def generate_value_with_same_type(value: 'values.Value'):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
if isinstance(value, values.NumberValue):
ret = values.NumberValue(None)
if isinstance(value, values.StrValue):
ret = values.StrValue(None)
if isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
if ret is None and isinstance(value, values.Value):
ret = values.Value()
if ret is not None:
ret.name = value.name + '_st'
return ret
def __init__(self):
super().__init__()
self.name = 'array'
fa = functions.FunctionArg()
fa.name = 'object'
fa.obj = values.Object(values.NoneValue())
self.funcArgs.append(fa)
fa = functions.FunctionArg()
fa.name = 'dtype'
fa.obj = values.Object(values.NoneValue())
self.funcArgs.append(fa)
fa = functions.FunctionArg()
fa.name = 'copy'
fa.obj = values.Object(values.BoolValue(True))
self.funcArgs.append(fa)
fa = functions.FunctionArg()
fa.name = 'order'
fa.obj = values.Object(values.StrValue('K'))
self.funcArgs.append(fa)
fa = functions.FunctionArg()
fa.name = 'subok'
fa.obj = values.Object(values.BoolValue(False))
self.funcArgs.append(fa)
fa = functions.FunctionArg()
fa.name = 'ndmin'
fa.obj = values.Object(values.NumberValue(0))
self.funcArgs.append(fa)
def veval_with_default(nast, default_value):
if nast is None:
ret = values.NumberValue(default_value)
ret.name = '@SliceDefault'
return ret
obj = veval_ast(astc.c(nast), local_field, graph)
return try_get_value(obj, 'subscript', lineprop)
def generate_value_with_same_type(value: 'values.Value'):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
if isinstance(value, values.NumberValue):
ret = values.NumberValue(None)
if value.internal_value is None:
ret.dtype = value.dtype
elif isinstance(value.internal_value, int):
ret.dtype = np.array(value.internal_value).dtype
elif isinstance(value.internal_value, float):
ret.dtype = np.array(value.internal_value).dtype
if isinstance(value, values.StrValue):
ret = values.StrValue(None)
if isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
if ret is None and isinstance(value, values.Value):
ret = values.Value()
if ret is not None:
ret.name = value.name + '_st'
return ret
def generate_copied_value(value : 'values.Value'):
if isinstance(value, values.NumberValue):
copied = values.NumberValue(value.internal_value)
return copied
if isinstance(value, values.TensorValue):
copied = values.TensorValue()
copied.shape = value.shape
return copied
if isinstance(value, values.NoneValue):
copied = values.NoneValue()
return copied
if isinstance(value, values.BoolValue):
copied = values.BoolValue(value.internal_value)
return copied
if isinstance(value, values.StrValue):
copied = values.StrValue(value.internal_value)
return copied
if isinstance(value, values.RangeValue):
copied = values.RangeValue()
return copied
if isinstance(value, values.TupleValue):
copied = values.TupleValue(value.values)
return copied
if config.show_warnings:
print('Unknown type {} is copied'.format(value))
return values.Value()
def generate_value_with_same_type(value : 'values.Value'):
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
return ret
if isinstance(value, values.NumberValue):
ret = values.NumberValue(None)
return ret
if isinstance(value, values.StrValue):
ret = values.StrValue(None)
return ret
if isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
return ret
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
return ret
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
return ret
if isinstance(value, values.Value):
ret = values.Value()
return ret
return None
def veval_ast_num(astc : 'AstContext', local_field : 'values.Field', graph : 'Graph'):
'''
Ex. 1, 2, ...
'''
assert(isinstance(astc.nast, gast.gast.Num))
lineprop = utils.LineProperty(astc.lineno)
value = values.NumberValue(astc.nast.n)
return value
def __init__(self):
super().__init__()
self.name = 'array'
self.args.add_arg('object', values.NoneValue())
self.args.add_arg('dtype', values.NoneValue())
self.args.add_arg('copy', values.BoolValue(True))
self.args.add_arg('order', values.StrValue('K'))
self.args.add_arg('subok', values.BoolValue(False))
self.args.add_arg('ndmin', values.NumberValue(0))
def veval(op: 'nodes.UnaryOpType', value: 'values.Value'):
if isinstance(value, values.NumberValue):
if value.internal_value is not None:
if op == nodes.UnaryOpType.UAdd:
return values.NumberValue(value.internal_value)
if op == nodes.UnaryOpType.USub:
return values.NumberValue(-value.internal_value)
else:
return functions.generate_value_with_same_type(value)
else:
return functions.generate_value_with_same_type(value)
if isinstance(value, values.BoolValue):
return functions.generate_value_with_same_type(value)
if op == nodes.UnaryOpType.Not and isinstance(value, values.ListValue):
return values.BoolValue(None)
return values.Value()
def vcall(self,
module: 'Field',
graph: 'Graph',
inst: 'values.ValueRef',
args: 'functions.FunctionArgInput',
line=-1):
node = nodes.NodeLen(
args.inputs[0].get_value(), # TODO: Check this.
line)
graph.add_node(node)
value = values.NumberValue(None)
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
return values.ValueRef(value)
def veval_ast_num(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
Ex. 1, 2, ...
'''
assert (isinstance(astc.nast, gast.gast.Num))
lineprop = utils.LineProperty(astc.lineno)
value = values.NumberValue(astc.nast.n)
ret = values.ValueRef(value)
name = values.create_ref_value_name_with_constant(ret)
ret.name = name
ret.get_value().name = name
return ret
def generate_copied_value(value: 'values.Value'):
assert (isinstance(value, values.Value))
if isinstance(value, values.NumberValue):
copied = values.NumberValue(value.internal_value)
copied.dtype = value.dtype
return copied
if isinstance(value, values.TensorValue):
copied = values.TensorValue()
copied.value = value.value
copied.shape = value.shape
copied.dtype = value.dtype
return copied
if isinstance(value, values.ListValue):
copied = values.ListValue()
copied.is_any = value.is_any
if value.internal_value is not None:
copied.internal_value = value.internal_value.copy()
return copied
if isinstance(value, values.NoneValue):
copied = values.NoneValue()
return copied
if isinstance(value, values.BoolValue):
copied = values.BoolValue(value.internal_value)
return copied
if isinstance(value, values.StrValue):
copied = values.StrValue(value.internal_value)
return copied
if isinstance(value, values.RangeValue):
copied = values.RangeValue()
return copied
if isinstance(value, values.TupleValue):
if value.internal_value is not None:
copied = values.TupleValue(value.internal_value.copy())
else:
copied = values.TupleValue(value.internal_value)
return copied
if config.show_warnings:
print('Unknown type {} is copied'.format(value))
return values.Value()
def generate_copied_value(value : 'values.Value'):
if isinstance(value, values.NumberValue):
copied = values.NumberValue(value.internal_value)
return copied
if isinstance(value, values.TensorValue):
copied = values.TensorValue()
copied.shape = value.shape
return copied
if isinstance(value, values.NoneValue):
copied = values.NoneValue()
return copied
if config.show_warnings:
print('Warning : Unimplemented copied_value {}'.format(value))
return values.Value()
def vcall(self,
module: 'Field',
graph: 'Graph',
inst: 'values.ValueRef',
args: 'functions.FunctionArgInput',
line=-1):
args = functions.FunctionArgInput()
args.inputs.append(inst)
args.keywords['self'] = inst
node = nodes.NodeCall(self, args, line)
value = values.NumberValue(None)
value.dtype = np.array(0).dtype
value.name = '@F.{}.{}'.format(line, self.name)
node.set_outputs([value])
graph.add_node(node)
return values.ValueRef(value)
def veval_ast_for(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
for target in iter:
...
