def _apply(self):
shape = self.op.attr("shape")
dtype = self.op.attr("dtype")
value = self.op.attr("value")
print("shape: ", shape)
print("dtype: ", dtype)
print("value: ", value)
tensor = self._create_ge_tensor(shape, dtype, value)
const = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor)
self._mark_as_input(const)
if self.op.block.var(self.op.output('Out')[0]).persistable:
print("%s fill_constant" % (self.op.output('Out')[0]))
var = core.GEOperatorFactory.create_operator(
self.op.output('Out')[0], "Variable")
var.update_output_desc("y",
core.GETensorDesc(
core.GEShape(shape),
core.GEFormat.FORMAT_ND,
core.GEDataType.DT_FLOAT))
assign = core.GEOperatorFactory.create_operator(
"assign" + self._accumulated_op_id(), "Assign").set_input(
"value", const).set_input("ref", var)
return [const], [[0]]
else:
print(
"self.op.output('Out')[0] is not persistable in fill_constant")
return [const], [[0]]
def _create_ge_tensor(self, shape, dtype, value):
tensor_desc = core.GETensorDesc(
core.GEShape(shape), core.GEFormat.FORMAT_ND,
self.ascend_helper.dtype2ge(dtype))
tensor = core.GETensor(tensor_desc)
data = (value * np.ones((
shape))).reshape(shape).astype(self.ascend_helper.dtype2np(dtype))
buf = data.tobytes()
data_8 = np.frombuffer(buf, dtype=np.uint8)
tensor.set_data(data_8)
return tensor
def _construct_input_map(self, input_varlist):
ret_map = {}
ge_in_operator = []
for id, var in enumerate(input_varlist):
if var.is_data: # input data
ge_input = core.GEOperatorFactory.create_operator(
var.name, "Data").set_attr_int32("index", id)
ret_map[var.name] = ge_input
ge_in_operator.append(ge_input)
else: # param, learning ...
ge_input = core.GEOperatorFactory.create_operator(var.name,
"Variable")
ge_input.update_output_desc("y",
core.GETensorDesc(
core.GEShape(var.shape),
core.GEFormat.FORMAT_ND,
core.GEDataType.DT_FLOAT))
ret_map[var.name] = ge_input
return ge_in_operator, ret_map
def _apply(self):
shape = self.op.attr("shape")
dtype = self.op.attr("dtype")
mean = self.op.attr("mean")
std = self.op.attr("std")
seed = self.op.attr("seed")
tensor1 = self._create_ge_tensor([len(shape)], 2, shape)
shape_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor1)
tensor2 = self._create_ge_tensor([1], dtype, mean)
mean_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor2)
tensor3 = self._create_ge_tensor([1], dtype, std)
std_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor3)
tensor4 = self._create_ge_tensor([1], dtype, mean - 2 * std)
min_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor4)
tensor5 = self._create_ge_tensor([1], dtype, mean + 2 * std)
max_tensor = core.GEOperatorFactory.create_operator(
"const" + self._accumulated_op_id(), "Const").set_attr_tensor(
"value", tensor5)
self._mark_as_input(shape_tensor)
self._mark_as_input(mean_tensor)
self._mark_as_input(std_tensor)
self._mark_as_input(min_tensor)
self._mark_as_input(max_tensor)
truncated_normal = core.GEOperatorFactory.create_operator(
"truncated_normal" + self._accumulated_op_id(),
"ParameterizedTruncatedNormal").set_input(
"shape", shape_tensor).set_input(
"means", mean_tensor).set_input(
"stdevs", std_tensor).set_input(
"min", min_tensor).set_input(
"max", max_tensor).set_attr_int32("seed", 0)
## wirte the output of truncatedNormal from startup_program to main_program
if self.op.block.var(self.op.output('Out')[0]).persistable:
print("%s is Persistable in truncated_normal" %
(self.op.output('Out')[0]))
#var = core.GEOperatorFactory.create_operator(self.op.output('Out')[0], "Variable").set_input("x", truncated_normal)
var = core.GEOperatorFactory.create_operator(
self.op.output('Out')[0], "Variable")
var.update_output_desc("y",
core.GETensorDesc(
core.GEShape(shape),
core.GEFormat.FORMAT_ND,
core.GEDataType.DT_FLOAT))
assign = core.GEOperatorFactory.create_operator(
"assign" + self._accumulated_op_id(), "Assign").set_input(
"value", truncated_normal).set_input("ref", var)
return [
shape_tensor, mean_tensor, std_tensor, min_tensor, max_tensor,
truncated_normal
], [[-1]]
else:
print(
"self.op.output('Out')[0] is not persistable in truncated_noraml"
)
return [truncated_normal], [[0]] #[assign]