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提交 140d0a06 authored 作者: abergeron's avatar abergeron 提交者： GitHub
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Merge pull request #4876 from Sentient07/cgt-opt

Cgt opt
上级 c49d23bd 085b71c8
隐藏空白字符变更
内嵌 并排
正在显示 包含 320 行增加 和 80 行删除
theano/compile/mode.py
浏览文件 @ 140d0a06
......@@ -150,6 +150,21 @@ optdb = gof.SequenceDB()
optdb.register('merge1', gof.MergeOptimizer(),
0, 'fast_run', 'fast_compile', 'merge')
# After scan1 opt at 0.5 and before ShapeOpt at 1
# This should only remove nodes.
# The opt should not do anything that need shape inference.
# New nodes that don't have infer_shape need that the original node
# also don't have infer_shape
local_useless = gof.optdb.LocalGroupDB(apply_all_opts=True, profile=True)
optdb.register(
'useless',
gof.optdb.TopoDB(local_useless,
failure_callback=gof.opt.NavigatorOptimizer.warn_inplace),
0.6, 'fast_run', 'fast_compile')
optdb.register('merge1.1', gof.MergeOptimizer(),
0.65, 'fast_run', 'fast_compile', 'merge')
# rearranges elemwise expressions
optdb.register('canonicalize', gof.EquilibriumDB(ignore_newtrees=False),
1, 'fast_run', 'fast_compile', 'canonicalize_db')
......
theano/compile/profiling.py
浏览文件 @ 140d0a06
......@@ -52,6 +52,7 @@ def _atexit_print_fn():
destination_file = sys.stdout
else:
destination_file = open(config.profiling.destination, 'w')
# Reverse sort in the order of compile+exec time
for ps in sorted(_atexit_print_list,
key=lambda a:a.compile_time + a.fct_call_time)[::-1]:
......
theano/gof/opt.py
浏览文件 @ 140d0a06
......@@ -5,7 +5,7 @@ amount of useful generic optimization tools.
"""
from __future__ import absolute_import, print_function, division
from collections import deque
from collections import deque, defaultdict
import copy
import inspect
import logging
......@@ -38,7 +38,6 @@ def _list_of_nodes(fgraph):
class Optimizer(object):
"""
WRITEME
An L{Optimizer} can be applied to an L{FunctionGraph} to transform it.
It can represent an optimization or in general any kind
......@@ -64,7 +63,6 @@ class Optimizer(object):
def apply(self, fgraph):
"""
WRITEME
Applies the optimization to the provided L{FunctionGraph}. It may
use all the methods defined by the L{FunctionGraph}. If the
......@@ -76,7 +74,6 @@ class Optimizer(object):
def optimize(self, fgraph, *args, **kwargs):
"""
WRITEME
This is meant as a shortcut to:
opt.add_requirements(fgraph)
......@@ -94,7 +91,6 @@ class Optimizer(object):
def __call__(self, fgraph):
"""
WRITEME
Same as self.optimize(fgraph).
......@@ -103,7 +99,6 @@ class Optimizer(object):
def add_requirements(self, fgraph):
"""
WRITEME
Add features to the fgraph that are required to apply the optimization.
For example:
......@@ -179,7 +174,6 @@ def inplace_optimizer(f):
class SeqOptimizer(Optimizer, list):
# inherit from Optimizer first to get Optimizer.__hash__
"""
WRITEME
Takes a list of L{Optimizer} instances and applies them
sequentially.
......@@ -201,17 +195,23 @@ class SeqOptimizer(Optimizer, list):
def __init__(self, *opts, **kw):
"""
WRITEME
Parameters
----------
*opts :
The List of optimizers to be applied to a node
failure_callback : callable or None
Keyword only argument. A callback used when a failure
happen during optimization.
"""
if len(opts) == 1 and isinstance(opts[0], (list, tuple)):
opts = opts[0]
self[:] = opts
self.failure_callback = kw.pop('failure_callback', None)
assert len(kw) == 0
def apply(self, fgraph):
"""
WRITEME
Applies each L{Optimizer} in self in turn.
