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91bfb0d1822a058ca7681fd4d08f956ac2345106
triton/python/src/bindings.cc
Philippe Tillet 2dd3fcb270 [CORE] Fixed bug for Multi-GPU
2020-05-04 18:36:44 -04:00

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#include <pybind11/pybind11.h>
#include <pybind11/buffer_info.h>
#include <pybind11/stl.h>
#include <pybind11/functional.h>
#include <string>
#include <regex>
#include <algorithm>
#include "triton/runtime/function.h"
#include "triton/runtime/arg.h"
#include "triton/lang/code_gen.h"
#include "triton/lang/parser.h"
#include "triton/lang/cpp.h"
#include "triton/driver/device.h"
#include "triton/driver/stream.h"
#include "triton/driver/kernel.h"
#include "triton/driver/module.h"
#include "triton/ir/module.h"
#include "triton/ir/function.h"
#include "triton/tools/bench.hpp"
using namespace triton;
namespace rt = triton::runtime;
typedef std::pair<size_t, size_t> map_key_t;
std::map<map_key_t, std::shared_ptr<rt::function::grid_fn_ty>> id_grid_map;
std::map<map_key_t, std::shared_ptr<rt::function>> id_fn_map;
std::map<size_t, double> fp64scalar_map;
std::map<size_t, int64_t> i64scalar_map;
/* Grid map */
void register_grid(const map_key_t& key,
const rt::function::grid_fn_ty& grid_fn) {
id_grid_map[key].reset(new rt::function::grid_fn_ty(grid_fn));
}
void delete_grid(const map_key_t& key) {
id_grid_map.erase(key);
}
/* Function map */
void register_fn(const map_key_t& key,
const std::string& src,
const rt::function::options_space_t& opt,
const std::string &cache_ref) {
id_fn_map[key].reset(new rt::function(src, opt, cache_ref));
}
void delete_fn(const map_key_t& key) {
id_fn_map.erase(key);
}
void register_cst(const map_key_t& key, const std::string& name, pybind11::buffer& data) {
pybind11::buffer_info info = data.request();
id_fn_map[key]->set_cst(name, info.ptr, info.size*info.itemsize);
}
void cleanup() {
id_grid_map.clear();
id_fn_map.clear();
i64scalar_map.clear();
}
size_t make_op_id() {
return id_fn_map.size();
}
/* TF scalar wrapper */
size_t make_scalar_id() {
size_t ret = i64scalar_map.size();
i64scalar_map[ret] = int64_t();
return ret;
}
bool has_scalar(size_t id) {
return i64scalar_map.find(id) != i64scalar_map.end();
}
int64_t retrieve_scalar(size_t id) {
return i64scalar_map.at(id);
}
/* TF source-code generation */
inline std::string to_tf_ty(ir::type *ty) {
if(ty->is_integer_ty(1))
return "bool";
if(ty->is_integer_ty(8))
return "int8";
if(ty->is_integer_ty(16))
return "int16";
if(ty->is_integer_ty(32))
return "int32";
if(ty->is_integer_ty(64))
return "int64";
if(ty->is_half_ty())
return "float16";
if(ty->is_float_ty())
return "float";
if(ty->is_double_ty())
return "double";
if(ty->is_pointer_ty())
return "Tensor";
throw std::runtime_error("unknown type");
}
inline std::string to_tf_scalar_ty(ir::type *ty) {
if(ty->is_pointer_ty())
return to_tf_ty(ty->get_pointer_element_ty());
else {
return to_tf_ty(ty);
}
}
inline std::string ref_to_tf_ty(ir::type *ty) {
std::string res = to_tf_ty(ty);
if(ty->is_pointer_ty())
res = "const " + res + "&";
return res;
}
std::string tf_normalize(const std::string& name) {
std::string ret = name;
auto tolower = [](char c) { return std::tolower(c);};
std::transform(ret.begin(), ret.end(), ret.begin(), tolower);
return ret;
}
struct tf_alloc_t{
enum type_t{
OUTPUT,
TEMP
};
tf_alloc_t(const std::string& _name, type_t _type)
: name(_name), type(_type), tf_name(tf_normalize(_name)){ }
std::string tf_name;
std::string name;
type_t type;
size_t shape_id;
};
typedef std::vector<tf_alloc_t> alloc_map_t;
void gen_extract_inputs(std::ostream &os, const std::vector<ir::argument*>& args, const alloc_map_t& allocs) {
for(unsigned i = 0; i < args.size(); i++){
ir::value *arg = args[i];
const std::string& name = arg->get_name();
std::string ty = to_tf_ty(arg->get_type());
if(!arg->get_type()->is_pointer_ty())
os << " " << ty << " " << name << " = context->input(" << i << ").scalar<" << ty << ">()();\n ";