'''
assert (isinstance(astc.nast, gast.gast.For))
lineprop = utils.LineProperty(astc.lineno)
# for target in iter:
iter_ = veval_ast(astc.c(astc.nast.iter), local_field, graph)
input_iter_value = try_get_value(iter_, 'for', lineprop)
body_iter_value = functions.generate_value_with_same_type(
input_iter_value, suffix_type=functions.SuffixType.Input)
# get target name
target_name = ''
if isinstance(astc.nast.target, gast.gast.Name):
target_name = astc.nast.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
# unroll?
if isinstance(input_iter_value,
values.ListValue) and input_iter_value.has_constant_value(
) and input_iter_value.dtype is None:
return veval_ast_for_unroll(astc, target_name, input_iter_value,
local_field, graph)
for_guid = utils.get_guid()
for_id = 'for_' + str(for_guid)
body_id = 'body_' + str(for_guid)
values.push_history(for_id)
# body
body_graph = Graph()
body_graph.root_graph = graph.root_graph
body_graph.name = 'Body_' + str(for_guid)
# generate a node for input
node_input = nodes.NodeInput('input')
body_graph.add_node(node_input)
body_counter_value = values.NumberValue(None)
body_counter_value.dtype = np.array(0).dtype
body_counter_value.name = 'for_counter_' + str(for_guid)
body_cond_value = values.BoolValue(None)
body_cond_value.name = 'for_cond_' + str(for_guid)
# create a node to lookup a value from sequence
node_forgen = nodes.NodeForGenerator(body_counter_value, body_iter_value)
# generate iterator
target_ref = input_iter_value.get_iterator()
if target_ref is None:
target_ref = values.ValueRef(values.UnknownValue())
if config.show_warnings:
print('unknown iteratable type in L.{}'.format(astc.lineno))
target_value = target_ref.get_value()
node_forgen.set_outputs([target_ref.get_value()])
target_attribute = local_field.get_attribute(target_name)
target_attribute.revise(target_ref)
body_graph.add_node(node_forgen)
# veval body
body = veval_ast(astc.c(astc.nast.body), local_field, body_graph)
value_inputs = values.get_inputs()
value_outputs = values.get_outputs()
values.pop_history()
inputs = []
outputs = []
node_input_outputs = []
# default input for subgraph's input
body_graph.add_input_value(body_counter_value)
body_graph.add_input_value(body_cond_value)
body_graph.add_input_value(body_iter_value)
# default output for subgraph's output
body_graph.add_output_value(body_cond_value)
body_graph.add_output_value(body_iter_value)
# default output
outputs.append(functions.generate_value_with_same_type(input_iter_value))
# generate pairs
value_pairs = {}
for v in value_inputs:
key = str(v.field.id) + '_' + v.name
if not (key in value_pairs.keys()):
value_pairs[key] = {}
value_pairs[key]['field'] = v.field
value_pairs[key]['name'] = v.name
value_pairs[key]['input_value'] = v.input_value
value_pairs[key]['input_body_value'] = v.value
for v in value_outputs:
key = str(v.field.id) + '_' + v.name
if not (key in value_pairs.keys()):
value_pairs[key] = {}
value_pairs[key]['field'] = v.field
value_pairs[key]['name'] = v.name
value_pairs[key]['output_body_value'] = v.value
for k, v in value_pairs.items():
name = v['name']
field = v['field']
if 'input_body_value' in v:
inputs.append(v['input_value'])
body_graph.add_input_value(v['input_body_value'])
else:
temp_value1 = functions.generate_value_with_same_type(
v['output_body_value'],
is_dummy_value=True,
suffix_type=functions.SuffixType.Dummy)
temp_value2 = functions.generate_value_with_same_type(
v['output_body_value'], suffix_type=functions.SuffixType.Dummy)
inputs.append(temp_value1)
body_graph.add_input_value(temp_value2)
node_input_outputs.append(temp_value2)
if 'output_body_value' in v:
body_graph.add_output_value(v['output_body_value'])
output_value = functions.generate_value_with_same_type(
v['output_body_value'])
outputs.append(output_value)
if field.get_attribute(name).has_obj():
field.get_attribute(name).get_ref().revise(output_value)
else:
field.get_attribute(name).revise(values.ValueRef(output_value))
else:
temp_value1 = v['input_body_value']
temp_value2 = functions.generate_value_with_same_type(
v['input_body_value'])
body_graph.add_output_value(temp_value1)
outputs.append(temp_value2)
node = nodes.NodeFor(input_iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
node_input.set_outputs(node_input_outputs)
graph.add_node(node)
return None
def generate_value_with_same_type(value: 'values.Value',
has_default=False,
suffix_type=SuffixType.Unknown):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
ret.dtype = value.dtype
if isinstance(value, values.NumberValue):
dtype = None
if value.internal_value is None:
dtype = value.dtype
elif isinstance(value.internal_value, int):
dtype = np.array(value.internal_value).dtype
elif isinstance(value.internal_value, float):
dtype = np.array(value.internal_value).dtype
if has_default:
if dtype == np.array(0).dtype:
ret = values.NumberValue(0)
elif dtype == np.array(0.0).dtype:
ret = values.NumberValue(0.0)
else:
ret = values.NumberValue(None)
else:
ret = values.NumberValue(None)
ret.dtype = dtype
if isinstance(value, values.StrValue):
if has_default:
ret = values.StrValue('')
else:
ret = values.StrValue(None)
if isinstance(value, values.BoolValue):
if has_default:
ret = values.BoolValue(False)
else:
ret = values.BoolValue(None)
