[RUNTIME] Lower-level interface for executing functions
This commit is contained in:
Philippe Tillet
committed by
Philippe Tillet
Philippe Tillet
parent
f4f216b88a
commit
acff1b5e05
@@ -3,20 +3,13 @@
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#include <pybind11/stl.h>
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#include <pybind11/functional.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/runtime/function.h"
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#include "triton/runtime/arg.h"
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#include "triton/lang/code_gen.h"
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#include "triton/lang/parser.h"
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#include "triton/lang/cpp.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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using namespace triton;
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@@ -83,196 +76,6 @@ int64_t retrieve_scalar(size_t id) {
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return i64scalar_map.at(id);
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}
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/* TF source-code generation */
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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 "float";
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if(ty->is_double_ty())
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return "double";
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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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std::string tf_normalize(const std::string& name) {
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std::string ret = name;
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auto tolower = [](char c) { return std::tolower(c);};
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std::transform(ret.begin(), ret.end(), ret.begin(), tolower);
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return ret;
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}
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struct tf_alloc_t{
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enum type_t{
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OUTPUT,
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TEMP
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};
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tf_alloc_t(const std::string& _name, type_t _type)
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: name(_name), type(_type), tf_name(tf_normalize(_name)){ }
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std::string tf_name;
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std::string name;
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type_t type;
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size_t shape_id;
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};
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typedef std::vector<tf_alloc_t> alloc_map_t;
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void gen_extract_inputs(std::ostream &os, const std::vector<ir::argument*>& args, const alloc_map_t& allocs) {
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for(unsigned i = 0; i < args.size(); i++){
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ir::value *arg = args[i];
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const std::string& name = arg->get_name();
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std::string ty = to_tf_ty(arg->get_type());
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if(!arg->get_type()->is_pointer_ty())
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os << " " << ty << " " << name << " = context->input(" << i << ").scalar<" << ty << ">()();\n ";
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else if(std::find_if(allocs.begin(), allocs.end(),
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[&](tf_alloc_t x) {
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return x.name == name;
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}) == allocs.end())
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os << " const Tensor* " << name << " = &context->input(" << i << ");\n ";
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else
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os << " Tensor* " << name << " = nullptr;\n ";
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}
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}
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void gen_set_outputs(std::ostream &os, const std::vector<ir::argument*>& args, const alloc_map_t& allocs) {
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// initialize shapes
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for(const auto& x: allocs)
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os << " TensorShape " << x.name << "_shape;\n ";
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for(const auto& x: allocs)
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os << " const Tensor& " << x.name << "_shape_tensor = context->input(" << x.shape_id << ");\n ";
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for(const auto& x: allocs)
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os << " const int32* " << x.name << "_shape_data = (const int32*)" << x.name << "_shape_tensor.tensor_data().data();\n ";
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for(const auto& x: allocs)
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os << " size_t " << x.name << "_rank = " << x.name << "_shape_tensor.dim_size(0);\n ";
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for(const auto& x: allocs)
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os << " for(size_t d = 0; d < " << x.name << "_rank ; d++) "
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<< x.name << "_shape.AddDim(" << x.name << "_shape_data[d]);\n ";
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// allocate
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int output = 0;
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for(const auto& x: allocs){
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if(x.type == tf_alloc_t::OUTPUT)
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os << " OP_REQUIRES_OK(context, context->allocate_output(" << output++ << ", " << x.name << "_shape, &" << x.name << "));\n ";
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else
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os << " OP_REQUIRES_OK(context, context->allocate_temp(" << x.name << "_type, " << x.name << "_shape, " << x.name << "));\n ";
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}
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}
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void gen_make_handles(std::ostream &os, const std::vector<ir::argument*>& args) {
