pycuda_fct(inputs[0][0],
inputs[1][0],
z[0],
numpy.intc(inputs[1][0].size),
block=block,
grid=grid)
thunk.inputs = inputs
thunk.outputs = outputs
thunk.lazy = False
return thunk
pycuda_optimizer = EquilibriumDB()
gpu_seqopt.register("pycuda_optimizer", pycuda_optimizer, 1.5, "fast_run")
@local_optimizer([GpuElemwise])
def local_pycuda_gpu_elemwise(node):
"""
GpuElemwise -> PycudaElemwiseSourceModuleOp
"""
if isinstance(node.op, GpuElemwise):
if (not any([any(i.type.broadcastable) for i in node.inputs])
and all([i.ndim <= 2 for i in node.inputs])):
new_op = PycudaElemwiseSourceModuleOp(
node.op.scalar_op, node.op.inplace_pattern)(*node.inputs)
return [new_op]
c_code,
"pycuda_elemwise_kernel_%s"%str(self.scalar_op),
preamble="""#include<Python.h>
#include <numpy/arrayobject.h>""")
return out_node
def perform(self, node, inputs, out):
#TODO assert all input have the same shape
z, = out
if z[0] is None or z[0].shape!=inputs[0].shape:
z[0] = theano.sandbox.cuda.CudaNdarray.zeros(inputs[0].shape)
i = inputs + z
self.pycuda_fct(*i)
pycuda_optimizer = EquilibriumDB()
gpu_seqopt.register("pycuda_optimizer", pycuda_optimizer, 1.5, "fast_run")
@local_optimizer([])
def local_pycuda_gpu_elemwise(node):
"""
GpuElemwise -> PycudaElemwiseSourceModuleOp
"""
if isinstance(node.op, GpuElemwise):
if not any([ any(i.type.broadcastable) for i in node.inputs]) and all([i.ndim<=2 for i in node.inputs]):
new_op = PycudaElemwiseSourceModuleOp(node.op.scalar_op, node.op.inplace_pattern)(*node.inputs)
return [new_op]
pycuda_optimizer.register("local_pycuda_gpu_elemwise", local_pycuda_gpu_elemwise)
@local_optimizer([])
def local_pycuda_gpu_elemwise_kernel(node):
ins = gpu_contiguous(ins)
out = GpuDnnSoftmax('bc01', 'accurate', 'channel')(ins)
out = as_cuda_ndarray_variable(out.dimshuffle(0, 1))
return [out]
class NoCuDNNRaise(Optimizer):
def apply(self, fgraph):
""" Raise a RuntimeError if cudnn can't be used"""
if not dnn_available():
# Make an assert error as we want Theano to fail, not
# just skip this optimization.
raise AssertionError(
"cuDNN optimization was enabled, but Theano was not able"
" to use it. We got this error: \n" +
dnn_available.msg)
gpu_seqopt.register("NoCuDNNRaise", NoCuDNNRaise(), 0, 'cudnn')
@register_opt('cudnn')
@local_optimizer([SoftmaxGrad])
def local_softmax_dnn_grad(node):
if (
isinstance(node.op, SoftmaxGrad)
and (isinstance(node.inputs[0].owner.op, HostFromGpu)
or isinstance(node.inputs[1].owner.op, HostFromGpu))
):
if not dnn_available():
return
ins = []
for n in node.inputs:
if isinstance(n.owner.op, HostFromGpu):
n = n.owner.inputs[0]
ins = gpu_contiguous(ins)
out = GpuDnnSoftmax('bc01', 'accurate', 'channel')(ins)
out = as_cuda_ndarray_variable(out.dimshuffle(0, 1))
return [out]
class NoCuDNNRaise(Optimizer):
def apply(self, fgraph):
""" Raise a RuntimeError if cudnn can't be used"""
if not dnn_available():
# Make an assert error as we want Theano to fail, not
# just skip this optimization.
raise AssertionError(
"cuDNN optimization was enabled, but Theano was not able"
" to use it. We got this error: \n" + dnn_available.msg)
gpu_seqopt.register("NoCuDNNRaise", NoCuDNNRaise(), 0, 'cudnn')
@register_opt('cudnn')
@local_optimizer([SoftmaxGrad])
def local_softmax_dnn_grad(node):
if (isinstance(node.op, SoftmaxGrad)
and (isinstance(node.inputs[0].owner.op, HostFromGpu)
or isinstance(node.inputs[1].owner.op, HostFromGpu))):
ins = []
for n in node.inputs:
if isinstance(n.owner.op, HostFromGpu):
n = n.owner.inputs[0]
ins.append(n.dimshuffle(0, 1, 'x', 'x'))
out = GpuDnnSoftmaxGrad('bc01', 'accurate',
'channel')(gpu_contiguous(ins[0]),