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Added a test to validate the computation of exp and log on 16bit floats

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This commit is contained in:
Benoit Steiner 2016-05-03 12:06:07 -07:00
parent 6c3e5b85bc
commit 2c5568a757
1 changed files with 63 additions and 0 deletions
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63
unsupported/test/cxx11_tensor_of_float16_cuda.cu
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@ -134,6 +134,68 @@ void test_cuda_elementwise() {
gpu_device.deallocate(d_res_float);
}
void test_cuda_trancendental() {
Eigen::CudaStreamDevice stream;
Eigen::GpuDevice gpu_device(&stream);
int num_elem = 101;
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::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(
d_res1_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res1_float(
d_res1_float, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res2_half(
d_res2_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<float, 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>();
Tensor<float, 1> input1(num_elem);
Tensor<float, 1> half_prec1(num_elem);
Tensor<float, 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);
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.synchronize();
for (int i = 0; i < num_elem; ++i) {
std::cout << "Checking elemwise exp " << i << " input = " << input1(i) << " full = " << full_prec1(i) << " half = " << half_prec1(i) << std::endl;
VERIFY_IS_APPROX(full_prec1(i), half_prec1(i));
}
for (int i = 0; i < num_elem; ++i) {
std::cout << "Checking elemwise log " << i << " input = " << input2(i) << " full = " << full_prec2(i) << " half = " << half_prec2(i) << std::endl;
VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
}
gpu_device.deallocate(d_float1);
gpu_device.deallocate(d_float2);
gpu_device.deallocate(d_res1_half);
gpu_device.deallocate(d_res1_float);
gpu_device.deallocate(d_res2_half);
gpu_device.deallocate(d_res2_float);
}
void test_cuda_contractions() {
Eigen::CudaStreamDevice stream;
@ -280,6 +342,7 @@ void test_cxx11_tensor_of_float16_cuda()
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());