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提交 950634de authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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Deployed optdb

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正在显示 包含 249 行增加 和 103 行删除
examples/logistic_regression.py
浏览文件 @ 950634de
......@@ -4,6 +4,7 @@ import theano
from theano import tensor as T
from theano.sandbox import nnet_ops
from theano.sandbox import module
from theano.sandbox import pprint
import numpy as N
......@@ -32,6 +33,10 @@ class LogisticRegressionN(module.FancyModule):
xent, y = nnet_ops.crossentropy_softmax_1hot(
T.dot(self.x, self.w) + self.b, self.targ)
xent = T.sum(xent)
self.y = y
self.xent = xent
gparams = T.grad(xent, self.params)
......@@ -75,15 +80,25 @@ class LogisticRegression2(module.FancyModule):
self.update = module.Method([self.x, self.targ], xent,
updates = dict((p, p - self.lr * g) for p, g in zip(self.params, gparams)))
self.apply = module.Method([self.x], T.argmax(T.dot(self.x, self.w) + self.b, axis=1))
if __name__ == '__main__':
pprint.pp.assign(nnet_ops.crossentropy_softmax_1hot_with_bias_dx, pprint.FunctionPrinter('xsoftmaxdx'))
pprint.pp.assign(nnet_ops.crossentropy_softmax_argmax_1hot_with_bias, pprint.FunctionPrinter('nll', 'softmax', 'argmax'))
if 1:
lrc = LogisticRegressionN()
#lr = lrc.make(10, 2, mode='FAST_RUN')
lr = lrc.make(10, 2, mode=theano.Mode('c|py', 'merge')) #'FAST_RUN')
print '================'
print lrc.update.pretty()
print '================'
print lrc.update.pretty(mode = theano.Mode('py', 'fast_run'))
print '================'
# sys.exit(0)
lr = lrc.make(10, 2, mode=theano.Mode('py', 'fast_run'))
#lr = lrc.make(10, 2, mode=theano.Mode('py', 'merge')) #'FAST_RUN')
data_x = N.random.randn(5, 10)
data_y = (N.random.randn(5) > 0)
......
theano/compile/function_module.py
浏览文件 @ 950634de
......@@ -53,7 +53,7 @@ class Supervisor:
return True
for r in self.protected + list(env.outputs):
if env.destroyers(r):
raise gof.InconsistencyError("Trying to destroy a protected Result.")
raise gof.InconsistencyError("Trying to destroy a protected Result.", r)
def std_env(input_specs, output_specs, accept_inplace = False):
......@@ -88,7 +88,7 @@ def std_env(input_specs, output_specs, accept_inplace = False):
break
# We need to protect all immutable inputs from inplace operations.
env.extend(Supervisor(input for spec, input in zip(input_specs, inputs) if not spec.mutable))
env.extend(Supervisor(input for spec, input in zip(input_specs, inputs) if not (spec.mutable or (hasattr(env, 'destroyers') and env.destroyers(input)))))
return env, map(SymbolicOutput, updates)
......
