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enable vectorization path when testing half on cuda, and add test for log1p

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This commit is contained in:
Gael Guennebaud 2016-08-26 14:55:51 +02:00
parent 965e595f02
commit 0f56b5a6de
1 changed files with 36 additions and 14 deletions
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50
unsupported/test/cxx11_tensor_of_float16_cuda.cu
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@ -181,30 +181,39 @@ void test_cuda_trancendental() {
float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float)); float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float)); float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
float* d_float3 = (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_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_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_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::half* d_res2_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::half* d_res3_half = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::half* d_res3_float = (Eigen::half*)gpu_device.allocate(num_elem * sizeof(Eigen::half));
Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1( Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float1(d_float1, num_elem);
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_float2( Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float3(d_float3, num_elem);
d_float2, num_elem); Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half(d_res1_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_half( Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float(d_res1_float, num_elem);
d_res1_half, num_elem); Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half(d_res2_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res1_float( Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_float(d_res2_float, num_elem);
d_res1_float, num_elem); Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_half(d_res3_half, num_elem);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res2_half( Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res3_float(d_res3_float, num_elem);
d_res2_half, num_elem);
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_float1.constant(0.5f); gpu_float1.device(gpu_device) = gpu_float1.random() - gpu_float1.constant(0.5f);
gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f); gpu_float2.device(gpu_device) = gpu_float2.random() + gpu_float1.constant(0.5f);
gpu_float3.device(gpu_device) = gpu_float3.random();
gpu_res1_float.device(gpu_device) = gpu_float1.exp().cast<Eigen::half>(); 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_res2_float.device(gpu_device) = gpu_float2.log().cast<Eigen::half>();
gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().exp(); gpu_res3_float.device(gpu_device) = gpu_float3.log1p().cast<Eigen::half>();
gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>().log();
gpu_res1_half.device(gpu_device) = gpu_float1.cast<Eigen::half>();
gpu_res1_half.device(gpu_device) = gpu_res1_half.exp();
gpu_res2_half.device(gpu_device) = gpu_float2.cast<Eigen::half>();
gpu_res2_half.device(gpu_device) = gpu_res2_half.log();
gpu_res3_half.device(gpu_device) = gpu_float3.cast<Eigen::half>();
gpu_res3_half.device(gpu_device) = gpu_res3_half.log1p();
Tensor<float, 1> input1(num_elem); Tensor<float, 1> input1(num_elem);
Tensor<Eigen::half, 1> half_prec1(num_elem); Tensor<Eigen::half, 1> half_prec1(num_elem);
@ -212,12 +221,18 @@ void test_cuda_trancendental() {
Tensor<float, 1> input2(num_elem); Tensor<float, 1> input2(num_elem);
Tensor<Eigen::half, 1> half_prec2(num_elem); Tensor<Eigen::half, 1> half_prec2(num_elem);
Tensor<Eigen::half, 1> full_prec2(num_elem); Tensor<Eigen::half, 1> full_prec2(num_elem);
Tensor<float, 1> input3(num_elem);
Tensor<Eigen::half, 1> half_prec3(num_elem);
Tensor<Eigen::half, 1> full_prec3(num_elem);
gpu_device.memcpyDeviceToHost(input1.data(), d_float1, num_elem*sizeof(float)); 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(input2.data(), d_float2, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(input3.data(), d_float3, num_elem*sizeof(float));
gpu_device.memcpyDeviceToHost(half_prec1.data(), d_res1_half, num_elem*sizeof(Eigen::half)); 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(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(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.memcpyDeviceToHost(full_prec2.data(), d_res2_float, num_elem*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(half_prec3.data(), d_res3_half, num_elem*sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec3.data(), d_res3_float, num_elem*sizeof(Eigen::half));
gpu_device.synchronize(); gpu_device.synchronize();
for (int i = 0; i < num_elem; ++i) { for (int i = 0; i < num_elem; ++i) {
@ -231,12 +246,19 @@ void test_cuda_trancendental() {
else else
VERIFY_IS_APPROX(full_prec2(i), half_prec2(i)); VERIFY_IS_APPROX(full_prec2(i), half_prec2(i));
} }
for (int i = 0; i < num_elem; ++i) {
std::cout << "Checking elemwise plog1 " << i << " input = " << input3(i) << " full = " << full_prec3(i) << " half = " << half_prec3(i) << std::endl;
VERIFY_IS_APPROX(full_prec3(i), half_prec3(i));
}
gpu_device.deallocate(d_float1); gpu_device.deallocate(d_float1);
gpu_device.deallocate(d_float2); gpu_device.deallocate(d_float2);
gpu_device.deallocate(d_float3);
gpu_device.deallocate(d_res1_half); gpu_device.deallocate(d_res1_half);
gpu_device.deallocate(d_res1_float); gpu_device.deallocate(d_res1_float);
gpu_device.deallocate(d_res2_half); gpu_device.deallocate(d_res2_half);
gpu_device.deallocate(d_res2_float); gpu_device.deallocate(d_res2_float);
gpu_device.deallocate(d_res3_float);
gpu_device.deallocate(d_res3_half);
} }
template<typename> template<typename>