# -*- coding: utf-8 -*-
# File   : batchnorm.py
# Author : Jiayuan Mao
# Email  : maojiayuan@gmail.com
# Date   : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.

import collections

import torch
import torch.nn.functional as F

from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast

from .comm import SyncMaster
from .replicate import DataParallelWithCallback

__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d',
           'SynchronizedBatchNorm3d', 'convert_model']


def _sum_ft(tensor):
  """sum over the first and last dimention"""
  return tensor.sum(dim=0).sum(dim=-1)


def _unsqueeze_ft(tensor):
  """add new dementions at the front and the tail"""
  return tensor.unsqueeze(0).unsqueeze(-1)


_ChildMessage = collections.namedtuple(
    '_ChildMessage', ['sum', 'ssum', 'sum_size'])
_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])


class _SynchronizedBatchNorm(_BatchNorm):
  def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
    super(_SynchronizedBatchNorm, self).__init__(
        num_features, eps=eps, momentum=momentum, affine=affine)

    self._sync_master = SyncMaster(self._data_parallel_master)

    self._is_parallel = False
    self._parallel_id = None
    self._slave_pipe = None

  def forward(self, input):
    # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
    if not (self._is_parallel and self.training):
      return F.batch_norm(
          input, self.running_mean, self.running_var, self.weight, self.bias,
          self.training, self.momentum, self.eps)

    # Resize the input to (B, C, -1).
    input_shape = input.size()
    input = input.view(input.size(0), self.num_features, -1)

    # Compute the sum and square-sum.
    sum_size = input.size(0) * input.size(2)
    input_sum = _sum_ft(input)
    input_ssum = _sum_ft(input ** 2)

    # Reduce-and-broadcast the statistics.
    if self._parallel_id == 0:
      mean, inv_std = self._sync_master.run_master(
          _ChildMessage(input_sum, input_ssum, sum_size))
    else:
      mean, inv_std = self._slave_pipe.run_slave(
          _ChildMessage(input_sum, input_ssum, sum_size))

    # Compute the output.
    if self.affine:
      # MJY:: Fuse the multiplication for speed.
      output = (input - _unsqueeze_ft(mean)) * \
          _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
    else:
      output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)

    # Reshape it.
    return output.view(input_shape)

  def __data_parallel_replicate__(self, ctx, copy_id):
    self._is_parallel = True
    self._parallel_id = copy_id

    # parallel_id == 0 means master device.
    if self._parallel_id == 0:
      ctx.sync_master = self._sync_master
    else:
      self._slave_pipe = ctx.sync_master.register_slave(copy_id)

  def _data_parallel_master(self, intermediates):
    """Reduce the sum and square-sum, compute the statistics, and broadcast it."""

    # Always using same "device order" makes the ReduceAdd operation faster.
    # Thanks to:: Tete Xiao (http://tetexiao.com/)
    intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())

    to_reduce = [i[1][:2] for i in intermediates]
    to_reduce = [j for i in to_reduce for j in i]  # flatten
    target_gpus = [i[1].sum.get_device() for i in intermediates]

    sum_size = sum([i[1].sum_size for i in intermediates])
    sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
    mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)

    broadcasted = Broadcast.apply(target_gpus, mean, inv_std)

    outputs = []
    for i, rec in enumerate(intermediates):
      outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))

    return outputs

  def _compute_mean_std(self, sum_, ssum, size):
    """Compute the mean and standard-deviation with sum and square-sum. This method
    also maintains the moving average on the master device."""
    assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
    mean = sum_ / size
    sumvar = ssum - sum_ * mean
    unbias_var = sumvar / (size - 1)
    bias_var = sumvar / size

    self.running_mean = (1 - self.momentum) * \
        self.running_mean + self.momentum * mean.data
    self.running_var = (1 - self.momentum) * \
        self.running_var + self.momentum * unbias_var.data

    return mean, bias_var.clamp(self.eps) ** -0.5


class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
  r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
  mini-batch.

  .. math::

      y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta

  This module differs from the built-in PyTorch BatchNorm1d as the mean and
  standard-deviation are reduced across all devices during training.

  For example, when one uses `nn.DataParallel` to wrap the network during
  training, PyTorch's implementation normalize the tensor on each device using
  the statistics only on that device, which accelerated the computation and
  is also easy to implement, but the statistics might be inaccurate.
  Instead, in this synchronized version, the statistics will be computed
  over all training samples distributed on multiple devices.

  Note that, for one-GPU or CPU-only case, this module behaves exactly same
  as the built-in PyTorch implementation.

  The mean and standard-deviation are calculated per-dimension over
  the mini-batches and gamma and beta are learnable parameter vectors
  of size C (where C is the input size).

  During training, this layer keeps a running estimate of its computed mean
  and variance. The running sum is kept with a default momentum of 0.1.

  During evaluation, this running mean/variance is used for normalization.

  Because the BatchNorm is done over the `C` dimension, computing statistics
  on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm

  Args:
      num_features: num_features from an expected input of size
          `batch_size x num_features [x width]`
      eps: a value added to the denominator for numerical stability.
          Default: 1e-5
      momentum: the value used for the running_mean and running_var
          computation. Default: 0.1
      affine: a boolean value that when set to ``True``, gives the layer learnable
          affine parameters. Default: ``True``

  Shape:
      - Input: :math:`(N, C)` or :math:`(N, C, L)`
      - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)

  Examples:
      >>> # With Learnable Parameters
      >>> m = SynchronizedBatchNorm1d(100)
      >>> # Without Learnable Parameters
      >>> m = SynchronizedBatchNorm1d(100, affine=False)
      >>> input = torch.autograd.Variable(torch.randn(20, 100))
      >>> output = m(input)
  """

  def _check_input_dim(self, input):
    if input.dim() != 2 and input.dim() != 3:
      raise ValueError('expected 2D or 3D input (got {}D input)'
                       .format(input.dim()))
    super(SynchronizedBatchNorm1d, self)._check_input_dim(input)


class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
  r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
  of 3d inputs

  .. math::

      y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta

  This module differs from the built-in PyTorch BatchNorm2d as the mean and
  standard-deviation are reduced across all devices during training.