theano/compile/mode.py
浏览文件 @ 950634de
......@@ -16,6 +16,9 @@ def check_equal(x, y):
raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y})
# If a string is passed as the linker argument in the constructor for
# Mode, it will be used as the key to retrieve the real linker in this
# dictionary
......@@ -35,12 +38,22 @@ def register_linker(name, linker):
predefined_linkers[name] = linker
# If a string is passed as the optimizer argument in the constructor
# for Mode, it will be used as the key to retrieve the real optimizer
# in this dictionary
OPT_FAST_RUN = gof.Query(include = ['fast_run'])
OPT_FAST_RUN_STABLE = OPT_FAST_RUN.requiring('stable')
OPT_FAST_COMPILE = gof.Query(include = ['fast_compile'])
predefined_optimizers = {
None : lambda env: None,
'merge' : gof.MergeOptimizer(),
'fast_run' : OPT_FAST_RUN,
'fast_run_stable' : OPT_FAST_RUN_STABLE,
'fast_compile' : OPT_FAST_COMPILE
}
default_optimizer = 'merge'
......@@ -50,6 +63,12 @@ def register_optimizer(name, opt):
raise ValueError('Optimizer name already taken: %s' % name)
predefined_optimizers[name] = opt
optdb = gof.SequenceDB()
optdb.register('merge1', gof.MergeOptimizer(), 0, 'fast_run', 'fast_compile')
optdb.register('canonicalize', gof.EquilibriumDB(), 1, 'fast_run')
optdb.register('specialize', gof.EquilibriumDB(), 2, 'fast_run')
optdb.register('merge2', gof.EquilibriumDB(), 100, 'fast_run')
class Mode(object):
"""
......@@ -81,15 +100,32 @@ class Mode(object):
self.linker = linker
if isinstance(optimizer, str) or optimizer is None:
optimizer = predefined_optimizers[optimizer]
if isinstance(optimizer, gof.Query):
self.provided_optimizer = optimizer
optimizer = optdb.query(optimizer)
self.optimizer = optimizer
def __str__(self):
return "Mode(linker = %s, optimizer = %s)" % (self.provided_linker, self.provided_optimizer)
def including(self, *tags):
return Mode(self.provided_linker, self.provided_optimizer.including(*tags))
def excluding(self, *tags):
return Mode(self.provided_linker, self.provided_optimizer.excluding(*tags))
def requiring(self, *tags):
return Mode(self.provided_linker, self.provided_optimizer.requiring(*tags))
# If a string is passed as the mode argument in function or
# FunctionMaker, the Mode will be taken from this dictionary using the
# string as the key
predefined_modes = {'FAST_COMPILE': Mode('py', 'merge')}
FAST_COMPILE = Mode('py', 'fast_compile')
FAST_RUN = Mode('c|py', 'fast_run')
predefined_modes = {'FAST_COMPILE': FAST_COMPILE,
'FAST_RUN': FAST_RUN}
default_mode = 'FAST_COMPILE'
def register_mode(name, mode):
......
theano/sandbox/module.py
浏览文件 @ 950634de
......@@ -70,7 +70,7 @@ class Component(object):
def __str__(self):
return self.__class__.__name__
def pretty(self):
def pretty(self, **kwargs):
raise NotImplementedError
def __get_name__(self):
......@@ -99,7 +99,7 @@ class _RComponent(Component):
def __str__(self):
return "%s(%s)" % (self.__class__.__name__, self.r)
def pretty(self):
def pretty(self, **kwargs):
rval = '%s :: %s' % (self.__class__.__name__, self.r.type)
return rval
......@@ -114,7 +114,7 @@ class External(_RComponent):
def build(self, mode, memo):
return None
def pretty(self):
def pretty(self, **kwargs):
rval = super(External, self).pretty()
if self.r.owner:
rval += '\n= %s' % (pprint.pp2.process(self.r, dict(target = self.r)))
......@@ -216,7 +216,7 @@ class Method(Component):
inputs += [(kit, get_storage(kit, True)) for kit in self.kits]
return compile.function(inputs, outputs, mode)
def pretty(self, header = True, **kwargs):
def pretty(self, **kwargs):
self.resolve_all()
# cr = '\n ' if header else '\n'
# rval = ''
......@@ -226,7 +226,20 @@ class Method(Component):
rval = 'inputs: %s\n' % ", ".join(map(str, self.inputs))
else:
rval = ''
rval += pprint.pp.process_graph(self.inputs, self.outputs, self.updates, False)
mode = kwargs.pop('mode', None)
inputs, outputs, updates = self.inputs, self.outputs if isinstance(self.outputs, (list, tuple)) else [self.outputs], self.updates
