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jitify_test.cu
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/*
* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifdef LINUX // Only supported by gcc on Linux (defined in Makefile)
#define JITIFY_ENABLE_EMBEDDED_FILES 1
#endif
#define JITIFY_PRINT_INSTANTIATION 1
#define JITIFY_PRINT_SOURCE 1
#define JITIFY_PRINT_LOG 1
#define JITIFY_PRINT_PTX 1
#define JITIFY_PRINT_LINKER_LOG 1
#define JITIFY_PRINT_LAUNCH 1
#define JITIFY_PRINT_HEADER_PATHS 1
#include "jitify.hpp"
#include "example_headers/class_arg_kernel.cuh"
#include "example_headers/my_header1.cuh.jit"
#ifdef LINUX // Only supported by gcc on Linux (defined in Makefile)
JITIFY_INCLUDE_EMBEDDED_FILE(example_headers_my_header2_cuh);
#endif
#include "gtest/gtest.h"
#include <cstdio>
#include <fstream>
#include <iostream>
#include <memory>
#define CHECK_CUDA(call) \
do { \
CUresult status = call; \
if (status != CUDA_SUCCESS) { \
const char* str; \
cuGetErrorName(status, &str); \
std::cout << "(CUDA) returned " << str; \
std::cout << " (" << __FILE__ << ":" << __LINE__ << ":" << __func__ \
<< "())" << std::endl; \
ASSERT_EQ(status, CUDA_SUCCESS); \
} \
} while (0)
#define CHECK_CUDART(call) \
do { \
cudaError_t status = call; \
if (status != cudaSuccess) { \
std::cout << "(CUDART) returned " << cudaGetErrorString(status); \
std::cout << " (" << __FILE__ << ":" << __LINE__ << ":" << __func__ \
<< "())" << std::endl; \
ASSERT_EQ(status, cudaSuccess); \
} \
} while (0)
std::istream* file_callback(std::string filename, std::iostream& tmp_stream) {
// User returns NULL or pointer to stream containing file source
// Note: tmp_stream is provided for convenience
if (filename == "example_headers/my_header4.cuh") {
tmp_stream << "#pragma once\n"
"template<typename T>\n"
"T pointless_func(T x) {\n"
" return x;\n"
"}\n";
return &tmp_stream;
} else {
// Find this file through other mechanisms
return 0;
}
}
static const char* const simple_program_source =
"my_program\n"
"template<int N, typename T>\n"
"__global__\n"
"void my_kernel(T* data) {\n"
" if (blockIdx.x != 0 || threadIdx.x != 0) return;\n"
" T data0 = data[0];\n"
" for( int i=0; i<N-1; ++i ) {\n"
" data[0] *= data0;\n"
" }\n"
"}\n";
TEST(JitifyTest, Simple) {
static jitify::JitCache kernel_cache;
jitify::Program program = kernel_cache.program(simple_program_source);
typedef float T;
T* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(T)));
dim3 grid(1);
dim3 block(1);
using jitify::reflection::type_of;
auto kernel_inst =
program.kernel("my_kernel").instantiate(3, type_of(*d_data));
T h_data = 5;
CHECK_CUDART(cudaMemcpy(d_data, &h_data, sizeof(T), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst.configure(grid, block).launch(d_data));
CHECK_CUDART(cudaMemcpy(&h_data, d_data, sizeof(T), cudaMemcpyDeviceToHost));
EXPECT_FLOAT_EQ(h_data, 125.f);
h_data = 5;
CHECK_CUDART(cudaMemcpy(d_data, &h_data, sizeof(T), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst.configure_1d_max_occupancy().launch(d_data));
CHECK_CUDART(cudaMemcpy(&h_data, d_data, sizeof(T), cudaMemcpyDeviceToHost));
EXPECT_FLOAT_EQ(h_data, 125.f);
CHECK_CUDART(cudaFree(d_data));
}
TEST(JitifyTest, Simple_experimental) {
std::vector<std::string> opts;
jitify::experimental::Program program_orig(simple_program_source, {}, opts);
auto program =
jitify::experimental::Program::deserialize(program_orig.serialize());
typedef float T;
T* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(T)));
dim3 grid(1);
dim3 block(1);
using jitify::reflection::type_of;
auto kernel_inst_orig =
program.kernel("my_kernel").instantiate(3, type_of(*d_data));
auto kernel_inst = jitify::experimental::KernelInstantiation::deserialize(
kernel_inst_orig.serialize());
T h_data = 5;
CHECK_CUDART(cudaMemcpy(d_data, &h_data, sizeof(T), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst.configure(grid, block).launch(d_data));
CHECK_CUDART(cudaMemcpy(&h_data, d_data, sizeof(T), cudaMemcpyDeviceToHost));
EXPECT_FLOAT_EQ(h_data, 125.f);
h_data = 5;
CHECK_CUDART(cudaMemcpy(d_data, &h_data, sizeof(T), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst.configure_1d_max_occupancy().launch(d_data));
CHECK_CUDART(cudaMemcpy(&h_data, d_data, sizeof(T), cudaMemcpyDeviceToHost));
EXPECT_FLOAT_EQ(h_data, 125.f);
CHECK_CUDART(cudaFree(d_data));
}
TEST(JitifyTest, DefaultConstructable) {
// Same as Simple, but uses default + move constructors of Program, Kernel,
// KernelInstantiation, and KernelLauncher classes.
