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[RUNTIME] Lower-level interface for executing functions

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This commit is contained in:
Philippe Tillet
2020-08-11 20:10:39 -04:00
committed by Philippe Tillet
parent f4f216b88a
commit acff1b5e05
25 changed files with 219 additions and 916 deletions
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6
python/examples/tutorials/conv2d.py
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@@ -8,7 +8,9 @@ class _conv(torch.autograd.Function):
TYPE *C __noalias __aligned(16),
float alpha,
// equivalent matmul
int M, int N, int K,
int M __retune,
int N __retune,
int K __retune,
// convolution properties
int pad_h, int pad_w, int stride_h, int stride_w,
// pointer increment
@@ -197,4 +199,4 @@ c = conv(a, b, pad, stride, time)
print((cc - c).abs().max() / max(cc.max(), c.max()))
print(time[0], 2*Z*H*W*CI*CO*R*S/(time[0]*1e-9)*1e-12)
#zc = torch.matmul(a,b)
#zc_ = dot(a,b)
#zc_ = dot(a,b)

4
python/examples/tutorials/mat_copy.py
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@@ -4,7 +4,9 @@ import triton
class _copy(torch.autograd.Function):
src = """
__global__ void copy(TYPE * X, TYPE * Y,
int M, int N, int ldx __multipleof(8)) {
int M __retune,
int N __retune,
int ldx __multipleof(8)) {
// extract program ID
int pidm = get_program_id(0); //(1)
int pidn = get_program_id(1); //(2)

6
python/examples/tutorials/mat_mul.py
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@@ -7,7 +7,9 @@ class _dot(torch.autograd.Function):
TYPE *B __noalias __readonly __aligned(16),
TYPE *C __noalias __aligned(16),
float alpha,
int M, int N, int K,
int M __retune,
int N __retune,
int K __retune,
int lda __multipleof(8),
int ldb __multipleof(8),
int ldc __multipleof(8)) {
@@ -128,4 +130,4 @@ b = torch.rand((K, N)).cuda()
#zc = torch.matmul(a,b)
zc_ = dot(a,b)
#print(torch.allclose(zc, zc_))
#print(torch.allclose(zc, zc_))

4
python/examples/tutorials/mat_transpose.py
View File

@@ -4,7 +4,9 @@ import triton
class _transpose(torch.autograd.Function):
src = """
__global__ void transpose(TYPE * X, TYPE * Y,
int M, int N, int ldx __multipleof(8), int ldy __multipleof(8)) {
int M __retune,
int N __retune,
int ldx __multipleof(8), int ldy __multipleof(8)) {
// extract program ID
int pidm = get_program_id(0); //(1)
int pidn = get_program_id(1); //(2)

11
python/setup.py
View File

@@ -8,9 +8,11 @@ import distutils
import glob
from distutils.version import LooseVersion
from setuptools import setup, Extension, find_packages
from torch.utils.cpp_extension import include_paths, library_paths
from setuptools.command.build_ext import build_ext
from setuptools.command.test import test as TestCommand
import distutils.spawn
import torch
def find_llvm():
@@ -58,12 +60,17 @@ class CMakeBuild(build_ext):
# python directories
python_include_dirs = distutils.sysconfig.get_python_inc()
python_lib_dirs = distutils.sysconfig.get_config_var('LIBDIR')
torch_include_dirs = include_paths(True)
torch_library_dirs = library_paths(True)
abi = torch._C._GLIBCXX_USE_CXX11_ABI
cmake_args = ['-DCMAKE_LIBRARY_OUTPUT_DIRECTORY=' + extdir,
'-DBUILD_TESTS=OFF',
'-DBUILD_PYTHON_MODULE=ON',
#'-DPYTHON_EXECUTABLE=' + sys.executable,
#'-DCMAKE_VERBOSE_MAKEFILE:BOOL=ON,
'-DPYTHON_INCLUDE_DIRS=' + python_include_dirs,
'-DPYTHON_INCLUDE_DIRS=' + ';'.join([python_include_dirs] + include_paths(True)),
'-DPYTHON_LINK_DIRS=' + ';'.join(library_paths(True)),
'-DTORCH_LIBRARIES=c10;c10_cuda;torch;torch_cuda;torch_cpu;torch_python;triton',
'-DLLVM_CONFIG=' + find_llvm()]
# configuration
cfg = 'Debug' if self.debug else 'Release'
@@ -80,8 +87,6 @@ class CMakeBuild(build_ext):
build_args += ['--', '-j4']
env = os.environ.copy()
env['CXXFLAGS'] = '{} -DVERSION_INFO=\\"{}\\"'.format(env.get('CXXFLAGS', ''),
self.distribution.get_version())
if not os.path.exists(self.build_temp):
os.makedirs(self.build_temp)
sourcedir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'src'))

537
python/src/bindings.cc
View File

@@ -3,20 +3,13 @@
#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;
@@ -83,196 +76,6 @@ 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 + "->nbytes(), (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;
@@ -290,339 +93,6 @@ void make_module(const std::string& src, ir::module* ir,
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 + ".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
View 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);

