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https://github.com/ROCm/composable_kernel.git
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ad57f6ef0b
* Wrap ck host utitlies in CK namespace. The CK and CK-Tile source code bases are incompatible because CK is not properly using namespaces everywhere. In particular, we need to put hip_check_error in the ck namespace. Move all functions in include/ck_/host_utility that were in global namespace into the ck namespace. There may be additional namespace problems like this, and it's possible we'll have namespace clashes. But it is good design to properly guard our to code bases (CK and CKTile) so that they can both coexist. Moreover, estabilishing this compatiblity is essential if we are going to allow the builder to instantiate kernels from either template library. * Add using declarations to test code. After moving some of the untils into the ck namespace, most examples and a few tests had to be updated to recognize the new namespace declarations. We add using declarations to individual compute units for functions that were previously in the global namespace. * Add using declarations to client examples.
130 lines
4.8 KiB
C++
130 lines
4.8 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#include <iostream>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_elementwise_dynamic_vector_dims_impl.hpp"
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#include "ck/library/utility/algorithm.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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using ::ck::DeviceMem;
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using ::ck::HostTensorDescriptor;
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using ::ck::Tensor;
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using F16 = ck::half_t;
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using F32 = float;
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using ABDataType = F16;
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using CDataType = F16;
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using Add = ck::tensor_operation::element_wise::Add;
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using DeviceElementwiseAddInstance =
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ck::tensor_operation::device::DeviceElementwiseImpl<ck::Tuple<ABDataType, ABDataType>,
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ck::Tuple<CDataType>,
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Add,
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4,
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64,
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2,
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128,
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2,
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2,
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ck::Sequence<1, 0>,
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ck::Sequence<2, 2>,
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ck::Sequence<2>>;
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template <typename HostTensorA, typename HostTensorB, typename HostTensorC, typename Functor>
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void host_elementwise4D(HostTensorC& C,
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const HostTensorA& A,
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const HostTensorB& B,
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const std::vector<std::size_t>& shape,
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Functor functor)
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{
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using ctype = ck::remove_reference_t<decltype(C(0, 0, 0, 0))>;
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for(std::size_t n = 0; n < shape[0]; ++n)
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for(std::size_t c = 0; c < shape[1]; ++c)
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for(std::size_t h = 0; h < shape[2]; ++h)
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for(std::size_t w = 0; w < shape[3]; ++w)
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{
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auto a_val = A(n, c, h, w);
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auto b_val = B(n, c, h, w);
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ctype c_val = 0;
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functor(c_val, a_val, b_val);
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C(n, c, h, w) = c_val;
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}
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}
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int main()
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{
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bool do_verification = true;
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bool time_kernel = false;
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std::vector<std::size_t> nchw = {4, 16, 32, 32};
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Tensor<ABDataType> a(nchw);
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Tensor<ABDataType> b(nchw);
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Tensor<CDataType> c(nchw);
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a.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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b.GenerateTensorValue(GeneratorTensor_3<ABDataType>{0.0, 1.0});
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DeviceMem a_device_buf(sizeof(ABDataType) * a.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(ABDataType) * b.mDesc.GetElementSpaceSize());
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DeviceMem c_device_buf(sizeof(CDataType) * c.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a.mData.data());
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b_device_buf.ToDevice(b.mData.data());
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std::array<const void*, 2> input = {a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer()};
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std::array<void*, 1> output = {c_device_buf.GetDeviceBuffer()};
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std::array<ck::index_t, 4> abc_lengths;
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std::array<ck::index_t, 4> a_strides;
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std::array<ck::index_t, 4> b_strides;
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std::array<ck::index_t, 4> c_strides;
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ck::ranges::copy(nchw, abc_lengths.begin());
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ck::ranges::copy(a.mDesc.GetStrides(), a_strides.begin());
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ck::ranges::copy(b.mDesc.GetStrides(), b_strides.begin());
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ck::ranges::copy(c.mDesc.GetStrides(), c_strides.begin());
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auto broadcastAdd = DeviceElementwiseAddInstance{};
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auto argument = broadcastAdd.MakeArgumentPointer(
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abc_lengths, {a_strides, b_strides}, {c_strides}, input, output, Add{});
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if(!broadcastAdd.IsSupportedArgument(argument.get()))
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{
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throw std::runtime_error(
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"The runtime parameters seems not supported by the device instance, exiting!");
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};
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auto broadcastAdd_invoker_ptr = broadcastAdd.MakeInvokerPointer();
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float ave_time =
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broadcastAdd_invoker_ptr->Run(argument.get(), StreamConfig{nullptr, time_kernel});
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std::cout << "Perf: " << ave_time << " ms" << std::endl;
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bool pass = true;
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if(do_verification)
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{
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c_device_buf.FromDevice(c.mData.data());
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Tensor<CDataType> host_c(nchw);
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host_elementwise4D<Tensor<ABDataType>, Tensor<ABDataType>, Tensor<CDataType>, Add>(
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host_c, a, b, nchw, Add{});
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pass &= ck::utils::check_err(c, host_c, "Error: Incorrect results c", 1e-3, 1e-3);
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}
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return pass ? 0 : 1;
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}
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