# do not add 'fast_run' to these two as this would always enable gpuarray mode
optdb.register("gpuarray_opt", gpu_seqopt, optdb.__position__.get("add_destroy_handler", 49.5) - 1, "gpuarray")
def register_opt(*tags, **kwargs):
def f(local_opt):
name = (kwargs and kwargs.pop("name")) or local_opt.__name__
gpu_optimizer.register(name, local_opt, "fast_run", "gpuarray", *tags)
return local_opt
return f
register_opt("fast_compile")(theano.tensor.opt.local_track_shape_i)
gpu_optimizer.register("local_remove_all_assert", theano.tensor.opt.local_remove_all_assert, "unsafe")
def safe_to_gpu(x, ctx_name):
if isinstance(x.type, tensor.TensorType):
return GpuFromHost(ctx_name)(x)
else:
return x
def safe_to_cpu(x):
if isinstance(x.type, GpuArrayType):
return host_from_gpu(x)
else:
return x
optdb.register('gpuarray_opt', gpu_seqopt,
optdb.__position__.get('add_destroy_handler', 49.5) - 1,
'gpuarray')
def register_opt(*tags, **kwargs):
def f(local_opt):
name = (kwargs and kwargs.pop('name')) or local_opt.__name__
gpu_optimizer.register(name, local_opt, 'fast_run', 'gpuarray', *tags)
return local_opt
return f
register_opt('fast_compile')(theano.tensor.opt.local_track_shape_i)
gpu_optimizer.register('local_remove_all_assert',
theano.tensor.opt.local_remove_all_assert,
'unsafe')
def safe_to_gpu(x):
if isinstance(x.type, tensor.TensorType):
return gpu_from_host(x)
else:
return x
def safe_to_cpu(x):
if isinstance(x.type, GpuArrayType):
return host_from_gpu(x)
else:
return x
except TypeError, e:
# This could fail if the inputs are not TensorTypes
pass
gpu_seqopt.register('InputToGpuArrayOptimizer', InputToGpuOptimizer(),
0, 'fast_run', 'fast_compile', 'merge')
@local_optimizer([gpu_from_host, host_from_gpu])
def local_cut_gpu_host_gpu(node):
if tensor.opt.opt.check_chain(node, gpu_from_host, host_from_gpu):
return [node.inputs[0].owner.inputs[0]]
if tensor.opt.opt.check_chain(node, host_from_gpu, gpu_from_host):
return [node.inputs[0].owner.inputs[0]]
return False
gpu_cut_copies.register('cut_gpua_host_transfers', local_cut_gpu_host_gpu,
'fast_run', 'inplace', 'gpuarray')
gpu_cut_copies.register('cut_gpua_constant_transfers',
tensor.opt.constant_folding,
'fast_run', 'gpuarray')
optdb['canonicalize'].register('local_cut_gpua_host_gpua',
local_cut_gpu_host_gpu, 'fast_run', 'gpuarray')
@register_opt()
@op_lifter([tensor.Alloc])
def local_gpualloc(node):
return gpu_alloc
@register_opt()
@local_optimizer([GpuAlloc])
@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]
pycuda_optimizer.register("local_pycuda_gpu_elemwise",
local_pycuda_gpu_elemwise)
"""
@local_optimizer([GpuElemwise])
def local_pycuda_gpu_elemwise_kernel(node):
""
GpuElemwise -> PycudaElemwiseKernelOp
""
if isinstance(node.op, GpuElemwise):
if not any([any(i.type.broadcastable) for i in node.inputs]):
new_op = PycudaElemwiseKernelOp(node.op.scalar_op,
node.op.inplace_pattern)(
*node.inputs)
return [new_op]
pycuda_optimizer.register("local_pycuda_gpu_elemwise_kernel",
local_pycuda_gpu_elemwise_kernel, 1.5)
def f(local_opt):
name = (kwargs and kwargs.pop('name')) or local_opt.__name__
optdb.register(
name,
TopoOptimizer(local_opt,
failure_callback=TopoOptimizer.warn_inplace), 60,
'fast_run', 'inplace', 'gpuarray', *tags)
return local_opt
return f
register_opt('fast_compile')(theano.tensor.opt.local_track_shape_i)
register_opt(final_opt=True,
name='gpua_constant_folding')(tensor.opt.constant_folding)
gpu_optimizer.register('local_remove_all_assert',
