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import argparse
import copy
import glob
import os
import random
import numpy as np
import torch
import wandb
from data import get_data
from distributed import init_distributed_device, world_info_from_env
from torch.nn.parallel import DistributedDataParallel as DDP
from train_utils import get_checkpoint, train_one_epoch
from transformers import (
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_linear_schedule_with_warmup,
)
from open_flamingo import create_model_and_transforms
def random_seed(seed=42, rank=0):
torch.manual_seed(seed + rank)
np.random.seed(seed + rank)
random.seed(seed + rank)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--vision_encoder_path", default="ViT-L-14", type=str)
parser.add_argument("--vision_encoder_pretrained", default="openai", type=str)
parser.add_argument("--lm_path", default="facebook/opt-1.3b", type=str)
parser.add_argument(
"--tokenizer_path",
default="facebook/opt-30b",
type=str,
help="path to tokenizer",
)
parser.add_argument(
"--cross_attn_every_n_layers",
type=int,
default=1,
help="how often to add a cross-attention layer after each transformer layer",
)
parser.add_argument(
"--run_name",
type=str,
default="openflamingo3B",
help="used to name saving directory and wandb run",
)
parser.add_argument("--use_media_placement_augmentation", action="store_true")
parser.add_argument("--offline", action="store_true")
parser.add_argument("--num_epochs", type=int, default=1)
parser.add_argument(
"--logging_steps", type=int, default=100, help="log loss every n steps"
)
# Sum of gradient optimization batch size
parser.add_argument("--batch_size_mmc4", type=int, default=128)
parser.add_argument("--batch_size_laion", type=int, default=128)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument(
"--resume_from_checkpoint",
type=str,
help="path to checkpoint to resume from, this should contain model, optimizer, and lr_scheduler states",
default=None,
)
parser.add_argument(
"--delete_previous_checkpoint",
action="store_true",
help="delete previous checkpoint when saving new checkpoint",
)
parser.add_argument(
"--laion_shards",
type=str,
help="path to laion shards, this should be a glob pattern such as /path/to/shards/shard-{0000..0999}.tar",
)
parser.add_argument(
"--mmc4_shards",
type=str,
help="path to c4 shards, this should be a glob pattern such as /path/to/shards/shard-{0000..0999}.tar",
)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--learning_rate", default=1e-4, type=float)
parser.add_argument(
"--lr_scheduler",
default="constant",
type=str,
help="constant, linear, or cosine",
)
parser.add_argument("--loss_multiplier_mmc4", type=float, default=1.0)
parser.add_argument("--loss_multiplier_laion", type=float, default=1.0)
parser.add_argument("--warmup_steps", default=5000, type=int)
parser.add_argument("--weight_decay", default=0.1, type=float)
parser.add_argument(
"--precision",
choices=["amp_bf16", "amp_bfloat16", "bf16", "fp16", "fp32"],
default="fp32",
help="Floating point precision.",
)
# data args
parser.add_argument("--workers", type=int, default=1)
parser.add_argument("--train_num_samples_mmc4", type=int, default=10000)
parser.add_argument("--train_num_samples_laion", type=int, default=10000)
parser.add_argument("--dataset_resampled", action="store_true")
# distributed training args
parser.add_argument(
"--dist-url",
default="env://",
type=str,
help="url used to set up distributed training",
)
parser.add_argument(
"--dist-backend", default="nccl", type=str, help="distributed backend"
)
parser.add_argument(
"--horovod",
default=False,
action="store_true",
help="Use horovod for distributed training.",
)
parser.add_argument(
"--no-set-device-rank",
default=False,
action="store_true",
help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc).",
)
# wandb args
parser.add_argument("--report_to_wandb", default=False, action="store_true")
parser.add_argument(
"--wandb_project",
type=str,
)
parser.add_argument(
"--wandb_entity",
type=str,
)
parser.add_argument(
"--save_checkpoints_to_wandb",
default=False,
action="store_true",
help="save checkpoints to wandb",
)
parser.add_argument(
"--mmc4_textsim_threshold",
default=30,
type=float,
help="threshold for filtering images in mmc4 based on image-text similarity",
)
args = parser.parse_args()
if args.laion_shards.startswith("s3"):
args.laion_shards = f"pipe:aws s3 cp {args.laion_shards} -"
if args.mmc4_shards.startswith("s3"):
args.mmc4_shards = f"pipe:aws s3 cp {args.mmc4_shards} -"
if args.save_checkpoints_to_wandb and not args.report_to_wandb:
raise ValueError("save_checkpoints_to_wandb requires report_to_wandb")
assert (args.train_num_samples_laion // args.batch_size_laion) == (
args.train_num_samples_mmc4 // args.batch_size_mmc4
), "number of samples per epoch must be equal for mmc4 and laion"
if args.offline:
os.environ["WANDB_MODE"] = "offline"
os.environ["TRANSFORMERS_OFFLINE"] = "1"
args.local_rank, args.rank, args.world_size = world_info_from_env()
device_id = init_distributed_device(args)
random_seed(args.seed)
model, image_processor, tokenizer = create_model_and_transforms(
args.vision_encoder_path,
args.vision_encoder_pretrained,
args.lm_path,
args.tokenizer_path if args.tokenizer_path else args.lm_path,
cross_attn_every_n_layers=args.cross_attn_every_n_layers,
use_local_files=args.offline,
use_media_placement_augmentation=args.use_media_placement_augmentation,
)
random_seed(args.seed, args.rank)
print(f"Start running training on rank {args.rank}.")
