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run_training_4m.py
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run_training_4m.py
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# Copyright 2024 EPFL and Apple Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import datetime
import json
import math
import os
import resource
import sys
import time
import warnings
from contextlib import nullcontext
from pathlib import Path
from typing import Iterable, List, Optional
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import yaml
from tokenizers import Tokenizer
import fourm.utils as utils
from fourm.data import build_mixture_dataloader, get_train_dataloader, get_val_dataloader, setup_sampling_mod_info
from fourm.models import fm
from fourm.data.modality_info import MODALITY_INFO
from fourm.utils import NativeScalerWithGradNormCount as NativeScaler
from fourm.utils import create_model
from fourm.utils.optim_factory import create_optimizer
def get_args():
config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
help='YAML config file specifying default arguments')
parser = argparse.ArgumentParser('4M pre-training script (using DDP)', add_help=True)
parser.add_argument('--run_name', type=str, default='auto')
parser.add_argument('--batch_size', default=256, type=int,
help='Batch size per GPU (default: %(default)s). '
'Effective batch size is batch_size * accum_iter * # gpus')
parser.add_argument('--epochs', default=100, type=int,
help='Number of epochs (default: %(default)s)')
parser.add_argument('--total_tokens', default=-1, type=int,
help='Number of total input tokens (in billions), only applicable if epochs is negative. '
'Sets the number of epochs to approximate this amount of tokens.')
parser.add_argument('--accum_iter', default=1, type=int,
help='Accumulate gradient iterations (for increasing the effective batch size under memory constraints)')
parser.add_argument('--save_ckpt_freq', default=20, type=int,
help='Checkpoint saving frequency in epochs (default: %(default)s)')
# Model parameters
parser.add_argument('--model', default='fm_base_12e_12d_swiglu_nobias', type=str,
help='Name of model to train (default: %(default)s)')
parser.add_argument('--patch_size', default=16, type=int,
help='Base patch size for image-like modalities (default: %(default)s)')
parser.add_argument('--input_size', default=224, type=int,
help='Images input size for backbone (default: %(default)s)')
parser.add_argument('--num_register_tokens', default=0, type=int,
help='Number of register tokens to add to encoder (default: %(default)s)')
parser.add_argument('--dtype', type=str, default='bfloat16',
choices=['float16', 'bfloat16', 'float32', 'bf16', 'fp16', 'fp32'],
help='Data type (default: %(default)s')
parser.add_argument('--num_input_tokens', type=int, default=128,
help="Token budget for the input")
parser.add_argument('--num_target_tokens', type=int, default=128,
help="Token budget for the target")
parser.add_argument('--min_input_tokens', type=int, default=None,
help="Minimum token budget for the input (None to set it to num_input_tokens)")
parser.add_argument('--min_target_tokens', type=int, default=None,
help="Minimum token budget for the target (None to set it to num_target_tokens)")
parser.add_argument('--loss_type', type=str, choices=['mod', 'token'], default='mod',
help="If mod, loss is the mean of the per-modality loss. If token, loss is the mean of the per-token loss (default: %(default)s)")
# Weight init / fine-tune parameters
parser.add_argument('--finetune', default='', help='finetune from checkpoint (for two-stage training)')
# Optimizer parameters
parser.add_argument('--opt', default='adamw', type=str,
help='Optimizer (default: %(default)s)')
parser.add_argument('--opt_eps', default=1e-8, type=float,
help='Optimizer epsilon (default: %(default)s)')
parser.add_argument('--opt_betas', default=[0.9, 0.95], type=float, nargs='+',
help='Optimizer betas (default: %(default)s)')
parser.add_argument('--compute_grad_norm', action='store_true')
parser.add_argument('--no_compute_grad_norm', action='store_false', dest='compute_grad_norm')
parser.set_defaults(compute_grad_norm=True)
parser.add_argument('--clip_grad', type=float, default=None,
help='Clip gradient norm (default: %(default)s)')
parser.add_argument('--skip_grad', type=float, default=None,
help='Skip update if gradient norm larger than threshold (default: %(default)s)')
parser.add_argument('--momentum', type=float, default=0.9,
help='SGD momentum (default: %(default)s)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='Weight decay (default: %(default)s)')
parser.add_argument('--weight_decay_end', type=float, default=None,
help="Final value of the weight decay. (Set the same value as args.weight_decay to keep weight decay value constant)")
parser.add_argument('--blr', type=float, default=1e-4,
