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SFT trainer with COMPLETION-ONLY loss (per MATS paper §3.6).
Handles HF datasets with 'prompt' + 'completion' columns.
Uses Qwen chat template; masks prompt tokens with -100 in labels.
"""
import argparse, os
os.environ.setdefault("PYTHONNOUSERSITE", "1")
import torch
from datasets import load_from_disk
from transformers import (
AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments,
DataCollatorForSeq2Seq,
)
CHAT_TEMPLATES = {
"qwen": {
"user_head": "<|im_start|>user\n",
"user_tail": "<|im_end|>\n",
"asst_head": "<|im_start|>assistant\n",
"asst_tail": "<|im_end|>",
},
"llama3": {
"user_head": "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n",
"user_tail": "<|eot_id|>",
"asst_head": "<|start_header_id|>assistant<|end_header_id|>\n\n",
"asst_tail": "<|eot_id|>",
},
}
def main():
p = argparse.ArgumentParser()
p.add_argument("--base", required=True)
p.add_argument("--data", required=True)
p.add_argument("--out", required=True)
p.add_argument("--epochs", type=float, default=4.0)
p.add_argument("--lr", type=float, default=2e-5)
p.add_argument("--bs", type=int, default=1)
p.add_argument("--grad_accum", type=int, default=16)
p.add_argument("--max_len", type=int, default=6144)
p.add_argument("--warmup", type=float, default=0.05)
p.add_argument("--chat_format", default="qwen", choices=["qwen", "llama3"])
args = p.parse_args()
print(f"loading base={args.base}", flush=True)
tok = AutoTokenizer.from_pretrained(args.base, trust_remote_code=True,
cache_dir="/weka/s225250685/Huggingface/hub")
if tok.pad_token is None:
tok.pad_token = tok.eos_token
model = AutoModelForCausalLM.from_pretrained(
args.base, torch_dtype=torch.bfloat16, trust_remote_code=True,
attn_implementation="sdpa",
cache_dir="/weka/s225250685/Huggingface/hub",
)
print(f"loading data={args.data}", flush=True)
dd = load_from_disk(args.data)
print(f"train={len(dd['train'])} test={len(dd['test'])}", flush=True)
tpl = CHAT_TEMPLATES[args.chat_format]
USER_HEAD = tpl["user_head"]
USER_TAIL = tpl["user_tail"]
ASSISTANT_HEAD = tpl["asst_head"]
ASSISTANT_TAIL = tpl["asst_tail"]
print(f"chat_format={args.chat_format}", flush=True)
def encode(ex):
prompt_text = f"{USER_HEAD}{ex['prompt']}{USER_TAIL}{ASSISTANT_HEAD}"
completion_text = f"{ex['completion']}{ASSISTANT_TAIL}"
full_text = prompt_text + completion_text
# Tokenize full
full_ids = tok(full_text, truncation=True, max_length=args.max_len,
padding=False, add_special_tokens=False)["input_ids"]
# Tokenize prompt-only (to find length for label masking)
prompt_ids = tok(prompt_text, truncation=True, max_length=args.max_len,
padding=False, add_special_tokens=False)["input_ids"]
prompt_len = len(prompt_ids)
# Build labels: -100 for prompt, real ids for completion
labels = [-100] * prompt_len + full_ids[prompt_len:]
labels = labels[:len(full_ids)]
attention = [1] * len(full_ids)
return {"input_ids": full_ids, "attention_mask": attention, "labels": labels}
print("Tokenizing...", flush=True)
train_ds = dd["train"].map(encode, remove_columns=dd["train"].column_names, num_proc=4)
eval_ds = dd["test"].map(encode, remove_columns=dd["test"].column_names, num_proc=4)
# DataCollatorForSeq2Seq pads input_ids with pad_token and labels with -100
collator = DataCollatorForSeq2Seq(tok, padding=True, label_pad_token_id=-100)
targs = TrainingArguments(
output_dir=args.out,
num_train_epochs=args.epochs,
per_device_train_batch_size=args.bs,
per_device_eval_batch_size=args.bs,
gradient_accumulation_steps=args.grad_accum,
learning_rate=args.lr,
warmup_ratio=args.warmup,
lr_scheduler_type="cosine",
bf16=True,
logging_steps=20,
save_strategy="epoch",
eval_strategy="epoch",
save_total_limit=1,
report_to=[],
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
remove_unused_columns=False,
dataloader_num_workers=2,
)
trainer = Trainer(
model=model, args=targs,
train_dataset=train_ds, eval_dataset=eval_ds,
tokenizer=tok, data_collator=collator,
)
trainer.train()
trainer.save_model(args.out)
tok.save_pretrained(args.out)
print(f"SAVED: {args.out}", flush=True)
if __name__ == "__main__":
main()
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