Source code for onmt.utils.optimizers

""" Optimizers class """
import torch
import torch.optim as optim
from torch.nn.utils import clip_grad_norm_
import operator
import functools
from copy import copy
from math import sqrt
import types
import importlib
from onmt.utils.misc import fn_args


def build_torch_optimizer(model, opt):
    """Builds the PyTorch optimizer.

    We use the default parameters for Adam that are suggested by
    the original paper https://arxiv.org/pdf/1412.6980.pdf
    These values are also used by other established implementations,
    e.g. https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer
    https://keras.io/optimizers/
    Recently there are slightly different values used in the paper
    "Attention is all you need"
    https://arxiv.org/pdf/1706.03762.pdf, particularly the value beta2=0.98
    was used there however, beta2=0.999 is still arguably the more
    established value, so we use that here as well

    Args:
      model: The model to optimize.
      opt. The dictionary of options.

    Returns:
      A ``torch.optim.Optimizer`` instance.
    """
    params = [p for p in model.parameters() if p.requires_grad]
    betas = [opt.adam_beta1, opt.adam_beta2]
    if opt.optim == 'sgd':
        optimizer = optim.SGD(params, lr=opt.learning_rate)
    elif opt.optim == 'adagrad':
        optimizer = optim.Adagrad(
            params,
            lr=opt.learning_rate,
            initial_accumulator_value=opt.adagrad_accumulator_init)
    elif opt.optim == 'adadelta':
        optimizer = optim.Adadelta(params, lr=opt.learning_rate)
    elif opt.optim == 'adafactor':
        optimizer = AdaFactor(
            params,
            non_constant_decay=True,
            enable_factorization=True,
            weight_decay=0)
    elif opt.optim == 'adam':
        optimizer = optim.Adam(
            params,
            lr=opt.learning_rate,
            betas=betas,
            eps=1e-9)
    elif opt.optim == 'sparseadam':
        dense = []
        sparse = []
        for name, param in model.named_parameters():
            if not param.requires_grad:
                continue
            # TODO: Find a better way to check for sparse gradients.
            if 'embed' in name:
                sparse.append(param)
            else:
                dense.append(param)
        optimizer = MultipleOptimizer(
            [optim.Adam(
                dense,
                lr=opt.learning_rate,
                betas=betas,
                eps=1e-8),
             optim.SparseAdam(
                 sparse,
                 lr=opt.learning_rate,
                 betas=betas,
                 eps=1e-8)])
    elif opt.optim == 'fusedadam':
        # we use here a FusedAdam() copy of an old Apex repo
        optimizer = FusedAdam(
            params,
            lr=opt.learning_rate,
            betas=betas)
    else:
        raise ValueError('Invalid optimizer type: ' + opt.optim)

    if opt.model_dtype == 'fp16':
        import apex
        if opt.optim != 'fusedadam':
            # In this case use the new AMP API from apex
            loss_scale = "dynamic" if opt.loss_scale == 0 else opt.loss_scale
            model, optimizer = apex.amp.initialize(
                [model, model.generator],
                optimizer,
                opt_level=opt.apex_opt_level,
                loss_scale=loss_scale,
                keep_batchnorm_fp32=None)
        else:
            # In this case use the old FusedAdam with FP16_optimizer wrapper
            static_loss_scale = opt.loss_scale
            dynamic_loss_scale = opt.loss_scale == 0
            optimizer = apex.optimizers.FP16_Optimizer(
                optimizer,
                static_loss_scale=static_loss_scale,
                dynamic_loss_scale=dynamic_loss_scale)
    return optimizer


def make_learning_rate_decay_fn(opt):
    """Returns the learning decay function from options."""
    if opt.decay_method == 'noam':
        return functools.partial(
            noam_decay,
            warmup_steps=opt.warmup_steps,
            model_size=opt.rnn_size)
    elif opt.decay_method == 'noamwd':
        return functools.partial(
            noamwd_decay,
            warmup_steps=opt.warmup_steps,
            model_size=opt.rnn_size,
            rate=opt.learning_rate_decay,
            decay_steps=opt.decay_steps,
            start_step=opt.start_decay_steps)
    elif opt.decay_method == 'rsqrt':
        return functools.partial(
            rsqrt_decay, warmup_steps=opt.warmup_steps)
    elif opt.start_decay_steps is not None:
        return functools.partial(
            exponential_decay,
            rate=opt.learning_rate_decay,
            decay_steps=opt.decay_steps,
            start_step=opt.start_decay_steps)