help='Base learning rate: absolute_lr = base_lr * total_batch_size / 256 (default: %(default)s)')
parser.add_argument('--min_blr', type=float, default=0.,
help='Lower base lr bound for cyclic schedulers that hit 0 (default: %(default)s)')
parser.add_argument('--frozen_model_blr', type=float, default=-1,
help='base lr bound for frozen model (default: %(default)s)')
parser.add_argument('--scheduler', type=str, default='cosine',
choices=['cosine', 'inverse_sqrt-10000'],
help='Learning rate scheduler type (default: %(default)s')
parser.add_argument('--warmup_epochs', type=int, default=10,
help='Epochs to warmup LR, if scheduler supports (default: %(default)s)')
parser.add_argument('--warmup_steps', type=int, default=-1,
help='Steps to warmup LR, if scheduler supports (default: %(default)s)')
parser.add_argument('--warmup_tokens', type=int, default=-1,
help='Total tokens to warmup LR, if scheduler supports (default: %(default)s)')
# Cooldown for inverse sqrt and other "infinite" LR schedules
parser.add_argument('--cooldown_epochs', type=int, default=10,
help='Epochs to cool down LR, if scheduler supports (default: %(default)s)')
parser.add_argument('--cooldown_steps', type=int, default=-1,
help='Steps to cool down LR, if scheduler supports (default: %(default)s)')
parser.add_argument('--cooldown_tokens', type=int, default=-1,
help='Total tokens to cool down LR, if scheduler supports (default: %(default)s)')
# For warm-starting from a trained model
parser.add_argument('--frozen_model_epochs', default=0, type=int,
help='Number of epochs where only input/output embeddings are trained (default: %(default)s)')
parser.add_argument('--frozen_model_tokens', default=0, type=int,
help='Number of tokens where only input/output embeddings are trained (default: %(default)s)')
parser.add_argument('--frozen_embedding_domain', default=None, type=str,
help='Embeddings of domains that are frozen during training (default: %(default)s)')
# Dataset parameters
parser.add_argument('--data_config', type=str, default="",
help="Path to data config to specify dataset and modality mixture parameters.")
parser.add_argument('--epoch_size', type=int, help="Number of samples per epoch")
parser.add_argument('--s3_endpoint', default='', type=str, help='S3 endpoint URL')
parser.add_argument('--s3_data_endpoint', default=None, type=str,
help='S3 endpoint URL for the data (if different). If set to None, will be set to s3_endpoint')
parser.add_argument('--s3_multipart_chunksize_mb', default=512, type=int)
parser.add_argument('--s3_multipart_threshold_mb', default=512, type=int)
parser.add_argument('--s3_max_io_queue', default=100, type=int)
# Text tokenizer
parser.add_argument('--text_tokenizer_path', default='fourm/utils/tokenizer/trained/text_tokenizer_4m_wordpiece_30k.json',
help="Path to trained text tokenizer")
# Eval
parser.add_argument('--eval_freq', default=10, type=int, help="frequency of evaluation")
parser.add_argument('--dist_eval', action='store_true', default=False,
help='Enabling distributed evaluation')
parser.add_argument('--no_dist_eval', action='store_false', dest='dist_eval',
help='Disabling distributed evaluation')
parser.set_defaults(dist_eval=True)
parser.add_argument('--fixed_eval', action='store_true')
parser.add_argument('--no_fixed_eval', action='store_false', dest='fixed_eval')
parser.set_defaults(fixed_eval=True)
parser.add_argument('--fixed_eval_input_tokens', default=128, type=int,
help="Number of input tokens for the fixed evaluation")
parser.add_argument('--fixed_eval_target_tokens', default=128, type=int,
help="Number of target tokens for the fixed evaluation")
parser.add_argument('--fixed_eval_batch_size', default=32, type=int,
help="Batch size for the fixed evaluation")
parser.add_argument('--eval', action='store_true',
help='Perform evaluation only')
# Misc.
parser.add_argument('--output_dir', default='',
help='Path where to save, empty for no saving')
parser.add_argument('--device', default='cuda',
help='Device to use for training / testing')
parser.add_argument('--seed', default=0, type=int, help='Random seed ')
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--auto_resume', action='store_true')
parser.add_argument('--no_auto_resume', action='store_false', dest='auto_resume')
parser.set_defaults(auto_resume=True)
parser.add_argument('--start_epoch', default=0, type=int, help='start epoch')
parser.add_argument('--num_workers', default=10, type=int)
parser.add_argument('--pin_mem', action='store_true',
help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem',
help='')
parser.set_defaults(pin_mem=True)
parser.add_argument('--find_unused_params', action='store_true')
parser.add_argument('--no_find_unused_params', action='store_false', dest='find_unused_params')
parser.set_defaults(find_unused_params=False)
parser.add_argument('--rlimit', default=4096, type=int,
help='Increase rlimit to avoid "RuntimeError: received 0 items of ancdata".')
