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main.py
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main.py
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#!/usr/bin/env python
# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import argparse
import os
import math
import sys
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from transformers import (
SchedulerType,
get_scheduler,
)
import deepspeed
from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
from utils.model.model_utils import create_critic_model
from utils.data.data_utils import create_prompt_dataset, DataCollatorReward
from utils.utils import print_rank_0, to_device, save_hf_format, set_random_seed, get_all_reduce_mean, get_optimizer_grouped_parameters, save_zero_three_model, load_hf_tokenizer
from utils.ds_utils import get_train_ds_config
from utils.module.lora import convert_linear_layer_to_lora, convert_lora_to_linear_layer, only_optimize_lora_parameters, make_model_gradient_checkpointing_compatible
def parse_args():
parser = argparse.ArgumentParser(
description=
"Finetune a transformers model on a causal language modeling task")
parser.add_argument('--data_path',
nargs='*',
default=['Dahoas/rm-static'],
help='Path to the training dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-path dataset2-path ...')
parser.add_argument('--data_split',
type=str,
default='2,4,4',
help='Comma-separated list of proportions for training'
'phase 1, 2, and 3 data. For example the split `2,4,4`'
'will use 60%% of data for phase 1, 20%% for phase 2'
'and 20%% for phase 3.')
parser.add_argument(
'--data_output_path',
type=str,
default='/tmp/data_files/',
help='Where to store the data-related files such as shuffle index.')
parser.add_argument(
"--model_name_or_path",
type=str,
help=
"Path to pretrained model or model identifier from huggingface.co/models.",
required=True,
)
parser.add_argument(
"--num_padding_at_beginning",
type=int,
default=1,
help=
"OPT model has a fixed number (1) of padding tokens at the beginning of the input. "
"We did not see this in other models but keep it as an option for now.",
)
parser.add_argument(
"--per_device_train_batch_size",
type=int,
default=16,
help="Batch size (per device) for the training dataloader.",
)
parser.add_argument(
"--per_device_eval_batch_size",
type=int,
default=16,
help="Batch size (per device) for the evaluation dataloader.",
)
parser.add_argument(
"--max_seq_len",
type=int,
default=512,
help="The maximum sequence length.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=5e-5,
help=
"Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument("--weight_decay",
type=float,
default=0.,
help="Weight decay to use.")
parser.add_argument("--num_train_epochs",
type=int,
default=1,
help="Total number of training epochs to perform.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--lr_scheduler_type",
type=SchedulerType,
default="cosine",
help="The scheduler type to use.",
choices=[
"linear", "cosine", "cosine_with_restarts", "polynomial",
"constant", "constant_with_warmup"
],
)
parser.add_argument(
"--num_warmup_steps",
type=int,
default=0,
help="Number of steps for the warmup in the lr scheduler.")
parser.add_argument("--output_dir",
type=str,
default=None,
help="Where to store the model.")
parser.add_argument("--seed",
type=int,
default=1234,
help="A seed for reproducible training.")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--gradient_checkpointing',
action='store_true',
help='Enable HF gradient checkpointing for Actor model.')
parser.add_argument('--disable_dropout',
action='store_true',
help='Disable the dropout of the model.')
# deepspeed features
parser.add_argument('--offload',
action='store_true',
help='Enable ZeRO Offload techniques.')
parser.add_argument(
'--zero_stage',
type=int,
default=0,
help='ZeRO optimization stage for Actor model (and clones).')
## LoRA for efficient training setting
parser.add_argument("--lora_dim",
type=int,
default=0,
help="If > 0, use LoRA for efficient training.")
parser.add_argument("--lora_module_name",
type=str,
default="decoder.layers.",
help="The scope of LoRA.")
parser.add_argument('--only_optimize_lora',
action='store_true',
help='Only optimize the LoRA parameters.')
parser.add_argument(
"--lora_learning_rate",
type=float,
default=5e-4,
help=
"Initial LoRA learning rate (after the potential warmup period) to use."
)
## Tensorboard logging
parser.add_argument('--enable_tensorboard',
action='store_true',
help='Enable tensorboard logging')
parser.add_argument('--tensorboard_path',
type=str,
default="step2_tensorboard")
parser = deepspeed.add_config_arguments(parser)
args = parser.parse_args()
return args
def main():
args = parse_args()
if args.local_rank == -1:
device = torch.device("cuda")
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
# torch.distributed.init_process_group(backend='nccl')
deepspeed.init_distributed()
args.global_rank = torch.distributed.get_rank()
ds_config = get_train_ds_config(offload=args.offload,
stage=args.zero_stage,
enable_tensorboard=args.enable_tensorboard,
tb_path=args.tensorboard_path,
tb_name="step2_model")
ds_config[
'train_micro_batch_size_per_gpu'] = args.per_device_train_batch_size
ds_config[
'train_batch_size'] = args.per_device_train_batch_size * torch.distributed.get_world_size(