static jitify::JitCache kernel_cache;
jitify::Program program;
program = kernel_cache.program(simple_program_source);
typedef float T;
T* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(T)));
dim3 grid(1);
dim3 block(1);
using jitify::reflection::type_of;
jitify::Kernel kernel;
kernel = program.kernel("my_kernel");
jitify::KernelInstantiation kernel_inst;
kernel_inst = kernel.instantiate(3, type_of(*d_data));
jitify::KernelLauncher kernel_launcher;
kernel_launcher = kernel_inst.configure(grid, block);
T h_data = 5;
CHECK_CUDART(cudaMemcpy(d_data, &h_data, sizeof(T), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_launcher.launch(d_data));
CHECK_CUDART(cudaMemcpy(&h_data, d_data, sizeof(T), cudaMemcpyDeviceToHost));
EXPECT_FLOAT_EQ(h_data, 125.f);
kernel_launcher = kernel_inst.configure_1d_max_occupancy();
h_data = 5;
CHECK_CUDART(cudaMemcpy(d_data, &h_data, sizeof(T), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_launcher.launch(d_data));
CHECK_CUDART(cudaMemcpy(&h_data, d_data, sizeof(T), cudaMemcpyDeviceToHost));
EXPECT_FLOAT_EQ(h_data, 125.f);
CHECK_CUDART(cudaFree(d_data));
}
static const char* const multiple_kernels_program_source =
"my_program1\n"
"#include \"example_headers/my_header1.cuh\"\n"
"#include \"example_headers/my_header2.cuh\"\n"
"#include \"example_headers/my_header3.cuh\"\n"
"#include \"example_headers/my_header4.cuh\"\n"
"\n"
"__global__\n"
"void my_kernel1(float const* indata, float* outdata) {\n"
" outdata[0] = indata[0] + 1;\n"
" outdata[0] -= 1;\n"
"}\n"
"\n"
"template<int C, typename T>\n"
"__global__\n"
"void my_kernel2(float const* indata, float* outdata) {\n"
" for( int i=0; i<C; ++i ) {\n"
" outdata[0] = "
"pointless_func(identity(sqrt(square(negate(indata[0])))));\n"
" }\n"
"}\n";
TEST(JitifyTest, MultipleKernels) {
using jitify::reflection::instance_of;
using jitify::reflection::NonType;
using jitify::reflection::reflect;
using jitify::reflection::Type;
using jitify::reflection::type_of;
thread_local static jitify::JitCache kernel_cache;
jitify::Program program = kernel_cache.program(
multiple_kernels_program_source, // Code string specified above
{example_headers_my_header1_cuh}, // Code string generated by stringify
{"--use_fast_math", "-I" CUDA_INC_DIR}, file_callback);
typedef float T;
T* indata;
T* outdata;
CHECK_CUDART(cudaMalloc((void**)&indata, sizeof(T)));
CHECK_CUDART(cudaMalloc((void**)&outdata, sizeof(T)));
T inval = 3.14159f;
CHECK_CUDART(cudaMemcpy(indata, &inval, sizeof(T), cudaMemcpyHostToDevice));
dim3 grid(1);
dim3 block(1);
CHECK_CUDA(program.kernel("my_kernel1")
.instantiate()
.configure(grid, block)
.launch(indata, outdata));
enum { C = 123 };
// These invocations are all equivalent and will come from cache after the 1st
CHECK_CUDA((program.kernel("my_kernel2")
.instantiate<NonType<int, C>, T>()
.configure(grid, block)
.launch(indata, outdata)));
CHECK_CUDA(program.kernel("my_kernel2")
.instantiate({reflect((int)C), reflect<T>()})
.configure(grid, block)
.launch(indata, outdata));
// Recommended versions
CHECK_CUDA(program.kernel("my_kernel2")
.instantiate((int)C, Type<T>())
.configure(grid, block)
.launch(indata, outdata));
CHECK_CUDA(program.kernel("my_kernel2")
.instantiate((int)C, type_of(*indata))
.configure(grid, block)
.launch(indata, outdata));
CHECK_CUDA(program.kernel("my_kernel2")
.instantiate((int)C, instance_of(*indata))
.configure(grid, block)
.launch(indata, outdata));
T outval = 0;
CHECK_CUDART(cudaMemcpy(&outval, outdata, sizeof(T), cudaMemcpyDeviceToHost));
CHECK_CUDART(cudaFree(outdata));
CHECK_CUDART(cudaFree(indata));
EXPECT_FLOAT_EQ(inval, outval);
}
TEST(JitifyTest, MultipleKernels_experimental) {
using jitify::reflection::instance_of;
using jitify::reflection::NonType;
using jitify::reflection::reflect;
using jitify::reflection::Type;
using jitify::reflection::type_of;
jitify::experimental::Program program_orig(
multiple_kernels_program_source, // Code string specified above
{example_headers_my_header1_cuh}, // Code string generated by stringify
{"--use_fast_math", "-I" CUDA_INC_DIR}, file_callback);
auto program =
jitify::experimental::Program::deserialize(program_orig.serialize());
typedef float T;
T* indata;
T* outdata;
CHECK_CUDART(cudaMalloc((void**)&indata, sizeof(T)));
CHECK_CUDART(cudaMalloc((void**)&outdata, sizeof(T)));
T inval = 3.14159f;
CHECK_CUDART(cudaMemcpy(indata, &inval, sizeof(T), cudaMemcpyHostToDevice));
dim3 grid(1);
dim3 block(1);
CHECK_CUDA(program.kernel("my_kernel1")
.instantiate()
.configure(grid, block)
.launch(indata, outdata));
enum { C = 123 };
// These invocations are all equivalent.
