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提交 b5af3406 authored 作者: Frédéric Bastien's avatar Frédéric Bastien
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Merge pull request #1669 from abergeron/eq_opt

make EquilibriumOptimizer use a dict to map ops to their nodes rather than running everything on everything.
上级 c4ad1383 7ec1f28d
隐藏空白字符变更
内嵌 并排
正在显示 包含 181 行增加 和 152 行删除
theano/gof/opt.py
浏览文件 @ b5af3406
......@@ -736,6 +736,14 @@ class LocalOptimizer(object):
_optimizer_idx[0] += 1
return self._optimizer_idx
def tracks(self):
"""
Return the list of op classes that this opt applies to.
Return None to apply to all nodes.
"""
return None
def transform(self, node):
"""Transform a subgraph whose output is `node`.
......@@ -772,8 +780,6 @@ class LocalOptimizer(object):
class FromFunctionLocalOptimizer(LocalOptimizer):
"""WRITEME"""
def __init__(self, fn, tracks=None):
if tracks is None:
tracks = []
self.transform = fn
self._tracks = tracks
......@@ -791,9 +797,15 @@ class FromFunctionLocalOptimizer(LocalOptimizer):
id(self))
def local_optimizer(*tracks):
def local_optimizer(tracks):
def decorator(f):
"""WRITEME"""
if tracks is not None:
if len(tracks) is 0:
raise ValueError, ("Use None instead of an empty list to apply to all nodes.", f.__module__, f.__name__)
for t in tracks:
if not (isinstance(t, op.Op) or issubclass(t, op.PureOp)):
raise ValueError, ("Tracks are op classes or instances", f.__module__, f.__name__)
rval = FromFunctionLocalOptimizer(f, tracks)
rval.__name__ = f.__name__
return rval
......@@ -870,7 +882,7 @@ class OpSub(LocalOptimizer):
return self.op1
def tracks(self):
return [[self.op1]]
return [self.op1]
def transform(self, node):
if node.op != self.op1:
......@@ -901,7 +913,7 @@ class OpRemove(LocalOptimizer):
return self.op
def tracks(self):
return [[self.op]]
return [self.op]
def transform(self, node):
if node.op != self.op:
......@@ -1008,17 +1020,7 @@ class PatternSub(LocalOptimizer):
return self.op
def tracks(self):
def helper(pattern, sofar):
if isinstance(pattern, (list, tuple)):
sofar = sofar + (pattern[0],)
return reduce(tuple.__add__,
tuple(helper(p, sofar) for p in pattern[1:]),
())
elif isinstance(pattern, dict):
return helper(pattern['pattern'], sofar)
else:
return (sofar,)
return set(helper(self.in_pattern, ()))
return [self.op]
def transform(self, node):
"""
......@@ -1500,12 +1502,17 @@ class EquilibriumOptimizer(NavigatorOptimizer):
None,
ignore_newtrees=True,
failure_callback=failure_callback)
self.local_optimizers = []
self.local_optimizers_map = dict()
self.local_optimizers_all = []
self.global_optimizers = []
for opt in optimizers:
if isinstance(opt, LocalOptimizer):
self.local_optimizers.append(opt)
if opt.tracks() is None:
self.local_optimizers_all.append(opt)
else:
for c in opt.tracks():
self.local_optimizers_map.setdefault(c, []).append(opt)
else:
self.global_optimizers.append(opt)
self.max_depth = max_depth
......@@ -1513,10 +1520,21 @@ class EquilibriumOptimizer(NavigatorOptimizer):
assert self.max_use_ratio is not None, (
'max_use_ratio has to be a number')
def get_local_optimizers(self):
for opt in self.local_optimizers_all:
yield opt
# if repeat is not a problem we can drop the set
s = set()
for lopt in self.local_optimizers_map.values():
for opt in lopt:
if opt not in s:
yield opt
s.add(opt)
def add_requirements(self, fgraph):
super(EquilibriumOptimizer, self).add_requirements(fgraph)
fgraph.attach_feature(ChangeTracker())
for opt in self.local_optimizers:
for opt in self.get_local_optimizers():
opt.add_requirements(fgraph)
for opt in self.global_optimizers:
opt.add_requirements(fgraph)
......@@ -1542,7 +1560,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
time_opts = {}
io_toposort_timing = []
nb_nodes = []
for opt in self.global_optimizers + self.local_optimizers:
for opt in self.global_optimizers + list(self.get_local_optimizers()):
global_process_count.setdefault(opt, 0)
time_opts.setdefault(opt, 0)
......@@ -1595,7 +1613,9 @@ class EquilibriumOptimizer(NavigatorOptimizer):
node = q.pop()
current_node = node
for lopt in self.local_optimizers:
for lopt in (self.local_optimizers_all +
self.local_optimizers_map.get(type(node.op), []) +
self.local_optimizers_map.get(node.op, [])):
t_opt = time.time()
lopt_change = self.process_node(fgraph, node, lopt)
time_opts[lopt] += time.time() - t_opt
......@@ -1634,7 +1654,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
print >> stream, "%s%s %s id=%i" % (
(' ' * level), self.__class__.__name__, name, id(self))
if depth != 0:
for lopt in self.local_optimizers:
for lopt in self.get_local_optimizers():
lopt.print_summary(stream, level=(level + 2),
depth=(depth - 1))
......@@ -1654,7 +1674,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
start_nb_nodes, end_nb_nodes, max_nb_nodes)
print >> stream, blanc, " time io_toposort %.3fs" % sum(
io_toposort_timing)
s = sum([time_opts[o] for o in opt.local_optimizers])
s = sum([time_opts[o] for o in opt.get_local_optimizers()])
print >> stream, blanc, " time in local optimizers %.3fs" % s
s = sum([time_opts[o] for o in opt.global_optimizers])
print >> stream, blanc, " time in global optimizers %.3fs" % s
......@@ -1679,7 +1699,7 @@ class EquilibriumOptimizer(NavigatorOptimizer):
not_used = 0
not_used_time = 0
process_count = {}
for o in opt.global_optimizers + opt.local_optimizers:
for o in opt.global_optimizers + list(opt.get_local_optimizers()):
process_count.setdefault(o, 0)
for count in loop_process_count:
for o, v in count.iteritems():
......@@ -1707,8 +1727,8 @@ class EquilibriumOptimizer(NavigatorOptimizer):
#(opt, loop_timing, loop_process_count, max_nb_nodes,
# global_opt_timing, nb_nodes, time_opts, io_toposort_timing) = prof1
local_optimizers = set(prof1[0].local_optimizers).union(
prof2[0].local_optimizers)
local_optimizers = set(prof1[0].get_local_optimizers()).union(
prof2[0].get_local_optimizers())
global_optimizers = set(prof1[0].global_optimizers).union(
prof2[0].global_optimizers)
new_opt = EquilibriumOptimizer(
......
