forked from mrq/DL-Art-School
529 lines
25 KiB
Python
529 lines
25 KiB
Python
import copy
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import logging
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import os
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from time import time
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import torch
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from torch import distributed
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from torch.nn.parallel import DataParallel
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import torch.nn as nn
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import trainer.lr_scheduler as lr_scheduler
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import trainer.networks as networks
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from trainer.base_model import BaseModel
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from trainer.batch_size_optimizer import create_batch_size_optimizer
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from trainer.inject import create_injector
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from trainer.steps import ConfigurableStep
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from trainer.experiments.experiments import get_experiment_for_name
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import torchvision.utils as utils
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from utils.loss_accumulator import LossAccumulator, InfStorageLossAccumulator
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from utils.util import opt_get, denormalize
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logger = logging.getLogger('base')
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# State is immutable to reduce complexity. Overwriting existing state keys is not supported.
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class OverwrittenStateError(Exception):
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def __init__(self, k, keys):
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super().__init__(f'Attempted to overwrite state key: {k}. The state should be considered '
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f'immutable and keys should not be overwritten. Current keys: {keys}')
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class ExtensibleTrainer(BaseModel):
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def __init__(self, opt, cached_networks={}):
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super(ExtensibleTrainer, self).__init__(opt)
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if opt['dist']:
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self.rank = torch.distributed.get_rank()
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else:
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self.rank = -1 # non dist training
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train_opt = opt['train']
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# env is used as a global state to store things that subcomponents might need.
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self.env = {'device': self.device,
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'rank': self.rank,
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'opt': opt,
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'step': 0,
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'dist': opt['dist']
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}
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if opt['path']['models'] is not None:
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self.env['base_path'] = os.path.join(opt['path']['models'])
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self.mega_batch_factor = 1
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if self.is_train:
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self.mega_batch_factor = train_opt['mega_batch_factor']
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self.env['mega_batch_factor'] = self.mega_batch_factor
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self.batch_factor = self.mega_batch_factor
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self.ema_rate = opt_get(train_opt, ['ema_rate'], .999)
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# It is advantageous for large networks to do this to save an extra copy of the model weights.
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# It does come at the cost of a round trip to CPU memory at every batch.
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self.do_emas = opt_get(train_opt, ['ema_enabled'], True)
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self.ema_on_cpu = opt_get(train_opt, ['ema_on_cpu'], False)
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self.checkpointing_cache = opt['checkpointing_enabled']
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self.auto_recover = opt_get(opt, ['automatically_recover_nan_by_reverting_n_saves'], None)
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self.batch_size_optimizer = create_batch_size_optimizer(train_opt)
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self.netsG = {}
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self.netsD = {}
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for name, net in opt['networks'].items():
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# Trainable is a required parameter, but the default is simply true. Set it here.
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if 'trainable' not in net.keys():
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net['trainable'] = True
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if name in cached_networks.keys():
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new_net = cached_networks[name]
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else:
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new_net = None
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if net['type'] == 'generator':
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if new_net is None:
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new_net = networks.create_model(opt, net, self.netsG).to(self.device)
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self.netsG[name] = new_net
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elif net['type'] == 'discriminator':
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if new_net is None:
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new_net = networks.create_model(opt, net, self.netsD).to(self.device)
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self.netsD[name] = new_net
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else:
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raise NotImplementedError("Can only handle generators and discriminators")
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if not net['trainable']:
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new_net.eval()
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if net['wandb_debug'] and self.rank <= 0:
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import wandb
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wandb.watch(new_net, log='all', log_freq=3)
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# Initialize the train/eval steps
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self.step_names = []
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self.steps = []
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for step_name, step in opt['steps'].items():
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step = ConfigurableStep(step, self.env)
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self.step_names.append(step_name) # This could be an OrderedDict, but it's a PITA to integrate with AMP below.
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self.steps.append(step)
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# step.define_optimizers() relies on the networks being placed in the env, so put them there. Even though
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# they aren't wrapped yet.
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self.env['generators'] = self.netsG
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self.env['discriminators'] = self.netsD
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# Define the optimizers from the steps
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for s in self.steps:
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s.define_optimizers()
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self.optimizers.extend(s.get_optimizers())
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if self.is_train:
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# Find the optimizers that are using the default scheduler, then build them.
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def_opt = []
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for s in self.steps:
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def_opt.extend(s.get_optimizers_with_default_scheduler())
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self.schedulers = lr_scheduler.get_scheduler_for_name(train_opt['default_lr_scheme'], def_opt, train_opt)
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# Set the starting step count for the scheduler.
