fused_adam.py

import types
import torch
import importlib
from apex.multi_tensor_apply import multi_tensor_applier

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)
        use_mt (boolean, optional): use multi tensor apply for lower launch
            latency. (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, use_mt=False,
                 amp_scale_adjustment=1.0):
        global fused_adam_cuda
        fused_adam_cuda = importlib.import_module("fused_adam_cuda")

        self._use_multi_tensor = False
        if use_mt:
            if not multi_tensor_applier.available:
                print("Warning:  multi_tensor_applier is unavailable")
            else:
                self._use_multi_tensor = True
                self._overflow_buf = torch.cuda.IntTensor([0])

        self._amp_scale_adjustment = amp_scale_adjustment

        if amsgrad:
            raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
        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 hasattr(self, "_amp_stash"):
            grads = self._amp_stash.grads
            output_params = self._amp_stash.output_params
            scale = self._amp_stash.scale*self._amp_scale_adjustment
            grad_norms = self._amp_stash.grad_norms

        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

            if self._use_multi_tensor:
                if output_params:
                    tensorlists = [[],[],[],[],[]]
                else:
                    tensorlists = [[],[],[],[]]
                tensordevice = None

            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
                if self._use_multi_tensor:
                    pl = [p.data, exp_avg, exp_avg_sq, grad]
                    if output_param is not None:
                        pl.append(out_p)

                    for tl, t in zip(tensorlists, pl):
                        tl.append(t)

                    if tensordevice is None:
                        tensordevice = p.device
                    elif tensordevice != p.device:
                        raise RuntimeError('FusedAdam does not support use_mt with tensors on multiple device')

                else:
                    with torch.cuda.device(p.device):
                        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'])

            if self._use_multi_tensor:
                with torch.cuda.device(tensordevice):
                    multi_tensor_applier(
                        fused_adam_cuda.adam_mt,
                        self._overflow_buf,
                        tensorlists,
                        group['lr'],
                        beta1,
                        beta2,
                        group['eps'],
                        combined_scale,
                        state['step'],
                        self.eps_mode,
                        bias_correction,
                        group['weight_decay'])

        return loss