if isinstance(value, values.ListValue):
ret = values.ListValue(None)
if isinstance(value, values.NoneValue):
ret = values.NoneValue()
if isinstance(value, values.TupleValue):
ret = values.TupleValue()
if isinstance(value, values.UnknownValue):
ret = values.UnknownValue()
if has_default:
ret.internal_value = 0
if ret is None and isinstance(value, values.Value):
ret = values.Value()
if ret is not None:
if suffix_type == SuffixType.Unknown:
ret.name = value.name + '_st'
if suffix_type == SuffixType.Unused:
ret.name = value.name + '_unused'
return ret
def veval_ast_listcomp(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
Ex. [x for x in xx]
[elt for target in iter]
'''
assert (isinstance(astc.nast, gast.gast.ListComp))
lineprop = utils.LineProperty(astc.lineno)
listcomp_guid = str(utils.get_guid())
listcomp_id = 'listcomp_' + listcomp_guid
body_id = 'listcomp_body_' + listcomp_guid
internal_counter_id = '@internal/listcomp_counter_' + listcomp_guid
internal_list_id = '@internal/listcomp_list_' + listcomp_guid
internal_cond_id = '@internal/listcomp_cond_' + listcomp_guid
generator = astc.nast.generators[0]
iter_value = try_get_value(
veval_ast(astc.c(generator.iter), local_field, graph), 'generator',
lineprop)
list_value = values.ListValue()
list_obj = values.ValueRef(list_value)
node_generate_list = nodes.NodeGenerate('List', [], lineprop)
node_generate_list.set_outputs([list_value])
graph.add_node(node_generate_list)
# body
target_name = ''
if isinstance(generator.target, gast.gast.Name):
target_name = generator.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
counter_value = values.NumberValue(None)
counter_value.dtype = np.array(0).dtype
counter_value.name = internal_counter_id
cond_value = values.BoolValue(None)
cond_value.name = internal_cond_id
# set values with internal name
local_field.get_attribute(internal_list_id).revise(list_obj)
values.push_history(listcomp_id)
body_graph = Graph()
body_graph.root_graph = graph.root_graph
body_graph.name = 'Body_' + listcomp_guid
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
target_ref = iter_value.get_iterator()
if target_ref is None:
target_ref = values.ValueRef(values.UnknownValue())
if config.show_warnings:
print('unknown iteratable type in L.{}'.format(astc.lineno))
target_value = target_ref.get_value()
node_forgen.set_outputs([target_ref.get_value()])
local_field.get_attribute(target_name,
from_module=False).revise(target_ref)
body_graph.add_node(node_forgen)
elt = veval_ast(astc.c(astc.nast.elt), local_field, body_graph)
elt_obj = try_get_ref(elt, 'listcomp', lineprop)
finput = functions.FunctionArgInput()
finput.inputs.append(elt_obj)
append_value = local_field.get_attribute(internal_list_id).get_ref(
).get_field().get_attribute('append').get_ref().get_value()
append_value.func.vcall(
local_field.module, body_graph,
local_field.get_attribute(internal_list_id).get_ref(), finput,
lineprop)
value_inputs = values.get_inputs()
value_outputs = values.get_outputs()
values.pop_history()
inputs = []
outputs = []
# default input for subgraph's input
body_graph.add_input_value(counter_value)
body_graph.add_input_value(cond_value)
body_graph.add_input_value(iter_value)
# default output for subgraph's output
body_graph.add_output_value(cond_value)
body_graph.add_output_value(iter_value)
# default output
outputs.append(functions.generate_value_with_same_type(iter_value))
# generate pairs
value_pairs = {}
for v in value_inputs:
key = str(v.field.id) + '_' + v.name
if not (key in value_pairs.keys()):
value_pairs[key] = {}
value_pairs[key]['field'] = v.field
value_pairs[key]['name'] = v.name
value_pairs[key]['input_value'] = v.input_value
value_pairs[key]['input_body_value'] = v.value
for v in value_outputs:
key = str(v.field.id) + '_' + v.name
if not (key in value_pairs.keys()):
value_pairs[key] = {}
value_pairs[key]['field'] = v.field
value_pairs[key]['name'] = v.name
value_pairs[key]['output_body_value'] = v.value
# remove iterator
removed_name = str(local_field.id) + '_' + target_value.name
del value_pairs[removed_name]
for k, v in value_pairs.items():
name = v['name']
field = v['field']
if 'input_body_value' in v:
inputs.append(v['input_value'])
body_graph.add_input_value(v['input_body_value'])
else:
temp_value1 = functions.generate_value_with_same_type(
v['output_body_value'])
temp_value2 = functions.generate_value_with_same_type(
v['output_body_value'])
inputs.append(temp_value1)
body_graph.add_input_value(temp_value2)
if 'output_body_value' in v:
body_graph.add_output_value(v['output_body_value'])
output_value = functions.generate_value_with_same_type(
v['output_body_value'])
outputs.append(output_value)
if field.get_attribute(name).has_obj():
field.get_attribute(name).get_ref().revise(output_value)
else:
field.get_attribute(name).revise(values.ValueRef(output_value))
else:
temp_value1 = v['input_body_value']
temp_value2 = functions.generate_value_with_same_type(
v['input_body_value'])
body_graph.add_output_value(temp_value1)
outputs.append(temp_value2)
node = nodes.NodeListcomp(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
return local_field.get_attribute(internal_list_id).get_ref()
def veval_ast_for(astc : 'AstContext', local_field : 'values.Field', graph : 'Graph'):
'''
for target in iter:
...
