451 lines
15 KiB
Python
Executable File
451 lines
15 KiB
Python
Executable File
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 torch
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import torch.distributed as dist
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from torch.utils.data import DataLoader
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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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class Trainer:
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"""
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Base class for training.
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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='inflat_all',
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fp16_scale_growth=1e-3,
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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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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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self.fp16_mode = fp16_mode
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self.fp16_scale_growth = fp16_scale_growth
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self.log_param_stats = log_param_stats
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self.prefetch_data = prefetch_data
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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.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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@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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@abstractmethod
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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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pass
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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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@abstractmethod
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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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pass
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@abstractmethod
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def save(self):
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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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pass
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@abstractmethod
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def finetune_from(self, finetune_ckpt):
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"""
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Finetune from a checkpoint.
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Should be called by all processes.
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"""
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pass
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@abstractmethod
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def run_snapshot(self, num_samples, batch_size=4, verbose=False, **kwargs):
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"""
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Run a snapshot of the model.
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"""
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pass
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@torch.no_grad()
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def visualize_sample(self, sample):
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"""
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Convert a sample to an image.
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"""
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if hasattr(self.dataset, 'visualize_sample'):
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return self.dataset.visualize_sample(sample)
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else:
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return sample
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@torch.no_grad()
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def snapshot_dataset(self, num_samples=100):
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"""
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Sample images from the dataset.
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"""
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dataloader = torch.utils.data.DataLoader(
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self.dataset,
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batch_size=num_samples,
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num_workers=0,
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shuffle=True,
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collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
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)
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data = next(iter(dataloader))
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data = recursive_to_device(data, self.device)
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vis = self.visualize_sample(data)
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if isinstance(vis, dict):
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save_cfg = [(f'dataset_{k}', v) for k, v in vis.items()]
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else:
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save_cfg = [('dataset', vis)]
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for name, image in save_cfg:
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utils.save_image(
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image,
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os.path.join(self.output_dir, 'samples', f'{name}.jpg'),
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nrow=int(np.sqrt(num_samples)),
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normalize=True,
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value_range=self.dataset.value_range,
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)
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@torch.no_grad()
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def snapshot(self, suffix=None, num_samples=64, batch_size=4, verbose=False):
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"""
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Sample images from the model.
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NOTE: This function should be called by all processes.
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"""
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if self.is_master:
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print(f'\nSampling {num_samples} images...', end='')
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if suffix is None:
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suffix = f'step{self.step:07d}'
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# Assign tasks
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num_samples_per_process = int(np.ceil(num_samples / self.world_size))
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samples = self.run_snapshot(num_samples_per_process, batch_size=batch_size, verbose=verbose)
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# Preprocess images
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for key in list(samples.keys()):
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if samples[key]['type'] == 'sample':
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vis = self.visualize_sample(samples[key]['value'])
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if isinstance(vis, dict):
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for k, v in vis.items():
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samples[f'{key}_{k}'] = {'value': v, 'type': 'image'}
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del samples[key]
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else:
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samples[key] = {'value': vis, 'type': 'image'}
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# Gather results
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if self.world_size > 1:
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for key in samples.keys():
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samples[key]['value'] = samples[key]['value'].contiguous()
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if self.is_master:
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all_images = [torch.empty_like(samples[key]['value']) for _ in range(self.world_size)]
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else:
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all_images = []
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dist.gather(samples[key]['value'], all_images, dst=0)
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if self.is_master:
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samples[key]['value'] = torch.cat(all_images, dim=0)[:num_samples]
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# Save images
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if self.is_master:
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os.makedirs(os.path.join(self.output_dir, 'samples', suffix), exist_ok=True)
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for key in samples.keys():
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if samples[key]['type'] == 'image':
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utils.save_image(
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samples[key]['value'],
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os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
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nrow=int(np.sqrt(num_samples)),
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normalize=True,
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value_range=self.dataset.value_range,
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)
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elif samples[key]['type'] == 'number':
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min = samples[key]['value'].min()
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max = samples[key]['value'].max()
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images = (samples[key]['value'] - min) / (max - min)
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images = utils.make_grid(
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images,
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nrow=int(np.sqrt(num_samples)),
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normalize=False,
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)
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save_image_with_notes(
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images,
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os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
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notes=f'{key} min: {min}, max: {max}',
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)
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if self.is_master:
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print(' Done.')
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@abstractmethod
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def update_ema(self):
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"""
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Update exponential moving average.