......@@ -890,6 +890,7 @@ class MergeOptimizer(Optimizer):
@staticmethod
def print_profile(stream, prof, level=0):
(nb_fail, replace_time, validate_time,
callback_time, callbacks_time, nb_merged, nb_constant) = prof
......@@ -1232,21 +1233,56 @@ def local_optimizer(tracks, inplace=False, requirements=()):
class LocalOptGroup(LocalOptimizer):
"""
WRITEME
"""Takes a list of LocalOptimizer and applies them to the node.
Parameters
----------
optimizers :
The List of optimizers to be applied to a node
reentrant : bool (Default True)
Keyword only argument. Reentrant information. Some global
optimizer like NavigatorOptimizer can use this value to
determine if it ignore new nodes during a pass on the
nodes. Sometimes, ignore_newtrees is not reentrant.
apply_all_opts : bool (Default False)
If False, it will return after the new node after the first optimizer
applied. Otherwise, it will start again with the new node until no new
optimization apply.
"""
def __init__(self, *optimizers):
def __init__(self, *optimizers, **kwargs):
if len(optimizers) == 1 and isinstance(optimizers[0], list):
# This happen when created by LocalGroupDB.
optimizers = tuple(optimizers[0])
self.opts = optimizers
assert isinstance(self.opts, tuple)
self.reentrant = any(getattr(opt, 'reentrant', True)
for opt in optimizers)
self.retains_inputs = all(getattr(opt, 'retains_inputs', False)
for opt in optimizers)
self.apply_all_opts = kwargs.pop('apply_all_opts', False)
self.profile = kwargs.pop('profile', False)
self.track_map = defaultdict(lambda: [])
assert len(kwargs) == 0
if self.profile:
self.time_opts = {}
self.process_count = {}
self.applied_true = {}
self.node_created = {}
for o in self.opts:
if self.profile:
self.time_opts.setdefault(o, 0)
self.process_count.setdefault(o, 0)
self.applied_true.setdefault(o, 0)
self.node_created.setdefault(o, 0)
for c in o.tracks():
self.track_map[c].append(o)
def __str__(self):
return getattr(self, '__name__',
('LocalOptGroup(%s)' %
......@@ -1261,10 +1297,77 @@ class LocalOptGroup(LocalOptimizer):
return t
def transform(self, node):
for opt in self.opts:
repl = opt.transform(node)
if repl:
if len(self.opts) == 0:
return
fgraph = node.fgraph
repl = None
while True:
opts = self.track_map[type(node.op)] + self.track_map[node.op] + self.track_map[None]
new_repl = None
for opt in opts:
opt_start = time.time()
new_repl = opt.transform(node)
opt_finish = time.time()
if self.profile:
self.time_opts[opt] += opt_start - opt_finish
self.process_count[opt] += 1
if not new_repl:
continue
else:
assert len(new_repl) == 1
if self.profile:
self.node_created[opt] += len(graph.ops(fgraph.variables, new_repl))
self.applied_true[opt] += 1
break # break from the for loop over optimization.
if not new_repl: # No optimization applied in the last iteration
return repl
# only 1 iteration or we are at the start of the graph.
if not self.apply_all_opts or not new_repl[0].owner:
return new_repl
repl = new_repl
node = repl[0].owner
@staticmethod
def print_profile(stream, prof, level=0):
(time_opts, process_count, applied_true, node_created, profile) = prof
if not profile:
return
blanc = (' ' * int(level))
print(blanc, "LocalOptGroup", file=stream)
print(blanc, "---------------------", file=stream)
count_opt = []
not_used = []
not_used_time = 0
for o, count in iteritems(process_count):
if count > 0:
count_opt.append((time_opts[o], applied_true[o], count, o, node_created[o]))
else:
not_used.append((time_opts[o], o))
not_used_time += time_opts[o]
if count_opt:
print(blanc,
' time taken - times applied - times tried - name - node_created:',
file=stream)
count_opt.sort()
for (t, a_t, count, o, n_c) in count_opt[::-1]:
print(blanc, ' %.3fs - %d - %d - %s - %d' % (
t, a_t, count, o, n_c), file=stream)
print(blanc, ' %.3fs - in %d optimization that were not used (display those with runtime greater than 0)' % (
not_used_time, len(not_used)), file=stream)
not_used.sort(key=lambda nu: (nu[0], str(nu[1])))
for (t, o) in not_used[::-1]:
if t > 0:
