forked from jiuyuan/InfiniTensor
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6 Commits
Author | SHA1 | Date |
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wanghailu | 8737f5773d | |
wanghailu | fbaa5116b4 | |
wanghailu | 5472c3c61b | |
wanghailu | 4eb55542de | |
wanghailu | a7972bbb9b | |
wanghailu | 0699ffbd13 |
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#pragma once
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#include "cnrt.h"
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#include <string>
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#include <vector>
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namespace infini {
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class BangGenCode {
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private:
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int64_t nram_size = 589824;
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int64_t deal_size;
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int64_t deal_size_align;
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std::vector<std::string> inputs;
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std::string output;
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std::vector<std::string> compute;
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int64_t totalNum;
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int64_t workerNum;
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public:
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BangGenCode(std::vector<std::string> inputs_list, std::string output_value, std::vector<std::string> compute_list, int64_t total, int64_t worker);
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private:
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std::string genHead();
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std::string genNramSplit();
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std::string genFunctionHead();
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std::string genFunctionEnd();
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std::string genTaskCycle();
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std::string genOffset();
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std::string computeKernel(std::string size);
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std::string genKernelCycle();
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public:
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std::string genElementwiseFusion();
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};
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}
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#pragma once
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#include <string>
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#include <vector>
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namespace infini {
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enum class BangOpName {
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None,
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Add,
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Sub,
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Mul,
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Relu,
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Sigmoid,
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};
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class BangOperator {
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};
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class BangGraph {
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public:
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};
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}
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#include "bang/bang_gencode.h"
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namespace infini {
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BangGenCode::BangGenCode(std::vector<std::string> inputs_list, std::string output_value, std::vector<std::string> compute_list, int64_t total, int64_t worker) : inputs(inputs_list), output(output_value), compute(compute_list), totalNum(total), workerNum(worker) {}
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std::string BangGenCode::genHead() {
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std::string temp = "#include <bang.h>\n";
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temp += "#define NRAM_SIZE " + std::to_string(nram_size) + "\n";
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return temp;
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}
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std::string BangGenCode::genNramSplit() {
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int64_t num = this->inputs.size();
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deal_size = nram_size / (num + 1);
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deal_size_align = ((deal_size) / (64) * (64));
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std::string temp = "";
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for(uint32_t i = 0; i < num; ++i) {
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temp += "__nram__ char nram_buffer" + std::to_string(i) + "[" + std::to_string(deal_size_align) + "];\n";
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}
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temp += "__nram__ char nram_buffer_output[" + std::to_string(deal_size_align) + "];\n";
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return temp;
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}
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std::string BangGenCode::genFunctionHead() {
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std::string temp = "";
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temp += "template<typename T>;\n";
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temp += "__mlu_device__ void FusionFunction(";
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temp += "T*" + this->output + ",\n";
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for(uint32_t i = 0; i < this->inputs.size(); ++i) {
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temp += "T*" + this->inputs[i] + ",\n";
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}
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temp += "int64_t taskNum) {\n";
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return temp;
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}
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std::string BangGenCode::genFunctionEnd() {
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return "}\n";
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}
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std::string BangGenCode::genTaskCycle() {
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int64_t num_per_core = this->totalNum / this->workerNum;
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int64_t num_rem = this->totalNum % this->workerNum;
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int64_t easy_job = num_per_core;
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int64_t hard_job = num_per_core + ( num_rem != 0 ? 1 : 0);
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std::string temp = "";
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temp += "int64_t my_job = taskId < " + std::to_string(num_rem) + " ? " + std::to_string(hard_job) + " : " + std::to_string(easy_job) + ";\n";
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temp += "int64_t start = ( taskId <= " + std::to_string(num_rem) + " ) ? ( taskId *" + std::to_string(hard_job) + " )\n";
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temp += ": ( " + std::to_string(num_rem) + " * " + std::to_string(hard_job) + " + ( taskId - " + std::to_string(num_rem) + ") * " + std::to_string(easy_job) + ");\n";
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return temp;
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}
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std::string BangGenCode::genOffset() {
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std::string temp = "";
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for(uint32_t i = 0; i < this->inputs.size(); ++i) {
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temp += "char *" + this->inputs[i] + "_start = (char*)" + this->inputs[i] + "start * sizeof(T);\n";
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}
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temp += "char *" + this->output + "_start = (char*)" + this->output + "start * sizeof(T);\n";
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return temp;
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}
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std::string BangGenCode::computeKernel(std::string size) {
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std::string temp = "";
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for(uint32_t i = 0; i < this->inputs.size(); ++i) {
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temp += "__memcpy(nram_buffer" + std::to_string(i) + ", " + this->inputs[i] + "_start, " + size + ", GDRAM2NRAM);\n";
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}
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////////////////////////////////////////
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// Compute
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////////////////////////////////////////
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temp += "__memcpy(" + this->output + "_start, nram_buffer_output, " + size + ", NRAM2GDRAM);\n";
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for(uint32_t i = 0; i < this->inputs.size(); ++i) {
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temp += this->inputs[i] + "_start += " + std::to_string(deal_size_align) + ";\n";
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}
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temp += this->output + "_start += " + std::to_string(deal_size_align) + ";\n";
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return temp;
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}
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std::string BangGenCode::genKernelCycle() {
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std::string temp = "";
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temp += "int64_t deal_num_align = " + std::to_string(deal_size_align) + " / sizeof(T);\n";
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temp += "int64_t repeat = my_job / deal_num_align;\n";
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temp += "int64_t rem = my_job % deal_num_align;\n";
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temp += "for(int i = 0; i < repeat; ++i) {;\n";
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temp += this->computeKernel(std::to_string(deal_size_align));
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temp += "}\n";
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temp += "if(rem){;\n";
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temp += this->computeKernel("rem * sizeof(T)");
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temp += "}\n";
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return temp;
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}
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std::string BangGenCode::genElementwiseFusion() {
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std::string temp = "";
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temp += genHead();
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temp += genNramSplit();
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temp += genFunctionHead();
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temp += genTaskCycle();
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temp += genOffset();
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temp += genKernelCycle();
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return temp;
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}
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}
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@ -0,0 +1,24 @@
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#include "bang/bang_gencode.h"
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#include "test.h"
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namespace infini {
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void testCodegen() {
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std::vector<std::string> inputs;
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std::vector<std::string> compute;
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inputs.push_back("inputa");
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inputs.push_back("inputb");
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compute.push_back("add");
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std::string output;
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BangGenCode gen(inputs, output, compute, 1024, 12);
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std::cout << gen.genElementwiseFusion();
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EXPECT_TRUE(1);
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}
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TEST(cnnl_Clip, run) {
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testCodegen();
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}
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} // namespace infini
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