parser.add_argument('--print_all', action='store_true', default=False)
parser.add_argument('--show_user_warnings', default=False, action='store_true')
parser.add_argument('--s3_save_dir', type=str, default="")
# Distributed training parameters
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
# Wandb logging
parser.add_argument('--log_wandb', default=False, action='store_true',
help='Log training and validation metrics to wandb')
parser.add_argument('--no_log_wandb', action='store_false', dest='log_wandb')
parser.set_defaults(log_wandb=False)
parser.add_argument('--wandb_project', default=None, type=str,
help='Project name on wandb')
parser.add_argument('--wandb_entity', default=None, type=str,
help='User or team name on wandb')
parser.add_argument('--wandb_run_name', default='auto', type=str,
help='Run name on wandb')
# Parse config file if there is one
args_config, remaining = config_parser.parse_known_args()
if args_config.config:
with open(args_config.config, 'r') as f:
cfg = yaml.safe_load(f)
parser.set_defaults(**cfg)
# The main arg parser parses the rest of the args, the usual
# defaults will have been overridden if config file is specified.
args = parser.parse_args(remaining)
# Add the config path as a final args if given
args.config_path = args_config.config
return args
def setup_modality_info(args):
# Global modality info
modality_info = {mod: MODALITY_INFO[mod] for mod in args.all_domains}
# Max tokens
for mod in modality_info:
image_size, patch_size = modality_info[mod].get('input_size', args.input_size), modality_info[mod].get('patch_size', args.patch_size)
num_patches = (image_size // patch_size) ** 2
if modality_info[mod]['type'] == 'img':
modality_info[mod]['max_tokens'] = num_patches
return modality_info
def setup_data(args):
# Set number of tokens for the sampling
if args.min_input_tokens is None:
args.min_input_tokens = args.num_input_tokens
if args.min_target_tokens is None:
args.min_target_tokens = args.num_target_tokens
# Load text tokenizer
text_tokenizer = Tokenizer.from_file(args.text_tokenizer_path)
print(f"Loading data config from: {args.data_config}")
with open(args.data_config, "r") as f:
data_config = yaml.safe_load(f)
# Train
train_config = data_config['train']['datasets']
# All input and output domains from potentially multiple datasets
args.in_domains = sorted(set.union(*[set(cfg['in_domains'].split('-')) for cfg in train_config.values()]))
args.out_domains = sorted(set.union(*[set(cfg['out_domains'].split('-')) for cfg in train_config.values()]))
args.all_domains = sorted(list(set(args.in_domains) | set(args.out_domains)))
# Set up shared modality info
modality_info = setup_modality_info(args)
# Initialize (multiple) train loaders
# Each train loader needs to be split by node if there are multiple
if any([cfg['data_path'].startswith('s3') for cfg in train_config.values()]):
utils.s3_utils.override_wds_s3_tar_loading(args.s3_data_endpoint, args.s3_multipart_threshold_mb, args.s3_multipart_chunksize_mb, args.s3_max_io_queue)
num_trainsets = len(train_config)
train_iters = []
shards_per_dataset = [] # For computing max number of workers
for dataset_name, dataset_cfg in train_config.items():
print(f'Setting up dataset {dataset_name} / train')
dataset_mod_info, sampling_weights = setup_sampling_mod_info(dataset_cfg, modality_info)
dataset_batch_size = None #args.batch_size if num_trainsets == 1 else None
epoch_size = None #args.epoch_size if num_trainsets == 1 else None
dataiter = get_train_dataloader(
dataset_config=dataset_cfg, modality_info=dataset_mod_info,
sampling_weights=sampling_weights, text_tokenizer=text_tokenizer, input_size=args.input_size,
num_input_tokens=args.num_input_tokens, num_target_tokens=args.num_target_tokens,
min_input_tokens=args.min_input_tokens, min_target_tokens=args.min_target_tokens,
num_tasks=args.num_tasks, num_workers=args.num_workers, dataset_batch_size=dataset_batch_size,
epoch_size=epoch_size
)
train_iters.append(dataiter)
if hasattr(dataiter, 'n_shards'):
shards_per_dataset.append(dataiter.n_shards)
num_workers = min(min(shards_per_dataset), args.num_workers) if shards_per_dataset else args.num_workers
# When there are multiple train loaders, create a wrapper to sample from all of them
weights = data_config['train'].get('weights', [1.0] * num_trainsets) # Default is equal weighting
epoch_size = args.epoch_size
data_loader_train = build_mixture_dataloader(
data_iters=train_iters, weights=weights, modality_info=modality_info,