'''
assert(isinstance(astc.nast, gast.gast.For))
# for target in iter:
iter_ = veval_ast(astc.c(astc.nast.iter), local_field, graph)
# get target name
target_name = ''
if isinstance(astc.nast.target, gast.gast.Name):
target_name = astc.nast.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
for_id = 'for_' + str(utils.get_guid())
body_id = 'body_' + str(utils.get_guid())
values.commit(for_id)
# Body
body_graph = Graph()
body_graph.name = 'Body'
counter_value = values.NumberValue(0)
counter_value.name = 'for_counter'
cond_value = values.BoolValue(True)
cond_value.name = 'for_cond'
iter_value = iter_.get_value()
# node to lookup a value from sequence
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
target_value = values.Value()
node_forgen.set_outputs([target_value])
target_attribute = local_field.get_attribute(target_name)
target_attribute.revise(target_value)
body_graph.add_node(node_forgen)
body = veval_ast(astc.c(astc.nast.body), local_field, body_graph)
values.commit(body_id)
body_input_attributes = get_input_attritubtes(local_field, for_id, body_id)
body_output_attributes = get_output_attritubtes(local_field, for_id, body_id)
input_attributes_2_values = {}
for attribute in body_input_attributes:
input_attributes_2_values[attribute] = attribute.get_value()
output_attributes_2_values = {}
for attribute in body_output_attributes:
output_attributes_2_values[attribute] = attribute.get_value()
# Exports
values.checkout(for_id)
# generate attribute pairs
name2attributes = {}
for attribute in body_input_attributes:
key = str(attribute.parent.id) + '_' + attribute.name
if key in name2attributes.keys():
name2attributes[key][0] = attribute
else:
name2attributes[key] = [attribute, None]
for attribute in body_output_attributes:
key = str(attribute.parent.id) + '_' + attribute.name
if key in name2attributes.keys():
name2attributes[key][1] = attribute
else:
name2attributes[key] = [None, attribute]
# remove defaule values
name_removing = []
defaule_values = [counter_value, cond_value, iter_value]
for k, v in name2attributes.items():
if v[0] in defaule_values:
name_removing.append(k)
if v[1] in defaule_values:
name_removing.append(k)
for nr in name_removing:
if nr in name2attributes.keys():
name2attributes.pop(nr)
#
inputs = []
outputs = []
non_volatiles = []
# default input for subgraph's input
body_graph.add_input_value(counter_value)
body_graph.add_input_value(cond_value)
body_graph.add_input_value(iter_value)
# default output for subgrap's output
body_graph.add_output_value(cond_value)
body_graph.add_output_value(iter_value)
# default output
outputs.append(functions.generate_value_with_same_type(iter_value))
for attributes in name2attributes.values():
name = ''
parent = None
input_value = None
output_value = None
if attributes[0] is not None:
name = attributes[0].name
parent = attributes[0].parent
if attributes[1] is not None:
name = attributes[1].name
parent = attributes[1].parent
if attributes[0] is not None:
input_value = input_attributes_2_values[attributes[0]]
else:
# value with same type
input_value = functions.generate_value_with_same_type(output_attributes_2_values[attributes[1]])
if attributes[1] is not None:
output_value = output_attributes_2_values[attributes[1]]
else:
# copy value
output_value = input_attributes_2_values[attributes[0]]
output_value_in_node = functions.generate_value_with_same_type(output_value)
inputs.append(input_value)
outputs.append(output_value_in_node)
body_graph.add_input_value(input_value)
body_graph.add_output_value(output_value)
if attributes[1].is_non_volatile:
non_volatiles.append((attribute[1].initial_value,output_value_in_node))
attributes[1].parent.get_attribute(name).revise(output_value_in_node)
node = nodes.NodeFor(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
# add non-volatiles
for tv, v in non_volatiles:
node_nv = nodes.NodeNonVolatileAssign(tv, v)
graph.add_node(node_nv)
return None
def convert_model(model: 'chainer.Chain', args=[]):
# reset values
values.reset_field_and_attributes()
utils.reset_guid()
# generate default module
default_module = values.Module(sys.modules[model.__module__])
# chainer.functions
chainer_functions_module_name = get_module_name(
F, default_module.internal_module)
if chainer_functions_module_name != '':
f_dict = values.Object(values.ModuleValue())
f_relu = values.FuncValue(functions_builtin.ReluFunction(), None)
f_dict.get_field().get_attribute('relu').revise(values.Object(f_relu))
f_softmax = values.FuncValue(functions_builtin.SoftmaxFunction(), None)
f_dict.get_field().get_attribute('softmax').revise(
values.Object(f_softmax))
f_softmax_cross_entropy = values.FuncValue(
functions_builtin.SoftmaxCrossEntropyFunction(), None)
f_dict.get_field().get_attribute('softmax_cross_entropy').revise(
values.Object(f_softmax_cross_entropy))
f_pad_sequence = values.FuncValue(
functions_builtin.PadSequenceFunction(), None)
f_dict.get_field().get_attribute('pad_sequence').revise(
values.Object(f_pad_sequence))
default_module.set_default_value(chainer_functions_module_name, f_dict)
# numpy
numpy_module_name = get_module_name(np, default_module.internal_module)
if numpy_module_name != '':
f_dict = values.Object(values.ModuleValue())
f_array = values.FuncValue(functions_builtin.NDArrayFunction(), None)
f_dict.get_field().get_attribute('array').revise(
values.Object(f_array))
f_dict.get_field().get_attribute('int32').revise(
values.Object(values.NumberValue(utils.numpy_type_2_int(
np.int32))))
f_dict.get_field().get_attribute('float32').revise(
values.Object(
values.NumberValue(utils.numpy_type_2_int(np.float32))))
default_module.set_default_value(numpy_module_name, f_dict)
m_range = values.FuncValue(functions_builtin.RangeFunction(), None)
default_module.set_default_value('range', values.Object(m_range))
m_list = values.FuncValue(functions_builtin.ListFunction(), None)
default_module.set_default_value('list', values.Object(m_list))
model_inst = values.parse_instance(default_module, '', model)
forward_func = model_inst.try_get_and_store_obj('forward')
# convert args
value_args = []
function_args = []
ind = 0
for arg in args:
varg = values.parse_instance(default_module, '', arg, None, True)
varg.name = 'in_' + str(ind)
varg.get_value().name = 'in_' + str(ind)
farg = functions.FunctionArg()
farg.obj = varg
value_args.append(varg.get_value())
function_args.append(farg)