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for sched in self.schedulers:
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sched.last_epoch = opt['current_step']
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else:
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self.schedulers = []
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# Wrap networks in distributed shells.
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dnets = []
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all_networks = [g for g in self.netsG.values()] + [d for d in self.netsD.values()]
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for anet in all_networks:
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has_any_trainable_params = False
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for p in anet.parameters():
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if not hasattr(p, 'DO_NOT_TRAIN'):
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has_any_trainable_params = True
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break
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if has_any_trainable_params and opt['dist']:
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if opt['dist_backend'] == 'apex':
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# Use Apex to enable delay_allreduce, which is compatible with gradient checkpointing.
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from apex.parallel import DistributedDataParallel
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dnet = DistributedDataParallel(anet, delay_allreduce=True)
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else:
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from torch.nn.parallel.distributed import DistributedDataParallel
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# Do NOT be tempted to put find_unused_parameters=True here. It will not work when checkpointing is
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# used and in a few other cases. But you can try it if you really want.
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dnet = DistributedDataParallel(anet, device_ids=[torch.cuda.current_device()],
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output_device=torch.cuda.current_device(),
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find_unused_parameters=opt_get(opt, ['ddp_find_unused_parameters'], False))
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# DDP graphs cannot be used with gradient checkpointing unless you use find_unused_parameters=True,
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# which does not work with this trainer (as stated above). However, if the graph is not subject
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# to control flow alterations, you can set this option to allow gradient checkpointing. Beware that
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# if you are wrong about control flow, DDP will not train all your model parameters! User beware!
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if opt_get(opt, ['ddp_static_graph'], False):
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dnet._set_static_graph()
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else:
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dnet = DataParallel(anet, device_ids=[torch.cuda.current_device()])
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if self.is_train:
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dnet.train()
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else:
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dnet.eval()
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dnets.append(dnet)
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# Backpush the wrapped networks into the network dicts. Also build the EMA parameters.
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self.networks = {}
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self.emas = {}
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found = 0
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for dnet in dnets:
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for net_dict in [self.netsD, self.netsG]:
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for k, v in net_dict.items():
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if v == dnet.module:
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net_dict[k] = dnet
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self.networks[k] = dnet
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if self.is_train and self.do_emas:
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self.emas[k] = copy.deepcopy(v)
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if self.ema_on_cpu:
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self.emas[k] = self.emas[k].cpu()
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found += 1
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assert found == len(self.netsG) + len(self.netsD)
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# Replace the env networks with the wrapped networks
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self.env['generators'] = self.netsG
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self.env['discriminators'] = self.netsD
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self.env['emas'] = self.emas
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self.print_network() # print network
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self.load() # load networks from save states as needed
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# Load experiments
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self.experiments = []
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if 'experiments' in opt.keys():
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self.experiments = [get_experiment_for_name(e) for e in opt['experiments']]
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# Setting this to false triggers SRGAN to call the models update_model() function on the first iteration.
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self.updated = True
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def feed_data(self, data, step, need_GT=True, perform_micro_batching=True):
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self.env['step'] = step
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self.batch_factor = self.mega_batch_factor
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self.opt['checkpointing_enabled'] = self.checkpointing_cache
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# The batch factor can be adjusted on a period to allow known high-memory steps to fit in GPU memory.
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if 'train' in self.opt.keys() and \
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'mod_batch_factor' in self.opt['train'].keys() and \
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self.env['step'] % self.opt['train']['mod_batch_factor_every'] == 0:
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self.batch_factor = self.opt['train']['mod_batch_factor']
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if self.opt['train']['mod_batch_factor_also_disable_checkpointing']:
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self.opt['checkpointing_enabled'] = False
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self.eval_state = {}
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for o in self.optimizers:
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o.zero_grad()
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torch.cuda.empty_cache()
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sort_key = opt_get(self.opt, ['train', 'sort_key'], None)
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if sort_key is not None:
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sort_indices = torch.sort(data[sort_key], descending=True).indices
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else:
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sort_indices = None
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batch_factor = self.batch_factor if perform_micro_batching else 1
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self.dstate = {}
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for k, v in data.items():
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if sort_indices is not None:
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if isinstance(v, list):
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v = [v[i] for i in sort_indices]
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else:
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v = v[sort_indices]
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if isinstance(v, torch.Tensor):
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self.dstate[k] = [t.to(self.device) for t in torch.chunk(v, chunks=batch_factor, dim=0)]
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if opt_get(self.opt, ['train', 'auto_collate'], False):
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for k, v in self.dstate.items():
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if f'{k}_lengths' in self.dstate.keys():
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for c in range(len(v)):
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maxlen = self.dstate[f'{k}_lengths'][c].max()
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if len(v[c].shape) == 2:
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self.dstate[k][c] = self.dstate[k][c][:, :maxlen]
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elif len(v[c].shape) == 3:
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self.dstate[k][c] = self.dstate[k][c][:, :, :maxlen]
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elif len(v[c].shape) == 4:
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self.dstate[k][c] = self.dstate[k][c][:, :, :, :maxlen]
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def optimize_parameters(self, it, optimize=True, return_grad_norms=False):
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grad_norms = {}
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# Some models need to make parametric adjustments per-step. Do that here.
