337 lines
18 KiB
Python
337 lines
18 KiB
Python
from torch.cuda.amp import GradScaler
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from torch.distributed.optim import ZeroRedundancyOptimizer
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from utils.loss_accumulator import LossAccumulator
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from torch.nn import Module
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import logging
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from trainer.losses import create_loss
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import torch
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from collections import OrderedDict
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from trainer.inject import create_injector
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from utils.util import recursively_detach, opt_get, clip_grad_norm
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logger = logging.getLogger('base')
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# Defines the expected API for a single training step
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class ConfigurableStep(Module):
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def __init__(self, opt_step, env):
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super(ConfigurableStep, self).__init__()
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self.step_opt = opt_step
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self.env = env
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self.opt = env['opt']
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self.gen_outputs = opt_step['generator_outputs']
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self.loss_accumulator = LossAccumulator(buffer_sz=opt_get(opt_step, ['loss_log_buffer'], 50))
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self.optimizers = None
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self.scaler = GradScaler(enabled=self.opt['fp16'] or opt_get(self.opt, ['grad_scaler_enabled'], False))
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self.grads_generated = False
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self.min_total_loss = opt_step['min_total_loss'] if 'min_total_loss' in opt_step.keys() else -999999999
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self.clip_grad_eps = opt_get(opt_step, ['clip_grad_eps'], None)
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# This is a half-measure that can be used between anomaly_detection and running a potentially problematic
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# trainer bare. With this turned on, the optimizer will not step() if a nan grad is detected. If a model trips
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# this warning 10 times in a row, the training session is aborted and the model state is saved. This has a
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# noticeable affect on training speed, but nowhere near as bad as anomaly_detection.
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self.check_grads_for_nan = opt_get(opt_step, ['check_grads_for_nan'], False)
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self.nan_counter = 0
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self.injectors = []
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if 'injectors' in self.step_opt.keys():
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injector_names = []
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for inj_name, injector in self.step_opt['injectors'].items():
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assert inj_name not in injector_names # Repeated names are always an error case.
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injector_names.append(inj_name)
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self.injectors.append(create_injector(injector, env))
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losses = []
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self.weights = {}
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if 'losses' in self.step_opt.keys():
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for loss_name, loss in self.step_opt['losses'].items():
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assert loss_name not in self.weights.keys() # Repeated names are always an error case.
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losses.append((loss_name, create_loss(loss, env)))
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self.weights[loss_name] = loss['weight']
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self.losses = OrderedDict(losses)
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def get_network_for_name(self, name):
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return self.env['generators'][name] if name in self.env['generators'].keys() \
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else self.env['discriminators'][name]
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# Subclasses should override this to define individual optimizers. They should all go into self.optimizers.
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# This default implementation defines a single optimizer for all Generator parameters.
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# Must be called after networks are initialized and wrapped.
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def define_optimizers(self):
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opt_configs = [opt_get(self.step_opt, ['optimizer_params'], None)]
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self.optimizers = []
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if opt_configs[0] is None:
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return
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training = self.step_opt['training']
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training_net = self.get_network_for_name(training)
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nets = [training_net]
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training = [training]
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for net_name, net, opt_config in zip(training, nets, opt_configs):
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# Configs can organize parameters by-group and specify different learning rates for each group. This only
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# works in the model specifically annotates which parameters belong in which group using PARAM_GROUP.
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optim_params = {'default': {'params': [], 'lr': opt_config['lr']}}
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if opt_config is not None and 'param_groups' in opt_config.keys():
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for k, pg in opt_config['param_groups'].items():
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optim_params[k] = {'params': [], 'lr': pg['lr']}
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import torch.nn as nn
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norm_modules = (nn.BatchNorm2d, nn.InstanceNorm2d, nn.BatchNorm1d, nn.InstanceNorm1d,
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nn.BatchNorm3d, nn.InstanceNorm3d, nn.GroupNorm, nn.LayerNorm)
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emb_modules = (nn.Embedding, nn.EmbeddingBag)
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param_names_notweights = set()
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all_param_names = set()
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param_map = {}
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for mn, m in net.named_modules():
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for k, v in m.named_parameters():
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v.is_bias = k.endswith(".bias")
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v.is_weight = k.endswith(".weight")
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v.is_norm = isinstance(m, norm_modules)
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v.is_emb = isinstance(m, emb_modules)
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fpn = '%s.%s' % (mn, k) if mn else k # full param name
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all_param_names.add(fpn)
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param_map[fpn] = v
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if v.is_bias or v.is_norm or v.is_emb:
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param_names_notweights.add(fpn)
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# Some models can specify some parameters to be in different groups.
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param_group = "default"
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if hasattr(v, 'PARAM_GROUP'):
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if v.PARAM_GROUP in optim_params.keys():
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param_group = v.PARAM_GROUP
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else:
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logger.warning(f'Model specifies a custom param group {v.PARAM_GROUP} which is not configured. '
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f'The same LR will be used for all parameters.')
