import torch
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
import ctypes

lib = ctypes.cdll.LoadLibrary(None)
lib.THCudaHalfTensor_normall.argtypes=[ctypes.c_void_p, ctypes.c_void_p]
lib.THCudaHalfTensor_normall.restype = ctypes.c_float

def fused_norm(input):
    if input.type() == 'torch.cuda.HalfTensor':
        # 16384 is half 2 if you stare at it long enough
        return lib.THCudaHalfTensor_normall(torch.cuda._state_cdata,
            input._cdata, 16384)
    else:
        return input.norm()

class FP16_Optimizer(object):
    """
    :class:`FP16_Optimizer` A cutdown version of apex.fp16_utils.FP16_Optimizer.
    Design to be used in the same way but support only fused optimizers in apex.
    Refer to apex.fp16_utils documents for more information.

    Example::

        model = torch.nn.Linear(D_in, D_out).cuda().half()
        optimizer = apex.optimizers.FusedAdam(model.parameters())
        # Name the FP16_Optimizer instance to replace the existing optimizer
        # (recommended but not required):
        optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0)
        ...
        # loss.backward() becomes:
        optimizer.backward(loss)
        ...

    Example with dynamic loss scaling::

        ...
        optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
                                   # optional arg to control dynamic loss scaling behavior
                                   # dynamic_loss_args={'scale_window' : 500})
                                   # Usually, dynamic_loss_args is not necessary.
    """

    def __init__(self,
                 init_optimizer,
                 static_loss_scale=1.0,
                 dynamic_loss_scale=False,
                 dynamic_loss_args=None,
                 verbose=True):

        # The fused optimizer does all the work. We need this layer for two reason:
        # 1. maintain same user API from apex.fp16_utils
        # 2. keep common stuff here in case we need to add new fused optimizer later

        # differences from apex.fp16_utils:
        # - assume all model params in fp16
        # - assume all params requires grad
        # - flat by groups, not keeping state. TODO: remove state explicitly?
        # - master gard and unflat master weight never exist. TODO: a way to save out unflat master?
        if not torch.cuda.is_available:
            raise SystemError("Cannot use fp16 without CUDA.")
        self.optimizer = init_optimizer

        # param flattened by groups
        self.fp16_groups = []
        self.fp16_groups_flat = []
        self.fp32_groups_flat = []

        # loop to deal with groups
        for i, param_group in enumerate(self.optimizer.param_groups):
            # push this group to list before modify
            self.fp16_groups.append(param_group['params'])
            # init fp16 weight buffer, flattened
            self.fp16_groups_flat.append(_flatten_dense_tensors([p.clone().detach() for p in self.fp16_groups[i]]))
            # set model fp16 weight to slices of flattened buffer
            updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i], self.fp16_groups[i])
            for p,q in zip(self.fp16_groups[i], updated_params):
                p.data = q.data
            # init master weight, flattened
            self.fp32_groups_flat.append(self.fp16_groups_flat[i].clone().float().detach())
            # modify optimizer of have flat master weight
            self.fp32_groups_flat[i].requires_grad = True # keep this in case internal optimizer uses it
            param_group['params'] = [self.fp32_groups_flat[i]]

        # we may have a way of fusing dynamic scale. Do not support for now
        if dynamic_loss_scale:
            if dynamic_loss_args is not None:
                raise SystemError("Do not support dynamic loss scale args for now.")
            self.dynamic_loss_scale = True
            self.cur_scale = 2**32
            self.cur_iter = 0
            self.last_overflow_iter = -1
            self.scale_factor = 2
            self.scale_window = 1000
        else:
            self.dynamic_loss_scale = False
            self.cur_iter = 0
            self.cur_scale = static_loss_scale

    def zero_grad(self, set_grads_to_None=True):
        """
        Zero FP16 parameter grads.
        """
        # FP32 grad should never exist.
        # For speed, set model fp16 grad to None by default
        for group in self.fp16_groups:
            for p in group:
                if set_grads_to_None:
                    p.grad = None
                else:
                    if p.grad is not None:
                        p.grad.detach_()
                        p.grad.zero_()

    def _compute_grad_norm(self, fp16_grads_flat, norm_type=2):
        """
        Compute fp16 grad norm for later clipping(fused with update).
        Internal accumulated in fp32.
        Also fused in NaN check. Possibly other reduction needed for grad.

        Args:
            fp16_grads_flat (tensor): fp16 grad flattened
            norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
                infinity norm.

        Returns:
            Total norm of the current fp16 gradients (viewed as a single vector).
            Returns -1 if the most recently computed fp16 gradients overflowed
        """
        # TODO: currently using pre-1.0 api, and not most efficient with copy to cpu and sync
        # only support 2-norm now
        norm = float(fused_norm(fp16_grads_flat))
        if norm == float('inf') or norm == -float('inf') or norm != norm:
            return -1
        else:
            return norm

    def step(self, closure=None):
        """
        Not supporting closure.
        """
        # First compute norm for all group so we know if there is overflow
        grads_groups_flat = []
        norm_groups = []
        skip = False
        for i, group in enumerate(self.fp16_groups):
            grads_groups_flat.append(_flatten_dense_tensors([p.grad for p in group]))
            norm_groups.append(self._compute_grad_norm(grads_groups_flat[i]))
            if norm_groups[i] == -1: #TODO: early break
                skip = True

        if skip:
            self._update_scale(skip)
            return

        # norm is in fact norm*cur_scale
        self.optimizer.step(grads=[[g] for g in grads_groups_flat],
                            output_params=[[p] for p in self.fp16_groups_flat],
                            scale=self.cur_scale,
                            grad_norms=norm_groups)

        # TODO: we probably don't need this? just to be safe
        for i in range(len(norm_groups)):
            updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i], self.fp16_groups[i])
            for p,q in zip(self.fp16_groups[i], updated_params):
                p.data = q.data

        self._update_scale(False)
        return

    def backward(self, loss):
        """
        :attr:`backward` performs the following conceptual steps:

        1. fp32_loss = loss.float()
        2. scaled_loss = fp32_loss*loss_scale
        3. scaled_loss.backward(), which accumulates scaled gradients into the ``.grad`` attributes of the model's fp16 leaves
        """
        scaled_loss = (loss.float()) * self.cur_scale
        scaled_loss.backward()

    def _update_scale(self, skip):
        if self.dynamic_loss_scale:
            if skip:
                print("\nGrad overflow on iteration", self.cur_iter)
                print("Using dynamic loss scale of", self.cur_scale)
                self.cur_scale = max(self.cur_scale/self.scale_factor, 1)
                self.last_overflow_iter = self.cur_iter
            else:
                if (self.cur_iter - self.last_overflow_iter) % self.scale_window == 0:
                    self.cur_scale *= self.scale_factor
        else:
            if skip:
                print("\nGrad overflow on iteration", self.cur_iter)
                print("Using static loss scale of", self.cur_scale)
        self.cur_iter +=1
        return

    # Promote state so it can be retrieved or set via "fp16_optimizer_instance.state"
    def _get_state(self):
        return self.optimizer.state

    def _set_state(self, value):
        self.optimizer.state = value

    state = property(_get_state, _set_state)

    # Promote param_groups so it can be retrieved or set via "fp16_optimizer_instance.param_groups"
    # (for example, to adjust the learning rate)
    def _get_param_groups(self):
        return self.optimizer.param_groups

    def _set_param_groups(self, value):
        self.optimizer.param_groups = value

    param_groups = property(_get_param_groups, _set_param_groups)
    
    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)