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for(unsigned i = 0; i < args.size(); i++){
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ir::argument *arg = args[i];
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if(!arg->get_type()->is_pointer_ty())
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continue;
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const std::string& name = arg->get_name();
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os << " drv::cu_buffer cu_" + name + "(ctx, " + name + "->nbytes(), (CUdeviceptr)" + name + "->tensor_data().data(), false);\n ";
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}
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}
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void gen_make_launch_function(std::ostream &os, const std::vector<ir::argument*>& args) {
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os << " std::function<void()> run = [&](){\n ";
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os << " (*id_fn_map.at(id_))({";
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for(unsigned i = 0; i < args.size() ; i++){
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ir::argument *arg = args[i];
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std::string name = arg->get_name();
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if(arg->get_type()->is_pointer_ty())
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name = "&cu_" + name;
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if(i > 0)
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os << ", ";
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os << name;
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}
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os << "}, *id_grid_map.at(id_), stream);\n ";
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os << " };\n ";
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os << " run();\n ";
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os << " if(bench_ > 0)\n ";
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os << " i64scalar_map[bench_id_] = triton::tools::bench(run, stream);\n ";
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}
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void gen_tf_register_kernel_builder(std::ostream &os, const std::string &name,
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const std::string &opname,
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const std::vector<ir::argument*>& args,
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const alloc_map_t& allocs){
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os << "REGISTER_KERNEL_BUILDER(Name(\"" + name + "\").Device(DEVICE_GPU)";
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for(size_t i = 0; i < args.size(); i++){
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ir::argument *arg = args[i];
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std::string name = tf_normalize(arg->get_name());
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if(!arg->get_type()->is_pointer_ty())
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os << ".HostMemory(\"" + name + "\")";
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}
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for(const auto& x: allocs)
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os << ".HostMemory(\"" << x.tf_name << "_shape\")";
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os << ", " + opname << ");\n";
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}
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void gen_tf_register_op(std::ostream &os, const std::string &name,
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const std::vector<ir::argument*>& args,
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const alloc_map_t& allocs){
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os << "REGISTER_OP(\"" << name << "\")\n";
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for(size_t i = 0; i < args.size(); i++)
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os << " .Attr(\"T" << i << " : {bool, int8, int16, int32, int64, float16, float32, float64}\")" << std::endl;
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for(size_t i = 0; i < args.size(); i++){
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ir::argument *arg = args[i];
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std::string name = tf_normalize(arg->get_name());
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if(std::find_if(allocs.begin(), allocs.end(),
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[&](tf_alloc_t x) {
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return name == x.tf_name;
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}) == allocs.end())
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os << " .Input(\"" << name << ": T" << i << "\")\n";
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else
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os << " .Input(\"" << name << "_shape: int32\")\n";
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}
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for(const auto& x: allocs)
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if(x.type == tf_alloc_t::OUTPUT)
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os << " .Output(\"" << x.tf_name << ": T" << x.shape_id << "\")\n";
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os << " .Attr(\"id: int\")\n";
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os << " .Attr(\"bench: int\")\n";
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os << " .Attr(\"bench_id: int\")\n";
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os << " .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* ctx) {\n";
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size_t current = 0;
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for(const auto& x: allocs)
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if(x.type == tf_alloc_t::OUTPUT){
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os << " shape_inference::ShapeHandle " << x.tf_name << "_handle;\n";
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os << " ctx->MakeShapeFromShapeTensor(" << x.shape_id << ", &" << x.tf_name << "_handle);\n";
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os << " ctx->set_output(" << current++ << ", " << x.tf_name << "_handle);\n";
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}
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os << " return Status::OK();\n";
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os << " })\n";
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os << ";\n";
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}
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void make_module(const std::string& src, ir::module* ir,
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const runtime::function::options_space_t& opt) {
