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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 json | |
import torch | |
import os | |
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 utils import * | |
from tqdm import tqdm | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--mode', default='') | |
parser.add_argument('--port', default='') | |
parser.add_argument('--config', default='configs/caption.yaml') | |
parser.add_argument('--from_checkpoint', action='store_true') | |
parser.add_argument('--evaluate', action='store_true') | |
parser.add_argument('--target_dataset', default='coco', type=str) | |
parser.add_argument('--shard_grad_op', action='store_true') | |
parser.add_argument('--full_shard', action='store_true') | |
parser.add_argument('--exp_name', default='', type=str) | |
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)[args.target_dataset] | |
torch.manual_seed(args.seed) | |
np.random.seed(args.seed) | |
random.seed(args.seed) | |
train_dataset, test_dataset = create_dataset('caption', 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) | |
tokenizer = model.tokenizer | |
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/caption_{args.exp_name}/pytorch_model.bin', map_location='cpu') | |
if os.path.exists(f'logging/caption_{args.exp_name}/epoch.pt'): | |
start_epoch = torch.load(f'logging/caption_{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) | |
best = 0 | |
start_time = time.time() | |
if not args.evaluate: | |
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 | |
model.eval() | |
result = [] | |
with torch.no_grad(): | |
for step, (experts, data_ids) in enumerate(tqdm(test_loader)): | |
captions = model(experts, train=False, prefix=config['prefix']) | |
if accelerator.use_distributed: | |
captions = tokenizer(captions, max_length=30, padding='max_length', return_tensors='pt').input_ids | |
captions = captions.to(experts['rgb'].device) | |
data_ids, captions = accelerator.gather_for_metrics((data_ids, captions)) | |
for data_id, caption in zip(data_ids, captions): | |
caption = tokenizer.decode(caption, skip_special_tokens=True) | |
if args.target_dataset == 'coco': | |
image_id = int(test_loader.dataset.data_list[data_id]['image'].split('/')[-1].strip('.jpg').split('_')[-1]) | |
result.append({"image_id": image_id, "caption": caption.capitalize() + '.'}) | |
elif args.target_dataset == 'nocaps': | |
result.append({"image_id": test_loader.dataset.data_list[data_id]['img_id'], | |
"caption": caption.capitalize() + '.'}) | |
accelerator.wait_for_everyone() | |
if accelerator.is_main_process: | |
json.dump(result, open(f'/results/caption_results_{args.exp_name}_{args.target_dataset}.json', 'w')) | |
if args.target_dataset == 'coco': | |
coco_eval = coco_caption_eval(f'{config["data_path"]}/coco_karpathy_test_gt.json', result) | |
torch.save([coco_eval.eval['CIDEr']], f'logging/caption_{args.exp_name}/temp_cider.pt') | |
if not os.path.isfile(f'logging/caption_{args.exp_name}/cider.pt'): | |
torch.save([coco_eval.eval['CIDEr']], f'logging/caption_{args.exp_name}/cider.pt') | |
accelerator.wait_for_everyone() | |
cider = torch.load(f'logging/caption_{args.exp_name}/cider.pt')[0] | |
curr_cider = torch.load(f'logging/caption_{args.exp_name}/temp_cider.pt')[0] | |
if cider < curr_cider: | |
train_loss /= num_train_elems | |
accelerator.print(f"Epoch {epoch:03d} | loss: {train_loss:.4f} || Time: {(time.time() - start_time):.4f}") | |
accelerator.save_state(f'logging/caption_{args.exp_name}') | |
accelerator.save([epoch], f'logging/caption_{args.exp_name}/epoch.pt') | |
accelerator.save([curr_cider], f'logging/caption_{args.exp_name}/cider.pt') | |
model.eval() | |
if accelerator.is_main_process: | |
result = [] | |
with torch.no_grad(): | |
for step, (experts, data_ids) in enumerate(tqdm(test_loader)): | |
captions = model(experts, train=False, prefix=config['prefix']) | |
if accelerator.use_distributed: | |
captions = tokenizer(captions, max_length=30, padding='max_length', return_tensors='pt').input_ids | |
captions = captions.to(experts['rgb'].device) | |
data_ids, captions = accelerator.gather_for_metrics((data_ids, captions)) | |
if accelerator.is_main_process: | |
for data_id, caption in zip(data_ids, captions): | |
caption = tokenizer.decode(caption, skip_special_tokens=True) | |
if args.target_dataset == 'coco': | |
image_id = int(test_loader.dataset.data_list[data_id]['image'].split('/')[-1].strip('.jpg').split('_')[-1]) | |
result.append({"image_id": image_id, "caption": caption.capitalize() + '.'}) | |
elif args.target_dataset == 'nocaps': | |
result.append({"image_id": test_loader.dataset.data_list[data_id]['img_id'], | |
"caption": caption.capitalize() + '.'}) | |
accelerator.wait_for_everyone() | |
if accelerator.is_main_process: | |
json.dump(result, open(f'/results/caption_results_{args.exp_name}_{args.target_dataset}.json', 'w')) | |
if args.target_dataset == 'coco': | |
coco_caption_eval(f'{config["data_path"]}/coco_karpathy_test_gt.json', result) | |