theano/ifelse.py
浏览文件 @ b5af3406
......@@ -384,7 +384,7 @@ def ifelse(condition, then_branch, else_branch, name=None):
return tuple(rval)
@gof.local_optimizer([None])
@gof.local_optimizer([IfElse])
def cond_make_inplace(node):
op = node.op
if isinstance(op, IfElse) and not op.as_view:
......@@ -445,7 +445,7 @@ acceptable_ops = (theano.tensor.basic.Dot,
theano.tensor.elemwise.DimShuffle)
@gof.local_optimizer([None])
@gof.local_optimizer(acceptable_ops)
def ifelse_lift_single_if_through_acceptable_ops(main_node):
"""This optimization lifts up certain ifelse instances.
......@@ -493,7 +493,7 @@ def ifelse_lift_single_if_through_acceptable_ops(main_node):
return nw_outs
@gof.local_optimizer([None])
@gof.local_optimizer([IfElse])
def cond_merge_ifs_true(node):
op = node.op
if not isinstance(op, IfElse):
......@@ -517,7 +517,7 @@ def cond_merge_ifs_true(node):
return op(*old_ins, **dict(return_list=True))
@gof.local_optimizer([None])
@gof.local_optimizer([IfElse])
def cond_merge_ifs_false(node):
op = node.op
if not isinstance(op, IfElse):
......@@ -592,7 +592,7 @@ class CondMerge(gof.Optimizer):
fgraph.replace_all_validate(pairs, reason='cond_merge')
@gof.local_optimizer([None])
@gof.local_optimizer([IfElse])
def cond_remove_identical(node):
op = node.op
......@@ -643,7 +643,7 @@ def cond_remove_identical(node):
return rval
@gof.local_optimizer([None])
@gof.local_optimizer([IfElse])
def cond_merge_random_op(main_node):
if isinstance(main_node.op, IfElse):
return False
......
theano/sandbox/cuda/GpuConv3D.py
浏览文件 @ b5af3406
......@@ -284,7 +284,7 @@ conv_rows_stack( float* img, float* kern, float* bias, float* out,
gpu_convd = GpuConv3D()
@register_opt()
@local_optimizer([])
@local_optimizer([Conv3D])
def local_gpu_conv3d(node):
if isinstance(node.op, Conv3D):
if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu) for i in node.inputs]):
......
theano/sandbox/cuda/GpuConvGrad3D.py
浏览文件 @ b5af3406
......@@ -341,7 +341,7 @@ convgrad_rows_stack( float* img, float* dCdH, float* dCdW,
gpu_conv_grad3d = GpuConvGrad3D()
@register_opt()
@local_optimizer([])
@local_optimizer([ConvGrad3D])
def local_gpu_conv_gradd(node):
if isinstance(node.op, ConvGrad3D):
if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu) for i in node.inputs]):
......
theano/sandbox/cuda/GpuConvTransp3D.py
浏览文件 @ b5af3406
......@@ -348,7 +348,7 @@ conv_transp_rows_stack( float* H, float* kern, float* bias, float* R,
gpu_conv_transpd = GpuConvTransp3D()
@register_opt()
@local_optimizer([])
@local_optimizer([ConvTransp3D])
def local_gpu_conv_transpd(node):
if isinstance(node.op, ConvTransp3D):
if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu) for i in node.inputs]):
......
theano/sandbox/cuda/neighbours.py
浏览文件 @ b5af3406
......@@ -405,7 +405,7 @@ def gpu_images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
return GpuImages2Neibs(mode)(ten4, neib_shape, neib_step)
@local_optimizer()
@local_optimizer([Images2Neibs])
def use_gpu_images2neibs(node):
if (type(node.op) is Images2Neibs and
node.inputs[0].dtype == 'float32' and
......
theano/sandbox/cuda/opt.py
浏览文件 @ b5af3406
......@@ -121,7 +121,7 @@ gpu_seqopt.register('InputToGpuOptimizer', InputToGpuOptimizer(),
'merge') # TODO: how to make it mandatory for gpu_seqopt?
@local_optimizer([])
@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]]
......@@ -170,7 +170,7 @@ def dtype_in_elemwise_supported(op):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.Elemwise])
def local_gpu_elemwise_0(node):
"""elemwise(..., host_from_gpu, ...)