else if(std::find_if(allocs.begin(), allocs.end(),
[&](tf_alloc_t x) {
return x.name == name;
}) == allocs.end())
os << " const Tensor* " << name << " = &context->input(" << i << ");\n ";
else
os << " Tensor* " << name << " = nullptr;\n ";
}
}
void gen_set_outputs(std::ostream &os, const std::vector<ir::argument*>& args, const alloc_map_t& allocs) {
// initialize shapes
for(const auto& x: allocs)
os << " TensorShape " << x.name << "_shape;\n ";
for(const auto& x: allocs)
os << " const Tensor& " << x.name << "_shape_tensor = context->input(" << x.shape_id << ");\n ";
for(const auto& x: allocs)
os << " const int32* " << x.name << "_shape_data = (const int32*)" << x.name << "_shape_tensor.tensor_data().data();\n ";
for(const auto& x: allocs)
os << " size_t " << x.name << "_rank = " << x.name << "_shape_tensor.dim_size(0);\n ";
for(const auto& x: allocs)
os << " for(size_t d = 0; d < " << x.name << "_rank ; d++) "
<< x.name << "_shape.AddDim(" << x.name << "_shape_data[d]);\n ";
// allocate
int output = 0;
for(const auto& x: allocs){
if(x.type == tf_alloc_t::OUTPUT)
os << " OP_REQUIRES_OK(context, context->allocate_output(" << output++ << ", " << x.name << "_shape, &" << x.name << "));\n ";
else
os << " OP_REQUIRES_OK(context, context->allocate_temp(" << x.name << "_type, " << x.name << "_shape, " << x.name << "));\n ";
}
}
void gen_make_handles(std::ostream &os, const std::vector<ir::argument*>& args) {
for(unsigned i = 0; i < args.size(); i++){
ir::argument *arg = args[i];
if(!arg->get_type()->is_pointer_ty())
continue;
const std::string& name = arg->get_name();
os << " drv::cu_buffer cu_" + name + "(ctx, " + name + "->tensor_data().size(), (CUdeviceptr)" + name + "->tensor_data().data(), false);\n ";
}
}
void gen_make_launch_function(std::ostream &os, const std::vector<ir::argument*>& args) {
os << " std::function<void()> run = [&](){\n ";
os << " (*id_fn_map.at(id_))({";
for(unsigned i = 0; i < args.size() ; i++){
ir::argument *arg = args[i];
std::string name = arg->get_name();
if(arg->get_type()->is_pointer_ty())
name = "&cu_" + name;
if(i > 0)
os << ", ";
os << name;
}
os << "}, *id_grid_map.at(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_tf_register_kernel_builder(std::ostream &os, const std::string &name,
const std::string &opname,
const std::vector<ir::argument*>& args,
const alloc_map_t& allocs){
os << "REGISTER_KERNEL_BUILDER(Name(\"" + name + "\").Device(DEVICE_GPU)";
for(size_t i = 0; i < args.size(); i++){
ir::argument *arg = args[i];
std::string name = tf_normalize(arg->get_name());
if(!arg->get_type()->is_pointer_ty())
os << ".HostMemory(\"" + name + "\")";
}
for(const auto& x: allocs)
os << ".HostMemory(\"" << x.tf_name << "_shape\")";
os << ", " + opname << ");\n";
}
void gen_tf_register_op(std::ostream &os, const std::string &name,
const std::vector<ir::argument*>& args,
const alloc_map_t& allocs){
os << "REGISTER_OP(\"" << name << "\")\n";
for(size_t i = 0; i < args.size(); i++)
os << " .Attr(\"T" << i << " : {bool, int8, int16, int32, int64, float16, float32, float64}\")" << std::endl;
for(size_t i = 0; i < args.size(); i++){
ir::argument *arg = args[i];
std::string name = tf_normalize(arg->get_name());
if(std::find_if(allocs.begin(), allocs.end(),
[&](tf_alloc_t x) {
return name == x.tf_name;
}) == allocs.end())
os << " .Input(\"" << name << ": T" << i << "\")\n";
else
os << " .Input(\"" << name << "_shape: int32\")\n";
}
for(const auto& x: allocs)
if(x.type == tf_alloc_t::OUTPUT)
os << " .Output(\"" << x.tf_name << ": T" << x.shape_id << "\")\n";
os << " .Attr(\"id: int\")\n";
os << " .Attr(\"bench: int\")\n";
os << " .Attr(\"bench_id: int\")\n";
os << " .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* ctx) {\n";
size_t current = 0;
for(const auto& x: allocs)
if(x.type == tf_alloc_t::OUTPUT){
os << " shape_inference::ShapeHandle " << x.tf_name << "_handle;\n";
os << " ctx->MakeShapeFromShapeTensor(" << x.shape_id << ", &" << x.tf_name << "_handle);\n";
