def _forward_xp_core(self, x, gy, xp):
# Compute filter weight gradient.
# (n, _, out_1, out_2, ..., out_N)
out_axes = (0,) + tuple(moves.range(2, self.ndim + 2))
# (n, _, _, ..., _, out_1, out_2, ..., out_N)
col_axes = (0,) + tuple(moves.range(self.ndim + 2, self.ndim * 2 + 2))
# NumPy raises an error when the array is not contiguous.
# See: https://github.com/chainer/chainer/issues/2744
# TODO(niboshi): Remove this code when NumPy is fixed.
if (xp is numpy and
not (gy.flags.c_contiguous or gy.flags.f_contiguous) and
1 in gy.shape):
gy = numpy.ascontiguousarray(gy)
if xp is numpy:
col = conv_nd.im2col_nd_cpu(
x, self.ksize, self.stride, self.pad,
cover_all=self.cover_all, dilate=self.dilate)
else:
col = conv_nd.im2col_nd_gpu(
x, self.ksize, self.stride, self.pad,
cover_all=self.cover_all, dilate=self.dilate)
gW = xp.tensordot(gy, col, (out_axes, col_axes)).astype(
self.W_dtype, copy=False)
return gW,
def _forward_xp(self, x, W, b, xp):
ndim = self.ndim
ksize = W.shape[2:]
stride = self.stride
pad = self.pad
# Make patch array.
if xp is numpy:
self.col = conv_nd.im2col_nd_cpu(x,
ksize,
stride,
pad,
cover_all=self.cover_all)
else:
self.col = conv_nd.im2col_nd_gpu(x,
ksize,
stride,
pad,
cover_all=self.cover_all)
# Compute correlation.
axes = tuple(moves.range(1, ndim + 2)) # (1, 2, ..., N+1)
y = xp.tensordot(self.col, W, (axes, axes)).astype(x.dtype)
# Apply bias if given.
if b is not None:
y += b
# Roll c_O before the second in (n, y_1, y_2, ..., y_N, c_O).
return xp.rollaxis(y, ndim + 1, 1),
def _backward_xp(self, x, W, b, gy, xp):
ndim = self.ndim
ksize = W.shape[2:]
stride = self.stride
pad = self.pad
if xp is numpy:
col = conv_nd.im2col_nd_cpu(gy, ksize, stride, pad)
else:
col = conv_nd.im2col_nd_gpu(gy, ksize, stride, pad)
# x : n, C_I, d_1, d_2, ..., d_N
# col: n, C_I, k_1, k_2, ..., k_N, d_1, d_2, ..., d_N
x_axes = (0,) + tuple(six.moves.range(2, ndim + 2))
col_axes = (0,) + tuple(six.moves.range(ndim + 2, ndim * 2 + 2))
gW = xp.tensordot(x, col, (x_axes, col_axes)).astype(
W.dtype, copy=False)
# col: n, C_I, k_1, k_2, ..., k_N, d_1, d_2, ..., d_N
# W : C_I, C_O, k_1, k_2, ..., k_N
axes = (1,) + tuple(six.moves.range(2, ndim + 2))
gx = xp.tensordot(col, W, (axes, axes)).astype(x.dtype, copy=False)
gx = xp.rollaxis(gx, ndim + 1, 1)
if b is None:
return gx, gW
else:
sum_axis = (0,) + tuple(six.moves.range(2, ndim + 2))
gb = gy.sum(axis=sum_axis)
return gx, gW, gb
def _backward_xp(self, x, W, b, gy, xp):
ndim = self.ndim
ksize = W.shape[2:]
stride = self.stride
pad = self.pad
if xp is numpy:
col = conv_nd.im2col_nd_cpu(gy, ksize, stride, pad)
else:
col = conv_nd.im2col_nd_gpu(gy, ksize, stride, pad)
# x : n, C_I, d_1, d_2, ..., d_N
# col: n, C_I, k_1, k_2, ..., k_N, d_1, d_2, ..., d_N
x_axes = (0, ) + tuple(six.moves.range(2, ndim + 2))
col_axes = (0, ) + tuple(six.moves.range(ndim + 2, ndim * 2 + 2))
gW = xp.tensordot(x, col, (x_axes, col_axes)).astype(W.dtype,
copy=False)
# col: n, C_I, k_1, k_2, ..., k_N, d_1, d_2, ..., d_N
# W : C_I, C_O, k_1, k_2, ..., k_N
axes = (1, ) + tuple(six.moves.range(2, ndim + 2))
gx = xp.tensordot(col, W, (axes, axes)).astype(x.dtype, copy=False)
gx = xp.rollaxis(gx, ndim + 1, 1)
if b is None:
return gx, gW
else:
sum_axis = (0, ) + tuple(six.moves.range(2, ndim + 2))
gb = gy.sum(axis=sum_axis)
return gx, gW, gb
def _forward_xp_core(self, x, W, b, xp):
ndim = self.ndim
ksize = W.shape[2:]
