888 lines
31 KiB
Python
888 lines
31 KiB
Python
"""
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The trainer handles all the logic for running a val loop, training loop, distributing, etc...
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"""
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import subprocess
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import traceback
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import warnings
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import os
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import pdb
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import re
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import torch
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from torch.utils.data.distributed import DistributedSampler
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import torch.multiprocessing as mp
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import torch.distributed as dist
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import numpy as np
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import tqdm
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from pytorch_lightning.root_module.memory import get_gpu_memory_map
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from pytorch_lightning.root_module.model_saving import TrainerIO
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from pytorch_lightning.pt_overrides.override_data_parallel import LightningDistributedDataParallel, LightningDataParallel
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from pytorch_lightning.utils.debugging import MisconfigurationException
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try:
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from apex import amp
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APEX_AVAILABLE = True
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except Exception:
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APEX_AVAILABLE = False
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def reduce_distributed_output(output, nb_gpus):
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if nb_gpus <= 1:
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return output
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# when using DP, we get one output per gpu
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# average outputs and return
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if type(output) is torch.Tensor:
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return output.mean()
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for k, v in output.items():
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# recurse on nested dics
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if isinstance(output[k], dict):
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output[k] = reduce_distributed_output(output[k], nb_gpus)
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# reduce only metrics that have the same nb of gpus
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elif output[k].size(0) == nb_gpus:
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reduced = torch.mean(output[k])
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output[k] = reduced
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return output
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class Trainer(TrainerIO):
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def __init__(self,
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experiment,
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early_stop_callback=None,
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checkpoint_callback=None,
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gradient_clip=0,
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cluster=None,
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process_position=0,
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current_gpu_name=0,
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nb_gpu_nodes=1,
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gpus=None,
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progress_bar=True,
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overfit_pct=0.0,
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track_grad_norm=-1,
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check_val_every_n_epoch=1,
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fast_dev_run=False,
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accumulate_grad_batches=1,
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max_nb_epochs=1000, min_nb_epochs=1,
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train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0,
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val_check_interval=0.95,
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log_save_interval=100, add_log_row_interval=10,
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distributed_backend='dp',
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use_amp=False,
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print_nan_grads=False,
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print_weights_summary=True,
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amp_level='O2',
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nb_sanity_val_steps=5):
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"""
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:param experiment: Test-tube experiment
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:param early_stop_callback: from pytorch_lightning import EarlyStopping
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:param checkpoint_callback: from pytorch_lightning import Checkpoint
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:param gradient_clip:
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:param cluster:
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:param process_position:
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:param current_gpu_name:
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:param nb_gpu_nodes:
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:param gpus:
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:param progress_bar:
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:param overfit_pct:
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:param track_grad_norm:
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:param check_val_every_n_epoch:
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:param fast_dev_run:
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:param accumulate_grad_batches:
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:param max_nb_epochs:
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:param min_nb_epochs:
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:param train_percent_check:
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:param val_percent_check:
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:param test_percent_check:
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:param val_check_interval:
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:param log_save_interval:
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:param add_log_row_interval:
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:param distributed_backend: 'np' to use DistributedParallel, 'ddp' to use DistributedDataParallel
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:param use_amp:
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:param print_nan_grads:
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:param print_weights_summary:
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:param amp_level:
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:param nb_sanity_val_steps:
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"""
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# Transfer params
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self.nb_gpu_nodes = nb_gpu_nodes
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self.gradient_clip = gradient_clip
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self.check_val_every_n_epoch = check_val_every_n_epoch
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self.enable_early_stop = early_stop_callback is not None
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self.track_grad_norm = track_grad_norm
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self.fast_dev_run = fast_dev_run
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self.on_gpu = gpus is not None and torch.cuda.is_available()
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self.progress_bar = progress_bar
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self.experiment = experiment
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self.exp_save_path = experiment.get_data_path(experiment.name, experiment.version)
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self.cluster = cluster
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self.process_position = process_position
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self.current_gpu_name = current_gpu_name
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self.print_weights_summary = print_weights_summary
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self.checkpoint_callback = checkpoint_callback
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if self.checkpoint_callback is not None:
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self.checkpoint_callback.save_function = self.save_checkpoint
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self.early_stop = early_stop_callback
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self.model = None
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self.max_nb_epochs = max_nb_epochs
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self.accumulate_grad_batches = accumulate_grad_batches
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self.early_stop_callback = early_stop_callback
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self.min_nb_epochs = min_nb_epochs
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self.nb_sanity_val_steps = nb_sanity_val_steps
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self.lr_schedulers = []
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self.amp_level = amp_level
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self.print_nan_grads = print_nan_grads
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self.data_parallel_device_ids = None
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self.world_size = 1
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self.node_rank = 0
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self.use_ddp = False
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self.use_dp = False
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# training bookeeping
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self.total_batch_nb = 0
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self.running_loss = []
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self.avg_loss = 0
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self.batch_nb = 0
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self.tqdm_metrics = {}
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self.nb_val_batches = None
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self.nb_tng_batches = None
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self.nb_test_batches = None
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# gpus come in as a string.