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std::string copy = triton::runtime::function::preheader() + src;
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@@ -290,339 +93,6 @@ void make_module(const std::string& src, ir::module* ir,
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gen.Gen(ir);
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}
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std::tuple<std::string,
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std::string> make_tensorflow_src(const std::string& src,
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const std::vector<std::string>& outputs,
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const std::vector<std::string>& tmp,
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const runtime::function::options_space_t& opt)
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{
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// triton-ir code-gen
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ir::context ctx;
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auto ir = std::shared_ptr<ir::module>(new ir::module("", ctx));
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make_module(src, &*ir, opt);
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// function
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ir::function* fn = ir->get_function_list().front();
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const std::vector<ir::argument*>& args = fn->args();
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std::string name = fn->get_name();
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std::string cc_name = name;
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cc_name[0] = static_cast<char>(std::toupper(cc_name[0]));
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std::string opname = cc_name + "Op";
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// allocation info
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alloc_map_t allocs;
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for(size_t i = 0; i < outputs.size(); i++)
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allocs.push_back(tf_alloc_t(outputs[i], tf_alloc_t::OUTPUT));
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for(size_t i = 0; i < tmp.size(); i++)
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allocs.push_back(tf_alloc_t(tmp[i], tf_alloc_t::TEMP));
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for(auto &x: allocs){
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size_t idx;
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for(idx = 0; idx < args.size(); idx++)
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if(args[idx]->get_name() == x.name)
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break;
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if(idx == args.size())
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throw std::runtime_error("unknown output");
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x.shape_id = idx;
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}
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std::ostringstream oss;
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oss << 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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#include "triton/tools/bench.hpp"
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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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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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extern std::map<size_t, std::shared_ptr<rt::function::grid_fn_ty>> id_grid_map;
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extern std::map<size_t, std::shared_ptr<rt::function>> id_fn_map;
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extern std::map<size_t, int64_t> i64scalar_map;
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class )" << opname << R"(: public OpKernel {
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public:
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explicit )" << opname << R"((OpKernelConstruction* context)
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: OpKernel(context) {
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OP_REQUIRES_OK(context, context->GetAttr("id", &id_));
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OP_REQUIRES_OK(context, context->GetAttr("bench", &bench_));
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OP_REQUIRES_OK(context, context->GetAttr("bench_id", &bench_id_));
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)";
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for(const auto& alloc: allocs)
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oss << " OP_REQUIRES_OK(context, context->GetAttr(\"T" << alloc.shape_id << "\", &" << alloc.name << "_type));\n ";
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oss << R"(
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}
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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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)";
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gen_extract_inputs(oss, args, allocs);
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oss << R"(
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// set outputs
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)";
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gen_set_outputs(oss, args, allocs);
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oss << R"(
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// wrap tensors
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)";
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gen_make_handles(oss, args);
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oss << R"(
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)";
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oss << R"(
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// launch function
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)";
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gen_make_launch_function(oss, args);
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oss << R"(
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}
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private:
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int id_;
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int bench_;
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int64 bench_id_;
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)";
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for(const auto& alloc: allocs)
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oss << "DataType " << alloc.name << "_type;\n ";
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oss << R"(
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};