ind += 1
graph = Graph()
forward_func_value = forward_func.get_value()
ret = forward_func_value.func.vcall(default_module, graph,
forward_func_value.obj, function_args)
assert (ret is None or isinstance(ret, values.Object))
ret_ = []
if isinstance(ret.get_value(), values.TupleValue):
ret_.extend([v.get_obj().get_value() for v in ret.get_value().values])
elif isinstance(ret, list):
ret_ = [r.get_value() for r in ret]
else:
ret_ = [ret.get_value()]
for v in value_args:
graph.add_input_value(v)
for v in ret_:
graph.add_output_value(v)
return (value_args, ret_, graph)
def convert_model(model: 'chainer.Chain', args=[]):
# reset values
values.reset_field_and_attributes()
utils.reset_guid()
values.instance_converters.clear()
def instance_converter(m, i):
if links_builtin.is_builtin_chainer_link(i):
return links_builtin.ChainerLinkInstance(m, i)
return None
values.instance_converters.append(instance_converter)
# generate default module
default_module = values.Module(sys.modules[model.__module__])
# chainer
chainer_module_name = get_module_name(
C, default_module.internal_module)
if chainer_module_name != '':
c_dict = values.ValueRef(values.ModuleValue())
# a substitute of Variable
c_variable = values.FuncValue(functions_ndarray.NDArrayFunction(), None)
c_dict.get_field().get_attribute('Variable').revise(values.ValueRef(c_variable))
default_module.set_default_value(chainer_module_name, c_dict)
# chainer.functions
chainer_functions_module_name = get_module_name(
F, default_module.internal_module)
if chainer_functions_module_name != '':
f_dict = values.ValueRef(values.ModuleValue())
def add_chainer_funtion(name:'str', func, ret_value_func = None):
if ret_value_func is None:
f = values.FuncValue(
functions_builtin.ChainerFunction(func), None)
else:
f = values.FuncValue(
functions_builtin.ChainerFunction(func, ret_value_func=ret_value_func), None)
f_dict.get_field().get_attribute(name).revise(values.ValueRef(f))
values.function_converters[func] = f
def ret_tuple():
return values.TupleValue()
add_chainer_funtion('relu', F.relu)
add_chainer_funtion('softmax', F.softmax)
add_chainer_funtion('softmax_cross_entropy', F.softmax_cross_entropy)
add_chainer_funtion('pad_sequence', F.pad_sequence)
add_chainer_funtion('average_pooling_2d', F.average_pooling_2d)
add_chainer_funtion('unpooling_2d', F.unpooling_2d)
add_chainer_funtion('reshape', F.reshape)
add_chainer_funtion('split_axis', F.split_axis, ret_value_func=ret_tuple)
add_chainer_funtion('reshape', F.reshape)
add_chainer_funtion('swapaxes', F.swapaxes)
add_chainer_funtion('dropout', F.dropout)
add_chainer_funtion('concat', F.concat)
add_chainer_funtion('matmul', F.matmul)
add_chainer_funtion('max_pooling_2d', F.max_pooling_2d)
add_chainer_funtion('resize_images', F.resize_images)
if int(chainer.__version__[0]) >= 6:
add_chainer_funtion('roi_max_pooling_2d', F.roi_max_pooling_2d)
add_chainer_funtion('roi_average_pooling_2d', F.roi_average_pooling_2d)
add_chainer_funtion('roi_max_align_2d', F.roi_max_align_2d)
add_chainer_funtion('roi_average_align_2d', F.roi_average_align_2d)
default_module.set_default_value(chainer_functions_module_name, f_dict)
# numpy
numpy_module_name = get_module_name(np, default_module.internal_module)
if numpy_module_name != '':
f_dict = values.ValueRef(values.ModuleValue())
f_array = values.FuncValue(functions_ndarray.NDArrayFunction(), None)
f_dict.get_field().get_attribute('array').revise(values.ValueRef(f_array))
f_zeros = values.FuncValue(functions_ndarray.NDArrayZerosFunction(), None)
f_dict.get_field().get_attribute('zeros').revise(values.ValueRef(f_zeros))
f_full = values.FuncValue(functions_ndarray.NDArrayFullFunction(), None)
f_dict.get_field().get_attribute('full').revise(values.ValueRef(f_full))
f_ceil = values.FuncValue(functions_ndarray.NDArrayCeilFunction(), None)
f_dict.get_field().get_attribute('ceil').revise(values.ValueRef(f_ceil))
f_dict.get_field().get_attribute('int32').revise(
values.ValueRef(values.NumberValue(utils.numpy_type_2_int(np.int32))))
f_dict.get_field().get_attribute('float32').revise(
values.ValueRef(values.NumberValue(utils.numpy_type_2_int(np.float32))))
default_module.set_default_value(numpy_module_name, f_dict)
m_range = values.FuncValue(functions_builtin.RangeFunction(), None)
default_module.set_default_value('range', values.ValueRef(m_range))
m_list = values.FuncValue(functions_builtin.ListFunction(), None)
default_module.set_default_value('list', values.ValueRef(m_list))
model_inst = values.parse_instance(default_module, '', model)
forward_func = model_inst.try_get_and_store_obj('forward')
# convert args
finput = functions.FunctionArgInput()
value_args = []
ind = 0
node_input = nodes.NodeInput('input')
for arg in args:
varg = values.parse_instance(default_module, '', arg, None, True)
varg.name = 'in_' + str(ind)
varg.get_value().name = 'in_' + str(ind)
# make value unknown
# if isinstance(varg.get_value(), values.TupleValue):
# for i in range(len(varg.get_value().internal_value)):
# varg.get_value().internal_value[i] = None
# else:
varg.get_value().internal_value = None
finput.inputs.append(varg)
value_args.append(varg.get_value())
ind += 1
node_input.set_outputs(value_args)
graph = Graph()
graph.add_node(node_input)
forward_func_value = forward_func.get_value()
ret = forward_func_value.func.vcall(
default_module, graph, forward_func_value.obj, finput)
assert(ret is None or isinstance(ret, values.ValueRef))
def try_get_value(value) -> 'values.Value':
if isinstance(value, values.Value):
return value
if isinstance(value, values.ValueRef):
return value.get_value()
if isinstance(value, values.Attribute):
return value.get_ref().get_value()
if ret is None or isinstance(ret, values.NoneValue):
if config.show_warnings:
print('Failed to compile. output is None.')
return (value_args, None, graph)
ret_ = []
if isinstance(ret.get_value(), values.TupleValue):
if ret.get_value().internal_value is not None:
for v in ret.get_value().internal_value:
assert(v is not None)
ret_.append(try_get_value(v))
else:
ret_ = [ret.get_value()]
elif isinstance(ret, list):
ret_ = [r.get_value() for r in ret]
else:
ret_ = [ret.get_value()]
for v in value_args:
graph.add_input_value(v)
for v in ret_:
graph.add_output_value(v)
return (value_args, ret_, graph)
def veval_ast_for(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
for target in iter:
...