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# if gpus = -1 then use all available devices
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# otherwise, split the string using commas
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if gpus is not None:
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if type(gpus) is list:
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self.data_parallel_device_ids = gpus
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elif type(gpus) is str:
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if gpus == '-1':
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self.data_parallel_device_ids = list(range(0, torch.cuda.device_count()))
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else:
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self.data_parallel_device_ids = [int(x.strip()) for x in gpus.split(',')]
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else:
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raise Exception('gpus has to be a string or list of ids')
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# set the correct cuda visible devices (using pci order)
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = ','.join([str(x) for x in self.data_parallel_device_ids])
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print(f'VISIBLE GPUS: {os.environ["CUDA_VISIBLE_DEVICES"]}')
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# make DP and DDP mutually exclusive
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# single GPU will also use DP with devices=[0]
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have_gpus = self.data_parallel_device_ids is not None and len(self.data_parallel_device_ids) > 0
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if have_gpus:
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self.use_dp = distributed_backend == 'dp'
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self.use_ddp = distributed_backend == 'ddp'
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# use ddp automatically if nb_gpu_nodes > 1
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if nb_gpu_nodes > 1 and self.use_dp: # pragma: no cover
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self.use_ddp = True
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self.use_dp = False
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w = 'DataParallel does not support nb_gpu_nodes > 1. ' \
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'Switching to DistributedDataParallel for you. ' \
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'To silence this warning set distributed_backend=ddp'
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warnings.warn(w)
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# extract SLURM flag vars
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# whenever we have the correct number of tasks, we let slurm manage processes
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# otherwise we launch the required number of processes
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if self.use_ddp:
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self.nb_requested_gpus = len(self.data_parallel_device_ids) * self.nb_gpu_nodes
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self.nb_slurm_tasks = 0
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try:
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self.nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])
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self.is_slurm_managing_tasks = self.nb_slurm_tasks == self.nb_requested_gpus
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except Exception as e:
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# likely not on slurm, so set the slurm managed flag to false
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self.is_slurm_managing_tasks = False
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# process info
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self.proc_rank = 0
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# training state
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self.optimizers = None
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self.prog_bar = None
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self.global_step = 0
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self.current_epoch = 0
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self.total_batches = 0
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# logging
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self.log_save_interval = log_save_interval
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self.val_check_interval = val_check_interval
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self.add_log_row_interval = add_log_row_interval
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# dataloaders
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self.tng_dataloader = None
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self.test_dataloader = None
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self.val_dataloader = None
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# how much of the data to use
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self.__determine_data_use_amount(train_percent_check, val_percent_check, test_percent_check, overfit_pct)
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print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))
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# 16 bit mixed precision training using apex
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self.use_amp = use_amp and APEX_AVAILABLE
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if self.use_amp:
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print('using 16bit precision')
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if use_amp and not APEX_AVAILABLE: # pragma: no cover
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msg = '''
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You set use_amp=True but do not have apex installed.
