def backward_gpu(self, x, gy):
out_c, out_h, out_w = gy[0].shape[1:]
n, c, h, w = x[0].shape
if cudnn.enabled and self.use_cudnn:
handle = cudnn.get_default_handle()
x_desc = cudnn.get_tensor_desc(x[0], h, w)
gy_desc = cudnn.get_tensor_desc(gy[0], out_h, out_w)
if self.b is not None:
libcudnn.cudnnConvolutionBackwardBias(
handle, 1, gy_desc.value, cudnn.get_ptr(gy[0]),
1, self.bias_desc.value, cudnn.get_ptr(self.gb))
libcudnn.cudnnConvolutionBackwardFilter(
handle, 1, x_desc.value, cudnn.get_ptr(x[0]),
gy_desc.value, cudnn.get_ptr(gy[0]), self.conv_desc.value,
1, self.filter_desc.value, cudnn.get_ptr(self.gW))
gx = cuda.empty_like(x[0])
libcudnn.cudnnConvolutionBackwardData(
handle, 1, self.filter_desc.value, cudnn.get_ptr(self.W),
gy_desc.value, cudnn.get_ptr(gy[0]), self.conv_desc.value,
0, x_desc.value, cudnn.get_ptr(gx))
else:
handle = cuda.get_cublas_handle()
if self.gb is not None:
# TODO(beam2d): Unify kernels
with cuda.using_cumisc(handle):
tmp = cuda.cumisc.sum(
gy[0].reshape(n * out_c, out_h * out_w), axis=1)
tmp = cuda.cumisc.sum(tmp.reshape(n, out_c), axis=0)
self.gb += tmp
# TODO(beam2d): Use streams
gW_mat = self.gW.reshape(out_c, c * self.kh * self.kw)
col_mats = self.col.reshape(
n, c * self.kh * self.kw, out_h * out_w)
gy_mats = gy[0].reshape(n, out_c, out_h * out_w)
for i in moves.range(n):
cuda.culinalg.add_dot(
gy_mats[i], col_mats[i], gW_mat, transb='T', handle=handle)
W_mat = self.W.reshape(out_c, c * self.kh * self.kw)
gcol = cuda.empty_like(self.col)
gcol_mats = gcol.reshape(n, c * self.kh * self.kw, out_h * out_w)
for i in moves.range(n):
cuda.culinalg.dot(W_mat, gy_mats[i], transa='T', handle=handle,
out=gcol_mats[i])
gx = conv.col2im_gpu(
gcol, self.sy, self.sx, self.ph, self.pw, h, w)
return gx,
def forward_gpu(self, x):
n, out_c, out_h, out_w = x[0].shape
c = self.W.shape[1]
h = get_deconv_outsize(out_h, self.kh, self.sy, self.ph)
w = get_deconv_outsize(out_w, self.kw, self.sx, self.pw)
if cudnn.enabled and self.use_cudnn:
handle = cudnn.get_default_handle()
x_desc = cudnn.get_tensor_desc(x[0], out_h, out_w)
y = cuda.empty((n, c, h, w), dtype=numpy.float32)
y_desc = cudnn.get_tensor_desc(y, h, w)
self.filter_desc = cudnn.get_filter4d_desc(self.W)
self.conv_desc = cudnn.get_conv2d_desc(
(self.ph, self.pw), (self.sy, self.sx))
if self.b is not None:
self.bias_desc = cudnn.get_conv_bias_desc(self.b)
libcudnn.cudnnConvolutionBackwardData(
handle, 1, self.filter_desc.value, cudnn.get_ptr(self.W),
x_desc.value, cudnn.get_ptr(x[0]), self.conv_desc.value,
0, y_desc.value, cudnn.get_ptr(y))
if self.b is not None:
libcudnn.cudnnAddTensor(
handle, libcudnn.cudnnAddMode['CUDNN_ADD_SAME_C'],
1, self.bias_desc.value, cudnn.get_ptr(self.b),
1, y_desc.value, cudnn.get_ptr(y))
else:
handle = cuda.get_cublas_handle()
# TODO(beam2d): Use streams
W_mat = self.W.reshape(out_c, c * self.kh * self.kw)
x_mats = x[0].reshape(n, out_c, out_h * out_w)
gcol = cuda.empty((n, c, self.kh, self.kw, out_h, out_w), dtype=numpy.float32)
gcol_mats = gcol.reshape(n, c * self.kh * self.kw, out_h * out_w)
for i in moves.range(n):
cuda.culinalg.dot(W_mat, x_mats[i], transa='T', handle=handle,
out=gcol_mats[i])
y = conv.col2im_gpu(
gcol, self.sy, self.sx, self.ph, self.pw, h, w)
# TODO(beam2d): Support unshared bias
if self.b is not None:
cuda.elementwise(
'float* y, const float* b, int c, int hw',
'y[i] += b[i / hw % c]',
'conv_bias_fwd')(y, self.b, c, h * w)
return y,