# Skip opt that have 0 times, they probably wasn't even tried.
print(blanc + " ", ' %.3fs - %s' % (t, o), file=stream)
else:
print(blanc, " The Optimizer wasn't successful ", file=stream)
print(file=stream)
def merge_profile(prof1, prof2):
raise NotImplementedError
def print_summary(self, stream=sys.stdout, level=0, depth=-1):
print("%s%s id=%i" % (
......@@ -1281,7 +1384,6 @@ class LocalOptGroup(LocalOptimizer):
class OpSub(LocalOptimizer):
"""
WRITEME
Replaces the application of a certain op by the application of
another op that takes the same inputs as what they are replacing.
......@@ -1331,7 +1433,6 @@ class OpSub(LocalOptimizer):
class OpRemove(LocalOptimizer):
"""
WRITEME
Removes all applications of an op by transferring each of its
outputs to the corresponding input.
......@@ -1367,7 +1468,6 @@ class OpRemove(LocalOptimizer):
class PatternSub(LocalOptimizer):
"""
WRITEME
@todo update
......@@ -1887,7 +1987,8 @@ class NavigatorOptimizer(Optimizer):
class TopoOptimizer(NavigatorOptimizer):
"""
WRITEME
TopoOptimizer has one local optimizer. It tries to apply to each node, in topological order (or reverse).
Each time the local optimizer applies, the node gets replaced, and the topooptimizer moves on to the next one.
"""
......@@ -1937,7 +2038,7 @@ class TopoOptimizer(NavigatorOptimizer):
callback_time = fgraph.execute_callbacks_time - callback_before
nb_nodes_end = len(fgraph.apply_nodes)
return (self, nb, nb_nodes_start, nb_nodes_end,
io_t, loop_t, callback_time)
io_t, loop_t, callback_time, self.local_opt)
@staticmethod
def print_profile(stream, prof, level=0):
......@@ -1948,7 +2049,7 @@ class TopoOptimizer(NavigatorOptimizer):
return
(opt, nb, nb_nodes_start, nb_nodes_end,
io_t, loop_t, callback_time) = prof
io_t, loop_t, callback_time, lopt) = prof
print(blanc, "TopoOptimizer ",
getattr(opt, "name", getattr(opt, "__name__", "")), file=stream)
......@@ -1958,12 +2059,62 @@ class TopoOptimizer(NavigatorOptimizer):
print(blanc, " init io_toposort", io_t, file=stream)
print(blanc, " loop time", loop_t, file=stream)
print(blanc, " callback_time", callback_time, file=stream)
if isinstance(lopt, LocalOptGroup):
if lopt.profile:
lopt.print_profile(stream, (lopt.time_opts,
lopt.process_count,
lopt.applied_true,
lopt.node_created,
lopt.profile),
level=level + 1)
def __str__(self):
return getattr(self, '__name__',
'<TopoOptimizer instance>')
def out2in(*local_opts, **kwargs):
"""
Uses the TopoOptimizer from the output nodes to input nodes of the graph.
"""
name = (kwargs and kwargs.pop('name', None))
if len(local_opts) > 1:
# Don't wrap it uselessly if their is only 1 optimization.
local_opts = LocalOptGroup(*local_opts)
else:
local_opts, = local_opts
if not name:
name = local_opts.__name__
ret = TopoOptimizer(local_opts,
order='out_to_in',
failure_callback=TopoOptimizer.warn_inplace,
**kwargs)
if name:
ret.__name__ = name
return ret
def in2out(*local_opts, **kwargs):
"""
Uses the TopoOptimizer from the input nodes to output nodes of the graph.
