255 lines
8.6 KiB
Python
255 lines
8.6 KiB
Python
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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import os
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import sys
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import torch
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import torch.nn as nn
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import hydra
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import logging
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import time
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import argparse
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from io import BytesIO
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from contextlib import nullcontext
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import torch.distributed as dist
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from omegaconf import DictConfig, OmegaConf
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from torch.cuda.amp import autocast, GradScaler
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.distributed.algorithms.join import Join
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from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
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from funasr.train_utils.average_nbest_models import average_checkpoints
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from funasr.register import tables
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from funasr.optimizers import optim_classes
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from funasr.train_utils.trainer import Trainer
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from funasr.schedulers import scheduler_classes
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from funasr.train_utils.initialize import initialize
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from funasr.download.download_from_hub import download_model
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from funasr.models.lora.utils import mark_only_lora_as_trainable
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from funasr.train_utils.set_all_random_seed import set_all_random_seed
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from funasr.train_utils.load_pretrained_model import load_pretrained_model
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from funasr.utils.misc import prepare_model_dir
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from funasr.train_utils.model_summary import model_summary
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from funasr import AutoModel
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@hydra.main(config_name=None, version_base=None)
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def main_hydra(kwargs: DictConfig):
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if kwargs.get("debug", False):
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import pdb
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pdb.set_trace()
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assert "model" in kwargs
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if "model_conf" not in kwargs:
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logging.info("download models from model hub: {}".format(kwargs.get("hub", "ms")))
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kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
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main(**kwargs)
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def main(**kwargs):
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# set random seed
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set_all_random_seed(kwargs.get("seed", 0))
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torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
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torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
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torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
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# open tf32
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torch.backends.cuda.matmul.allow_tf32 = kwargs.get("enable_tf32", True)
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local_rank = int(os.environ.get("LOCAL_RANK", 0))
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if local_rank == 0:
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tables.print()
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# Check if we are using DDP or FSDP
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use_ddp = "WORLD_SIZE" in os.environ and int(os.environ["WORLD_SIZE"]) > 1
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use_fsdp = kwargs.get("use_fsdp", False)
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# use_ddp = False if use_fsdp else use_fsdp
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if use_ddp or use_fsdp:
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dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method="env://")
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torch.cuda.set_device(local_rank)
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logging.info("Build model, frontend, tokenizer")
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device = kwargs.get("device", "cuda")
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kwargs["device"] = "cpu"
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model = AutoModel(**kwargs)
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# save config.yaml
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if (
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(use_ddp or use_fsdp)
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and dist.get_rank() == 0
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or not (use_ddp or use_fsdp)
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and local_rank == 0
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):
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prepare_model_dir(**kwargs)
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# parse kwargs
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kwargs = model.kwargs
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kwargs["device"] = device
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tokenizer = kwargs["tokenizer"]
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frontend = kwargs["frontend"]
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model = model.model
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del kwargs["model"]
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# freeze_param
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freeze_param = kwargs.get("freeze_param", None)
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if freeze_param is not None:
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if "," in freeze_param:
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freeze_param = eval(freeze_param)
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if not isinstance(freeze_param, (list, tuple)):
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freeze_param = (freeze_param,)
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logging.info("freeze_param is not None: %s", freeze_param)
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for t in freeze_param:
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for k, p in model.named_parameters():
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if k.startswith(t + ".") or k == t:
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logging.info(f"Setting {k}.requires_grad = False")
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p.requires_grad = False
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if local_rank == 0:
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logging.info(f"{model_summary(model)}")
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if use_ddp:
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model = model.cuda(local_rank)
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model = DDP(
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model,
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device_ids=[local_rank],
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find_unused_parameters=kwargs.get("train_conf", {}).get(
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"find_unused_parameters", False
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),
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)
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elif use_fsdp:
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# model = FSDP(model).cuda(local_rank)
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def custom_auto_wrap_policy(
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module: nn.Module,
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recurse: bool,
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nonwrapped_numel: int,
