ROCm/composable_kernel
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Tensor permutation (#479)

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[ROCm/composable_kernel commit: 685860c2a9]
This commit is contained in:
arai713
2022-10-18 21:24:19 -07:00
committed by GitHub
parent 88822f0d02
commit ce151db598
2 changed files with 18 additions and 8 deletions
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0
example/38_elementwise_permute/CMakeLists.txt → example/44_elementwise_permute/CMakeLists.txt
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26
example/38_elementwise_permute/elementwise_permute_4D_fp16.cpp → example/44_elementwise_permute/elementwise_permute_4D_fp16.cpp
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@@ -3,7 +3,7 @@
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_elementwise.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
@@ -42,10 +42,10 @@ void host_elementwise4D(HostTensorB& B_nhwc, const HostTensorA& A_nchw, Functor
int main()
{
bool do_verification = true;
bool time_kernel = false;
bool time_kernel = true;
std::vector<std::size_t> nchw = {4, 4, 8, 8};
std::vector<std::size_t> nhwc = {4, 8, 8, 4};
std::vector<std::size_t> nchw = {16, 128, 32, 64};
std::vector<std::size_t> nhwc = {16, 32, 64, 128};
Tensor<ADataType> a(nchw);
Tensor<BDataType> b(nhwc);
@@ -55,7 +55,6 @@ int main()
DeviceMem b_device_buf(sizeof(BDataType) * b.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a.mData.data());
// LogRangeAsType<float>(std::cout << "Tensor a : ", a.mData, ",") << std::endl;
std::array<const void*, 1> input = {a_device_buf.GetDeviceBuffer()};
std::array<void*, 1> output = {b_device_buf.GetDeviceBuffer()};
@@ -81,22 +80,33 @@ int main()
throw std::runtime_error(
"The runtime parameters seems not supported by the device instance, exiting!");
};
std::cout << "A (nchw): " << a.mDesc << std::endl;
std::cout << "B (nhwc): " << b.mDesc << std::endl;
auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
float ave_time =
broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = std::size_t(2) * nchw[0] * nchw[1] * nchw[2] * nchw[3];
std::cout << "Perf: " << ave_time << " ms" << std::endl;
std::size_t num_btype = sizeof(ADataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]) +
sizeof(BDataType) * (nchw[0] * nchw[1] * nchw[2] * nchw[3]);
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s"
<< std::endl;
bool pass = true;
if(do_verification)
{
b_device_buf.FromDevice(b.mData.data());
// LogRangeAsType<float>(std::cout << "Tensor b : ", b.mData, ",") << std::endl;
Tensor<BDataType> host_b(nhwc);
host_elementwise4D(host_b, a, PassThrough{});
// LogRangeAsType<float>(std::cout << "Host b : ", host_b.mData, ",") << std::endl;
pass &=
ck::utils::check_err(b.mData, host_b.mData, "Error: Incorrect results b", 1e-3, 1e-3);
}