"""
name = (kwargs and kwargs.pop('name', None))
if len(local_opts) > 1:
# Don't wrap it uselessly if their is only 1 optimization.
local_opts = LocalOptGroup(*local_opts)
else:
local_opts, = local_opts
if not name:
name = local_opts.__name__
ret = TopoOptimizer(local_opts,
order='in_to_out',
failure_callback=TopoOptimizer.warn_inplace,
**kwargs)
if name:
ret.__name__ = name
return ret
class OpKeyOptimizer(NavigatorOptimizer):
"""
WRITEME
......
theano/gof/optdb.py
浏览文件 @ 140d0a06
......@@ -321,8 +321,11 @@ class SequenceDB(DB):
def register(self, name, obj, position, *tags):
super(SequenceDB, self).register(name, obj, *tags)
assert isinstance(position, (integer_types, float))
self.__position__[name] = position
if position == 'last':
self.__position__[name] = max(self.__position__.values())
else:
assert isinstance(position, (integer_types, float))
self.__position__[name] = position
def query(self, *tags, **kwtags):
"""
......@@ -390,7 +393,7 @@ class SequenceDB(DB):
return sio.getvalue()
class LocalGroupDB(SequenceDB):
class LocalGroupDB(DB):
"""
Generate a local optimizer of type LocalOptGroup instead
of a global optimizer.
......@@ -399,11 +402,41 @@ class LocalGroupDB(SequenceDB):
"""
seq_opt = opt.LocalOptGroup
def __init__(self, failure_callback=opt.SeqOptimizer.warn):
def __init__(self, apply_all_opts=False, profile=False):
super(LocalGroupDB, self).__init__()
self.failure_callback = None
self.apply_all_opts = apply_all_opts
self.profile = profile
def query(self, *tags, **kwtags):
# For the new `useless` optimizer
opts = super(LocalGroupDB, self).query(*tags, **kwtags)
ret = opt.LocalOptGroup(*opts,
apply_all_opts=self.apply_all_opts,
profile=self.profile)
return ret
class TopoDB(DB):
"""
Generate a Global Optimizer of type TopoOptimizer.
"""
def __init__(self, db, order='in_to_out', ignore_newtrees=False,
failure_callback=None):
super(TopoDB, self).__init__()
self.db = db
self.order = order
self.ignore_newtrees = ignore_newtrees
self.failure_callback = failure_callback
def query(self, *tags, **kwtags):
return opt.TopoOptimizer(self.db.query(*tags, **kwtags),
self.order,
self.ignore_newtrees,
self.failure_callback)
class ProxyDB(DB):
......
theano/gpuarray/opt.py
浏览文件 @ 140d0a06
......@@ -736,7 +736,11 @@ gpu_local_elemwise_fusion = tensor.opt.local_elemwise_fusion_op(
GpuElemwise,
max_inputs_to_GpuElemwise)
optdb.register('gpua_elemwise_fusion',
tensor.opt.FusionOptimizer(gpu_local_elemwise_fusion), 71.00,
# 48.5 move to gpu
# 48.6 specialize
# 49 cpu fusion
# 49.5 add destroy handler
tensor.opt.FusionOptimizer(gpu_local_elemwise_fusion), 49,
'fast_run', 'fusion', 'local_elemwise_fusion', 'gpuarray')
inplace_gpu_elemwise_opt = tensor.opt.inplace_elemwise_optimizer_op(
......
