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/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#if GOOGLE_CUDA
#define EIGEN_USE_GPU
#include <numeric>
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/cuda_kernel_helper.h"
#define CUDA_EXPECT_SUCCESS \
{ \
cudaDeviceSynchronize(); \
cudaError_t err = cudaGetLastError(); \
EXPECT_EQ(cudaSuccess, err) << cudaGetErrorString(err); \
}
#define CUDA_ASSERT_SUCCESS \
{ \
cudaDeviceSynchronize(); \
cudaError_t err = cudaGetLastError(); \
ASSERT_EQ(cudaSuccess, err) << cudaGetErrorString(err); \
}
namespace tensorflow {
namespace {
__global__ void SetOutbufZero(CudaLaunchConfig config, int* outbuf) {
CUDA_1D_KERNEL_LOOP(x, config.virtual_thread_count) { outbuf[x] = 0; }
}
// counting number of jobs by using atomic +1
__global__ void Count1D(CudaLaunchConfig config, int bufsize, int* outbuf) {
CUDA_1D_KERNEL_LOOP(x, config.virtual_thread_count) {
if (x < 0) { // x might overflow when testing extreme case
break;
}
atomicAdd(&outbuf[x % bufsize], 1);
}
}
__global__ void Count2D(Cuda2DLaunchConfig config, int bufsize, int* outbuf) {
CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count.x, X) {
if (x < 0) { // x might overflow when testing extreme case
break;
}
CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count.y, Y) {
if (y < 0) { // y might overflow when testing extreme case
break;
}
int idx = x * config.virtual_thread_count.y + y;
atomicAdd(&outbuf[idx % bufsize], 1);
}
}
}
__global__ void Count3D(Cuda3DLaunchConfig config, int bufsize, int* outbuf) {
CUDA_AXIS_KERNEL_LOOP(x, config.virtual_thread_count.x, X) {
if (x < 0) { // x might overflow when testing extreme case
break;
}
CUDA_AXIS_KERNEL_LOOP(y, config.virtual_thread_count.y, Y) {
if (y < 0) { // y might overflow when testing extreme case
break;
}
CUDA_AXIS_KERNEL_LOOP(z, config.virtual_thread_count.z, Z) {
if (z < 0) { // z might overflow when testing extreme case
break;
}
int idx =
x * config.virtual_thread_count.y * config.virtual_thread_count.z +
y * config.virtual_thread_count.z + z;
atomicAdd(&outbuf[idx % bufsize], 1);
}
}
}
}
__global__ void CudaShuffleGetSrcLaneTest(unsigned* failure_count) {
unsigned lane_id = CudaLaneId();
for (int width = warpSize; width > 1; width /= 2) {
auto check_result = [&](const char* op_name, int param, unsigned actual,
unsigned expected) {
if (actual != expected) {
printf("Cuda%sGetSrcLane(%d, %d) for lane %d returned %d, not %d\n",
op_name, param, width, lane_id, actual, expected);
CudaAtomicAdd(failure_count, 1);
}
};
for (int src_lane = -warpSize; src_lane <= warpSize; ++src_lane) {
unsigned actual_lane = detail::CudaShuffleGetSrcLane(src_lane, width);
unsigned expect_lane =
CudaShuffleSync(kCudaWarpAll, lane_id, src_lane, width);
check_result("Shuffle", src_lane, actual_lane, expect_lane);
}
for (unsigned delta = 0; delta <= warpSize; ++delta) {
unsigned actual_lane = detail::CudaShuffleUpGetSrcLane(delta, width);
