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authorGravatar Rohit Santhanam <rohit.santhanam@amd.com>2021-05-28 20:06:48 +0000
committerGravatar Rohit Santhanam <rohit.santhanam@amd.com>2021-05-28 20:06:48 +0000
commitc8d40a7bf1915015c991b108cf2cd6a32138fdc8 (patch)
tree16d4e2ec436780afe5efbe5bb623305ef9ec18ab
parent91cd67f057f90101cf858d63916ee56a58511b0d (diff)
Removed dead code from GPU float16 unit test.
-rw-r--r--unsupported/test/cxx11_tensor_of_float16_gpu.cu42
1 files changed, 16 insertions, 26 deletions
diff --git a/unsupported/test/cxx11_tensor_of_float16_gpu.cu b/unsupported/test/cxx11_tensor_of_float16_gpu.cu
index 062f76e26..30bcc1d28 100644
--- a/unsupported/test/cxx11_tensor_of_float16_gpu.cu
+++ b/unsupported/test/cxx11_tensor_of_float16_gpu.cu
@@ -329,26 +329,22 @@ void test_gpu_reductions(int size1, int size2, int redux) {
int num_elem = size1*size2;
int result_size = (redux == 1 ? size1 : size2);
- float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
- float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+ float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(result_size * sizeof(Eigen::half));
- Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float1(
- d_float1, size1, size2);
- Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float2(
- d_float2, size1, size2);
+ Eigen::TensorMap<Eigen::Tensor<float, 2>, Eigen::Aligned> gpu_float(
+ d_float, size1, size2);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_half(
d_res_half, result_size);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 1>, Eigen::Aligned> gpu_res_float(
d_res_float, result_size);
- gpu_float1.device(gpu_device) = gpu_float1.random() * 2.0f;
- gpu_float2.device(gpu_device) = gpu_float2.random() * 2.0f;
+ gpu_float.device(gpu_device) = gpu_float.random() * 2.0f;
Eigen::array<int, 1> redux_dim = {redux};
- 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_res_float.device(gpu_device) = gpu_float.sum(redux_dim).cast<Eigen::half>();
+ gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().sum(redux_dim);
Tensor<Eigen::half, 1> half_prec(result_size);
Tensor<Eigen::half, 1> full_prec(result_size);
@@ -361,8 +357,7 @@ void test_gpu_reductions(int size1, int size2, int redux) {
VERIFY_IS_APPROX(full_prec(i), half_prec(i));
}
- gpu_device.deallocate(d_float1);
- gpu_device.deallocate(d_float2);
+ gpu_device.deallocate(d_float);
gpu_device.deallocate(d_res_half);
gpu_device.deallocate(d_res_float);
}
@@ -386,25 +381,21 @@ void test_gpu_full_reductions() {
int size = 13;
int num_elem = size*size;
- float* d_float1 = (float*)gpu_device.allocate(num_elem * sizeof(float));
- float* d_float2 = (float*)gpu_device.allocate(num_elem * sizeof(float));
+ float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
Eigen::half* d_res_half = (Eigen::half*)gpu_device.allocate(1 * sizeof(Eigen::half));
Eigen::half* d_res_float = (Eigen::half*)gpu_device.allocate(1 * 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, 2>, Eigen::Aligned> gpu_float(
+ d_float, size, size);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_half(
d_res_half);
Eigen::TensorMap<Eigen::Tensor<Eigen::half, 0>, Eigen::Aligned> gpu_res_float(
d_res_float);
- gpu_float1.device(gpu_device) = gpu_float1.random();
- gpu_float2.device(gpu_device) = gpu_float2.random();
+ gpu_float.device(gpu_device) = gpu_float.random();
- gpu_res_float.device(gpu_device) = gpu_float1.sum().cast<Eigen::half>();
- gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum();
+ gpu_res_float.device(gpu_device) = gpu_float.sum().cast<Eigen::half>();
+ gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().sum();
Tensor<Eigen::half, 0> half_prec;
Tensor<Eigen::half, 0> full_prec;
@@ -414,16 +405,15 @@ void test_gpu_full_reductions() {
VERIFY_IS_APPROX(full_prec(), half_prec());
- gpu_res_float.device(gpu_device) = gpu_float1.maximum().cast<Eigen::half>();
- gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().maximum();
+ gpu_res_float.device(gpu_device) = gpu_float.maximum().cast<Eigen::half>();
+ gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().maximum();
gpu_device.memcpyDeviceToHost(half_prec.data(), d_res_half, sizeof(Eigen::half));
gpu_device.memcpyDeviceToHost(full_prec.data(), d_res_float, sizeof(Eigen::half));
gpu_device.synchronize();
VERIFY_IS_APPROX(full_prec(), half_prec());
- gpu_device.deallocate(d_float1);
- gpu_device.deallocate(d_float2);
+ gpu_device.deallocate(d_float);
gpu_device.deallocate(d_res_half);
gpu_device.deallocate(d_res_float);
}