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for net in self.networks.values():
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if hasattr(net.module, "update_for_step"):
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net.module.update_for_step(it, os.path.join(self.opt['path']['models'], ".."))
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# Iterate through the steps, performing them one at a time.
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state = self.dstate
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for step_num, step in enumerate(self.steps):
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train_step = True
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# 'every' is used to denote steps that should only occur at a certain integer factor rate. e.g. '2' occurs every 2 steps.
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# Note that the injection points for the step might still be required, so address this by setting train_step=False
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if 'every' in step.step_opt.keys() and it % step.step_opt['every'] != 0:
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train_step = False
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# Steps can opt out of early (or late) training, make sure that happens here.
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if 'after' in step.step_opt.keys() and it < step.step_opt['after'] or 'before' in step.step_opt.keys() and it > step.step_opt['before']:
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continue
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# Steps can choose to not execute if a state key is missing.
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if 'requires' in step.step_opt.keys():
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requirements_met = True
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for requirement in step.step_opt['requires']:
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if requirement not in state.keys():
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requirements_met = False
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if not requirements_met:
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continue
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if train_step:
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# Only set requires_grad=True for the network being trained.
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nets_to_train = step.get_networks_trained()
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enabled = 0
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for name, net in self.networks.items():
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net_enabled = name in nets_to_train
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if net_enabled:
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enabled += 1
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# Networks can opt out of training before a certain iteration by declaring 'after' in their definition.
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if 'after' in self.opt['networks'][name].keys() and it < self.opt['networks'][name]['after']:
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net_enabled = False
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for p in net.parameters():
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do_not_train_flag = hasattr(p, "DO_NOT_TRAIN") or (hasattr(p, "DO_NOT_TRAIN_UNTIL") and it < p.DO_NOT_TRAIN_UNTIL)
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if p.dtype != torch.int64 and p.dtype != torch.bool and not do_not_train_flag:
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p.requires_grad = net_enabled
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else:
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p.requires_grad = False
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assert enabled == len(nets_to_train)
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# Update experiments
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[e.before_step(self.opt, self.step_names[step_num], self.env, nets_to_train, state) for e in self.experiments]
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for o in step.get_optimizers():
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o.zero_grad()
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# Now do a forward and backward pass for each gradient accumulation step.
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new_states = {}
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self.batch_size_optimizer.focus(net)
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for m in range(self.batch_factor):
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ns = step.do_forward_backward(state, m, step_num, train=train_step, no_ddp_sync=(m+1 < self.batch_factor))
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# Call into post-backward hooks.
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for name, net in self.networks.items():
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if hasattr(net.module, "after_backward"):
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net.module.after_backward(it)
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for k, v in ns.items():
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if k not in new_states.keys():
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new_states[k] = [v]
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else:
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new_states[k].append(v)
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# Push the detached new state tensors into the state map for use with the next step.
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for k, v in new_states.items():
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if k in state.keys():
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raise OverwrittenStateError(k, list(state.keys()))
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state[k] = v
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# (Maybe) perform a step.
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if train_step and optimize and self.batch_size_optimizer.should_step(it):
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# Call into pre-step hooks.
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for name, net in self.networks.items():
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if hasattr(net.module, "before_step"):
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net.module.before_step(it)
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# Unscale gradients within the step. (This is admittedly pretty messy but the API contract between step & ET is pretty much broken at this point)
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# This is needed to accurately log the grad norms.