os << " ctx->set_output(" << current++ << ", " << x.tf_name << "_handle);\n";
}
os << " return Status::OK();\n";
os << " })\n";
os << ";\n";
}
void make_module(const std::string& src, ir::module* ir,
const runtime::function::options_space_t& opt) {
std::string copy = triton::runtime::function::preheader() + src;
// pre-process
TokenSequence tokens;
Preprocessor cpp(&copy, true);
for(auto it: opt.defines){
cpp.AddMacro(it.first, &it.second[0]);
}
cpp.Process(tokens);
// parse
Parser parser(tokens);
parser.Parse();
Generator gen(&parser);
gen.Gen(ir);
}
std::tuple<std::string,
std::string> make_tensorflow_src(const std::string& src,
const std::vector<std::string>& outputs,
const std::vector<std::string>& tmp,
const runtime::function::options_space_t& opt)
{
// triton-ir code-gen
ir::context ctx;
auto ir = std::shared_ptr<ir::module>(new ir::module("", ctx));
make_module(src, &*ir, opt);
// function
ir::function* fn = ir->get_function_list().front();
const std::vector<ir::argument*>& args = fn->args();
std::string name = fn->get_name();
std::string cc_name = name;
cc_name[0] = static_cast<char>(std::toupper(cc_name[0]));
std::string opname = cc_name + "Op";
// allocation info
alloc_map_t allocs;
for(size_t i = 0; i < outputs.size(); i++)
allocs.push_back(tf_alloc_t(outputs[i], tf_alloc_t::OUTPUT));
for(size_t i = 0; i < tmp.size(); i++)
allocs.push_back(tf_alloc_t(tmp[i], tf_alloc_t::TEMP));
for(auto &x: allocs){
size_t idx;
for(idx = 0; idx < args.size(); idx++)
if(args[idx]->get_name() == x.name)
break;
if(idx == args.size())
throw std::runtime_error("unknown output");
x.shape_id = idx;
}
std::ostringstream oss;
oss << R"(
#include "triton/driver/buffer.h"
#include "triton/driver/backend.h"
#include "triton/driver/stream.h"
#include "triton/runtime/function.h"
#include "triton/tools/bench.hpp"
#define EIGEN_USE_GPU
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/op_kernel.h"
using namespace tensorflow;
using GPUDevice = Eigen::GpuDevice;
namespace rt = triton::runtime;
namespace drv = triton::driver;
extern std::map<size_t, std::shared_ptr<rt::function::grid_fn_ty>> id_grid_map;
extern std::map<size_t, std::shared_ptr<rt::function>> id_fn_map;
extern std::map<size_t, int64_t> i64scalar_map;
class )" << opname << R"(: public OpKernel {
public:
explicit )" << opname << R"((OpKernelConstruction* context)
: OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("id", &id_));
OP_REQUIRES_OK(context, context->GetAttr("bench", &bench_));
OP_REQUIRES_OK(context, context->GetAttr("bench_id", &bench_id_));
)";
for(const auto& alloc: allocs)
oss << " OP_REQUIRES_OK(context, context->GetAttr(\"T" << alloc.shape_id << "\", &" << alloc.name << "_type));\n ";
oss << R"(
}
void Compute(OpKernelContext* context){
// get device/stream
GPUDevice device = context->eigen_device<GPUDevice>();
drv::cu_stream sstream(device.stream(), false);
drv::context* ctx = sstream.context();
drv::stream* stream = &sstream;
// extract inputs
)";
gen_extract_inputs(oss, args, allocs);
oss << R"(
// set outputs
)";
gen_set_outputs(oss, args, allocs);
oss << R"(
// wrap tensors
)";
gen_make_handles(oss, args);
oss << R"(
)";
oss << R"(
// launch function
)";
gen_make_launch_function(oss, args);
oss << R"(
}
private:
int id_;
int bench_;
int64 bench_id_;
)";
for(const auto& alloc: allocs)
oss << "DataType " << alloc.name << "_type;\n ";
oss << R"(
};
// register kernel builder
)";
gen_tf_register_kernel_builder(oss, cc_name, opname, args, allocs);
oss << R"(
// register op
)";
gen_tf_register_op(oss, cc_name, args, allocs);
return std::tuple<std::string, std::string>{oss.str(), name};
}
inline std::string to_torch_ty(ir::type *ty) {
if(ty->is_integer_ty())
return "int64_t";
if(ty->is_half_ty())
return "double";
if(ty->is_float_ty())
return "double";
if(ty->is_double_ty())
return "double";
if(ty->is_pointer_ty())
return "torch::Tensor";
throw std::runtime_error("unknown type");
}
inline std::string to_torch_ty(rt::arg_type ty){
