def local_gpua_pool_dnn_grad_stride(fgraph, op, ctx_name, inputs, outputs):
if not dnn_available(ctx_name):
return
if not op.ignore_border:
return
inp, out, out_grad, ws, stride, pad = inputs
nd = op.ndim
if nd not in (2, 3):
return
inp = gpu_contiguous(as_gpuarray_variable(inp, ctx_name))
out = gpu_contiguous(as_gpuarray_variable(out, ctx_name))
out_grad = gpu_contiguous(as_gpuarray_variable(out_grad, ctx_name))
mode = op.mode
# the GPU ops expect exactly 2 non-pooling dimensions
if inp.ndim == nd + 2:
return GpuDnnPoolGrad(mode=mode)(inp, out, out_grad, ws, stride, pad)
else:
# reshape to 4D or 5D with 2 non-pooling dimensions
inp_padded = pad_dims(inp, 2, nd)
out_padded = pad_dims(out, 2, nd)
out_grad_padded = pad_dims(out_grad, 2, nd)
ret_padded = GpuDnnPoolGrad(mode=mode)(inp_padded, out_padded,
out_grad_padded, ws, stride,
pad)
return unpad_dims(ret_padded, inp, 2, nd)
def local_gpua_avg_pool_dnn_grad_stride(fgraph, op, ctx_name, inputs, outputs):
if not dnn_available(ctx_name):
return
if not op.ignore_border:
return
inp, out_grad, ws, stride, pad = inputs
nd = op.ndim
if nd not in (2, 3):
return
inp = gpu_contiguous(as_gpuarray_variable(inp, ctx_name))
out_grad = gpu_contiguous(as_gpuarray_variable(out_grad, ctx_name))
mode = op.mode
# the GPU ops expect exactly 2 non-pooling dimensions
if inp.ndim == nd + 2:
# We reuse out_grad because cuDNN does not use the value of the `out`
# argument but still checks its shape for average pooling. This
# has been observed in v2 and v3 as far as I know.
return GpuDnnPoolGrad(mode=mode)(inp, out_grad, out_grad, ws, stride,
pad)
else:
# reshape to 4D or 5D with 2 non-pooling dimensions
inp_padded = pad_dims(inp, 2, nd)
out_grad_padded = pad_dims(out_grad, 2, nd)
ret_padded = GpuDnnPoolGrad(mode=mode)(inp_padded, out_grad_padded,
out_grad_padded, ws, stride,
pad)
return unpad_dims(ret_padded, inp, 2, nd)
def make_node(self, inp1, inp2):
if not cublas_available:
raise RuntimeError("CUBLAS is not available and "
"GpuCublasTriangularSolve Op "
"can not be constructed.")
context_name = infer_context_name(inp1, inp2)
inp1 = as_gpuarray_variable(inp1, context_name)
inp2 = as_gpuarray_variable(inp2, context_name)
inp1 = gpu_contiguous(inp1)
inp2 = gpu_contiguous(inp2)
assert inp1.ndim == 2
assert inp2.ndim in [1, 2]
assert inp1.dtype == inp2.dtype
return Apply(
self,
[inp1, inp2],
[
GpuArrayType(
inp1.dtype,
broadcastable=inp2.broadcastable,
context_name=context_name,
)()
],
)
def make_node(self, inp1, inp2):
if not cusolver_available:
raise RuntimeError("CUSOLVER is not available and "
"GpuCusolverSolve Op can not be constructed.")