CHECK_CUDA(jitify::experimental::KernelInstantiation::deserialize(
program.kernel("my_kernel2")
.instantiate<NonType<int, C>, T>()
.serialize())
.configure(grid, block)
.launch(indata, outdata));
CHECK_CUDA(jitify::experimental::KernelInstantiation::deserialize(
program.kernel("my_kernel2")
.instantiate({reflect((int)C), reflect<T>()})
.serialize())
.configure(grid, block)
.launch(indata, outdata));
// Recommended versions
CHECK_CUDA(jitify::experimental::KernelInstantiation::deserialize(
program.kernel("my_kernel2")
.instantiate((int)C, Type<T>())
.serialize())
.configure(grid, block)
.launch(indata, outdata));
CHECK_CUDA(jitify::experimental::KernelInstantiation::deserialize(
program.kernel("my_kernel2")
.instantiate((int)C, type_of(*indata))
.serialize())
.configure(grid, block)
.launch(indata, outdata));
CHECK_CUDA(jitify::experimental::KernelInstantiation::deserialize(
program.kernel("my_kernel2")
.instantiate((int)C, instance_of(*indata))
.serialize())
.configure(grid, block)
.launch(indata, outdata));
T outval = 0;
CHECK_CUDART(cudaMemcpy(&outval, outdata, sizeof(T), cudaMemcpyDeviceToHost));
CHECK_CUDART(cudaFree(outdata));
CHECK_CUDART(cudaFree(indata));
EXPECT_FLOAT_EQ(inval, outval);
}
static const char* const constmem_program_source =
"constmem_program\n"
"#pragma once\n"
"\n"
"__constant__ int a;\n"
"__device__ int d;\n"
"namespace b { __constant__ int a; __device__ int d; }\n"
"namespace c { namespace b { __constant__ int a; __device__ int d; } }\n"
"namespace x { __constant__ int a = 3; __device__ int d = 7; }\n"
"namespace y { __constant__ int a[] = {4, 5}; __device__ int d[] = {8, 9}; "
"}\n"
"\n"
"__global__ void constant_test(int *x) {\n"
" x[0] = a;\n"
" x[1] = b::a;\n"
" x[2] = c::b::a;\n"
" x[3] = d;\n"
" x[4] = b::d;\n"
" x[5] = c::b::d;\n"
" x[6] = x::a;\n"
" x[7] = x::d;\n"
" x[8] = y::a[0];\n"
" x[9] = y::a[1];\n"
" x[10] = y::d[0];\n"
" x[11] = y::d[1];\n"
"}\n";
TEST(JitifyTest, ConstantMemory) {
using jitify::reflection::Type;
thread_local static jitify::JitCache kernel_cache;
constexpr int n_const = 12;
int* outdata;
CHECK_CUDART(cudaMalloc((void**)&outdata, n_const * sizeof(int)));
dim3 grid(1);
dim3 block(1);
{ // test __constant__ look up in kernel string using diffrent namespaces
jitify::Program program = kernel_cache.program(
constmem_program_source, 0, {"--use_fast_math", "-I" CUDA_INC_DIR});
auto instance = program.kernel("constant_test").instantiate();
int inval[] = {2, 4, 8, 12, 14, 18, 22, 26, 30, 34, 38, 42};
int dval;
CHECK_CUDA(instance.get_global_value("x::a", &dval));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(dval, 3);
CHECK_CUDA(instance.get_global_value("x::d", &dval));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(dval, 7);
int darr[2];
CHECK_CUDA(instance.get_global_array("y::a", &darr[0], 2));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(darr[0], 4);
EXPECT_EQ(darr[1], 5);
CHECK_CUDA(instance.get_global_value("y::d", &darr));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(darr[0], 8);
EXPECT_EQ(darr[1], 9);
CHECK_CUDA(instance.set_global_value("a", inval[0]));
CHECK_CUDA(instance.set_global_value("b::a", inval[1]));
CHECK_CUDA(instance.set_global_value("c::b::a", inval[2]));
CHECK_CUDA(instance.set_global_value("d", inval[3]));
CHECK_CUDA(instance.set_global_value("b::d", inval[4]));
CHECK_CUDA(instance.set_global_value("c::b::d", inval[5]));
CHECK_CUDA(instance.set_global_value("x::a", inval[6]));
