def do_plan(context, graph):
""" Take the ATerm expression graph and do inner-most evaluation to
generate a linear sequence of instructions from that together with
the table of inputs and outputs, built kernels forms the execution
plan.
Example::
::
a + b * c
::
vars %a %b %c
%0 = Elemwise[np.mul,nogil](%b, %c)
%0 = Elemwise[np.add,nogil,inplace](%a, %0)
ret %0
"""
context = dict(context)
aterm_graph = context['aterm_graph']
ivisitor = InstructionGen(have_numbapro=have_numbapro)
plan = ivisitor.visit(aterm_graph)
context['instructions'] = ivisitor.result()
print ivisitor.result()
return context, plan
def do_plan(context, aterm_graph):
""" Take the ATerm expression graph and do inner-most evaluation to
generate a linear sequence of instructions from that together with
the table of inputs and outputs, built kernels forms the execution
plan.
Example::
::
a + b * c
::
vars %a %b %c
%0 = Elemwise[np.mul,nogil](%b, %c)
%0 = Elemwise[np.add,nogil,inplace](%a, %0)
ret %0
"""
context = dict(context)
ivisitor = InstructionGen(context['executors'],
have_numbapro=have_numbapro)
plan = ivisitor.visit(aterm_graph)
context['instructions'] = ivisitor.result()
context['symbols'] = ivisitor.symbols
return context, plan
def do_plan(context, graph):
""" Take the ATerm expression graph and do inner-most evaluation to
generate a linear sequence of instructions from that together with
the table of inputs and outputs, built kernels forms the execution
plan.
Example::
::
a + b * c
::
vars %a %b %c
%0 = Elemwise[np.mul,nogil](%b, %c)
%0 = Elemwise[np.add,nogil,inplace](%a, %0)
ret %0
"""
context = dict(context)
aterm_graph = context['aterm_graph']
igen = InstructionGen()
igen.visit(aterm_graph) # effectful
plan = igen.plan()
context['plan'] = plan
return context, graph