def transition_copy_graph(func, env):
"""
Transition to a new phase:
- create a copy of all functions in the call graph
- update and cache copied function results in the 'flypy.state.copies'
dict
"""
if not isinstance(func, ir.Function):
return func, env
graph = callgraph.callgraph(func)
envs = env['flypy.state.envs']
phase = env['flypy.state.phase']
func_copies = env['flypy.state.copies']
if phase in func_copies:
_, new_func, new_env = func_copies[phase]
return new_func, new_env
all_copies = find_copies(graph, envs, phase)
copying.copy_graph(func, env, all_copies, graph)
update_copies(all_copies, phase)
_, new_func, new_env = func_copies[phase]
return new_func, new_env
def transition_copy_graph(func, env):
"""
Transition to a new phase:
- create a copy of all functions in the call graph
- update and cache copied function results in the 'numba.state.copies'
dict
"""
if not isinstance(func, ir.Function):
return func, env
graph = callgraph.callgraph(func)
envs = env['numba.state.envs']
phase = env['numba.state.phase']
func_copies = env['numba.state.copies']
if phase in func_copies:
_, new_func, new_env = func_copies[phase]
return new_func, new_env
all_copies = find_copies(graph, envs, phase)
copying.copy_graph(func, env, all_copies, graph)
update_copies(all_copies, phase)
_, new_func, new_env = func_copies[phase]
return new_func, new_env
def test_simple_callgraph(self):
g = self.m.get_function('g')
f = self.m.get_function('f')
G = callgraph.callgraph(g)
assert f in G.node
assert g in G.node
assert f in G.successors(g)
assert not G.successors(f)
def test_simple_callgraph(self):
g = self.m.get_function('g')
f = self.m.get_function('f')
G = callgraph.callgraph(g)
assert f in G.node
assert g in G.node
assert f in G.successors(g)
assert not G.successors(f)
def generator_fusion(func, env):
changed = True
envs = env['flypy.state.envs']
dependences = callgraph.callgraph(func).node
while changed:
changed = False
for f in dependences:
e = envs[f]
consumers = identify_consumers(f, e)
#print("consumers", f.name, consumers)
fuse_generators(f, e, consumers)
changed |= bool(consumers)
def generator_fusion(func, env):
changed = True
envs = env['flypy.state.envs']
dependences = callgraph.callgraph(func).node
while changed:
changed = False
for f in dependences:
e = envs[f]
consumers = identify_consumers(f, e)
#print("consumers", f.name, consumers)
fuse_generators(f, e, consumers)
changed |= bool(consumers)
def apply_all(phase, func, env, dependences=None):
"""Apply a phase to a function and its dependences"""
if dependences is None:
dependences = callgraph.callgraph(func).node
envs = env["flypy.state.envs"]
for dep in dependences:
if func != dep:
dep_env = envs[dep]
phase.apply_single(dep, dep_env)
return phase.apply_single(func, env)
def copy_graph(func, env, funcs=None, graph=None):
"""
Copy a function graph.
"""
if funcs is None:
funcs = {}
graph = graph or callgraph.callgraph(func)
envs = env['flypy.state.envs']
copy_functions(graph, funcs, envs)
update_callgraph(graph, funcs)
return funcs
def _deps(func, debug=False):
"""
Locate dependences for a function.
"""
graph = callgraph.callgraph(func)
if debug:
import networkx as nx
G = nx.DiGraph()
for src in graph.node:
for dst in graph.neighbors(src):
G.add_edge(src.name, dst.name)
dump(G, os.path.expanduser("~/callgraph.dot"))
return graph.node
def copy_graph(func, env, funcs=None, graph=None):
"""
Copy a function graph.
"""
if funcs is None:
funcs = {}
graph = graph or callgraph.callgraph(func)
envs = env['flypy.state.envs']
copy_functions(graph, funcs, envs)
update_callgraph(graph, funcs)
return funcs
def code_generation(func, env, codegen=None):
"""
Invoke the code generator after initializing all functions in the call graph
"""
codegen = codegen or env["codegen.impl"]
cache = env["codegen.cache"]
graph = callgraph.callgraph(func)
for callee in graph.node:
if callee not in cache:
cache[callee] = codegen.initialize(callee, env)
# TODO: Different environments for each function?
results = {}
for callee in graph.node:
results[callee] = codegen.translate(callee, env, cache[callee])
return results[func], env
def code_generation(func, env, codegen=None):
"""
Invoke the code generator after initializing all functions in the call graph
"""
codegen = codegen or env["codegen.impl"]
cache = env["codegen.cache"]
graph = callgraph.callgraph(func)
for callee in graph.node:
if callee not in cache:
cache[callee] = codegen.initialize(callee, env)
# TODO: Different environments for each function?
results = {}
for callee in graph.node:
results[callee] = codegen.translate(callee, env, cache[callee])
return results[func], env
def run(func, env):
envs = env["flypy.state.envs"]
dependences = [d for d in callgraph.callgraph(func).node]
env["flypy.state.dependences"] = dependences
def _deps(func):
return callgraph.callgraph(func).node
def apply_and_resolve(phase, func, env, graph=None):
"""Apply a phase to a function and its dependences"""
graph = graph or callgraph.callgraph(func)
envs = env["numba.state.envs"]
for f in graph.node:
phase(f, envs[f])
def run(func, env):
envs = env["flypy.state.envs"]
dependences = [d for d in callgraph.callgraph(func).node]
env["flypy.state.dependences"] = dependences