if skcuda.__version__ <= "0.5.1":
warnings.warn(
"The GpuSolve op requires scikit-cuda > 0.5.1 to work with CUDA 8"
)
context_name = infer_context_name(inp1, inp2)
inp1 = as_gpuarray_variable(inp1, context_name)
inp2 = as_gpuarray_variable(inp2, context_name)
inp1 = gpu_contiguous(inp1)
inp2 = gpu_contiguous(inp2)
assert inp1.ndim == 2
assert inp2.ndim == 2
assert inp1.dtype == inp2.dtype
return Apply(
self,
[inp1, inp2],
[
GpuArrayType(
inp1.dtype,
broadcastable=inp1.broadcastable,
context_name=context_name,
)()
],
)
def local_gpua_softmax_dnn_grad(op, ctx_name, inputs, outputs):
if not dnn_available(ctx_name):
return
ins = []
for n in inputs:
n = as_gpuarray_variable(n, ctx_name)
if n.ndim != 2:
return
ins.append(n.dimshuffle(0, "x", 1, "x"))
out = GpuDnnSoftmaxGrad("accurate", "instance")(gpu_contiguous(ins[0]),
gpu_contiguous(ins[1]))
return [out.dimshuffle(0, 2)]
def make_node(self, inp, s=None):
# A shape parameter s can be provided as an input. For now this is used to
# manage odd transform sizes.
# Later this could be extended to handle padding and trunkation,
# following numpy's interface. However, cuFFT expects array that match
# the shape given to the plan, so padding will have to be done in the op.
# The effect of padding on gradients has yet to be investigated.
if not skcuda_available:
raise RuntimeError("skcuda is needed for CuFFTOp")
if not pygpu_available:
raise RuntimeError("pygpu is needed for CuFFTOp")
if not pycuda_available:
raise RuntimeError("pycuda is needed for CuFFTOp")
inp = gpu_contiguous(as_gpuarray_variable(inp, infer_context_name(inp)))
# If no shape is provided as input, default to input data shape.
if s is None:
s = inp.shape[1:]
s = as_tensor_variable(s)
assert inp.dtype == "float32"
assert s.ndim == 1
assert s.dtype in integer_dtypes
return Apply(self, [inp, s], [self.output_type(inp)()])
def make_node(self, A):
ctx_name = infer_context_name(A)
A = as_gpuarray_variable(A, ctx_name)
A = gpu_contiguous(A)
if A.ndim != 2:
raise LinAlgError("Matrix rank error")
if A.dtype != "float32":
raise TypeError("only `float32` is supported for now")
if self.compute_uv:
return Apply(
self,
[A],
# return S, U, VT
[
GpuArrayType(A.dtype,
broadcastable=[False],
context_name=ctx_name)(),
A.type(),
A.type(),
],
)
else:
return Apply(
self,
[A],
# return only S
[
GpuArrayType(A.dtype,
broadcastable=[False],
context_name=ctx_name)()
],
)
def local_softmax_dnn(fgraph, node):
if isinstance(node.op, GpuSoftmax):
if not dnn_available(node.outputs[0].type.context_name):
return
ins = node.inputs[0].dimshuffle(0, 1, "x", "x")
ins = gpu_contiguous(ins)
out = GpuDnnSoftmax("accurate", "channel")(ins)
out = as_gpuarray_variable(out.dimshuffle(0, 1), out.type.context_name)
return [out]
def make_node(self, A):
ctx_name = infer_context_name(A)
A = as_gpuarray_variable(A, ctx_name)
A = gpu_contiguous(A)
if A.ndim != 2:
raise LinAlgError("Matrix rank error")
if A.dtype != "float32":
raise TypeError("only `float32` is supported for now")
return Apply(self, [A], [A.type()])
def local_gpua_logsoftmax_to_dnn(op, ctx_name, inputs, outputs):
# Transform the input in the format expected by GpuDnnSoftmax
inp = inputs[0]
if inp.ndim != 2:
return
if not dnn_available(ctx_name):
return
inp = inp.dimshuffle(0, 1, "x", "x")
inp.tag.context_name = ctx_name
# Apply GpuDnnSoftmax and return the result
out = GpuDnnSoftmax("log", "channel")(gpu_contiguous(inp))
return [out.dimshuffle(0, 1)]
def make_node(self, activations, labels, input_lengths):
context_name = infer_context_name(activations)
t_activations = as_gpuarray_variable(activations,
context_name=context_name)
# Ensure activations array is C-contiguous
t_activations = gpu_contiguous(t_activations)
# Labels and input lengths are always on the CPU
t_labels = as_tensor_variable(labels)
t_input_lengths = as_tensor_variable(input_lengths)
if t_activations.type.dtype != "float32":
raise TypeError("activations must use the float32 type.")
