optimization_fp16.py

# coding=utf-8
"""PyTorch optimization for BERT model."""

from apex.optimizers import FP16_Optimizer


class FP16_Optimizer_State(FP16_Optimizer):
    def __init__(self,
                 init_optimizer,
                 static_loss_scale=1.0,
                 dynamic_loss_scale=False,
                 dynamic_loss_args=None,
                 verbose=True):
        super(FP16_Optimizer_State, self).__init__(init_optimizer,
                                                   static_loss_scale, dynamic_loss_scale, dynamic_loss_args, verbose)

    def state_dict(self):
        """
        Returns a dict containing the current state of this :class:`FP16_Optimizer` instance.
        This dict contains attributes of :class:`FP16_Optimizer`, as well as the state_dict
        of the contained Pytorch optimizer.
        Example::
            checkpoint = {}
            checkpoint['model'] = model.state_dict()
            checkpoint['optimizer'] = optimizer.state_dict()
            torch.save(checkpoint, "saved.pth")
        """
        state_dict = {}
        state_dict['dynamic_loss_scale'] = self.dynamic_loss_scale
        state_dict['cur_scale'] = self.cur_scale
        state_dict['cur_iter'] = self.cur_iter
        if state_dict['dynamic_loss_scale']:
            state_dict['last_overflow_iter'] = self.last_overflow_iter
            state_dict['scale_factor'] = self.scale_factor
            state_dict['scale_window'] = self.scale_window
        state_dict['optimizer_state_dict'] = self.optimizer.state_dict()
        state_dict['fp32_groups_flat'] = self.fp32_groups_flat
        return state_dict

    def load_state_dict(self, state_dict):
        """
        Loads a state_dict created by an earlier call to state_dict(). 
        If ``fp16_optimizer_instance`` was constructed from some ``init_optimizer``, 
        whose parameters in turn came from ``model``, it is expected that the user 
        will call ``model.load_state_dict()`` before
        ``fp16_optimizer_instance.load_state_dict()`` is called.
        Example::
            model = torch.nn.Linear(D_in, D_out).cuda().half()
            optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
            optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0)
            ...
            checkpoint = torch.load("saved.pth")
            model.load_state_dict(checkpoint['model'])
            optimizer.load_state_dict(checkpoint['optimizer'])
        """
        # I think it should actually be ok to reload the optimizer before the model.
        self.dynamic_loss_scale = state_dict['dynamic_loss_scale']
        self.cur_scale = state_dict['cur_scale']
        self.cur_iter = state_dict['cur_iter']
        if state_dict['dynamic_loss_scale']:
            self.last_overflow_iter = state_dict['last_overflow_iter']
            self.scale_factor = state_dict['scale_factor']
            self.scale_window = state_dict['scale_window']
        self.optimizer.load_state_dict(state_dict['optimizer_state_dict'])
        # At this point, the optimizer's references to the model's fp32 parameters are up to date.
        # The optimizer's hyperparameters and internal buffers are also up to date.
        # However, the fp32 master copies of the model's fp16 params stored by the optimizer are still
        # out of date.  There are two options.
        # 1:  Refresh the master params from the model's fp16 params.
        # This requires less storage but incurs precision loss.
        # 2:  Save and restore the fp32 master copies separately.
        # We choose option 2.
        #
        # Pytorch Optimizer.load_state_dict casts saved buffers (e.g. momentum) to the type and device
        # of their associated parameters, because it's possible those buffers might not exist yet in
        # the current optimizer instance.  In our case, as long as the current FP16_Optimizer has been
        # constructed in the same way as the one whose state_dict we are loading, the same master params
        # are guaranteed to exist, so we can just copy_() from the saved master params.
        for current, saved in zip(self.fp32_groups_flat, state_dict['fp32_groups_flat']):
            current.data.copy_(saved.data)