from torch.cuda.amp import GradScaler from utils.loss_accumulator import LossAccumulator from torch.nn import Module import logging from trainer.losses import create_loss import torch from collections import OrderedDict from trainer.inject import create_injector from utils.util import recursively_detach logger = logging.getLogger('base') # Defines the expected API for a single training step class ConfigurableStep(Module): def __init__(self, opt_step, env): super(ConfigurableStep, self).__init__() self.step_opt = opt_step self.env = env self.opt = env['opt'] self.gen_outputs = opt_step['generator_outputs'] self.loss_accumulator = LossAccumulator() self.optimizers = None self.scaler = GradScaler(enabled=self.opt['fp16']) self.grads_generated = False self.min_total_loss = opt_step['min_total_loss'] if 'min_total_loss' in opt_step.keys() else -999999999 self.injectors = [] if 'injectors' in self.step_opt.keys(): injector_names = [] for inj_name, injector in self.step_opt['injectors'].items(): assert inj_name not in injector_names # Repeated names are always an error case. injector_names.append(inj_name) self.injectors.append(create_injector(injector, env)) losses = [] self.weights = {} if 'losses' in self.step_opt.keys(): for loss_name, loss in self.step_opt['losses'].items(): assert loss_name not in self.weights.keys() # Repeated names are always an error case. losses.append((loss_name, create_loss(loss, env))) self.weights[loss_name] = loss['weight'] self.losses = OrderedDict(losses) def get_network_for_name(self, name): return self.env['generators'][name] if name in self.env['generators'].keys() \ else self.env['discriminators'][name] # Subclasses should override this to define individual optimizers. They should all go into self.optimizers. # This default implementation defines a single optimizer for all Generator parameters. # Must be called after networks are initialized and wrapped. def define_optimizers(self): training = self.step_opt['training'] training_net = self.get_network_for_name(training) # When only training one network, optimizer params can just embedded in the step params. if 'optimizer_params' not in self.step_opt.keys(): opt_configs = [self.step_opt] else: opt_configs = [self.step_opt['optimizer_params']] nets = [training_net] training = [training] self.optimizers = [] for net_name, net, opt_config in zip(training, nets, opt_configs): # Configs can organize parameters by-group and specify different learning rates for each group. This only # works in the model specifically annotates which parameters belong in which group using PARAM_GROUP. optim_params = {'default': {'params': [], 'lr': opt_config['lr']}} if 'param_groups' in opt_config.keys(): for k, pg in opt_config['param_groups'].items(): optim_params[k] = {'params': [], 'lr': pg['lr']} for k, v in net.named_parameters(): # can optimize for a part of the model # Make some inference about these parameters, which can be used by some optimizers to treat certain # parameters differently. For example, it is considered good practice to not do weight decay on # BN & bias parameters. TODO: process the module tree instead of the parameter tree to accomplish the # same thing, but in a more effective way. if k.endswith(".bias"): v.is_bias = True if k.endswith(".weight"): v.is_weight = True if ".bn" in k or '.batchnorm' in k or '.bnorm' in k: v.is_bn = True # Some models can specify some parameters to be in different groups. param_group = "default" if hasattr(v, 'PARAM_GROUP'): if v.PARAM_GROUP in optim_params.keys(): param_group = v.PARAM_GROUP else: logger.warning(f'Model specifies a custom param group {v.PARAM_GROUP} which is not configured. ' f'The same LR will be used for all parameters.') if v.requires_grad: optim_params[param_group]['params'].append(v) else: if self.env['rank'] <= 0: logger.warning('Params [{:s}] will not optimize.'.format(k)) if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adam': opt = torch.optim.Adam(list(optim_params.values()), weight_decay=opt_config['weight_decay'], betas=(opt_config['beta1'], opt_config['beta2'])) elif self.step_opt['optimizer'] == 'lars': from trainer.optimizers.larc import LARC from trainer.optimizers.sgd import SGDNoBiasMomentum optSGD = SGDNoBiasMomentum(list(optim_params.values()), lr=opt_config['lr'], momentum=opt_config['momentum'], weight_decay=opt_config['weight_decay']) opt = LARC(optSGD, trust_coefficient=opt_config['lars_coefficient']) opt._config = opt_config # This is a bit seedy, but we will need these configs later. opt._config['network'] = net_name self.optimizers.append(opt) # Returns all optimizers used in this step. def get_optimizers(self): assert self.optimizers is not None return self.optimizers # Returns optimizers which are opting in for default LR scheduling. def get_optimizers_with_default_scheduler(self): assert self.optimizers is not None return self.optimizers # Returns the names of the networks this step will