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for opt in step.optimizers:
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from torch.cuda.amp.grad_scaler import OptState
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if step.scaler.is_enabled() and step.scaler._per_optimizer_states[id(opt)]["stage"] is not OptState.UNSCALED:
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step.scaler.unscale_(opt)
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if return_grad_norms and train_step:
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for name in nets_to_train:
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model = self.networks[name]
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if hasattr(model.module, 'get_grad_norm_parameter_groups'):
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pgroups = {f'{name}_{k}': v for k, v in model.module.get_grad_norm_parameter_groups().items()}
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else:
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pgroups = {f'{name}_all_parameters': list(model.parameters())}
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for name in pgroups.keys():
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stacked_grads = []
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for p in pgroups[name]:
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if hasattr(p, 'grad') and p.grad is not None:
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stacked_grads.append(torch.norm(p.grad.detach(), 2))
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if not stacked_grads:
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continue
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grad_norms[name] = torch.norm(torch.stack(stacked_grads), 2)
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if distributed.is_available() and distributed.is_initialized():
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# Gather the metric from all devices if in a distributed setting.
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distributed.all_reduce(grad_norms[name], op=distributed.ReduceOp.SUM)
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grad_norms[name] /= distributed.get_world_size()
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grad_norms[name] = grad_norms[name].cpu()
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self.consume_gradients(state, step, it)
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# Record visual outputs for usage in debugging and testing.
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if 'visuals' in self.opt['logger'].keys() and self.rank <= 0 and it % self.opt['logger']['visual_debug_rate'] == 0:
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def fix_image(img):
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if opt_get(self.opt, ['logger', 'is_mel_spectrogram'], False):
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img = img.unsqueeze(dim=1)
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# Normalize so spectrogram is easier to view.
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img = (img - img.mean()) / img.std()
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if img.shape[1] > 3:
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img = img[:, :3, :, :]
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if opt_get(self.opt, ['logger', 'reverse_n1_to_1'], False):
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img = (img + 1) / 2
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if opt_get(self.opt, ['logger', 'reverse_imagenet_norm'], False):
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img = denormalize(img)
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return img
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sample_save_path = os.path.join(self.opt['path']['models'], "..", "visual_dbg")
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for v in self.opt['logger']['visuals']:
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if v not in state.keys():
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continue # This can happen for several reasons (ex: 'after' defs), just ignore it.
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for i, dbgv in enumerate(state[v]):
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if 'recurrent_visual_indices' in self.opt['logger'].keys() and len(dbgv.shape)==5:
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for rvi in self.opt['logger']['recurrent_visual_indices']:
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rdbgv = fix_image(dbgv[:, rvi])
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os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
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utils.save_image(rdbgv.float(), os.path.join(sample_save_path, v, "%05i_%02i_%02i.png" % (it, rvi, i)))
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else:
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dbgv = fix_image(dbgv)
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os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
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utils.save_image(dbgv.float(), os.path.join(sample_save_path, v, "%05i_%02i.png" % (it, i)))
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# Some models have their own specific visual debug routines.
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for net_name, net in self.networks.items():
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if hasattr(net.module, "visual_dbg"):
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model_vdbg_dir = os.path.join(sample_save_path, net_name)
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os.makedirs(model_vdbg_dir, exist_ok=True)
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net.module.visual_dbg(it, model_vdbg_dir)
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return grad_norms
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def consume_gradients(self, state, step, it):
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[e.before_optimize(state) for e in self.experiments]
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self.restore_optimizers()
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step.do_step(it)
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self.stash_optimizers()
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# Call into custom step hooks as well as update EMA params.
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for name, net in self.networks.items():
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if hasattr(net.module, "after_step"):
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net.module.after_step(it)
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if self.do_emas:
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ema_params = self.emas[name].parameters()
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net_params = net.parameters()
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for ep, np in zip(ema_params, net_params):
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if self.ema_on_cpu:
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np = np.cpu()
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ep.detach().mul_(self.ema_rate).add_(np, alpha=1 - self.ema_rate)
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[e.after_optimize(state) for e in self.experiments]
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def test(self):
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for net in self.netsG.values():
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net.eval()
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accum_metrics = InfStorageLossAccumulator()
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with torch.no_grad():
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# This can happen one of two ways: Either a 'validation injector' is provided, in which case we run that.
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# Or, we run the entire chain of steps in "train" mode and use eval.output_state.