CHECK_CUDA(instance.set_global_value("x::d", inval[7]));
CHECK_CUDA(instance.set_global_array("y::a", &inval[8], 2));
int inarr[] = {inval[10], inval[11]};
CHECK_CUDA(instance.set_global_value("y::d", inarr));
CHECK_CUDA(instance.configure(grid, block).launch(outdata));
CHECK_CUDART(cudaDeviceSynchronize());
int outval[n_const];
CHECK_CUDART(
cudaMemcpy(outval, outdata, sizeof(outval), cudaMemcpyDeviceToHost));
for (int i = 0; i < n_const; i++) {
EXPECT_EQ(inval[i], outval[i]);
}
}
{ // test __constant__ array look up in header nested in both anonymous and
// explicit namespace
jitify::Program program =
kernel_cache.program("example_headers/constant_header.cuh", 0,
{"--use_fast_math", "-I" CUDA_INC_DIR});
auto instance = program.kernel("constant_test2").instantiate();
constexpr int n_anon_const = 6;
int inval[] = {3, 5, 9, 13, 15, 19};
CHECK_CUDA(
cuMemcpyHtoD(instance.get_constant_ptr("(anonymous namespace)::b::a"),
inval, sizeof(inval) / 2));
CHECK_CUDA(
cuMemcpyHtoD(instance.get_global_ptr("(anonymous namespace)::b::d"),
inval + 3, sizeof(inval) / 2));
CHECK_CUDA(instance.configure(grid, block).launch(outdata));
int outval[n_anon_const];
CHECK_CUDART(
cudaMemcpy(outval, outdata, sizeof(outval), cudaMemcpyDeviceToHost));
for (int i = 0; i < n_anon_const; i++) {
EXPECT_EQ(inval[i], outval[i]);
}
}
CHECK_CUDART(cudaFree(outdata));
}
TEST(JitifyTest, ConstantMemory_experimental) {
using jitify::reflection::Type;
constexpr int n_const = 12;
int* outdata;
CHECK_CUDART(cudaMalloc((void**)&outdata, n_const * sizeof(int)));
dim3 grid(1);
dim3 block(1);
{ // test __constant__ look up in kernel string using different namespaces
jitify::experimental::Program program_orig(
constmem_program_source, {}, {"--use_fast_math", "-I" CUDA_INC_DIR});
auto program =
jitify::experimental::Program::deserialize(program_orig.serialize());
auto instance = jitify::experimental::KernelInstantiation::deserialize(
program.kernel("constant_test").instantiate().serialize());
int inval[] = {2, 4, 8, 12, 14, 18, 22, 26, 30, 34, 38, 42};
int dval;
CHECK_CUDA(instance.get_global_value("x::a", &dval));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(dval, 3);
CHECK_CUDA(instance.get_global_value("x::d", &dval));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(dval, 7);
int darr[2];
CHECK_CUDA(instance.get_global_array("y::a", &darr[0], 2));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(darr[0], 4);
EXPECT_EQ(darr[1], 5);
CHECK_CUDA(instance.get_global_value("y::d", &darr));
CHECK_CUDART(cudaDeviceSynchronize());
EXPECT_EQ(darr[0], 8);
EXPECT_EQ(darr[1], 9);
CHECK_CUDA(instance.set_global_value("a", inval[0]));
CHECK_CUDA(instance.set_global_value("b::a", inval[1]));
CHECK_CUDA(instance.set_global_value("c::b::a", inval[2]));
CHECK_CUDA(instance.set_global_value("d", inval[3]));
CHECK_CUDA(instance.set_global_value("b::d", inval[4]));
CHECK_CUDA(instance.set_global_value("c::b::d", inval[5]));
CHECK_CUDA(instance.set_global_value("x::a", inval[6]));
CHECK_CUDA(instance.set_global_value("x::d", inval[7]));
CHECK_CUDA(instance.set_global_array("y::a", &inval[8], 2));
int inarr[] = {inval[10], inval[11]};
CHECK_CUDA(instance.set_global_value("y::d", inarr));
CHECK_CUDA(instance.configure(grid, block).launch(outdata));
CHECK_CUDART(cudaDeviceSynchronize());
int outval[n_const];
CHECK_CUDART(
cudaMemcpy(outval, outdata, sizeof(outval), cudaMemcpyDeviceToHost));
for (int i = 0; i < n_const; i++) {
EXPECT_EQ(inval[i], outval[i]);