'''
assert (isinstance(astc.nast, gast.gast.For))
lineprop = utils.LineProperty(astc.lineno)
# for target in iter:
iter_ = veval_ast(astc.c(astc.nast.iter), local_field, graph)
# get target name
target_name = ''
if isinstance(astc.nast.target, gast.gast.Name):
target_name = astc.nast.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
for_guid = utils.get_guid()
for_id = 'for_' + str(for_guid)
body_id = 'body_' + str(for_guid)
values.push_history(for_id)
# body
body_graph = Graph()
body_graph.name = 'Body_' + str(for_guid)
counter_value = values.NumberValue(0)
counter_value.name = 'for_counter_' + str(for_guid)
cond_value = values.BoolValue(True)
cond_value.name = 'for_cond_' + str(for_guid)
iter_value = try_get_value(iter_, 'for', lineprop)
# create a node to lookup a value from sequence
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
# estimate type
# TODO : more types
if isinstance(iter_value, values.RangeValue):
target_value = values.NumberValue(None)
target_value.dtype = np.array(0).dtype
else:
target_value = values.Value()
target_obj = values.Object(target_value)
node_forgen.set_outputs([target_value])
target_attribute = local_field.get_attribute(target_name)
target_attribute.revise(target_obj)
body_graph.add_node(node_forgen)
# veval body
body = veval_ast(astc.c(astc.nast.body), local_field, body_graph)
value_inputs = values.get_inputs()
value_outputs = values.get_outputs()
values.pop_history()
inputs = []
outputs = []
# default input for subgraph's input
body_graph.add_input_value(counter_value)
body_graph.add_input_value(cond_value)
body_graph.add_input_value(iter_value)
# default output for subgraph's output
body_graph.add_output_value(cond_value)
body_graph.add_output_value(iter_value)
# default output
outputs.append(functions.generate_value_with_same_type(iter_value))
# generate pairs
value_pairs = {}
for v in value_inputs:
key = str(v.field.id) + '_' + v.name
if not (key in value_pairs.keys()):
value_pairs[key] = {}
value_pairs[key]['field'] = v.field
value_pairs[key]['name'] = v.name
value_pairs[key]['input_value'] = v.input_value
value_pairs[key]['input_body_value'] = v.value
for v in value_outputs:
key = str(v.field.id) + '_' + v.name
if not (key in value_pairs.keys()):
value_pairs[key] = {}
value_pairs[key]['field'] = v.field
value_pairs[key]['name'] = v.name
value_pairs[key]['output_body_value'] = v.value
for k, v in value_pairs.items():
name = v['name']
field = v['field']
if 'input_body_value' in v:
inputs.append(v['input_value'])
body_graph.add_input_value(v['input_body_value'])
else:
temp_value1 = functions.generate_value_with_same_type(
v['output_body_value'])
temp_value2 = functions.generate_value_with_same_type(
v['output_body_value'])
inputs.append(temp_value1)
body_graph.add_input_value(temp_value2)
if 'output_body_value' in v:
body_graph.add_output_value(v['output_body_value'])
output_value = functions.generate_value_with_same_type(
v['output_body_value'])
outputs.append(output_value)
if field.get_attribute(name).has_obj():
field.get_attribute(name).get_obj().revise(output_value)
else:
field.get_attribute(name).revise(values.Object(output_value))
else:
temp_value1 = v['input_body_value']
temp_value2 = functions.generate_value_with_same_type(
v['input_body_value'])
body_graph.add_output_value(temp_value1)
outputs.append(temp_value2)
node = nodes.NodeFor(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
return None
def generate_value_with_same_type(value: 'values.Value',
is_dummy_value=False,
suffix_type=SuffixType.Unknown):
assert (isinstance(value, values.Value))
ret = None
if isinstance(value, values.TensorValue):
ret = values.TensorValue()
ret.shape = value.shape
ret.dtype = value.dtype
elif isinstance(value, values.NumberValue):
dtype = None
if value.internal_value is None:
dtype = value.dtype
elif isinstance(value.internal_value, int):
dtype = np.array(value.internal_value).dtype
elif isinstance(value.internal_value, float):
dtype = np.array(value.internal_value).dtype
ret = values.NumberValue(None)
ret.dtype = dtype
elif isinstance(value, values.StrValue):
ret = values.StrValue(None)
elif isinstance(value, values.BoolValue):
ret = values.BoolValue(None)
elif isinstance(value, values.ListValue):
ret = values.ListValue(None)
elif isinstance(value, values.NoneValue):
ret = values.NoneValue()
elif isinstance(value, values.TupleValue):
ret = values.TupleValue()
elif isinstance(value, values.RangeValue):
ret = values.RangeValue()
elif isinstance(value, values.UnknownValue):
ret = values.UnknownValue()
elif ret is None and isinstance(value, values.Value):
ret = values.Value()
else:
assert (False)
assert (ret is not None)
ret.is_dummy_value = is_dummy_value
if suffix_type == SuffixType.Unknown:
ret.name = value.name + '_st'
elif suffix_type == SuffixType.Unused:
ret.name = value.name + '_unused'
elif suffix_type == SuffixType.Dummy:
ret.name = value.name + '_dummy'
elif suffix_type == SuffixType.Input:
ret.name = value.name + '_in'
else:
assert (False)
return ret
def veval_ast_listcomp(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
Ex. [x for x in xx]
[elt for target in iter]
'''
assert (isinstance(astc.nast, gast.gast.ListComp))
lineprop = utils.LineProperty(astc.lineno)
listcomp_guid = str(utils.get_guid())
listcomp_id = 'listcomp_' + listcomp_guid
body_id = 'listcomp_body_' + listcomp_guid
internal_iter_id = '@internal/iter_' + listcomp_guid
generator = astc.nast.generators[0]
iter_value = try_get_value(
veval_ast(astc.c(generator.iter), local_field, graph), 'generator',
lineprop)
list_value = values.ListValue()
list_obj = values.Object(list_value)
node_generate_list = nodes.NodeGenerate('List', [], lineprop)
node_generate_list.set_outputs([list_value])
graph.add_node(node_generate_list)
# body
target_name = ''
if isinstance(generator.target, gast.gast.Name):
target_name = generator.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
counter_value = values.NumberValue(0)
counter_value.name = 'listcomp_counter_' + listcomp_guid
cond_value = values.BoolValue(True)
cond_value.name = 'listcomp_cond_' + listcomp_guid
body_field = values.Field()
body_field.set_module(local_field.module)
body_field.set_parent(local_field)
# set iter with internal name
body_field.get_attribute(internal_iter_id).revise(list_obj)
values.commit(listcomp_id)
body_graph = Graph()
body_graph.name = 'Body_' + listcomp_guid
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
target_value = values.Value()
target_obj = values.Object(target_value)
node_forgen.set_outputs([target_value])
body_field.get_attribute(target_name).revise(target_obj)
body_graph.add_node(node_forgen)
elt = veval_ast(astc.c(astc.nast.elt), body_field, body_graph)
elt_obj = try_get_obj(elt, 'listcomp', lineprop)
farg = functions.FunctionArg()
farg.name = ''
farg.obj = elt_obj
append_value = list_obj.get_field().get_attribute(
'append').get_obj().get_value()
append_value.func.vcall(local_field.module, body_graph, list_obj, [farg],
lineprop)
values.commit(body_id)
body_input_attributes = get_input_attritubtes(
body_field, listcomp_id, body_id) + get_input_attritubtes(
local_field, listcomp_id, body_id)
body_output_attributes = get_output_attritubtes(