stride = self.stride
pad = self.pad
dilate = self.dilate
# Make patch array.
if xp is numpy:
col = conv_nd.im2col_nd_cpu(
x, ksize, stride, pad, cover_all=self.cover_all, dilate=dilate)
else:
col = conv_nd.im2col_nd_gpu(
x, ksize, stride, pad, cover_all=self.cover_all, dilate=dilate)
# Compute correlation.
axes = tuple(moves.range(1, ndim + 2)) # (1, 2, ..., N+1)
y = xp.tensordot(col, W, (axes, axes)).astype(x.dtype, copy=False)
# Apply bias if given.
if b is not None:
y += b
# Roll c_O before the second in (n, y_1, y_2, ..., y_N, c_O).
return xp.rollaxis(y, ndim + 1, 1),
def _forward_xp_core(self, x, gy, xp):
# Compute filter weight gradient.
# (n, _, out_1, out_2, ..., out_N)
out_axes = (0, ) + tuple(moves.range(2, self.ndim + 2))
# (n, _, _, ..., _, out_1, out_2, ..., out_N)
col_axes = (0, ) + tuple(moves.range(self.ndim + 2, self.ndim * 2 + 2))
# NumPy raises an error when the array is not contiguous.
# See: https://github.com/chainer/chainer/issues/2744
# TODO(niboshi): Remove this code when NumPy is fixed.
if (xp is numpy
and not (gy.flags.c_contiguous or gy.flags.f_contiguous)
and 1 in gy.shape):
gy = numpy.ascontiguousarray(gy)
if xp is numpy:
col = conv_nd.im2col_nd_cpu(x,
self.ksize,
self.stride,
self.pad,
cover_all=self.cover_all,
dilate=self.dilate)
else:
col = conv_nd.im2col_nd_gpu(x,
self.ksize,
self.stride,
self.pad,
cover_all=self.cover_all,
dilate=self.dilate)
gW = xp.tensordot(gy, col, (out_axes, col_axes)).astype(self.W_dtype,
copy=False)
return gW,
def test_im2col_nd_gpu_parameter_ranks(self):
img_gpu = cuda.to_gpu(self.img)
# Invalid ksize length.
with self.assertRaises(AssertionError):
conv_nd.im2col_nd_gpu(img_gpu, (2,), self.stride, self.pad)
# Invalid stride length.
with self.assertRaises(AssertionError):
conv_nd.im2col_nd_gpu(img_gpu, self.ksize, (1,), self.pad)