if args.rank == 0 and args.report_to_wandb:
wandb.init(
project=args.wandb_project,
entity=args.wandb_entity,
name=args.run_name,
config=vars(args),
)
device_id = args.rank % torch.cuda.device_count()
model = model.to(device_id)
ddp_model = DDP(model, device_ids=[device_id])
laion_dataset = get_data(args, image_processor, tokenizer, "image_text")
mmc4_dataset = get_data(args, image_processor, tokenizer, "mmc4")
def get_grouped_params(model):
params_with_wd, params_without_wd = [], []
def apply_decay(x):
return (
"gated_cross_attn_layer" in x
and "ff_gate" not in x
and "attn_gate" not in x
and "norm" not in x
and "bias" not in x
)
for n, p in model.named_parameters():
# if p.requires_grad:
if apply_decay(n):
params_with_wd.append(p)
else:
params_without_wd.append(p)
return [
{"params": params_with_wd, "weight_decay": args.weight_decay},
{"params": params_without_wd, "weight_decay": 0.0},
]
optimizer = torch.optim.AdamW(get_grouped_params(ddp_model), lr=args.learning_rate)
total_training_steps = (
(args.train_num_samples_mmc4) // (args.batch_size_mmc4 * args.world_size)
) * args.num_epochs
if args.rank == 0:
print(f"Total training steps: {total_training_steps}")
if args.lr_scheduler == "linear":
lr_scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=total_training_steps,
)
elif args.lr_scheduler == "cosine":
lr_scheduler = get_cosine_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=total_training_steps,
)
else:
lr_scheduler = get_constant_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps
)
# check if a checkpoint exists for this run
if os.path.exists(f"{args.run_name}") and args.resume_from_checkpoint is None:
checkpoint_list = glob.glob(f"{args.run_name}/checkpoint_*.pt")
if len(checkpoint_list) == 0:
print(f"Found no checkpoints for run {args.run_name}.")
else:
args.resume_from_checkpoint = sorted(
checkpoint_list, key=lambda x: int(x.split("_")[-1].split(".")[0])
)[-1]
print(
f"Found checkpoint {args.resume_from_checkpoint} for run {args.run_name}."
)
resume_from_epoch = 0
if args.resume_from_checkpoint is not None:
if args.rank == 0:
print(f"Loading checkpoint from {args.resume_from_checkpoint}")
checkpoint = torch.load(args.resume_from_checkpoint, map_location="cpu")
ddp_model.load_state_dict(checkpoint["model_state_dict"], False)
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
lr_scheduler.load_state_dict(checkpoint["lr_scheduler_state_dict"])
resume_from_epoch = checkpoint["epoch"] + 1
ddp_model.train()
for epoch in range(resume_from_epoch, args.num_epochs):
laion_dataset.set_epoch(epoch)
laion_loader = laion_dataset.dataloader
mmc4_dataset.set_epoch(epoch)
mmc4_loader = mmc4_dataset.dataloader
train_one_epoch(
args=args,
model=ddp_model,
epoch=epoch,
tokenizer=tokenizer,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
laion_loader=laion_loader,
mmc4_loader=mmc4_loader,
device_id=device_id,
wandb=wandb,
)
if args.rank == 0:
if not os.path.exists(args.run_name):
os.makedirs(args.run_name)
checkpoint_dict = {
"epoch": epoch,
"model_state_dict": get_checkpoint(ddp_model),
"optimizer_state_dict": optimizer.state_dict(),
"lr_scheduler_state_dict": lr_scheduler.state_dict(),
}
print(f"Saving checkpoint to {args.run_name}/checkpoint_{epoch}.pt")
torch.save(checkpoint_dict, f"{args.run_name}/checkpoint_{epoch}.pt")
if args.report_to_wandb and args.save_checkpoints_to_wandb:
wandb.save(f"{args.run_name}/checkpoint_{epoch}.pt")
if args.delete_previous_checkpoint:
if epoch > 0:
os.remove(f"{args.run_name}/checkpoint_{epoch-1}.pt")
if args.rank == 0:
if not os.path.exists(args.run_name):
os.makedirs(args.run_name)
torch.save(get_checkpoint(ddp_model), f"{args.run_name}/final_weights.pt")
if args.report_to_wandb and args.save_checkpoints_to_wandb:
wandb.save(f"{args.run_name}/final_weights.pt")
if __name__ == "__main__":
main()
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