def noam_decay(step, warmup_steps, model_size):
    """Learning rate schedule described in
    https://arxiv.org/pdf/1706.03762.pdf.
    """
    return (
        model_size ** (-0.5) *
        min(step ** (-0.5), step * warmup_steps**(-1.5)))


def noamwd_decay(step, warmup_steps,
                 model_size, rate, decay_steps, start_step=0):
    """Learning rate schedule optimized for huge batches
    """
    return (
        model_size ** (-0.5) *
        min(step ** (-0.5), step * warmup_steps**(-1.5)) *
        rate ** (max(step - start_step + decay_steps, 0) // decay_steps))


def exponential_decay(step, rate, decay_steps, start_step=0):
    """A standard exponential decay, scaling the learning rate by :obj:`rate`
    every :obj:`decay_steps` steps.
    """
    return rate ** (max(step - start_step + decay_steps, 0) // decay_steps)


def rsqrt_decay(step, warmup_steps):
    """Decay based on the reciprocal of the step square root."""
    return 1.0 / sqrt(max(step, warmup_steps))


class MultipleOptimizer(object):
    """ Implement multiple optimizers needed for sparse adam """

    def __init__(self, op):
        """ ? """
        self.optimizers = op

    @property
    def param_groups(self):
        param_groups = []
        for optimizer in self.optimizers:
            param_groups.extend(optimizer.param_groups)
        return param_groups

    def zero_grad(self):
        """ ? """
        for op in self.optimizers:
            op.zero_grad()

    def step(self):
        """ ? """
        for op in self.optimizers:
            op.step()

    @property
    def state(self):
        """ ? """
        return {k: v for op in self.optimizers for k, v in op.state.items()}

    def state_dict(self):
        """ ? """
        return [op.state_dict() for op in self.optimizers]

    def load_state_dict(self, state_dicts):
        """ ? """
        assert len(state_dicts) == len(self.optimizers)
        for i in range(len(state_dicts)):
            self.optimizers[i].load_state_dict(state_dicts[i])


