mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 21:21:22 +00:00
4a2a56c22f
* Rangify STL algorithms This commit adapts rangified std::copy(), std::fill() & std::transform() * Rangify check_err() By rangifying check_err(), we can not only compare values between std::vector<>s, but also compare any ranges which have same value type. * Allow constructing Tensor<> like a HostTensorDescriptor * Simplify Tensor<> object construction logics * Remove more unnecessary 'HostTensorDescriptor' objects * Re-format example code * Re-write more HostTensorDescriptor ctor call
121 lines
4.3 KiB
C++
121 lines
4.3 KiB
C++
// SPDX-License-Identifier: MIT
|
|
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
|
|
|
#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/impl/device_elementwise.hpp"
|
|
|
|
#include "ck/library/utility/algorithm.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 ABDataType = F16;
|
|
using CDataType = F16;
|
|
|
|
using Add = ck::tensor_operation::element_wise::Add;
|
|
|
|
using DeviceElementwiseAddInstance =
|
|
ck::tensor_operation::device::DeviceElementwise<ck::Tuple<ABDataType, ABDataType>,
|
|
ck::Tuple<CDataType>,
|
|
Add,
|
|
4,
|
|
8,
|
|
ck::Sequence<8, 8>,
|
|
ck::Sequence<8>>;
|
|
|
|
template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
|
|
void host_elementwise4D(HostTensorC& C,
|
|
const HostTensorA& A,
|
|
const HostTensorB& B,
|
|
const std::vector<std::size_t>& shape,
|
|
Functor functor)
|
|
{
|
|
using ctype = ck::remove_reference_t<decltype(C(0, 0, 0, 0))>;
|
|
|
|
for(std::size_t n = 0; n < shape[0]; ++n)
|
|
for(std::size_t c = 0; c < shape[1]; ++c)
|
|
for(std::size_t h = 0; h < shape[2]; ++h)
|
|
for(std::size_t w = 0; w < shape[3]; ++w)
|
|
{
|
|
auto a_val = A(n, c, h, w);
|
|
auto b_val = B(n, c, h, w);
|
|
ctype c_val = 0;
|
|
functor(c_val, a_val, b_val);
|
|
C(n, c, h, w) = c_val;
|
|
}
|
|
}
|
|
|
|
int main()
|
|
{
|
|
bool do_verification = true;
|
|
bool time_kernel = false;
|
|
|
|
std::vector<std::size_t> nchw = {4, 16, 32, 32};
|
|
|
|
Tensor<ABDataType> a(nchw);
|
|
Tensor<ABDataType> b(nchw);
|
|
Tensor<CDataType> c(nchw);
|
|
|
|
a.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
|
|
b.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
|
|
|
|
DeviceMem a_device_buf(sizeof(ABDataType) * a.mDesc.GetElementSpaceSize());
|
|
DeviceMem b_device_buf(sizeof(ABDataType) * b.mDesc.GetElementSpaceSize());
|
|
DeviceMem c_device_buf(sizeof(CDataType) * c.mDesc.GetElementSpaceSize());
|
|
|
|
a_device_buf.ToDevice(a.mData.data());
|
|
b_device_buf.ToDevice(b.mData.data());
|
|
|
|
std::array<const void*, 2> input = {a_device_buf.GetDeviceBuffer(),
|
|
b_device_buf.GetDeviceBuffer()};
|
|
std::array<void*, 1> output = {c_device_buf.GetDeviceBuffer()};
|
|
|
|
std::array<ck::index_t, 4> abc_lengths;
|
|
std::array<ck::index_t, 4> a_strides;
|
|
std::array<ck::index_t, 4> b_strides;
|
|
std::array<ck::index_t, 4> c_strides;
|
|
|
|
ck::ranges::copy(nchw, abc_lengths.begin());
|
|
ck::ranges::copy(a.mDesc.GetStrides(), a_strides.begin());
|
|
ck::ranges::copy(b.mDesc.GetStrides(), b_strides.begin());
|
|
ck::ranges::copy(c.mDesc.GetStrides(), c_strides.begin());
|
|
|
|
auto broadcastAdd = DeviceElementwiseAddInstance{};
|
|
auto argument = broadcastAdd.MakeArgumentPointer(
|
|
abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
|
|
|
|
if(!broadcastAdd.IsSupportedArgument(argument.get()))
|
|
{
|
|
throw std::runtime_error(
|
|
"The runtime parameters seems not supported by the device instance, exiting!");
|
|
};
|
|
|
|
auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
|
|
float ave_time =
|
|
broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
|
|
|
|
std::cout << "Perf: " << ave_time << " ms" << std::endl;
|
|
|
|
bool pass = true;
|
|
if(do_verification)
|
|
{
|
|
c_device_buf.FromDevice(c.mData.data());
|
|
Tensor<CDataType> host_c(nchw);
|
|
|
|
host_elementwise4D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
|
|
host_c, a, b, nchw, Add{});
|
|
|
|
pass &= ck::utils::check_err(c, host_c, "Error: Incorrect results c", 1e-3, 1e-3);
|
|
}
|
|
|
|
return pass ? 0 : 1;
|
|
}
|