theano/tensor/opt.py
浏览文件 @ 140d0a06
......@@ -22,7 +22,7 @@ from theano import gof
from theano.compat import izip
from theano.gof import opt, InconsistencyError, TopoOptimizer, graph
from theano.gof import Variable, Constant
from theano.gof.opt import copy_stack_trace
from theano.gof.opt import copy_stack_trace, in2out
from theano.gof.utils import MethodNotDefined
from theano.gradient import DisconnectedType
from theano.configparser import config
......@@ -57,44 +57,6 @@ _logger = logging.getLogger('theano.tensor.opt')
# Utilities
def out2in(*local_opts, **kwargs):
"""WRITEME """
name = (kwargs and kwargs.pop('name', None))
if len(local_opts) > 1:
# Don't wrap it uselessly if their is only 1 optimization.
local_opts = opt.LocalOptGroup(*local_opts)
else:
local_opts, = local_opts
if not name:
name = local_opts.__name__
ret = opt.TopoOptimizer(local_opts,
order='out_to_in',
failure_callback=TopoOptimizer.warn_inplace,
**kwargs)
if name:
ret.__name__ = name
return ret
def in2out(*local_opts, **kwargs):
"""WRITEME """
name = (kwargs and kwargs.pop('name', None))
if len(local_opts) > 1:
# Don't wrap it uselessly if their is only 1 optimization.
local_opts = opt.LocalOptGroup(*local_opts)
else:
local_opts, = local_opts
if not name:
name = local_opts.__name__
ret = opt.TopoOptimizer(local_opts,
order='in_to_out',
failure_callback=TopoOptimizer.warn_inplace,
**kwargs)
if name:
ret.__name__ = name
return ret
def _fill_chain(new_out, orig_inputs):
for i in orig_inputs:
new_out = T.fill(i, new_out)
......@@ -409,6 +371,19 @@ compile.optdb.register('inplace_elemwise_opt', inplace_elemwise_optimizer, 75,
'fast_run', 'inplace')
def register_useless(lopt, *tags, **kwargs):
if type(lopt) == str:
def register(inner_lopt):
return register_useless(inner_lopt, lopt, *tags, **kwargs)
return register
else:
name = kwargs.pop('name', None) or lopt.__name__
compile.mode.local_useless.register(name, lopt, 'last', 'fast_run',
*tags, **kwargs)
return lopt
def register_canonicalize(lopt, *tags, **kwargs):
if type(lopt) == str:
def register(inner_lopt):
......@@ -1756,6 +1731,7 @@ compile.optdb.register('local_elemwise_alloc',
@register_canonicalize("fast_compile")
@register_useless
@gof.local_optimizer([T.fill])
def local_useless_fill(node):
"""fill(s,v) -> v
......@@ -1776,6 +1752,7 @@ def local_useless_fill(node):
@register_specialize
@register_stabilize
@register_canonicalize
@register_useless
@gof.local_optimizer([T.alloc])
def local_useless_alloc(node):
"""
......@@ -1796,6 +1773,35 @@ def local_useless_alloc(node):
# We don't need to copy over any stack traces here
return [input]
@register_specialize
@register_stabilize
@register_canonicalize
@gof.local_optimizer([T.alloc])
def local_canonicalize_alloc(node):
"""If the input type is the same as the output type (dtype and broadcast)
there is no change in the shape of the input. So this is just a simple copy
of the input. This is not needed. (as local_useless_alloc)
Also, it will canonicalize alloc by creating Dimshuffle after the
alloc to introduce the dimensions of constant size 1.
See https://github.com/Theano/Theano/issues/4072 to know why this
is needed.
"""
op = node.op
if not isinstance(op, Alloc):
return False
input = node.inputs[0]
output = node.outputs[0]
# Check if dtype and broadcast remain the same.
if input.type == output.type:
# We don't need to copy over any stack traces here
return [input]
# Allow local_merge_alloc to do its work first
clients = getattr(output, 'clients', [])
for client, i in clients:
......@@ -1803,6 +1809,7 @@ def local_useless_alloc(node):
return
# Check if alloc adds a broadcastable dimension with shape 1.
output_shape = node.inputs[1:]
num_dims_with_size_1_added_to_left = 0
for i in range(len(output_shape) - input.ndim):
......@@ -1925,6 +1932,7 @@ def local_subtensor_remove_broadcastable_index(node):
@register_specialize
@register_canonicalize('fast_compile_gpu')
@register_useless
@gof.local_optimizer([Subtensor, AdvancedSubtensor1])
def local_subtensor_make_vector(node):
"""
......@@ -2009,6 +2017,7 @@ def local_subtensor_make_vector(node):
