def test_LocalGroupDB(self):
lg_db = LocalGroupDB()
lg_db.register("a", TestOpt(), 1)
assert "a" in lg_db.__position__
with pytest.raises(TypeError, match=r"`position` must be.*"):
lg_db.register("b", TestOpt(), position=object())
import aesara.printing
print("PrintCurrentFunctionGraph:", self.header)
aesara.printing.debugprint(fgraph.outputs)
optdb = SequenceDB()
optdb.register("merge1", 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 = 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),
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",
)
pool_db = LocalGroupDB()
pool_db2 = LocalGroupDB(local_opt=GraphToGPULocalOptGroup)
pool_db2.__name__ = "pool_db2"
matrix_ops_db = LocalGroupDB()
matrix_ops_db2 = LocalGroupDB(local_opt=GraphToGPULocalOptGroup)
matrix_ops_db2.__name__ = "matrix_ops_db2"
abstract_batch_norm_db = LocalGroupDB()
abstract_batch_norm_db2 = LocalGroupDB(local_opt=GraphToGPULocalOptGroup)
abstract_batch_norm_db2.__name__ = "abstract_batch_norm_db2"
abstract_batch_norm_groupopt = LocalGroupDB()
abstract_batch_norm_groupopt.__name__ = "gpuarray_batchnorm_opts"