-> host_from_gpu(elemwise(gpu_from_host, ..., gpu_from_host)
......@@ -229,7 +229,7 @@ def local_gpu_elemwise_0(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host])
def local_gpu_elemwise_1(node):
"""
gpu_from_host(Elemwise)) -> GpuElemwise(gpu_from_host(...))
......@@ -265,7 +265,7 @@ def local_gpu_elemwise_1(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.DimShuffle, gpu_from_host])
def local_gpu_dimshuffle_0(node):
"""
dimshuffle(host_from_gpu()) -> host_from_gpu(gpu_dimshuffle)
......@@ -290,7 +290,7 @@ def local_gpu_dimshuffle_0(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.SpecifyShape, gpu_from_host])
def local_gpu_specifyShape_0(node):
"""
specify_shape(host_from_gpu()) -> host_from_gpu(specify_shape)
......@@ -313,7 +313,7 @@ def local_gpu_specifyShape_0(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host]) # XXX: broken: tensor.basic.dot is not an op
def local_gpu_dot_to_dot22(node):
"""
gpu_from_host(dot) -> gpudot(gpu_from_host)
......@@ -376,7 +376,7 @@ def local_gpu_dot_to_dot22(node):
@register_opt()
@local_optimizer([])
@local_optimizer([theano.ifelse.IfElse, gpu_from_host])
def local_gpu_lazy_ifelse(node):
"""
gpu_from_host(ifelse) -> gpu_ifelse(gpu_from_host)
......@@ -434,7 +434,7 @@ def local_gpu_lazy_ifelse(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.blas._dot22])
def local_gpu_dot22(node):
"""
gpu_from_host(dot22) -> gpudot(gpu_from_host)
......@@ -456,7 +456,7 @@ def local_gpu_dot22(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.blas._dot22scalar])
def local_gpu_dot22scalar(node):
"""
gpu_from_host(dot22scalar) -> gpudot(gpu_from_host)
......@@ -482,7 +482,7 @@ def local_gpu_dot22scalar(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.blas_c.CGemv, tensor.blas.Gemv])
def local_gpu_gemv(node):
"""
gpu_from_host(gemv) -> gpu_gemv(gpu_from_host)
......@@ -523,7 +523,8 @@ def local_gpu_gemv(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.blas_c.CGer, tensor.blas.Ger,
tensor.blas_scipy.ScipyGer])
def local_gpu_ger(node):
"""
gpu_from_host(ger) -> gpu_ger(gpu_from_host)
......@@ -566,7 +567,7 @@ def local_gpu_ger(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.blas.gemm_no_inplace, gpu_from_host])
def local_gpu_gemm(node):
"""
gpu_from_host(gemm) -> gpu_gemm(gpu_from_host)
......@@ -601,7 +602,13 @@ def local_gpu_gemm(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.elemwise.CAReduce,
tensor.elemwise.All,
tensor.elemwise.Any,
tensor.elemwise.CAReduceDtype,
tensor.elemwise.Sum,
tensor.elemwise.Prod,
tensor.elemwise.ProdWithoutZeros])
def local_gpu_careduce(node):
if isinstance(node.op, tensor.elemwise.CAReduce):
scalar_op = node.op.scalar_op
......@@ -671,7 +678,7 @@ def local_gpu_careduce(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.Reshape])
def local_gpu_reshape(node):
if node.op == gpu_from_host:
host_input = node.inputs[0]
......@@ -705,7 +712,7 @@ def local_gpu_reshape(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.Flatten])
def local_gpu_flatten(node):
if node.op == gpu_from_host:
host_input = node.inputs[0]
......@@ -724,7 +731,7 @@ def local_gpu_flatten(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.Subtensor])
def local_gpu_subtensor(node):
if node.op == gpu_from_host:
host_input = node.inputs[0]
......@@ -745,7 +752,7 @@ def local_gpu_subtensor(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.AdvancedSubtensor1])
def local_gpu_advanced_subtensor1(node):
if node.op == gpu_from_host:
host_input = node.inputs[0]
......@@ -764,7 +771,7 @@ def local_gpu_advanced_subtensor1(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.AdvancedIncSubtensor1])
def local_gpu_advanced_incsubtensor1(node):
if node.op == gpu_from_host:
host_input = node.inputs[0]
......@@ -838,7 +845,7 @@ def local_gpu_advanced_incsubtensor1(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.IncSubtensor])
def local_gpu_incsubtensor(node):
if node.op == gpu_from_host:
host_output = node.inputs[0]
......@@ -885,7 +892,7 @@ def local_gpu_incsubtensor(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.Shape])
def local_gpu_shape(node):
if isinstance(node.op, tensor.Shape):
x, = node.inputs
......@@ -896,7 +903,7 @@ def local_gpu_shape(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.Rebroadcast])
def local_gpu_rebroadcast(node):
'''rebroadcast(host_from_gpu(x)) -> host_from_gpu(rebroadcast(x))'''
if isinstance(node.op, tensor.Rebroadcast):
......@@ -911,7 +918,7 @@ def gpu_print_wrapper(op, cnda):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.printing.Print])
def local_gpu_print_op(node):
if isinstance(node.op, tensor.printing.Print):
x, = node.inputs
......@@ -932,7 +939,7 @@ import theano.tensor.nnet
