mirror of
https://github.com/nomic-ai/gpt4all
synced 2024-11-04 12:00:10 +00:00
232 lines
8.6 KiB
Python
232 lines
8.6 KiB
Python
import os
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AdamW, get_scheduler
|
|
from transformers.trainer_pt_utils import get_parameter_names
|
|
import torch
|
|
import torch.nn as nn
|
|
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
|
|
|
|
|
|
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().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'])
|
|
# llama has no pad token, set it to new token
|
|
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
|
|
)
|
|
|
|
no_decay = ["bias", "LayerNorm.weight"]
|
|
optimizer_grouped_parameters = [
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
|
|
"weight_decay": config["weight_decay"],
|
|
},
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
|
|
"weight_decay": 0.0,
|
|
},
|
|
]
|
|
|
|
# karpathy doesn't decay embeddding, maybe we should exclude
|
|
# https://github.com/karpathy/minGPT/commit/bbbdac74fa9b2e55574d70056163ffbae42310c1#diff-2075fa9c224b395be5bda85544dd36572b59c76c54562819eadadbf268602834R157s
|
|
optimizer = optimizer_cls(optimizer_grouped_parameters, lr=config["lr"])
|
|
|
|
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 spcified 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 * accelerator.num_processes,
|
|
)
|
|
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_local_main_process and config["wandb"]:
|
|
wandb.watch(model, log_freq=config["log_grads_every"])
|
|
|
|
for epoch in range(config["num_epochs"]):
|
|
train_loss = MeanMetric().to(model.device)
|
|
for step, batch in enumerate(tqdm(train_dataloader)):
|
|
model.train()
|
|
outputs = model(**batch)
|
|
loss = outputs.loss
|
|
loss = loss / gradient_accumulation_steps
|
|
|
|
accelerator.backward(loss)
|
|
|
|
# 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()
|
|
|
|
loss_values = accelerator.gather_for_metrics({"loss": loss.detach()})
|
|
train_loss.update(loss_values["loss"])
|
|
|
|
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(config, 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)
|
|
if accelerator.is_main_process:
|
|
unwrapped_model.push_to_hub(config["save_name"] + f"-epoch_{epoch}", private=True)
|
|
|
|
|
|
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)
|