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Commit 0e2be9c2 authored by Astha Rai's avatar Astha Rai
Browse files

added example file with updated device elementwise call

parent 9e07a42f
#include <iostream>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_elementwise_2d.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
using F16 = ck::half_t;
using F32 = float;
using ADataType = F16;
using BDataType = F16;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceElementwisePermuteInstance =
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ADataType>,
ck::Tuple<BDataType>,
PassThrough,
3,
1,
8,
8,
ck::Sequence<1>,
ck::Sequence<1>>;
template <typename HostTensorA, typename HostTensorB, typename Functor>
void host_elementwise4D(HostTensorB& B_nhwc,
const HostTensorA& A_nchw,
const std::vector<std::size_t>& shape_nchw,
Functor functor)
{
for(std::size_t n = 0; n < shape_nchw[0]; ++n)
for(std::size_t c = 0; c < shape_nchw[1]; ++c)
for(std::size_t h = 0; h < shape_nchw[2]; ++h)
for(std::size_t w = 0; w < shape_nchw[3]; ++w)
{
auto a_val = A_nchw(n, c, h, w);
functor(B_nhwc(n, h, w, c), a_val);
}
}
int main()
{
bool do_verification = true;
bool time_kernel = false;
std::vector<std::size_t> nchw = {4, 4, 8, 8};
std::vector<std::size_t> nhwc = {4, 8, 8, 4};
Tensor<ADataType> a(nchw);
Tensor<BDataType> b(nhwc);
a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
DeviceMem a_device_buf(sizeof(ADataType) * a.mDesc.GetElementSpaceSize());
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()};
std::array<ck::index_t, 4> ab_lengths;
std::array<ck::index_t, 4> a_strides = {static_cast<int>(nchw[1] * nchw[2] * nchw[3]),
static_cast<int>(nchw[2] * nchw[3]),
static_cast<int>(nchw[3]),
1};
std::array<ck::index_t, 4> b_strides = {
static_cast<int>(nhwc[1] * nhwc[2] * nhwc[3]), 1, 32, 4};
// std::cout << "Length: " << ab_lengths << std::endl;
// std::cout << "A stride: " << a_strides << std::endl;
// std::cout << "B stride: " << b_strides << std::endl;
std::copy(nchw.begin(), nchw.end(), ab_lengths.begin());
// std::copy(a.mDesc.GetStrides().begin(), a.mDesc.GetStrides().end(), a_strides.begin());
// std::copy(b.mDesc.GetStrides().begin(), b.mDesc.GetStrides().end(), b_strides.begin());
auto broadcastPermute = DeviceElementwisePermuteInstance{};
auto argument = broadcastPermute.MakeArgumentPointer(
ab_lengths, {a_strides}, {b_strides}, input, output, PassThrough{});
if(!broadcastPermute.IsSupportedArgument(argument.get()))
{
throw std::runtime_error(
"The runtime parameters seems not supported by the device instance, exiting!");
};
auto broadcastPermute_invoker_ptr = broadcastPermute.MakeInvokerPointer();
float ave_time =
broadcastPermute_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
std::cout << "Perf: " << ave_time << " ms" << 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<Tensor<ADataType>, Tensor<BDataType>, PassThrough>(
host_b, a, nchw, 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);
}
return pass ? 0 : 1;
}
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