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translation (transliteration -> English/Swedish). Loss is computed on the answer tokens only.
Data (jsonl):
mt : {"id", "src", "tgt", "lang"}
ocr: {"id", "image", "target"}
"""
import argparse
import json
import math
import os
import random
import sys
import time
import torch
from torch.utils.data import DataLoader, Dataset
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from ocr_noise import corrupt # noqa: E402
from qwen_common import encode_kwargs, end_text, load_image, load_model, prompt_text # noqa: E402
def read_jsonl(paths, limit=None):
rows = []
for p in paths.split(","):
with open(p) as f:
rows.extend(json.loads(l) for l in f if l.strip())
if limit:
rows = rows[:limit]
return rows
class Examples(Dataset):
def __init__(self, rows, processor, task, image_root, noise_p=0.0, noise_max=0.5, seed=0, tags=False):
self.rows, self.processor, self.task, self.tags = rows, processor, task, tags
self.image_root, self.noise_p, self.noise_max = image_root, noise_p, noise_max
self.rng = random.Random(seed)
self.end = end_text(processor.tokenizer)
self.kw = encode_kwargs(processor)
def __len__(self):
return len(self.rows)
def __getitem__(self, i):
r = self.rows[i]
if self.task == "ocr":
img = load_image(os.path.join(self.image_root, r["image"]))
prompt = prompt_text(self.processor, "ocr")
target = r["target"]
full = self.processor(text=[prompt + target + self.end], images=[img], return_tensors="pt")
n_prompt = self.processor(text=[prompt], images=[img], return_tensors="pt")["input_ids"].shape[1]
else:
src = r["src"]
rng = random.Random(int.from_bytes(os.urandom(8), "little"))
if self.noise_p > 0 and rng.random() < self.noise_p:
src = corrupt(src, rng.uniform(0.05, self.noise_max), rng)
prompt = prompt_text(self.processor, "mt", src, r["lang"], r["rl"] if self.tags else None)
target = r["tgt"]
full = self.processor(text=[prompt + target + self.end], return_tensors="pt", **self.kw)
n_prompt = self.processor(text=[prompt], return_tensors="pt", **self.kw)["input_ids"].shape[1]
item = {k: v for k, v in full.items()}
labels = item["input_ids"].clone()
labels[:, :n_prompt] = -100
item["labels"] = labels
return item
def collate(batch, pad_id, max_len):
out = {}
seq_keys = [k for k, v in batch[0].items() if v.dim() == 2 and v.shape[0] == 1 and k != "image_grid_thw"]
L = min(max(b["input_ids"].shape[1] for b in batch), max_len)
for k in seq_keys:
pad = pad_id if k == "input_ids" else (-100 if k == "labels" else 0)
rows = []
for b in batch:
t = b[k][0, :L]
rows.append(torch.cat([t, torch.full((L - t.shape[0],), pad, dtype=t.dtype)]))
out[k] = torch.stack(rows)
for k in batch[0]:
if k not in seq_keys:
out[k] = torch.cat([b[k] for b in batch], dim=0)
return out
@torch.no_grad()
def val_loss(model, loader, device, max_batches=50):
model.eval()
tot, n = 0.0, 0
for bi, batch in enumerate(loader):
if bi >= max_batches:
break
batch = {k: v.to(device) for k, v in batch.items()}
with torch.autocast("cuda", dtype=torch.bfloat16):
tot += model(**batch).loss.item()
n += 1
model.train()
return tot / max(n, 1)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--task", choices=["mt", "ocr"], required=True)
ap.add_argument("--model", required=True)
ap.add_argument("--train_file", required=True)
ap.add_argument("--val_file", required=True)
ap.add_argument("--image_root", default="")
ap.add_argument("--out", required=True)
ap.add_argument("--epochs", type=float, default=3)
ap.add_argument("--lr", type=float, default=2e-4)
ap.add_argument("--bs", type=int, default=8)
ap.add_argument("--grad_accum", type=int, default=2)
ap.add_argument("--rank", type=int, default=16)
ap.add_argument("--alpha", type=int, default=32)
ap.add_argument("--max_len", type=int, default=1536)
ap.add_argument("--max_steps", type=int, default=0)
ap.add_argument("--limit", type=int, default=0)
ap.add_argument("--noise_p", type=float, default=0.0)
ap.add_argument("--seed", type=int, default=13)