batch_size=args.batch_size, num_workers=num_workers,
epoch_size=epoch_size, num_gpus=args.num_tasks
)
num_training_steps_per_epoch = epoch_size // (args.batch_size * args.num_tasks)
# Val
if 'val' in data_config:
val_config = data_config['val']['datasets']
data_loaders_val, data_loaders_fixed_eval = {}, {}
for dataset_name, dataset_cfg in val_config.items():
dataset_mod_info, sampling_weights = setup_sampling_mod_info(train_config[dataset_name], modality_info)
data_loaders_val[dataset_name] = get_val_dataloader(
dataset_config=dataset_cfg, dataset_name=dataset_name, train_configs=train_config,
modality_info=dataset_mod_info, sampling_weights=sampling_weights, text_tokenizer=text_tokenizer,
input_size=args.input_size, num_input_tokens=args.num_input_tokens, num_target_tokens=args.num_target_tokens,
min_input_tokens=args.min_input_tokens, min_target_tokens=args.min_target_tokens, fixed_eval=False,
fixed_eval_input_tokens=args.fixed_eval_input_tokens, fixed_eval_target_tokens=args.fixed_eval_target_tokens,
dist_eval=args.dist_eval, num_tasks=args.num_tasks, num_workers=args.num_workers,
batch_size=int(1.5*args.batch_size), pin_mem=args.pin_mem,
)
if args.fixed_eval:
data_loaders_fixed_eval[dataset_name] = get_val_dataloader(
dataset_config=dataset_cfg, dataset_name=dataset_name, train_configs=train_config,
modality_info=dataset_mod_info, sampling_weights=sampling_weights, text_tokenizer=text_tokenizer,
input_size=args.input_size, num_input_tokens=args.num_input_tokens, num_target_tokens=args.num_target_tokens,
min_input_tokens=args.min_input_tokens, min_target_tokens=args.min_target_tokens, fixed_eval=True,
fixed_eval_input_tokens=args.fixed_eval_input_tokens, fixed_eval_target_tokens=args.fixed_eval_target_tokens,
dist_eval=args.dist_eval, num_tasks=args.num_tasks, num_workers=args.num_workers,
batch_size=int(1.5*args.batch_size), pin_mem=args.pin_mem,
)
data_loaders_fixed_eval = data_loaders_fixed_eval if data_loaders_fixed_eval else None
else:
data_loaders_val, data_loaders_fixed_eval = None, None
return modality_info, data_loader_train, num_training_steps_per_epoch, data_loaders_val, data_loaders_fixed_eval
def get_model(args, modality_info):
"""Creates and returns model from arguments
"""
print(f"Creating model: {args.model} for modalities {list(modality_info.keys())}")
encoder_embeddings = {}
for mod in args.in_domains:
info = modality_info[mod]
if info.get("encoder_embedding", None) is not None:
if info["type"] == "img":
image_size, patch_size = info.get('input_size', args.input_size), info.get('patch_size', args.patch_size)
encoder_embeddings[mod] = info["encoder_embedding"](patch_size=patch_size, image_size=image_size)
else:
encoder_embeddings[mod] = info["encoder_embedding"]()
decoder_embeddings = {}
for mod in args.out_domains:
info = modality_info[mod]
if info.get("decoder_embedding", None) is not None:
if info["type"] == "img":
image_size, patch_size = info.get('input_size', args.input_size), info.get('patch_size', args.patch_size)
decoder_embeddings[mod] = info["decoder_embedding"](patch_size=patch_size, image_size=image_size)
else:
decoder_embeddings[mod] = info["decoder_embedding"]()
model = create_model(
args.model,
encoder_embeddings=encoder_embeddings,
decoder_embeddings=decoder_embeddings,
modality_info=modality_info,
num_register_tokens=args.num_register_tokens,
)
return model
def main(args):
## Distributed init
utils.init_distributed_mode(args)
device = torch.device(args.device)
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
if not args.show_user_warnings:
warnings.filterwarnings("ignore", category=UserWarning)
if args.dtype in ['float16', 'fp16']:
dtype = torch.float16
elif args.dtype in ['bfloat16', 'bf16']:
dtype = torch.bfloat16
elif args.dtype in ['float32', 'fp32']:
dtype = torch.float32
else:
raise ValueError(f"Invalid dtype: {args.dtype}")
# Distributed training variables
num_tasks = utils.get_world_size()
args.num_tasks = num_tasks
global_rank = utils.get_rank()
## Data
modality_info, data_loader_train, num_training_steps_per_epoch, data_loaders_val, data_loaders_fixed_eval = setup_data(args)
## Model
model = get_model(args, modality_info)
# Logger
if global_rank == 0 and args.log_wandb:
log_writer = utils.WandbLogger(args)
else:
log_writer = None
## Training phases / epochs
if args.epochs < 0:
if args.total_tokens < 0:
print("Epochs and total tokens are both set to negative values, stopping training.")
