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# -*- encoding: utf-8 -*-
import os
import sys
import torch
import torch.nn as nn
import hydra
import logging
import time
import argparse
from io import BytesIO
from contextlib import nullcontext
import torch.distributed as dist
from omegaconf import DictConfig, OmegaConf
from torch.cuda.amp import autocast, GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.algorithms.join import Join
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
from funasr.train_utils.average_nbest_models import average_checkpoints
from funasr.register import tables
from funasr.optimizers import optim_classes
from funasr.train_utils.trainer_ds import Trainer
from funasr.schedulers import scheduler_classes
from funasr.train_utils.initialize import initialize
from funasr.download.download_model_from_hub import download_model
from funasr.models.lora.utils import mark_only_lora_as_trainable
from funasr.train_utils.set_all_random_seed import set_all_random_seed
from funasr.train_utils.load_pretrained_model import load_pretrained_model
from funasr.utils.misc import prepare_model_dir
from funasr.train_utils.model_summary import model_summary
from funasr import AutoModel
try:
import deepspeed
except:
deepspeed = None
@hydra.main(config_name=None, version_base=None)
def main_hydra(kwargs: DictConfig):
if kwargs.get("debug", False):
import pdb
pdb.set_trace()
assert "model" in kwargs
if "model_conf" not in kwargs:
logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
main(**kwargs)
def main(**kwargs):
# set random seed
set_all_random_seed(kwargs.get("seed", 0))
torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
# open tf32
torch.backends.cuda.matmul.allow_tf32 = kwargs.get("enable_tf32", True)
rank = int(os.environ.get("RANK", 0))
local_rank = int(os.environ.get("LOCAL_RANK", 0))
world_size = int(os.environ.get("WORLD_SIZE", 1))
if local_rank == 0:
tables.print()
use_ddp = world_size > 1
use_fsdp = kwargs.get("use_fsdp", False)
use_deepspeed = kwargs.get("use_deepspeed", False)
if use_deepspeed:
logging.info(f"use_deepspeed: {use_deepspeed}")
deepspeed.init_distributed(dist_backend=kwargs.get("backend", "nccl"))
elif use_ddp or use_fsdp:
logging.info(f"use_ddp: {use_ddp}, use_fsdp: {use_fsdp}")
dist.init_process_group(
backend=kwargs.get("backend", "nccl"),
init_method="env://",
)
torch.cuda.set_device(local_rank)
# rank = dist.get_rank()
logging.info("Build model, frontend, tokenizer")
device = kwargs.get("device", "cuda")
kwargs["device"] = "cpu"
model = AutoModel(**kwargs)
# save config.yaml
if rank == 0:
prepare_model_dir(**kwargs)
# parse kwargs
kwargs = model.kwargs
kwargs["device"] = device
tokenizer = kwargs["tokenizer"]
frontend = kwargs["frontend"]
model = model.model
del kwargs["model"]
# freeze_param
freeze_param = kwargs.get("freeze_param", None)
if freeze_param is not None:
if "," in freeze_param:
freeze_param = eval(freeze_param)
if not isinstance(freeze_param, (list, tuple)):
freeze_param = (freeze_param,)
logging.info("freeze_param is not None: %s", freeze_param)
for t in freeze_param:
for k, p in model.named_parameters():
if k.startswith(t + ".") or k == t:
logging.info(f"Setting {k}.requires_grad = False")
p.requires_grad = False
if local_rank == 0:
logging.info(f"{model_summary(model)}")
trainer = Trainer(
rank=rank,
local_rank=local_rank,
world_size=world_size,
use_ddp=use_ddp,
use_fsdp=use_fsdp,
device=kwargs["device"],
excludes=kwargs.get("excludes", None),
output_dir=kwargs.get("output_dir", "./exp"),
**kwargs.get("train_conf"),
)
model = trainer.warp_model(model, **kwargs)
kwargs["device"] = int(os.environ.get("LOCAL_RANK", 0))
trainer.device = int(os.environ.get("LOCAL_RANK", 0))
model, optim, scheduler = trainer.warp_optim_scheduler(model, **kwargs)
# dataset
logging.info("Build dataloader")
dataloader_class = tables.dataloader_classes.get(
kwargs["dataset_conf"].get("dataloader", "DataloaderMapStyle")
)
dataloader = dataloader_class(**kwargs)
# dataloader_tr, dataloader_val = dataloader_class(**kwargs)
scaler = GradScaler(enabled=True) if trainer.use_fp16 or trainer.use_bf16 else None
scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler
trainer.resume_checkpoint(
model=model,
optim=optim,
scheduler=scheduler,
scaler=scaler,
)
dataloader_tr, dataloader_val = None, None
for epoch in range(trainer.start_epoch, trainer.max_epoch):
time1 = time.perf_counter()
for data_split_i in range(trainer.start_data_split_i, dataloader.data_split_num):
time_slice_i = time.perf_counter()
dataloader_tr, dataloader_val = dataloader.build_iter(
epoch, data_split_i=data_split_i, start_step=trainer.start_step
)
trainer.train_epoch(
model=model,
optim=optim,
scheduler=scheduler,
scaler=scaler,
dataloader_train=dataloader_tr,
dataloader_val=dataloader_val,
epoch=epoch,
data_split_i=data_split_i,
data_split_num=dataloader.data_split_num,
start_step=trainer.start_step,
)
trainer.start_step = 0
device = next(model.parameters()).device
if device.type == "cuda":
with torch.cuda.device(device):
torch.cuda.empty_cache()
time_escaped = (time.perf_counter() - time_slice_i) / 3600.0
logging.info(
f"\n\nrank: {local_rank}, "
f"time_escaped_epoch: {time_escaped:.3f} hours, "
f"estimated to finish {dataloader.data_split_num} data_slices, remaining: {dataloader.data_split_num-data_split_i} slices, {(dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours, "
f"epoch: {trainer.max_epoch - epoch} epochs, {((trainer.max_epoch - epoch - 1)*dataloader.data_split_num + dataloader.data_split_num-data_split_i)*time_escaped:.3f} hours\n"
)
trainer.start_data_split_i = 0
trainer.validate_epoch(model=model, dataloader_val=dataloader_val, epoch=epoch + 1)
scheduler.step()
trainer.step_in_epoch = 0
trainer.save_checkpoint(
epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler
)
time2 = time.perf_counter()
time_escaped = (time2 - time1) / 3600.0
logging.info(
f"\n\nrank: {local_rank}, "
f"time_escaped_epoch: {time_escaped:.3f} hours, "
f"estimated to finish {trainer.max_epoch} "
f"epoch: {(trainer.max_epoch - epoch) * time_escaped:.3f} hours\n"
)
trainer.train_acc_avg = 0.0
trainer.train_loss_avg = 0.0
if trainer.rank == 0:
average_checkpoints(
trainer.output_dir, trainer.avg_nbest_model, use_deepspeed=trainer.use_deepspeed
)
trainer.close()
if __name__ == "__main__":
main_hydra()
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