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if v.requires_grad:
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optim_params[param_group]['params'].append(v)
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else:
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if self.env['rank'] <= 0:
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logger.warning('Params [{:s}] will not optimize.'.format(k))
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params_names_notweights = sorted(list(param_names_notweights))
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params_notweights = [param_map[k] for k in params_names_notweights]
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params_names_weights = sorted(list(all_param_names ^ param_names_notweights))
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params_weights = [param_map[k] for k in params_names_weights]
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if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adam':
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opt = torch.optim.Adam(list(optim_params.values()), lr=opt_config['lr'],
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weight_decay=opt_config['weight_decay'],
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betas=(opt_config['beta1'], opt_config['beta2']))
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opt._group_names = sorted(list(all_param_names))
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elif self.step_opt['optimizer'] == 'adamw':
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groups = [
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{ 'params': params_weights, 'weight_decay': opt_get(opt_config, ['weight_decay'], 0) },
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{ 'params': params_notweights, 'weight_decay': 0 }
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]
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opt = torch.optim.AdamW(groups, lr=opt_config['lr'],
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weight_decay=opt_get(opt_config, ['weight_decay'], 1e-2),
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betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999)))
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opt._group_names = [params_names_weights, params_names_notweights]
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elif self.step_opt['optimizer'] == 'adamw_zero':
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# The torch ZeRO implementation does not seem to support parameter groups, so do not shard the non-weighted
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# parameters and just use a normal AdamW implementation. In a large network, these weights will normally
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# be a tiny fraction of the total weights.
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opt_unweighted = torch.optim.AdamW(params_notweights, lr=opt_config['lr'], weight_decay=0,
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betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999)))
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opt_unweighted._config = opt_config
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opt_unweighted._config['network'] = net_name
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self.optimizers.append(opt_unweighted)
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# Not setting these means abnormal gradient detection below no longer works.
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opt_unweighted._group_names = []
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opt = ZeroRedundancyOptimizer(params_weights, optimizer_class=torch.optim.AdamW, lr=opt_config['lr'],
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weight_decay=opt_get(opt_config, ['weight_decay'], 1e-2),
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betas=(opt_get(opt_config, ['beta1'], .9), opt_get(opt_config, ['beta2'], .999)))
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opt._group_names = []
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elif self.step_opt['optimizer'] == 'lars':
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from trainer.optimizers.larc import LARC
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from trainer.optimizers.sgd import SGDNoBiasMomentum
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optSGD = SGDNoBiasMomentum(list(optim_params.values()), lr=opt_config['lr'], momentum=opt_config['momentum'],
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weight_decay=opt_config['weight_decay'])
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opt = LARC(optSGD, trust_coefficient=opt_config['lars_coefficient'])
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opt._group_names = sorted(list(all_param_names))
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elif self.step_opt['optimizer'] == 'sgd':
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from torch.optim import SGD
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opt = SGD(list(optim_params.values()), lr=opt_config['lr'], momentum=opt_config['momentum'], weight_decay=opt_config['weight_decay'])
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opt._group_names = sorted(list(all_param_names))
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opt._config = opt_config # This is a bit seedy, but we will need these configs later.
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opt._config['network'] = net_name
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self.optimizers.append(opt)
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# Returns all optimizers used in this step.
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def get_optimizers(self):
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assert self.optimizers is not None
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return self.optimizers
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# Returns optimizers which are opting in for default LR scheduling.
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def get_optimizers_with_default_scheduler(self):
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assert self.optimizers is not None
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return self.optimizers
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# Returns the names of the networks this step will train. Other networks will be frozen.
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def get_networks_trained(self):
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if isinstance(self.step_opt['training'], list):
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return self.step_opt['training']
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else:
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return [self.step_opt['training']]
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def get_training_network_name(self):
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if isinstance(self.step_opt['training'], list):
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return self.step_opt['training'][0]
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else:
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return self.step_opt['training']
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# Performs all forward and backward passes for this step given an input state. All input states are lists of
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# chunked tensors. Use grad_accum_step to dereference these steps. Should return a dict of tensors that later
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# steps might use. These tensors are automatically detached and accumulated into chunks.
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def do_forward_backward(self, state, grad_accum_step, amp_loss_id, train=True, no_ddp_sync=False, loss_accumulator=None):
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local_state = {} # <-- Will store the entire local state to be passed to injectors & losses.
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new_state = {} # <-- Will store state values created by this step for returning to ExtensibleTrainer.
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for k, v in state.items():
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local_state[k] = v[grad_accum_step]
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local_state['train_nets'] = str(self.get_networks_trained())
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loss_accumulator = self.loss_accumulator if loss_accumulator is None else loss_accumulator
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# Some losses compute backward() internally. Accommodate this by stashing the amp_loss_id in env.
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self.env['amp_loss_id'] = amp_loss_id
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self.env['current_step_optimizers'] = self.optimizers
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self.env['training'] = train
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# Inject in any extra dependencies.
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for inj in self.injectors:
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# Don't do injections tagged with eval unless we are not in train mode.