exit(1)
else:
train_dataset_size = args.epoch_size # or len(dataset_train)
args.epochs = math.ceil(args.total_tokens * 1e9 / ((args.num_input_tokens + args.num_target_tokens) * train_dataset_size))
print(f"Total tokens: {args.total_tokens}B")
print(f"Setting the number of epochs accordingly to {args.epochs}")
elif args.total_tokens > 0:
print("Epochs and total tokens are both non-negative, stopping training.")
exit(1)
# Warmup
if args.warmup_epochs < 0 and args.warmup_steps < 0:
if args.warmup_tokens < 0:
print("Warmup epochs, steps and total tokens all set to negative values, stopping training.")
exit(1)
else:
args.warmup_steps = math.ceil(args.warmup_tokens * 1e9 / ((args.num_input_tokens + args.num_target_tokens) * args.batch_size * utils.get_world_size()))
# Cooldown
if args.cooldown_epochs < 0 and args.cooldown_steps < 0:
if args.cooldown_tokens < 0 and args.lr_schedule in ['inverse_sqrt']:
print("Cooldown epochs, steps and total tokens all set to negative values, stopping training.")
exit(1)
else:
args.cooldown_steps = math.ceil(args.cooldown_tokens * 1e9 / ((args.num_input_tokens + args.num_target_tokens) * args.batch_size * utils.get_world_size()))
# Frozen
if args.frozen_model_epochs <= 0:
if args.frozen_model_tokens > 0:
train_dataset_size = args.epoch_size # or len(dataset_train)
args.frozen_model_epochs = math.ceil(args.frozen_model_tokens * 1e9 / ((args.num_input_tokens + args.num_target_tokens) * train_dataset_size))
else:
print("No frozen models during training.")
else:
if args.frozen_model_tokens > 0:
print("Frozen_model_epochs and frozen_model_tokens are both non-negative, stopping training.")
exit(1)
print(args)
## Starting from pre-trained model
if args.finetune:
if args.finetune.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(
args.finetune, map_location='cpu')
else:
checkpoint = torch.load(args.finetune, map_location='cpu')
# Remove pos_emb
# TODO: In the future, find a way to not have to store the pos_embs here
checkpoint['model'] = {k: v for k, v in checkpoint['model'].items() if ".pos_emb" not in k}
msg = model.load_state_dict(checkpoint['model'], strict=False)
print(msg)
model.to(device)
model_without_ddp = model
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Model = %s" % str(model_without_ddp))
print(f"Number of params: {n_parameters / 1e6} M")
batch_size_no_accum = args.batch_size * utils.get_world_size()
total_batch_size = args.batch_size * args.accum_iter * utils.get_world_size()
args.lr = args.blr * total_batch_size / 256
args.min_lr = args.min_blr * total_batch_size / 256
if args.frozen_model_blr > 0:
args.frozen_model_lr = args.frozen_model_blr * total_batch_size / 256
else:
args.frozen_model_lr = args.blr * total_batch_size / 256
print("LR = %.8f" % args.lr)
print("Min LR = %.8f" % args.min_lr)
print("Total (effective) batch size = %d" % total_batch_size)
print("Accumulate grad iterations = %d" % args.accum_iter)
print("Number of training steps = %d" % num_training_steps_per_epoch)
print("Number of training examples per epoch = %d" % (batch_size_no_accum * num_training_steps_per_epoch))
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu], find_unused_parameters=args.find_unused_params)
model_without_ddp = model.module
optimizer = create_optimizer(args, model_without_ddp)
loss_scaler = NativeScaler(enabled=dtype == torch.float16)
## LR and WD schedules
if args.weight_decay_end is None:
args.weight_decay_end = args.weight_decay
if args.frozen_model_epochs > 0:
frozen_lr_schedule_values = utils.constant_scheduler(args.frozen_model_lr, args.frozen_model_epochs, num_training_steps_per_epoch)
frozen_wd_schedule_values = utils.constant_scheduler(args.weight_decay, args.frozen_model_epochs, num_training_steps_per_epoch)
main_schedule_epochs = args.epochs - args.frozen_model_epochs
