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batch_training.py
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batch_training.py
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import os
from datasets import load_dataset
from accelerate import Accelerator, DeepSpeedPlugin
import torch
from tqdm import tqdm
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
import argparse
import logging
from accelerate.logging import get_logger
logger = get_logger(__name__)
import numpy as np
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
def g_func(losses, l):
return torch.exp(losses/l)
def h_func(losses, delta=1., l_min=0.75, l_max=1.8):
return 2. * delta * losses / max(l_max - l_min, 1e-6) - delta * (l_max + l_min) / max(l_max - l_min, 1e-6)
def f_func(losses, delta=1.):
return 1 - losses**2 / delta**2
def main(args):
set_seed(args.seed)
deepspeed_plugin = DeepSpeedPlugin(zero_stage=2, gradient_accumulation_steps=1)
accelerator = Accelerator(mixed_precision='bf16', deepspeed_plugin=deepspeed_plugin,
log_with="all")
if accelerator.is_main_process:
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
raw_dataset = load_dataset("c4", "en", cache_dir=args.data_path, split="train", streaming=True)
raw_dataset = raw_dataset.shuffle(buffer_size=10_000, seed=42)
for item in raw_dataset:
print(item)
break
hf_token = args.hf_token
# tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m",
tokenizer = AutoTokenizer.from_pretrained(args.model,
cache_dir=args.model_path,
token=hf_token,
padding_side="left")
tokenizer.pad_token_id = 0
def tokenize_function(examples):
data = tokenizer(examples["text"], padding=True, truncation=True, max_length=512)
data = {k: torch.tensor(v) for k, v in data.items()}
return data
tokenized_dataset = raw_dataset.map(tokenize_function, batched=True)
tokenized_dataset = tokenized_dataset.remove_columns(["url", "timestamp", "text"])
# tokenized_datasets = tokenized_datasets.remove_columns(["text"])
bs = args.batch_size
lr = args.lr
# model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m",
model = AutoModelForCausalLM.from_pretrained(args.model,
cache_dir=args.model_path,
token=hf_token)
vocab_size = model.config.vocab_size
train_dataloader = torch.utils.data.DataLoader(tokenized_dataset, shuffle=False, batch_size=bs)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
train_dataloader, model, optimizer = accelerator.prepare(
train_dataloader, model, optimizer
)
# save_dir = f'/v-zhendwang/datasets/C4/filterd_llama2_7b_num{capacity}'
save_dir = args.save_dir
os.makedirs(save_dir, exist_ok=True)
num_batch = args.num_batch
frac = args.frac
buffers = []
all_weights = []
for i, batch in enumerate(tqdm(train_dataloader, mininterval=60)):
# text = batch['text'][0]
# del batch['text']
if i > num_batch:
break
labels = batch['input_ids']
batch['labels'] = labels
outputs = model(**batch)
if accelerator.is_main_process:
logger.info(f"Step: {i}, Official Loss: {outputs['loss']}")
logits = outputs.logits
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = torch.nn.functional.cross_entropy(
shift_logits.view(-1, vocab_size), shift_labels.view(-1), reduction='none')
loss = loss.reshape(bs, -1)
mask = shift_labels.reshape(bs, -1) != -100
num_non_zeros = mask.sum(1)
loss = (loss * mask).sum(1) / num_non_zeros
gathered_loss = loss
if args.mode == 'rank':
capacity = int(frac * gathered_loss.shape[0])
sorted_loss, _ = torch.sort(gathered_loss, descending=True)
if args.patience > 1:
buffers.append(gathered_loss)
if len(buffers) == args.patience:
collected_loss = torch.cat(buffers)
if accelerator.is_main_process:
logger.info(f"Step: {i}, Loss: {collected_loss.mean()}")
sorted_loss, _ = torch.sort(collected_loss, descending=True)
gathered_loss = sorted_loss[args.portion*capacity:(args.portion + 1)*capacity].mean()
optimizer.zero_grad()
accelerator.backward(gathered_loss)
optimizer.step()
buffers = []
else:
gathered_loss = sorted_loss[args.portion*capacity:(args.portion + 1)*capacity]
gathered_loss = gathered_loss.mean()
# print(gathered_loss)
if accelerator.is_main_process:
logger.info(f"Step: {i}, Loss: {gathered_loss}")
optimizer.zero_grad()
accelerator.backward(gathered_loss)
optimizer.step()
elif args.mode == 'dro':
kl_reg = args.kl_reg
if args.patience > 1:
buffers.append(gathered_loss)
if len(buffers) == args.patience:
collected_loss = torch.cat(buffers)
if accelerator.is_main_process:
logger.info(f"Step: {i}, Loss: {collected_loss.mean()}")
g_losses = g_func(collected_loss.detach() - collected_loss.max().detach(), l=kl_reg)
weights = g_losses / g_losses.sum()
all_weights.append(weights)
collected_loss = torch.sum(weights.detach() * collected_loss)
# pdb.set_trace()
optimizer.zero_grad()
accelerator.backward(collected_loss)
optimizer.step()
buffers = []
else:
g_losses = g_func(gathered_loss.detach() - gathered_loss.max().detach(), l=kl_reg)
weights = g_losses / g_losses.sum()
all_weights.append(weights)
gathered_loss = torch.sum(weights.detach() * gathered_loss)
# print(gathered_loss)
if accelerator.is_main_process:
logger.info(f"Step: {i}, Loss: {gathered_loss.mean()}")
optimizer.zero_grad()
accelerator.backward(gathered_loss)
optimizer.step()
if (i + 1) % args.save_freq == 0:
unwrapped_model = accelerator.unwrap_model(model)
accelerator.save(
unwrapped_model.state_dict(),
os.path.join(save_dir, f"model_{i}.pt"))
unwrapped_model = accelerator.unwrap_model(model)
accelerator.save(
unwrapped_model.state_dict(),
os.path.join(save_dir, "model_final.pt"))
# get local rank
local_rank = accelerator.local_process_index
torch.save(all_weights, os.path.join(save_dir, f"weights_{local_rank}.pt"))
# torch.save(model.state_dict(), os.path.join(save_dir, "model.pt"))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--gpu", type=str, default="0")
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--data_path", type=str, required=True)
parser.add_argument("--model", type=str, default='meta-llama/Llama-2-7b-hf')
parser.add_argument("--model_path", type=str, required=True)
parser.add_argument("--save_dir", type=str, required=True)
parser.add_argument("--mode", type=str, default='naive')
parser.add_argument("--num_batch", type=int, default=100)
parser.add_argument("--frac", type=float, default=0.125)
parser.add_argument("--lr", type=float, default=5e-6)
parser.add_argument("--kl_reg", type=float, default=1)
parser.add_argument("--patience", type=int, default=1)
parser.add_argument("--seed", type=int, default=1)
parser.add_argument("--portion", type=int, default=0)
parser.add_argument("--save_freq", type=int, default=1000)
parser.add_argument("--hf_token", type=str, required=True)
args = parser.parse_args()
main(args)