5
python/triton/__init__.py
View File

@@ -1,7 +1,6 @@
from .kernel import *
from .utils import *
import triton.ops
import triton.nn
#import triton.ops
#import triton.nn
# clean-up libtriton resources

28
python/triton/frameworks.py
View File

@@ -1,28 +0,0 @@
import sys
import os
import triton._C.libtriton as libtriton
torch = None
tensorflow = None
def _import_torch():
global torch
if torch is None:
import torch
def _import_tensorflow():
global tensorflow
if tensorflow is None:
import tensorflow
def has_tensorflow():
result = 'tensorflow' in sys.modules
if result:
_import_tensorflow()
return result
def has_torch():
result = 'torch' in sys.modules
if result:
_import_torch()
return result

206
python/triton/kernel.py
View File

@@ -1,181 +1,71 @@
# import for cache
import os
import tempfile
import shutil
import hashlib
import sysconfig
import sys
import weakref
import contextlib
import io
import torch.utils.cpp_extension
# import for just-in-time compilation
import distutils
import setuptools.command.build_ext
import setuptools
# triton
import triton.frameworks as fw
import triton.utils
import triton._C.libtriton as libtriton
import os
import time
import platform
from struct import pack
import torch
@contextlib.contextmanager
def quiet():
old_stdout, old_stderr = sys.stdout, sys.stderr
sys.stdout, sys.stderr = io.StringIO(), io.StringIO()
try:
yield
finally:
sys.stdout, sys.stderr = old_stdout, old_stderr
codes = {
libtriton.arg_type.int1: 'B',
libtriton.arg_type.int8: 'B',
libtriton.arg_type.int32: 'I',
libtriton.arg_type.int64: 'Q',
libtriton.arg_type.half: 'H',
libtriton.arg_type.float: 'f',
libtriton.arg_type.double: 'd',
libtriton.arg_type.buffer: 'P'
}
def _build(src, path, name):
ccdir = os.path.join(libtriton.__file__, os.path.pardir)
ccdir = os.path.realpath(ccdir)
# include / libraries
include_dirs = [os.path.join(ccdir, 'include')]
library_dirs = [ccdir]
libraries = ['triton']
# create extension module
abi = fw.torch._C._GLIBCXX_USE_CXX11_ABI
extra_compile_args = ['-fPIC', '-Wno-deprecated-declarations', f'-D_GLIBCXX_USE_CXX11_ABI={str(int(abi))}']
extra_compile_args += ['-DTORCH_EXTENSION_NAME={}'.format(name)]
extra_compile_args += ['-DTORCH_API_INCLUDE_EXTENSION_H']
ext = torch.utils.cpp_extension.CUDAExtension(
name = name,
language = 'c++',
sources = [src],
include_dirs = include_dirs,
library_dirs = library_dirs,
libraries = libraries,
extra_compile_args = extra_compile_args,
depends = [os.path.realpath(libtriton.__file__)]
)
# build extension module
args = ['build_ext']
tmp = tempfile.mkdtemp()
args.append('--build-temp=' + tmp)
args.append('--build-lib=' + path)
args.append('-q')
args = dict(
name = name,
ext_modules = [ext],
script_args = args,
)
with quiet():
setuptools.setup(**args)
shutil.rmtree(tmp)
def _cvt_to_def_str(obj):
# bool
if isinstance(obj, bool):
return str(int(obj))
# torch type
if fw.has_torch():
if isinstance(obj, fw.torch.dtype):
return {fw.torch.int8: 'char',
fw.torch.int16: 'short',
fw.torch.int32: 'int',
fw.torch.int64: 'long',
fw.torch.float16: 'half',
fw.torch.float32: 'float',
fw.torch.float64: 'double'}[obj]
else:
assert False
# default
return str(obj)
sizes = {
libtriton.arg_type.int1: 1,
libtriton.arg_type.int8: 1,
libtriton.arg_type.int32: 4,
libtriton.arg_type.int64: 8,
libtriton.arg_type.half: 2,
libtriton.arg_type.float: 4,
libtriton.arg_type.double: 8,
libtriton.arg_type.buffer: 8
}
def _encode(arg_types):
codes = {
libtriton.arg_type.int1: 'i1',
libtriton.arg_type.int8: 'i8',
libtriton.arg_type.int32: 'i32',
libtriton.arg_type.int64: 'i64',
libtriton.arg_type.half: 'f16',
libtriton.arg_type.float: 'f32',
libtriton.arg_type.double: 'f64',
libtriton.arg_type.buffer: 'buf'
def th_to_triton(obj):
tys = {
torch.int8: 'char',
torch.int16: 'short',
torch.int32: 'int',
torch.int64: 'long',
torch.float16: 'half',
torch.float32: 'float',
torch.float64: 'double'
}
ret = '_'.join(map(codes.get, arg_types))
return ret
if isinstance(obj, torch.dtype):
return [tys[obj]]
if isinstance(obj, list):
return [th_to_triton(x)[0] for x in obj]
return [str(obj)]
def _make_framework_op(arg_types):
name = _encode(arg_types)
# path of .cpp and .so file
home = os.path.expanduser('~')
root = os.path.join(home, '.triton', 'torch', name)
try:
os.makedirs(root)
except FileExistsError:
pass
suffix = sysconfig.get_config_var('EXT_SUFFIX')
so = os.path.join(root, f'op{suffix}')
cpp = os.path.join(root, f'op.cpp')
# handle cached .so file
if os.path.exists(so) and os.stat(so).st_size > 0:
tt_mtime = os.stat(os.path.realpath(libtriton.__file__)).st_mtime
so_mtime = os.stat(so).st_mtime
# can use cached if libtriton is older than the .so
if tt_mtime < so_mtime:
fw.torch.ops.load_library(so)
return getattr(fw.torch.ops.triton, name)
# create torch source code
print('[TRITON] Compiling op...')
baton = torch.utils.file_baton.FileBaton(os.path.join(root, 'lock'))
if baton.try_acquire():
try:
src, _ = libtriton.make_torch_src(name, arg_types)
with open(cpp, 'w+') as handle:
handle.writelines(src)
ccdir = os.path.join(libtriton.__file__, os.path.pardir)
ccdir = os.path.realpath(ccdir)
_build(cpp, root, 'op')
finally:
baton.release()
else:
baton.wait()
print('[TRITON] Done compiling...')
fw.torch.ops.load_library(so)
return getattr(fw.torch.ops.triton, name)
def cdiv(a, b):
return (a + b - 1) // b
class kernel:
def __init__(self, src, defines = dict(), num_warps = [2, 4, 8]):
self.src = src
# create constants
self.cst = dict()
# create triton op
macros = []
for k, v in defines.items():
cvt = lambda x: _cvt_to_def_str(x)
if(isinstance(v, list)):
values = list(map(cvt, v))
else:
values = [cvt(v)]
macros.append((k, values))
opt = libtriton.options_space()
opt.defines = macros
opt.num_warps = num_warps
self.opt = libtriton.options_space()
self.opt.defines = [(k, th_to_triton(v)) for k, v in defines.items()]
self.opt.num_warps = num_warps
self.op_id = libtriton.make_op_id()
self.opt = opt
self.registered = set()
# create pytorch hook
arg_types = libtriton.get_fn_signature(self.src, opt)
self.fw_op = _make_framework_op(arg_types)
arg_types = libtriton.get_fn_signature(self.src, self.opt)
size = sum([sizes[x] for x in arg_types])
self.tys = ''.join([codes[x] for x in arg_types])
def set_constant(self, device, name, value):
libtriton.register_cst((self.op_id, device), name, value)
def __call__(self, *args, **kwargs):
for x in args:
if isinstance(x, fw.torch.Tensor):
if isinstance(x, torch.Tensor):
device = x.device.index
break
# lazily register function for device
@@ -191,6 +81,6 @@ class kernel:
grid = kwargs['grid']
libtriton.register_grid((self.op_id, device), grid)
# launch
self.fw_op(self.op_id, device, bench, bench_id, *args)
if bench > 0:
return libtriton.retrieve_scalar(bench_id)
params = pack(self.tys, *[x.data_ptr() if isinstance(x, torch.Tensor) else x for x in args])
torch.cuda.synchronize()
torch.ops.triton.launch_kernel(self.op_id, device, params)