# TODO: the other optimization for and, or, xor, le and ge see ticket #496.
@register_useless
@register_canonicalize('fast_compile')
@register_specialize
@gof.local_optimizer([T.Elemwise])
......@@ -2428,6 +2437,7 @@ def local_upcast_elemwise_constant_inputs(node):
##################
@register_useless
@register_canonicalize
@register_specialize
@gof.local_optimizer([IncSubtensor])
......@@ -2518,6 +2528,7 @@ def local_set_to_inc_subtensor(node):
return [ret]
@register_useless
@register_canonicalize
@register_specialize
@gof.local_optimizer([Subtensor])
......@@ -2558,6 +2569,11 @@ def local_useless_subtensor(node):
list/vector or the ARange op.
"""
# If the optimization is tried over a node that is not a part of graph before
if not hasattr(node, 'fgraph'):
return
# This optimization needs ShapeOpt and fgraph.shape_feature
if not hasattr(node.fgraph, 'shape_feature'):
return
......@@ -2988,11 +3004,18 @@ def local_subtensor_merge(node):
return [out]
@register_useless
@register_canonicalize
@register_specialize
@gof.local_optimizer([Subtensor])
def local_subtensor_of_alloc(node):
"""alloc[x:y] -> alloc"""
"""
alloc(val)[x:y] -> alloc(val[...])
alloc(val)[x:y] -> alloc(val)
This can be seen as a lift, but it also reduce the number of computation/memory.
"""
if not isinstance(node.op, Subtensor):
return False
u = node.inputs[0]
......@@ -3373,6 +3396,7 @@ def local_adv_sub1_adv_inc_sub1(node):
@register_specialize
@register_stabilize
@register_canonicalize
@register_useless
@gof.local_optimizer([IncSubtensor,
AdvancedIncSubtensor,
AdvancedIncSubtensor1])
......@@ -3484,6 +3508,7 @@ def local_useless_inc_subtensor_alloc(node):
# Rebroadcast opts #
####################
@register_useless
@register_canonicalize
@register_specialize
@gof.local_optimizer([T.Rebroadcast])
......@@ -3611,6 +3636,7 @@ def apply_rebroadcast_opt(rval):
#############
@register_specialize
@register_canonicalize
@register_useless
@gof.local_optimizer([T.Join])
def local_join_1(node):
"""Join(i, x) => x
......@@ -3627,6 +3653,8 @@ def local_join_1(node):
return [tensors[0]]
# TODO: merge in local_useless_join
@register_useless
@register_specialize
@register_canonicalize
@gof.local_optimizer([T.Join])
......@@ -3683,6 +3711,7 @@ def local_join_empty(node):
@register_specialize
@register_canonicalize
@register_useless
@gof.local_optimizer([T.Join])
def local_join_make_vector(node):
"""Join(0, make_vector1, make_vector2, ...) => Join(0, make_vector12, ...)
......@@ -3785,6 +3814,7 @@ def local_expm1(node):
###############
# Switch opts #
###############
@register_useless('local_remove_switch_const_cond')
@register_canonicalize('fast_compile', 'local_remove_switch_const_cond')
@register_specialize
@gof.local_optimizer([T.Elemwise])
......@@ -4053,6 +4083,7 @@ def local_merge_switch_same_cond(node):
#############
# Tile Opts #
#############
@register_useless
@register_canonicalize
@register_stabilize
@gof.local_optimizer([T.Tile])
......@@ -4099,6 +4130,7 @@ def local_useless_tile(node):
##############
# Split Opts #
##############
@register_useless
@register_canonicalize
@register_specialize
@gof.local_optimizer([T.Split])
......@@ -4179,6 +4211,7 @@ register_canonicalize(local_reshape_chain(T.Reshape),
name='local_reshape_chain')
@register_useless
@register_canonicalize
@register_stabilize
@gof.local_optimizer([T.Reshape])
......@@ -4987,6 +5020,7 @@ def local_elemwise_sub_zeros(node):
return [T.zeros_like(node.inputs[0])]
@register_useless
@register_specialize
@register_stabilize
@register_canonicalize
......@@ -5435,9 +5469,10 @@ def local_reduce_join(node):
return [ret]
@register_canonicalize('fast_compile')
@register_canonicalize('fast_compile', 'local_cut_useless_reduce')
@register_useless('local_cut_useless_reduce')
@gof.local_optimizer(ALL_REDUCE)
def local_cut_useless_reduce(node):
def local_useless_reduce(node):
"""Sum(a, axis=[]) -> a """
if isinstance(node.op, T.CAReduce):
summed, = node.inputs
......@@ -7213,6 +7248,7 @@ def local_grad_clip(node):
return node.inputs
@register_useless
@register_canonicalize
@register_stabilize
@register_specialize
......
