911 lines
39 KiB
Python
911 lines
39 KiB
Python
from abc import abstractmethod
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import os
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import time
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import json
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import copy
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import threading
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from functools import partial
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from contextlib import nullcontext
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import torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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from torch.nn.parallel import DistributedDataParallel as DDP
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import numpy as np
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from torchvision import utils
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from torch.utils.tensorboard import SummaryWriter
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from .utils import *
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from ..utils.general_utils import *
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from ..utils.data_utils import recursive_to_device, cycle, ResumableSampler
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from ..utils.dist_utils import *
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from ..utils import grad_clip_utils, elastic_utils
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class BasicTrainer:
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"""
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Trainer for basic training loop.
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Args:
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models (dict[str, nn.Module]): Models to train.
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dataset (torch.utils.data.Dataset): Dataset.
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output_dir (str): Output directory.
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load_dir (str): Load directory.
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step (int): Step to load.
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batch_size (int): Batch size.
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batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
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batch_split (int): Split batch with gradient accumulation.
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max_steps (int): Max steps.
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optimizer (dict): Optimizer config.
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lr_scheduler (dict): Learning rate scheduler config.
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elastic (dict): Elastic memory management config.
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grad_clip (float or dict): Gradient clip config.
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ema_rate (float or list): Exponential moving average rates.
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mix_precision_mode (str):
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- None: No mixed precision.
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- 'inflat_all': Hold a inflated fp32 master param for all params.
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- 'amp': Automatic mixed precision.
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mix_precision_dtype (str): Mixed precision dtype.
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fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
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parallel_mode (str): Parallel mode. Options are 'ddp'.
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finetune_ckpt (dict): Finetune checkpoint.
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log_param_stats (bool): Log parameter stats.
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i_print (int): Print interval.
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i_log (int): Log interval.
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i_sample (int): Sample interval.
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i_save (int): Save interval.
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i_ddpcheck (int): DDP check interval.
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"""
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def __init__(self,
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models,
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dataset,
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*,
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output_dir,
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load_dir,
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step,
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max_steps,
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batch_size=None,
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batch_size_per_gpu=None,
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batch_split=None,
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optimizer={},
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lr_scheduler=None,
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elastic=None,
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grad_clip=None,
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ema_rate=0.9999,
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fp16_mode=None,
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mix_precision_mode='inflat_all',
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mix_precision_dtype='float16',
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fp16_scale_growth=1e-3,
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parallel_mode='ddp',
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finetune_ckpt=None,
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log_param_stats=False,
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prefetch_data=True,
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snapshot_batch_size=4,
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i_print=1000,
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i_log=500,
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i_sample=10000,
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i_save=10000,
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i_ddpcheck=10000,
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**kwargs
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):
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assert batch_size is not None or batch_size_per_gpu is not None, 'Either batch_size or batch_size_per_gpu must be specified.'
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self.models = models
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self.dataset = dataset
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self.batch_split = batch_split if batch_split is not None else 1
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self.max_steps = max_steps
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self.optimizer_config = optimizer
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self.lr_scheduler_config = lr_scheduler
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self.elastic_controller_config = elastic
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self.grad_clip = grad_clip
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self.ema_rate = [ema_rate] if isinstance(ema_rate, float) else ema_rate
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if fp16_mode is not None:
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mix_precision_dtype = 'float16'
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mix_precision_mode = fp16_mode
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self.mix_precision_mode = mix_precision_mode
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self.mix_precision_dtype = str_to_dtype(mix_precision_dtype)
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self.fp16_scale_growth = fp16_scale_growth
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self.parallel_mode = parallel_mode
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self.log_param_stats = log_param_stats
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self.prefetch_data = prefetch_data
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self.snapshot_batch_size = snapshot_batch_size
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self.log = []
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if self.prefetch_data:
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self._data_prefetched = None
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self.output_dir = output_dir
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self.i_print = i_print
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self.i_log = i_log
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self.i_sample = i_sample
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self.i_save = i_save
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self.i_ddpcheck = i_ddpcheck
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if dist.is_initialized():
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# Multi-GPU params
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self.world_size = dist.get_world_size()
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self.rank = dist.get_rank()
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self.local_rank = dist.get_rank() % torch.cuda.device_count()
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self.is_master = self.rank == 0
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else:
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# Single-GPU params
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self.world_size = 1
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self.rank = 0
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self.local_rank = 0
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self.is_master = True
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self.batch_size = batch_size if batch_size_per_gpu is None else batch_size_per_gpu * self.world_size
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self.batch_size_per_gpu = batch_size_per_gpu if batch_size_per_gpu is not None else batch_size // self.world_size
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assert self.batch_size % self.world_size == 0, 'Batch size must be divisible by the number of GPUs.'