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# Additional custom arguments
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min_num_params: int = int(1e8),
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) -> bool:
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# 根据自定义逻辑决定是否包装模块
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is_large = unwrapped_params >= min_num_params
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requires_grad_uniform = len({p.requires_grad for p in module.parameters()}) == 1
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return is_large and requires_grad_uniform
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# Configure a custom `min_num_params`
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my_auto_wrap_policy = functools.partial(custom_auto_wrap_policy, min_num_params=int(1e5))
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torch.cuda.set_device(local_rank)
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model = FSDP(
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model,
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auto_wrap_policy=custom_auto_wrap_policy,
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mixed_precision=None,
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device_id=torch.cuda.current_device(),
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)
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else:
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model = model.to(device=kwargs.get("device", "cuda"))
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kwargs["device"] = next(model.parameters()).device
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# optim
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logging.info("Build optim")
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optim = kwargs.get("optim", "adam")
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assert optim in optim_classes
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optim_class = optim_classes.get(optim)
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optim = optim_class(model.parameters(), **kwargs.get("optim_conf"))
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# scheduler
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logging.info("Build scheduler")
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scheduler = kwargs.get("scheduler", "warmuplr")
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assert scheduler in scheduler_classes
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scheduler_class = scheduler_classes.get(scheduler)
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scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
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# dataset
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logging.info("Build dataloader")
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dataloader_class = tables.dataloader_classes.get(
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kwargs["dataset_conf"].get("dataloader", "DataloaderMapStyle")
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)
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dataloader = dataloader_class(**kwargs)
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# dataloader_tr, dataloader_val = dataloader_class(**kwargs)
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trainer = Trainer(
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local_rank=local_rank,
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use_ddp=use_ddp,
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use_fsdp=use_fsdp,
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device=kwargs["device"],
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output_dir=kwargs.get("output_dir", "./exp"),
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**kwargs.get("train_conf"),
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)
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scaler = GradScaler(enabled=trainer.use_fp16) if trainer.use_fp16 else None
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scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler
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trainer.resume_checkpoint(
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model=model,
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optim=optim,
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scheduler=scheduler,
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scaler=scaler,
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)
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tensorboard_dir = os.path.join(kwargs.get("output_dir"), "tensorboard")
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os.makedirs(tensorboard_dir, exist_ok=True)
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try:
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from tensorboardX import SummaryWriter
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writer = SummaryWriter(tensorboard_dir) # if trainer.rank == 0 else None
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except:
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writer = None
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dataloader_tr, dataloader_val = None, None
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for epoch in range(trainer.start_epoch, trainer.max_epoch):
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time1 = time.perf_counter()
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for data_split_i in range(trainer.start_data_split_i, dataloader.data_split_num):
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dataloader_tr, dataloader_val = dataloader.build_iter(
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epoch, data_split_i=data_split_i, start_step=trainer.start_step
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)
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trainer.train_epoch(
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model=model,
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optim=optim,
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scheduler=scheduler,
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scaler=scaler,
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dataloader_train=dataloader_tr,
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dataloader_val=dataloader_val,
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epoch=epoch,
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writer=writer,
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data_split_i=data_split_i,
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data_split_num=dataloader.data_split_num,
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start_step=trainer.start_step,
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)
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trainer.start_step = 0
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torch.cuda.empty_cache()
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trainer.start_data_split_i = 0
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trainer.validate_epoch(
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model=model, dataloader_val=dataloader_val, epoch=epoch + 1, writer=writer
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)
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scheduler.step()
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trainer.step_in_epoch = 0
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trainer.save_checkpoint(
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epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler
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)
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time2 = time.perf_counter()
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time_escaped = (time2 - time1) / 3600.0
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logging.info(
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f"rank: {local_rank}, "
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f"time_escaped_epoch: {time_escaped:.3f} hours, "
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f"estimated to finish {trainer.max_epoch} "
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f"epoch: {(trainer.max_epoch - epoch) * time_escaped:.3f} hours\n"
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)
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trainer.train_acc_avg = 0.0
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trainer.train_loss_avg = 0.0
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if trainer.rank == 0:
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average_checkpoints(trainer.output_dir, trainer.avg_nbest_model)
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trainer.close()
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if __name__ == "__main__":
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main_hydra()
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