if mode:
nin = len(inputs)
nout = len(outputs)
k, v = zip(*updates.items()) if updates else ((), ())
nup = len(k)
eff_in = tuple(inputs) + tuple(k)
eff_out = tuple(outputs) + tuple(v)
env = gof.Env(*gof.graph.clone(eff_in + tuple(gof.graph.inputs(eff_out)),
eff_out))
mode.optimizer.optimize(env)
inputs, outputs, updates = env.inputs[:nin], env.outputs[:nout], dict(zip(env.inputs[nin:], env.outputs[nout:]))
rval += pprint.pp.process_graph(inputs, outputs, updates, False)
return rval
def __str__(self):
......@@ -395,13 +408,13 @@ class ComponentList(Composite):
def __str__(self):
return str(self._components)
def pretty(self, header = True, **kwargs):
def pretty(self, **kwargs):
cr = '\n ' #if header else '\n'
strings = []
#if header:
# rval += "ComponentList:"
for i, c in self.components_map():
strings.append('%i:%s%s' % (i, cr, c.pretty().replace('\n', cr)))
strings.append('%i:%s%s' % (i, cr, c.pretty(**kwargs).replace('\n', cr)))
#rval += cr + '%i -> %s' % (i, c.pretty(header = True, **kwargs).replace('\n', cr))
return '\n'.join(strings)
......@@ -469,7 +482,7 @@ class Module(Composite):
value.bind(self, item)
self._components[item] = value
def pretty(self, header = True, **kwargs):
def pretty(self, **kwargs):
cr = '\n ' #if header else '\n'
strings = []
# if header:
......@@ -477,7 +490,7 @@ class Module(Composite):
for name, component in self.components_map():
if name.startswith('_'):
continue
strings.append('%s:%s%s' % (name, cr, component.pretty().replace('\n', cr)))
strings.append('%s:%s%s' % (name, cr, component.pretty(**kwargs).replace('\n', cr)))
strings.sort()
return '\n'.join(strings)
......
theano/sandbox/nnet_ops.py
浏览文件 @ 950634de
......@@ -416,7 +416,8 @@ class CrossentropySoftmaxArgmax1HotWithBias(theano.Op):
if g_sm is not None or g_am is not None:
raise NotImplementedError()
nll, sm = crossentropy_softmax_1hot_with_bias(x, b, y_idx)
dx = CrossentropySoftmax1HotWithBiasDx()(g_nll, sm, y_idx)
#dx = CrossentropySoftmax1HotWithBiasDx()(g_nll, sm, y_idx)
dx = crossentropy_softmax_1hot_with_bias_dx(g_nll, sm, y_idx)
db = tensor.sum(dx, axis = [0])
return dx, db, None
......@@ -597,6 +598,9 @@ class CrossentropySoftmax1HotWithBiasDx (theano.Op):
crossentropy_softmax_argmax_1hot_with_bias = \
CrossentropySoftmaxArgmax1HotWithBias()
crossentropy_softmax_1hot_with_bias_dx = \
CrossentropySoftmax1HotWithBiasDx()
def crossentropy_softmax_1hot_with_bias(x, b, y_idx, **kwargs):
return crossentropy_softmax_argmax_1hot_with_bias(x, b, y_idx, **kwargs)[0:2]
......
theano/sandbox/pprint.py
浏览文件 @ 950634de
......@@ -88,7 +88,7 @@ class FunctionPrinter:
raise TypeError("function %s cannot represent a result with no associated operation" % self.names)
idx = node.outputs.index(output)
name = self.names[idx]
return "%s(%s)" % (name, ", ".join([pprinter.process(input, pstate.clone(precedence = -1000))
return "%s(%s)" % (name, ", ".join([pprinter.process(input, pstate.clone(precedence = 1000))
for input in node.inputs]))
class MemberPrinter:
......@@ -123,8 +123,8 @@ class DimShufflePrinter:
def __p(self, new_order, pstate, r):
if new_order != () and new_order[0] == 'x':
return "%s" % self.__p(new_order[1:], pstate, r)
# return "[%s]" % self.__p(new_order[1:], pstate, r)
# return "%s" % self.__p(new_order[1:], pstate, r)
return "[%s]" % self.__p(new_order[1:], pstate, r)
if list(new_order) == range(r.type.ndim):
return pstate.pprinter.process(r)
if list(new_order) == list(reversed(range(r.type.ndim))):
......@@ -161,7 +161,7 @@ class SubtensorPrinter:
sidxs.append("%s:%s%s" % ("" if entry.start is None or entry.start == 0 else entry.start,
"" if entry.stop is None or entry.stop == sys.maxint else entry.stop,
"" if entry.step is None else ":%s" % entry.step))
return "%s[%s]" % (pstate.clone(precedence = 1000).pprinter.process(input),
return "%s[%s]" % (pstate.pprinter.process(input, pstate.clone(precedence = 1000)),
", ".join(sidxs))
else:
raise TypeError("Can only print Subtensor.")
