def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True):
assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
super(_SynchronizedBatchNorm,
self).__init__(num_features,
eps=eps,
momentum=momentum,
affine=affine,
track_running_stats=track_running_stats)
if not self.track_running_stats:
import warnings
warnings.warn(
'track_running_stats=False is not supported by the SynchronizedBatchNorm.'
)
self._sync_master = SyncMaster(self._data_parallel_master)
self._is_parallel = False
self._parallel_id = None
self._slave_pipe = None
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
groups=1,
bias=True,
dilation=1,
eps=1e-5,
n_iter=5,
momentum=0.1,
block=64,
sampling_stride=3):
assert ReduceAddCoalesced is not None, 'Can not use SynchronizedDeconvTransposed without CUDA support.'
super(SynchronizedDeconv,
self).__init__(in_channels, out_channels, kernel_size, stride,
padding, output_padding, groups, bias, dilation,
eps, n_iter, momentum, block, sampling_stride)
if not self.track_running_stats:
import warnings
warnings.warn(
'track_running_stats=False is not supported by the SynchronizedDeconvTransposed.'
)
self._sync_master = SyncMaster(self._data_parallel_master)
self._is_parallel = False
self._parallel_id = None
self._slave_pipe = None
def __init__(self,
num_features,
num_category=0,
eps=1e-5,
momentum=0.1,
affine=True):
assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
super(_SynchronizedBatchNorm, self).__init__(num_features,
eps=eps,
momentum=momentum,
affine=affine)
self.num_category = num_category
if self.affine:
######
# add num_category in here
self.weight = torch.nn.Parameter(
torch.Tensor(num_category + 1, num_features))
self.bias = torch.nn.Parameter(
torch.Tensor(num_category + 1, num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self._sync_master = SyncMaster(self._data_parallel_master)
self._is_parallel = False
self._parallel_id = None
self._slave_pipe = None
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
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 __init__(self,
in_features,
out_features,
bias=True,
eps=1e-5,
n_iter=5,
momentum=0.1,
block=512):
assert ReduceAddCoalesced is not None, 'Can not use SynchronizedDelinear without CUDA support.'
super(SynchronizedDelinear,
self).__init__(in_features, out_features, bias, eps, n_iter,
momentum, block)
self._sync_master = SyncMaster(self._data_parallel_master)
self._is_parallel = False
self._parallel_id = None
self._slave_pipe = None
class SynchronizedDelinear(Delinear):
def __init__(self,
in_features,
out_features,
bias=True,
eps=1e-5,
n_iter=5,
momentum=0.1,
block=512):
assert ReduceAddCoalesced is not None, 'Can not use SynchronizedDelinear without CUDA support.'
super(SynchronizedDelinear,
self).__init__(in_features, out_features, bias, eps, n_iter,
momentum, block)
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 the original implementation.
if not (self._is_parallel and self.training):
return super().forward(input)
X = input.view(-1, self.block)
X_sum = X.sum(0)
XY_sum = X.t() @ X
sum_size = X.shape[0]
# Reduce-and-broadcast the statistics.
if self._parallel_id == 0:
X_mean, cov_isqrt = self._sync_master.run_master(
_ChildMessage(X_sum, XY_sum, sum_size))
else:
X_mean, cov_isqrt = self._slave_pipe.run_slave(
_ChildMessage(X_sum, XY_sum, sum_size))
w = self.weight.view(-1, self.block) @ cov_isqrt
if self.bias is None:
b = -(w @ (X_mean.unsqueeze(1))).view(self.weight.shape[0],
-1).sum(1)
else:
b = self.bias - (w @ (X_mean.unsqueeze(1))).view(
self.weight.shape[0], -1).sum(1)
w = w.view(self.weight.shape)
return F.linear(input, w, b)
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].X_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].X_sum.get_device() for i in intermediates]
total_sum = sum([i[1].sum_size for i in intermediates])
X_sum, XY_sum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
X_mean, cov_isqrt = self._compute_mean_isqrt(X_sum, XY_sum, total_sum)
broadcasted = Broadcast.apply(target_gpus, X_mean, cov_isqrt)
outputs = []
for i, rec in enumerate(intermediates):
outputs.append(
(rec[0], _MasterMessage(*broadcasted[i * 2:i * 2 + 2])))
return outputs
def _compute_mean_isqrt(self, X_sum, XY_sum, 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.'