theano.tensor.opt.local_remove_all_assert, 'unsafe')
def safe_to_gpu(x, ctx_name):
if isinstance(x.type, tensor.TensorType):
return GpuFromHost(ctx_name)(x)
else:
return x
def safe_to_cpu(x):
if isinstance(x.type, GpuArrayType):
return host_from_gpu(x)
else:
return x
gpu_seqopt.register('InputToGpuArrayOptimizer', InputToGpuOptimizer(), 0,
'fast_run', 'fast_compile', 'merge')
@local_optimizer([])
def local_cut_gpu_host_gpu(node):
if tensor.opt.opt.check_chain(node, gpu_from_host, host_from_gpu):
return [node.inputs[0].owner.inputs[0]]
if tensor.opt.opt.check_chain(node, host_from_gpu, gpu_from_host):
return [node.inputs[0].owner.inputs[0]]
return False
gpu_cut_copies.register('cut_gpua_host_transfers', local_cut_gpu_host_gpu,
'fast_run', 'inplace', 'gpuarray')
gpu_cut_copies.register('cut_gpua_constant_transfers',
tensor.opt.constant_folding, 'fast_run', 'gpuarray')
optdb['canonicalize'].register('local_cut_gpua_host_gpua',
local_cut_gpu_host_gpu, 'fast_run', 'gpuarray')
@register_opt()
@op_lifter(tensor.Alloc)
def local_gpualloc(node):
return gpu_alloc
@register_opt()
@op_lifter(tensor.Reshape)
def local_gpureshape(node):
pass
gpu_seqopt.register("InputToGpuArrayOptimizer", InputToGpuOptimizer(), 0, "fast_run", "fast_compile", "merge")
@local_optimizer([gpu_from_host, host_from_gpu])
def local_cut_gpu_host_gpu(node):
if tensor.opt.opt.check_chain(node, gpu_from_host, host_from_gpu):
return [node.inputs[0].owner.inputs[0]]
if tensor.opt.opt.check_chain(node, host_from_gpu, gpu_from_host):
return [node.inputs[0].owner.inputs[0]]
return False
gpu_cut_copies.register("cut_gpua_host_transfers", local_cut_gpu_host_gpu, "fast_run", "inplace", "gpuarray")
gpu_cut_copies.register("cut_gpua_constant_transfers", tensor.opt.constant_folding, "fast_run", "gpuarray")
optdb["canonicalize"].register("local_cut_gpua_host_gpua", local_cut_gpu_host_gpu, "fast_run", "gpuarray")
@register_opt()
@local_optimizer([tensor.Alloc])
def local_gpuaalloc2(node):
"""
Join(axis, Alloc, Alloc, ...) -> Join(axis, GpuAlloc, Alloc, ...)
Moves an alloc that is an input to join to the gpu.
"""
if isinstance(node.op, tensor.Alloc) and all(
c != "output"
and c.op == tensor.join
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):
"""
GpuElemwise -> PycudaElemwiseKernelOp
"""
if isinstance(node.op, GpuElemwise):
if not any([ any(i.type.broadcastable) for i in node.inputs]):
new_op = PycudaElemwiseKernelOp(node.op.scalar_op, node.op.inplace_pattern)(*node.inputs)
return [new_op]
pycuda_optimizer.register("local_pycuda_gpu_elemwise_kernel", local_pycuda_gpu_elemwise_kernel, 1.5)
@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]
pycuda_optimizer.register("local_pycuda_gpu_elemwise",
local_pycuda_gpu_elemwise)
"""
@local_optimizer([GpuElemwise])
def local_pycuda_gpu_elemwise_kernel(node):
""
GpuElemwise -> PycudaElemwiseKernelOp
""
if isinstance(node.op, GpuElemwise):
if not any([any(i.type.broadcastable) for i in node.inputs]):
new_op = PycudaElemwiseKernelOp(node.op.scalar_op,
node.op.inplace_pattern)(
*node.inputs)
return [new_op]
pycuda_optimizer.register("local_pycuda_gpu_elemwise_kernel",
@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):
"""
GpuElemwise -> PycudaElemwiseKernelOp
"""
if isinstance(node.op, GpuElemwise):
if not any([any(i.type.broadcastable) for i in node.inputs]):
new_op = PycudaElemwiseKernelOp(node.op.scalar_op,
node.op.inplace_pattern)(
*node.inputs)
return [new_op]
pycuda_optimizer.register("local_pycuda_gpu_elemwise_kernel",