}
}
{ // test __constant__ array look up in header nested in both anonymous and
// explicit namespace
jitify::experimental::Program program_orig(
"example_headers/constant_header.cuh", {},
{"--use_fast_math", "-I" CUDA_INC_DIR});
auto program =
jitify::experimental::Program::deserialize(program_orig.serialize());
auto instance = jitify::experimental::KernelInstantiation::deserialize(
program.kernel("constant_test2").instantiate().serialize());
constexpr int n_anon_const = 6;
int inval[] = {3, 5, 9, 13, 15, 19};
CHECK_CUDA(
cuMemcpyHtoD(instance.get_constant_ptr("(anonymous namespace)::b::a"),
inval, sizeof(inval) / 2));
CHECK_CUDA(
cuMemcpyHtoD(instance.get_global_ptr("(anonymous namespace)::b::d"),
inval + 3, sizeof(inval) / 2));
CHECK_CUDA(instance.configure(grid, block).launch(outdata));
int outval[n_anon_const];
CHECK_CUDART(
cudaMemcpy(outval, outdata, sizeof(outval), cudaMemcpyDeviceToHost));
for (int i = 0; i < n_anon_const; i++) {
EXPECT_EQ(inval[i], outval[i]);
}
}
CHECK_CUDART(cudaFree(outdata));
}
TEST(JitifyTest, ParallelFor) {
int n = 10000;
typedef float T;
T* d_out;
CHECK_CUDART(cudaMalloc((void**)&d_out, n * sizeof(T)));
T val = 3.14159f;
jitify::ExecutionPolicy policy(jitify::DEVICE);
auto lambda = JITIFY_LAMBDA((d_out, val), d_out[i] = (float)i * val);
CHECK_CUDA(jitify::parallel_for(policy, 0, n, lambda));
std::vector<T> h_out(n);
CHECK_CUDART(
cudaMemcpy(&h_out[0], d_out, n * sizeof(T), cudaMemcpyDeviceToHost));
CHECK_CUDART(cudaFree(d_out));
for (int i = 0; i < n; ++i) {
EXPECT_FLOAT_EQ(h_out[i], (T)i * val);
}
}
TEST(JitifyTest, InvalidPrograms) {
jitify::JitCache kernel_cache;
auto program_v1 = kernel_cache.program("empty_program\n"); // OK
EXPECT_THROW(auto program_v2 = kernel_cache.program("missing_filename"),
std::runtime_error);
EXPECT_THROW(
auto program_v3 = kernel_cache.program("bad_program\nNOT CUDA C!"),
std::runtime_error);
jitify::experimental::Program program_v4("empty_program\n"); // OK
EXPECT_THROW(jitify::experimental::Program program_v5("missing_filename"),
std::runtime_error);
EXPECT_THROW(
jitify::experimental::Program program_v6("bad_program\nNOT CUDA C!"),
std::runtime_error);
}
static const char* const pragma_repl_program_source = R"(my_program
template <int N, typename T>
__global__ void my_kernel(T* data) {
if (blockIdx.x != 0 || threadIdx.x != 0) return;
T data0 = data[0];
#pragma unroll
for (int i = 0; i < N - 1; ++i) data[0] *= data0;
#pragma unroll 1
for (int i = 0; i < N - 1; ++i) data[0] *= data0;
#pragma unroll 1 // Make sure parsing works with comments
for (int i = 0; i < N - 1; ++i) data[0] *= data0;
// TODO: Add support for block comments.
//#pragma unroll 1 /* Make sure parsing works with comments */
//for (int i = 0; i < N - 1; ++i) data[0] *= data0;
}
)";
TEST(JitifyTest, PragmaReplacement) {
static jitify::JitCache kernel_cache;
jitify::Program program = kernel_cache.program(pragma_repl_program_source);
typedef float T;
T* d_data = nullptr;
using jitify::reflection::type_of;
auto kernel_inst =
program.kernel("my_kernel").instantiate(3, type_of(*d_data));
}
// TODO: Expand this to include more Thrust code.
static const char* const thrust_program_source =
"thrust_program\n"
"#include <thrust/iterator/counting_iterator.h>\n"
"__global__ void my_kernel(thrust::counting_iterator<int> begin,\n"
" thrust::counting_iterator<int> end) {\n"
"}\n";
TEST(JitifyTest, ThrustHeaders) {
// Checks that basic Thrust headers can be compiled.