else:
frozen_lr_schedule_values = np.array([])
frozen_wd_schedule_values = np.array([])
main_schedule_epochs = args.epochs
if args.scheduler == 'cosine':
main_lr_schedule_values = utils.cosine_scheduler(
args.lr, args.min_lr, main_schedule_epochs, num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps
)
wd_schedule_values = utils.cosine_scheduler(
args.weight_decay, args.weight_decay_end, main_schedule_epochs, num_training_steps_per_epoch
)
elif 'inverse_sqrt' in args.scheduler:
try:
timescale = int(args.scheduler.split('-')[-1])
except:
timescale = 10_000
main_lr_schedule_values = utils.inverse_sqrt_scheduler(
args.lr, args.min_lr, main_schedule_epochs, num_training_steps_per_epoch,
warmup_epochs=args.warmup_epochs, warmup_steps=args.warmup_steps,
cooldown_epochs=args.cooldown_epochs, cooldown_steps=args.cooldown_steps,
timescale=timescale
)
wd_schedule_values = utils.inverse_sqrt_scheduler(
args.weight_decay, args.weight_decay_end, main_schedule_epochs, num_training_steps_per_epoch,
cooldown_epochs=args.cooldown_epochs, cooldown_steps=args.cooldown_steps,
timescale=timescale
)
else:
raise NotImplementedError(f"Scheduler {args.scheduler} not implemented.")
lr_schedule_values = np.concatenate((frozen_lr_schedule_values, main_lr_schedule_values))
wd_schedule_values = np.concatenate((frozen_wd_schedule_values, wd_schedule_values))
print("Max WD = %.7f, Min WD = %.7f" % (max(wd_schedule_values), min(wd_schedule_values)))
# Auto-load from checkpoint
utils.auto_load_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
## Eval (on trained model)
if args.eval:
if data_loaders_val is not None:
for dataset_name, data_loader_val in data_loaders_val.items():
prefix = '[Eval] ' if not dataset_name else f'[Eval ({dataset_name})] '
eval_stats = evaluate(model, data_loader_val, device,
num_input_tokens=args.num_input_tokens,
num_target_tokens=args.num_target_tokens,
all_domains=args.all_domains, dtype=dtype,
prefix=prefix, loss_type=args.loss_type)
print("Eval Stats:" if not dataset_name else f"Eval Stats ({dataset_name}):")
print(eval_stats)
print()
if data_loaders_fixed_eval is not None:
for dataset_name, data_loader_fixed_eval in data_loaders_fixed_eval.items():
prefix = '[Fixed Eval] ' if not dataset_name else f'[Fixed Eval ({dataset_name})] '
fixed_eval_stats = evaluate(model, data_loader_fixed_eval, device,
num_input_tokens=args.fixed_eval_input_tokens,
num_target_tokens=args.fixed_eval_target_tokens,
all_domains=args.all_domains, dtype=dtype,
prefix=prefix, loss_type=args.loss_type)
print("Fixed Eval Stats:" if not dataset_name else f"Fixed Eval Stats ({dataset_name}):")
print(fixed_eval_stats)
print()
exit(0)
## Training
print(f"Start training for {args.epochs} epochs")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if log_writer is not None:
log_writer.set_step(epoch * num_training_steps_per_epoch)
train_stats = train_one_epoch(
model=model,
data_loader=data_loader_train,
optimizer=optimizer,
device=device,
epoch=epoch,
frozen_model_epochs=args.frozen_model_epochs,
loss_scaler=loss_scaler,
accum_iter=args.accum_iter,
max_norm=args.clip_grad,
max_skip_norm=args.skip_grad,
log_writer=log_writer,
start_steps=epoch * num_training_steps_per_epoch,
lr_schedule_values=lr_schedule_values,
wd_schedule_values=wd_schedule_values,
num_input_tokens=args.num_input_tokens,
num_target_tokens=args.num_target_tokens,
all_domains=args.all_domains,
dtype=dtype,
loader_len=num_training_steps_per_epoch,
output_dir=args.output_dir,
compute_grad_norm=args.compute_grad_norm,
loss_type=args.loss_type,
total_batch_size=total_batch_size,
)
if args.output_dir:
if (epoch + 1) % args.save_ckpt_freq == 0 or epoch + 1 == args.epochs:
utils.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch)