X_mean = X_sum / size
Cov = (XY_sum -
(X_sum.unsqueeze(1)) @ X_mean.unsqueeze(0)) / size #check here.
Id = torch.eye(Cov.shape[1], dtype=Cov.dtype, device=Cov.device)
cov_isqrt = isqrt_newton_schulz_autograd(Cov + self.eps * Id,
self.n_iter)
self.running_mean.mul_(1 - self.momentum)
self.running_mean.add_(X_mean.detach() * self.momentum)
self.running_cov_isqrt.mul_(1 - self.momentum)
self.running_cov_isqrt.add_(cov_isqrt.detach() * self.momentum)
return X_mean, cov_isqrt
class SynchronizedDeconvTransposed(FastDeconvTransposed):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
groups=1,
bias=True,
dilation=1,
eps=1e-5,
n_iter=5,
momentum=0.1,
block=64,
sampling_stride=3):
assert ReduceAddCoalesced is not None, 'Can not use SynchronizedDeconvTransposed without CUDA support.'
super(SynchronizedDeconv,
self).__init__(in_channels, out_channels, kernel_size, stride,
padding, output_padding, groups, bias, dilation,
eps, n_iter, momentum, block, sampling_stride)
if not self.track_running_stats:
import warnings
warnings.warn(
'track_running_stats=False is not supported by the SynchronizedDeconvTransposed.'
)
self._sync_master = SyncMaster(self._data_parallel_master)
self._is_parallel = False
self._parallel_id = None
self._slave_pipe = None
def forward(self, x, output_size=None):
# If it is not parallel computation or is in evaluation mode, use the original implementation.
if not (self._is_parallel and self.training):
return super().forward(x, output_size)
N, C, H, W = x.shape
B = self.block
if self.training and self.track_running_stats:
self.counter += 1
#im2col: N x cols x pixels -> N*pixles x cols
if self.kernel_size[0] > 1:
#the adjoint of a conv operation is a full correlation operation. So pad first.
padding = (self.padding[0] + self.kernel_size[0] - 1,
self.padding[1] + self.kernel_size[1] - 1)
X = torch.nn.functional.unfold(x, self.kernel_size, self.dilation,
self.padding,
self.sampling_stride).transpose(
1, 2).contiguous()
else:
#channel wise
X = x.permute(0, 2, 3,
1).contiguous().view(-1,
C)[::self.sampling_stride**2, :]
if self.groups == 1:
# (C//B*N*pixels,k*k*B)
X = X.view(-1, self.num_features,
C // B).transpose(1, 2).contiguous().view(
-1, self.num_features)
else:
X = X.view(-1, X.shape[-1])
X_sum = X.sum(0)
if self.groups == 1:
XY_sum = X.t() @ X
sum_size = X.shape[0]
else:
XY_sum = X.transpose(1, 2) @ X
sum_size = X.shape[1]
