* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
#include "onnx_common.h"
#include "op_math_proto_extend.h"
#include "math/muls/op_graph/muls_proto.h"
#include "math/shape/op_graph/shape_proto.h"
using namespace ge;
namespace domi {
static Status ParseParamsRandomNormal(const Message* op_src, ge::Operator& op_dest)
{
const ge::onnx::NodeProto* node = dynamic_cast<const ge::onnx::NodeProto*>(op_src);
if (node == nullptr) {
OP_LOGE(GetOpName(op_dest).c_str(), "Dynamic cast op_src to NodeProto failed.");
return FAILED;
}
op_dest.DynamicInputRegister("x", 1);
op_dest.DynamicOutputRegister("y", 1);
op_dest.SetAttr("original_type", "ai.onnx::11::RandomNormal");
int dtype = 1;
float mean = 0.0f;
float scale = 1.0f;
int seed = 0;
std::vector<int> shape_list;
for (const auto& attr : node->attribute()) {
if (attr.name() == "dtype" && attr.type() == ge::onnx::AttributeProto::INT) {
dtype = attr.i();
} else if (attr.name() == "mean" && attr.type() == ge::onnx::AttributeProto::FLOAT) {
mean = attr.f();
} else if (attr.name() == "scale" && attr.type() == ge::onnx::AttributeProto::FLOAT) {
scale = attr.f();
} else if (attr.name() == "seed" && attr.type() == ge::onnx::AttributeProto::FLOAT) {
seed = (int)attr.f();
} else if (attr.name() == "shape" && attr.type() == ge::onnx::AttributeProto::INTS) {
for (int dim : attr.ints()) {
shape_list.push_back(dim);
}
};
}
if (shape_list.empty()) {
OP_LOGE(GetOpName(op_dest).c_str(), "Attr of shape must be not null.");
return FAILED;
}
op_dest.SetAttr("name", node->name());
op_dest.SetAttr("mean", mean);
op_dest.SetAttr("scale", scale);
op_dest.SetAttr("seed", seed);
op_dest.SetAttr("dtype", dtype);
op_dest.SetAttr("shape", shape_list);
return SUCCESS;
}
static Status ParseOpToGraphRandomNormal(const ge::Operator& op, ge::Graph& graph)
{
std::string ori_name;
if (op.GetAttr("name", ori_name) != SUCCESS) {
OP_LOGE(GetOpName(op).c_str(), "get name from op failed.");
return FAILED;
}
float mean = 0.0f;
op.GetAttr("mean", mean);
float scale = 1.0f;
op.GetAttr("scale", scale);
int dtype = 1;
op.GetAttr("dtype", dtype);
std::vector<int> shape;
op.GetAttr("shape", shape);
if (shape.empty()) {
OP_LOGE(GetOpName(op).c_str(), "Attr of shape must be not null.");
return FAILED;
}
std::map<int, ge::DataType> kvlist = {{1, ge::DT_FLOAT}, {10, ge::DT_FLOAT16}, {11, ge::DT_DOUBLE}};
if (kvlist.find(dtype) == kvlist.end()) {
OP_LOGE(GetOpName(op).c_str(), "only support float32/half/double, but got %d", dtype);
return FAILED;
}
int seed = 0;
op.GetAttr("seed", seed);
ge::TensorDesc tensorDescInput;
int64_t shapeLen = shape.size();
std::vector<int64_t> dimsInput = {shapeLen};
ge::Shape shapeInput(dimsInput);
tensorDescInput.SetShape(shapeInput);
tensorDescInput.SetDataType(ge::DT_INT32);
ge::Tensor tensor_shape(tensorDescInput, reinterpret_cast<uint8_t*>(shape.data()), shapeLen * sizeof(int));
auto shape_op = op::Const((ori_name + "_input_shape").c_str()).set_attr_value(tensor_shape);
auto random_op = op::RandomStandardNormal((ori_name + "_randomNormal").c_str())
.set_input_shape(shape_op)
.set_attr_dtype(kvlist[dtype])
.set_attr_seed(seed)
.set_attr_seed2(seed);
auto mul_op = op::Muls((ori_name + "_mul").c_str()).set_input_x(random_op).set_attr_value(scale);
auto add_op = op::Adds((ori_name + "_add").c_str()).set_input_x(mul_op).set_attr_value(mean);
std::vector<ge::Operator> inputs{shape_op};
std::vector<std::pair<ge::Operator, std::vector<size_t>>> outputs;
outputs.emplace_back(add_op, std::vector<std::size_t>{0});
graph.SetInputs(inputs).SetOutputs(outputs);
return SUCCESS;
}
REGISTER_CUSTOM_OP("PartitionedCall")
.FrameworkType(ONNX)
.OriginOpType({ge::AscendString("ai.onnx::8::RandomNormal"),
ge::AscendString("ai.onnx::9::RandomNormal"),
ge::AscendString("ai.onnx::10::RandomNormal"),
ge::AscendString("ai.onnx::11::RandomNormal"),
ge::AscendString("ai.onnx::12::RandomNormal"),
ge::AscendString("ai.onnx::13::RandomNormal"),
ge::AscendString("ai.onnx::14::RandomNormal"),
ge::AscendString("ai.onnx::15::RandomNormal"),
ge::AscendString("ai.onnx::16::RandomNormal"),
ge::AscendString("ai.onnx::17::RandomNormal"),
ge::AscendString("ai.onnx::18::RandomNormal")})
.ParseParamsFn(ParseParamsRandomNormal)
.ParseOpToGraphFn(ParseOpToGraphRandomNormal)
.ImplyType(ImplyType::TVM);
}