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assert self.batch_size_per_gpu % self.batch_split == 0, 'Batch size per GPU must be divisible by batch split.'
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self.init_models_and_more(**kwargs)
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self.prepare_dataloader(**kwargs)
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# Load checkpoint
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self.step = 0
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if load_dir is not None and step is not None:
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self.load(load_dir, step)
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elif finetune_ckpt is not None:
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self.finetune_from(finetune_ckpt)
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if self.is_master:
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os.makedirs(os.path.join(self.output_dir, 'ckpts'), exist_ok=True)
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os.makedirs(os.path.join(self.output_dir, 'samples'), exist_ok=True)
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self.writer = SummaryWriter(os.path.join(self.output_dir, 'tb_logs'))
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if self.parallel_mode == 'ddp' and self.world_size > 1:
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self.check_ddp()
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if self.is_master:
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print('\n\nTrainer initialized.')
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print(self)
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def __str__(self):
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lines = []
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lines.append(self.__class__.__name__)
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lines.append(f' - Models:')
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for name, model in self.models.items():
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lines.append(f' - {name}: {model.__class__.__name__}')
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lines.append(f' - Dataset: {indent(str(self.dataset), 2)}')
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lines.append(f' - Dataloader:')
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lines.append(f' - Sampler: {self.dataloader.sampler.__class__.__name__}')
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lines.append(f' - Num workers: {self.dataloader.num_workers}')
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lines.append(f' - Number of steps: {self.max_steps}')
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lines.append(f' - Number of GPUs: {self.world_size}')
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lines.append(f' - Batch size: {self.batch_size}')
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lines.append(f' - Batch size per GPU: {self.batch_size_per_gpu}')
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lines.append(f' - Batch split: {self.batch_split}')
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lines.append(f' - Optimizer: {self.optimizer.__class__.__name__}')
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lines.append(f' - Learning rate: {self.optimizer.param_groups[0]["lr"]}')
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if self.lr_scheduler_config is not None:
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lines.append(f' - LR scheduler: {self.lr_scheduler.__class__.__name__}')
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if self.elastic_controller_config is not None:
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lines.append(f' - Elastic memory: {indent(str(self.elastic_controller), 2)}')
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if self.grad_clip is not None:
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lines.append(f' - Gradient clip: {indent(str(self.grad_clip), 2)}')
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lines.append(f' - EMA rate: {self.ema_rate}')
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lines.append(f' - Mixed precision dtype: {self.mix_precision_dtype}')
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lines.append(f' - Mixed precision mode: {self.mix_precision_mode}')
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if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
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lines.append(f' - FP16 scale growth: {self.fp16_scale_growth}')
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lines.append(f' - Parallel mode: {self.parallel_mode}')
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return '\n'.join(lines)
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@property
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def device(self):
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for _, model in self.models.items():
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if hasattr(model, 'device'):
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return model.device
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return next(list(self.models.values())[0].parameters()).device
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def init_models_and_more(self, **kwargs):
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"""
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Initialize models and more.
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"""
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if self.world_size > 1:
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# Prepare distributed data parallel
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self.training_models = {
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name: DDP(
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model,
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device_ids=[self.local_rank],
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output_device=self.local_rank,
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bucket_cap_mb=128,
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find_unused_parameters=False
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)
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for name, model in self.models.items()
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}
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else:
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self.training_models = self.models
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# Build master params
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self.model_params = sum(
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[[p for p in model.parameters() if p.requires_grad] for model in self.models.values()]
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, [])
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if self.mix_precision_mode == 'amp':
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self.master_params = self.model_params
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if self.mix_precision_dtype == torch.float16:
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self.scaler = torch.GradScaler()
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elif self.mix_precision_mode == 'inflat_all':
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self.master_params = make_master_params(self.model_params)
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if self.mix_precision_dtype == torch.float16:
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self.log_scale = 20.0
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elif self.mix_precision_mode is None:
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self.master_params = self.model_params
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else:
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raise NotImplementedError(f'Mix precision mode {self.mix_precision_mode} is not implemented.')
