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miaobytemiaobyte
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changeshape:补充expand等shape算子
1 parent bcaf203 commit 22c724c

24 files changed

Lines changed: 495 additions & 438 deletions

‎excuter/op-mem-ompsimd/src/deepx/op/changeshape.hpp‎

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#ifndef DEEPX_OP_CHANGESHAPE_HPP
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#define DEEPX_OP_CHANGESHAPE_HPP
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#include "deepx/op/op.hpp"
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#include "deepx/tensorfunc/changeshape.hpp"
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#include "deepx/dtype.hpp"
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namespace deepx::op
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{
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template <typename T>
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class Concat : public Op{
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public:
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Concat(){
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this->init("concat",deepx::dtype<T>::name(), {}, {}, false, {}, {});
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}
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Concat(vector< string> args, vector< string> returns, bool require_grad = false, vector< string> args_grad = {}, vector< string> returns_grad = {}){
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this->init("concat",deepx::dtype<T>::name(), args, returns, require_grad, args_grad, returns_grad);
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}
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Concat(initializer_list< string> args, initializer_list< string> returns, bool require_grad = false, initializer_list< string> args_grad = {}, initializer_list< string> returns_grad = {}){
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this->init("concat",deepx::dtype<T>::name(), args, returns, require_grad, args_grad, returns_grad);
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}
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void setexample() override {
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this->init("concat", "float32", {"T1", "T2", "3"}, {"T3"}, false, {}, {});
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}
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string math_formula() const override {
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return "T3 = concat([T1, T2], axis=3)";
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}
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void forward(mem::Mem &mem) override
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{
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std::vector<Tensor<T>*> input;
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for (int i=0;i<this->args.size()-1;i++){
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input.push_back(mem.gettensor<T>(this->args[i]).get());
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}
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auto output = mem.gettensor<T>(this->returns[0]).get();
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int axis = mem.getarg<int>(this->args.back());
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tensorfunc::concat(input,axis,*output);
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};
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void backward(mem::Mem &mem) override
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{
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std::vector<Tensor<T>*> input;
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for (int i=0;i<this->args.size()-1;i++){
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input.push_back(mem.gettensor<T>(this->args[i]).get());
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}
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int axis = mem.getarg<int>(this->args.back());
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auto output = mem.gettensor<T>(this->returns[0]).get();
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tensorfunc::split(*output,axis,input);
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};
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};
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template <typename T>
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class Reshape : public Op
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{
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public:
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Reshape()
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{
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this->init("reshape", "any", {}, {}, false, {}, {});
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}
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void forward(mem::Mem &mem) override
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{
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auto input = mem.gettensor<T>(this->args[0]).get();
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auto output = mem.gettensor<T>(this->returns[0]).get();
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vector<int> shape;
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if (this->args.size() == 2 && !is_integer(this->args[1]))
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{
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shape = mem.getvector<int32_t>(this->args[1]);
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}
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else
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{
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for (int i = 1; i < this->args.size(); i++)
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{
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shape.push_back(atoi(this->args[i].c_str()));
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}
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}
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tensorfunc::reshape(*input, *output, shape);
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}
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void backward(mem::Mem &mem) override
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{
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auto return_grad = mem.gettensor<T>(this->returns_grad[0]).get();
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auto input_grad = mem.gettensor<T>(this->args_grad[0]).get();
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auto input = mem.gettensor<T>(this->args[0]).get();
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vector<int> shape = input->shape.shape;
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tensorfunc::reshape(*return_grad, *input_grad, shape);
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}
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void setexample() override {
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this->init("reshape", "float32", {"T1", "2","3","4"}, {"T2"}, false, {}, {});
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}
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string math_formula() const override {
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return "T2 = reshape(T1, [2,3,4])";
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}
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};
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template <typename T>
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class Transpose : public Op {
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public:
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Transpose() {
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this->init("transpose", "any", {}, {}, false, {}, {});
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}
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Transpose(vector<string> args, vector<string> returns, bool require_grad = false, vector<string> args_grad = {}, vector<string> returns_grad = {}) {
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this->init("transpose", "any", args, returns, require_grad, args_grad, returns_grad);
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}
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Transpose(initializer_list<string> args, initializer_list<string> returns, bool require_grad = false, initializer_list<string> args_grad = {}, initializer_list<string> returns_grad = {}) {
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this->init("transpose", "any", args, returns, require_grad, args_grad, returns_grad);
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}
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void forward(mem::Mem &mem) override {
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auto input = mem.gettensor<T>(this->args[0]).get();
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vector<int> dimOrder;
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if (this->args.size()==2&&!is_integer(this->args[1])){
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dimOrder=mem.getvector<int32_t>(this->args[1]);
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}else if (this->args.size()>2){
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for (int i = 1; i < this->args.size(); i++) {
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dimOrder.push_back(atoi(this->args[i].c_str()));
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}
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}
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auto output = mem.gettensor<T>(this->returns[0]).get();
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tensorfunc::transpose(*input, *output, dimOrder);
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}
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void backward(mem::Mem &mem) override {
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auto input_grad = mem.gettensor<T>(this->args_grad[0]).get();
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vector<int> dimOrder;
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if (this->args.size()==2&&!is_integer(this->args[1])){
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dimOrder=mem.getvector<int32_t>(this->args[1]);
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}else if (this->args.size()>2){
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for (int i = 1; i < this->args.size(); i++) {
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dimOrder.push_back(atoi(this->args[i].c_str()));
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}
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}
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auto output_grad = mem.gettensor<T>(this->returns_grad[0]).get();
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tensorfunc::transpose(*output_grad, *input_grad, dimOrder);
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}
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void setexample() override {
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this->init("transpose", "float32", {"T1", "1","0"}, {"T2"}, false, {}, {});
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}
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string math_formula() const override {
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return "T2 = transpose(T1, dimorder=[1,0])";
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}
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};
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}
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#endif // DEEPX_OP_CONCAT_HPP

