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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.
259 lines
11 KiB
C++
259 lines
11 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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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "ck/ck.hpp"
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#include "ck/stream_config.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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#include "ck/library/utility/literals.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.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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template <ck::index_t... Is>
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using S = ck::Sequence<Is...>;
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using F16 = ck::half_t;
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using F32 = float;
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using BF16 = ck::bhalf_t;
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using INT8 = std::int8_t;
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using INT32 = std::int32_t;
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#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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using INT4 = ck::int4_t;
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#endif
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CElementwiseOperation,
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typename DeviceCGemmInstance,
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typename ReferenceCGemmInstance,
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typename KernelADataType = ADataType,
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typename KernelBDataType = BDataType,
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typename KernelCDataType = CDataType>
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bool run_cgemm_xdl(ck::index_t M,
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ck::index_t N,
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ck::index_t K,
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ck::index_t StrideA,
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ck::index_t StrideB,
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ck::index_t StrideC,
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bool do_verification,
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int init_method,
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bool time_kernel)
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{
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#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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static_assert(sizeof(ck::int4_t) == sizeof(int8_t),
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"sizeof ck::int4_t and int8_t is different!");
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static_assert(sizeof(ADataType) == sizeof(KernelADataType),
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"sizeof ADataType and KernelADataType is different!");
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static_assert(sizeof(BDataType) == sizeof(KernelBDataType),
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"sizeof BDataType and KernelBDataType is different!");
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static_assert(sizeof(CDataType) == sizeof(KernelCDataType),
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"sizeof CDataType and KernelCDataType is different!");
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#endif
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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using namespace ck::literals;
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor({row, col}, {stride, 1_uz});
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}
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else
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{
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return HostTensorDescriptor({row, col}, {1_uz, stride});
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}
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};
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Tensor<ADataType> a_m_k_real(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<ADataType> a_m_k_imag(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n_real(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<BDataType> b_k_n_imag(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<KernelCDataType> c_m_n_real_device_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<KernelCDataType> c_m_n_imag_device_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k_real: " << a_m_k_real.mDesc << std::endl;
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std::cout << "a_m_k_imag: " << a_m_k_imag.mDesc << std::endl;
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std::cout << "b_k_n_real: " << b_k_n_real.mDesc << std::endl;
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std::cout << "b_k_n_imag: " << b_k_n_imag.mDesc << std::endl;
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std::cout << "c_m_n_real: " << c_m_n_real_device_result.mDesc << std::endl;
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std::cout << "c_m_n_imag: " << c_m_n_imag_device_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_m_k_real.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
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break;
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default:
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a_m_k_real.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
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a_m_k_imag.GenerateTensorValue(GeneratorTensor_3<ADataType>{-0.5, 0.5});
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b_k_n_real.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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b_k_n_imag.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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}
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auto cgemm = DeviceCGemmInstance{};
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DeviceMem a_m_k_real_device_buf(sizeof(KernelADataType) *
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a_m_k_real.mDesc.GetElementSpaceSize());
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DeviceMem a_m_k_imag_device_buf(sizeof(KernelADataType) *
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a_m_k_imag.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_real_device_buf(sizeof(KernelBDataType) *
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b_k_n_real.mDesc.GetElementSpaceSize());
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DeviceMem b_k_n_imag_device_buf(sizeof(KernelBDataType) *
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b_k_n_imag.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_real_device_buf(sizeof(KernelCDataType) *
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c_m_n_real_device_result.mDesc.GetElementSpaceSize());
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DeviceMem c_m_n_imag_device_buf(sizeof(KernelCDataType) *
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c_m_n_imag_device_result.mDesc.GetElementSpaceSize());
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DeviceMem workspace_device_buf(cgemm.GetWorkspaceSize(M, N, K, StrideA, StrideB, StrideC));
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#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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if constexpr(std::is_same_v<ADataType, ck::int4_t>)
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{
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Tensor<KernelADataType> a_m_k_real_converted(a_m_k_real);