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias])
def local_gpu_crossentorpy_softmax_argmax_1hot_with_bias(node):
if isinstance(node.op, tensor.nnet.CrossentropySoftmaxArgmax1HotWithBias):
x, b, y = node.inputs
......@@ -962,7 +969,7 @@ def local_gpu_crossentorpy_softmax_argmax_1hot_with_bias(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.nnet.CrossentropySoftmax1HotWithBiasDx])
def local_gpu_crossentorpy_softmax_1hot_with_bias_dx(node):
if isinstance(node.op, tensor.nnet.CrossentropySoftmax1HotWithBiasDx):
dnll, sm, yidx = node.inputs
......@@ -977,7 +984,7 @@ def local_gpu_crossentorpy_softmax_1hot_with_bias_dx(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.nnet.Softmax])
def local_gpu_softmax(node):
if isinstance(node.op, tensor.nnet.Softmax):
x, = node.inputs
......@@ -989,7 +996,7 @@ def local_gpu_softmax(node):
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.nnet.SoftmaxWithBias])
def local_gpu_softmax_with_bias(node):
if isinstance(node.op, tensor.nnet.SoftmaxWithBias):
x, b = node.inputs
......@@ -1005,7 +1012,7 @@ from theano.tensor.nnet import conv
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, conv.ConvOp])
def local_gpu_conv(node):
"""
gpu_from_host(conv) -> gpu_conv(gpu_from_host)
......@@ -1105,7 +1112,7 @@ import theano.tensor.signal.downsample as downsample
@register_opt()
@local_optimizer([])
@local_optimizer([downsample.DownsampleFactorMax])
def local_gpu_downsample_factor_max(node):
if isinstance(node.op, downsample.DownsampleFactorMax):
x, = node.inputs
......@@ -1115,7 +1122,7 @@ def local_gpu_downsample_factor_max(node):
@register_opt()
@local_optimizer([])
@local_optimizer([downsample.DownsampleFactorMaxGrad])
def local_gpu_downsample_factor_max_grad(node):
if isinstance(node.op, downsample.DownsampleFactorMaxGrad):
x, z, gz = node.inputs
......@@ -1127,11 +1134,11 @@ def local_gpu_downsample_factor_max_grad(node):
gpu_from_host(gz)))]
from theano.sandbox.cuda.basic_ops import gpu_join
from theano.sandbox.cuda.basic_ops import gpu_join, GpuJoin
@register_opt()
@local_optimizer([])
@local_optimizer([tensor.Join])
def local_gpu_join(node):
"""
Inspired by the opt for convop.
......@@ -1188,6 +1195,14 @@ def local_gpu_join(node):
return [replacement_node]
# This is a copy of the same opt in tensor to make the tests happy,
# but I'm not convinced it is actually needed.
@register_opt()
@local_optimizer([GpuJoin])
def local_gpujoin_1(node):
tensors = node.inputs[1:]
if len(tensors) == 1:
return [tensors[0]]
# Commented out because it can result in
# shared = dimshuffle(gemm_inplace(dimshuffle(shared)))
......@@ -1205,7 +1220,7 @@ def local_inplace_gemv(node):
return [gpu_gemv_inplace(*node.inputs)]
@local_optimizer([gpu_gemm_no_inplace])
@local_optimizer([gpu_ger_no_inplace])
def local_inplace_ger(node):
if node.op == gpu_ger_no_inplace:
return [gpu_ger_inplace(*node.inputs)]
......@@ -1336,7 +1351,7 @@ optdb.register('gpu_inplace_elemwise_opt', gpu_inplace_elemwise_optimizer, 75,
@register_opt()
@local_optimizer([tensor.Alloc])
@local_optimizer([tensor.alloc])
def local_gpualloc(node):
replace = False
if node.op == tensor.alloc:
......@@ -1383,7 +1398,7 @@ def local_gpualloc(node):
@register_opt()
@local_optimizer([tensor.Alloc])
@local_optimizer([GpuAlloc])
def local_gpualloc_memset_0(node):
if isinstance(node.op, GpuAlloc) and not node.op.memset_0:
inp = node.inputs[0]
......@@ -1395,7 +1410,7 @@ def local_gpualloc_memset_0(node):
@register_opt()
@local_optimizer([])
@local_optimizer([gpu_from_host, tensor.Eye])
def local_gpu_eye(node):
"""
gpu_from_host(eye) -> gpueye(gpu_from_host)
......@@ -1479,7 +1494,7 @@ def tensor_to_cuda(x):
@register_opt()
@local_optimizer([])
@local_optimizer(None) # XXX: linalg is in sandbox, so don't import it globally
def local_gpu_extract_diagonal(node):
"""
extract_diagonal(host_from_gpu()) -> host_from_gpu(extract_diagonal)
......@@ -1505,7 +1520,7 @@ def local_gpu_extract_diagonal(node):
@register_opt('scan')
@local_optimizer([])
@local_optimizer([gpu_from_host, scan_op.Scan])
def gpuScanOptimization(node):
"""
scan(host_from_gpu) -> host_from_gpu(GPUscan)
......
theano/sandbox/cuda/rng_curand.py
浏览文件 @ b5af3406
......@@ -346,7 +346,7 @@ class CURAND_RandomStreams(object):
return rval
@local_optimizer([None])
@local_optimizer([CURAND_Base])
def local_destructive(node):
op = node.op
if isinstance(op, CURAND_Base) and not op.destructive:
......
theano/sandbox/gpuarray/opt.py
浏览文件 @ b5af3406
......@@ -112,7 +112,7 @@ gpu_seqopt.register('InputToGpuArrayOptimizer', InputToGpuOptimizer(),
0, 'fast_run', 'fast_compile', 'merge')
@local_optimizer([])
@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]]
......
