def local_cudnn_maxandargmax(node):
if not isinstance(node.op, GpuMaxAndArgmax):
return
if not dnn_available(node.inputs[0].type.context_name):
return
if version(raises=False) < 6000:
return
if node.inputs[0].ndim > 8:
return
if node.inputs[0].dtype != node.outputs[0].dtype:
return
if node.inputs[0].dtype not in ["float16", "float32", "float64"]:
return
# order of the axes influences the output indices
if node.op.axis is not None and tuple(sorted(
node.op.axis)) != node.op.axis:
return
max, arg = GpuDnnReduction("maximum", node.op.axis, node.outputs[0].dtype,
node.outputs[0].dtype, True)(node.inputs[0])
# cudnn can only return int32 indices
return (
max,
as_gpuarray_variable(arg.astype("int64"),
node.outputs[1].type.context_name),
)
def local_dnn_argmax(op, ctx_name, inputs, outputs):
if not dnn_available(ctx_name):
return
if version(raises=False) < 6000:
return
if inputs[0].ndim > 8:
return
if inputs[0].dtype not in ["float16", "float32", "float64"]:
return
# order of the axes influences the output indices
if op.axis is not None and tuple(sorted(op.axis)) != op.axis:
return
max, arg = GpuDnnReduction("maximum", op.axis, inputs[0].dtype,
inputs[0].dtype, True)(*inputs)
return [as_gpuarray_variable(arg.astype("int64"), ctx_name)]
def local_abstractconv_cudnn_alt(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]
def local_dnn_reduction(node):
if not isinstance(node.op, GpuCAReduceCuda):
return
if not dnn_available(node.inputs[0].type.context_name):
return
if version(raises=False) < 6000:
return
if node.inputs[0].ndim > 8:
return
acc_dtype = node.op._acc_dtype(node.inputs[0].dtype)
if node.inputs[0].dtype != node.outputs[0].dtype:
# We can mix float16 and float32, but not float64.
if node.inputs[0].dtype == "float64" or node.outputs[
0].dtype == "float64":
return
if acc_dtype != "float32":
return
if node.inputs[0].dtype not in ["float16", "float32", "float64"]:
return
if node.inputs[0].dtype == "float64" and acc_dtype != "float64":
return
if node.inputs[0].dtype == "float32" and acc_dtype != "float32":
return
if node.inputs[0].dtype == "float16" and acc_dtype == "float64":
return
def _identity(a):
return a
def _square(a):
return GpuElemwise(theano.scalar.basic.sqr)(a)
scal = node.op.scalar_op.name
post = _identity
if node.op.pre_scalar_op is not None:
if isinstance(node.op.scalar_op, theano.scalar.basic.Add):
if isinstance(node.op.pre_scalar_op, theano.scalar.basic.Sqr):
scal = "norm2"
post = _square
elif isinstance(node.op.pre_scalar_op, theano.scalar.basic.Abs):
scal = "norm1"
else:
return
elif isinstance(node.op.scalar_op,
theano.scalar.basic.Maximum) and isinstance(
node.op.pre_scalar_op, theano.scalar.basic.Abs):
scal = "absmax"
else:
return
if not cudnn.cudnnReduceTensorOp_t.has_alias(scal):
return
with inherit_stack_trace(node.outputs):
ret = GpuDnnReduction(scal, node.op.axis, acc_dtype, node.op.dtype,
False)(node.inputs[0])
return [post(ret)]
# We use FWD 2D to check it.
# Based on documentation, algo small (CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM)
# should support all configurations, for both v5.1, v6 and v7.
inputs = theano.shared(np.zeros((1, 1, 2, 2), dtype=dtype))
filters = theano.shared(np.zeros((1, 1, 2, 2), dtype=dtype))
conv = dnn_conv(inputs, filters, precision=precision, algo="small")
f = theano.function([], conv, mode=mode_with_gpu)
try:
f()
except RuntimeError as e:
assert "CUDNN_STATUS_ARCH_MISMATCH" in str(e)
return False
return True
cudnn = cudnn_defs.get_definitions(version(raises=False))
class ConvCase:
"""
Helper class to describe a special test case quickly.
This handles only 2D and 3D cases.
"""
FWD, GRADINPUT, GRADWEIGHT = 0, 1, 2
def __init__(
self,
type,
inputs_shape,
filters_shape,
# We use FWD 2D to check it.
# Based on documentation, algo small (CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM)
# should support all configurations, for both v5.1, v6 and v7.
inputs = theano.shared(np.zeros((1, 1, 2, 2), dtype=dtype))
filters = theano.shared(np.zeros((1, 1, 2, 2), dtype=dtype))
conv = dnn_conv(inputs, filters, precision=precision, algo='small')
f = theano.function([], conv, mode=mode_with_gpu)
try:
f()
except RuntimeError as e:
assert 'CUDNN_STATUS_ARCH_MISMATCH' in e.message
return False
return True
cudnn = cudnn_defs.get_definitions(version(raises=False))
class ConvCase:
"""
Helper class to describe a special test case quickly.
This handles only 2D and 3D cases.
"""
FWD, GRADINPUT, GRADWEIGHT = 0, 1, 2
def __init__(self, type,
inputs_shape, filters_shape,
algo=None, dtype=None, precision=None,
subsample=None, dilation=None, border_mode='valid',
conv_mode='conv', alpha=1, beta=0,