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Install apex first using this guide and rerun with use_amp=True:
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https://github.com/NVIDIA/apex#linux
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this run will NOT use 16 bit precision
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'''
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raise ModuleNotFoundError(msg)
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@property
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def data_parallel(self):
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return self.use_dp or self.use_ddp
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def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct):
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"""
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Use less data for debugging purposes
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"""
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self.train_percent_check = train_percent_check
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self.val_percent_check = val_percent_check
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self.test_percent_check = test_percent_check
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if overfit_pct > 0:
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self.train_percent_check = overfit_pct
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self.val_percent_check = overfit_pct
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self.test_percent_check = overfit_pct
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def __get_model(self):
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return self.model.module if self.data_parallel else self.model
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def __is_function_implemented(self, f_name):
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model = self.__get_model()
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f_op = getattr(model, f_name, None)
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return callable(f_op)
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@property
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def __tng_tqdm_dic(self):
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# ForkedPdb().set_trace()
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tqdm_dic = {
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'tng_loss': '{0:.3f}'.format(self.avg_loss),
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'v_nb': '{}'.format(self.experiment.version),
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'epoch': '{}'.format(self.current_epoch),
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'batch_nb':'{}'.format(self.batch_nb),
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}
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tqdm_dic.update(self.tqdm_metrics)
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if self.on_gpu:
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tqdm_dic['gpu'] = '{}'.format(self.current_gpu_name)
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return tqdm_dic
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@property
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def tng_tqdm_dic(self):
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"""
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Read-only for tqdm metrics
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:return:
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"""
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return self.__tng_tqdm_dic
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def __layout_bookeeping(self):
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# determine number of training batches
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self.nb_tng_batches = len(self.tng_dataloader)
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self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)
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# determine number of validation batches
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self.nb_val_batches = len(self.val_dataloader)
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self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)
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self.nb_val_batches = max(1, self.nb_val_batches)
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self.nb_val_batches = self.nb_val_batches
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# determine number of test batches
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self.nb_test_batches = len(self.test_dataloader)
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self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)
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# determine when to check validation
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self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)
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def __add_tqdm_metrics(self, metrics):
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for k, v in metrics.items():
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if type(v) is torch.Tensor:
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v = v.item()
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self.tqdm_metrics[k] = v
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def validate(self, model, dataloader, max_batches):
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"""
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Run validation code
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:param model: PT model
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:param dataloader: PT dataloader
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:param max_batches: Scalar
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:return:
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"""
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# enable eval mode
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model.zero_grad()
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model.eval()
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# disable gradients to save memory
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torch.set_grad_enabled(False)
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# bookkeeping
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outputs = []
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# run training
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for batch_i, data_batch in enumerate(dataloader):
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if data_batch is None: # pragma: no cover
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continue
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# stop short when on fast dev run
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if max_batches is not None and batch_i >= max_batches:
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break
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# -----------------
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# RUN VALIDATION STEP
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# -----------------
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if self.use_ddp:
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output = model(data_batch, batch_i)
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elif self.use_dp:
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output = model(data_batch, batch_i)
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output = reduce_distributed_output(output, len(self.data_parallel_device_ids))
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else:
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output = model.validation_step(data_batch, batch_i)
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outputs.append(output)
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# batch done
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if self.progress_bar and self.prog_bar is not None:
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self.prog_bar.update(1)
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# give model a chance to do something with the outputs
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if self.data_parallel:
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val_results = model.module.validation_end(outputs)
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else:
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val_results = model.validation_end(outputs)
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# enable train mode again
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model.train()
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# enable gradients to save memory
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torch.set_grad_enabled(True)
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return val_results
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def get_dataloaders(self, model):
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"""
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Dataloaders are provided by the model
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:param model:
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:return:
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"""
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self.tng_dataloader = model.tng_dataloader
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self.test_dataloader = model.test_dataloader
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self.val_dataloader = model.val_dataloader
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if self.use_ddp and not isinstance(self.tng_dataloader.sampler, DistributedSampler):
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msg = '''
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when using multiple gpus and multiple nodes you must pass a DistributedSampler to DataLoader(sampler).
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ie: this:
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dataset = myDataset()
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dataloader = Dataloader(dataset)
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becomes:
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dataset = myDataset()
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dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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dataloader = Dataloader(dataset, sampler=dist_sampler)
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'''
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raise MisconfigurationException(msg)
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# -----------------------------
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# MODEL TRAINING
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# -----------------------------
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def fit(self, model):
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# when using multi-node or DDP within a node start each module in a separate process
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if self.use_ddp:
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# must copy only the meta of the exp so it survives pickle/unpickle when going to new process
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self.experiment = self.experiment.get_meta_copy()
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if self.is_slurm_managing_tasks:
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task = int(os.environ['SLURM_LOCALID'])
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self.ddp_train(task, model)
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else:
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msg = f"""
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You requested {self.nb_requested_gpus} GPUs but launched {self.nb_slurm_tasks} slurm tasks.