body_field, listcomp_id, body_id) + get_output_attritubtes(
local_field, listcomp_id, body_id)
body_input_attributes = filter_attributes(body_input_attributes)
body_output_attributes = filter_attributes(body_output_attributes)
# get objects whose values are changed
value_changed_objs = get_output_objs(body_field, listcomp_id,
body_id) + get_output_objs(
local_field, listcomp_id, body_id)
changed_values = {}
for obj in value_changed_objs:
in_value = obj.get_value_log(listcomp_id)
out_value = obj.get_value_log(body_id)
changed_values[in_value] = (obj, out_value)
output_attributes_2_values = {}
for attribute in body_output_attributes:
output_attributes_2_values[attribute] = attribute.get_obj().get_value()
# get inputs
values.checkout(listcomp_id)
input_attributes_2_values = {}
for attribute in body_input_attributes:
input_attributes_2_values[attribute] = attribute.get_obj().get_value()
# Exports
values.checkout(listcomp_id)
# generate attribute pairs
name2attributes = {}
for attribute in body_input_attributes:
key = str(attribute.parent.id) + '_' + attribute.name
if key in name2attributes.keys():
name2attributes[key][0] = attribute
else:
name2attributes[key] = [attribute, None]
for attribute in body_output_attributes:
key = str(attribute.parent.id) + '_' + attribute.name
if key in name2attributes.keys():
name2attributes[key][1] = attribute
else:
name2attributes[key] = [None, attribute]
# remove defaule values
name_removing = []
defaule_values = [counter_value, cond_value, iter_value]
for k, v in name2attributes.items():
if v[0] is not None and input_attributes_2_values[
v[0]] in defaule_values:
name_removing.append(k)
if v[1] is not None and output_attributes_2_values[
v[1]] in defaule_values:
name_removing.append(k)
for nr in name_removing:
if nr in name2attributes.keys():
name2attributes.pop(nr)
#
inputs = []
outputs = []
non_volatiles = []
# default input for subgraph's input
body_graph.add_input_value(counter_value)
body_graph.add_input_value(cond_value)
body_graph.add_input_value(iter_value)
# default output for subgrap's output
body_graph.add_output_value(cond_value)
body_graph.add_output_value(iter_value)
# default output
outputs.append(functions.generate_value_with_same_type(iter_value))
for attributes in name2attributes.values():
name = ''
parent = None
input_value = None
output_value = None
if attributes[0] is not None:
name = attributes[0].name
parent = attributes[0].parent
if attributes[1] is not None:
name = attributes[1].name
parent = attributes[1].parent
if attributes[0] is not None:
input_value = input_attributes_2_values[attributes[0]]
else:
# value with same type
input_value = functions.generate_value_with_same_type(
output_attributes_2_values[attributes[1]])
if attributes[1] is not None:
output_value = output_attributes_2_values[attributes[1]]
else:
if input_attributes_2_values[
attributes[0]] in changed_values.keys():
# change only values
output_value = changed_values[input_attributes_2_values[
attributes[0]]]
else:
# copy value
output_value = input_attributes_2_values[attributes[0]]
output_value_in_node = functions.generate_value_with_same_type(
output_value)
inputs.append(input_value)
outputs.append(output_value_in_node)
body_graph.add_input_value(input_value)
body_graph.add_output_value(output_value)
if attributes[1] is not None and attributes[1].is_non_volatile:
non_volatiles.append(
(attribute[1].initial_obj.get_value(), output_value_in_node))
output_obj_in_node = values.Object(output_value_in_node)
parent.get_attribute(name).revise(output_obj_in_node)
for changed_value_in, changed_obj_value_out in changed_values.items():
if changed_value_in is None:
continue
if changed_value_in in inputs:
continue
inputs.append(changed_value_in)
body_graph.add_input_value(changed_value_in)
body_graph.add_output_value(changed_obj_value_out[1])
value = functions.generate_value_with_same_type(
changed_obj_value_out[1])
changed_obj_value_out[0].revise(value)
outputs.append(value)
node = nodes.NodeListcomp(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
# add non-volatiles
for tv, v in non_volatiles:
node_nv = nodes.NodeNonVolatileAssign(tv, v)
graph.add_node(node_nv)
if body_field.get_attribute(internal_iter_id).has_obj():
return body_field.get_attribute(internal_iter_id).get_obj()
else:
return list_obj
def veval_ast_for(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
for target in iter:
...
'''
assert (isinstance(astc.nast, gast.gast.For))
# for target in iter:
iter_ = veval_ast(astc.c(astc.nast.iter), local_field, graph)
# get target name
target_name = ''
if isinstance(astc.nast.target, gast.gast.Name):
target_name = astc.nast.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
for_id = 'for_' + str(utils.get_guid())
body_id = 'body_' + str(utils.get_guid())
values.commit(for_id)
# Body
body_graph = Graph()
body_graph.name = 'Body'
counter_value = values.NumberValue(0)
counter_value.name = 'for_counter'
cond_value = values.BoolValue(True)
cond_value.name = 'for_cond'
iter_value = iter_.get_value()
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
target_value = values.Value()
node_forgen.set_outputs([target_value])
target_attribute = local_field.get_attribute(target_name)
target_attribute.revise(target_value)
body_graph.add_node(node_forgen)
body = veval_ast(astc.c(astc.nast.body), local_field, body_graph)
values.commit(body_id)
body_output_attributes = get_input_attritubtes(local_field, for_id,
body_id)
body_intput_attributes = get_output_attritubtes(local_field, for_id,
body_id)
input_attributes_2_values = {}
for attribute in body_intput_attributes:
input_attributes_2_values[attribute] = attribute.get_value()
output_attributes_2_values = {}
for attribute in body_output_attributes:
output_attributes_2_values[attribute] = attribute.get_value()
# Exports
values.checkout(for_id)
input_attributes = set(body_intput_attributes)
inputs = [i.get_value() for i in input_attributes if i.has_value()]
output_attributes = body_output_attributes
output_attributes.remove(target_attribute)
output_attributes = [target_attribute] + output_attributes
outputs = []
non_volatiles = []
# default output
body_graph.add_output_value(cond_value)
body_graph.add_output_value(iter_value)
for attribute in output_attributes:
name = attribute.name
body_graph.add_output_value(output_attributes_2_values[attribute])
value = functions.generate_value_with_same_type(
output_attributes_2_values[attribute])
outputs.append(value)
if attribute.is_non_volatile:
non_volatiles.append((attribute.initial_value, value))
attribute.parent.get_attribute(name).revise(value)
# default input
'''
body_graph.add_input_value(counter_value)
body_graph.add_input_value(cond_value)
body_graph.add_input_value(iter_value)
inputs.remove(counter_value)
inputs.remove(cond_value)
inputs.remove(iter_value)
'''
for input in inputs:
body_graph.add_input_value(input)
node = nodes.NodeFor(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
# add non-volatiles
for tv, v in non_volatiles:
node_nv = nodes.NodeNonVolatileAssign(tv, v)
graph.add_node(node_nv)
return None
def veval_ast_for(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
for target in iter:
...