[docs]class Optimizer(object): """ Controller class for optimization. Mostly a thin wrapper for `optim`, but also useful for implementing rate scheduling beyond what is currently available. Also implements necessary methods for training RNNs such as grad manipulations. """ def __init__(self, optimizer, learning_rate, learning_rate_decay_fn=None, max_grad_norm=None): """Initializes the controller. Args: optimizer: A ``torch.optim.Optimizer`` instance. learning_rate: The initial learning rate. learning_rate_decay_fn: An optional callable taking the current step as argument and return a learning rate scaling factor. max_grad_norm: Clip gradients to this global norm. """ self._optimizer = optimizer self._learning_rate = learning_rate self._learning_rate_decay_fn = learning_rate_decay_fn self._max_grad_norm = max_grad_norm or 0 self._training_step = 1 self._decay_step = 1 self._fp16 = None
[docs] @classmethod def from_opt(cls, model, opt, checkpoint=None): """Builds the optimizer from options. Args: cls: The ``Optimizer`` class to instantiate. model: The model to optimize. opt: The dict of user options. checkpoint: An optional checkpoint to load states from. Returns: An ``Optimizer`` instance. """ optim_opt = opt optim_state_dict = None if opt.train_from and checkpoint is not None: optim = checkpoint['optim'] ckpt_opt = checkpoint['opt'] ckpt_state_dict = {} if isinstance(optim, Optimizer): # Backward compatibility. ckpt_state_dict['training_step'] = optim._step + 1 ckpt_state_dict['decay_step'] = optim._step + 1 ckpt_state_dict['optimizer'] = optim.optimizer.state_dict() else: ckpt_state_dict = optim if opt.reset_optim == 'none': # Load everything from the checkpoint. optim_opt = ckpt_opt optim_state_dict = ckpt_state_dict elif opt.reset_optim == 'all': # Build everything from scratch. pass elif opt.reset_optim == 'states': # Reset optimizer, keep options. optim_opt = ckpt_opt optim_state_dict = ckpt_state_dict del optim_state_dict['optimizer'] elif opt.reset_optim == 'keep_states': # Reset options, keep optimizer. optim_state_dict = ckpt_state_dict optimizer = cls( build_torch_optimizer(model, optim_opt), optim_opt.learning_rate, learning_rate_decay_fn=make_learning_rate_decay_fn(optim_opt), max_grad_norm=optim_opt.max_grad_norm) if opt.model_dtype == "fp16": if opt.optim == "fusedadam": optimizer._fp16 = "legacy" else: optimizer._fp16 = "amp" if optim_state_dict: optimizer.load_state_dict(optim_state_dict) return optimizer
@property def training_step(self): """The current training step.""" return self._training_step
[docs] def learning_rate(self): """Returns the current learning rate.""" if self._learning_rate_decay_fn is None: return self._learning_rate scale = self._learning_rate_decay_fn(self._decay_step) return scale * self._learning_rate
def state_dict(self): return { 'training_step': self._training_step, 'decay_step': self._decay_step, 'optimizer': self._optimizer.state_dict() } def load_state_dict(self, state_dict): self._training_step = state_dict['training_step'] # State can be partially restored. if 'decay_step' in state_dict: self._decay_step = state_dict['decay_step'] if 'optimizer' in state_dict: self._optimizer.load_state_dict(state_dict['optimizer'])
[docs] def zero_grad(self): """Zero the gradients of optimized parameters.""" self._optimizer.zero_grad()
[docs] def backward(self, loss): """Wrapper for backward pass. Some optimizer requires ownership of the backward pass.""" if self._fp16 == "amp": import apex with apex.amp.scale_loss(loss, self._optimizer) as scaled_loss: scaled_loss.backward() elif self._fp16 == "legacy": kwargs = {} if "update_master_grads" in fn_args(self._optimizer.backward): kwargs["update_master_grads"] = True self._optimizer.backward(loss, **kwargs) else: loss.backward()
[docs] def step(self): """Update the model parameters based on current gradients. Optionally, will employ gradient modification or update learning rate. """ learning_rate = self.learning_rate() if self._fp16 == "legacy": if hasattr(self._optimizer, "update_master_grads"): self._optimizer.update_master_grads() if hasattr(self._optimizer, "clip_master_grads") and \ self._max_grad_norm > 0: self._optimizer.clip_master_grads(self._max_grad_norm) for group in self._optimizer.param_groups: group['lr'] = learning_rate if self._fp16 is None and self._max_grad_norm > 0: clip_grad_norm_(group['params'], self._max_grad_norm) self._optimizer.step() self._decay_step += 1 self._training_step += 1