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We will launch {self.nb_requested_gpus} processes for you.
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We recommend you let slurm manage the processes by setting: --ntasks-per-node={self.nb_requested_gpus}
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If you're not using SLURM, ignore this message!
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"""
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warnings.warn(msg)
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mp.spawn(self.ddp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))
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# 1 gpu or dp option triggers training using DP module
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# easier to avoid NCCL issues
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elif self.use_dp:
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self.__dp_train(model)
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# ON CPU
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else:
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# run through amp wrapper
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if self.use_amp:
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raise MisconfigurationException('amp + cpu is not supported. Please use a GPU option')
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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self.optimizers = model.configure_optimizers()
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if len(self.optimizers) == 2:
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self.optimizers, self.lr_schedulers = self.optimizers
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self.__run_pretrain_routine(model)
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# return 1 when finished
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# used for testing or when we need to know that training succeeded
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return 1
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def __dp_train(self, model):
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# CHOOSE OPTIMIZER
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# allow for lr schedulers as well
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self.optimizers = model.configure_optimizers()
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if len(self.optimizers) == 2:
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self.optimizers, self.lr_schedulers = self.optimizers
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model.cuda(self.data_parallel_device_ids[0])
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# check for this bug (amp + dp + !01 doesn't work)
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# https://github.com/NVIDIA/apex/issues/227
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if self.use_dp and self.use_amp:
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m = f'amp level {self.amp_level} with DataParallel is not supported. ' \
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f'See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227. ' \
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f'We recommend you switch to ddp if you want to use amp'
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raise MisconfigurationException(m)
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model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)
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self.__run_pretrain_routine(model)
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def ddp_train(self, gpu_nb, model):
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"""
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Entry point into a DP thread
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:param gpu_nb:
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:param model:
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:param cluster_obj:
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:return:
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"""
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# node rank using relative slurm id