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// register kernel builder
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)";
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gen_tf_register_kernel_builder(oss, cc_name, opname, args, allocs);
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oss << R"(
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// register op
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)";
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gen_tf_register_op(oss, cc_name, args, allocs);
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return std::tuple<std::string, std::string>{oss.str(), name};
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}
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inline std::string to_torch_ty(ir::type *ty) {
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if(ty->is_integer_ty())
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return "int64_t";
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if(ty->is_half_ty())
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return "double";
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if(ty->is_float_ty())
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return "double";
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if(ty->is_double_ty())
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return "double";
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if(ty->is_pointer_ty())
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return "torch::Tensor";
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throw std::runtime_error("unknown type");
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}
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inline std::string to_torch_ty(rt::arg_type ty){
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switch(ty){
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case rt::INT1_T: return "int64_t";
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case rt::INT8_T: return "int64_t";
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case rt::INT16_T: return "int64_t";
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case rt::INT32_T: return "int64_t";
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case rt::INT64_T: return "int64_t";
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case rt::HALF_T: return "double";
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case rt::FLOAT_T: return "double";
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case rt::DOUBLE_T: return "double";
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case rt::BUFFER_T: return "torch::Tensor";
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default: return "UNKNOWN";
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}
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}
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inline std::string to_c_ty(rt::arg_type ty){
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switch(ty){
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case rt::INT1_T: return "bool";
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case rt::INT8_T: return "int8_t";
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case rt::INT16_T: return "int16_t";
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case rt::INT32_T: return "int32_t";
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case rt::INT64_T: return "int64_t";
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case rt::HALF_T: return "half";
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case rt::FLOAT_T: return "float";
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case rt::DOUBLE_T: return "double";
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case rt::BUFFER_T: return "drv::cu_buffer";
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default: return "UNKNOWN";
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}
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}
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inline std::string to_c_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_t";
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if(ty->is_integer_ty(16))
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return "int16_t";
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if(ty->is_integer_ty(32))
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return "int32_t";
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if(ty->is_integer_ty(64))
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return "int64_t";
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if(ty->is_half_ty())
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return "half";
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if(ty->is_float_ty())
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return "float";
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if(ty->is_double_ty())
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return "double";
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if(ty->is_pointer_ty())
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return "drv::cu_buffer";
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throw std::runtime_error("unknown type");
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}
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void gen_torch_signature(std::ostringstream& oss,
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const std::string& name,
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const std::vector<rt::arg_type>& args) {
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std::string ret_ty = "void";
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oss << ret_ty << " " << name << "(";
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oss << "int64_t id, ";
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oss << "int64_t dev_id, ";
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oss << "int64_t bench, ";
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oss << "int64_t bench_id, ";
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for(size_t i = 0; i < args.size(); i++) {
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if(i > 0)
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oss << ", ";
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oss << to_torch_ty(args[i]) << " " << "th_arg_" << i;
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}
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oss << ")";
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}
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void gen_torch_init_driver(std::ostringstream &oss,
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const std::vector<rt::arg_type>&args) {
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// Find index of first buffer
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size_t i;
|
||||
for(i = 0; i < args.size(); i++)
|
||||
if(args[i] == rt::BUFFER_T)
|
||||
break;
|
||||
