import os
from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler, LlamaForCausalLM
import torch
from torch.optim import AdamW
from argparse import ArgumentParser
from read import read_config
from accelerate import Accelerator
from accelerate.utils import DummyScheduler, DummyOptim, set_seed
from peft import get_peft_model, LoraConfig, TaskType
from data import load_data
from torchmetrics import MeanMetric
from tqdm import tqdm
import wandb
torch.backends.cuda.matmul.allow_tf32 = True
def format_metrics(metrics, split, prefix=""):
log = f"[{split}]" + prefix
log += " ".join([f"{key}: {value:.4f}" for key, value in metrics.items()])
return log
def evaluate(model, val_dataloader):
model.eval()
val_loss = MeanMetric(nan_strategy="error").to(model.device)
with torch.no_grad():
for batch in tqdm(val_dataloader):
loss = model(**batch).loss
loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
val_loss.update(loss_values["loss"])
return val_loss
def train(accelerator, config):
set_seed(config['seed'])
accelerator.print(config)
accelerator.print(f"Using {accelerator.num_processes} GPUs")
tokenizer = AutoTokenizer.from_pretrained(config['tokenizer_name'], model_max_length=config['max_length'])
# if no pad token, set it to eos
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
with accelerator.main_process_first():
train_dataloader, val_dataloader = load_data(config, tokenizer)
checkpoint = config["gradient_checkpointing"]
model = AutoModelForCausalLM.from_pretrained(config["model_name"],
use_cache=False if checkpoint else True,
trust_remote_code=True)
if checkpoint:
model.gradient_checkpointing_enable()
if config["lora"]:
peft_config = LoraConfig(
# should R be configurable?
task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
optimizer_cls = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
# karpathy doesn't decay embeddding, maybe we should exclude
# https://github.com/karpathy/minGPT/commit/bbbdac74fa9b2e55574d70056163ffbae42310c1#diff-2075fa9c224b395be5bda85544dd36572b59c76c54562819eadadbf268602834R157s
optimizer = optimizer_cls(model.parameters(), lr=config["lr"], weight_decay=config["weight_decay"])
if accelerator.state.deepspeed_plugin is not None:
gradient_accumulation_steps = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
# decay to min_lr instead of 0
lr_ratio = config["min_lr"] / config["lr"]
accelerator.print(f"Len of train_dataloader: {len(train_dataloader)}")
total_num_steps = (len(train_dataloader) / gradient_accumulation_steps) * config["num_epochs"]
# instead of decaying to zero, decay to ratio of min_lr / lr
total_num_steps += int(total_num_steps * lr_ratio) + config["warmup_steps"]
accelerator.print(f"Total training steps: {total_num_steps}")
# Creates Dummy Scheduler if `scheduler` was specified in the config file else creates `args.lr_scheduler_type` Scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
scheduler = get_scheduler(
name="cosine",
optimizer=optimizer,
num_warmup_steps=config["warmup_steps"] * accelerator.num_processes,
num_training_steps=total_num_steps,
)
else:
scheduler = DummyScheduler(
optimizer, total_num_steps=config["warmup_steps"], warmup_num_steps=config["warmup_steps"]
)
model, optimizer, train_dataloader, val_dataloader, scheduler = accelerator.prepare(
model, optimizer, train_dataloader, val_dataloader, scheduler
)
# setup for saving training states in case preemption
accelerator.register_for_checkpointing(scheduler)
if config["checkpoint"]:
accelerator.load_state(config["checkpoint"])
accelerator.print(f"Resumed from checkpoint: {config['checkpoint']}")
path = os.path.basename(config["train_args"]["resume_from_checkpoint"])
training_difference = os.path.splitext(path)[0]
resume_step = int(training_difference.replace("step_", ""))
accelerator.skip_first_batches(train_dataloader, resume_step)
accelerator.print(f"Resuming from step {resume_step}")
# log gradients
if accelerator.is_main_process and config["wandb"]:
wandb.watch(model, log_freq=config["log_grads_every"], log="all")
for epoch in range(config["num_epochs"]):
train_loss = MeanMetric(nan_strategy="error").to(model.device)
for step, batch in enumerate(tqdm(train_dataloader)):
model.train()
outputs = model(**batch)
loss = outputs.loss
# gather loss before backprop in case of gradient accumulation
loss_values = accelerator.gather_for_metrics({"loss": loss.detach().float()})
train_loss.update(loss_values["loss"])
loss = loss / gradient_accumulation_steps
accelerator.backward(loss)
# get gradient norm of all params
# log LR in case something weird happens
if step > 0 and step % (config["eval_every"] // 10) == 0:
if config["wandb"]:
curr_step = step + epoch * len(train_dataloader)
accelerator.log({"lr": scheduler.get_last_lr()[0]}, step=curr_step)
if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
optimizer.step()
scheduler.step()
optimizer.zero_grad()
if step > 0 and step % config["save_every"] == 0:
curr_step = step + epoch * len(train_dataloader)
accelerator.save_state(f"{config['output_dir']}/step_{curr_step}")
if step > 0 and (step % config["eval_every"] == 0 or step == len(train_dataloader) - 1):
val_loss = evaluate(model, val_dataloader)
log_train = {
"train_loss": train_loss.compute()
}
log_val = {
"val_loss": val_loss.compute()
}
if config["wandb"]:
curr_step = step + epoch * len(train_dataloader)
accelerator.log({**log_train, **log_val}, step=curr_step)
accelerator.print(f"Current LR: {scheduler.get_last_lr()[0]}")
accelerator.print(format_metrics(log_train, "train", f" step {step} "))
accelerator.print(format_metrics(log_val, "val", f" step {step} "))
train_loss.reset()
accelerator.print(f"Epoch {epoch} finished")
accelerator.print(f"Pushing to HF hub")
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
try:
if accelerator.is_main_process:
unwrapped_model.push_to_hub(config["save_name"] + f"-epoch_{epoch}", private=True)
except Exception as e:
accelerator.print(e)
accelerator.print(f"Failed to push to hub")
unwrapped_model.save_pretrained(
f"{config['output_dir']}/epoch_{epoch}",
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
state_dict=accelerator.get_state_dict(model),
)
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(
f"{config['output_dir']}/final",
is_main_process=accelerator.is_main_process,
save_function=accelerator.save,
state_dict=accelerator.get_state_dict(model),
)
accelerator.end_training()
if __name__ == "__main__":
# parse arguments by reading in a config
parser = ArgumentParser()
parser.add_argument("--config", type=str, default="config.yaml")
args = parser.parse_args()
config = read_config(args.config)
if config["wandb"]:
accelerator = Accelerator(log_with="wandb")
accelerator.init_trackers(
project_name=config["wandb_project_name"],
config=config,
init_kwargs={"wandb": {"entity": config["wandb_entity"]}},
)
else:
accelerator = Accelerator()
train(accelerator, config=config)