def test_badoptimization_opt_err():
# This variant of test_badoptimization() replace the working code
# with a new apply node that will raise an error.
@gof.local_optimizer([theano.tensor.add])
def insert_bigger_b_add(node):
if node.op == theano.tensor.add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
inputs[-1] = theano.tensor.concatenate(
(inputs[-1], inputs[-1]))
return [node.op(*inputs)]
return False
@gof.local_optimizer([theano.tensor.add])
def insert_bad_dtype(node):
if node.op == theano.tensor.add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
return [node.outputs[0].astype("float32")]
return False
edb = gof.EquilibriumDB()
edb.register("insert_bigger_b_add", insert_bigger_b_add, "all")
opt = edb.query("+all")
edb2 = gof.EquilibriumDB()
edb2.register("insert_bad_dtype", insert_bad_dtype, "all")
opt2 = edb2.query("+all")
a = theano.tensor.dvector()
b = theano.tensor.dvector()
f = theano.function([a, b], a + b, mode=debugmode.DebugMode(optimizer=opt))
with pytest.raises(ValueError, match=r"insert_bigger_b_add"):
f(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
# Test that opt that do an illegal change still get the error from gof.
with pytest.raises(theano.gof.toolbox.BadOptimization,
match=r"insert_bad_dtype") as einfo:
with theano.change_flags(on_opt_error="raise"):
f2 = theano.function(
[a, b],
a + b,
mode=debugmode.DebugMode(optimizer=opt2, stability_patience=1),
)
f2(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
# Test that we can reraise the error with an extended message
with pytest.raises(theano.gof.toolbox.BadOptimization):
e = einfo.value
new_e = e.__class__("TTT" + str(e))
exc_type, exc_value, exc_trace = sys.exc_info()
exc_value = new_e
reraise(e.__class__, exc_value, exc_trace)
def test_stochasticoptimization():
# this optimization alternates between triggering and not triggering.
last_time_replaced = [False]
@gof.local_optimizer([theano.tensor.add])
def insert_broken_add_sometimes(node):
if node.op == theano.tensor.add:
last_time_replaced[0] = not last_time_replaced[0]
if last_time_replaced[0]:
return [off_by_half(*node.inputs)]
return False
edb = gof.EquilibriumDB()
edb.register("insert_broken_add_sometimes", insert_broken_add_sometimes,
"all")
opt = edb.query("+all")
a = theano.tensor.dvector()
b = theano.tensor.dvector()
with pytest.raises(debugmode.StochasticOrder):
theano.function(
[a, b],
theano.tensor.add(a, b),
mode=debugmode.DebugMode(
optimizer=opt,
check_c_code=True,
stability_patience=max(2, config.DebugMode.patience),
),
)
def test_badoptimization():
@gof.local_optimizer([theano.tensor.add])
def insert_broken_add(node):
if node.op == theano.tensor.add:
return [off_by_half(*node.inputs)]
return False
edb = gof.EquilibriumDB()
edb.register('insert_broken_add', insert_broken_add, 'all')
opt = edb.query('+all')
a = theano.tensor.dvector()
b = theano.tensor.dvector()
f = theano.function([a, b], a + b, mode=debugmode.DebugMode(optimizer=opt))
try:
f(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
except debugmode.BadOptimization as e:
assert str(e.reason) == 'insert_broken_add'
return # TEST PASS
assert False
def test_stochasticoptimization():
# this optimization alternates between triggering and not triggering.
last_time_replaced = [False]
@gof.local_optimizer([theano.tensor.add])
def insert_broken_add_sometimes(node):
if node.op == theano.tensor.add:
last_time_replaced[0] = not last_time_replaced[0]
if last_time_replaced[0]:
return [off_by_half(*node.inputs)]
return False
edb = gof.EquilibriumDB()
edb.register('insert_broken_add_sometimes', insert_broken_add_sometimes,
'all')
opt = edb.query('+all')
a = theano.tensor.dvector()
b = theano.tensor.dvector()
try:
f = theano.function([a, b],
theano.tensor.add(a, b),
mode=debugmode.DebugMode(optimizer=opt,
check_c_code=True))
except debugmode.StochasticOrder:
return #TEST PASS
assert False
def test_badoptimization_opt_err():
"""This variant of test_badoptimization() replace the working code
with a new apply node that will raise an error.