  For example, when one uses `nn.DataParallel` to wrap the network during
  training, PyTorch's implementation normalize the tensor on each device using
  the statistics only on that device, which accelerated the computation and
  is also easy to implement, but the statistics might be inaccurate.
  Instead, in this synchronized version, the statistics will be computed
  over all training samples distributed on multiple devices.

  Note that, for one-GPU or CPU-only case, this module behaves exactly same
  as the built-in PyTorch implementation.

  The mean and standard-deviation are calculated per-dimension over
  the mini-batches and gamma and beta are learnable parameter vectors
  of size C (where C is the input size).

  During training, this layer keeps a running estimate of its computed mean
  and variance. The running sum is kept with a default momentum of 0.1.

  During evaluation, this running mean/variance is used for normalization.

  Because the BatchNorm is done over the `C` dimension, computing statistics
  on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm

  Args:
      num_features: num_features from an expected input of
          size batch_size x num_features x height x width
      eps: a value added to the denominator for numerical stability.
          Default: 1e-5
      momentum: the value used for the running_mean and running_var
          computation. Default: 0.1
      affine: a boolean value that when set to ``True``, gives the layer learnable
          affine parameters. Default: ``True``

  Shape:
      - Input: :math:`(N, C, H, W)`
      - Output: :math:`(N, C, H, W)` (same shape as input)

  Examples:
      >>> # With Learnable Parameters
      >>> m = SynchronizedBatchNorm2d(100)
      >>> # Without Learnable Parameters
      >>> m = SynchronizedBatchNorm2d(100, affine=False)
      >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
      >>> output = m(input)
  """

  def _check_input_dim(self, input):
    if input.dim() != 4:
      raise ValueError('expected 4D input (got {}D input)'
                       .format(input.dim()))
    super(SynchronizedBatchNorm2d, self)._check_input_dim(input)


class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
  r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
  of 4d inputs

  .. math::

      y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta

  This module differs from the built-in PyTorch BatchNorm3d as the mean and
  standard-deviation are reduced across all devices during training.

  For example, when one uses `nn.DataParallel` to wrap the network during
  training, PyTorch's implementation normalize the tensor on each device using
  the statistics only on that device, which accelerated the computation and
  is also easy to implement, but the statistics might be inaccurate.
  Instead, in this synchronized version, the statistics will be computed
  over all training samples distributed on multiple devices.

  Note that, for one-GPU or CPU-only case, this module behaves exactly same
  as the built-in PyTorch implementation.

  The mean and standard-deviation are calculated per-dimension over
  the mini-batches and gamma and beta are learnable parameter vectors
  of size C (where C is the input size).

  During training, this layer keeps a running estimate of its computed mean
  and variance. The running sum is kept with a default momentum of 0.1.

  During evaluation, this running mean/variance is used for normalization.

  Because the BatchNorm is done over the `C` dimension, computing statistics
  on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
  or Spatio-temporal BatchNorm

  Args:
      num_features: num_features from an expected input of
          size batch_size x num_features x depth x height x width
      eps: a value added to the denominator for numerical stability.
          Default: 1e-5
      momentum: the value used for the running_mean and running_var
          computation. Default: 0.1
      affine: a boolean value that when set to ``True``, gives the layer learnable
          affine parameters. Default: ``True``

  Shape:
      - Input: :math:`(N, C, D, H, W)`
      - Output: :math:`(N, C, D, H, W)` (same shape as input)

  Examples:
      >>> # With Learnable Parameters
      >>> m = SynchronizedBatchNorm3d(100)
      >>> # Without Learnable Parameters
      >>> m = SynchronizedBatchNorm3d(100, affine=False)
      >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
      >>> output = m(input)
  """

  def _check_input_dim(self, input):
    if input.dim() != 5:
      raise ValueError('expected 5D input (got {}D input)'
                       .format(input.dim()))
    super(SynchronizedBatchNorm3d, self)._check_input_dim(input)


def convert_model(module):
  """Traverse the input module and its child recursively
     and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d
     to SynchronizedBatchNorm*N*d 

  Args:
      module: the input module needs to be convert to SyncBN model

  Examples:
      >>> import torch.nn as nn
      >>> import torchvision
      >>> # m is a standard pytorch model
      >>> m = torchvision.models.resnet18(True)
      >>> m = nn.DataParallel(m)
      >>> # after convert, m is using SyncBN
      >>> m = convert_model(m)
  """
  if isinstance(module, torch.nn.DataParallel):
    mod = module.module
    mod = convert_model(mod)
    mod = DataParallelWithCallback(mod)
    return mod

  mod = module
  for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d,
                                      torch.nn.modules.batchnorm.BatchNorm2d,
                                      torch.nn.modules.batchnorm.BatchNorm3d],
                                     [SynchronizedBatchNorm1d,
                                      SynchronizedBatchNorm2d,
                                      SynchronizedBatchNorm3d]):
    if isinstance(module, pth_module):
      mod = sync_module(module.num_features, module.eps,
                        module.momentum, module.affine)
      mod.running_mean = module.running_mean
      mod.running_var = module.running_var
      if module.affine:
        mod.weight.data = module.weight.data.clone().detach()
        mod.bias.data = module.bias.data.clone().detach()

  for name, child in module.named_children():
    mod.add_module(name, convert_model(child))

  return mod