......@@ -173,7 +173,7 @@ class MakeVectorPrinter:
if r.owner is None:
raise TypeError("Can only print make_vector.")
elif isinstance(r.owner.op, T.MakeVector):
return "[%s]" % ", ".join(pstate.clone(precedence = 1000).pprinter.process(input) for input in r.owner.inputs)
return "[%s]" % ", ".join(pstate.pprinter.process(input, pstate.clone(precedence = 1000)) for input in r.owner.inputs)
else:
raise TypeError("Can only print make_vector.")
......@@ -311,7 +311,7 @@ def pprinter():
pp.assign(T.shape, MemberPrinter('shape'))
pp.assign(T.fill, FunctionPrinter('fill'))
#pp.assign(T.vertical_stack, FunctionPrinter('vstack'))
#pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.MakeVector), MakeVectorPrinter())
pp.assign(lambda pstate, r: r.owner and isinstance(r.owner.op, T.MakeVector), MakeVectorPrinter())
return pp
pp = pprinter()
......
theano/tensor/basic.py
浏览文件 @ 950634de
......@@ -1198,8 +1198,11 @@ class Subtensor(Op):
return type(self) == type(other) and self.idx_list == other.idx_list
def __hash__(self):
# FIXME: this doesn't work if there are slices in the list because for some mysterious reason slice is unhashable
return hash(tuple(self.idx_list))
idx_list = tuple((entry.start, entry.stop, entry.step)
if isinstance(entry, slice)
else entry
for entry in self.idx_list)
return hash(idx_list)
def __str__(self):
indices = []
......@@ -1605,27 +1608,28 @@ if 0: #vertical and horizontal stacking are deprecated. Better to use stack() a
return gz[:xs[0]], gz[xs[0]:]
vertical_stack = VerticalStack()
class MakeVector(Op):
"""WRITEME"""
def __init__(self, stype):
self.stype = stype
def make_node(self, *inputs):
inputs = map(as_tensor, inputs)
assert all(a.type == self.stype for a in inputs)
return Apply(self, inputs, [Tensor(broadcastable = (False,),
dtype = self.stype.dtype)()])
def perform(self, node, inputs, (out,)):
out[0] = numpy.asarray(inputs)
def grad(self, inputs, (gout,)):
return [None]*len(inputs)
make_lvector = MakeVector(lscalar)
"""WRITEME"""
else:
pass
class MakeVector(Op):
"""WRITEME"""
def __init__(self, stype):
self.stype = stype
def make_node(self, *inputs):
inputs = map(as_tensor, inputs)
assert all(a.type == self.stype for a in inputs)
return Apply(self, inputs, [Tensor(broadcastable = (False,),
dtype = self.stype.dtype)()])
def perform(self, node, inputs, (out,)):
out[0] = numpy.asarray(inputs)
def grad(self, inputs, (gout,)):
return [None]*len(inputs)
make_lvector = MakeVector(lscalar)
"""WRITEME"""
#########################
# Linalg : Dot
#########################
......