if epoch + 1 == args.epochs:
use_s3 = len(args.s3_save_dir) > 0
utils.save_model(
args=args, model=model, model_without_ddp=model_without_ddp, optimizer=optimizer,
loss_scaler=loss_scaler, epoch=epoch, ckpt_name='final', use_s3=use_s3)
log_stats = {**{k: v for k, v in train_stats.items()},
'epoch': epoch,
'n_parameters': n_parameters,
'input_tokens_seen_b': (epoch + 1) * num_training_steps_per_epoch * (total_batch_size / args.accum_iter) * args.num_input_tokens / 1e9,
'target_tokens_seen_b': (epoch + 1) * num_training_steps_per_epoch * (total_batch_size / args.accum_iter) * args.num_target_tokens / 1e9,
'total_tokens_seen_b': (epoch + 1) * num_training_steps_per_epoch * (total_batch_size / args.accum_iter) * (args.num_input_tokens + args.num_target_tokens) / 1e9,
}
if data_loaders_val is not None and ((epoch + 1) % args.eval_freq == 0 or epoch + 1 == args.epochs):
for dataset_name, data_loader_val in data_loaders_val.items():
prefix = '[Eval] ' if not dataset_name else f'[Eval ({dataset_name})] '
eval_stats = evaluate(model, data_loader_val, device, num_input_tokens=args.num_input_tokens, num_target_tokens=args.num_target_tokens,
all_domains=args.all_domains, dtype=dtype, prefix=prefix, loss_type=args.loss_type)
extra_stats = {**{k: v for k, v in eval_stats.items()}}
log_stats.update(extra_stats)
if data_loaders_fixed_eval is not None and ((epoch + 1) % args.eval_freq == 0 or epoch + 1 == args.epochs):
for dataset_name, data_loader_fixed_eval in data_loaders_fixed_eval.items():
prefix = '[Fixed Eval] ' if not dataset_name else f'[Fixed Eval ({dataset_name})] '
fixed_eval_stats = evaluate(model, data_loader_fixed_eval, device, num_input_tokens=args.fixed_eval_input_tokens, num_target_tokens=args.fixed_eval_target_tokens,
all_domains=args.all_domains, dtype=dtype, prefix=prefix, loss_type=args.loss_type)
extra_stats = {**{k: v for k, v in fixed_eval_stats.items()}}
log_stats.update(extra_stats)
if log_writer is not None:
log_writer.update(log_stats)
if args.output_dir and utils.is_main_process():
with open(os.path.join(args.output_dir, "log.txt"), mode="a", encoding="utf-8") as f:
f.write(json.dumps(log_stats) + "\n")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
def train_one_epoch(model: torch.nn.Module, data_loader: Iterable, optimizer: torch.optim.Optimizer,
num_input_tokens: int, num_target_tokens: int, loss_type: str, device: torch.device, epoch: int, frozen_model_epochs: int,
loss_scaler, accum_iter, max_norm: float = None, max_skip_norm: float = None, log_writer=None,
lr_scheduler=None, start_steps=None, lr_schedule_values=None, wd_schedule_values=None,
all_domains: List[str] = [], dtype: torch.dtype = torch.float16, loader_len: Optional[int] = None,
output_dir=None, compute_grad_norm=True, total_batch_size=None):
model.train()
if frozen_model_epochs > 0 and epoch < frozen_model_epochs:
if args.frozen_embedding_domain is None:
model.module.freeze_shared_params()
else:
model.module.freeze_params_except_specific_embeddings(args.frozen_embedding_domain)
else:
model.module.unfreeze_all()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
for step, x in enumerate(metric_logger.log_every(data_loader, print_freq, iter_len=loader_len, header=header)):
# Assign learning rate & weight decay for each step
it = start_steps + step # global training iteration
update_grad = (step + 1) % accum_iter == 0
if step % accum_iter == 0:
if lr_schedule_values is not None or wd_schedule_values is not None:
for i, param_group in enumerate(optimizer.param_groups):
if lr_schedule_values is not None:
param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
if wd_schedule_values is not None and param_group["weight_decay"] > 0:
param_group["weight_decay"] = wd_schedule_values[it]
mod_dict = {
