mirror of
https://github.com/nomic-ai/gpt4all.git
synced 2025-09-06 11:00:48 +00:00
mono repo structure
This commit is contained in:
Richard Guo
233
gpt4all-training/train.py
Normal file
233
gpt4all-training/train.py
Normal file
@@ -0,0 +1,233 @@
|
||||
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 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,
|
||||
)
|
||||
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)
|
||||
Reference in New Issue
Block a user