# Reduce-and-broadcast the statistics.
if self._parallel_id == 0:
X_mean, cov_isqrt = self._sync_master.run_master(
_ChildMessage(X_sum, XY_sum, sum_size))
else:
X_mean, cov_isqrt = self._slave_pipe.run_slave(
_ChildMessage(X_sum, XY_sum, sum_size))
# X * deconv * conv = X * (deconv * conv)
weight = torch.flip(self.weight, [2, 3])
if self.groups == 1:
w = weight.view(-1, self.num_features, C // B).transpose(
1, 2).contiguous().view(-1, self.num_features) @ cov_isqrt
b = self.bias - (w @ (X_mean.unsqueeze(1))).view(
self.weight.shape[0], -1).sum(1)
w = w.view(-1, C // B,
self.num_features).transpose(1, 2).contiguous()
else:
w = self.weight.view(C // B, -1, self.num_features) @ cov_isqrt
b = self.bias - (w @ (X_mean.view(-1, self.num_features, 1))).view(
self.bias.shape)
w = w.view(self.weight.shape)
w = torch.flip(w.view(weight.shape), [2, 3])
output_padding = self._output_padding(x, output_size, self.stride,
self.padding, self.kernel_size)
x = F.conv_transpose2d(x, w, b, self.stride, self.padding,
output_padding, self.groups, self.dilation)
return x
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].X_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].X_sum.get_device() for i in intermediates]
total_sum = sum([i[1].sum_size for i in intermediates])
X_sum, XY_sum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
X_mean, cov_isqrt = self._compute_mean_isqrt(X_sum, XY_sum, total_sum)
broadcasted = Broadcast.apply(target_gpus, X_mean, cov_isqrt)
outputs = []
for i, rec in enumerate(intermediates):
outputs.append(
(rec[0], _MasterMessage(*broadcasted[i * 2:i * 2 + 2])))
return outputs
def _compute_mean_isqrt(self, X_sum, XY_sum, 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.'
X_mean = X_sum / size
if self.groups == 1:
Cov = (XY_sum - (X_sum.unsqueeze(1)) @ X_mean.unsqueeze(0)
) / size #check here.
Id = torch.eye(Cov.shape[1], dtype=Cov.dtype, device=Cov.device)
cov_isqrt = isqrt_newton_schulz_autograd(Cov + self.eps * Id,
self.n_iter)
else:
Cov = (XY_sum - (X_sum.unsqueeze(2)) @ X_mean.unsqueeze(1)
) / size #check here.
Id = torch.eye(self.num_features,
dtype=Cov.dtype,
device=Cov.device).expand(self.groups,
self.num_features,
self.num_features)
cov_isqrt = isqrt_newton_schulz_autograd_batch(
Cov + self.eps * Id, self.n_iter)
self.running_mean.mul_(1 - self.momentum)
self.running_mean.add_(X_mean.detach() * self.momentum)
self.running_cov_isqrt.mul_(1 - self.momentum)
self.running_cov_isqrt.add_(cov_isqrt.detach() * self.momentum)
return X_mean, cov_isqrt
class _SynchronizedBatchNorm(_BatchNorm):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
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
if hasattr(torch, 'no_grad'):
with torch.no_grad():
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
else:
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 _SynchronizedBatchNorm(_BatchNorm):
def __init__(self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True):
assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
super(_SynchronizedBatchNorm,
self).__init__(num_features,
eps=eps,
momentum=momentum,
affine=affine,
track_running_stats=track_running_stats)
if not self.track_running_stats:
import warnings
warnings.warn(
'track_running_stats=False is not supported by the SynchronizedBatchNorm.'
)
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 not (self._is_parallel and self.training):
return F.batch_norm(
input, self.running_mean if self.track_running_stats else None,
self.running_var if self.track_running_stats else None,
self.weight, self.bias,
self.training if self.track_running_stats else True,
self.momentum, self.eps)
input_shape = input.size()
input = input.view(input.size(0), self.num_features, -1)
sum_size = input.size(0) * input.size(2)
input_sum = _sum_ft(input)
input_ssum = _sum_ft(input**2)
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))
if self.affine:
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)
return output.view(input_shape)
def __data_parallel_replicate__(self, ctx, copy_id):
self._is_parallel = True
self._parallel_id = copy_id
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."""
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]
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
if hasattr(torch, 'no_grad'):
with torch.no_grad():
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
else:
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