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Should only be called by the rank 0 process.
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"""
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pass
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@abstractmethod
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def check_ddp(self):
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"""
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Check if DDP is working properly.
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Should be called by all process.
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"""
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pass
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@abstractmethod
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def training_losses(**mb_data):
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"""
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Compute training losses.
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"""
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pass
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def load_data(self):
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"""
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Load data.
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"""
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if self.prefetch_data:
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if self._data_prefetched is None:
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self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
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data = self._data_prefetched
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self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
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else:
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data = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
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# if the data is a dict, we need to split it into multiple dicts with batch_size_per_gpu
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if isinstance(data, dict):
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if self.batch_split == 1:
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data_list = [data]
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else:
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batch_size = list(data.values())[0].shape[0]
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data_list = [
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{k: v[i * batch_size // self.batch_split:(i + 1) * batch_size // self.batch_split] for k, v in data.items()}
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for i in range(self.batch_split)
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]
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elif isinstance(data, list):
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data_list = data
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else:
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raise ValueError('Data must be a dict or a list of dicts.')
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return data_list
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@abstractmethod
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def run_step(self, data_list):
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"""
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Run a training step.
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"""
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pass
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def run(self):
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"""
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Run training.
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"""
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if self.is_master:
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print('\nStarting training...')
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self.snapshot_dataset()
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if self.step == 0:
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self.snapshot(suffix='init')
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else: # resume
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self.snapshot(suffix=f'resume_step{self.step:07d}')
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log = []
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time_last_print = 0.0
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time_elapsed = 0.0
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while self.step < self.max_steps:
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time_start = time.time()
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data_list = self.load_data()
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step_log = self.run_step(data_list)
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time_end = time.time()
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time_elapsed += time_end - time_start
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self.step += 1
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# Print progress
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if self.is_master and self.step % self.i_print == 0:
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speed = self.i_print / (time_elapsed - time_last_print) * 3600
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columns = [
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f'Step: {self.step}/{self.max_steps} ({self.step / self.max_steps * 100:.2f}%)',
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f'Elapsed: {time_elapsed / 3600:.2f} h',
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f'Speed: {speed:.2f} steps/h',
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f'ETA: {(self.max_steps - self.step) / speed:.2f} h',
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]
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print(' | '.join([c.ljust(25) for c in columns]), flush=True)
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time_last_print = time_elapsed
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# Check ddp
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if self.world_size > 1 and self.i_ddpcheck is not None and self.step % self.i_ddpcheck == 0:
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self.check_ddp()
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# Sample images
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if self.step % self.i_sample == 0:
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self.snapshot()
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if self.is_master:
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log.append((self.step, {}))
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# Log time
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log[-1][1]['time'] = {
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'step': time_end - time_start,
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'elapsed': time_elapsed,
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}
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# Log losses
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if step_log is not None:
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log[-1][1].update(step_log)
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# Log scale
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if self.fp16_mode == 'amp':
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log[-1][1]['scale'] = self.scaler.get_scale()
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elif self.fp16_mode == 'inflat_all':
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log[-1][1]['log_scale'] = self.log_scale
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# Save log
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if self.step % self.i_log == 0:
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## save to log file
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log_str = '\n'.join([
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f'{step}: {json.dumps(log)}' for step, log in log
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])
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with open(os.path.join(self.output_dir, 'log.txt'), 'a') as log_file:
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log_file.write(log_str + '\n')
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# show with mlflow
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log_show = [l for _, l in log if not dict_any(l, lambda x: np.isnan(x))]
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log_show = dict_reduce(log_show, lambda x: np.mean(x))
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log_show = dict_flatten(log_show, sep='/')
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for key, value in log_show.items():
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self.writer.add_scalar(key, value, self.step)
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log = []
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# Save checkpoint
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if self.step % self.i_save == 0:
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self.save()
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if self.is_master:
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self.snapshot(suffix='final')
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self.writer.close()
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print('Training finished.')
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def profile(self, wait=2, warmup=3, active=5):
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"""
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Profile the training loop.
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"""
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with torch.profiler.profile(
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schedule=torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=1),
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on_trace_ready=torch.profiler.tensorboard_trace_handler(os.path.join(self.output_dir, 'profile')),
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profile_memory=True,
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with_stack=True,
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) as prof:
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for _ in range(wait + warmup + active):
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self.run_step()
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prof.step()
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