jitify::JitCache kernel_cache;
#if CUDA_VERSION < 11000
const char* cppstd = "-std=c++98";
#else
const char* cppstd = "-std=c++11";
#endif
auto program_v1 = kernel_cache.program(thrust_program_source, {},
{"-I" CUDA_INC_DIR, cppstd});
auto program_v2 = jitify::experimental::Program(thrust_program_source, {},
{"-I" CUDA_INC_DIR, cppstd});
}
static const char* const cub_program_source =
"cub_program\n"
"#include <cub/block/block_load.cuh>\n"
"#include <cub/block/block_radix_sort.cuh>\n"
"#include <cub/block/block_reduce.cuh>\n"
"#include <cub/block/block_store.cuh>\n"
"\n"
"template<int BLOCK_SIZE, int PER_THREAD>\n"
"__global__ void my_kernel(float* data) {\n"
" typedef cub::BlockLoad<float, BLOCK_SIZE, PER_THREAD,\n"
" cub::BLOCK_LOAD_VECTORIZE> BlockLoad;\n"
" typedef cub::BlockRadixSort<float, BLOCK_SIZE, PER_THREAD>\n"
" BlockSort;\n"
" typedef cub::BlockReduce<float, BLOCK_SIZE> BlockReduce;\n"
" typedef cub::BlockStore<float, BLOCK_SIZE, PER_THREAD,\n"
" cub::BLOCK_STORE_VECTORIZE> BlockStore;\n"
" __shared__ union {\n"
" typename BlockLoad::TempStorage load;\n"
" typename BlockSort::TempStorage sort;\n"
" typename BlockReduce::TempStorage reduce;\n"
" typename BlockStore::TempStorage store;\n"
" float sum;\n"
" } temp_storage;\n"
" float thread_data[PER_THREAD];\n"
" BlockLoad(temp_storage.load).Load(data, thread_data);\n"
" __syncthreads();\n"
" BlockSort(temp_storage.sort).Sort(thread_data);\n"
" __syncthreads();\n"
" float sum = BlockReduce(temp_storage.reduce).Sum(thread_data);\n"
" __syncthreads();\n"
" if (threadIdx.x == 0) {\n"
" temp_storage.sum = sum;\n"
" }\n"
" __syncthreads();\n"
" sum = temp_storage.sum;\n"
" #pragma unroll\n"
" for (int i = 0; i < PER_THREAD; ++i) {\n"
" thread_data[i] *= 1.f / sum;\n"
" }\n"
" __syncthreads();\n"
" BlockStore(temp_storage.store).Store(data, thread_data);\n"
"}\n";
TEST(JitifyTest, CubBlockPrimitives) {
int block_size = 64;
int per_thread = 4;
int n = block_size * per_thread;
std::vector<float> h_data(n);
float sum = 0;
for (int i = 0; i < n; ++i) {
// Start with values sorted in reverse.
h_data[i] = (float)(n - 1 - i);
sum += h_data[i];
}
// Shuffle the values a bit.
std::swap(h_data[3], h_data[7]);
std::swap(h_data[10], h_data[20]);
std::vector<float> h_expected(n);
for (int i = 0; i < n; ++i) {
// Expected sorted and normalized.
h_expected[i] = (float)i / sum;
}
std::vector<float> h_result(n);
float* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, n * sizeof(float)));
jitify::JitCache kernel_cache;
auto program_v1 = kernel_cache.program(cub_program_source, {},
{"-I" CUB_DIR, "-I" CUDA_INC_DIR});
CHECK_CUDART(cudaMemcpy(d_data, h_data.data(), n * sizeof(float),
cudaMemcpyHostToDevice));
CHECK_CUDA(program_v1.kernel("my_kernel")
.instantiate(block_size, per_thread)
.configure(1, block_size)
.launch(d_data));
CHECK_CUDART(cudaMemcpy(h_result.data(), d_data, n * sizeof(float),
cudaMemcpyDeviceToHost));
for (int i = 0; i < n; ++i) {
EXPECT_FLOAT_EQ(h_result[i], h_expected[i]);
}
auto program_v2 = jitify::experimental::Program::deserialize(
jitify::experimental::Program(cub_program_source, {},
{"-I" CUB_DIR, "-I" CUDA_INC_DIR})
.serialize());
auto kernel_inst_v2 = jitify::experimental::KernelInstantiation::deserialize(
program_v2.kernel("my_kernel")
.instantiate(block_size, per_thread)
.serialize());
CHECK_CUDART(cudaMemcpy(d_data, h_data.data(), n * sizeof(float),
cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst_v2.configure(1, block_size).launch(d_data));
CHECK_CUDART(cudaMemcpy(h_result.data(), d_data, n * sizeof(float),
cudaMemcpyDeviceToHost));
for (int i = 0; i < n; ++i) {
EXPECT_FLOAT_EQ(h_result[i], h_expected[i]);
}
CHECK_CUDART(cudaFree(d_data));
}
static const char* const unused_globals_source =
"unused_globals_program\n"
"struct Foo { static const int value = 7; };\n"
"struct Bar { int a; double b; };\n"
"__device__ float used_scalar;\n"
"__device__ float used_array[2];\n"
"__device__ Bar used_struct;\n"
"__device__ float unused_scalar;\n"
"__device__ float unused_array[3];\n"
"__device__ Bar unused_struct;\n"
"__device__ float reg, ret, bra;\n" // Tricky names
"__global__ void foo_kernel(int* data) {\n"
" if (blockIdx.x != 0 || threadIdx.x != 0) return;\n"
" used_scalar = 1.f;\n"
" used_array[1] = 2.f;\n"
" used_struct.b = 3.f;\n"
" __syncthreads();\n"
" *data += Foo::value + used_scalar + used_array[1] + used_struct.b;\n"
" printf(\"*data = %i\\n\", *data);\n" // Produces global symbols named
// $str
"}\n";
TEST(JitifyTest, RemoveUnusedGlobals) {
cudaFree(0);
auto program_v2 = jitify::experimental::Program(
unused_globals_source, {},
// Note: Flag added twice to test handling of repeats.