theano/tensor/opt.py
浏览文件 @ 950634de
......@@ -87,17 +87,25 @@ def _insert_inplace_optimizer(env):
break
insert_inplace_optimizer = gof.optimizer(_insert_inplace_optimizer)
inplace_optimizer = gof.InplaceOptimizer(
gof.SeqOptimizer(out2in(gemm_pattern_1),
out2in(dot_to_gemm),
insert_inplace_optimizer,
failure_callback = gof.keep_going))
compile.optdb.register('inplace', inplace_optimizer, 99, 'fast_run')
inplace_optimizer = gof.SeqOptimizer(out2in(gemm_pattern_1),
out2in(dot_to_gemm),
insert_inplace_optimizer)
def register_canonicalize(lopt, *tags, **kwargs):
compile.optdb['canonicalize'].register((kwargs and kwargs.pop('name')) or lopt.__name__, lopt, 'fast_run', *tags)
def register_specialize(lopt, *tags, **kwargs):
compile.optdb['specialize'].register((kwargs and kwargs.pop('name')) or lopt.__name__, lopt, 'fast_run', *tags)
######################
# DimShuffle lifters #
######################
@gof.local_optimizer
@gof.local_optimizer([None, None])
def local_dimshuffle_lift(node):
"""
"Lifts" DimShuffle through Elemwise operations and merges
......@@ -129,14 +137,15 @@ def local_dimshuffle_lift(node):
else:
return DimShuffle(iinput.type.broadcastable, new_order, inplace).make_node(iinput).outputs
dimshuffle_lift = out2in(local_dimshuffle_lift)
register_canonicalize(local_dimshuffle_lift)
#################
# Shape lifters #
#################
@gof.local_optimizer
@gof.local_optimizer([T.shape, None])
def local_shape_lift_elemwise(node):
"""
shape(elemwise_op(..., x, ...)) -> shape(x)
......@@ -155,7 +164,10 @@ def local_shape_lift_elemwise(node):
return False
@gof.local_optimizer
register_canonicalize(local_shape_lift_elemwise)
@gof.local_optimizer([T.shape, None])
def local_shape_lift_sum(node):
"""
shape(sum{n}(x)) -> [shape(x)[0], ..., shape(x)[n-1], shape(x)[n+1], ...]
......@@ -173,7 +185,10 @@ def local_shape_lift_sum(node):
return T.make_lvector.make_node(*(ish[i] for i in xrange(input.type.ndim) if i not in axis)).outputs
# return T.vertical_stack.make_node(ish[:axis], ish[axis+1:]).outputs
@gof.local_optimizer
register_canonicalize(local_shape_lift_sum)
@gof.local_optimizer([T.shape, T.dot])
def local_shape_lift_dot(node):
"""
shape(dot(a, b)) -> [shape(a)[0], shape(b)[1]]
......@@ -183,9 +198,12 @@ def local_shape_lift_dot(node):
a, b = node.inputs[0].owner.inputs
return T.make_lvector.make_node(T.shape(a)[0], T.shape(b)[1]).outputs
local_shape_lift = opt.LocalOptGroup(local_shape_lift_elemwise,
local_shape_lift_sum,
local_shape_lift_dot)
register_canonicalize(local_shape_lift_dot)
# local_shape_lift = opt.LocalOptGroup(local_shape_lift_elemwise,
# local_shape_lift_sum,
# local_shape_lift_dot)
################
......@@ -201,7 +219,7 @@ def encompasses_broadcastable(b1, b2):
def merge_broadcastables(broadcastables):
return [all(bcast) for bcast in zip(*broadcastables)]
@gof.local_optimizer
@gof.local_optimizer([T.fill, None])
def local_fill_lift(node):
"""
fill(f(a), b) -> fill(a, b)
......@@ -217,10 +235,10 @@ def local_fill_lift(node):
mb, fb = model.type.broadcastable, filling.type.broadcastable
if model.type.dtype == filling.type.dtype and encompasses_broadcastable(fb, mb):
return [filling]
return False# [filling]
parent = model.owner
if parent is None:
if parent is None or not isinstance(parent, T.Elemwise):
return False
for input in parent.inputs:
if input.type == model.type:
......@@ -228,13 +246,15 @@ def local_fill_lift(node):
return False
register_canonicalize(local_fill_lift)
##################
# Subtensor opts #
##################
@gof.local_optimizer
@gof.local_optimizer([None, None])
def local_subtensor_make_vector(node):
"""
[a,b,c][0] -> a
......@@ -242,7 +262,7 @@ def local_subtensor_make_vector(node):
If the index or slice is constant.