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# Build EMA params
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if self.is_master:
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self.ema_params = [copy.deepcopy(self.master_params) for _ in self.ema_rate]
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# Initialize optimizer
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if hasattr(torch.optim, self.optimizer_config['name']):
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self.optimizer = getattr(torch.optim, self.optimizer_config['name'])(self.master_params, **self.optimizer_config['args'])
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else:
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self.optimizer = globals()[self.optimizer_config['name']](self.master_params, **self.optimizer_config['args'])
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# Initalize learning rate scheduler
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if self.lr_scheduler_config is not None:
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if hasattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name']):
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self.lr_scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name'])(self.optimizer, **self.lr_scheduler_config['args'])
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else:
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self.lr_scheduler = globals()[self.lr_scheduler_config['name']](self.optimizer, **self.lr_scheduler_config['args'])
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# Initialize elastic memory controller
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if self.elastic_controller_config is not None:
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assert any([isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)) for model in self.models.values()]), \
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'No elastic module found in models, please inherit from ElasticModule or ElasticModuleMixin'
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self.elastic_controller = getattr(elastic_utils, self.elastic_controller_config['name'])(**self.elastic_controller_config['args'])
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for model in self.models.values():
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if isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)):
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model.register_memory_controller(self.elastic_controller)
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# Initialize gradient clipper
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if self.grad_clip is not None:
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if isinstance(self.grad_clip, (float, int)):
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self.grad_clip = float(self.grad_clip)
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else:
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self.grad_clip = getattr(grad_clip_utils, self.grad_clip['name'])(**self.grad_clip['args'])
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def prepare_dataloader(self, **kwargs):
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"""
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Prepare dataloader.
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"""
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self.data_sampler = ResumableSampler(
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self.dataset,
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shuffle=True,
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)
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self.dataloader = DataLoader(
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self.dataset,
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batch_size=self.batch_size_per_gpu,
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num_workers=int(np.ceil(os.cpu_count() / torch.cuda.device_count())),
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pin_memory=True,
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drop_last=True,
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persistent_workers=True,
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collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
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sampler=self.data_sampler,
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)
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self.data_iterator = cycle(self.dataloader)
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def _master_params_to_state_dicts(self, master_params):
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"""
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Convert master params to dict of state_dicts.
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"""
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if self.mix_precision_mode == 'inflat_all':
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master_params = unflatten_master_params(self.model_params, master_params)
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state_dicts = {name: model.state_dict() for name, model in self.models.items()}
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master_params_names = sum(
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[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
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, [])
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for i, (model_name, param_name) in enumerate(master_params_names):
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state_dicts[model_name][param_name] = master_params[i]
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return state_dicts
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def _state_dicts_to_master_params(self, master_params, state_dicts):
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"""
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Convert a state_dict to master params.
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"""
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master_params_names = sum(
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[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
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, [])
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params = [state_dicts[name][param_name] for name, param_name in master_params_names]
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if self.mix_precision_mode == 'inflat_all':
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model_params_to_master_params(params, master_params)
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else:
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for i, param in enumerate(params):
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master_params[i].data.copy_(param.data)
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def load(self, load_dir, step=0):
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"""
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Load a checkpoint.
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Should be called by all processes.
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"""
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if self.is_master:
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print(f'\nLoading checkpoint from step {step}...', end='')
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model_ckpts = {}
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for name, model in self.models.items():
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model_ckpt = torch.load(read_file_dist(os.path.join(load_dir, 'ckpts', f'{name}_step{step:07d}.pt')), map_location=self.device, weights_only=True)
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model_ckpts[name] = model_ckpt
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model.load_state_dict(model_ckpt)
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self._state_dicts_to_master_params(self.master_params, model_ckpts)
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del model_ckpts
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if self.is_master:
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for i, ema_rate in enumerate(self.ema_rate):
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ema_ckpts = {}
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for name, model in self.models.items():
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ema_ckpt = torch.load(os.path.join(load_dir, 'ckpts', f'{name}_ema{ema_rate}_step{step:07d}.pt'), map_location=self.device, weights_only=True)
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ema_ckpts[name] = ema_ckpt
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self._state_dicts_to_master_params(self.ema_params[i], ema_ckpts)
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del ema_ckpts
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misc_ckpt = torch.load(read_file_dist(os.path.join(load_dir, 'ckpts', f'misc_step{step:07d}.pt')), map_location=torch.device('cpu'), weights_only=False)
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self.optimizer.load_state_dict(misc_ckpt['optimizer'])
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self.step = misc_ckpt['step']
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self.data_sampler.load_state_dict(misc_ckpt['data_sampler'])
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if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
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self.scaler.load_state_dict(misc_ckpt['scaler'])
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elif self.mix_precision_mode == 'inflat_all' and self.mix_precision_dtype == torch.float16:
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self.log_scale = misc_ckpt['log_scale']
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if self.lr_scheduler_config is not None:
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self.lr_scheduler.load_state_dict(misc_ckpt['lr_scheduler'])
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if self.elastic_controller_config is not None:
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self.elastic_controller.load_state_dict(misc_ckpt['elastic_controller'])
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if self.grad_clip is not None and not isinstance(self.grad_clip, float):
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self.grad_clip.load_state_dict(misc_ckpt['grad_clip'])
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del misc_ckpt
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if self.world_size > 1:
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dist.barrier()
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if self.is_master:
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print(' Done.')