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# otherwise default to node rank 0
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try:
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node_id = os.environ['SLURM_NODEID']
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self.node_rank = int(node_id)
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except Exception as e:
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self.node_rank = 0
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# recover original exp before went into process
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# init in write mode only on proc 0
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self.experiment.debug = self.proc_rank > 0
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self.experiment = self.experiment.get_non_ddp_exp()
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|
|
# show progbar only on prog_rank 0
|
|
self.prog_bar = self.prog_bar and self.node_rank == 0 and gpu_nb == 0
|
|
|
|
# determine which process we are and world size
|
|
self.proc_rank = self.node_rank * len(self.data_parallel_device_ids) + gpu_nb
|
|
self.world_size = self.nb_gpu_nodes * len(self.data_parallel_device_ids)
|
|
|
|
# let the exp know the rank to avoid overwriting logs
|
|
self.experiment.rank = self.proc_rank
|
|
|
|
# set up server using proc 0's ip address
|
|
# try to init for 20 times at max in case ports are taken
|
|
# where to store ip_table
|
|
self.__init_tcp_connection()
|
|
|
|
# CHOOSE OPTIMIZER
|
|
# allow for lr schedulers as well
|
|
self.optimizers = model.configure_optimizers()
|
|
if len(self.optimizers) == 2:
|
|
self.optimizers, self.lr_schedulers = self.optimizers
|
|
|
|
# MODEL
|
|
# copy model to each gpu
|
|
torch.cuda.set_device(gpu_nb)
|
|
model.cuda(gpu_nb)
|
|
|
|
# AMP
|
|
# run through amp wrapper before going to distributed DP
|
|
if self.use_amp:
|
|
# An example
|
|
model, optimizers = amp.initialize(
|
|
model, self.optimizers, opt_level=self.amp_level,
|
|
)
|
|
self.optimizers = optimizers
|
|
|
|
model = LightningDistributedDataParallel(model, device_ids=[gpu_nb], find_unused_parameters=True)
|
|
|
|
# continue training routine
|
|
self.__run_pretrain_routine(model)
|
|
|
|
def __init_tcp_connection(self):
|
|
"""
|
|
Connect all procs in the world using the env:// init
|
|
Use the first node as the root address
|
|
:param port:
|
|
:param tries:
|
|
:return:
|
|
"""
|
|
# sets the appropriate port
|
|
try:
|
|
port = os.environ['MASTER_PORT']
|
|
except Exception as e:
|
|
port = 12910
|
|
os.environ['MASTER_PORT'] = f'{port}'
|
|
|
|
# figure out the root node addr
|
|
try:
|
|
root_node = os.environ['SLURM_NODELIST'].split(' ')[0]
|
|
except Exception as e:
|
|
root_node = '127.0.0.2'
|
|
|
|
root_node = self.resolve_root_node_address(root_node)
|
|
os.environ['MASTER_ADDR'] = root_node
|
|
|
|
dist.init_process_group("nccl", rank=self.proc_rank, world_size=self.world_size)
|
|
|
|
def resolve_root_node_address(self, root_node):
|
|
if '[' in root_node:
|
|
name = root_node.split('[')[0]
|
|
number = root_node.split(',')[0]
|
|
if '-' in number:
|
|
number = number.split('-')[0]
|
|
|
|
number = re.sub('[^0-9]', '', number)
|
|
root_node = name + number
|
|
|
|
return root_node
|
|
|
|
def __run_pretrain_routine(self, model):
|
|
"""
|
|
Sanity check a few things before starting actual training
|
|
:param model:
|
|
:return:
|
|
"""
|
|
ref_model = model
|
|
if self.data_parallel:
|
|
ref_model = model.module
|
|
|
|
ref_model.trainer = self
|
|
|
|
# set local properties on the model
|
|
ref_model.on_gpu = self.on_gpu
|
|
|
|
# transfer data loaders from model
|
|
self.get_dataloaders(ref_model)
|
|
|
|
# init training constants
|
|
self.__layout_bookeeping()
|
|
|
|
# print model summary
|
|
if self.proc_rank == 0 and self.print_weights_summary:
|
|
ref_model.summarize()
|
|
|
|
# give model convenience properties
|
|
ref_model.trainer = self
|
|
ref_model.experiment = self.experiment
|
|
|
|
# run tiny validation to make sure program won't crash during val
|
|
_ = self.validate(model, self.val_dataloader, max_batches=self.nb_sanity_val_steps)
|
|
|
|