{"-remove-unused-globals", "--remove-unused-globals"});
auto kernel_inst_v2 = program_v2.kernel("foo_kernel").instantiate();
std::string ptx = kernel_inst_v2.ptx();
EXPECT_TRUE(ptx.find(".global .align 4 .f32 used_scalar;") !=
std::string::npos);
// Note: PTX represents arrays and structs as .b8 instead of the actual type.
EXPECT_TRUE(ptx.find(".global .align 4 .b8 used_array[8];") !=
std::string::npos);
EXPECT_TRUE(ptx.find(".global .align 8 .b8 used_struct[16];") !=
std::string::npos);
EXPECT_FALSE(ptx.find("_ZN3Foo5valueE") != std::string::npos);
EXPECT_FALSE(ptx.find("unused_scalar;") != std::string::npos);
EXPECT_FALSE(ptx.find("unused_array;") != std::string::npos);
EXPECT_FALSE(ptx.find("unused_struct;") != std::string::npos);
EXPECT_FALSE(ptx.find(".global .align 4 .f32 reg;") != std::string::npos);
EXPECT_FALSE(ptx.find(".global .align 4 .f32 ret;") != std::string::npos);
EXPECT_FALSE(ptx.find(".global .align 4 .f32 bra;") != std::string::npos);
int* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(int)));
int h_data = 3;
CHECK_CUDART(
cudaMemcpy(d_data, &h_data, sizeof(int), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst_v2.configure(1, 1).launch(d_data));
CHECK_CUDART(
cudaMemcpy(&h_data, d_data, sizeof(int), cudaMemcpyDeviceToHost));
EXPECT_EQ(h_data, 16);
CHECK_CUDART(cudaFree(d_data));
}
static const char* const curand_program_source =
"curand_program\n"
"#include <curand_kernel.h>\n"
"__global__ void my_kernel() {}\n"
"\n";
TEST(JitifyTest, CuRandKernel) {
auto program_v2 = jitify::experimental::Program(
curand_program_source, {},
// Note: --remove-unused-globals is added to remove huge precomputed
// arrays that come from CURAND.
{"-I" CUDA_INC_DIR, "--remove-unused-globals"});
auto kernel_inst_v2 = program_v2.kernel("my_kernel").instantiate();
// TODO: Expand this test to actually call curand kernels and check outputs.
}
static const char* const linktest_program1_source =
"linktest_program1\n"
"__constant__ int c = 5;\n"
"__device__ int d = 7;\n"
"__device__ int f(int i) { return i + 11; }\n"
"\n";
static const char* const linktest_program2_source =
"linktest_program2\n"
"extern __constant__ int c;\n"
"extern __device__ int d;\n"
"extern __device__ int f(int);\n"
"__global__ void my_kernel(int* data) {\n"
" *data = f(*data + c + d);\n"
"}\n"
"\n";
TEST(JitifyTest, LinkExternalFiles) {
cudaFree(0);
// Ensure temporary file is deleted at the end.
std::unique_ptr<const char, int (*)(const char*)> ptx_filename(
"example_headers/linktest.ptx", std::remove);
{
std::ofstream ptx_file(ptx_filename.get());
ptx_file.exceptions(std::ofstream::failbit | std::ofstream::badbit);
ptx_file << jitify::experimental::Program(linktest_program1_source, {},
{"-rdc=true"})
.kernel("")
.instantiate()
.ptx();
}
auto program_v2 = jitify::experimental::Program(
linktest_program2_source, {},
{"-rdc=true", "-Lexample_headers", "-llinktest.ptx"});
auto kernel_inst_v2 = program_v2.kernel("my_kernel").instantiate();
int* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(int)));
int h_data = 3;
CHECK_CUDART(
cudaMemcpy(d_data, &h_data, sizeof(int), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst_v2.configure(1, 1).launch(d_data));
CHECK_CUDART(
cudaMemcpy(&h_data, d_data, sizeof(int), cudaMemcpyDeviceToHost));
EXPECT_EQ(h_data, 26);
CHECK_CUDART(cudaFree(d_data));
}
namespace a {
__host__ __device__ int external_device_func(int i) { return i + 1; }
} // namespace a
static const char* const selflink_program_source =
"selflink_program\n"
"namespace a {\n"
"extern __device__ int external_device_func(int);\n"
"}\n"
"__global__ void my_kernel(int* data) {\n"
" *data = a::external_device_func(*data);\n"
"}\n"
"\n";
TEST(JitifyTest, LinkCurrentExecutable) {
cudaFree(0);
using namespace jitify::experimental;
auto program = Program(selflink_program_source, {}, {"-l."});
auto kernel_inst = program.kernel("my_kernel").instantiate();
int* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(int)));
int h_data = 3;
CHECK_CUDART(
