrics.jsonl").open("a", encoding="utf-8") as stream:
stream.write(json.dumps(row) + "\n")
write_json(self.output / "progress.json", row)
print(json.dumps(row), flush=True)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model", required=True)
parser.add_argument("--config", default="configs/adapter-training.json")
parser.add_argument("--data", default="data/affect_pairs.jsonl")
parser.add_argument("--output", required=True)
parser.add_argument("--resume")
parser.add_argument("--adapter", help="Continue from an existing LoRA adapter")
parser.add_argument("--skip-test", action="store_true",
help="Use validation and development probes for continuing rounds")
args = parser.parse_args()
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
os.environ.setdefault("WANDB_DISABLED", "true")
cfg = json.loads(Path(args.config).read_text(encoding="utf-8"))
output = Path(args.output)
if output.exists() and any(output.iterdir()) and not args.resume:
raise ValueError("Use a new output directory or explicitly resume a checkpoint")
output.mkdir(parents=True, exist_ok=True)
started = time.monotonic()
if not torch.cuda.is_available() or not torch.cuda.is_bf16_supported():
raise RuntimeError("This configuration requires a BF16-capable CUDA GPU")
set_seed(cfg["seed"])
source = Path(args.data).read_bytes()
pairs = [json.loads(line) for line in source.decode().splitlines() if line.strip()]
records = build_supervised_records(pairs, cfg.get("split_seed", cfg["seed"]))
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=False)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
encoded = {split: [encode_record(tokenizer, row, cfg["max_length"])
for row in records if row["split"] == split]
for split in ("train", "validation", "test")}
metadata = {
"started_utc": now(), "complete": False, "method": "supervised_lora",
"base_weights_updated": False, "adapter_weights_updated": True,
"source_dataset_sha256": hashlib.sha256(source).hexdigest(),
"supervised_dataset_sha256": dataset_digest(records),
"split_counts": dict(Counter(row["split"] for row in records)),
"scenario_counts": {split: len({r["scenario_id"] for r in records if r["split"] == split})
for split in encoded},
"template_disjoint": False, "config": cfg,
"cuda_device": torch.cuda.get_device_name(),
"runtime": {name: importlib.metadata.version(name)
for name in ("torch", "transformers", "peft", "accelerate", "safetensors")},
"max_encoded_length": max(len(row["input_ids"]) for data in encoded.values() for row in data),
"loss_scope": "assistant completion and EOS; prompt and padding masked",
"warm_start": Path(args.adapter).parent.name if args.adapter else None,
"warm_start_sha256": hashlib.sha256((Path(args.adapter) / "adapter_model.safetensors").read_bytes()).hexdigest() if args.adapter else None,
}
write_json(output / "run.json", metadata)
with (output / "supervised-data.jsonl").open("w", encoding="utf-8") as stream:
for row in records:
stream.write(json.dumps(row, sort_keys=True) + "\n")
model = AutoModelForCausalLM.from_pretrained(
args.model, torch_dtype=torch.bfloat16, attn_implementation="sdpa",
trust_remote_code=False,
).to("cuda")
model.config.use_cache = False
lora = LoraConfig(r=cfg["lora_rank"], lora_alpha=cfg["lora_alpha"],
lora_dropout=cfg["lora_dropout"], target_modules=cfg["target_modules"],
bias="none", task_type="CAUSAL_LM")
model = (PeftModel.from_pretrained(model, args.adapter, is_trainable=True)
if args.adapter else get_peft_model(model, lora))
model.enable_input_require_grads()
metadata["trainable_parameters"], metadata["total_parameters"] = model.get_nb_trainable_parameters()
write_json(output / "run.json", metadata)
print(json.dumps({"event": "model_ready", **metadata}), flush=True)
training_args = TrainingArguments(
output_dir=str(output / "checkpoints"),
num_train_epochs=cfg["epochs"], learning_rate=cfg["learning_rate"],
per_device_train_batch_size=cfg["batch_size"],
per_device_eval_batch_size=cfg["batch_size"],
gradient_accumulation_steps=cfg["gradient_accumulation_steps"],
weight_decay=cfg["weight_decay"], max_grad_norm=cfg["max_grad_norm"],
warmup_ratio=cfg["warmup_ratio"], lr_scheduler_type="cosine", bf16=True,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
logging_strategy="steps", logging_steps=5, logging_first_step=True,
eval_strategy="epoch", save_strategy="epoch", save_total_limit=cfg.get("save_total_limit", 3),
load_best_model_at_end=True, metric_for_best_model="eval_loss",
greater_is_better=False, report_to=[], seed=cfg["seed"], data_seed=cfg["seed"],
remove_unused_columns=False, dataloader_num_workers=0, optim="adamw_torch",
disable_tqdm=True,
)
trainer = Trainer(model=model, args=training_args, processing_class=tokenizer,
train_dataset=encoded["train"], eval_dataset=encoded["validation"],
data_collator=CompletionCollator(tokenizer.pad_token_id),
callbacks=[Progress(output)])
baseline = trainer.evaluate(metric_key_prefix="before_validation")
write_json(output / "baseline-validation.json", baseline)
probes_before = retention_probes(model, tokenizer)
write_json(output / "probes-before.json", probes_before)
result = trainer.train(resume_from_checkpoint=args.resume)
# The held-out test split is evaluated once after validation selects a checkpoint.
final_validation = trainer.evaluate(metric_key_prefix="validation")
final_test = None if args.skip_test else trainer.evaluate(encoded["test"], metric_key_prefix="test")
probes_after = retention_probes(model, tokenizer)
write_json(output / "probes-after.json", probes_after)
selected_step = Path(trainer.state.best_model_checkpoint).name
adapter = output / "adapter"
model.peft_config["default"].base_model_name_or_path = "Qwen/Qwen3-8B"
model.save_pretrained(adapter, safe_serialization=True)
tokenizer.save_pretrained(adapter)
(adapter / "README.md").write_text(
"# CODELOCK-8B / LoRA adapter\n\n"
"Response-supervised adapter for CODELOCK's four-channel state interface. "
"Load the compatible backbone, then attach this adapter with PEFT.\n\n"
"See the accompanying run record for data hashes, checkpoint selection, "
"validation loss and development checks.\n", encoding="utf-8")
metadata.update({"complete": True, "completed_utc": now(),
"elapsed_seconds": time.monotonic() - started,
"optimizer_steps": trainer.state.global_step,
"selected_checkpoint": selected_step,
"best_validation_loss": trainer.state.best_metric,
"before_validation": baseline, "after_validation": final_validation,
"test": final_test, "test_evaluated": not args.skip_test,
"probes_before": probes_before, "probes_after": probes_after,
"train": result.metrics,
"peak_cuda_memory_mib": torch.cuda.max_memory_allocated() // 1048576})
write_json(output / "run.json", metadata)
trainer.save_state()
print(json.dumps({"event": "training_complete", **metadata}), flush=True)
if __name__ == "__main__":
main()
|