theano/sandbox/linalg/ops.py
浏览文件 @ b5af3406
......@@ -72,7 +72,7 @@ def hints(variable):
@register_canonicalize
@local_optimizer([])
@local_optimizer([Hint])
def remove_hint_nodes(node):
if is_hint_node(node):
# transfer hints from graph to Feature
......@@ -224,7 +224,7 @@ def is_positive(v):
@register_stabilize
@local_optimizer([])
@local_optimizer([Dot, Dot22])
def inv_as_solve(node):
if not imported_scipy:
return False
......@@ -242,7 +242,7 @@ def inv_as_solve(node):
@register_canonicalize
@register_stabilize
@register_specialize
@local_optimizer([])
@local_optimizer([DimShuffle])
def no_transpose_symmetric(node):
if isinstance(node.op, DimShuffle):
x = node.inputs[0]
......@@ -253,7 +253,7 @@ def no_transpose_symmetric(node):
@register_stabilize
@local_optimizer([])
@local_optimizer(None) # XXX: solve is defined later and can't be used here
def psd_solve_with_chol(node):
if node.op == solve:
A, b = node.inputs # result is solution Ax=b
......@@ -269,7 +269,7 @@ def psd_solve_with_chol(node):
@register_stabilize
@register_specialize
@local_optimizer([])
@local_optimizer(None) # XXX: det is defined later and can't be used here
def local_det_chol(node):
"""
If we have det(X) and there is already an L=cholesky(X)
......@@ -287,7 +287,7 @@ def local_det_chol(node):
@register_canonicalize
@register_stabilize
@register_specialize
@local_optimizer([])
@local_optimizer([tensor.log])
def local_log_prod_sqr(node):
if node.op == tensor.log:
x, = node.inputs
......@@ -307,7 +307,7 @@ def local_log_prod_sqr(node):
@register_canonicalize
@register_stabilize
@register_specialize
@local_optimizer([])
@local_optimizer([tensor.log])
def local_log_pow(node):
if node.op == tensor.log:
x, = node.inputs
......
theano/sandbox/multinomial.py
浏览文件 @ b5af3406
......@@ -337,7 +337,7 @@ class GpuMultinomialFromUniform(MultinomialFromUniform, GpuOp):
""" % locals()
@local_optimizer()
@local_optimizer([MultinomialFromUniform])
def local_gpu_multinomial(node):
if type(node.op) is MultinomialFromUniform:
p, u = node.inputs
......
theano/sandbox/rng_mrg.py
浏览文件 @ b5af3406
......@@ -941,7 +941,7 @@ class MRG_RandomStreams(object):
return final_samples
@local_optimizer([None])
@local_optimizer([mrg_uniform])
def mrg_random_make_inplace(node):
op = node.op
if isinstance(op, mrg_uniform) and not op.inplace:
......
theano/scan_module/scan_opt.py
浏览文件 @ b5af3406
......@@ -49,7 +49,7 @@ def info(*msg):
_logger.info('INFO theano.scan: ' + ' '.join(msg))
@gof.local_optimizer([None])
@gof.local_optimizer([scan_op.Scan])
def remove_constants_and_unused_inputs_scan(node):
'''
Move constants into the inner graph, and remove unused inputs.
......@@ -1337,7 +1337,7 @@ def make_equiv(lo, li):
return left, right
@gof.local_optimizer([None])
@gof.local_optimizer([scan_op.Scan])
def scan_merge_inouts(node):
if not isinstance(node.op, scan_op.Scan):
return False
......
theano/sparse/opt.py
浏览文件 @ b5af3406
......@@ -32,7 +32,7 @@ sparse.register_specialize(local_csm_properties_csm)
# This is tested in tests/test_basic.py:test_remove0
@gof.local_optimizer([None])
@gof.local_optimizer([sparse.Remove0])
def local_inplace_remove0(node):
"""
Optimization to insert inplace versions of Remove0.
......@@ -49,7 +49,7 @@ theano.compile.optdb.register('local_inplace_remove0',
gof.TopoOptimizer(local_inplace_remove0,
failure_callback=gof.TopoOptimizer.warn_inplace),
60, 'fast_run', 'inplace')
@gof.local_optimizer([None])
@gof.local_optimizer([sparse.AddSD])
def local_inplace_addsd(node):
"""
Optimization to insert inplace versions of AddSD.
......
theano/tensor/blas.py
浏览文件 @ b5af3406
......@@ -1645,7 +1645,7 @@ class Dot22(GemmRelated):
_dot22 = Dot22()
@local_optimizer([T._dot])
@local_optimizer([T.Dot])
def local_dot_to_dot22(node):
# This works for tensor.outer too because basic.outer is a macro that
# produces a dot(dimshuffle,dimshuffle) of form 4 below
......@@ -2025,7 +2025,7 @@ blas_optdb.register('local_dot22_to_dot22scalar',
#from opt import register_specialize, register_canonicalize
#@register_specialize
@local_optimizer([])
@local_optimizer([T.sub, T.add])
def local_print_as_we_go_along(node):
if node.op in (T.sub, T.add):
debugprint(node)
theano/tensor/nnet/conv3d2d.py
浏览文件 @ b5af3406
......@@ -266,7 +266,7 @@ def make_gpu_optimizer(op, to_gpu):
:param to_gpu: a list of op inputs that are moved to the GPU.