oss << " // Wrap CUDA handles" << std::endl;
|
||||
oss << " c10::DeviceIndex device = th_arg_" << i << ".storage().device().index();" << std::endl;
|
||||
oss << " // Get stream" << std::endl;
|
||||
oss << " CUstream custream = (CUstream)at::cuda::getCurrentCUDAStream(device).stream();" << std::endl;
|
||||
oss << " triton::driver::cu_stream stream(custream, false);" << std::endl;
|
||||
oss << " triton::driver::context* ctx = stream.context();" << std::endl;
|
||||
}
|
||||
|
||||
void gen_torch_make_handles(std::ostream &os,
|
||||
const std::vector<rt::arg_type>& args) {
|
||||
for(unsigned i = 0; i < args.size(); i++){
|
||||
rt::arg_type arg = args[i];
|
||||
const std::string th_name = "th_arg_" + std::to_string(i);
|
||||
const std::string name = "arg_" + std::to_string(i);
|
||||
if(arg != rt::BUFFER_T)
|
||||
os << " " << to_c_ty(arg) << " " << name << " = " << th_name << ";" << std::endl;
|
||||
else{
|
||||
os << " CHECK_INPUT(" << th_name << ");" << std::endl;
|
||||
os << " drv::cu_buffer " + name + "(ctx, " + th_name + ".nbytes(), "
|
||||
" (CUdeviceptr)((char*)" + th_name + ".storage().data() + " + th_name + ".storage_offset() * " + th_name + ".itemsize()), false);" << std::endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::string get_val_struct_name(rt::arg_type ty){
|
||||
switch(ty){
|
||||
case rt::INT1_T: return "int1";
|
||||
case rt::INT8_T: return "int8";
|
||||
case rt::INT16_T: return "int16";
|
||||
case rt::INT32_T: return "int32";
|
||||
case rt::INT64_T: return "int64";
|
||||
case rt::HALF_T: return "fp16";
|
||||
case rt::FLOAT_T: return "fp32";
|
||||
case rt::DOUBLE_T: return "fp64";
|
||||
case rt::BUFFER_T: return "buf";
|
||||
default: return "";
|
||||
}
|
||||
}
|
||||
|
||||
void gen_torch_make_launch_function(std::ostream &os,
|
||||
const std::vector<rt::arg_type>& args) {
|
||||
os << " namespace rt = triton::runtime;\n ";
|
||||
os << " std::vector<rt::arg> args;\n ";
|
||||
for(unsigned i = 0; i < args.size(); i++){
|
||||
std::string name = "arg_" + std::to_string(i);
|
||||
if(args[i] == rt::BUFFER_T)
|
||||
name = "&" + name;
|
||||
if(args[i] == rt::HALF_T)
|
||||
name = "*((uint16_t*)&" + name + ")";
|
||||
os << "rt::arg_type ty" << i << " = (rt::arg_type)(" << args[i] << ");\n ";
|
||||
os << "rt::arg::value_t val" << i << ";\n ";
|
||||
os << "val" << i << "." << get_val_struct_name(args[i]) << " = " << name << ";\n ";
|
||||
os << "args.push_back(rt::arg(ty" << i << ", val" << i << "));\n ";
|
||||
}
|
||||
os << " std::function<void()> run = [&](){\n ";
|
||||
os << " (*id_fn_map.at({id, dev_id}))(args , *id_grid_map.at({id, dev_id}), &stream);\n";
|
||||
os << " };\n";
|
||||
os << " run();\n";
|
||||
os << " if(bench > 0)\n ";
|
||||
os << " i64scalar_map[bench_id] = triton::tools::bench(run, &stream);\n ";
|
||||
}
|
||||
|
||||
void gen_torch_ret(std::ostream &os, const std::vector<std::string>& outputs) {
|
||||
if(outputs.size() == 1){
|
||||
os << " return " << outputs[0] << ";" << std::endl;
|
||||
return;
|
||||
}
|
||||
os << " return {";
|
||||
for(size_t i = 0; i < outputs.size(); i++){
|
||||
if(i > 0)
|
||||
os << ", ";
|
||||
os << outputs[i];
|
||||
}
|
||||
os << "};" << std::endl;
|
||||
}
|
||||
|
||||
std::tuple<std::string,
|
||||
std::string> make_torch_src(const std::string& name, std::vector<rt::arg_type> args) {
|
||||
// generate framework code
|
||||
std::ostringstream oss;
|
||||
oss << R"(
|
||||
#include "triton/driver/buffer.h"
|
||||
#include "triton/driver/stream.h"
|
||||
#include "triton/runtime/function.h"
|
||||
#include "triton/tools/bench.hpp"
|
||||
#include "torch/script.h"
|
||||
#include "ATen/cuda/CUDAContext.h"
|
||||
|
||||
#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
|
||||
#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
|
||||
#define CHECK_INPUT(x) CHECK_CUDA(x);
|
||||
|
||||
namespace rt = triton::runtime;
|
||||
namespace drv = triton::driver;
|
||||
|
||||
typedef std::pair<size_t, size_t> map_key_t;
|
||||
extern std::map<map_key_t, std::shared_ptr<rt::function::grid_fn_ty>> id_grid_map;
|
||||
extern std::map<map_key_t, std::shared_ptr<rt::function>> id_fn_map;
|
||||
extern std::map<size_t, int64_t> i64scalar_map;
|
||||
|
||||
)";
|
||||
|
||||
gen_torch_signature(oss, name, args);
|
||||
oss << " {" << std::endl;
|
||||
gen_torch_init_driver(oss, args);
|
||||
gen_torch_make_handles(oss, args);
|
||||
gen_torch_make_launch_function(oss, args);
|
||||
//gen_torch_ret(oss);
|
||||
oss << "}" << std::endl;
|
||||
|
||||
oss << std::endl;
|
||||
oss << std::endl;
|
||||
oss << "static auto registry = torch::RegisterOperators(\"triton::" << name << "\", &" << name << ");" << std::endl;
|
||||
|
||||
return std::tuple<std::string, std::string>{oss.str(), name};
|
||||
}
|
||||
|
||||
/* Function signature */
|
||||
std::vector<rt::arg_type> get_fn_signature(const std::string& src,
|
||||
const runtime::function::options_space_t& opt) {
|
||||
@@ -646,13 +116,6 @@ typedef triton::runtime::function::options_space_t options_space_t;
|
||||
PYBIND11_MODULE(libtriton, m) {
|
||||
m.doc() = "Python bindings to the C++ Triton API";
|
||||
|
||||
// framework binding source code generation
|
||||
m.def("make_tensorflow_src", &make_tensorflow_src,
|
||||
"Creates C++ source code for a custom Tensorflow op "
|
||||
"corresponding to the specified Triton kernel");
|
||||
m.def("make_torch_src", &make_torch_src,
|
||||
"Creates C++ source code for a custom PyTorch op ");
|
||||
|
||||
// bindings for triton classes
|
||||
pybind11::enum_<rt::arg_type>(m, "arg_type")
|
||||
.value("int1", rt::INT1_T)
|
||||
|
||||
27
python/src/launch.cc
Normal file
27
python/src/launch.cc
Normal file
@@ -0,0 +1,27 @@
|
||||
#include "triton/driver/buffer.h"
|
||||
#include "triton/driver/stream.h"
|
||||
#include "triton/runtime/function.h"
|
||||
#include "triton/tools/bench.hpp"
|
||||
#include "torch/script.h"
|
||||
#include "ATen/cuda/CUDAContext.h"
|
||||
|
||||
#define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
|
||||
#define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
|
||||
#define CHECK_INPUT(x) CHECK_CUDA(x);
|
||||
|
||||
namespace rt = triton::runtime;
|
||||
namespace drv = triton::driver;
|
||||
|
||||
typedef std::pair<size_t, size_t> map_key_t;
|
||||
extern std::map<map_key_t, std::shared_ptr<rt::function::grid_fn_ty>> id_grid_map;
|
||||
extern std::map<map_key_t, std::shared_ptr<rt::function>> id_fn_map;
|
||||
|
||||
void launch_kernel(int64_t op_id, int64_t dev_id, const std::string& args){
|
||||
CUstream custream = (CUstream)at::cuda::getCurrentCUDAStream(dev_id).stream();
|
||||
triton::driver::cu_stream stream(custream, false);
|
||||
triton::driver::context* ctx = stream.context();
|
||||
(*id_fn_map.at({op_id, dev_id}))((void**)args.c_str(), args.size(), *id_grid_map.at({op_id, dev_id}), &stream);
|
||||
}
|
||||
|
||||
|
||||
static auto registry = torch::RegisterOperators("triton::launch_kernel", &launch_kernel);
|
||||
Reference in New Issue
Block a user