"""
@gof.local_optimizer([theano.tensor.add])
def insert_bigger_b_add(node):
if node.op == theano.tensor.add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
inputs[-1] = theano.tensor.concatenate(
(inputs[-1], inputs[-1]))
return [node.op(*inputs)]
return False
edb = gof.EquilibriumDB()
edb.register('insert_bigger_b_add', insert_bigger_b_add, 'all')
opt = edb.query('+all')
a = theano.tensor.dvector()
b = theano.tensor.dvector()
f = theano.function([a, b], a + b, mode=debugmode.DebugMode(optimizer=opt))
try:
f(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
except Exception as e:
assert 'insert_bigger_b_add' in exc_message(e)
return # TEST PASS
assert False
def test_badoptimization():
@gof.local_optimizer([theano.tensor.add])
def insert_broken_add(node):
if node.op == theano.tensor.add:
return [off_by_half(*node.inputs)]
return False
edb = gof.EquilibriumDB()
edb.register("insert_broken_add", insert_broken_add, "all")
opt = edb.query("+all")
a = theano.tensor.dvector()
b = theano.tensor.dvector()
f = theano.function([a, b], a + b, mode=debugmode.DebugMode(optimizer=opt))
with pytest.raises(debugmode.BadOptimization) as einfo:
f(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
assert str(einfo.value.reason) == "insert_broken_add"
# 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')
# Register in the canonizer Equilibrium as a clean up opt the merge opt.
# Without this, as the equilibrium have ignore_newtrees=False, we
# won't merge all nodes if it is set as a global optimizer with
# final_opt=True.
# We need a new instance of MergeOptimizer to don't have its name
# changed by other usage of it.
optdb['canonicalize'].register("merge",
gof.opt.MergeOptimizer(),
'fast_run',
"fast_compile",
cleanup=True)
optdb.register('merge1.2', gof.MergeOptimizer(), 1.2, 'fast_run',
given point.
"""
def __init__(self, header):
self.header = header
def apply(self, fgraph):
import theano.printing
print "PrintCurrentFunctionGraph:", self.header
theano.printing.debugprint(fgraph.outputs)
optdb = gof.SequenceDB()
optdb.register('merge1', gof.MergeOptimizer(), 0, 'fast_run', 'fast_compile')
# rearranges elemwise expressions
optdb.register('canonicalize', gof.EquilibriumDB(), 1, 'fast_run',
'fast_compile')
optdb.register('merge1.2', gof.MergeOptimizer(), 1.2, 'fast_run',
'fast_compile')
optdb.register(
'Print1.21',
PrintCurrentFunctionGraph('Post-canonicalize'),
1.21,
) # 'fast_run', 'fast_compile')
# replace unstable subgraphs
optdb.register('stabilize', gof.EquilibriumDB(), 1.5, 'fast_run')
optdb.register(
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",
)
# Register in the canonizer Equilibrium as a clean up opt the merge opt.
# Without this, as the equilibrium have ignore_newtrees=False, we
# won't merge all nodes if it is set as a global optimizer with
# final_opt=True.
# We need a new instance of MergeOptimizer to don't have its name
# changed by other usage of it.
optdb["canonicalize"].register(
"merge", gof.opt.MergeOptimizer(), "fast_run", "fast_compile", cleanup=True
)
"""
def __init__(self, header):
self.header = header
def apply(self, fgraph):
import theano.printing
print("PrintCurrentFunctionGraph:", self.header)
theano.printing.debugprint(fgraph.outputs)
optdb = gof.SequenceDB()
optdb.register('merge1', gof.MergeOptimizer(),
0, 'fast_run', 'fast_compile', 'merge')
# rearranges elemwise expressions
optdb.register('canonicalize', gof.EquilibriumDB(ignore_newtrees=False),
1, 'fast_run', 'fast_compile', 'canonicalize_db')