"""
@theano.gof.local_optimizer([])
@theano.gof.local_optimizer([op, cuda.gpu_from_host])
def local_to_gpu(node):
"""
op(host_from_gpu()) -> host_from_gpu(op)
......@@ -302,7 +302,7 @@ if cuda.cuda_available:
make_gpu_optimizer(IncDiagonalSubtensor, [0, 3])
@theano.gof.local_optimizer([None])
@theano.gof.local_optimizer([DiagonalSubtensor, IncDiagonalSubtensor])
def local_inplace_DiagonalSubtensor(node):
""" also work for IncDiagonalSubtensor """
if (isinstance(node.op, (DiagonalSubtensor, IncDiagonalSubtensor)) and
......
theano/tensor/nnet/nnet.py
浏览文件 @ b5af3406
......@@ -589,7 +589,7 @@ opt.local_mul_canonizer.add_simplifier(softmax_simplifier,
if 0:
@opt.register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([tensor.add])
def local_softmax_grad(node):
'''dy*sm - DimShuffle{0,'x'}(sum{1}(dy*sm))*sm -> softmax_grad(dy,sm)'''
#TODO what if the signs are changed?
......@@ -1417,7 +1417,7 @@ def _is_const(z, val, approx=False):
@opt.register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([subtensor.AdvancedSubtensor, tensor.log])
def local_advanced_indexing_crossentropy_onehot(node):
log = None
sm = None
......
theano/tensor/opt.py
浏览文件 @ b5af3406
......@@ -347,7 +347,7 @@ compile.optdb['canonicalize'].register(
@register_canonicalize
@register_stabilize
@gof.local_optimizer([None])
@gof.local_optimizer([T.Dot])
def local_0_dot_x(node):
if not isinstance(node.op, T.Dot):
return False
......@@ -390,7 +390,7 @@ def local_0_dot_x(node):
######################
@gof.local_optimizer([None, None])
@gof.local_optimizer([DimShuffle])
def local_dimshuffle_lift(node):
"""
"Lifts" DimShuffle through Elemwise operations and merges
......@@ -431,7 +431,7 @@ def local_dimshuffle_lift(node):
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.DimShuffle])
def local_lift_transpose_through_dot(node):
"""
dot(x,y).T -> dot(y.T, x.T)
......@@ -456,7 +456,7 @@ def local_lift_transpose_through_dot(node):
return [T.dot(y.T, x.T)]
@gof.local_optimizer([])
@gof.local_optimizer([DimShuffle])
def dimshuffle_as_view(node):
op = node.op
if not isinstance(op, DimShuffle) or op.inplace:
......@@ -476,7 +476,7 @@ register_specialize(local_dimshuffle_lift)
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.DimShuffle])
def local_dimshuffle_no_inplace_at_canonicalize(node):
if isinstance(node.op, T.DimShuffle) and node.op.inplace:
return [T.DimShuffle(node.op.input_broadcastable,
......@@ -1211,9 +1211,9 @@ def local_useless_alloc(node):
@register_specialize
@register_canonicalize
@gof.local_optimizer([T._shape])
@gof.local_optimizer([T.shape])
def local_shape_to_shape_i(node):
if node.op == T._shape:
if node.op == T.shape:
# This optimization needs ShapeOpt and fgraph.shape_feature
if not hasattr(node.fgraph, 'shape_feature'):
return
......@@ -1221,9 +1221,10 @@ def local_shape_to_shape_i(node):
return [shape_feature.make_vector_shape(node.inputs[0])]
# TODO: Not sure what type of node we are expecting here
@register_specialize
@register_canonicalize
@gof.local_optimizer([T._shape])
@gof.local_optimizer(None)
def local_track_shape_i(node):
try:
shape_feature = node.fgraph.shape_feature
......@@ -1423,7 +1424,7 @@ def local_remove_useless_assert(node):
return [assert_(node.inputs[0], *cond)]
@gof.local_optimizer([T.Alloc])
@gof.local_optimizer([T.Elemwise])
def local_alloc_elemwise(node):
"""
elemwise(alloc(x, shp), ..., y.TensorType(BROADCAST CONDITION))
......@@ -1542,7 +1543,7 @@ else:
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.Elemwise])
def local_upcast_elemwise_constant_inputs(node):
"""This explicitly upcasts constant inputs to elemwise Ops, when
those Ops do implicit upcasting anyway.
......@@ -1690,7 +1691,7 @@ def local_useless_subtensor(node):
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([Subtensor])
def local_subtensor_lift(node):
"""
unary(x)[idx] -> unary(x[idx])#any broadcast pattern.
......@@ -1900,7 +1901,7 @@ def merge_two_slices(slice1, len1, slice2, len2):
@register_canonicalize
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([Subtensor])
def local_subtensor_merge(node):
"""
Refactored optimization to deal with all cases of tensor merging.
......@@ -1962,7 +1963,7 @@ def local_subtensor_merge(node):
@register_canonicalize
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([Subtensor])
def local_subtensor_of_alloc(node):
"""alloc[x:y] -> alloc"""
if not isinstance(node.op, Subtensor):
......@@ -2015,7 +2016,7 @@ def local_subtensor_of_alloc(node):
@register_canonicalize
@gof.local_optimizer([None])
@gof.local_optimizer([T.add])
def local_IncSubtensor_serialize(node):
"""
When using Subtensor, gradient graphs can be ugly.