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if self.world_size > 1:
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self.check_ddp()
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def save(self, non_blocking=True):
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"""
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Save a checkpoint.
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Should be called only by the rank 0 process.
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"""
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assert self.is_master, 'save() should be called only by the rank 0 process.'
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print(f'\nSaving checkpoint at step {self.step}...', end='')
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model_ckpts = self._master_params_to_state_dicts(self.master_params)
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for name, model_ckpt in model_ckpts.items():
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model_ckpt = {k: v.cpu() for k, v in model_ckpt.items()} # Move to CPU for saving
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if non_blocking:
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threading.Thread(
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target=torch.save,
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args=(model_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_step{self.step:07d}.pt')),
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).start()
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else:
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torch.save(model_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_step{self.step:07d}.pt'))
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for i, ema_rate in enumerate(self.ema_rate):
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ema_ckpts = self._master_params_to_state_dicts(self.ema_params[i])
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for name, ema_ckpt in ema_ckpts.items():
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ema_ckpt = {k: v.cpu() for k, v in ema_ckpt.items()} # Move to CPU for saving
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if non_blocking:
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threading.Thread(
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target=torch.save,
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args=(ema_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_ema{ema_rate}_step{self.step:07d}.pt')),
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).start()
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else:
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torch.save(ema_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_ema{ema_rate}_step{self.step:07d}.pt'))
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misc_ckpt = {
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'optimizer': self.optimizer.state_dict(),
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'step': self.step,
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'data_sampler': self.data_sampler.state_dict(),
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}
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if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
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misc_ckpt['scaler'] = self.scaler.state_dict()
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elif self.mix_precision_mode == 'inflat_all' and self.mix_precision_dtype == torch.float16:
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misc_ckpt['log_scale'] = self.log_scale
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if self.lr_scheduler_config is not None:
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misc_ckpt['lr_scheduler'] = self.lr_scheduler.state_dict()
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if self.elastic_controller_config is not None:
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misc_ckpt['elastic_controller'] = self.elastic_controller.state_dict()
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if self.grad_clip is not None and not isinstance(self.grad_clip, float):
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misc_ckpt['grad_clip'] = self.grad_clip.state_dict()
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if non_blocking:
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threading.Thread(
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target=torch.save,
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args=(misc_ckpt, os.path.join(self.output_dir, 'ckpts', f'misc_step{self.step:07d}.pt')),
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).start()
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else:
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torch.save(misc_ckpt, os.path.join(self.output_dir, 'ckpts', f'misc_step{self.step:07d}.pt'))
|
|
print(' Done.')
|
|
|
|
def finetune_from(self, finetune_ckpt):
|
|
"""
|
|
Finetune from a checkpoint.
|
|
Should be called by all processes.
|
|
"""
|
|
if self.is_master:
|
|
print('\nFinetuning from:')
|
|
for name, path in finetune_ckpt.items():
|
|
print(f' - {name}: {path}')
|
|
|
|
model_ckpts = {}
|
|
for name, model in self.models.items():
|
|
model_state_dict = model.state_dict()
|
|
if name in finetune_ckpt:
|
|
model_ckpt = torch.load(read_file_dist(finetune_ckpt[name]), map_location=self.device, weights_only=True)
|
|
for k, v in model_ckpt.items():
|
|
if k not in model_state_dict:
|
|
if self.is_master:
|
|
print(f'Warning: {k} not found in model_state_dict, skipped.')
|
|
model_ckpt[k] = None
|
|
elif model_ckpt[k].shape != model_state_dict[k].shape:
|
|
if self.is_master:
|
|
print(f'Warning: {k} shape mismatch, {model_ckpt[k].shape} vs {model_state_dict[k].shape}, skipped.')
|
|
model_ckpt[k] = model_state_dict[k]
|
|
model_ckpt = {k: v for k, v in model_ckpt.items() if v is not None}
|
|
model_ckpts[name] = model_ckpt
|
|
model.load_state_dict(model_ckpt)
|
|
else:
|
|
if self.is_master:
|
|
print(f'Warning: {name} not found in finetune_ckpt, skipped.')
|
|
model_ckpts[name] = model_state_dict
|
|
self._state_dicts_to_master_params(self.master_params, model_ckpts)
|
|
if self.is_master:
|
|
for i, ema_rate in enumerate(self.ema_rate):
|
|
self._state_dicts_to_master_params(self.ema_params[i], model_ckpts)
|
|
del model_ckpts
|
|
|
|
if self.world_size > 1:
|
|
dist.barrier()
|
|
if self.is_master:
|
|
print('Done.')