unsigned expect_lane =
CudaShuffleUpSync(kCudaWarpAll, lane_id, delta, width);
check_result("ShuffleUp", delta, actual_lane, expect_lane);
}
for (unsigned delta = 0; delta <= warpSize; ++delta) {
unsigned actual_lane = detail::CudaShuffleDownGetSrcLane(delta, width);
unsigned expect_lane =
CudaShuffleDownSync(kCudaWarpAll, lane_id, delta, width);
check_result("ShuffleDown", delta, actual_lane, expect_lane);
}
for (int lane_lane = warpSize; lane_lane > 0; lane_lane /= 2) {
unsigned actual_lane = detail::CudaShuffleXorGetSrcLane(lane_lane, width);
unsigned expect_lane =
CudaShuffleXorSync(kCudaWarpAll, lane_id, lane_lane, width);
check_result("ShuffleXor", lane_lane, actual_lane, expect_lane);
}
}
}
} // namespace
class CudaLaunchConfigTest : public ::testing::Test {
protected:
const int bufsize = 1024;
int* outbuf = nullptr;
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice d = Eigen::GpuDevice(&stream);
virtual void SetUp() {
cudaError_t err = cudaMallocManaged(&outbuf, sizeof(int) * bufsize);
ASSERT_EQ(cudaSuccess, err) << cudaGetErrorString(err);
}
virtual void TearDown() {
cudaDeviceSynchronize();
cudaFree(outbuf);
outbuf = nullptr;
}
};
TEST_F(CudaLaunchConfigTest, GetCudaLaunchConfig) {
CudaLaunchConfig cfg;
// test valid inputs
#define TEST_LAUNCH_PARAMETER(work_element_count) \
cfg = GetCudaLaunchConfig(bufsize, d); \
SetOutbufZero<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, outbuf); \
CUDA_ASSERT_SUCCESS \
cfg = GetCudaLaunchConfig(work_element_count, d); \
Count1D<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, bufsize, outbuf); \
CUDA_EXPECT_SUCCESS \
EXPECT_EQ(work_element_count, std::accumulate(outbuf, outbuf + bufsize, 0)); \
\
cfg = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \
SetOutbufZero<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, outbuf); \
CUDA_ASSERT_SUCCESS \
cfg = GetCudaLaunchConfig(work_element_count, d, Count1D, 0, 0); \
Count1D<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, bufsize, outbuf); \
CUDA_EXPECT_SUCCESS \
EXPECT_EQ(work_element_count, std::accumulate(outbuf, outbuf + bufsize, 0))
TEST_LAUNCH_PARAMETER(128);
TEST_LAUNCH_PARAMETER(129);
TEST_LAUNCH_PARAMETER(511);
TEST_LAUNCH_PARAMETER(512);
TEST_LAUNCH_PARAMETER(2048);
TEST_LAUNCH_PARAMETER(2049);
TEST_LAUNCH_PARAMETER(8191);
TEST_LAUNCH_PARAMETER(8192);
TEST_LAUNCH_PARAMETER(123456);
TEST_LAUNCH_PARAMETER(1 << 30);
#undef TEST_LAUNCH_PARAMETER
}
bool operator==(const Cuda2DLaunchConfig& a, const Cuda2DLaunchConfig& b) {
return a.thread_per_block.x == b.thread_per_block.x &&
a.thread_per_block.y == b.thread_per_block.y &&
a.thread_per_block.z == b.thread_per_block.z &&
a.block_count.x == b.block_count.x &&
a.block_count.y == b.block_count.y &&
a.block_count.z == b.block_count.z &&
a.thread_per_block.x == b.thread_per_block.x &&
a.thread_per_block.y == b.thread_per_block.y &&
a.thread_per_block.z == b.thread_per_block.z;