......@@ -2087,7 +2088,7 @@ compile.optdb.register('pre_local_IncSubtensor_serialize',
#after priority 50 Destructive inplace operations
#gemm is the first one now, at priority 70
@gof.local_optimizer([None])
@gof.local_optimizer([IncSubtensor]) # XXX: GPU
def local_inplace_setsubtensor(node):
"""
Also work for GpuIncSubtensor
......@@ -2106,7 +2107,7 @@ compile.optdb.register('local_inplace_setsubtensor',
'fast_run', 'inplace') # DEBUG
@gof.local_optimizer([None])
@gof.local_optimizer([AdvancedIncSubtensor1]) # XXX: GPU
def local_inplace_incsubtensor1(node):
""" also work for GpuAdvancedIncSubtensor1 """
if isinstance(node.op, AdvancedIncSubtensor1) and not node.op.inplace:
......@@ -2124,7 +2125,7 @@ compile.optdb.register('local_inplace_incsubtensor1',
@register_canonicalize
@register_stabilize
@gof.local_optimizer([None])
@gof.local_optimizer([IncSubtensor])
def local_incsubtensor_of_allocs(node):
"""
IncSubtensor(x, zeros, idx) -> x
......@@ -2147,7 +2148,7 @@ def local_incsubtensor_of_allocs(node):
@register_canonicalize
@register_stabilize
@gof.local_optimizer([None])
@gof.local_optimizer([IncSubtensor])
def local_setsubtensor_of_allocs(node):
"""
SetSubtensor(x, x[idx], idx) -> x
......@@ -2294,7 +2295,7 @@ def local_join_1(node):
###############
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.Elemwise])
def local_remove_switch_const_cond(node):
"""
This optimization makes the following changes in the graph:
......@@ -2377,7 +2378,7 @@ def local_mul_switch_sink(node):
@register_canonicalize
@gof.local_optimizer([T.true_div])
@gof.local_optimizer([T.true_div, T.int_div, T.floor_div])
def local_div_switch_sink(node):
"""
This optimization makes the folowing changes in the graph:
......@@ -2421,7 +2422,7 @@ def local_div_switch_sink(node):
################
@register_canonicalize
@register_stabilize
@gof.local_optimizer([])
@gof.local_optimizer([T.Flatten])
def local_flatten_lift(node):
"""
Flatten(UnaryElemwise(x)) -> UnaryElemwise(Flatten(x))
......@@ -2442,7 +2443,7 @@ def local_flatten_lift(node):
##################
@gof.local_optimizer([None, None])
@gof.local_optimizer([T.Reshape])
def local_reshape_chain(node):
"""
Reshape(Reshape(shape1),shape2) -> Reshape(shape2)
......@@ -2470,7 +2471,7 @@ register_canonicalize(local_reshape_chain)
@register_canonicalize
@register_stabilize
@gof.local_optimizer([])
@gof.local_optimizer([T.Reshape])
def local_reshape_lift(node):
"""
Reshape(UnaryElemwise(x)) -> UnaryElemwise(Reshape(x))
......@@ -2490,7 +2491,7 @@ def local_reshape_lift(node):
if 0:
# TODO: Test that this optimziation works.
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.Reshape])
def local_scalar_reshape(node):
"""Eliminate reshape Ops whose inputs and outputs are scalars """
if isinstance(node.op, T.Reshape):
......@@ -2506,7 +2507,7 @@ if 0:
# TODO: Remember to take into account the new sum dtype argument if this
# optimization is enabled.
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.Sum])
def local_sum_over_empty(node):
if isinstance(node.op, T.Sum):
# This optimization needs ShapeOpt and fgraph.shape_feature
......@@ -2528,7 +2529,7 @@ if 0:
##################
@gof.local_optimizer([None, T.fill])
@gof.local_optimizer([T.Elemwise])
def local_fill_cut(node):
"""
f(fill(a,b), c) -> f(b, c)
......@@ -2582,7 +2583,7 @@ register_canonicalize(local_fill_cut)
register_canonicalize(gof.OpRemove(T.tensor_copy), name='remove_tensor_copy')
@gof.local_optimizer([None, T.fill])
@gof.local_optimizer([T.Elemwise])
def local_fill_sink(node):
"""
f(fill(a, b), fill(c, d), e) -> fill(a, fill(c, f(b, d, e)))
......@@ -2670,8 +2671,7 @@ class Canonizer(gof.LocalOptimizer):
self.external_simplifiers.append((reason, simplifier))
def tracks(self):
return [[self.main, None], [self.inverse, None],
[self.reciprocal, None]]
return [self.main, self.inverse, self.reciprocal]
def get_num_denum(self, input):
"""
......@@ -3059,7 +3059,7 @@ register_canonicalize(local_neg_to_mul)
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([T.Sum])
def local_sum_mul_by_scalar(node):
"""sum(scalar * smth) -> scalar * sum(smth)
sum(-smth) -> -sum(smth)
......@@ -3096,7 +3096,7 @@ def local_sum_mul_by_scalar(node):
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([T.Elemwise])
def local_elemwise_sub_zeros(node):
"""
Elemwise{sub}(X,X) -> zeros_like(X)
......@@ -3110,7 +3110,7 @@ def local_elemwise_sub_zeros(node):
@register_canonicalize
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([T.Sum])
def local_sum_div_dimshuffle(node):
'''sum(a / dimshuffle{...}(b), axis=l) -> sum(a, axis={...}) / b,
if dimension l of the DimShuffle is 'x'.'''
