236 lines
7.2 KiB
C++
236 lines
7.2 KiB
C++
#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include <string>
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#include <regex>
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#include <algorithm>
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#include "triton/codegen/selection/selection.h"
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#include "triton/runtime/function.h"
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#include "triton/lang/lang.h"
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#include "triton/driver/device.h"
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#include "triton/driver/stream.h"
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#include "triton/driver/kernel.h"
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#include "triton/driver/module.h"
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#include "triton/ir/module.h"
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#include "triton/ir/function.h"
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#include "triton/tools/bench.hpp"
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typedef struct yy_buffer_state * YY_BUFFER_STATE;
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extern int yyparse();
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extern YY_BUFFER_STATE yy_scan_string(const char * str);
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extern void yy_delete_buffer(YY_BUFFER_STATE buffer);
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extern triton::lang::translation_unit *ast_root;
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using namespace triton;
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inline std::string to_tf_ty(ir::type *ty) {
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if(ty->is_integer_ty(1))
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return "bool";
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if(ty->is_integer_ty(8))
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return "int8";
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if(ty->is_integer_ty(16))
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return "int16";
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if(ty->is_integer_ty(32))
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return "int32";
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if(ty->is_integer_ty(64))
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return "int64";
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if(ty->is_half_ty())
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return "float16";
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if(ty->is_float_ty())
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return "float32";
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if(ty->is_double_ty())
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return "float64";
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if(ty->is_pointer_ty())
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return "Tensor";
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throw std::runtime_error("unknown type");
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}
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inline std::string to_tf_scalar_ty(ir::type *ty) {
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if(ty->is_pointer_ty())
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return to_tf_ty(ty->get_pointer_element_ty());
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else {
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return to_tf_ty(ty);
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}
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}
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inline std::string ref_to_tf_ty(ir::type *ty) {
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std::string res = to_tf_ty(ty);
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if(ty->is_pointer_ty())
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res = "const " + res + "&";
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return res;
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}
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inline triton::lang::translation_unit *make_ast(const char *src) {
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YY_BUFFER_STATE buffer = yy_scan_string(src);
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yyparse();
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yy_delete_buffer(buffer);
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triton::lang::translation_unit *program = ast_root;
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return program;
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}
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inline std::unique_ptr<ir::module> make_ir(ir::context& ctx, triton::lang::translation_unit *program) {
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// create Triton-IR from AST
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ir::module* module = new ir::module("", ctx);
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program->codegen(module);
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return std::unique_ptr<ir::module>(module);
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}
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std::string make_tensorflow_src(const std::string src,
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const std::vector<size_t>& outputs,
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const std::string& macro) {
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triton::lang::translation_unit *ast = make_ast(src.c_str());
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triton::ir::context context;
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std::unique_ptr<ir::module> ir = make_ir(context, ast);
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// extract function signature
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ir::function* fn = ir->get_function_list().front();
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ir::function_type* fn_ty = fn->get_fn_type();
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// numberof arguments
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size_t n_args = fn_ty->get_num_params();
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size_t n_outputs = outputs.size();
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// extract function name
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std::string name = fn->get_name();
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name[0] = static_cast<char>(std::toupper(name[0]));
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std::string classname = name + "Op";
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// extract argument name
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std::vector<std::string> arg_names;
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for(ir::argument *arg: fn->args())
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arg_names.push_back(arg->get_name());
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// cached int to str
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std::vector<std::string> str_i;
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for(size_t i = 0; i < fn_ty->get_num_params(); i++)
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str_i.push_back(std::to_string(i));
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// index of tensors
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std::vector<size_t> ptr_idx;
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for(unsigned i = 0; i < fn_ty->get_num_params(); i++)
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if(fn_ty->get_param_ty(i)->is_pointer_ty())
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ptr_idx.push_back(i);
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// extract tensorflow types
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std::vector<std::string> tf_scalar_tys;
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std::transform(fn_ty->params_begin(), fn_ty->params_end(), std::back_inserter(tf_scalar_tys), to_tf_scalar_ty);
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std::vector<std::string> tf_cref_tys;
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std::transform(fn_ty->params_begin(), fn_ty->params_end(), std::back_inserter(tf_cref_tys), ref_to_tf_ty);
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std::ostringstream oss;
