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import os
import argparse
import pandas as pd
from datasets import Dataset
from sacrebleu.metrics import BLEU, CHRF
from peft import LoraConfig, get_peft_model
from IndicTransToolkit import IndicProcessor, IndicDataCollator
from transformers import (
Seq2SeqTrainer,
Seq2SeqTrainingArguments,
AutoModelForSeq2SeqLM,
AutoTokenizer,
EarlyStoppingCallback,
)
bleu_metric = BLEU()
chrf_metric = CHRF()
def get_arg_parse():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
type=str,
)
parser.add_argument(
"--src_lang_list",
type=str,
help="comma separated list of source languages",
)
parser.add_argument(
"--tgt_lang_list",
type=str,
help="comma separated list of target languages",
)
parser.add_argument("--data_dir", type=str)
parser.add_argument("--output_dir", type=str)
parser.add_argument("--save_steps", type=int, default=1000)
parser.add_argument("--eval_steps", type=int, default=1000)
parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--num_train_epochs", type=int, default=100)
parser.add_argument("--max_steps", type=int, default=1000000)
parser.add_argument("--grad_accum_steps", type=int, default=4)
parser.add_argument("--warmup_steps", type=int, default=4000)
parser.add_argument("--warmup_ratio", type=int, default=0.0)
parser.add_argument("--max_grad_norm", type=float, default=1.0)
parser.add_argument("--learning_rate", type=float, default=5e-4)
parser.add_argument("--weight_decay", type=float, default=0.0)
parser.add_argument("--adam_beta1", type=float, default=0.9)
parser.add_argument("--adam_beta2", type=float, default=0.98)
parser.add_argument("--dropout", type=float, default=0.0)
parser.add_argument("--print_samples", action="store_true")
parser.add_argument(
"--optimizer",
type=str,
default="adamw_torch",
choices=[
"adam_hf",
"adamw_torch",
"adamw_torch_fused",
"adamw_apex_fused",
"adafactor",
],
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="inverse_sqrt",
choices=[
"inverse_sqrt",
"linear",
"polynomial",
"cosine",
"constant",
"constant_with_warmup",
],
)
parser.add_argument("--label_smoothing", type=float, default=0.0)
parser.add_argument("--num_workers", type=int, default=8)
parser.add_argument("--metric_for_best_model", type=str, default="eval_loss")
parser.add_argument("--greater_is_better", action="store_true")
parser.add_argument("--lora_target_modules", type=str, default="q_proj,k_proj")
parser.add_argument("--lora_dropout", type=float, default=0.1)
parser.add_argument("--lora_r", type=int, default=16)
parser.add_argument("--lora_alpha", type=int, default=32)
parser.add_argument(
"--report_to",
type=str,
default="none",
choices=["wandb", "tensorboard", "azure_ml", "none"],
)
parser.add_argument("--patience", type=int, default=5),
parser.add_argument("--threshold", type=float, default=1e-3)
return parser
def load_and_process_translation_dataset(
data_dir,
split="train",
tokenizer=None,
processor=None,
src_lang_list=None,
tgt_lang_list=None,
num_proc=8,
seed=42
):
complete_dataset = {
"sentence_SRC": [],
"sentence_TGT": [],
}
for src_lang in src_lang_list:
for tgt_lang in tgt_lang_list:
if src_lang == tgt_lang:
continue
src_path = os.path.join(
data_dir, split, f"{src_lang}-{tgt_lang}", f"{split}.{src_lang}"
)
tgt_path = os.path.join(
data_dir, split, f"{src_lang}-{tgt_lang}", f"{split}.{tgt_lang}"
)
if not os.path.exists(src_path) or not os.path.exists(tgt_path):
raise FileNotFoundError(
f"Source ({split}.{src_lang}) or Target ({split}.{tgt_lang}) file not found in {data_dir}"
)
with open(src_path, encoding="utf-8") as src_file, open(
tgt_path, encoding="utf-8"
) as tgt_file:
src_lines = src_file.readlines()
tgt_lines = tgt_file.readlines()
# Ensure both files have the same number of lines
assert len(src_lines) == len(
tgt_lines
), f"Source and Target files have different number of lines for {split}.{src_lang} and {split}.{tgt_lang}"
complete_dataset["sentence_SRC"] += processor.preprocess_batch(
src_lines, src_lang=src_lang, tgt_lang=tgt_lang, is_target=False
)
complete_dataset["sentence_TGT"] += processor.preprocess_batch(
tgt_lines, src_lang=tgt_lang, tgt_lang=src_lang, is_target=True
)
complete_dataset = Dataset.from_dict(complete_dataset).shuffle(seed=seed)
return complete_dataset.map(
lambda example: preprocess_fn(
example,
tokenizer=tokenizer
),
batched=True,
num_proc=num_proc,
)
def compute_metrics_factory(
tokenizer, metric_dict=None, print_samples=False, n_samples=10
):
def compute_metrics(eval_preds):
preds, labels = eval_preds
labels[labels == -100] = tokenizer.pad_token_id
preds[preds == -100] = tokenizer.pad_token_id
with tokenizer.as_target_tokenizer():
preds = [
x.strip()
for x in tokenizer.batch_decode(
preds, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
]
labels = [
x.strip()
for x in tokenizer.batch_decode(
labels, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
]
assert len(preds) == len(
labels
), "Predictions and Labels have different lengths"
df = pd.DataFrame({"Predictions": preds, "References": labels}).sample(
n=n_samples
)
if print_samples:
for pred, label in zip(df["Predictions"].values, df["References"].values):
print(f" | > Prediction: {pred}")
print(f" | > Reference: {label}\n")
return {
metric_name: metric.corpus_score(preds, [labels]).score
for (metric_name, metric) in metric_dict.items()
}
return compute_metrics
def preprocess_fn(example, tokenizer, **kwargs):
model_inputs = tokenizer(
example["sentence_SRC"], truncation=True, padding=False, max_length=256
)
with tokenizer.as_target_tokenizer():
labels = tokenizer(
example["sentence_TGT"], truncation=True, padding=False, max_length=256
)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
def main(args):
print(f" | > Loading {args.model} and tokenizer ...")