......@@ -3199,7 +3199,7 @@ def local_sum_div_dimshuffle(node):
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.Sum])
def local_sum_all_to_none(node):
"""Sum{0,1,...N} -> Sum{}"""
if isinstance(node.op, T.Sum):
......@@ -3212,7 +3212,7 @@ def local_sum_all_to_none(node):
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer([T.Sum])
def local_sum_sum(node):
"""
Sum(Sum()) -> Sum
......@@ -3278,9 +3278,12 @@ def local_sum_sum(node):
combined_sum = T.Sum(newaxis, dtype=out_dtype)
return [combined_sum(summed.owner.inputs[0])]
ALL_REDUCE = [T.elemwise.CAReduce, T.elemwise.All, T.elemwise.Any,
T.elemwise.Sum, T.elemwise.Prod,
T.elemwise.ProdWithoutZeros]
@register_canonicalize
@gof.local_optimizer([])
@gof.local_optimizer(ALL_REDUCE)
def local_cut_useless_reduce(node):
"""Sum(a, axis=[]) -> a """
if isinstance(node.op, T.CAReduce):
......@@ -3296,7 +3299,7 @@ def local_cut_useless_reduce(node):
#
#@register_canonicalize
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer(ALL_REDUCE)
def local_reduce_broadcastable(node):
"""Remove reduction over broadcastable dimensions"""
if isinstance(node.op, T.CAReduce):
......@@ -3335,7 +3338,7 @@ def local_reduce_broadcastable(node):
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([T.Sum])
def local_sum_alloc(node):
""" sum(alloc(constant,shapes...)) => constant*prod(shapes)"""
if isinstance(node.op, T.Sum):
......@@ -3742,7 +3745,7 @@ def local_abs_lift(node):
@register_specialize
@gof.local_optimizer([])
@gof.local_optimizer([T.mul, T.true_div])
def local_abs_merge(node):
"""
merge abs generated by local_abs_lift when the canonizer don't
......@@ -3917,8 +3920,7 @@ def attempt_distribution(factor, num, denum):
neg_pairs))), num, denum
@gof.local_optimizer([T.mul, T.add, T.mul], [T.mul, T.sub, T.mul],
[T.mul, T.add, T.true_div], [T.mul, T.sub, T.true_div])
@gof.local_optimizer([T.mul])
def local_greedy_distributor(node):
"""
This optimization tries to apply distributivity of multiplication
......@@ -3984,7 +3986,7 @@ register_canonicalize(local_greedy_distributor)
register_stabilize(local_greedy_distributor)
@gof.local_optimizer([None])
@gof.local_optimizer(None)
def constant_folding(node):
for input in node.inputs:
if not isinstance(input, Constant):
......
theano/tensor/opt_uncanonicalize.py
浏览文件 @ b5af3406
......@@ -55,7 +55,7 @@ def local_max_and_argmax(node):
return [new, None]
@register_uncanonicalize
@gof.local_optimizer([T._shape])
@gof.local_optimizer([T.neg])
def local_max_to_min(node):
"""
change -(max(-x)) to min
......
theano/tensor/raw_random.py
浏览文件 @ b5af3406
......@@ -816,7 +816,7 @@ def multinomial(random_state, size=None, n=1, pvals=[0.5, 0.5],
return op(random_state, size, n, pvals)
@gof.local_optimizer([None])
@gof.local_optimizer([RandomFunction])
def random_make_inplace(node):
op = node.op
if isinstance(op, RandomFunction) and not op.inplace:
......
theano/tensor/tests/test_basic.py
浏览文件 @ b5af3406
......@@ -3361,10 +3361,8 @@ class T_Join_and_Split(unittest.TestCase):
utt.verify_grad((lambda a, b: join(0, a, b)), [a_val, b_val], rng=rng)
def test_broadcastable_single_input_broadcastable_dimension(self):
"""
Test that all broadcastable flags are preserved by a
single-input join.
"""
# Test that all broadcastable flags are preserved by a
# single-input join.
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a_val = rng.rand(1, 4, 1).astype(self.floatX)
a = self.shared(a_val, broadcastable=(True, False, True))
......@@ -3387,10 +3385,8 @@ class T_Join_and_Split(unittest.TestCase):
#self.assertRaises(TypeError, f, bad_a_val)
def test_broadcastable_flags_many_dims_and_inputs(self):
"""
Test that the right broadcastable flags get set for a join
with many inputs and many input dimensions.
"""
# Test that the right broadcastable flags get set for a join
# with many inputs and many input dimensions.
a = TensorType(dtype=self.floatX, broadcastable=[1, 0, 1, 0, 0, 0])()
b = TensorType(dtype=self.floatX, broadcastable=[1, 1, 1, 0, 0, 0])()
c = TensorType(dtype=self.floatX, broadcastable=[1, 0, 0, 0, 0, 0])()
......@@ -3479,20 +3475,16 @@ class T_Join_and_Split(unittest.TestCase):
f(get_mat(3, 4), get_mat(3, 4), get_mat(2, 5))
def test_rebroadcast(self):
"""
Regression test for a crash that used to happen when rebroadcasting.
"""
# Regression test for a crash that used to happen when rebroadcasting.
x = tensor.TensorType(self.floatX, [False, False, True])()
u = tensor.TensorType(self.floatX, [False, False, True])()
# This line used to crash.
z = tensor.concatenate([x, -u], axis=2)
def test_concatenate_same(self):
"""
Test that we can concatenate the same tensor multiple time.
# Test that we can concatenate the same tensor multiple time.
In the past it was broken on the GPU.
"""
# In the past it was broken on the GPU.
rng = numpy.random.RandomState(seed=utt.fetch_seed())
T_shared = self.shared(rng.rand(3, 4).astype(self.floatX))
Tout = tensor.concatenate([T_shared, T_shared])
......
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