mirror of
https://github.com/ROCm/composable_kernel.git
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b5e2f26808
* 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.
[ROCm/composable_kernel commit: ad57f6ef0b]
224 lines
8.5 KiB
C++
224 lines
8.5 KiB
C++
// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <cstdlib>
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#include <iostream>
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#include <string>
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#include <vector>
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#include "ck/ck.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_generator.hpp"
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#include "ck/library/utility/numeric.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_contraction.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 Row = ck::tensor_layout::gemm::RowMajor;
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int run_contraction_scale_example(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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// A[M0, M1, K0, K1]
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std::vector<ck::index_t> a_ms_ks_lengths{30, 128, 32, 64};
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std::vector<ck::index_t> a_ms_ks_strides{524288, 4096, 128, 1};
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// B[N0, N1, K0, K1]
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std::vector<ck::index_t> b_ns_ks_lengths{32, 64, 32, 64};
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std::vector<ck::index_t> b_ns_ks_strides{524288, 4096, 128, 1};
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// E[M0, M1, N0, N1]
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std::vector<ck::index_t> e_ms_ns_lengths{30, 128, 32, 64};
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std::vector<ck::index_t> e_ms_ns_strides{524288, 4096, 128, 1};
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float scale = 1.f;
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if(argc == 1)
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{
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// use default case
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}
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else if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else if(argc == 23)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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const ck::index_t M0 = std::stoi(argv[4]);
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const ck::index_t M1 = std::stoi(argv[5]);
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const ck::index_t N0 = std::stoi(argv[6]);
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const ck::index_t N1 = std::stoi(argv[7]);
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const ck::index_t K0 = std::stoi(argv[8]);
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const ck::index_t K1 = std::stoi(argv[9]);
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a_ms_ks_lengths = {M0, M1, K0, K1};
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a_ms_ks_strides = {
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std::stoi(argv[10]), std::stoi(argv[11]), std::stoi(argv[12]), std::stoi(argv[13])};
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b_ns_ks_lengths = {N0, N1, K0, K1};
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b_ns_ks_strides = {
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std::stoi(argv[14]), std::stoi(argv[15]), std::stoi(argv[16]), std::stoi(argv[17])};
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e_ms_ns_lengths = {M0, M1, N0, N1};
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e_ms_ns_strides = {
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std::stoi(argv[18]), std::stoi(argv[19]), std::stoi(argv[20]), std::stoi(argv[21])};
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scale = std::stof(argv[22]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: time kernel (0=no, 1=yes)\n");
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printf("arg4 to 9: M0, M1, N0, N1, K0, K1\n");
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printf("arg10 to 13: Stride_A_M0, Stride_A_M1, Stride_A_K0, Stride_A_K1\n");
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printf("arg14 to 17: Stride_B_N0, Stride_B_N1, Stride_B_K0, Stride_B_K1\n");
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printf("arg18 to 21: Stride_E_M0, Stride_E_M1, Stride_E_N0, Stride_E_N1\n");
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printf("arg22: scale\n");
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exit(0);
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}
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Tensor<ADataType> a_ms_ks(a_ms_ks_lengths, a_ms_ks_strides, Row{});
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Tensor<BDataType> b_ns_ks(b_ns_ks_lengths, b_ns_ks_strides, Row{});
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Tensor<EDataType> e_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides, Row{});
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Tensor<EDataType> e_ms_ns_device_result(e_ms_ns_lengths, e_ms_ns_strides, Row{});
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std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
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std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
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std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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default:
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a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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break;
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_ms_ks.mData.data());
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b_device_buf.ToDevice(b_ns_ks.mData.data());
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// set zero
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e_device_buf.SetZero();
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto cde_element_op = CDEElementOp{scale};
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// device operation
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auto op = DeviceOpInstance{};
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auto invoker = op.MakeInvoker();
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auto argument = op.MakeArgument(a_device_buf.GetDeviceBuffer(),
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b_device_buf.GetDeviceBuffer(),
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std::array<const void*, 0>{},
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e_device_buf.GetDeviceBuffer(),
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a_ms_ks_lengths,
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a_ms_ks_strides,
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b_ns_ks_lengths,
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b_ns_ks_strides,
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std::array<std::vector<ck::index_t>, 0>{},
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std::array<std::vector<ck::index_t>, 0>{},
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e_ms_ns_lengths,
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e_ms_ns_strides,
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a_element_op,
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b_element_op,
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cde_element_op);
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if(!op.IsSupportedArgument(argument))
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{
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std::cout << op.GetTypeString() << " does not support this problem" << std::endl;
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return 0;
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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ck::index_t M =
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ck::accumulate_n<ck::index_t>(e_ms_ns_lengths.begin(), NumDimM, 1, std::multiplies<>{});
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ck::index_t N = ck::accumulate_n<ck::index_t>(
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e_ms_ns_lengths.begin() + NumDimM, NumDimN, 1, std::multiplies<>{});
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ck::index_t K = ck::accumulate_n<ck::index_t>(
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a_ms_ks_lengths.begin() + NumDimM, NumDimK, 1, std::multiplies<>{});
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std::size_t flop = std::size_t(2) * M * N * K;
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std::size_t num_btype =
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sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + +sizeof(EDataType) * M * N;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
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float gb_per_sec = num_btype / 1.E6 / ave_time;
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std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
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<< op.GetTypeString() << std::endl;
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e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
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if(do_verification)
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{
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Tensor<CShuffleDataType> c_ms_ns_host_result(e_ms_ns_lengths, e_ms_ns_strides, Row{});
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using ReferenceOpInstance =
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ck::tensor_operation::host::ReferenceContraction_M2_N2_K2<NumDimM,
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NumDimN,
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NumDimK,
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ADataType,
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BDataType,
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CShuffleDataType,
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AccDataType,
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ComputeDataType,
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AElementOp,
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BElementOp>;
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auto ref_op = ReferenceOpInstance{};
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auto ref_invoker = ref_op.MakeInvoker();
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auto ref_argument =
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ref_op.MakeArgument(a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op);
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ref_invoker.Run(ref_argument);
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for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
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{
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for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
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{
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for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++n0)
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{
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for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n1)
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{
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cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1),
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c_ms_ns_host_result(m0, m1, n0, n1));
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}
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}
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}
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}
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return ck::utils::check_err(e_ms_ns_device_result, e_ms_ns_host_result) ? 0 : 1;
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}
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return 0;
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}
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