# Register in the canonizer Equilibrium as a clean up opt the merge opt.
# Without this, as the equilibrium have ignore_newtrees=False, we
# won't merge all nodes if it is set as a global optimizer with
# final_opt=True.
# We need a new instance of MergeOptimizer to don't have its name
# changed by other usage of it.
optdb['canonicalize'].register("merge", gof.opt.MergeOptimizer(), 'fast_run',
"fast_compile", cleanup=True)
optdb.register('merge1.2', gof.MergeOptimizer(),
1.2, 'fast_run', 'fast_compile', 'merge')
optdb.register('Print1.21', PrintCurrentFunctionGraph('Post-canonicalize'),
"""
def __init__(self, header):
self.header = header
def apply(self, fgraph):
import theano.printing
print "PrintCurrentFunctionGraph:", self.header
theano.printing.debugprint(fgraph.outputs)
optdb = gof.SequenceDB()
optdb.register('merge1', gof.MergeOptimizer(),
0, 'fast_run', 'fast_compile', 'merge')
# rearranges elemwise expressions
optdb.register('canonicalize', gof.EquilibriumDB(),
1, 'fast_run', 'fast_compile')
optdb.register('merge1.2', gof.MergeOptimizer(),
1.2, 'fast_run', 'fast_compile', 'merge')
optdb.register('Print1.21', PrintCurrentFunctionGraph('Post-canonicalize'),
1.21,) # 'fast_run', 'fast_compile')
# replace unstable subgraphs
optdb.register('stabilize', gof.EquilibriumDB(),
1.5, 'fast_run')
optdb.register('Print1.51', PrintCurrentFunctionGraph('Post-stabilize'),
1.51,) # 'fast_run', 'fast_compile')
def test_badoptimization_opt_err():
# This variant of test_badoptimization() replace the working code
# with a new apply node that will raise an error.
@gof.local_optimizer([theano.tensor.add])
def insert_bigger_b_add(node):
if node.op == theano.tensor.add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
inputs[-1] = theano.tensor.concatenate(
(inputs[-1], inputs[-1]))
return [node.op(*inputs)]
return False
@gof.local_optimizer([theano.tensor.add])
def insert_bad_dtype(node):
if node.op == theano.tensor.add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
return [node.outputs[0].astype('float32')]
return False
edb = gof.EquilibriumDB()
edb.register('insert_bigger_b_add', insert_bigger_b_add, 'all')
opt = edb.query('+all')
edb2 = gof.EquilibriumDB()
edb2.register('insert_bad_dtype', insert_bad_dtype, 'all')
opt2 = edb2.query('+all')
a = theano.tensor.dvector()
b = theano.tensor.dvector()
f = theano.function([a, b], a + b, mode=debugmode.DebugMode(optimizer=opt))
try:
f(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
except ValueError as e:
assert 'insert_bigger_b_add' in exc_message(e)
else:
assert False
# Test that opt that do an illegal change still get the error from gof.
try:
with theano.change_flags(on_opt_error='raise'):
f2 = theano.function([a, b],
a + b,
mode=debugmode.DebugMode(
optimizer=opt2, stability_patience=1))
f2(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
except theano.gof.toolbox.BadOptimization as e:
assert 'insert_bad_dtype' in str(e)
# Test that we can reraise the error with an extended message
try:
new_e = e.__class__("TTT" + str(e))
exc_type, exc_value, exc_trace = sys.exc_info()
exc_value = new_e
reraise(e.__class__, exc_value, exc_trace)
except theano.gof.toolbox.BadOptimization as e:
pass
else:
assert False
else:
assert False
class PrintCurrentEnv(gof.Optimizer):
"""This optimizer is for debugging.
Toss it into the optimization pipeline to see the state of things at any given point.
"""
def __init__(self, header):
self.header =header
def apply(self, env):
import theano.printing
print "PrintCurrentEnv:", self.header
theano.printing.debugprint(env.outputs)
optdb = gof.SequenceDB()
optdb.register('merge1', gof.MergeOptimizer(),
0, 'fast_run', 'fast_compile')
optdb.register('canonicalize', gof.EquilibriumDB(), # rearranges elemwise expressions
1, 'fast_run', 'fast_compile')
optdb.register('merge1.2', gof.MergeOptimizer(skip_const_merge=False),
1.2, 'fast_run', 'fast_compile')
optdb.register('Print1.21', PrintCurrentEnv('Post-canonicalize'),
1.21,)# 'fast_run', 'fast_compile')
optdb.register('stabilize', gof.EquilibriumDB(), # replace unstable subgraphs
1.5, 'fast_run')
optdb.register('Print1.51', PrintCurrentEnv('Post-stabilize'),
1.51,) #'fast_run', 'fast_compile')
optdb.register('specialize', gof.EquilibriumDB(), # misc special cases for speed
2, 'fast_run')
optdb.register('Print2.01', PrintCurrentEnv('Post-specialize'),
2.01, )#'fast_run', 'fast_compile')
optdb.register('uncanonicalize', gof.EquilibriumDB(),# misc special cases for speed that break canonicalization