ap.add_argument("--workers", type=int, default=6)
ap.add_argument("--init_adapter", default="")
ap.add_argument("--tags", action="store_true", help="mt: prepend <runic-language> <2target> tags (field rl)")
ap.add_argument("--no_last", action="store_true", help="do not save last_adapter (disk)")
args = ap.parse_args()
print("ARGS", vars(args), flush=True)
torch.manual_seed(args.seed)
random.seed(args.seed)
os.makedirs(args.out, exist_ok=True)
processor, model = load_model(args.model, adapter=args.init_adapter or None, trainable=bool(args.init_adapter))
tok = processor.tokenizer
if not args.init_adapter:
from peft import LoraConfig, get_peft_model
cfg = LoraConfig(r=args.rank, lora_alpha=args.alpha, lora_dropout=0.05, target_modules="all-linear",
task_type="CAUSAL_LM")
model = get_peft_model(model, cfg)
model.print_trainable_parameters()
print("base dtype", next(p for n, p in model.named_parameters() if "lora" not in n).dtype,
"lora dtype", next(p for n, p in model.named_parameters() if "lora" in n).dtype, flush=True)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
model.enable_input_require_grads()
device = "cuda"
model.to(device)
model.train()
tr_rows = read_jsonl(args.train_file, args.limit or None)
va_rows = read_jsonl(args.val_file, 400 if not args.limit else min(args.limit, 40))
random.Random(args.seed).shuffle(tr_rows)
tr = Examples(tr_rows, processor, args.task, args.image_root, args.noise_p, seed=args.seed, tags=args.tags)
va = Examples(va_rows, processor, args.task, args.image_root, 0.0, seed=args.seed, tags=args.tags)
col = lambda b: collate(b, tok.pad_token_id, args.max_len) # noqa: E731
tl = DataLoader(tr, batch_size=args.bs, shuffle=True, num_workers=args.workers, collate_fn=col,
persistent_workers=args.workers > 0)
vl = DataLoader(va, batch_size=args.bs, shuffle=False, num_workers=args.workers, collate_fn=col)
params = [p for p in model.parameters() if p.requires_grad]
opt = torch.optim.AdamW(params, lr=args.lr, weight_decay=0.0)
steps_per_epoch = math.ceil(len(tl) / args.grad_accum)
total = args.max_steps or int(steps_per_epoch * args.epochs)
warm = max(1, int(0.03 * total))
sched = torch.optim.lr_scheduler.LambdaLR(
opt, lambda s: min(1.0, (s + 1) / warm) * 0.5 * (1 + math.cos(math.pi * min(1.0, s / max(1, total)))))
print(f"train {len(tr)} val {len(va)} steps/epoch {steps_per_epoch} total {total}", flush=True)
step, best, t0 = 0, float("inf"), time.time()
log = open(os.path.join(args.out, "train_log.jsonl"), "a")
done = False
epoch = 0
while not done:
for bi, batch in enumerate(tl):
batch = {k: v.to(device) for k, v in batch.items()}
with torch.autocast("cuda", dtype=torch.bfloat16):
loss = model(**batch).loss / args.grad_accum
loss.backward()
if (bi + 1) % args.grad_accum:
continue
torch.nn.utils.clip_grad_norm_(params, 1.0)
opt.step()
sched.step()
opt.zero_grad(set_to_none=True)
step += 1
if step % 20 == 0 or step == 1:
rec = dict(step=step, loss=loss.item() * args.grad_accum, lr=sched.get_last_lr()[0],
elapsed=round(time.time() - t0))
print(json.dumps(rec), flush=True)
log.write(json.dumps(rec) + "\n")
log.flush()
eval_every = max(1, steps_per_epoch // 2) if not args.max_steps else args.max_steps
if step % eval_every == 0 or step >= total:
vloss = val_loss(model, vl, device)
rec = dict(step=step, epoch=round(step / steps_per_epoch, 2), val_loss=vloss)
print(json.dumps(rec), flush=True)
log.write(json.dumps(rec) + "\n")
log.flush()
if vloss < best:
best = vloss
model.save_pretrained(os.path.join(args.out, "best_adapter"))
json.dump(rec, open(os.path.join(args.out, "best.json"), "w"))
if step >= total:
done = True
break
epoch += 1
if not args.no_last:
model.save_pretrained(os.path.join(args.out, "last_adapter"))
json.dump(dict(total_steps=step, best_val_loss=best, minutes=round((time.time() - t0) / 60, 1)),
open(os.path.join(args.out, "DONE.json"), "w"))
print("DONE", step, best, flush=True)
if __name__ == "__main__":
main()
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