) * args.gradient_accumulation_steps
# If passed along, set the training seed now.
set_random_seed(args.seed)
torch.distributed.barrier()
# load_hf_tokenizer will get the correct tokenizer and set padding tokens based on the model family
tokenizer = load_hf_tokenizer(args.model_name_or_path, fast_tokenizer=True)
rm_model = create_critic_model(args.model_name_or_path,
tokenizer,
ds_config,
args.num_padding_at_beginning,
disable_dropout=args.disable_dropout)
if args.lora_dim > 0:
rm_model = convert_linear_layer_to_lora(rm_model,
args.lora_module_name,
args.lora_dim)
if args.only_optimize_lora:
rm_model = only_optimize_lora_parameters(rm_model)
rm_model = make_model_gradient_checkpointing_compatible(rm_model)
train_phase = 2
train_dataset, eval_dataset = create_prompt_dataset(
args.local_rank, args.data_path, args.data_split,
args.data_output_path, train_phase, args.seed, tokenizer,
args.max_seq_len)
# DataLoaders creation:
data_collator = DataCollatorReward()
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
eval_sampler = SequentialSampler(eval_dataset)
else:
train_sampler = DistributedSampler(train_dataset)
eval_sampler = DistributedSampler(eval_dataset)
train_dataloader = DataLoader(train_dataset,
collate_fn=data_collator,
sampler=train_sampler,
batch_size=args.per_device_train_batch_size)
eval_sampler = SequentialSampler(eval_dataset)
eval_dataloader = DataLoader(eval_dataset,
collate_fn=data_collator,
sampler=eval_sampler,
batch_size=args.per_device_eval_batch_size)
def evaluation_reward(model, eval_dataloader):
model.eval()
correct_predictions = 0
total_predictions = 0
scores = 0
for step, batch in enumerate(eval_dataloader):
batch = to_device(batch, device)
with torch.no_grad():
outputs = model(**batch)
chosen = outputs["chosen_mean_scores"]
rejected = outputs["rejected_mean_scores"]
correct_predictions += (chosen > rejected).sum()
total_predictions += chosen.shape[0]
scores += outputs["chosen_mean_scores"].mean().float()
if step == 99: # For faster evaluation and debugging
break
acc = correct_predictions / total_predictions
scores = scores / (step + 1)
try:
acc = get_all_reduce_mean(acc).item()
scores = get_all_reduce_mean(scores).item()
except:
pass
return scores, acc
# Split weights in two groups, one with weight decay and the other not.
optimizer_grouped_parameters = get_optimizer_grouped_parameters(
rm_model, args.weight_decay, args.lora_learning_rate)
AdamOptimizer = DeepSpeedCPUAdam if args.offload else FusedAdam
optimizer = AdamOptimizer(optimizer_grouped_parameters,
lr=args.learning_rate,
betas=(0.9, 0.95))
num_update_steps_per_epoch = math.ceil(
len(train_dataloader) / args.gradient_accumulation_steps)
lr_scheduler = get_scheduler(
name=args.lr_scheduler_type,
optimizer=optimizer,
num_warmup_steps=args.num_warmup_steps,
num_training_steps=args.num_train_epochs * num_update_steps_per_epoch,
)
rm_model, optimizer, _, lr_scheduler = deepspeed.initialize(
model=rm_model,
optimizer=optimizer,
args=args,
config=ds_config,
lr_scheduler=lr_scheduler,
dist_init_required=True)
if args.gradient_checkpointing:
rm_model.gradient_checkpointing_enable()
# Train!
print_rank_0("***** Running training *****", args.global_rank)
print_rank_0(
f"***** Evaluating reward, Epoch {0}/{args.num_train_epochs} *****",
args.global_rank)
reward_score, acc = evaluation_reward(rm_model, eval_dataloader)
print_rank_0(
f"chosen_last_scores (higher is better) : {reward_score}, acc (higher is better) : {acc}",
args.global_rank)
for epoch in range(args.num_train_epochs):
print_rank_0(
f"Beginning of Epoch {epoch+1}/{args.num_train_epochs}, Total Micro Batches {len(train_dataloader)}",
args.global_rank)
rm_model.train()
mean_loss = 0
for step, batch in enumerate(train_dataloader):
batch = to_device(batch, device)
outputs = rm_model(**batch, use_cache=False)
loss = outputs["loss"]
rm_model.backward(loss)
rm_model.step()
mean_loss += loss.item()
print_rank_0(
f"Epoch {epoch+1}/{args.num_train_epochs} with loss {mean_loss/(step+1)}",
args.global_rank)
# Evaluate reward_loss on the validation set.
print_rank_0(
f"***** Evaluating reward, Epoch {epoch+1}/{args.num_train_epochs} *****",
args.global_rank)
reward_score, acc = evaluation_reward(rm_model, eval_dataloader)
print_rank_0(
f"chosen_last_scores (higher is better) : {reward_score}, acc (higher is better) : {acc}",
args.global_rank)
rm_model.tput_timer.update_epoch_count()
if args.output_dir is not None:
print_rank_0('saving model ...', args.global_rank)
rm_model = convert_lora_to_linear_layer(rm_model)
if args.global_rank == 0:
save_hf_format(rm_model, tokenizer, args)
if args.zero_stage == 3:
# for zero stage 3, each gpu only has a part of the model, so we need to save the model on each gpu by using DS-Engine
save_zero_three_model(rm_model,
args.global_rank,
args.output_dir,
zero_stage=args.zero_stage)
if __name__ == "__main__":
main()