modality: {k: v.to(device, non_blocking=True) for k, v in d.items()}
for modality, d in x.items()
if modality in all_domains
}
# Only sync if we update grad (for accum_iter)
# See https://muellerzr.github.io/blog/gradient_accumulation.html
with nullcontext() if update_grad else model.no_sync():
with torch.cuda.amp.autocast(dtype=dtype, enabled=dtype != torch.float32):
loss, mod_loss = model(mod_dict, num_encoder_tokens=num_input_tokens, num_decoder_tokens=num_target_tokens, loss_type=loss_type)
loss_value = loss.item()
mod_loss_values = {f'{mod}_loss': l.item() for mod, l in mod_loss.items()}
if not math.isfinite(loss_value):
torch.save(mod_dict, os.path.join(output_dir, "debug_mod_dict.pt"))
print(f"Loss is {loss_value}, stopping training", file=sys.stderr)
sys.exit(1)
loss = loss / accum_iter
grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm, skip_grad=max_skip_norm,
parameters=model.parameters(), compute_grad_norm=compute_grad_norm,
update_grad=update_grad)
if update_grad:
optimizer.zero_grad()
if dtype == torch.float16:
loss_scale_value = loss_scaler.state_dict()["scale"]
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
metric_logger.update(**mod_loss_values)
if dtype == torch.float16:
metric_logger.update(loss_scale=loss_scale_value)
min_lr = 1.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
metric_logger.update(min_lr=min_lr)
weight_decay_value = None
for group in optimizer.param_groups:
if group["weight_decay"] > 0:
weight_decay_value = group["weight_decay"]
metric_logger.update(weight_decay=weight_decay_value)
metric_logger.update(grad_norm=grad_norm)
if log_writer is not None:
log_writer.update(
{
'loss': loss_value,
'lr': max_lr,
'weight_decay': weight_decay_value,
'grad_norm': grad_norm,
}
)
log_writer.update(mod_loss_values)
if total_batch_size is not None:
log_writer.update(
{
'input_tokens_seen_b': it * (total_batch_size / accum_iter) * num_input_tokens / 1e9,
'target_tokens_seen_b': it * (total_batch_size /accum_iter) * num_target_tokens / 1e9,
'total_tokens_seen_b': it * (total_batch_size / accum_iter) * (num_input_tokens + num_target_tokens) / 1e9,
}
)
log_writer.set_step()
if lr_scheduler is not None:
lr_scheduler.step_update(start_steps + step)
# Gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
torch.cuda.empty_cache()
return {'[Epoch] ' + k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(model, data_loader, device, num_input_tokens, num_target_tokens, loss_type,
all_domains: List[str], dtype: torch.dtype = torch.float16, prefix="[Eval] "):
metric_logger = utils.MetricLogger(delimiter=" ")
header = prefix
# switch to evaluation mode
model.eval()
print_freq = 10
iter_len = len(data_loader) if hasattr(data_loader, '__len__') else -1 # Dealing with iterable datasets
for x in metric_logger.log_every(data_loader, print_freq, iter_len=iter_len, header=header):
mod_dict = {
modality: {k: v.to(device, non_blocking=True) for k, v in d.items()}
for modality, d in x.items()
if modality in all_domains
}
with torch.cuda.amp.autocast(dtype=dtype, enabled=dtype != torch.float32):
loss, mod_loss = model(mod_dict, num_encoder_tokens=num_input_tokens, num_decoder_tokens=num_target_tokens, loss_type=loss_type)
loss_value = loss.item()
mod_loss_values = {f'{mod}_loss': l.item() for mod, l in mod_loss.items()}
metric_logger.update(loss=loss_value)
metric_logger.update(**mod_loss_values)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Eval averaged stats:", metric_logger)
torch.cuda.empty_cache()
return {prefix + k: meter.global_avg for k, meter in metric_logger.meters.items()}
if __name__ == '__main__':
args = get_args()
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (args.rlimit, rlimit[1]))
utils.setup_run_name(args)
utils.setup_s3_args(args)
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)