def test_stochasticoptimization():
# this optimization alternates between triggering and not triggering.
last_time_replaced = [False]
@local_optimizer([add])
def insert_broken_add_sometimes(fgraph, node):
if node.op == add:
last_time_replaced[0] = not last_time_replaced[0]
if last_time_replaced[0]:
return [off_by_half(*node.inputs)]
return False
edb = EquilibriumDB()
edb.register("insert_broken_add_sometimes", insert_broken_add_sometimes,
"all")
opt = edb.query("+all")
a = dvector()
b = dvector()
with pytest.raises(StochasticOrder):
aesara.function(
[a, b],
add(a, b),
mode=DebugMode(
optimizer=opt,
check_c_code=True,
stability_patience=max(2, config.DebugMode__patience),
),
)
def test_badoptimization_opt_err():
# This variant of test_badoptimization() replace the working code
# with a new apply node that will raise an error.
@local_optimizer([add])
def insert_bigger_b_add(fgraph, node):
if node.op == add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
inputs[-1] = aet.concatenate((inputs[-1], inputs[-1]))
return [node.op(*inputs)]
return False
@local_optimizer([add])
def insert_bad_dtype(fgraph, node):
if node.op == add:
inputs = list(node.inputs)
if inputs[-1].owner is None:
return [node.outputs[0].astype("float32")]
return False
edb = EquilibriumDB()
edb.register("insert_bigger_b_add", insert_bigger_b_add, "all")
opt = edb.query("+all")
edb2 = EquilibriumDB()
edb2.register("insert_bad_dtype", insert_bad_dtype, "all")
opt2 = edb2.query("+all")
a = dvector()
b = dvector()
f = aesara.function([a, b], a + b, mode=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 graph.
with pytest.raises(TypeError) as einfo:
with config.change_flags(on_opt_error="raise"):
f2 = aesara.function(
[a, b],
a + b,
mode=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(TypeError):
e = einfo.value
new_e = e.__class__("TTT" + str(e))
exc_type, exc_value, exc_trace = sys.exc_info()
exc_value = new_e
raise exc_value.with_traceback(exc_trace)
def test_badoptimization():
@local_optimizer([add])
def insert_broken_add(fgraph, node):
if node.op == add:
return [off_by_half(*node.inputs)]
return False
edb = EquilibriumDB()
edb.register("insert_broken_add", insert_broken_add, "all")
opt = edb.query("+all")
a = dvector()
b = dvector()
f = aesara.function([a, b], a + b, mode=DebugMode(optimizer=opt))
with pytest.raises(BadOptimization) as einfo:
f(
[1.0, 2.0, 3.0],
[2, 3, 4],
)
assert str(einfo.value.reason) == "insert_broken_add"
local_useless = LocalGroupDB(apply_all_opts=True, profile=True)
optdb.register(
"useless",
TopoDB(local_useless, failure_callback=NavigatorOptimizer.warn_inplace),
0.6,
"fast_run",
"fast_compile",
)
optdb.register("merge1.1", MergeOptimizer(), 0.65, "fast_run", "fast_compile",
"merge")
# rearranges elemwise expressions
optdb.register(
"canonicalize",
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",
MergeOptimizer(),
"fast_run",
new_repl = opt.transform(fgraph, op, context_name, inputs, outputs)
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
if self.profile:
self.node_created[opt] += len(
list(applys_between(fgraph.variables, new_repl)))
self.applied_true[opt] += 1
return new_repl
gpu_optimizer = EquilibriumDB()
gpu_cut_copies = EquilibriumDB()
# Not used for an EquilibriumOptimizer. It has the "tracks" that we need for GraphToGPUDB.
gpu_optimizer2 = EquilibriumDB()
gpu_seqopt = SequenceDB()
# do not add 'fast_run' to these two as this would always enable gpuarray mode
optdb.register(
"gpuarray_opt",
gpu_seqopt,
optdb.__position__.get("add_destroy_handler", 49.5) - 1,
"gpuarray",
)
def test_EquilibriumDB(self):
eq_db = EquilibriumDB()
with pytest.raises(ValueError, match=r"`final_opt` and.*"):
eq_db.register("d", TestOpt(), final_opt=True, cleanup=True)
optdb.register(
"useless",
TopoDB(local_useless, failure_callback=NavigatorOptimizer.warn_inplace),
"fast_run",
"fast_compile",
position=0.6,
)
optdb.register(
"merge1.1", MergeOptimizer(), "fast_run", "fast_compile", "merge", position=0.65
)
# rearranges elemwise expressions
optdb.register(
"canonicalize",
EquilibriumDB(ignore_newtrees=False),
"fast_run",
"fast_compile",
"canonicalize_db",
position=1,
)
# 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", MergeOptimizer(), "fast_run", "fast_compile", cleanup=True
)