|
|
|
|
if self.world_size > 1:
|
|
self.check_ddp()
|
|
|
|
@abstractmethod
|
|
def run_snapshot(self, num_samples, batch_size=4, verbose=False, **kwargs):
|
|
"""
|
|
Run a snapshot of the model.
|
|
"""
|
|
pass
|
|
|
|
@torch.no_grad()
|
|
def visualize_sample(self, sample):
|
|
"""
|
|
Convert a sample to an image.
|
|
"""
|
|
if hasattr(self.dataset, 'visualize_sample'):
|
|
return self.dataset.visualize_sample(sample)
|
|
else:
|
|
return sample
|
|
|
|
@torch.no_grad()
|
|
def snapshot_dataset(self, num_samples=100, batch_size=4):
|
|
"""
|
|
Sample images from the dataset.
|
|
"""
|
|
dataloader = torch.utils.data.DataLoader(
|
|
self.dataset,
|
|
batch_size=batch_size,
|
|
num_workers=1,
|
|
shuffle=True,
|
|
collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
|
|
)
|
|
save_cfg = {}
|
|
for i in range(0, num_samples, batch_size):
|
|
data = next(iter(dataloader))
|
|
data = {k: v[:min(num_samples - i, batch_size)] for k, v in data.items()}
|
|
data = recursive_to_device(data, self.device)
|
|
vis = self.visualize_sample(data)
|
|
if isinstance(vis, dict):
|
|
for k, v in vis.items():
|
|
if f'dataset_{k}' not in save_cfg:
|
|
save_cfg[f'dataset_{k}'] = []
|
|
save_cfg[f'dataset_{k}'].append(v)
|
|
else:
|
|
if 'dataset' not in save_cfg:
|
|
save_cfg['dataset'] = []
|
|
save_cfg['dataset'].append(vis)
|
|
for name, image in save_cfg.items():
|
|
utils.save_image(
|
|
torch.cat(image, dim=0),
|
|
os.path.join(self.output_dir, 'samples', f'{name}.jpg'),
|
|
nrow=int(np.sqrt(num_samples)),
|
|
normalize=True,
|
|
value_range=self.dataset.value_range,
|
|
)
|
|
|
|
@torch.no_grad()
|
|
def snapshot(self, suffix=None, num_samples=64, batch_size=4, verbose=False):
|
|
"""
|
|
Sample images from the model.
|
|
NOTE: This function should be called by all processes.
|
|
"""
|
|
if self.is_master:
|
|
print(f'\nSampling {num_samples} images...', end='')
|
|
|
|
if suffix is None:
|
|
suffix = f'step{self.step:07d}'
|
|
|
|
# Assign tasks
|
|
num_samples_per_process = int(np.ceil(num_samples / self.world_size))
|
|
amp_context = partial(torch.autocast, device_type='cuda', dtype=self.mix_precision_dtype) if self.mix_precision_mode == 'amp' else nullcontext
|
|
with amp_context():
|
|
samples = self.run_snapshot(num_samples_per_process, batch_size=batch_size, verbose=verbose)
|
|
|
|
# Preprocess images
|
|
for key in list(samples.keys()):
|
|
if samples[key]['type'] == 'sample':
|
|
vis = self.visualize_sample(samples[key]['value'])
|
|
if isinstance(vis, dict):
|
|
for k, v in vis.items():
|
|
samples[f'{key}_{k}'] = {'value': v, 'type': 'image'}
|
|
del samples[key]
|
|
else:
|
|
samples[key] = {'value': vis, 'type': 'image'}
|
|
|
|
# Gather results
|
|
if self.world_size > 1:
|
|
for key in samples.keys():
|
|
samples[key]['value'] = samples[key]['value'].contiguous()
|
|
if self.is_master:
|
|
all_images = [torch.empty_like(samples[key]['value']) for _ in range(self.world_size)]
|
|
else:
|
|
all_images = []
|
|
dist.gather(samples[key]['value'], all_images, dst=0)
|
|
if self.is_master:
|
|
samples[key]['value'] = torch.cat(all_images, dim=0)[:num_samples]
|
|
|
|
# Save images
|
|
if self.is_master:
|
|
os.makedirs(os.path.join(self.output_dir, 'samples', suffix), exist_ok=True)
|
|
for key in samples.keys():
|
|
if samples[key]['type'] == 'image':
|
|
utils.save_image(
|
|
samples[key]['value'],
|
|
os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
|
|
nrow=int(np.sqrt(num_samples)),
|
|
normalize=True,
|
|
value_range=self.dataset.value_range,
|
|
)
|
|
elif samples[key]['type'] == 'number':
|
|
min = samples[key]['value'].min()
|
|
max = samples[key]['value'].max()
|
|
images = (samples[key]['value'] - min) / (max - min)
|
|
images = utils.make_grid(
|
|
images,
|
|
nrow=int(np.sqrt(num_samples)),
|
|
normalize=False,
|
|
)
|
|
save_image_with_notes(
|
|
images,
|
|
os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
|
|
notes=f'{key} min: {min}, max: {max}',
|
|
)
|
|
|
|
if self.is_master:
|
|
print(' Done.')