‎excuter/op-mem-ompsimd/src/deepx/op/concat.hpp‎

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‎excuter/op-mem-ompsimd/src/deepx/op/matmul.hpp‎

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#ifndef DEEPX_OP_MATMUL_HPP
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#define DEEPX_OP_MATMUL_HPP
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#include <iostream>
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#include "deepx/shape_transpose.hpp"
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#include "deepx/op/op.hpp"
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#include "deepx/mem/mem.hpp"
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#include "deepx/tensorfunc/new.hpp"
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#include "deepx/tensorfunc/matmul.hpp"
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#include "deepx/tensorfunc/transpose.hpp"
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#include "deepx/tensorfunc/changeshape.hpp"
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namespace deepx::op
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{
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using namespace std;

‎excuter/op-mem-ompsimd/src/deepx/op/opfactory.cpp‎

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#include "deepx/op/new.hpp"
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#include "deepx/op/arg.hpp"
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#include "deepx/op/print.hpp"
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#include "deepx/op/transpose.hpp"
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#include "deepx/op/reshape.hpp"
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#include "deepx/op/changeshape.hpp"
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namespace deepx::op
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{
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//new

‎excuter/op-mem-ompsimd/src/deepx/op/opfactory.hpp‎

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#include <algorithm>
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#include "deepx/op/op.hpp"
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#include "deepx/op/concat.hpp"
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namespace deepx::op
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{
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using Op_dtype = std::unordered_map<std::string, std::shared_ptr<Op>>;

‎excuter/op-mem-ompsimd/src/deepx/op/reduce.hpp‎

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#include "deepx/tensor.hpp"
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#include "deepx/tensorfunc/reduce.hpp"
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#include "deepx/tensorfunc/broadcast.hpp"
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#include "deepx/tensorfunc/changeshape.hpp"
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#include "deepx/tensorfunc/compare.hpp"
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#include "stdutil/num.hpp"
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‎excuter/op-mem-ompsimd/src/deepx/op/reshape.hpp‎

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‎excuter/op-mem-ompsimd/src/deepx/op/transpose.hpp‎

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