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import pdb
from os import path
import torch
import torch.distributed as dist
import torch.autograd as autograd
import torch.cuda.comm as comm
from torch.autograd.function import once_differentiable
from torch.utils.cpp_extension import load
_src_path = path.join(path.dirname(path.abspath(__file__)), "src")
_backend = load(name="inplace_abn",
extra_cflags=["-O3"],
sources=[path.join(_src_path, f) for f in [
"inplace_abn.cpp",
"inplace_abn_cpu.cpp",
"inplace_abn_cuda.cu",
"inplace_abn_cuda_half.cu"
]],
extra_cuda_cflags=["--expt-extended-lambda"])
# Activation names
ACT_RELU = "relu"
ACT_LEAKY_RELU = "leaky_relu"
ACT_ELU = "elu"
ACT_NONE = "none"
def _check(fn, *args, **kwargs):
success = fn(*args, **kwargs)
if not success:
raise RuntimeError("CUDA Error encountered in {}".format(fn))
def _broadcast_shape(x):
out_size = []
for i, s in enumerate(x.size()):
if i != 1:
out_size.append(1)
else:
out_size.append(s)
return out_size
def _reduce(x):
if len(x.size()) == 2:
return x.sum(dim=0)
else:
n, c = x.size()[0:2]
return x.contiguous().view((n, c, -1)).sum(2).sum(0)
def _count_samples(x):
count = 1
for i, s in enumerate(x.size()):
if i != 1:
count *= s
return count
def _act_forward(ctx, x):
if ctx.activation == ACT_LEAKY_RELU:
_backend.leaky_relu_forward(x, ctx.slope)
elif ctx.activation == ACT_ELU:
_backend.elu_forward(x)
elif ctx.activation == ACT_NONE:
pass
def _act_backward(ctx, x, dx):
if ctx.activation == ACT_LEAKY_RELU:
_backend.leaky_relu_backward(x, dx, ctx.slope)
elif ctx.activation == ACT_ELU:
_backend.elu_backward(x, dx)
elif ctx.activation == ACT_NONE:
pass
class InPlaceABN(autograd.Function):
@staticmethod
def forward(ctx, x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation=ACT_LEAKY_RELU, slope=0.01):
# Save context
ctx.training = training
ctx.momentum = momentum
ctx.eps = eps
ctx.activation = activation
ctx.slope = slope
ctx.affine = weight is not None and bias is not None
# Prepare inputs
count = _count_samples(x)
x = x.contiguous()
weight = weight.contiguous() if ctx.affine else x.new_empty(0)
bias = bias.contiguous() if ctx.affine else x.new_empty(0)
if ctx.training:
mean, var = _backend.mean_var(x)
# Update running stats
running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * count / (count - 1))
# Mark in-place modified tensors
ctx.mark_dirty(x, running_mean, running_var)
else:
mean, var = running_mean.contiguous(), running_var.contiguous()
ctx.mark_dirty(x)
# BN forward + activation
_backend.forward(x, mean, var, weight, bias, ctx.affine, ctx.eps)
_act_forward(ctx, x)
# Output
ctx.var = var
ctx.save_for_backward(x, var, weight, bias)
ctx.mark_non_differentiable(running_mean, running_var)
return x, running_mean, running_var
@staticmethod
@once_differentiable
def backward(ctx, dz, _drunning_mean, _drunning_var):
z, var, weight, bias = ctx.saved_tensors
dz = dz.contiguous()
# Undo activation
_act_backward(ctx, z, dz)
if ctx.training:
edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)
else:
# TODO: implement simplified CUDA backward for inference mode
edz = dz.new_zeros(dz.size(1))
eydz = dz.new_zeros(dz.size(1))
dx = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
# dweight = eydz * weight.sign() if ctx.affine else None
dweight = eydz if ctx.affine else None
if dweight is not None:
dweight[weight < 0] *= -1
dbias = edz if ctx.affine else None
return dx, dweight, dbias, None, None, None, None, None, None, None
class InPlaceABNSync(autograd.Function):
@classmethod
def forward(cls, ctx, x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation=ACT_LEAKY_RELU, slope=0.01, equal_batches=True):
# Save context
ctx.training = training
ctx.momentum = momentum
ctx.eps = eps
ctx.activation = activation
ctx.slope = slope
ctx.affine = weight is not None and bias is not None
# Prepare inputs
ctx.world_size = dist.get_world_size() if dist.is_initialized() else 1
# count = _count_samples(x)
batch_size = x.new_tensor([x.shape[0]], dtype=torch.long)
x = x.contiguous()
weight = weight.contiguous() if ctx.affine else x.new_empty(0)
bias = bias.contiguous() if ctx.affine else x.new_empty(0)
if ctx.training:
mean, var = _backend.mean_var(x)
if ctx.world_size > 1:
# get global batch size
if equal_batches:
batch_size *= ctx.world_size
else:
dist.all_reduce(batch_size, dist.ReduceOp.SUM)
ctx.factor = x.shape[0] / float(batch_size.item())
mean_all = mean.clone() * ctx.factor
dist.all_reduce(mean_all, dist.ReduceOp.SUM)
var_all = (var + (mean - mean_all) ** 2) * ctx.factor
dist.all_reduce(var_all, dist.ReduceOp.SUM)
mean = mean_all
var = var_all
# Update running stats
running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
count = batch_size.item() * x.view(x.shape[0], x.shape[1], -1).shape[-1]
running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * var * (float(count) / (count - 1)))
# Mark in-place modified tensors
ctx.mark_dirty(x, running_mean, running_var)
else:
mean, var = running_mean.contiguous(), running_var.contiguous()
ctx.mark_dirty(x)
# BN forward + activation
_backend.forward(x, mean, var, weight, bias, ctx.affine, ctx.eps)
_act_forward(ctx, x)
# Output
ctx.var = var
ctx.save_for_backward(x, var, weight, bias)
ctx.mark_non_differentiable(running_mean, running_var)
return x, running_mean, running_var
@staticmethod
@once_differentiable
def backward(ctx, dz, _drunning_mean, _drunning_var):
z, var, weight, bias = ctx.saved_tensors
dz = dz.contiguous()
# Undo activation
_act_backward(ctx, z, dz)
if ctx.training:
edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)
edz_local = edz.clone()
eydz_local = eydz.clone()
if ctx.world_size > 1:
edz *= ctx.factor
dist.all_reduce(edz, dist.ReduceOp.SUM)
eydz *= ctx.factor
dist.all_reduce(eydz, dist.ReduceOp.SUM)
else:
edz_local = edz = dz.new_zeros(dz.size(1))
eydz_local = eydz = dz.new_zeros(dz.size(1))
dx = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
# dweight = eydz_local * weight.sign() if ctx.affine else None
dweight = eydz_local if ctx.affine else None
if dweight is not None:
dweight[weight < 0] *= -1
dbias = edz_local if ctx.affine else None
return dx, dweight, dbias, None, None, None, None, None, None, None
inplace_abn = InPlaceABN.apply
inplace_abn_sync = InPlaceABNSync.apply
__all__ = ["inplace_abn", "inplace_abn_sync", "ACT_RELU", "ACT_LEAKY_RELU", "ACT_ELU", "ACT_NONE"]
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