# save exp to get started
|
|
if self.proc_rank == 0:
|
|
self.experiment.save()
|
|
|
|
# track model now.
|
|
# if cluster resets state, the model will update with the saved weights
|
|
self.model = model
|
|
|
|
# enable cluster checkpointing
|
|
# also restores training state
|
|
if self.cluster is not None: # pragma: no cover
|
|
self.enable_auto_hpc_walltime_manager()
|
|
|
|
# ---------------------------
|
|
# CORE TRAINING LOOP
|
|
# ---------------------------
|
|
self.__train()
|
|
|
|
def __train(self):
|
|
# run all epochs
|
|
for epoch_nb in range(self.current_epoch, self.max_nb_epochs):
|
|
# update the lr scheduler
|
|
if self.lr_schedulers is not None:
|
|
for lr_scheduler in self.lr_schedulers:
|
|
lr_scheduler.step()
|
|
|
|
model = self.__get_model()
|
|
model.current_epoch = epoch_nb
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_epoch_start'):
|
|
model = self.__get_model()
|
|
model.on_epoch_start()
|
|
|
|
self.current_epoch = epoch_nb
|
|
self.total_batches = self.nb_tng_batches + self.nb_val_batches
|
|
self.batch_loss_value = 0 # accumulated grads
|
|
|
|
# init progbar when requested
|
|
if self.progress_bar:
|
|
self.prog_bar = tqdm.tqdm(range(self.total_batches), position=self.process_position)
|
|
|
|
for batch_nb, data_batch in enumerate(self.tng_dataloader):
|
|
self.batch_nb = batch_nb
|
|
self.global_step += 1
|
|
|
|
model = self.__get_model()
|
|
model.global_step = self.global_step
|
|
|
|
# stop when the flag is changed or we've gone past the amount requested in the batches
|
|
self.total_batch_nb += 1
|
|
met_batch_limit = batch_nb > self.nb_tng_batches
|
|
if met_batch_limit:
|
|
break
|
|
|
|
# ---------------
|
|
# RUN TRAIN STEP
|
|
# ---------------
|
|
batch_result = self.__run_tng_batch(data_batch, batch_nb)
|
|
early_stop_epoch = batch_result == -1
|
|
|
|
# ---------------
|
|
# RUN VAL STEP
|
|
# ---------------
|
|
is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0
|
|
if self.fast_dev_run or is_val_check_batch or early_stop_epoch:
|
|
self.__run_validation()
|
|
|
|
# when batch should be saved
|
|
if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:
|
|
if self.proc_rank == 0:
|
|
self.experiment.save()
|
|
|
|
# when metrics should be logged
|
|
if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:
|
|
# count items in memory
|
|
# nb_params, nb_tensors = count_mem_items()
|
|
|
|
model = self.__get_model()
|
|
metrics = self.__tng_tqdm_dic
|
|
|
|
# add gpu memory
|
|
if self.on_gpu:
|
|
mem_map = get_gpu_memory_map()
|
|
metrics.update(mem_map)
|
|
|
|
# add norms
|
|
if self.track_grad_norm > 0:
|
|
model = self.__get_model()
|
|
grad_norm_dic = model.grad_norm(self.track_grad_norm)
|
|
metrics.update(grad_norm_dic)
|
|
|
|
if self.__is_function_implemented('on_tng_metrics'):
|
|
model.on_tng_metrics(metrics)
|
|
|
|
# log metrics
|
|
scalar_metrics = self.__metrics_to_scalars(metrics, blacklist=self.__log_vals_blacklist())
|
|
if self.proc_rank == 0:
|
|
self.experiment.log(scalar_metrics, global_step=self.global_step)
|
|
self.experiment.save()
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_batch_end'):
|
|
model = self.__get_model()
|
|
model.on_batch_end()
|
|
|
|
# end epoch early
|
|
if early_stop_epoch:
|
|
break
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_epoch_end'):
|
|
model = self.__get_model()
|
|
model.on_epoch_end()
|
|
|
|
# early stopping
|
|
met_min_epochs = epoch_nb > self.min_nb_epochs
|
|
if self.enable_early_stop and met_min_epochs:
|
|
should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb, logs=self.__tng_tqdm_dic)
|
|
|
|
# stop training
|
|
stop = should_stop and met_min_epochs
|
|
if stop:
|
|
return
|
|
|
|
def __metrics_to_scalars(self, metrics, blacklist=[]):
|
|
new_metrics = {}
|
|
for k, v in metrics.items():
|
|
if type(v) is torch.Tensor:
|
|
v = v.item()
|
|
|
|
if type(v) is dict:
|
|
v = self.__metrics_to_scalars(v)
|
|
|
|
if k not in blacklist:
|
|
new_metrics[k] = float(v)
|
|
|
|
return new_metrics
|
|
|
|
def __log_vals_blacklist(self):
|
|
"""avoid logging some vals lightning uses to maintain state"""
|
|
blacklist = {'batch_nb', 'v_nb', 'gpu'}
|
|
return blacklist
|
|
|
|
def __run_tng_batch(self, data_batch, batch_nb):