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std::string result = R"(
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#include "triton/driver/buffer.h"
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#include "triton/driver/backend.h"
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#include "triton/driver/stream.h"
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#include "triton/runtime/function.h"
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#define EIGEN_USE_GPU
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#include "tensorflow/core/framework/op.h"
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#include "tensorflow/core/framework/shape_inference.h"
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#include "tensorflow/core/framework/op_kernel.h"
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#include "tensorflow/core/util/cuda_kernel_helper.h"
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#include "tensorflow/core/util/padding.h"
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#include "tensorflow/core/util/tensor_format.h"
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#include "tensorflow/core/framework/common_shape_fns.h"
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using namespace tensorflow;
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using GPUDevice = Eigen::GpuDevice;
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namespace rt = triton::runtime;
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namespace drv = triton::driver;
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std::string src = R"TTKERNSRC( )" + src + ")TTKERNSRC\";" + R"(
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class )" + classname + R"(: public OpKernel {
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public:
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explicit )" + classname + R"((OpKernelConstruction* context)
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: OpKernel(context), fn_(src) { }
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void Compute(OpKernelContext* context){
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// get device/stream
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GPUDevice device = context->eigen_device<GPUDevice>();
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drv::cu_stream sstream(device.stream(), false);
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drv::context* ctx = sstream.context();
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drv::stream* stream = &sstream;
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// extract inputs)";
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for(unsigned i = 0; i < n_args; i++){
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std::string suffix = "";
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std::string ty = tf_cref_tys[i];
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if(!fn_ty->get_param_ty(i)->is_pointer_ty())
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suffix = ".scalar<" + ty + ">()()";
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result += R"(
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)" + ty + " " + arg_names[i] + " = context->input(" + str_i[i] + ")" + suffix + ";";
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}
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result += R"(
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// extract outputs)";
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for(unsigned i = 0; i < n_outputs; i++)
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result += R"(
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context->set_output()" + str_i[i] + ", " + arg_names[outputs[i]] + ");";
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result += R"(
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// wrap tensors)";
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for(size_t i: ptr_idx)
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result += R"(
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drv::cu_buffer cu_)" + arg_names[i] + "(ctx, " + arg_names[i] + ".tensor_data().size(), (CUdeviceptr)" + arg_names[i] + R"(.tensor_data().data(), false);)";
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std::regex regex("#([a-zA-Z]([a-zA-Z]|[0-9])*)");
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std::string grid_str = std::regex_replace(macro, regex, "x.at(\"$1\")");
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result += R"(
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// create launch grid;
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auto grid = [&](const rt::params_t& x) { return rt::grid_t{)" + grid_str + R"(}; };)";
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result += R"(
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// execute function
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fn_({
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)";
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for(unsigned i = 0; i < n_args; i++){
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std::string arg = arg_names[i];
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if(fn_ty->get_param_ty(i)->is_pointer_ty())
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arg = "&cu_" + arg;
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if(i > 0)
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result += ", ";
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result += arg;
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}
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result += R"(
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}, grid, stream);
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}
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private:
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rt::function fn_;
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};
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REGISTER_KERNEL_BUILDER(Name(")" + name + "\").Device(DEVICE_GPU)";
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for(size_t i = 0; i < tf_scalar_tys.size(); i++){
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std::string arg_name = arg_names[i];
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std::transform(arg_name.begin(), arg_name.end(), arg_name.begin(), [](char c) { return std::tolower(c);});
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if(!fn_ty->get_param_ty(i)->is_pointer_ty())
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result += ".HostMemory(\"" + arg_name + "\")";
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}
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result += ", " + classname + R"();
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REGISTER_OP(")" + name + "\")\n";
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for(size_t i = 0; i < tf_scalar_tys.size(); i++){
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bool is_output = std::find(outputs.begin(), outputs.end(), i) != outputs.end();
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std::string mode = is_output ? "Input" : "Input" ;
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std::string arg_name = arg_names[i];
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std::transform(arg_name.begin(), arg_name.end(), arg_name.begin(), [](char c) { return std::tolower(c);});
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result += " .Input(\"" + arg_name + ": " + tf_scalar_tys[i] + "\")\n";
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}
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for(size_t i = 0; i < outputs.size(); i++){
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result += " .Output(\"out: " + tf_scalar_tys[outputs[i]] + "\")\n";
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}
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result += ";\n";
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return result;
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}
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PYBIND11_MODULE(libtriton, m) {
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m.doc() = "Python bindings to the C++ Triton API";
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m.def("make_tensorflow_src", &make_tensorflow_src, "Creates C++ source code for a custom Tensorflow op corresponding to the specified Triton kernel");
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}
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