cudaMemcpy(d_data, &h_data, sizeof(int), cudaMemcpyHostToDevice));
CHECK_CUDA(kernel_inst.configure(1, 1).launch(d_data));
CHECK_CUDART(
cudaMemcpy(&h_data, d_data, sizeof(int), cudaMemcpyDeviceToHost));
EXPECT_EQ(h_data, 4);
CHECK_CUDART(cudaFree(d_data));
}
static const char* const reflection_program_source =
"reflection_program\n"
"struct Base { virtual ~Base() {} };\n"
"template <typename T>\n"
"struct Derived : public Base {};\n"
"template<typename T>\n"
"__global__ void type_kernel() {}\n"
"template<unsigned short N>\n"
"__global__ void nontype_kernel() {}\n"
"\n";
struct Base {
virtual ~Base() {}
};
template <typename T>
struct Derived : public Base {};
TEST(JitifyTest, Reflection) {
cudaFree(0);
using namespace jitify::experimental;
using jitify::reflection::instance_of;
Program program(reflection_program_source);
auto type_kernel = program.kernel("type_kernel");
#define JITIFY_TYPE_REFLECTION_TEST(T) \
EXPECT_EQ(type_kernel.instantiate<T>().mangled_name(), \
type_kernel.instantiate({#T}).mangled_name())
JITIFY_TYPE_REFLECTION_TEST(const volatile float);
JITIFY_TYPE_REFLECTION_TEST(const volatile float*);
JITIFY_TYPE_REFLECTION_TEST(const volatile float&);
JITIFY_TYPE_REFLECTION_TEST(Base * (const volatile float));
JITIFY_TYPE_REFLECTION_TEST(const volatile float[4]);
#undef JITIFY_TYPE_REFLECTION_TEST
typedef Derived<float> derived_type;
const Base& base = derived_type();
EXPECT_EQ(type_kernel.instantiate(instance_of(base)).mangled_name(),
type_kernel.instantiate<derived_type>().mangled_name());
auto nontype_kernel = program.kernel("nontype_kernel");
#define JITIFY_NONTYPE_REFLECTION_TEST(N) \
EXPECT_EQ(nontype_kernel.instantiate(N).mangled_name(), \
nontype_kernel.instantiate({#N}).mangled_name())
JITIFY_NONTYPE_REFLECTION_TEST(7);
JITIFY_NONTYPE_REFLECTION_TEST('J');
#undef JITIFY_NONTYPE_REFLECTION_TEST
}
static const char* const builtin_numeric_limits_program_source =
"builtin_numeric_limits_program\n"
"#include <limits>\n"
"struct MyType {};\n"
"namespace std {\n"
"template<> class numeric_limits<MyType> {\n"
" public:\n"
" static MyType min() { return {}; }\n"
" static MyType max() { return {}; }\n"
"};\n"
"} // namespace std\n"
"template <typename T>\n"
"__global__ void my_kernel(T* data) {\n"
" data[0] = std::numeric_limits<T>::min();\n"
" data[1] = std::numeric_limits<T>::max();\n"
"}\n";
TEST(JitifyTest, BuiltinNumericLimitsHeader) {
cudaFree(0);
using namespace jitify::experimental;
auto program = Program(builtin_numeric_limits_program_source);
for (const auto& type :
{"float", "double", "char", "signed char", "unsigned char", "short",
"unsigned short", "int", "unsigned int", "long", "unsigned long",
"long long", "unsigned long long", "MyType"}) {
program.kernel("my_kernel").instantiate({type});
}
}
TEST(JitifyTest, ClassKernelArg) {
using jitify::reflection::Type;
thread_local static jitify::JitCache kernel_cache;
int h_data;
int* d_data;
CHECK_CUDART(cudaMalloc((void**)&d_data, sizeof(int)));
dim3 grid(1);
dim3 block(1);
jitify::Program program =
kernel_cache.program("example_headers/class_arg_kernel.cuh", 0,
{"--use_fast_math", "-I" CUDA_INC_DIR});
{ // test that we can pass an arg object to a kernel
Arg arg(-1);
CHECK_CUDA(program.kernel("class_arg_kernel")
.instantiate(Type<Arg>())
.configure(grid, block)
.launch(d_data, arg));
CHECK_CUDART(cudaDeviceSynchronize());
CHECK_CUDART(
cudaMemcpy(&h_data, d_data, sizeof(int), cudaMemcpyDeviceToHost));
EXPECT_EQ(arg.x, h_data);
}
{ // test that we can pass an arg object rvalue to a kernel
int value = -2;
CHECK_CUDA(program.kernel("class_arg_kernel")
.instantiate(Type<Arg>())
.configure(grid, block)
.launch(d_data, Arg(value)));
CHECK_CUDART(cudaDeviceSynchronize());
CHECK_CUDART(
cudaMemcpy(&h_data, d_data, sizeof(int), cudaMemcpyDeviceToHost));
EXPECT_EQ(value, h_data);
}
{ // test that we can pass an arg object reference to a kernel
Arg* arg = new Arg(-3);
// references are passed as pointers since refernces are just pointers from
// an ABI point of view
CHECK_CUDA(program.kernel("class_arg_ref_kernel")