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Further improved the testing of fp16

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This commit is contained in:
Benoit Steiner 2016-05-05 15:58:05 -07:00
parent 0451940fa4
commit 9a48688d37
1 changed files with 60 additions and 56 deletions
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116
unsupported/test/cxx11_tensor_of_float16_cuda.cu
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@ -141,43 +141,43 @@ void test_cuda_trancendental() {
float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res1_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res1_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res2_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res2_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
Eigen::half* d_res1_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::half* d_res1_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::half* d_res2_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(
d_float1, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float2(
d_float2, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res1_half(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(
d_res1_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res1_float(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(
d_res1_float, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res2_half(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(
d_res2_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res2_float(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(
d_res2_float, num_elem);
gpu_float1.device(gpu_device) = gpu_float1.random();
gpu_float2.device(gpu_device) = gpu_float2.random();
gpu_res1_float.device(gpu_device) = gpu_float1.exp();
gpu_res2_float.device(gpu_device) = gpu_float2.log();
gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().exp().cast<float>();
gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>().log().cast<float>();
gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::half>();
gpu_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::half>();
gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().exp();
gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>().log();
Tensor<float, 1> input1(num_elem);
Tensor<float, 1> half_prec1(num_elem);
Tensor<float, 1> full_prec1(num_elem);
Tensor<Eigen::half, 1> half_prec1(num_elem);
Tensor<Eigen::half, 1> full_prec1(num_elem);
Tensor<float, 1> input2(num_elem);
Tensor<float, 1> half_prec2(num_elem);
Tensor<float, 1> full_prec2(num_elem);
Tensor<Eigen::half, 1> half_prec2(num_elem);
Tensor<Eigen::half, 1> full_prec2(num_elem);
gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(input2.data(), d_float2, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec1.data(), d_res1_float, num_elem*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(half_prec2.data(), d_res2_half, num_elem*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half));
gpu_device.synchronize();
for (int i = 0; i < num_elem; ++i) {
@ -206,16 +206,16 @@ void test_cuda_contractions() {
float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res_half = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(
d_float1, rows, cols);
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(
d_float2, rows, cols);
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_res_half(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_half(
d_res_half, rows, cols);
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_res_float(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 2>, Eigen::Aligned> gpu_res_float(
d_res_float, rows, cols);
gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
@ -223,13 +223,13 @@ void test_cuda_contractions() {
typedef Tensor<float, 2>::DimensionPair DimPair;
Eigen::array<DimPair, 1> dims(DimPair(1, 0));
gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims);
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims).cast<float>();
gpu_res_float.device(gpu_device) = gpu_float1.contract(gpu_float2, dims).cast<Eigen::half>();
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().contract(gpu_float2.cast<Eigen::half>(), dims);
Tensor<float, 2> half_prec(rows, cols);
Tensor<float, 2> full_prec(rows, cols);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(float));
Tensor<Eigen::half, 2> half_prec(rows, cols);
Tensor<Eigen::half, 2> full_prec(rows, cols);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, num_elem*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, num_elem*sizeof(Eigen::half));
gpu_device.synchronize();
for (int i = 0; i < rows; ++i) {
@ -254,29 +254,42 @@ void test_cuda_reductions() {
float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_res_half = (float*)gpu_device.allocate(size * sizeof(float));
float* d_res_float = (float*)gpu_device.allocate(size * sizeof(float));
Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(size * sizeof(Eigen::half));
Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(size * sizeof(Eigen::half));
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(
d_float1, size, size);
Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(
d_float2, size, size);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_half(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half(
d_res_half, size);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float(
d_res_float, size);
gpu_float1.device(gpu_device) = gpu_float1.random();
gpu_float2.device(gpu_device) = gpu_float2.random();
Eigen::array<int, 1> redux_dim = {{0}};
gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim);
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(redux_dim).cast<float>();
gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim).cast<Eigen::half>();
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(redux_dim);
Tensor<float, 1> half_prec(size);
Tensor<float, 1> full_prec(size);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, size*sizeof(float));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, size*sizeof(float));
Tensor<Eigen::half, 1> half_prec(size);
Tensor<Eigen::half, 1> full_prec(size);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, size*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, size*sizeof(Eigen::half));
gpu_device.synchronize();
for (int i = 0; i < size; ++i) {
std::cout << "Checking redux " << i << std::endl;
VERIFY_IS_APPROX(full_prec(i), half_prec(i));
}
redux_dim = {{1}};
gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim).cast<Eigen::half>();
gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(redux_dim);
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, size*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, size*sizeof(Eigen::half));
gpu_device.synchronize();
for (int i = 0; i < size; ++i) {
@ -333,23 +346,14 @@ void test_cuda_forced_evals() {
void test_cxx11_tensor_of_float16_cuda()
{
#ifdef EIGEN_HAS_CUDA_FP16
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice device(&stream);
if (device.majorDeviceVersion() > 5 ||
(device.majorDeviceVersion() == 5 && device.minorDeviceVersion() >= 3)) {
std::cout << "Running test on device with capability " << device.majorDeviceVersion() << "." << device.minorDeviceVersion() << std::endl;
CALL_SUBTEST_1(test_cuda_conversion());
CALL_SUBTEST_1(test_cuda_unary());
CALL_SUBTEST_1(test_cuda_elementwise());
CALL_SUBTEST_1(test_cuda_trancendental());
CALL_SUBTEST_2(test_cuda_contractions());
CALL_SUBTEST_3(test_cuda_reductions());
CALL_SUBTEST_4(test_cuda_forced_evals());
CALL_SUBTEST_1(test_cuda_conversion());
CALL_SUBTEST_1(test_cuda_unary());
CALL_SUBTEST_1(test_cuda_elementwise());
CALL_SUBTEST_1(test_cuda_trancendental());
CALL_SUBTEST_2(test_cuda_contractions());
CALL_SUBTEST_3(test_cuda_reductions());
CALL_SUBTEST_4(test_cuda_forced_evals());
}
else {
std::cout << "Half floats require compute capability of at least 5.3. This device only supports " << device.majorDeviceVersion() << "." << device.minorDeviceVersion() << ". Skipping the test" << std::endl;
}
#else
std::cout << "Half floats are not supported by this version of cuda: skipping the test" << std::endl;
#endif