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if train and 'eval' in inj.opt.keys() and inj.opt['eval']:
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continue
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# Likewise, don't do injections tagged with train unless we are not in eval.
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if not train and 'train' in inj.opt.keys() and inj.opt['train']:
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continue
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# Don't do injections tagged with 'after' or 'before' when we are out of spec.
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if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
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'before' in inj.opt.keys() and self.env['step'] > inj.opt['before'] or \
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'every' in inj.opt.keys() and self.env['step'] % inj.opt['every'] != 0:
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continue
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if 'no_accum' in inj.opt.keys() and grad_accum_step > 0:
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continue
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training_net = self.get_network_for_name(self.step_opt['training'])
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if no_ddp_sync and hasattr(training_net, 'no_sync'):
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with training_net.no_sync():
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injected = inj(local_state)
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elif opt_get(inj.opt, ['no_grad'], False):
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with torch.no_grad():
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injected = inj(local_state)
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else:
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injected = inj(local_state)
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local_state.update(injected)
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new_state.update(injected)
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if len(self.losses) > 0:
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# Finally, compute the losses.
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total_loss = 0
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for loss_name, loss in self.losses.items():
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# Some losses only activate after a set number of steps. For example, proto-discriminator losses can
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# be very disruptive to a generator.
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if 'after' in loss.opt.keys() and loss.opt['after'] > self.env['step'] or \
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'before' in loss.opt.keys() and self.env['step'] > loss.opt['before'] or \
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'every' in loss.opt.keys() and self.env['step'] % loss.opt['every'] != 0:
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continue
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if loss.is_stateful():
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l, lstate = loss(self.get_network_for_name(self.step_opt['training']), local_state)
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local_state.update(lstate)
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new_state.update(lstate)
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else:
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l = loss(self.get_network_for_name(self.step_opt['training']), local_state)
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if not l.isfinite():
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print(f'!!Detected non-finite loss {loss_name}')
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total_loss += l * self.weights[loss_name]
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# Record metrics.
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if isinstance(l, torch.Tensor):
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loss_accumulator.add_loss(loss_name, l)
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for n, v in loss.extra_metrics():
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loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
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loss.clear_metrics()
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# In some cases, the loss could not be set (e.g. all losses have 'after')
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if train and isinstance(total_loss, torch.Tensor):
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loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), total_loss)
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reset_required = total_loss < self.min_total_loss
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# Scale the loss down by the accumulation factor.
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total_loss = total_loss / self.env['mega_batch_factor']
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# Get dem grads!
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self.scaler.scale(total_loss).backward()
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if reset_required:
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# You might be scratching your head at this. Why would you zero grad as opposed to not doing a
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# backwards? Because DDP uses the backward() pass as a synchronization point and there is not a good
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# way to simply bypass backward. If you want a more efficient way to specify a min_loss, use or
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# implement it at the loss level.
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self.get_network_for_name(self.step_opt['training']).zero_grad()
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loss_accumulator.increment_metric("%s_skipped_steps" % (self.get_training_network_name(),))
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self.grads_generated = True
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# Detach all state variables. Within the step, gradients can flow. Once these variables leave the step
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# we must release the gradients.
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new_state = recursively_detach(new_state)
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# Prune state outputs that are not actually needed.
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if 'step_outputs' in self.step_opt.keys():
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nst = {}
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for k in self.step_opt['step_outputs']:
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nst[k] = new_state[k]
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new_state = nst
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return new_state
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# Performs the optimizer step after all gradient accumulation is completed. Default implementation simply steps()
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# all self.optimizers.
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def do_step(self, step):
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if not self.grads_generated:
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return
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self.grads_generated = False
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for opt in self.optimizers:
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# Optimizers can be opted out in the early stages of training.
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after = opt._config['after'] if 'after' in opt._config.keys() else 0
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after_network = self.opt['networks'][opt._config['network']]['after'] if 'after' in self.opt['networks'][opt._config['network']].keys() else 0
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after = max(after, after_network)
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if self.env['step'] < after:
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continue
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before = opt._config['before'] if 'before' in opt._config.keys() else -1
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if before != -1 and self.env['step'] > before:
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continue
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nan_found = False
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if self.check_grads_for_nan:
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for pg in opt.param_groups:
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for p in pg['params']:
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if not torch.isfinite(p.grad).any():
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nan_found = True
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break
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if nan_found:
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break
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if nan_found:
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print("NaN found in grads. Throwing this step out.")
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self.nan_counter += 1
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else:
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self.nan_counter = 0
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if self.clip_grad_eps is not None:
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for pgn, pg in zip(opt._group_names, opt.param_groups):
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grad_norm = clip_grad_norm(pg['params'], pgn, self.clip_grad_eps)
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if torch.isnan(grad_norm):
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nan_found = True
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self.nan_counter += 1
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if not nan_found:
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self.scaler.step(opt)
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self.scaler.update()
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else:
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opt.zero_grad()
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def get_metrics(self):
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return self.loss_accumulator.as_dict()
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