train. Other networks will be frozen. def get_networks_trained(self): if isinstance(self.step_opt['training'], list): return self.step_opt['training'] else: return [self.step_opt['training']] def get_training_network_name(self): if isinstance(self.step_opt['training'], list): return self.step_opt['training'][0] else: return self.step_opt['training'] # Performs all forward and backward passes for this step given an input state. All input states are lists of # chunked tensors. Use grad_accum_step to dereference these steps. Should return a dict of tensors that later # steps might use. These tensors are automatically detached and accumulated into chunks. def do_forward_backward(self, state, grad_accum_step, amp_loss_id, train=True): local_state = {} # <-- Will store the entire local state to be passed to injectors & losses. new_state = {} # <-- Will store state values created by this step for returning to ExtensibleTrainer. for k, v in state.items(): local_state[k] = v[grad_accum_step] local_state['train_nets'] = str(self.get_networks_trained()) # Some losses compute backward() internally. Accommodate this by stashing the amp_loss_id in env. self.env['amp_loss_id'] = amp_loss_id self.env['current_step_optimizers'] = self.optimizers self.env['training'] = train # Inject in any extra dependencies. for inj in self.injectors: # Don't do injections tagged with eval unless we are not in train mode. if train and 'eval' in inj.opt.keys() and inj.opt['eval']: continue # Likewise, don't do injections tagged with train unless we are not in eval. if not train and 'train' in inj.opt.keys() and inj.opt['train']: continue # Don't do injections tagged with 'after' or 'before' when we are out of spec. if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \ 'before' in inj.opt.keys() and self.env['step'] > inj.opt['before'] or \ 'every' in inj.opt.keys() and self.env['step'] % inj.opt['every'] != 0: continue injected = inj(local_state) local_state.update(injected) new_state.update(injected) if train and len(self.losses) > 0: # Finally, compute the losses. total_loss = 0 for loss_name, loss in self.losses.items(): # Some losses only activate after a set number of steps. For example, proto-discriminator losses can # be very disruptive to a generator. if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step'] or \ 'before' in loss.opt.keys() and self.env['step'] > loss.opt['before'] or \ 'every' in loss.opt.keys() and self.env['step'] % loss.opt['every'] != 0: continue if loss.is_stateful(): l, lstate = loss(self.get_network_for_name(self.step_opt['training']), local_state) local_state.update(lstate) new_state.update(lstate) else: l = loss(self.get_network_for_name(self.step_opt['training']), local_state) total_loss += l * self.weights[loss_name] # Record metrics. if isinstance(l, torch.Tensor): self.loss_accumulator.add_loss(loss_name, l) for n, v in loss.extra_metrics(): self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v) loss.clear_metrics() # In some cases, the loss could not be set (e.g. all losses have 'after') if isinstance(total_loss, torch.Tensor): self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss) reset_required = total_loss < self.min_total_loss # Scale the loss down by the accumulation factor. total_loss = total_loss / self.env['mega_batch_factor'] # Get dem grads! self.scaler.scale(total_loss).backward() if reset_required: # You might be scratching your head at this. Why would you zero grad as opposed to not doing a # backwards? Because DDP uses the backward() pass as a synchronization point and there is not a good # way to simply bypass backward. If you want a more efficient way to specify a min_loss, use or # implement it at the loss level. self.get_network_for_name(self.step_opt['training']).zero_grad() self.loss_accumulator.increment_metric("%s_skipped_steps" % (self.get_training_network_name(),)) self.grads_generated = True # Detach all state variables. Within the step, gradients can flow. Once these variables leave the step # we must release the gradients. new_state = recursively_detach(new_state) return new_state # Performs the optimizer step after all gradient accumulation is completed. Default implementation simply steps() # all self.optimizers. def do_step(self, step): if not self.grads_generated: return self.grads_generated = False for opt in self.optimizers: # Optimizers can be opted out in the early stages of training. after = opt._config['after'] if 'after' in opt._config.keys() else 0 after_network = self.opt['networks'][opt._config['network']]['after'] if 'after' in self.opt['networks'][opt._config['network']].keys() else 0 after = max(after, after_network) if self.env['step'] < after: continue before = opt._config['before'] if 'before' in opt._config.keys() else -1 if before != -1 and self.env['step'] > before: continue self.scaler.step(opt) self.scaler.update() def get_metrics(self): return self.loss_accumulator.as_dict()