'''
assert (isinstance(astc.nast, gast.gast.For))
lineprop = utils.LineProperty(astc.lineno)
# for target in iter:
iter_ = veval_ast(astc.c(astc.nast.iter), local_field, graph)
# get target name
target_name = ''
if isinstance(astc.nast.target, gast.gast.Name):
target_name = astc.nast.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
for_id = 'for_' + str(utils.get_guid())
body_id = 'body_' + str(utils.get_guid())
values.commit(for_id)
# body
body_graph = Graph()
body_graph.name = 'Body'
counter_value = values.NumberValue(0)
counter_value.name = 'for_counter'
cond_value = values.BoolValue(True)
cond_value.name = 'for_cond'
iter_value = try_get_value(iter_, 'for', lineprop)
# create a node to lookup a value from sequence
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
# estimate type
# TODO : more types
if isinstance(iter_value, values.RangeValue):
target_value = values.NumberValue(None)
target_value.dtype = np.array(0).dtype
else:
target_value = values.Value()
target_obj = values.Object(target_value)
node_forgen.set_outputs([target_value])
target_attribute = local_field.get_attribute(target_name)
target_attribute.revise(target_obj)
body_graph.add_node(node_forgen)
# veval body
body = veval_ast(astc.c(astc.nast.body), local_field, body_graph)
values.commit(body_id)
# get changed attributes
body_input_attributes = get_input_attritubtes(local_field, for_id, body_id)
body_output_attributes = get_output_attritubtes(local_field, for_id,
body_id)
body_input_attributes = filter_attributes(body_input_attributes)
body_output_attributes = filter_attributes(body_output_attributes)
# get objects whose values are changed
value_changed_objs = get_output_objs(local_field, for_id, body_id)
changed_values = {}
for obj in value_changed_objs:
in_value = obj.get_value_log(for_id)
out_value = obj.get_value_log(body_id)
changed_values[in_value] = out_value
# get outputs
output_attributes_2_values = {}
for attribute in body_output_attributes:
output_attributes_2_values[attribute] = attribute.get_obj().get_value()
# get inputs
values.checkout(for_id)
input_attributes_2_values = {}
for attribute in body_input_attributes:
input_attributes_2_values[attribute] = attribute.get_obj().get_value()
# export
values.checkout(for_id)
# generate attribute pairs
name2attributes = {}
for attribute in body_input_attributes:
key = str(attribute.parent.id) + '_' + attribute.name
if key in name2attributes.keys():
name2attributes[key][0] = attribute
else:
name2attributes[key] = [attribute, None]
for attribute in body_output_attributes:
key = str(attribute.parent.id) + '_' + attribute.name
if key in name2attributes.keys():
name2attributes[key][1] = attribute
else:
name2attributes[key] = [None, attribute]
# remove defaule values
name_removing = []
defaule_values = [counter_value, cond_value, iter_value]
for k, v in name2attributes.items():
if v[0] in defaule_values:
name_removing.append(k)
if v[1] in defaule_values:
name_removing.append(k)
for nr in name_removing:
if nr in name2attributes.keys():
name2attributes.pop(nr)
#
inputs = []
outputs = []
non_volatiles = []
# default input for subgraph's input
body_graph.add_input_value(counter_value)
body_graph.add_input_value(cond_value)
body_graph.add_input_value(iter_value)
# default output for subgrap's output
body_graph.add_output_value(cond_value)
body_graph.add_output_value(iter_value)
# default output
outputs.append(functions.generate_value_with_same_type(iter_value))
for attributes in name2attributes.values():
name = ''
parent = None
input_value = None
output_value = None
if attributes[0] is not None:
name = attributes[0].name
parent = attributes[0].parent
if attributes[1] is not None:
name = attributes[1].name
parent = attributes[1].parent
if attributes[0] is not None:
input_value = input_attributes_2_values[attributes[0]]
else:
# value with same type
input_value = functions.generate_value_with_same_type(
output_attributes_2_values[attributes[1]])
if attributes[1] is not None:
output_value = output_attributes_2_values[attributes[1]]
else:
if input_attributes_2_values[
attributes[0]] in changed_values.keys():
# change only values
output_value = changed_values[input_attributes_2_values[
attributes[0]]]
else:
# copy value
output_value = input_attributes_2_values[attributes[0]]
output_value_in_node = functions.generate_value_with_same_type(
output_value)
inputs.append(input_value)
outputs.append(output_value_in_node)
body_graph.add_input_value(input_value)
body_graph.add_output_value(output_value)
if attributes[1] is not None and attributes[1].is_non_volatile:
non_volatiles.append(
(attributes[1].initial_obj.get_value(), output_value_in_node))
output_obj_in_node = values.Object(output_value_in_node)
parent.get_attribute(name).revise(output_obj_in_node)
for changed_value_in, changed_value_out in changed_values.items():
if changed_value_in is None:
continue
if changed_value_in in inputs:
continue
inputs.append(changed_value_in)
body_graph.add_input_value(changed_value_in)
body_graph.add_output_value(changed_value_out)
value = functions.generate_value_with_same_type(changed_value_out)
obj.revise(value)
outputs.append(value)
node = nodes.NodeFor(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
# add non-volatiles
for tv, v in non_volatiles:
node_nv = nodes.NodeNonVolatileAssign(tv, v)
graph.add_node(node_nv)
return None
def veval_ast_listcomp(astc: 'AstContext', local_field: 'values.Field',
graph: 'Graph'):
'''
Ex. [x for x in xx]
[elt for target in iter]
'''
assert (isinstance(astc.nast, gast.gast.ListComp))
lineprop = utils.LineProperty(astc.lineno)
listcomp_id = 'listcomp_' + str(utils.get_guid())
body_id = 'listcomp_body_' + str(utils.get_guid())
values.commit(listcomp_id)
generator = astc.nast.generators[0]
iter_value = try_get_value(
veval_ast(astc.c(generator.iter), local_field, graph), 'generator',
lineprop)
list_value = values.ListValue()
node_generate_list = nodes.NodeGenerate('List', [], lineprop)
graph.add_node(node_generate_list)
node_generate_list.set_outputs([list_value])
# body
target_name = ''
if isinstance(generator.target, gast.gast.Name):
target_name = generator.target.id
else:
if config.show_warnings:
print('This for is not supported. in L.{}'.format(astc.lineno))
return None
counter_value = values.NumberValue(0)
counter_value.name = 'for_counter'
node_forgen = nodes.NodeForGenerator(counter_value, iter_value)
target_value = values.Value()
node_forgen.set_outputs([target_value])
body_field = values.Field()
body_field.set_module(local_field.module)
body_field.set_parent(local_field)
body_field.get_attribute(target_name).revise(target_value)
body_graph = Graph()
body_graph.name = 'Body'
body_graph.add_node(node_forgen)
elt = veval_ast(astc.c(astc.nast.elt), body_field, body_graph)
farg = functions.FunctionArg()
farg.name = ''
farg.value = elt
list_value.append_func.func.vcall(local_field.module, body_graph,
list_value, [farg], lineprop)
values.commit(body_id)
body_output_attributes = get_input_attritubtes(body_field, listcomp_id,
body_id)
body_intput_attributes = get_output_attritubtes(body_field, listcomp_id,
body_id)
# Exports
values.checkout(listcomp_id)
input_attributes = set(body_intput_attributes)
inputs = [i.get_value() for i in input_attributes]
output_attributes = set(body_output_attributes)
outputs = []
for attribute in output_attributes:
value = values.Value()
outputs.append(value)
attribute.revise(value)
node = nodes.NodeListcomp(iter_value, inputs, body_graph, astc.lineno)
node.set_outputs(outputs)
graph.add_node(node)
# compare
return list_value