|
|
|
|
def update_ema(self):
|
|
"""
|
|
Update exponential moving average.
|
|
Should only be called by the rank 0 process.
|
|
"""
|
|
assert self.is_master, 'update_ema() should be called only by the rank 0 process.'
|
|
for i, ema_rate in enumerate(self.ema_rate):
|
|
for master_param, ema_param in zip(self.master_params, self.ema_params[i]):
|
|
ema_param.detach().mul_(ema_rate).add_(master_param, alpha=1.0 - ema_rate)
|
|
|
|
def check_ddp(self):
|
|
"""
|
|
Check if DDP is working properly.
|
|
Should be called by all process.
|
|
"""
|
|
if self.is_master:
|
|
print('\nPerforming DDP check...')
|
|
|
|
if self.is_master:
|
|
print('Checking if parameters are consistent across processes...')
|
|
dist.barrier()
|
|
try:
|
|
for p in self.master_params:
|
|
# split to avoid OOM
|
|
for i in range(0, p.numel(), 10000000):
|
|
sub_size = min(10000000, p.numel() - i)
|
|
sub_p = p.detach().view(-1)[i:i+sub_size]
|
|
# gather from all processes
|
|
sub_p_gather = [torch.empty_like(sub_p) for _ in range(self.world_size)]
|
|
dist.all_gather(sub_p_gather, sub_p)
|
|
# check if equal
|
|
assert all([torch.equal(sub_p, sub_p_gather[i]) for i in range(self.world_size)]), 'parameters are not consistent across processes'
|
|
except AssertionError as e:
|
|
if self.is_master:
|
|
print(f'\n\033[91mError: {e}\033[0m')
|
|
print('DDP check failed.')
|
|
raise e
|
|
|
|
dist.barrier()
|
|
if self.is_master:
|
|
print('Done.')
|
|
|
|
@abstractmethod
|
|
def training_losses(**mb_data):
|
|
"""
|
|
Compute training losses.
|
|
"""
|
|
pass
|
|
|
|
def load_data(self):
|
|
"""
|
|
Load data.
|
|
"""
|
|
if self.prefetch_data:
|
|
if self._data_prefetched is None:
|
|
self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
|
|
data = self._data_prefetched
|
|
self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
|
|
else:
|
|
data = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
|
|
|
|
# if the data is a dict, we need to split it into multiple dicts with batch_size_per_gpu
|
|
if isinstance(data, dict):
|
|
if self.batch_split == 1:
|
|
data_list = [data]
|
|
else:
|
|
batch_size = list(data.values())[0].shape[0]
|
|
data_list = [
|
|
{k: v[i * batch_size // self.batch_split:(i + 1) * batch_size // self.batch_split] for k, v in data.items()}
|
|
for i in range(self.batch_split)
|
|
]
|
|
elif isinstance(data, list):
|
|
data_list = data
|
|
else:
|
|
raise ValueError('Data must be a dict or a list of dicts.')
|
|
|
|
return data_list
|
|
|
|
def run_step(self, data_list):
|
|
"""
|
|
Run a training step.