# Code below is an implementation of https://arxiv.org/pdf/1804.04235.pdf # inspired but modified from https://github.com/DeadAt0m/adafactor-pytorch class AdaFactor(torch.optim.Optimizer): def __init__(self, params, lr=None, beta1=0.9, beta2=0.999, eps1=1e-30, eps2=1e-3, cliping_threshold=1, non_constant_decay=True, enable_factorization=True, ams_grad=True, weight_decay=0): enable_momentum = beta1 != 0 if non_constant_decay: ams_grad = False defaults = dict(lr=lr, beta1=beta1, beta2=beta2, eps1=eps1, eps2=eps2, cliping_threshold=cliping_threshold, weight_decay=weight_decay, ams_grad=ams_grad, enable_factorization=enable_factorization, enable_momentum=enable_momentum, non_constant_decay=non_constant_decay) super(AdaFactor, self).__init__(params, defaults) def __setstate__(self, state): super(AdaFactor, self).__setstate__(state) def _experimental_reshape(self, shape): temp_shape = shape[2:] if len(temp_shape) == 1: new_shape = (shape[0], shape[1]*shape[2]) else: tmp_div = len(temp_shape) // 2 + len(temp_shape) % 2 new_shape = (shape[0]*functools.reduce(operator.mul, temp_shape[tmp_div:], 1), shape[1]*functools.reduce(operator.mul, temp_shape[:tmp_div], 1)) return new_shape, copy(shape) def _check_shape(self, shape): ''' output1 - True - algorithm for matrix, False - vector; output2 - need reshape ''' if len(shape) > 2: return True, True elif len(shape) == 2: return True, False elif len(shape) == 2 and (shape[0] == 1 or shape[1] == 1): return False, False else: return False, False def _rms(self, x): return sqrt(torch.mean(x.pow(2))) def step(self, closure=None): loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad.data if grad.is_sparse: raise RuntimeError('Adam does not support sparse \ gradients, use SparseAdam instead') is_matrix, is_need_reshape = self._check_shape(grad.size()) new_shape = p.data.size() if is_need_reshape and group['enable_factorization']: new_shape, old_shape = \ self._experimental_reshape(p.data.size()) grad = grad.view(new_shape) state = self.state[p] if len(state) == 0: state['step'] = 0 if group['enable_momentum']: state['exp_avg'] = torch.zeros(new_shape, dtype=torch.float32, device=p.grad.device) if is_matrix and group['enable_factorization']: state['exp_avg_sq_R'] = \ torch.zeros((1, new_shape[1]), dtype=torch.float32, device=p.grad.device) state['exp_avg_sq_C'] = \ torch.zeros((new_shape[0], 1), dtype=torch.float32, device=p.grad.device) else: state['exp_avg_sq'] = torch.zeros(new_shape, dtype=torch.float32, device=p.grad.device) if group['ams_grad']: state['exp_avg_sq_hat'] = \ torch.zeros(new_shape, dtype=torch.float32, device=p.grad.device) if group['enable_momentum']: exp_avg = state['exp_avg'] if is_matrix and group['enable_factorization']: exp_avg_sq_r = state['exp_avg_sq_R'] exp_avg_sq_c = state['exp_avg_sq_C'] else: exp_avg_sq = state['exp_avg_sq'] if group['ams_grad']: exp_avg_sq_hat = state['exp_avg_sq_hat'] state['step'] += 1 lr_t = group['lr'] lr_t *= max(group['eps2'], self._rms(p.data)) if group['enable_momentum']: if group['non_constant_decay']: beta1_t = group['beta1'] * \ (1 - group['beta1'] ** (state['step'] - 1)) \ / (1 - group['beta1'] ** state['step']) else: beta1_t = group['beta1'] exp_avg.mul_(beta1_t).add_(1 - beta1_t, grad) if group['non_constant_decay']: beta2_t = group['beta2'] * \ (1 - group['beta2'] ** (state['step'] - 1)) / \ (1 - group['beta2'] ** state['step']) else: beta2_t = group['beta2'] if is_matrix and group['enable_factorization']: exp_avg_sq_r.mul_(beta2_t). \ add_(1 - beta2_t, torch.sum(torch.mul(grad, grad). add_(group['eps1']), dim=0, keepdim=True)) exp_avg_sq_c.mul_(beta2_t). \ add_(1 - beta2_t, torch.sum(torch.mul(grad, grad). add_(group['eps1']), dim=1, keepdim=True)) v = torch.mul(exp_avg_sq_c, exp_avg_sq_r).div_(torch.sum(exp_avg_sq_r)) else: exp_avg_sq.mul_(beta2_t). \ addcmul_(1 - beta2_t, grad, grad). \ add_((1 - beta2_t)*group['eps1']) v = exp_avg_sq g = grad if group['enable_momentum']: g = torch.div(exp_avg, 1 - beta1_t ** state['step']) if group['ams_grad']: torch.max(exp_avg_sq_hat, v, out=exp_avg_sq_hat) v = exp_avg_sq_hat u = torch.div(g, (torch.div(v, 1 - beta2_t ** state['step'])).sqrt().add_(group['eps1'])) else: u = torch.div(g, v.sqrt()) u.div_(max(1, self._rms(u) / group['cliping_threshold'])) p.data.add_(-lr_t * (u.view(old_shape) if is_need_reshape and group['enable_factorization'] else u)) if group['weight_decay'] != 0: p.data.add_(-group['weight_decay'] * lr_t, p.data) return loss class FusedAdam(torch.optim.Optimizer): """Implements Adam algorithm. Currently GPU-only. Requires Apex to be installed via ``python setup.py install --cuda_ext --cpp_ext``. It has been proposed in `Adam: A Method for Stochastic Optimization`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups. lr (float, optional): learning rate. (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square. (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability. (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: False) NOT SUPPORTED in FusedAdam! eps_inside_sqrt (boolean, optional): in the 'update parameters' step, adds eps to the bias-corrected second moment estimate before evaluating square root instead of adding it to the square root of second moment estimate as in the original paper. (default: False) .. _Adam: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ """ def __init__(self, params, lr=1e-3, bias_correction=True, betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt=False, weight_decay=0., max_grad_norm=0., amsgrad=False): global fused_adam_cuda fused_adam_cuda = importlib.import_module("fused_adam_cuda") if amsgrad: raise RuntimeError('AMSGrad variant not supported.') defaults = dict(lr=lr, bias_correction=bias_correction, betas=betas, eps=eps, weight_decay=weight_decay, max_grad_norm=max_grad_norm) super(FusedAdam, self).__init__(params, defaults) self.eps_mode = 0 if eps_inside_sqrt else 1 def step(self, closure=None, grads=None, output_params=None, scale=1., grad_norms=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. grads (list of tensors, optional): weight gradient to use for the optimizer update. If gradients have type torch.half, parameters are expected to be in type torch.float. (default: None) output params (list of tensors, optional): A reduced precision copy of the updated weights written out in addition to the regular updated weights. Have to be of same type as gradients. (default: None) scale (float, optional): factor to divide gradient tensor values by before applying to weights. (default: 1) """ loss = None if closure is not None: loss = closure() if grads is None: grads_group = [None]*len(self.param_groups) # backward compatibility # assuming a list/generator of parameter means single group elif isinstance(grads, types.GeneratorType): grads_group = [grads] elif type(grads[0]) != list: grads_group = [grads] else: grads_group = grads if output_params is None: output_params_group = [None]*len(self.param_groups) elif isinstance(output_params, types.GeneratorType): output_params_group = [output_params] elif type(output_params[0]) != list: output_params_group = [output_params] else: output_params_group = output_params if grad_norms is None: grad_norms = [None]*len(self.param_groups) for group, grads_this_group, output_params_this_group, \ grad_norm in zip(self.param_groups, grads_group, output_params_group, grad_norms): if grads_this_group is None: grads_this_group = [None]*len(group['params']) if output_params_this_group is None: output_params_this_group = [None]*len(group['params']) # compute combined scale factor for this group combined_scale = scale if group['max_grad_norm'] > 0: # norm is in fact norm*scale clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm'] if clip > 1: combined_scale = clip * scale bias_correction = 1 if group['bias_correction'] else 0 for p, grad, output_param in zip(group['params'], grads_this_group, output_params_this_group): # note: p.grad should not ever be set for correct operation of # mixed precision optimizer that sometimes sends None gradients if p.grad is None and grad is None: continue if grad is None: grad = p.grad.data if grad.is_sparse: raise RuntimeError('FusedAdam does not support sparse \ gradients, please consider \ SparseAdam instead') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p.data) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 out_p = torch.tensor([], dtype=torch.float) if output_param \ is None else output_param fused_adam_cuda.adam(p.data, out_p, exp_avg, exp_avg_sq, grad, group['lr'], beta1, beta2, group['eps'], combined_scale, state['step'], self.eps_mode, bias_correction, group['weight_decay']) return loss