"""
if not opt.check_chain(node, T.Subtensor, T.Join):
if not opt.check_chain(node, T.Subtensor, T.MakeVector):
return False
joined_r = node.inputs[0]
......@@ -263,13 +283,15 @@ def local_subtensor_make_vector(node):
return T.make_vector(*(node.owner.inputs[0].owner.inputs.__getslice__(idx)))
except TypeError:
return False
register_canonicalize(local_subtensor_make_vector)
##################
# Middleman cuts #
##################
@gof.local_optimizer
@gof.local_optimizer([None, T.fill])
def local_fill_cut(node):
"""
f(fill(a,b), c) -> f(b, c)
......@@ -301,7 +323,10 @@ def local_fill_cut(node):
return False
return node.op.make_node(*new_inputs).outputs
@gof.local_optimizer
register_canonicalize(local_fill_cut)
@gof.local_optimizer([None, T.fill])
def local_fill_sink(node):
"""
f(fill(a, b), fill(c, d), e) -> fill(a, fill(c, f(b, d, e)))
......@@ -323,6 +348,8 @@ def local_fill_sink(node):
c = T.fill(model, c)
return [c]
register_canonicalize(local_fill_sink)
################
# Canonization #
......@@ -367,15 +394,23 @@ class Canonizer(gof.LocalOptimizer):
2 * x / 2 -> x
"""
def __init__(self, main, inverse, reciprocal, calculate):
def __init__(self, main, inverse, reciprocal, calculate, use_reciprocal = True):
self.main = main
self.inverse = inverse
self.reciprocal = reciprocal
self.calculate = calculate
self.use_reciprocal = use_reciprocal
def tracks(self):
#return [[None], [None, None], [None]*3, [None]*4, [None]*5]
return [[self.main, None], [self.inverse, None], [self.reciprocal, None]]
def get_num_denum(self, input):
if input.owner is None or input.owner.op not in [self.main, self.inverse, self.reciprocal]:
return [input], []
if input.owner and isinstance(input.owner.op, T.DimShuffle):
return self.get_num_denum(input.owner.inputs[0])
else:
return [input], []
num = []
denum = []
parent = input.owner
......@@ -396,7 +431,10 @@ class Canonizer(gof.LocalOptimizer):
if not ln and not ld:
return T.as_tensor(self.calculate([], []))
if not ln:
return self.reciprocal(self.merge_num_denum(denum, []))
if self.use_reciprocal:
return self.reciprocal(self.merge_num_denum(denum, []))
else:
ln = [self.calculate([], [], aslist = False)]
if not ld:
if ln == 1:
if isinstance(num[0], gof.Result):
......@@ -444,10 +482,13 @@ class Canonizer(gof.LocalOptimizer):
dcc += 1
denum.remove(v)
denumct.append(ct)
ct = self.calculate(numct, denumct, aslist = True)
if self.use_reciprocal:
ct = self.calculate(numct, denumct, aslist = True)
else:
ct = [self.calculate(numct, denumct, aslist = False)]
# if len(ct) and ncc == 1 and dcc == 0:
# return orig_num, orig_denum
if orig_num and ct == self.get_constant(orig_num[0]):
if orig_num and N.all(ct == self.get_constant(orig_num[0])):
return orig_num, orig_denum
return ct + num, denum
......@@ -471,14 +512,21 @@ class Canonizer(gof.LocalOptimizer):
num, denum = list(orig_num), list(orig_denum)
num, denum = self.simplify(num, denum)
if not reorg and orig_num == num and orig_denum == denum:
def same(x, y):
return len(x) == len(y) and all(N.all(xe == ye) for xe, ye in zip(x, y))