switch(ty){
case rt::INT1_T: return "int64_t";
case rt::INT8_T: return "int64_t";
case rt::INT16_T: return "int64_t";
case rt::INT32_T: return "int64_t";
case rt::INT64_T: return "int64_t";
case rt::HALF_T: return "double";
case rt::FLOAT_T: return "double";
case rt::DOUBLE_T: return "double";
case rt::BUFFER_T: return "torch::Tensor";
default: return "UNKNOWN";
}
}
inline std::string to_c_ty(rt::arg_type ty){
switch(ty){
case rt::INT1_T: return "bool";
case rt::INT8_T: return "int8_t";
case rt::INT16_T: return "int16_t";
case rt::INT32_T: return "int32_t";
case rt::INT64_T: return "int64_t";
case rt::HALF_T: return "half";
case rt::FLOAT_T: return "float";
case rt::DOUBLE_T: return "double";
case rt::BUFFER_T: return "drv::cu_buffer";
default: return "UNKNOWN";
}
}
inline std::string to_c_ty(ir::type *ty) {
if(ty->is_integer_ty(1))
return "bool";
if(ty->is_integer_ty(8))
return "int8_t";
if(ty->is_integer_ty(16))
return "int16_t";
if(ty->is_integer_ty(32))
return "int32_t";
if(ty->is_integer_ty(64))
return "int64_t";
if(ty->is_half_ty())
return "half";
if(ty->is_float_ty())
return "float";
if(ty->is_double_ty())
return "double";
if(ty->is_pointer_ty())
return "drv::cu_buffer";
throw std::runtime_error("unknown type");
}
void gen_torch_signature(std::ostringstream& oss,
const std::string& name,
const std::vector<rt::arg_type>& args) {
std::string ret_ty = "void";
oss << ret_ty << " " << name << "(";
oss << "int64_t id, ";
oss << "int64_t dev_id, ";
oss << "int64_t bench, ";
oss << "int64_t bench_id, ";
for(size_t i = 0; i < args.size(); i++) {
if(i > 0)
oss << ", ";
oss << to_torch_ty(args[i]) << " " << "th_arg_" << i;
}
oss << ")";
}
void gen_torch_init_driver(std::ostringstream &oss,
const std::vector<rt::arg_type>&args) {
// Find index of first buffer
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 + ".storage().size(), "
" (CUdeviceptr)((char*)" + th_name + ".storage().data() + " + th_name + ".storage_offset() * " + th_name + ".itemsize()), false);" << std::endl;
}
}
}
void gen_torch_make_launch_function(std::ostream &os,
const std::vector<rt::arg_type>& args) {
os << " std::function<void()> run = [&](){\n ";
os << " (*id_fn_map.at({id, dev_id}))({";
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(i > 0)
os << ", ";
os << name;
}
os << "}, *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) {
// triton-ir code-gen
ir::context ctx;
auto ir = std::shared_ptr<ir::module>(new ir::module("", ctx));
make_module(src, &*ir, opt);
// function
ir::function* fn = ir->get_function_list().front();
// extract signature
std::vector<rt::arg_type> ret;
ir::function_type* ty = fn->get_fn_type();
for(size_t i = 0; i < ty->get_num_params(); i++)
ret.push_back(rt::convert(ty->get_param_ty(i)));
return ret;
}
typedef triton::runtime::function::options_t options_t;
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)
.value("int8", rt::INT8_T)
.value("int16", rt::INT16_T)
.value("int32", rt::INT32_T)
.value("int64", rt::INT64_T)
.value("half", rt::HALF_T)
.value("float", rt::FLOAT_T)
.value("double", rt::DOUBLE_T)
.value("buffer", rt::BUFFER_T);
pybind11::class_<options_t>(m, "options")
.def(pybind11::init<>())
.def("d", &options_t::D<int>)
.def_readonly("num_warps", &options_t::num_warps);
pybind11::class_<options_space_t>(m, "options_space")
.def(pybind11::init<>())
.def_readwrite("defines", &options_space_t::defines)
.def_readwrite("num_warps", &options_space_t::num_warps);
// hooks into triton constructs since frameworks may not use pybind11
m.def("get_fn_signature", &get_fn_signature);
m.def("register_grid", &register_grid);
m.def("delete_grid", &delete_grid);
m.def("register_fn", &register_fn);
m.def("register_cst", &register_cst);
m.def("delete_fn", &delete_fn);
m.def("make_op_id", &make_op_id);
m.def("make_scalar_id", &make_scalar_id);
m.def("retrieve_scalar", &retrieve_scalar);
m.def("cleanup", &cleanup);
;
}
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