272 lines
13 KiB
Python
272 lines
13 KiB
Python
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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 models.steps.losses import create_loss
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import torch
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from apex import amp
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from collections import OrderedDict
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from .injectors import create_injector
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from models.novograd import NovoGrad
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from utils.util import recursively_detach
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logger = logging.getLogger('base')
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def define_recurrent_controller(opt, env):
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pass
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class RecurrentController:
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def __init__(self, opt, env):
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self.opt = opt
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self.env = env
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# This is the meat of the RecurrentController code. It is expected to return a recurrent_state which is fed into
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# the injectors and losses, or None if the recurrent loop is to be exited.
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# Note that on the first call, the recurrent_state parameter is set to None.
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def get_next_step(self, state, recurrent_state):
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return None
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# This class implements the logic necessary to gather the gradients resulting from recurrent network passes.
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class RecurrentStep(Module):
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def __init__(self, opt_step, env):
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super(RecurrentStep, 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()
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self.optimizers = None
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# Recurrent steps must have a bespoke "controller". This is a snippet of code responsible for determining
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# how many recurrent steps should be executed, and also compiles a "recurrent_state" which is passed to the
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# injectors and losses within the recurrent loop. Note that the recurrent state does not persist past the
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# recurrent loop.
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self.controller = define_recurrent_controller(self.step_opt)
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# Unlike a "normal" step, recurrent steps have 2 injection sites: "initial" and "recurrent". Initial injectors
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# are run once when the step is first executed. Recurrent injectors are run for every recurrent cycle and their
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# outputs are appended to a list.
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self.initial_injectors = []
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if 'initial_injectors' in self.step_opt.keys():
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for inj_name, injector in self.step_opt['initial_injectors'].items():
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self.initial_injectors.append(create_injector(injector, env))
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self.recurrent_injectors = []
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if 'recurrent_injectors' in self.step_opt.keys():
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for inj_name, injector in self.step_opt['recurrent_injectors'].items():
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self.recurrent_injectors.append(create_injector(injector, env))
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# Recurrent detach points are a list of state variables that get detached on every iteration. Since recurrent
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# injections are pushed into lists, detach points specify the exact tensor to detach by being a list of lists,
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# e.g.: [['var1', -2], ['var2', -1], ['var3', 0]]
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# The first element of the sublist is the state variable you want to detach. The second element is a list index
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# into that state variable.
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self.recurrent_detach_points = []
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if 'recurrent_detach_points' in self.step_opt.keys():
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for name, index in self.step_opt['recurrent_detach_points']:
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self.recurrent_detach_points.append(name, index)
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# Recurrent steps also have two types of losses: 'recurrent' and 'final'.
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# Similar to injection points, 'recurrent' losses are invoked every iteration.
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# 'final' losses are invoked after all iterations have completed.
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losses = []
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self.recurrent_weights = {}
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if 'recurrent_losses' in self.step_opt.keys():
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for loss_name, loss in self.step_opt['recurrent_losses'].items():
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losses.append((loss_name, create_loss(loss, env)))
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self.recurrent_weights[loss_name] = loss['weight']
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self.recurrent_losses = OrderedDict(losses)
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self.final_weights = {}
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if 'final_losses' in self.step_opt.keys():
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for loss_name, loss in self.step_opt['final_losses'].items():
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losses.append((loss_name, create_loss(loss, env)))
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self.final_weights[loss_name] = loss['weight']
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self.final_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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training = self.step_opt['training']
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if isinstance(training, list):
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self.training_net = [self.get_network_for_name(t) for t in training]
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opt_configs = [self.step_opt['optimizer_params'][t] for t in training]
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nets = self.training_net
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else:
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self.training_net = self.get_network_for_name(training)
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# When only training one network, optimizer params can just embedded in the step params.
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if 'optimizer_params' not in self.step_opt.keys():
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opt_configs = [self.step_opt]
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else:
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opt_configs = [self.step_opt['optimizer_params']]
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nets = [self.training_net]
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self.optimizers = []
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for net, opt_config in zip(nets, opt_configs):
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optim_params = []
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for k, v in net.named_parameters(): # can optimize for a part of the model
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if v.requires_grad:
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optim_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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if 'optimizer' not in self.step_opt.keys() or self.step_opt['optimizer'] == 'adam':
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opt = torch.optim.Adam(optim_params, 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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elif self.step_opt['optimizer'] == 'novograd':
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opt = NovoGrad(optim_params, lr=opt_config['lr'], weight_decay=opt_config['weight_decay'],
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betas=(opt_config['beta1'], opt_config['beta2']))
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opt._config = opt_config # This is a bit seedy, but we will need these configs later.
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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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def do_injection(self, injectors, local_state, train=True):
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injected_state = {}
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for inj in 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']:
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continue
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injected_state.update(inj(local_state))
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return injected_state
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def compute_gradients(self, losses, weights, local_state, amp_loss_id):
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total_loss = 0
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for loss_name, loss in 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']:
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continue
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l = loss(self.training_net, local_state)
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total_loss += l * weights[loss_name]
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# Record metrics.
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self.loss_accumulator.add_loss(loss_name, l)
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for n, v in loss.extra_metrics():
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self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
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self.loss_accumulator.add_loss("%s_total" % (self.get_training_network_name(),), 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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if self.env['amp']:
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with amp.scale_loss(total_loss, self.optimizers, amp_loss_id) as scaled_loss:
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scaled_loss.backward()
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else:
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total_loss.backward()
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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):
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new_state = {}
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# Prepare a de-chunked state dict which will be used for the injectors & losses.
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local_state = {}
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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.update(new_state)
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local_state['train_nets'] = str(self.get_networks_trained())
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# Some losses compute backward() internally. Accomodate 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 initial tensors.
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injected = self.do_injection(self.initial_injectors, local_state, train)
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local_state.update(injected)
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new_state.update(injected)
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recurrent_state = self.controller.get_next_step(state, None)
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while recurrent_state:
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# Detach items no longer needed from previous recursive loop.
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for name, ind in self.recurrent_detach_points:
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len_required = ind if ind > 0 else abs(ind)+1
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if len(local_state[name]) >= len_required:
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local_state[name][ind] = local_state[name][ind].detach()
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# Recurrent injectors and losses rely on state variables from recurrent_state. Combine that with local_state.
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combined_state = local_state
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combined_state.update(recurrent_state)
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# Inject recurrent injections.
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injected = self.do_injection(self.recurrent_injectors, combined_state, train)
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for k, v in injected.items():
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if k not in local_state.keys():
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local_state[k] = []
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combined_state[k] = []
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new_state[k] = []
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local_state[k].append(v)
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combined_state[k].append(v)
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new_state[k].append(v.detach())
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# Compute the recurrent losses.
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if train:
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self.compute_gradients(self.recurrent_losses, self.recurrent_weights, combined_state, amp_loss_id)
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# Zero out combined_state, it'll be repopulated in the next loop.
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combined_state = {}
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# Compute the final losses
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if train:
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self.compute_gradients(self.final_losses, self.final_weights, local_state, amp_loss_id)
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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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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):
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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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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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opt.step()
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def get_metrics(self):
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return self.loss_accumulator.as_dict()
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