|
|
"""
|
|
step_log = {'loss': {}, 'status': {}}
|
|
amp_context = partial(torch.autocast, device_type='cuda', dtype=self.mix_precision_dtype) if self.mix_precision_mode == 'amp' else nullcontext
|
|
elastic_controller_context = self.elastic_controller.record if self.elastic_controller_config is not None else nullcontext
|
|
|
|
# Train
|
|
losses = []
|
|
statuses = []
|
|
elastic_controller_logs = []
|
|
zero_grad(self.model_params)
|
|
for i, mb_data in enumerate(data_list):
|
|
## sync at the end of each batch split
|
|
sync_contexts = [self.training_models[name].no_sync for name in self.training_models] if i != len(data_list) - 1 and self.world_size > 1 else [nullcontext]
|
|
with nested_contexts(*sync_contexts), elastic_controller_context():
|
|
with amp_context():
|
|
loss, status = self.training_losses(**mb_data)
|
|
l = loss['loss'] / len(data_list)
|
|
## backward
|
|
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
|
|
self.scaler.scale(l).backward()
|
|
elif self.mix_precision_mode == 'inflat_all' and self.mix_precision_dtype == torch.float16:
|
|
scaled_l = l * (2 ** self.log_scale)
|
|
scaled_l.backward()
|
|
else:
|
|
l.backward()
|
|
## log
|
|
losses.append(dict_foreach(loss, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
|
|
statuses.append(dict_foreach(status, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
|
|
if self.elastic_controller_config is not None:
|
|
elastic_controller_logs.append(self.elastic_controller.log())
|
|
## gradient clip
|
|
if self.grad_clip is not None:
|
|
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
|
|
self.scaler.unscale_(self.optimizer)
|
|
elif self.mix_precision_mode == 'inflat_all':
|
|
model_grads_to_master_grads(self.model_params, self.master_params)
|
|
if self.mix_precision_dtype == torch.float16:
|
|
self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
|
|
if isinstance(self.grad_clip, float):
|
|
grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params, self.grad_clip)
|
|
else:
|
|
grad_norm = self.grad_clip(self.master_params)
|
|
if torch.isfinite(grad_norm):
|
|
statuses[-1]['grad_norm'] = grad_norm.item()
|
|
## step
|
|
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
|
|
prev_scale = self.scaler.get_scale()
|
|
self.scaler.step(self.optimizer)
|
|
self.scaler.update()
|
|
elif self.mix_precision_mode == 'inflat_all':
|
|
if self.mix_precision_dtype == torch.float16:
|
|
prev_scale = 2 ** self.log_scale
|
|
if not any(not p.grad.isfinite().all() for p in self.model_params):
|
|
if self.grad_clip is None:
|
|
model_grads_to_master_grads(self.model_params, self.master_params)
|
|
self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
|
|
self.optimizer.step()
|
|
master_params_to_model_params(self.model_params, self.master_params)
|
|
self.log_scale += self.fp16_scale_growth
|
|
else:
|
|
self.log_scale -= 1
|
|
else:
|
|
prev_scale = 1.0
|
|
if self.grad_clip is None:
|
|
model_grads_to_master_grads(self.model_params, self.master_params)
|
|
if not any(not p.grad.isfinite().all() for p in self.master_params):
|
|
self.optimizer.step()
|
|
master_params_to_model_params(self.model_params, self.master_params)
|
|
else:
|
|
print('\n\033[93mWarning: NaN detected in gradients. Skipping update.\033[0m')
|
|
else:
|
|
prev_scale = 1.0
|
|
if not any(not p.grad.isfinite().all() for p in self.model_params):
|
|
self.optimizer.step()
|
|
else:
|
|
print('\n\033[93mWarning: NaN detected in gradients. Skipping update.\033[0m')
|
|
## adjust learning rate
|
|
if self.lr_scheduler_config is not None:
|
|
statuses[-1]['lr'] = self.lr_scheduler.get_last_lr()[0]
|
|
self.lr_scheduler.step()
|
|
|
|
# Logs
|
|
step_log['loss'] = dict_reduce(losses, lambda x: np.mean(x))
|
|
step_log['status'] = dict_reduce(statuses, lambda x: np.mean(x), special_func={'min': lambda x: np.min(x), 'max': lambda x: np.max(x)})
|
|
if self.elastic_controller_config is not None:
|
|
step_log['elastic'] = dict_reduce(elastic_controller_logs, lambda x: np.mean(x))
|
|
if self.grad_clip is not None:
|
|
step_log['grad_clip'] = self.grad_clip if isinstance(self.grad_clip, float) else self.grad_clip.log()
|
|
|
|
# Check grad and norm of each param
|
|
if self.log_param_stats:
|
|
param_norms = {}
|
|
param_grads = {}
|
|
for model_name, model in self.models.items():
|
|
for name, param in model.named_parameters():
|
|
if param.requires_grad:
|
|
param_norms[f'{model_name}.{name}'] = param.norm().item()
|
|
if param.grad is not None and torch.isfinite(param.grad).all():
|
|
param_grads[f'{model_name}.{name}'] = param.grad.norm().item() / prev_scale
|
|
step_log['param_norms'] = param_norms
|
|
step_log['param_grads'] = param_grads
|
|
|
|
# Update exponential moving average
|
|
if self.is_master:
|
|
self.update_ema()
|
|
|
|
return step_log
|
|
|
|
def save_logs(self):
|
|
log_str = '\n'.join([
|
|
f'{step}: {json.dumps(dict_foreach(log, lambda x: float(x)))}' for step, log in self.log
|
|
])
|
|
with open(os.path.join(self.output_dir, 'log.txt'), 'a') as log_file:
|
|
log_file.write(log_str + '\n')
|
|
|
|
# show with mlflow
|
|
log_show = [l for _, l in self.log if not dict_any(l, lambda x: np.isnan(x))]
|
|
log_show = dict_reduce(log_show, lambda x: np.mean(x))
|
|
log_show = dict_flatten(log_show, sep='/')
|
|
for key, value in log_show.items():
|
|
self.writer.add_scalar(key, value, self.step)
|
|
self.log = []
|
|
|
|
def check_abort(self):
|
|
"""
|
|
Check if training should be aborted due to certain conditions.
