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import math
import bitsandbytes as bnb
import transformers
from torch import nn
from torch.optim.lr_scheduler import OneCycleLR
from transformers import EarlyStoppingCallback
from transformers.trainer_pt_utils import get_parameter_names
def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
total_num_steps = int(
math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
)
warmup_steps = cfg.warmup_steps if cfg.warmup_steps else min(int(0.03 * total_num_steps), 100)
logging_steps = max(min(int(0.005 * total_num_steps), 10), 1)
save_steps = eval_steps = cfg.save_steps if cfg.save_steps else min(int(0.05 * total_num_steps), 200)
training_arguments_kwargs = {}
if cfg.bf16 == "full":
training_arguments_kwargs["bf16_full_eval"] = True
else:
training_arguments_kwargs["bf16"] = cfg.bf16
training_arguments_kwargs["tf32"] = cfg.tf32
training_arguments_kwargs["warmup_steps"] = warmup_steps
training_arguments_kwargs["logging_steps"] = logging_steps
if cfg.gradient_checkpointing is not None:
training_arguments_kwargs["gradient_checkpointing"] = cfg.gradient_checkpointing
training_args = transformers.TrainingArguments(
per_device_train_batch_size=cfg.micro_batch_size,
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
num_train_epochs=cfg.num_epochs,
learning_rate=cfg.learning_rate,
evaluation_strategy="steps" if cfg.val_set_size > 0 else "no",
save_strategy="steps",
eval_steps=eval_steps if cfg.val_set_size > 0 else None,
save_steps=save_steps,
output_dir=cfg.output_dir,
save_total_limit=3,
load_best_model_at_end=True if cfg.val_set_size > 0 else False,
ddp_find_unused_parameters=False if cfg.ddp else None,
group_by_length=cfg.group_by_length,
report_to="wandb" if cfg.use_wandb else None,
run_name=cfg.wandb_run_id if cfg.use_wandb else None,
**training_arguments_kwargs,
)
trainer_kwargs = {}
if cfg.load_in_8bit and not cfg.load_4bit:
decay_parameters = get_parameter_names(model, [nn.LayerNorm])
decay_parameters = [name for name in decay_parameters if "bias" not in name]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if n in decay_parameters],
"weight_decay": training_args.weight_decay,
},
{
"params": [
p for n, p in model.named_parameters() if n not in decay_parameters
],
"weight_decay": 0.0,
},
]
optimizer = bnb.optim.Adam8bit(
optimizer_grouped_parameters,
betas=(training_args.adam_beta1, training_args.adam_beta2),
eps=training_args.adam_epsilon,
lr=training_args.learning_rate,
)
if cfg.lr_scheduler == "one_cycle":
lr_scheduler_kwargs = (
cfg.lr_scheduler_kwargs if cfg.lr_scheduler_kwargs else {}
)
lr_scheduler = OneCycleLR(
optimizer,
cfg.learning_rate,
total_steps=total_num_steps,
**lr_scheduler_kwargs,
)
else:
lr_scheduler = transformers.get_cosine_schedule_with_warmup(
optimizer,
training_args.warmup_steps,
total_num_steps,
)
trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)
# TODO on_save callback to sync checkpoints to GCP/AWS in background
if cfg.early_stopping_patience:
early_stop_cb = EarlyStoppingCallback(
cfg.early_stopping_patience,
)
trainer_kwargs["callbacks"] = [early_stop_cb]
data_collator_kwargs = {
"padding": True,
}
if cfg.collator_pad_to_longest:
data_collator_kwargs["padding"] = "longest"
else:
data_collator_kwargs["pad_to_multiple_of"] = 8
trainer = transformers.Trainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer,
return_tensors="pt",
**data_collator_kwargs,
),
**trainer_kwargs,
)
return trainer
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