if t_activations.ndim != 3:
raise ValueError("activations must have 3 dimensions.")
if t_labels.type.dtype != "int32":
raise TypeError("labels must use the int32 type.")
if t_labels.ndim != 2:
raise ValueError("labels must have 2 dimensions.")
if t_input_lengths.type.dtype != "int32":
raise TypeError("input_lengths must use the int32 type.")
if t_input_lengths.ndim != 1:
raise ValueError("input_lengths must have 1 dimension.")
costs = GpuArrayType(dtype="float32",
broadcastable=(False, ),
context_name=context_name)()
outputs = [costs]
if self.compute_grad:
gradients = GpuArrayType(
dtype="float32",
broadcastable=(
False,
False,
False,
),
context_name=context_name,
)()
outputs += [gradients]
return Apply(self,
inputs=[t_activations, t_labels, t_input_lengths],
outputs=outputs)
def test_gpu_contiguous():
a = tt.fmatrix("a")
i = tt.iscalar("i")
a_val = np.asarray(np.random.rand(4, 5), dtype="float32")
# The reshape is needed otherwise we make the subtensor on the CPU
# to transfer less data.
f = aesara.function(
[a, i], gpu_contiguous(a.reshape((5, 4))[::i]), mode=mode_with_gpu
)
topo = f.maker.fgraph.toposort()
assert any([isinstance(node.op, GpuSubtensor) for node in topo])
assert any([isinstance(node.op, GpuContiguous) for node in topo])
assert f(a_val, 1).flags.c_contiguous
assert f(a_val, 2).flags.c_contiguous
assert f(a_val, 2).flags.c_contiguous
def make_node(self, inp):
if not cusolver_available:
raise RuntimeError("CUSOLVER is not available and "
"GpuCholesky Op can not be constructed.")
if skcuda.__version__ <= "0.5.1":
warnings.warn("The GpuCholesky op requires scikit-cuda > "
"0.5.1 to work with CUDA 8")
if not pygpu_available:
raise RuntimeError("Missing pygpu or triu/tril functions."
"Install or update libgpuarray.")
context_name = infer_context_name(inp)
inp = as_gpuarray_variable(inp, context_name)
inp = gpu_contiguous(inp)
assert inp.ndim == 2
return Apply(self, [inp], [inp.type()])
def grad(self, inp, grads):
img, ws, stride, pad = inp
(grad, ) = grads
grad = gpu_contiguous(grad)
disc = [aesara.gradient.DisconnectedType()() for i in inp[1:]]
if self.mode == "max":
out = self(img, ws, stride, pad)
g_out = GpuMaxPoolGrad(ndim=self.ndim,
ignore_border=self.ignore_border)(img, out,
grad, ws,
stride,
pad)
return [g_out] + disc
else:
g_out = GpuAveragePoolGrad(ndim=self.ndim,
ignore_border=self.ignore_border,
mode=self.mode)(img, grad, ws, stride,
pad)
return [g_out] + disc
def local_gpua_pool_dnn_alternative(fgraph, op, ctx_name, inputs, outputs):
if not dnn_available(ctx_name):
return
if not op.ignore_border:
return
img, ws, stride, pad = inputs
nd = op.ndim
if nd not in (2, 3):
return
img = gpu_contiguous(as_gpuarray_variable(img, ctx_name))
mode = op.mode
# dnn_pool expects exactly 2 non-pooling dimensions
if img.ndim == nd + 2:
return dnn_pool(img, ws, stride=stride, pad=pad, mode=mode)
else:
# reshape to 4D or 5D with 2 non-pooling dimensions
img_padded = pad_dims(img, 2, nd)
ret_padded = dnn_pool(img_padded,
ws,
stride=stride,
pad=pad,
mode=mode)
return unpad_dims(ret_padded, img, 2, nd)
def local_dnn_convi_output_merge(node, *inputs):
inputs = inputs[0:2] + (gpu_contiguous(inputs[2]), ) + inputs[3:]
return [
GpuDnnConvGradI(algo=node.op.algo,
num_groups=node.op.num_groups)(*inputs)