|
|
if data_batch is None:
|
|
return 0
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_batch_start'):
|
|
model_ref = self.__get_model()
|
|
response = model_ref.on_batch_start(data_batch)
|
|
|
|
if response == -1:
|
|
return -1
|
|
|
|
if self.progress_bar:
|
|
self.prog_bar.update(1)
|
|
|
|
# forward pass
|
|
# return a scalar value and a dic with tqdm metrics
|
|
if self.use_ddp:
|
|
output = self.model(data_batch, batch_nb)
|
|
elif self.use_dp:
|
|
output = self.model(data_batch, batch_nb)
|
|
output = reduce_distributed_output(output, len(self.data_parallel_device_ids))
|
|
else:
|
|
output = self.model.training_step(data_batch, batch_nb)
|
|
|
|
try:
|
|
model_specific_tqdm_metrics_dic = output['prog']
|
|
except Exception as e:
|
|
model_specific_tqdm_metrics_dic = {}
|
|
|
|
# if output dict doesn't have the keyword loss
|
|
# then assume the output=loss if scalar
|
|
try:
|
|
loss = output['loss']
|
|
except Exception as e:
|
|
if type(output) is torch.Tensor:
|
|
loss = output
|
|
|
|
self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
|
|
|
|
# backward pass
|
|
if self.use_amp:
|
|
# scale loss when using amp
|
|
for optimizer in self.optimizers:
|
|
with amp.scale_loss(loss, optimizer) as scaled_loss:
|
|
scaled_loss.backward()
|
|
else:
|
|
loss.backward()
|
|
|
|
# insert after step hook
|
|
if self.__is_function_implemented('on_after_backward'):
|
|
model_ref = self.__get_model()
|
|
response = model_ref.on_after_backward()
|
|
|
|
if self.print_nan_grads:
|
|
model = self.__get_model()
|
|
for param in model.parameters():
|
|
print(param.grad.float().sum())
|
|
|
|
# avoid memory leaks
|
|
self.batch_loss_value += loss.item()
|
|
|
|
# gradient update with accumulated gradients
|
|
if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:
|
|
|
|
# clip gradients
|
|
if self.gradient_clip > 0:
|
|
model = self.__get_model()
|
|
torch.nn.utils.clip_grad_norm(model.parameters(), self.gradient_clip)
|
|
|
|
# update gradients across all optimizers
|
|
for optimizer in self.optimizers:
|
|
optimizer.step()
|
|
|
|
# insert after step hook
|
|
if self.__is_function_implemented('on_before_zero_grad'):
|
|
model_ref = self.__get_model()
|
|
response = model_ref.on_before_zero_grad(optimizer)
|
|
|
|
# clear gradients
|
|
optimizer.zero_grad()
|
|
|
|
# queuing loss across batches blows it up proportionally... divide out the number accumulated
|
|
self.batch_loss_value = self.batch_loss_value / self.accumulate_grad_batches
|
|
|
|
# track loss
|
|
self.running_loss.append(self.batch_loss_value)
|
|
self.batch_loss_value = 0
|
|
self.avg_loss = np.mean(self.running_loss[-100:])
|
|
|
|
# update progbar
|
|
if self.progress_bar:
|
|
# add model specific metrics
|
|
tqdm_metrics = self.__tng_tqdm_dic
|
|
self.prog_bar.set_postfix(**tqdm_metrics)
|
|
|
|
# activate batch end hook
|
|
if self.__is_function_implemented('on_batch_end'):
|
|
model = self.__get_model()
|
|
model.on_batch_end()
|
|
|
|
return 0
|
|
|
|
def __run_validation(self):
|
|
# decide if can check epochs
|
|
can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0
|
|
if self.fast_dev_run:
|
|
print('skipping to check performance bc of --fast_dev_run')
|
|
elif not can_check_epoch:
|
|
return
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_pre_performance_check'):
|
|
model = self.__get_model()
|
|
model.on_pre_performance_check()
|
|
|
|
# use full val set on end of epoch
|
|
# use a small portion otherwise
|
|
max_batches = None if not self.fast_dev_run else 1
|
|
model_specific_tqdm_metrics_dic = self.validate(
|
|
self.model,
|
|
self.val_dataloader,
|
|
max_batches
|
|
)
|
|
self.__add_tqdm_metrics(model_specific_tqdm_metrics_dic)
|
|
|
|
# hook
|
|
if self.__is_function_implemented('on_post_performance_check'):
|
|
model = self.__get_model()
|
|
model.on_post_performance_check()
|
|
|
|
if self.progress_bar:
|
|
# add model specific metrics
|
|
tqdm_metrics = self.__tng_tqdm_dic
|
|
self.prog_bar.set_postfix(**tqdm_metrics)
|
|
|
|
# model checkpointing
|
|
if self.proc_rank == 0 and self.checkpoint_callback is not None:
|
|
print('save callback...')
|
|
self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch, logs=self.__tng_tqdm_dic) |