75
python/triton/utils.py
View File

@@ -1,75 +0,0 @@
import triton.frameworks as fw
import triton._C.libtriton as libtriton
import numpy as np
import weakref
def cdiv(a, b):
return (a + b - 1) // b
class tf_empty_proxy:
def __init__(self, shape, dtype):
self.shape = shape
self.dtype = dtype
self.tensor = None
def to_tensor(self):
assert self.tensor is not None
return self.tensor
def empty(shape, dtype):
if fw.has_tensorflow():
shape = [fw.tensorflow.constant(x) for x in shape]
shape = fw.tensorflow.stack(shape)
return tf_empty_proxy(shape, dtype)
#return fw.tf_extra_ops.alloc_empty(args, T = dtype)
elif fw.has_torch():
return fw.torch.empty(shape, dtype=dtype, device='cuda:0')
def shape(A) :
if fw.has_tensorflow():
return A.shape.as_list()
elif fw.has_torch():
return A.shape
else:
assert False
class id_dict:
# Lazy entry for e.g., tensorflow, when value of benchmark is
# not known at graph compile time
class lazy_entry:
def __init__(self, id):
self.id = id
def get(self):
return libtriton.retrieve_scalar(self.id)
def __init__(self):
self.data = dict()
def __delitem__(self, key):
del self.data[key]
@staticmethod
def _get_key(key):
if fw.has_tensorflow():
if isinstance(key, fw.tensorflow.Tensor):
key = id(key.op)
if fw.has_torch():
if isinstance(key, fw.torch.Tensor):
key = id(key)
return key
def __getitem__(self, key):
ret = self.data[id_dict._get_key(key)]
if isinstance(ret, id_dict.lazy_entry):
return ret.get()
return ret
def __len__(self):
return len(self.data)
def __setitem__(self, key, value):
self.data[id_dict._get_key(key)] = value