model = AutoModelForSeq2SeqLM.from_pretrained(
args.model,
trust_remote_code=True,
attn_implementation="eager",
dropout=args.dropout
)
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
processor = IndicProcessor(inference=False) # pre-process before tokenization
data_collator = IndicDataCollator(
tokenizer=tokenizer,
model=model,
padding="longest", # saves padding tokens
pad_to_multiple_of=8, # better to have it as 8 when using fp16
label_pad_token_id=-100
)
if args.data_dir is not None:
train_dataset = load_and_process_translation_dataset(
args.data_dir,
split="train",
tokenizer=tokenizer,
processor=processor,
src_lang_list=args.src_lang_list.split(","),
tgt_lang_list=args.tgt_lang_list.split(","),
)
print(f" | > Loaded train dataset from {args.data_dir}. Size: {len(train_dataset)} ...")
eval_dataset = load_and_process_translation_dataset(
args.data_dir,
split="dev",
tokenizer=tokenizer,
processor=processor,
src_lang_list=args.src_lang_list.split(","),
tgt_lang_list=args.tgt_lang_list.split(","),
)
print(f" | > Loaded eval dataset from {args.data_dir}. Size: {len(eval_dataset)} ...")
else:
raise ValueError(" | > Data directory not provided")
lora_config = LoraConfig(
r=args.lora_r,
bias="none",
inference_mode=False,
task_type="SEQ_2_SEQ_LM",
lora_alpha=args.lora_alpha,
lora_dropout=args.lora_dropout,
target_modules=args.lora_target_modules.split(","),
)
model.set_label_smoothing(args.label_smoothing)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
print(f" | > Loading metrics factory with BLEU and chrF ...")
seq2seq_compute_metrics = compute_metrics_factory(
tokenizer=tokenizer,
print_samples=args.print_samples,
metric_dict={"BLEU": bleu_metric, "chrF": chrf_metric},
)
training_args = Seq2SeqTrainingArguments(
output_dir=args.output_dir,
do_train=True,
do_eval=True,
fp16=True, # use fp16 for faster training
logging_strategy="steps",
evaluation_strategy="steps",
save_strategy="steps",
logging_steps=100,
save_total_limit=1,
predict_with_generate=True,
load_best_model_at_end=True,
max_steps=args.max_steps, # max_steps overrides num_train_epochs
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
gradient_accumulation_steps=args.grad_accum_steps,
eval_accumulation_steps=args.grad_accum_steps,
weight_decay=args.weight_decay,
adam_beta1=args.adam_beta1,
adam_beta2=args.adam_beta2,
max_grad_norm=args.max_grad_norm,
optim=args.optimizer,
lr_scheduler_type=args.lr_scheduler,
warmup_ratio=args.warmup_ratio,
warmup_steps=args.warmup_steps,
learning_rate=args.learning_rate,
num_train_epochs=args.num_train_epochs,
save_steps=args.save_steps,
eval_steps=args.eval_steps,
dataloader_num_workers=args.num_workers,
metric_for_best_model=args.metric_for_best_model,
greater_is_better=args.greater_is_better,
report_to=args.report_to,
generation_max_length=256,
generation_num_beams=5,
sortish_sampler=True,
group_by_length=True,
include_tokens_per_second=True,
include_num_input_tokens_seen=True,
dataloader_prefetch_factor=2,
)
# Create Trainer instance
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=seq2seq_compute_metrics,
callbacks=[
EarlyStoppingCallback(
early_stopping_patience=args.patience,
early_stopping_threshold=args.threshold,
)
],
)
print(f" | > Starting training ...")
try:
trainer.train()
except KeyboardInterrupt:
print(f" | > Training interrupted ...")
# this will only save the LoRA adapter weights
model.save_pretrained(args.output_dir)
if __name__ == "__main__":
parser = get_arg_parse()
args = parser.parse_args()
main(args)
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