# Invalid pad length.
with self.assertRaises(AssertionError):
conv_nd.im2col_nd_gpu(img_gpu, self.ksize, self.stride, (0,))
def forward_gpu(self, inputs):
X, W, B, initial_ct = _as_contiguous(inputs[:4])
dtype = X.dtype
xp = cuda.get_array_module(W)
batchsize, feature_dimension, seq_length = X.shape
mask_x = inputs[4] if len(inputs) == 5 else None
if mask_x is not None:
X *= mask_x[..., None]
self.col = conv_nd.im2col_nd_gpu(X, (1, ), (1, ), (0, ),
cover_all=False)
self.U = _as_contiguous(
xp.tensordot(self.col, W[..., None],
((1, 2), (1, 2))).astype(X.dtype,
copy=False).transpose(
(0, 2, 1)))
# U = xp.matmul(W, X)
total_columns = feature_dimension * batchsize
thread_per_block = min(512, total_columns)
num_block = math.ceil(total_columns / thread_per_block)
assert thread_per_block * num_block >= total_columns
H = xp.empty((batchsize, feature_dimension, seq_length), dtype=dtype)
self.C = xp.empty((batchsize, feature_dimension, seq_length),
dtype=dtype)
self._cuda_elementwise("forward",
args=[
X.data.ptr, self.U.data.ptr, B.data.ptr,
initial_ct.data.ptr, self.C.data.ptr,
H.data.ptr, batchsize, feature_dimension,
seq_length, self.use_tanh
],
block=(thread_per_block, 1, 1),
grid=(num_block, 1, 1))
return H, self.C, self.C[..., -1]
def test_im2col_consistency(self):
col_cpu = conv_nd.im2col_nd_cpu(self.x, self.ksize, self.stride,
self.pad)
col_gpu = conv_nd.im2col_nd_gpu(cuda.to_gpu(self.x), self.ksize,
self.stride, self.pad)
testing.assert_allclose(col_cpu, col_gpu.get(), atol=0, rtol=0)
def test_im2col_consistency(self):
col_cpu = conv_nd.im2col_nd_cpu(
self.x, self.ksize, self.stride, self.pad)
col_gpu = conv_nd.im2col_nd_gpu(
cuda.to_gpu(self.x), self.ksize, self.stride, self.pad)
testing.assert_allclose(col_cpu, col_gpu.get(), atol=0, rtol=0)
def backward_gpu(self, inputs, grad_outputs):
X, W, B, initial_ct = _as_contiguous(inputs[:4])
dtype = X.dtype
xp = cuda.get_array_module(W)
batchsize, feature_dimension, seq_length = X.shape
mask_x = inputs[4] if len(inputs) == 5 else None
if mask_x is not None:
X *= mask_x[..., None]
total_columns = feature_dimension * batchsize
thread_per_block = min(512, total_columns)
num_block = total_columns // thread_per_block + 1
grad_x = xp.zeros_like(X)
grad_highway_x = xp.zeros_like(X)
grad_b = xp.zeros((batchsize, feature_dimension * 2, seq_length),
dtype=dtype)
grad_w = xp.zeros((batchsize, ) + W.shape, dtype=dtype)
grad_u = xp.zeros((batchsize, feature_dimension * 3, seq_length),
dtype=dtype)
grad_initial_ct = xp.zeros_like(initial_ct)
# initialize with zero
incoming_grad_ct = xp.zeros_like(
initial_ct) if grad_outputs[2] is None else _as_contiguous(
grad_outputs[2])
incoming_grad_h = xp.zeros_like(
X) if grad_outputs[0] is None else _as_contiguous(grad_outputs[0])
self._cuda_elementwise(
"backward",
args=[
X.data.ptr, self.U.data.ptr, B.data.ptr, self.C.data.ptr,
initial_ct.data.ptr, incoming_grad_h.data.ptr,
incoming_grad_ct.data.ptr, W.data.ptr, grad_highway_x.data.ptr,
grad_u.data.ptr, grad_b.data.ptr, grad_initial_ct.data.ptr,
batchsize, feature_dimension, seq_length, self.use_tanh
],
block=(thread_per_block, 1, 1),
grid=(num_block, 1, 1))
col = conv_nd.im2col_nd_gpu(grad_u, (1, ), (1, ), (0, ),
cover_all=False)
grad_x = xp.tensordot(col, W.T[..., None],
((1, 2),
(1, 2))).astype(dtype, copy=False).transpose(
(0, 2, 1)) + grad_highway_x
if mask_x is not None:
grad_x *= mask_x[..., None]
grad_b = xp.sum(grad_b, axis=(0, 2))
grad_w = xp.tensordot(grad_u, self.col,
((0, 2),
(0, 3))).astype(dtype, copy=False).reshape(
(feature_dimension * 3, feature_dimension))
if len(inputs) == 5:
return grad_x, grad_w, grad_b, grad_initial_ct, None
return grad_x, grad_w, grad_b, grad_initial_ct