if not reorg and same(orig_num, num) and same(orig_denum, denum):
return False
new = self.merge_num_denum(num, denum)
if new.type != out.type:
new = T.fill(out, new)
#new = T.fill(out, new)
new = T.fill(out, T.Elemwise(scalar.Identity(scalar.specific_out(getattr(scalar, out.type.dtype))))(new))
return [new]
def __str__(self):
return getattr(self, 'name', 'Canonizer(%s, %s, %s)' % (self.main, self.inverse, self.reciprocal))
def mul_calculate(num, denum, aslist = False):
v = reduce(N.multiply, num, 1.0) / reduce(N.multiply, denum, 1.0)
......@@ -489,28 +537,42 @@ def mul_calculate(num, denum, aslist = False):
return [v]
return v
local_mul_canonizer = Canonizer(T.mul, T.div, T.inv, mul_calculate)
local_mul_canonizer = Canonizer(T.mul, T.div, T.inv, mul_calculate, False)
@gof.local_optimizer
@gof.local_optimizer([T.neg])
def local_neg_to_mul(node):
if node.op == T.neg:
return [-1.0 * node.inputs[0]]
return [-1 * node.inputs[0]]
else:
return False
@gof.local_optimizer
@gof.local_optimizer([T.mul])
def local_mul_to_neg(node):
if node.op == T.mul and local_mul_canonizer.get_constant(node.inputs[0]) == -1.0:
if node.op == T.mul and N.all(local_mul_canonizer.get_constant(node.inputs[0]) == -1.0):
return [-local_mul_canonizer.merge_num_denum(node.inputs[1:], [])]
else:
return False
neg_to_mul = out2in(gof.LocalOptGroup(local_neg_to_mul))
mul_to_neg = out2in(gof.LocalOptGroup(local_mul_to_neg))
@gof.local_optimizer([T.div])
def local_div_to_inv(node):
if node.op == T.div and N.all(local_mul_canonizer.get_constant(node.inputs[0]) == 1.0):
return [T.inv(local_mul_canonizer.merge_num_denum(node.inputs[1:], []))]
else:
return False
register_canonicalize(local_neg_to_mul)
register_specialize(local_mul_to_neg)
register_specialize(local_div_to_inv)
register_canonicalize(local_mul_canonizer, name = 'local_mul_canonizer')
# neg_to_mul = out2in(gof.LocalOptGroup(local_neg_to_mul))
# mul_to_neg = out2in(gof.LocalOptGroup(local_mul_to_neg))
mul_canonizer = in2out(gof.LocalOptGroup(local_mul_canonizer, local_fill_cut, local_fill_sink))
def add_calculate(num, denum, aslist = False):
v = reduce(N.add, num, 0.0) - reduce(N.add, denum, 0.0)
if aslist:
......@@ -523,6 +585,8 @@ def add_calculate(num, denum, aslist = False):
local_add_canonizer = Canonizer(T.add, T.sub, T.neg, add_calculate)
add_canonizer = in2out(gof.LocalOptGroup(local_add_canonizer, local_fill_cut, local_fill_sink))
register_canonicalize(local_add_canonizer, name = 'local_add_canonizer')
##################
# Distributivity #
......@@ -583,7 +647,8 @@ def attempt_distribution(factor, num, denum):
list(itertools.starmap(local_mul_canonizer.merge_num_denum, pos_pairs)),
list(itertools.starmap(local_mul_canonizer.merge_num_denum, neg_pairs))), num, denum
@gof.local_optimizer
@gof.local_optimizer([T.mul, T.add, T.mul], [T.mul, T.sub, T.mul],
[T.mul, T.add, T.div], [T.mul, T.sub, T.div])
def local_greedy_distributor(node):
"""
This optimization tries to apply distributivity of multiplication
......@@ -638,41 +703,48 @@ def local_greedy_distributor(node):