]
def local_abstractconv_cudnn_alt(fgraph, node):
if not isinstance(node.op, (AbstractConv2d, AbstractConv2d_gradWeights,
AbstractConv2d_gradInputs)):
return
if version(raises=False) < 6000 and node.op.filter_dilation != (1, 1):
return None
if node.op.unshared:
return None
if isinstance(node.op.border_mode, tuple) and any(
isinstance(p, tuple) for p in node.op.border_mode):
# Asymmetric padding not yet supported
return None
inp1 = node.inputs[0]
inp2 = node.inputs[1]
if not dnn_available(inp1.type.context_name):
return
op = node.op
border_mode = node.op.border_mode
subsample = node.op.subsample
filter_dilation = node.op.filter_dilation
num_groups = node.op.num_groups
precision, _ = get_precision(None, [inp1, inp2])
if node.op.filter_flip:
conv_mode = "conv"
else:
conv_mode = "cross"
if isinstance(op, AbstractConv2d):
if border_mode == "half" or subsample != (1, 1) or num_groups != 1:
return None
if border_mode == "full":
direction_hint = "bprop inputs"
elif border_mode == "valid" and filter_dilation == (1, 1):
direction_hint = "bprop weights"
else:
return None
rval = dnn_conv(
inp1,
inp2,
border_mode=border_mode,
subsample=subsample,
dilation=filter_dilation,
direction_hint=direction_hint,
conv_mode=conv_mode,
num_groups=num_groups,
)
elif isinstance(op, AbstractConv2d_gradWeights):
if (border_mode == "valid" and subsample == (1, 1)
and filter_dilation == (1, 1) and num_groups == 1):
img = gpu_contiguous(inp1)
topgrad = gpu_contiguous(inp2)
ctx_name = infer_context_name(img, topgrad)
img = gpu_contiguous(img.dimshuffle(1, 0, 2, 3))
topgrad = gpu_contiguous(topgrad.dimshuffle(1, 0, 2, 3))
ishape = [shape_i_op(i)(img) for i in range(img.ndim)]
tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
out_shp = get_conv_output_shape(
ishape,
tshape,
border_mode=border_mode,
subsample=subsample,
filter_dilation=filter_dilation,
)
out_shp = assert_conv_shape(out_shp)
out = GpuAllocEmpty(dtype=img.dtype,
context_name=ctx_name)(*out_shp)
desc = GpuDnnConvDesc(
border_mode=border_mode,
subsample=subsample,
dilation=filter_dilation,
conv_mode="cross",
precision=precision,
)(out.shape)
conv = GpuDnnConv(algo=None, num_groups=num_groups)(img, topgrad,
out, desc)
if conv_mode == "conv":
conv = conv[:, :, ::-1, ::-1]
rval = as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3), ctx_name)
else:
return None
elif isinstance(op, AbstractConv2d_gradInputs):
if border_mode == "valid" and subsample == (1, 1) and num_groups == 1:
kerns = gpu_contiguous(inp1.dimshuffle(1, 0, 2, 3))
topgrad = gpu_contiguous(inp2)
ctx_name = infer_context_name(kerns, topgrad)
conv_mode = "cross" if conv_mode == "conv" else "conv"
desc = GpuDnnConvDesc(
border_mode="full",
subsample=subsample,
dilation=filter_dilation,
conv_mode=conv_mode,
precision=precision,
)(kerns.shape)
tshape = [shape_i_op(i)(topgrad) for i in range(topgrad.ndim)]
kshape = [shape_i_op(i)(kerns) for i in range(kerns.ndim)]
shape = get_conv_output_shape(
tshape,
kshape,
border_mode="full",
subsample=subsample,
filter_dilation=filter_dilation,
)
shape = assert_conv_shape(shape)
out = GpuAllocEmpty(dtype=topgrad.dtype,
context_name=ctx_name)(*shape)
rval = GpuDnnConv(algo=None, num_groups=num_groups)(topgrad, kerns,
out, desc)
else:
return None
return [rval]