# -------------------------------------------------------- # X-Decoder -- Generalized Decoding for Pixel, Image, and Language # Copyright (c) 2022 Microsoft # Licensed under The MIT License [see LICENSE for details] # Modified by Xueyan Zou (xueyan@cs.wisc.edu) # -------------------------------------------------------- from datetime import datetime import time import os import sys import importlib import json import random import logging import numpy as np import copy import contextlib import shutil from typing import Any, Callable, Union import torch import torch.nn as nn import torch.optim as optim import torch.optim.lr_scheduler as lr_scheduler from torch.utils.data import DataLoader from torch.utils.data.distributed import DistributedSampler from mpi4py import MPI from infinibatch import iterators from .distributed_trainer import DistributedTrainer from .utils.misc import * from .utils.serialization import JSONEncoder, filter_jsonable from utilities.distributed import get_world_size logger = logging.getLogger(__name__) class UtilsTrainer(DistributedTrainer): def __init__(self, opt): super().__init__(opt) def is_gradient_accumulation_boundary(self): return (self.train_params['num_updates'] + 1) % self.grad_acc_steps == 0 def get_batch_size(self, batch, module_name='default'): if hasattr(self.raw_models[module_name], 'get_batch_size'): if callable(self.raw_models[module_name].get_batch_size): return self.raw_models[module_name].get_batch_size(batch) return {} def _initialize_ddp(self): if self.opt['FP16']: from torch.cuda.amp import GradScaler self.grad_scaler = GradScaler() logger.warning("PyTorch AMP GradScaler initialized.") for module_name in self.model_names: if self.opt['world_size'] > 1: # ddp: wrap modules for distributed data parallel training self.models[module_name] = nn.parallel.DistributedDataParallel(self.models[module_name], device_ids=[self.opt['local_rank']], output_device=self.opt['local_rank'], find_unused_parameters=self.opt.get('FIND_UNUSED_PARAMETERS', True)) def _get_and_validate_current_optim_steps(self): current_optim_steps = set([self.train_params['optim_steps'][module_name] for module_name in self.model_names]) assert len(current_optim_steps) == 1, f"All modules should be at the same optim step: {self.train_params['optim_steps']}" return next(iter(current_optim_steps)) def load_model(self, load_path): for module_name in self.model_names: self.raw_models[module_name] = self.raw_models[module_name].from_pretrained(load_path) self.raw_models[module_name].to(self.opt['device']) def save_checkpoint(self, tag): tag = str(tag).zfill(8) logger.warning('Saving checkpoint...') resume_epoch_idx = self.train_params['current_epoch_idx'] resume_batch_idx = self.train_params['current_batch_idx'] + 1 if resume_batch_idx == self.train_params['updates_per_epoch']: self.train_params['start_batch_idx'] = 0 self.train_params['start_epoch_idx'] = resume_epoch_idx + 1 else: self.train_params['start_batch_idx'] = resume_batch_idx self.train_params['start_epoch_idx'] = resume_epoch_idx save_dir = os.path.join(self.save_folder, tag) if self.opt['world_size'] > 1: torch.distributed.barrier() if self.opt['rank'] == 0: os.makedirs(self.save_folder, exist_ok=True) if self.opt['world_size'] > 1: torch.distributed.barrier() if self.opt['rank'] == 0: os.makedirs(save_dir, exist_ok=True) if self.opt['rank'] == 0: if self.opt['FP16']: amp_state = self.grad_scaler.state_dict() else: amp_state = None for module_name in self.model_names: module_save_dir = os.path.join(save_dir, module_name) os.makedirs(module_save_dir, exist_ok=True) save_path = os.path.join(module_save_dir, 'module_training_states.pt') state = {'module': self.models[module_name].state_dict(), 'optimizer': self.optimizers[module_name].state_dict(), 'lr_scheduler': self.lr_schedulers[module_name].state_dict(), 'amp_state': amp_state,} torch.save(state, save_path) if self.opt['rank'] == 0: save_path = os.path.join(save_dir, 'trainer_states.pt') trainer_state = {'train_loss': self.train_loss, 'train_params': self.train_params,} torch.save(trainer_state, save_path) num_retries = 0 while num_retries < 3: try: random_state_path = os.path.join(save_dir, f"random_state_rank_{self.opt['rank']:04d}") random_state = {'random': random.getstate(), 'numpy_random': np.random.get_state(), 'torch_random': torch.get_rng_state(), 'torch_cuda_random': torch.cuda.get_rng_state(device=self.opt['device']) if self.opt['CUDA'] else None } torch.save(random_state, random_state_path) num_retries = 3 except Exception as err: num_retries += 1 logger.warning(err) logger.warning("Failed to save checkpoint at retry {}, waiting for 30s to retry.".format(num_retries)) time.sleep(30) if self.opt['rank'] == 0: for module_name in self.model_names: module_save_dir = os.path.join(save_dir, module_name) self.raw_models[module_name].save_pretrained(module_save_dir) if self.opt['rank'] == 0: # save the latest checkpoint location to json file checkpoint_location = {'checkpoint_tag': tag, 'checkpoint_path': os.path.relpath(self.save_folder, start=self.opt['SAVE_DIR'])} with open(os.path.join(self.opt['SAVE_DIR'], f"resume_checkpoint.json"), 'w', encoding='utf-8') as f: json.dump(checkpoint_location, f, cls=JSONEncoder) logger.warning(f'Finished saving checkpoint and model to {save_dir}.') def load_weight(self, checkpoint_path=None, must_exist=False): self.load_model(checkpoint_path) logger.warning(f'Load weights from {checkpoint_path}...') def load_checkpoint(self, checkpoint_path=None, must_exist=False): logger.warning(f'Resuming checkpoint from {checkpoint_path}...') for model_name in self.model_names: model_load_path = os.path.join(checkpoint_path, model_name, 'module_training_states.pt') state = torch.load(model_load_path, map_location=self.opt['device']) logger.warning(f'HACK to strip module from model state dict on single gpu debugging!') ckpt = state['module'] if get_world_size() <= 1: ckpt = {key.replace('module.',''):ckpt[key] for key in ckpt.keys()} self.models[model_name].load_state_dict(ckpt) self.optimizers[model_name].load_state_dict(state['optimizer']) self.lr_schedulers[model_name].load_state_dict(state['lr_scheduler']) if self.opt['FP16']: self.grad_scaler.load_state_dict(state['amp_state']) load_path = os.path.join(checkpoint_path, 'trainer_states.pt') trainer_state = torch.load(load_path, map_location='cpu') self.train_loss = trainer_state['train_loss'] self.train_params = trainer_state['train_params'] random_state_path = os.path.join(checkpoint_path, f"random_state_rank_{self.opt['rank']:04d}") if os.path.exists(random_state_path): random_state = torch.load(random_state_path, map_location='cpu') random.setstate(random_state['random']) np.random.set_state(random_state['numpy_random']) torch.set_rng_state(random_state['torch_random']) if self.opt['CUDA']: torch.cuda.set_rng_state(random_state['torch_cuda_random'], device=self.opt['device']) else: logging.warning("Could not find random state for rank {}".format(self.opt['rank'])) logger.warning(f'Finished loading checkpoint from {checkpoint_path}.')