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if 'injectors' in self.opt['eval'].keys():
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state = {}
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for inj in self.opt['eval']['injectors'].values():
|
|
# Need to move from mega_batch mode to batch mode (remove chunks)
|
|
for k, v in self.dstate.items():
|
|
state[k] = v[0]
|
|
inj = create_injector(inj, self.env)
|
|
state.update(inj(state))
|
|
else:
|
|
# Iterate through the steps, performing them one at a time.
|
|
state = self.dstate
|
|
for step_num, s in enumerate(self.steps):
|
|
ns = s.do_forward_backward(state, 0, step_num, train=False, loss_accumulator=accum_metrics)
|
|
for k, v in ns.items():
|
|
state[k] = [v]
|
|
|
|
self.eval_state = {}
|
|
for k, v in state.items():
|
|
if isinstance(v, list):
|
|
self.eval_state[k] = [s.detach().cpu() if isinstance(s, torch.Tensor) else s for s in v]
|
|
else:
|
|
self.eval_state[k] = [v.detach().cpu() if isinstance(v, torch.Tensor) else v]
|
|
|
|
for net in self.netsG.values():
|
|
net.train()
|
|
return accum_metrics
|
|
|
|
# Fetches a summary of the log.
|
|
def get_current_log(self, step):
|
|
log = {}
|
|
for s in self.steps:
|
|
log.update(s.get_metrics())
|
|
|
|
for e in self.experiments:
|
|
log.update(e.get_log_data())
|
|
|
|
# Some generators can do their own metric logging.
|
|
for net_name, net in self.networks.items():
|
|
if hasattr(net.module, "get_debug_values"):
|
|
log.update(net.module.get_debug_values(step, net_name))
|
|
|
|
# Log learning rate (from first param group) too.
|
|
for o in self.optimizers:
|
|
for pgi, pg in enumerate(o.param_groups):
|
|
log['learning_rate_%s_%i' % (o._config['network'], pgi)] = pg['lr']
|
|
|
|
# The batch size optimizer also outputs loggable data.
|
|
log.update(self.batch_size_optimizer.get_statistics())
|
|
|
|
# In distributed mode, get agreement on all single tensors.
|
|
if distributed.is_available() and distributed.is_initialized():
|
|
for k, v in log.items():
|
|
if not isinstance(v, torch.Tensor):
|
|
continue
|
|
if len(v.shape) != 1 or v.dtype != torch.float:
|
|
continue
|
|
distributed.all_reduce(v, op=distributed.ReduceOp.SUM)
|
|
log[k] = v / distributed.get_world_size()
|
|
|
|
return log
|
|
|
|
def get_current_visuals(self, need_GT=True):
|
|
# Conforms to an archaic format from MMSR.
|
|
res = {'rlt': self.eval_state[self.opt['eval']['output_state']][0].float().cpu()}
|
|
if 'hq' in self.eval_state.keys():
|
|
res['hq'] = self.eval_state['hq'][0].float().cpu(),
|
|
return res
|
|
|
|
def print_network(self):
|
|
for name, net in self.networks.items():
|
|
s, n = self.get_network_description(net)
|
|
net_struc_str = '{}'.format(net.__class__.__name__)
|
|
if self.rank <= 0:
|
|
logger.info('Network {} structure: {}, with parameters: {:,d}'.format(name, net_struc_str, n))
|
|
logger.info(s)
|
|
|
|
def load(self):
|
|
for netdict in [self.netsG, self.netsD]:
|
|
for name, net in netdict.items():
|
|
load_path = self.opt['path']['pretrain_model_%s' % (name,)]
|
|
if load_path is None:
|
|
return
|
|
if self.rank <= 0:
|
|
logger.info('Loading model for [%s]' % (load_path,))
|
|
self.load_network(load_path, net, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
|
|
load_path_ema = load_path.replace('.pth', '_ema.pth')
|
|
if self.is_train and self.do_emas:
|
|
ema_model = self.emas[name]
|
|
if os.path.exists(load_path_ema):
|
|
self.load_network(load_path_ema, ema_model, self.opt['path']['strict_load'], opt_get(self.opt, ['path', f'pretrain_base_path_{name}']))
|
|
else:
|
|
print("WARNING! Unable to find EMA network! Starting a new EMA from given model parameters.")
|
|
self.emas[name] = copy.deepcopy(net)
|
|
if self.ema_on_cpu:
|
|
self.emas[name] = self.emas[name].cpu()
|
|
if hasattr(net.module, 'network_loaded'):
|
|
net.module.network_loaded()
|
|
|
|
def save(self, iter_step):
|
|
for name, net in self.networks.items():
|
|
# Don't save non-trainable networks.
|
|
if self.opt['networks'][name]['trainable']:
|
|
self.save_network(net, name, iter_step)
|
|
if self.do_emas:
|
|
self.save_network(self.emas[name], f'{name}_ema', iter_step)
|
|
|
|
def force_restore_swapout(self):
|
|
# Legacy method. Do nothing.
|
|
pass
|