return [local_mul_canonizer.merge_num_denum(new_num, new_denum)]
register_canonicalize(local_greedy_distributor)
def _math_optimizer():
pass_1 = in2out(local_fill_sink)
pass_2 = out2in(local_dimshuffle_lift, local_shape_lift, local_fill_lift)#, local_fill_cut)
pass_3 = out2in(local_subtensor_make_vector, local_fill_cut)
# def _math_optimizer():
# pass_1 = in2out(local_fill_sink)
# pass_2 = out2in(local_dimshuffle_lift, local_shape_lift, local_fill_lift)#, local_fill_cut)
# pass_3 = out2in(local_subtensor_make_vector, local_fill_cut)
canonizer = in2out(local_add_canonizer,
local_mul_canonizer,
local_fill_sink)
# canonizer = in2out(local_add_canonizer,
# local_mul_canonizer,
# local_fill_sink)
# pass_4 = out2in(local_greedy_distributor)
# return gof.SeqOptimizer(pass_1,
# pass_2,
# pass_3,
# neg_to_mul,
# canonizer,
# pass_4,
# mul_to_neg)
# math_optimizer = _math_optimizer()
pass_4 = out2in(local_greedy_distributor)
return gof.SeqOptimizer(pass_1,
pass_2,
pass_3,
neg_to_mul,
canonizer,
pass_4,
mul_to_neg)
math_optimizer = _math_optimizer()
compile.register_optimizer('math',
gof.MergeOptMerge(
gof.PureThenInplaceOptimizer(
math_optimizer,
inplace_optimizer)))
# compile.register_optimizer('math',
# gof.MergeOptMerge(
# gof.PureThenInplaceOptimizer(
# math_optimizer,
# inplace_optimizer)))
compile.register_mode('SANITY_CHECK', compile.Mode('c&py', 'math'))
compile.register_mode('FAST_RUN', compile.Mode('c|py', 'math'))
compile.register_mode('EXPENSIVE_OPTIMIZATIONS', compile.Mode('c|py', 'math'))
# compile.register_mode('SANITY_CHECK', compile.Mode('c&py', 'math'))
# compile.register_mode('FAST_RUN', compile.Mode('c|py', 'math'))
# compile.register_mode('EXPENSIVE_OPTIMIZATIONS', compile.Mode('c|py', 'math'))
# @gof.local_optimizer
......
theano/tensor/tests/test_basic.py
浏览文件 @ 950634de
......@@ -1808,7 +1808,7 @@ class T_op_cache(unittest.TestCase):
if __name__ == '__main__':
if len(sys.argv) >= 2 and sys.argv[1] == 'OPT':
default_mode = compile.Mode(linker = 'c&py',
optimizer = 'math')
optimizer = 'fast_run')
sys.argv[1:] = sys.argv[2:]
if 1:
unittest.main()
......
theano/tensor/tests/test_opt.py
浏览文件 @ 950634de
......@@ -9,6 +9,7 @@ from theano import tensor
from theano.tensor import Tensor
from theano.gof import Env
from theano.tensor.elemwise import DimShuffle
from theano.sandbox import pprint
import numpy
#import scalar_opt
......@@ -129,12 +130,13 @@ class test_canonize(unittest.TestCase):
# e = (a * b) / (b * c) / (c * d)
# e = 2 * x / 2
# e = x / y / x
e = (x / x) * (y / y)
# e = (x / x) * (y / y)
e = (-1 * x) / y / (-2 * z)
g = Env([x, y, z, a, b, c, d], [e])
##print pprint.pp.process(g.outputs[0])
print pprint.pp.process(g.outputs[0])
mul_canonizer.optimize(g)
gof.TopoOptimizer(gof.LocalOptGroup(local_fill_cut, local_fill_lift), order = 'out_to_in').optimize(g)
##print pprint.pp.process(g.outputs[0])
print pprint.pp.process(g.outputs[0])
# def test_plusmin(self):
# x, y, z = inputs()
......
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