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, 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')
# 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',
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, 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)
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", "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",
# 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'])
OPT_STABILIZE = gof.Query(include = ['fast_run'])
OPT_STABILIZE.position_cutoff = 1.5000001
predefined_optimizers = {
None : lambda env: None,
'None' : lambda env: None,
'merge' : gof.MergeOptimizer(),
'fast_run' : OPT_FAST_RUN,
'fast_run_stable' : OPT_FAST_RUN_STABLE,
'fast_compile' : OPT_FAST_COMPILE,
'stabilize': OPT_STABILIZE
}
def register_optimizer(name, opt):
"""Add a `Optimizer` which can be referred to by `name` in `Mode`."""
if name in predefined_optimizers:
raise ValueError('Optimizer name already taken: %s' % name)
predefined_optimizers[name] = opt
def register_OutputGuard_c_code(type):
OutputGuard.c_code_types.append(type)
