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train_classification.py
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train_classification.py
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# Copyright (c) 2023, NVIDIA Corporation & Affiliates. All rights reserved.
#
# This work is made available under the Nvidia Source Code License-NC.
# To view a copy of this license, visit
# https://github.com/NVlabs/prismer/blob/main/LICENSE
import argparse
import numpy as np
import random
import time
import functools
import torch
try:
import ruamel_yaml as yaml
except ModuleNotFoundError:
import ruamel.yaml as yaml
from accelerate import Accelerator, FullyShardedDataParallelPlugin
from model.prismer_caption import PrismerCaption
from model.modules.utils import interpolate_pos_embed
from dataset import create_dataset, create_loader
from tqdm import tqdm
from utils import *
parser = argparse.ArgumentParser()
parser.add_argument('--mode', default='')
parser.add_argument('--port', default='')
parser.add_argument('--config', default='configs/classification.yaml')
parser.add_argument('--from_checkpoint', action='store_true')
parser.add_argument('--evaluate', action='store_true')
parser.add_argument('--exp_name', default='', type=str)
parser.add_argument('--shard_grad_op', action='store_true')
parser.add_argument('--full_shard', action='store_true')
parser.add_argument('--mixed_precision', default='fp16', type=str)
parser.add_argument('--seed', default=42, type=int)
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
train_dataset, test_dataset = create_dataset('classification', config)
train_loader = create_loader(train_dataset, batch_size=config['batch_size_train'], num_workers=8, train=True)
test_loader = create_loader(test_dataset, batch_size=config['batch_size_test'], num_workers=8, train=False)
model = PrismerCaption(config)
if args.shard_grad_op: # Model Sharding: ZeRO 2
from torch.distributed.fsdp import MixedPrecision, BackwardPrefetch, ShardingStrategy, StateDictType
fsdp_plugin = FullyShardedDataParallelPlugin(sharding_strategy=ShardingStrategy.SHARD_GRAD_OP,
backward_prefetch=BackwardPrefetch.BACKWARD_PRE,
mixed_precision_policy=MixedPrecision(param_dtype=torch.float16,
reduce_dtype=torch.float16,
buffer_dtype=torch.float16),
state_dict_type=StateDictType.FULL_STATE_DICT,
ignored_modules=model.ignored_modules)
accelerator = Accelerator(mixed_precision=args.mixed_precision, fsdp_plugin=fsdp_plugin)
model = accelerator.prepare(model)
elif args.full_shard: # Model Sharding: ZeRO 3
from torch.distributed.fsdp import MixedPrecision, BackwardPrefetch, ShardingStrategy, StateDictType
from torch.distributed.fsdp.wrap import transformer_auto_wrap_policy
from model.modules.vit import ResidualAttentionBlock
from model.modules.resampler import PerceiverAttentionBlock
from model.modules.roberta import RobertaLayer
auto_wrap_policy = functools.partial(
transformer_auto_wrap_policy,
transformer_layer_cls={
ResidualAttentionBlock,
PerceiverAttentionBlock,
RobertaLayer
},
)
fsdp_plugin = FullyShardedDataParallelPlugin(sharding_strategy=ShardingStrategy.FULL_SHARD,
backward_prefetch=BackwardPrefetch.BACKWARD_PRE,
mixed_precision_policy=MixedPrecision(param_dtype=torch.float16,
reduce_dtype=torch.float16,
buffer_dtype=torch.float16),
state_dict_type=StateDictType.FULL_STATE_DICT,
auto_wrap_policy=auto_wrap_policy,
ignored_modules=model.ignored_modules)
accelerator = Accelerator(mixed_precision=args.mixed_precision, fsdp_plugin=fsdp_plugin)
model = accelerator.prepare(model)
else:
accelerator = Accelerator(mixed_precision=args.mixed_precision)
# Reload saved states
if not args.from_checkpoint:
state_dict = torch.load(f'logging/pretrain_{args.exp_name}/pytorch_model.bin', map_location='cpu')
state_dict['expert_encoder.positional_embedding'] = interpolate_pos_embed(state_dict['expert_encoder.positional_embedding'],
len(model.expert_encoder.positional_embedding))
model.load_state_dict(state_dict)
start_epoch = 0
else:
state_dict = torch.load(f'logging/classification_{args.exp_name}/pytorch_model.bin', map_location='cpu')
if os.path.exists(f'logging/classification_{args.exp_name}/epoch.pt'):
start_epoch = torch.load(f'logging/classification_{args.exp_name}/epoch.pt')[0] + 1
else:
start_epoch = 0
model.load_state_dict(state_dict)
accelerator.print(f'Start re-training from checkpoint with Epoch {start_epoch}')
optimizer = torch.optim.AdamW(params=filter(lambda p: p.requires_grad, model.parameters()),
lr=config['init_lr'], weight_decay=config['weight_decay'])
if args.shard_grad_op or args.full_shard:
optimizer, train_loader, test_loader = accelerator.prepare(optimizer, train_loader, test_loader)
else:
model, optimizer, train_loader, test_loader = accelerator.prepare(model, optimizer, train_loader, test_loader)
start_time = time.time()
best = 0
for epoch in range(start_epoch, config['max_epoch']):
train_loss = 0
num_train_elems = 0
model.train()
for i, (experts, caption) in enumerate(tqdm(train_loader)):
cosine_lr_schedule(optimizer, epoch * len(train_loader) + i, config['max_epoch'] * len(train_loader), config['init_lr'], config['min_lr'])
loss = model(experts, caption, prefix=config['prefix'])
optimizer.zero_grad()
accelerator.backward(loss)
optimizer.step()
train_loss += loss.item()
num_train_elems += 1
train_loss /= num_train_elems
accelerator.print(f"Epoch {epoch:03d} | loss: {train_loss:.4f} || Time: {(time.time() - start_time):.4f}")
if (epoch + 1) % 5 == 0:
model.eval()
num_test_elems = 0
accurate = 0
with torch.no_grad():
answer_list = test_loader.dataset.answer_list
for step, (experts, gt) in enumerate(tqdm(test_loader)):
predictions = model(experts, answer=answer_list, train=False, prefix=config['prefix'], k_test=config['k_test'], inference='rank')
if accelerator.use_distributed:
predictions, gt = accelerator.gather_for_metrics((predictions, gt))
accurate_preds = predictions == gt
num_test_elems += accurate_preds.shape[0]
accurate += accurate_preds.long().sum()
eval_metric = accurate.item() / num_test_elems
accelerator.wait_for_everyone()
accelerator.print(f'{config["shots"]}-Shot Acc: {eval_metric}')
if eval_metric > best:
best = eval_metric
accelerator.save_state(f'logging/classification_{args.exp_name}')
accelerator.save([epoch], f'logging/classification_{args.exp_name}/epoch.pt')