ColossalAI/colossalai/nn/optimizer/fused_adam.py

# modified from https://github.com/NVIDIA/apex/blob/master/apex/optimizers/fused_adam.py
import torch

from colossalai.registry import OPTIMIZERS
from ..multi_tensor_apply import multi_tensor_applier


@OPTIMIZERS.register_module
class FusedAdam(torch.optim.Optimizer):
    """Implements Adam algorithm.

    Currently GPU-only.  Requires ColossalAI to be installed via
    ``pip install -v --no-cache-dir --global-option="--cuda_ext" ./``.

    This version of fused Adam implements 2 fusions.

      * Fusion of the Adam update's elementwise operations
      * A multi-tensor apply launch that batches the elementwise updates applied to all the model's parameters into one or a few kernel launches.

    :class:`colossalai.nn.optimizer.FusedAdam` may be used as a drop-in replacement for ``torch.optim.AdamW``,
    or ``torch.optim.Adam`` with ``adam_w_mode=False``

    :class:`colossalai.nn.optimizer.FusedAdam` may be used with or without Amp. 

    Adam was 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!
        adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
            True for decoupled weight decay(also known as AdamW) (default: True)
        set_grad_none (bool, optional): whether set grad to None when zero_grad()
            method is called. (default: True)

    .. _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, adam_w_mode=True,
                 weight_decay=0., amsgrad=False, set_grad_none=True):

        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)
        super(FusedAdam, self).__init__(params, defaults)
        self.adam_w_mode = 1 if adam_w_mode else 0
        self.set_grad_none = set_grad_none
        if multi_tensor_applier.available:
            import colossal_C
            # Skip buffer
            self._dummy_overflow_buf = torch.cuda.IntTensor([0])
            self.multi_tensor_adam = colossal_C.multi_tensor_adam
        else:
            raise RuntimeError(
                'apex.optimizers.FusedAdam requires cuda extensions')

    def zero_grad(self):
        if self.set_grad_none:
            for group in self.param_groups:
                for p in group['params']:
                    p.grad = None
        else:
            super(FusedAdam, self).zero_grad()

    def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None):
        """Performs a single optimization step.

        Arguments:
            closure (callable, optional): A closure that reevaluates the model
                and returns the loss.

        The remaining arguments are deprecated, and are only retained (for the moment) for error-checking purposes.
        """
        if any(p is not None for p in [grads, output_params, scale, grad_norms]):
            raise RuntimeError(
                'FusedAdam has been updated.  Simply initialize it identically to torch.optim.Adam, and call step() with no arguments.')
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            bias_correction = 1 if group['bias_correction'] else 0
            beta1, beta2 = group['betas']

            # assume same step across group now to simplify things
            # per parameter step can be easily support by making it tensor, or pass list into kernel
            if 'step' in group:
                group['step'] += 1
            else:
                group['step'] = 1

            # create lists for multi-tensor apply
            g_16, p_16, m_16, v_16 = [], [], [], []
            g_32, p_32, m_32, v_32 = [], [], [], []

            for p in group['params']:
                if p.grad is None:
                    continue
                if p.grad.data.is_sparse:
                    raise RuntimeError(
                        'FusedAdam does not support sparse gradients, please consider SparseAdam instead')

                state = self.state[p]
                # State initialization
                if len(state) == 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)

                if p.dtype == torch.float16:
                    g_16.append(p.grad.data)
                    p_16.append(p.data)
                    m_16.append(state['exp_avg'])
                    v_16.append(state['exp_avg_sq'])
                elif p.dtype == torch.float32:
                    g_32.append(p.grad.data)
                    p_32.append(p.data)
                    m_32.append(state['exp_avg'])
                    v_32.append(state['exp_avg_sq'])
                else:
                    raise RuntimeError('FusedAdam only support fp16 and fp32.')

            if (len(g_16) > 0):
                multi_tensor_applier(self.multi_tensor_adam,
                                     self._dummy_overflow_buf,
                                     [g_16, p_16, m_16, v_16],
                                     group['lr'],
                                     beta1,
                                     beta2,
                                     group['eps'],
                                     group['step'],
                                     self.adam_w_mode,
                                     bias_correction,
                                     group['weight_decay'])
            if (len(g_32) > 0):
                multi_tensor_applier(self.multi_tensor_adam,
                                     self._dummy_overflow_buf,
                                     [g_32, p_32, m_32, v_32],
                                     group['lr'],
                                     beta1,
                                     beta2,
                                     group['eps'],
                                     group['step'],
                                     self.adam_w_mode,
                                     bias_correction,
                                     group['weight_decay'])

        return loss