theano/tensor/tests/test_opt.py
浏览文件 @ 140d0a06
......@@ -39,12 +39,12 @@ from theano.tensor.opt import (
local_useless_reshape,
local_reshape_to_dimshuffle,
mul_canonizer,
out2in,
Shape_i,
Assert,
MakeVector,
make_vector,
local_expm1
local_expm1,
local_canonicalize_alloc
)
from theano import tensor
from theano import tensor as T
......@@ -70,7 +70,7 @@ from theano.tensor.elemwise import DimShuffle
from theano.tests import unittest_tools as utt
from theano.compile.mode import optdb
from theano.compile import Mode
from theano.gof.opt import check_stack_trace
from theano.gof.opt import check_stack_trace, out2in
from nose.plugins.attrib import attr
mode_opt = theano.config.mode
......@@ -3175,7 +3175,7 @@ class Test_local_elemwise_alloc(unittest.TestCase):
# Exclude local_useless_alloc, since it does not introduce
# assert in all the same cases.
self.fast_run_mode = self.fast_run_mode.excluding(
'local_useless_alloc')
'local_useless_alloc', 'local_canonicalize_alloc')
# No optimization on alloc
func = function(
[self.vec, self.mat],
......@@ -3676,7 +3676,7 @@ class Test_local_useless_elemwise_comparison(unittest.TestCase):
self.assert_eqs_const(f, 0)
class Test_local_useless_alloc(unittest.TestCase):
class Test_local_canonicalize_alloc(unittest.TestCase):
def setUp(self):
self.rng = numpy.random.RandomState(utt.fetch_seed())
......@@ -3698,11 +3698,11 @@ class Test_local_useless_alloc(unittest.TestCase):
self.assertRaises(ValueError, f)
# No need to check_stack_trace as the optimization
# local_useless_alloc only removes nodes.
# local_canonicalize_alloc only removes nodes.
def test1(self):
# Test that alloc never gets instantiated during optimization
mode = mode_opt.excluding('local_useless_alloc')
mode = mode_opt.excluding('local_canonicalize_alloc')
x = tensor.matrix('x')
xx = tensor.fill(x, x)
......@@ -3714,11 +3714,11 @@ class Test_local_useless_alloc(unittest.TestCase):
assert tensor.Alloc not in op_classes
# No need to check_stack_trace as the optimization
# local_useless_alloc only removes nodes.
# local_canonicalize_alloc only removes nodes.
def test2(self):
# Test that alloc never gets instantiated during optimization
mode = mode_opt.excluding('local_useless_alloc')
mode = mode_opt.excluding('local_canonicalize_alloc')
x = tensor.matrix('x')
y = tensor.tile(x, (1,)*2)
......@@ -3736,7 +3736,7 @@ class Test_local_useless_alloc(unittest.TestCase):
# The correct opt removes nodes, no need for check_stack_trace
def test_useless_alloc_with_shape_one(self):
alloc_lift = out2in(local_useless_alloc)
alloc_lift = out2in(local_canonicalize_alloc)
x = shared(self.rng.randn(2,))
y = shared(self.rng.randn())
z = shared(self.rng.randn(1, 1))
......
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