|
|
"""
|
|
# 1. If log_scale in inflat_all mode is less than 0
|
|
if self.mix_precision_dtype == torch.float16 and \
|
|
self.mix_precision_mode == 'inflat_all' and \
|
|
self.log_scale < 0:
|
|
if self.is_master:
|
|
print ('\n\n\033[91m')
|
|
print (f'ABORT: log_scale in inflat_all mode is less than 0 at step {self.step}.')
|
|
print ('This indicates that the model is diverging. You should look into the model and the data.')
|
|
print ('\033[0m')
|
|
self.save(non_blocking=False)
|
|
self.save_logs()
|
|
if self.world_size > 1:
|
|
dist.barrier()
|
|
raise ValueError('ABORT: log_scale in inflat_all mode is less than 0.')
|
|
|
|
def run(self):
|
|
"""
|
|
Run training.
|
|
"""
|
|
if self.is_master:
|
|
print('\nStarting training...')
|
|
self.snapshot_dataset(batch_size=self.snapshot_batch_size)
|
|
if self.step == 0:
|
|
self.snapshot(suffix='init', batch_size=self.snapshot_batch_size)
|
|
else: # resume
|
|
self.snapshot(suffix=f'resume_step{self.step:07d}', batch_size=self.snapshot_batch_size)
|
|
|
|
time_last_print = 0.0
|
|
time_elapsed = 0.0
|
|
while self.step < self.max_steps:
|
|
time_start = time.time()
|
|
|
|
data_list = self.load_data()
|
|
step_log = self.run_step(data_list)
|
|
|
|
time_end = time.time()
|
|
time_elapsed += time_end - time_start
|
|
|
|
self.step += 1
|
|
|
|
# Print progress
|
|
if self.is_master and self.step % self.i_print == 0:
|
|
speed = self.i_print / (time_elapsed - time_last_print) * 3600
|
|
columns = [
|
|
f'Step: {self.step}/{self.max_steps} ({self.step / self.max_steps * 100:.2f}%)',
|
|
f'Elapsed: {time_elapsed / 3600:.2f} h',
|
|
f'Speed: {speed:.2f} steps/h',
|
|
f'ETA: {(self.max_steps - self.step) / speed:.2f} h',
|
|
]
|
|
print(' | '.join([c.ljust(25) for c in columns]), flush=True)
|
|
time_last_print = time_elapsed
|
|
|
|
# Check ddp
|
|
if self.parallel_mode == 'ddp' and self.world_size > 1 and self.i_ddpcheck is not None and self.step % self.i_ddpcheck == 0:
|
|
self.check_ddp()
|
|
|
|
# Sample images
|
|
if self.step % self.i_sample == 0:
|
|
self.snapshot()
|
|
|
|
if self.is_master:
|
|
self.log.append((self.step, {}))
|
|
|
|
# Log time
|
|
self.log[-1][1]['time'] = {
|
|
'step': time_end - time_start,
|
|
'elapsed': time_elapsed,
|
|
}
|
|
|
|
# Log losses
|
|
if step_log is not None:
|
|
self.log[-1][1].update(step_log)
|
|
|
|
# Log scale
|
|
if self.mix_precision_dtype == torch.float16:
|
|
if self.mix_precision_mode == 'amp':
|
|
self.log[-1][1]['scale'] = self.scaler.get_scale()
|
|
elif self.mix_precision_mode == 'inflat_all':
|
|
self.log[-1][1]['log_scale'] = self.log_scale
|
|
|
|
# Save log
|
|
if self.step % self.i_log == 0:
|
|
self.save_logs()
|
|
|
|
# Save checkpoint
|
|
if self.step % self.i_save == 0:
|
|
self.save()
|
|
|
|
# Check abort
|
|
self.check_abort()
|
|
|
|
self.snapshot(suffix='final', batch_size=self.snapshot_batch_size)
|
|
if self.world_size > 1:
|
|
dist.barrier()
|
|
if self.is_master:
|
|
self.writer.close()
|
|
print('Training finished.')
|
|
|
|
def profile(self, wait=2, warmup=3, active=5):
|
|
"""
|
|
Profile the training loop.
|
|
"""
|
|
with torch.profiler.profile(
|
|
schedule=torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=1),
|
|
on_trace_ready=torch.profiler.tensorboard_trace_handler(os.path.join(self.output_dir, 'profile')),
|
|
profile_memory=True,
|
|
with_stack=True,
|
|
) as prof:
|
|
for _ in range(wait + warmup + active):
|
|
self.run_step()
|
|
prof.step()
|