}
TEST_F(CudaLaunchConfigTest, GetCuda2DLaunchConfig) {
Cuda2DLaunchConfig cfg;
CudaLaunchConfig cfg1d;
// test valid inputs
#define TEST_LAUNCH_PARAMETER(dimx, dimy) \
cfg1d = GetCudaLaunchConfig(bufsize, d); \
SetOutbufZero<<<cfg1d.block_count, cfg1d.thread_per_block, 0, d.stream()>>>( \
cfg1d, outbuf); \
CUDA_ASSERT_SUCCESS \
cfg = GetCuda2DLaunchConfig(dimx, dimy, d); \
Count2D<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, bufsize, outbuf); \
CUDA_EXPECT_SUCCESS \
EXPECT_EQ(dimx* dimy, std::accumulate(outbuf, outbuf + bufsize, 0)); \
\
cfg1d = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \
SetOutbufZero<<<cfg1d.block_count, cfg1d.thread_per_block, 0, d.stream()>>>( \
cfg1d, outbuf); \
CUDA_ASSERT_SUCCESS \
cfg = GetCuda2DLaunchConfig(dimx, dimy, d, Count2D, 0, 0); \
Count2D<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, bufsize, outbuf); \
CUDA_EXPECT_SUCCESS \
EXPECT_EQ(dimx* dimy, std::accumulate(outbuf, outbuf + bufsize, 0))
TEST_LAUNCH_PARAMETER(128, 128);
TEST_LAUNCH_PARAMETER(129, 64);
TEST_LAUNCH_PARAMETER(511, 2048);
TEST_LAUNCH_PARAMETER(512, 512);
TEST_LAUNCH_PARAMETER(2048, 1024);
TEST_LAUNCH_PARAMETER(2049, 32);
TEST_LAUNCH_PARAMETER(8191, 1);
TEST_LAUNCH_PARAMETER(8192, 10);
TEST_LAUNCH_PARAMETER(123456, 12);
TEST_LAUNCH_PARAMETER(1, 1 << 30);
TEST_LAUNCH_PARAMETER(1 << 30, 1);
#undef TEST_LAUNCH_PARAMETER
}
TEST_F(CudaLaunchConfigTest, GetCuda3DLaunchConfig) {
Cuda3DLaunchConfig cfg;
CudaLaunchConfig cfg1d;
// test valid inputs
#define TEST_LAUNCH_PARAMETER(dimx, dimy, dimz) \
cfg1d = GetCudaLaunchConfig(bufsize, d, SetOutbufZero, 0, 0); \
SetOutbufZero<<<cfg1d.block_count, cfg1d.thread_per_block, 0, d.stream()>>>( \
cfg1d, outbuf); \
CUDA_ASSERT_SUCCESS \
cfg = GetCuda3DLaunchConfig(dimx, dimy, dimz, d, Count3D, 0, 0); \
Count3D<<<cfg.block_count, cfg.thread_per_block, 0, d.stream()>>>( \
cfg, bufsize, outbuf); \
CUDA_EXPECT_SUCCESS \
EXPECT_EQ(dimx* dimy* dimz, std::accumulate(outbuf, outbuf + bufsize, 0))
TEST_LAUNCH_PARAMETER(128, 128, 128);
TEST_LAUNCH_PARAMETER(129, 64, 1024);
TEST_LAUNCH_PARAMETER(511, 2048, 128);
TEST_LAUNCH_PARAMETER(512, 512, 64);
TEST_LAUNCH_PARAMETER(2048, 1024, 128);
TEST_LAUNCH_PARAMETER(2049, 32, 1024);
TEST_LAUNCH_PARAMETER(8191, 1, 1024);
TEST_LAUNCH_PARAMETER(8192, 10, 32);
TEST_LAUNCH_PARAMETER(123456, 12, 21);
TEST_LAUNCH_PARAMETER(1, 1, 1 << 30);
TEST_LAUNCH_PARAMETER(1, 1 << 30, 1);
TEST_LAUNCH_PARAMETER(1 << 30, 1, 1);
#undef TEST_LAUNCH_PARAMETER
}
TEST(CudaDeviceFunctionsTest, ShuffleGetSrcLane) {
unsigned* failure_count;
ASSERT_EQ(cudaMallocManaged(&failure_count, sizeof(unsigned)), cudaSuccess);
*failure_count = 0;
CudaShuffleGetSrcLaneTest<<<1, 32>>>(failure_count);
ASSERT_EQ(cudaDeviceSynchronize(), cudaSuccess);
ASSERT_EQ(*failure_count, 0);
cudaFree(failure_count);
}
} // namespace tensorflow
#endif // GOOGLE_CUDA
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