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Tensor<KernelADataType> a_m_k_imag_converted(a_m_k_imag);
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Tensor<KernelBDataType> b_k_n_real_converted(b_k_n_real);
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Tensor<KernelBDataType> b_k_n_imag_converted(b_k_n_imag);
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a_m_k_real_device_buf.ToDevice(a_m_k_real_converted.mData.data());
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a_m_k_imag_device_buf.ToDevice(a_m_k_imag_converted.mData.data());
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b_k_n_real_device_buf.ToDevice(b_k_n_real_converted.mData.data());
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b_k_n_imag_device_buf.ToDevice(b_k_n_imag_converted.mData.data());
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}
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else
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#endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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{
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a_m_k_real_device_buf.ToDevice(a_m_k_real.mData.data());
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a_m_k_imag_device_buf.ToDevice(a_m_k_imag.mData.data());
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b_k_n_real_device_buf.ToDevice(b_k_n_real.mData.data());
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b_k_n_imag_device_buf.ToDevice(b_k_n_imag.mData.data());
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}
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auto a_element_op = AElementwiseOperation{};
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auto b_element_op = BElementwiseOperation{};
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auto c_element_op = CElementwiseOperation{};
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// do GEMM
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auto invoker = cgemm.MakeInvoker();
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auto argument =
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cgemm.MakeArgument(static_cast<KernelADataType*>(a_m_k_real_device_buf.GetDeviceBuffer()),
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static_cast<KernelADataType*>(a_m_k_imag_device_buf.GetDeviceBuffer()),
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static_cast<KernelBDataType*>(b_k_n_real_device_buf.GetDeviceBuffer()),
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static_cast<KernelBDataType*>(b_k_n_imag_device_buf.GetDeviceBuffer()),
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static_cast<KernelCDataType*>(c_m_n_real_device_buf.GetDeviceBuffer()),
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static_cast<KernelCDataType*>(c_m_n_imag_device_buf.GetDeviceBuffer()),
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static_cast<KernelCDataType*>(workspace_device_buf.GetDeviceBuffer()),
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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a_element_op,
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b_element_op,
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c_element_op);
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if(!cgemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_cgemm with the specified compilation parameters does "
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"not support this CGEMM problem");
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}
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(8) * M * N * K;
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std::size_t num_btype =
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std::size_t(2) *
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(sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(CDataType) * 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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<< cgemm.GetTypeString() << std::endl;
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if(do_verification)
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{
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Tensor<CDataType> c_m_n_real_host_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_imag_host_result(
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f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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auto ref_cgemm = ReferenceCGemmInstance{};
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auto ref_invoker = ref_cgemm.MakeInvoker();
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auto ref_argument = ref_cgemm.MakeArgument(a_m_k_real,
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a_m_k_imag,
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b_k_n_real,
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b_k_n_imag,
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c_m_n_real_host_result,
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c_m_n_imag_host_result,
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a_element_op,
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b_element_op,
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c_element_op);
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ref_invoker.Run(ref_argument);
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c_m_n_real_device_buf.FromDevice(c_m_n_real_device_result.mData.data());
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c_m_n_imag_device_buf.FromDevice(c_m_n_imag_device_result.mData.data());
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bool result = true;
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#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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if constexpr(std::is_same_v<ADataType, ck::int4_t>)
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{
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const Tensor<CDataType> c_m_n_real_device_result_converted(c_m_n_real_device_result);
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const Tensor<CDataType> c_m_n_imag_device_result_converted(c_m_n_imag_device_result);
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result = ck::utils::check_err(c_m_n_real_device_result_converted,
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c_m_n_real_host_result,
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"Verification error: incorrect results in real part!",
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1e-2f,
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1e-1f);
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result = result && ck::utils::check_err(
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c_m_n_imag_device_result_converted,
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c_m_n_imag_host_result,
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"Verification error: incorrect results in imaginary part!",
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1e-2f,
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1e-1f);
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}
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else
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#endif // CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
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{
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result = ck::utils::check_err(c_m_n_real_device_result,
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c_m_n_real_host_result,
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"Verification error: incorrect results in real part!",
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1e-2f,
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1e-1f);
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result = result && ck::utils::check_err(
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c_m_n_imag_device_result,
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c_m_n_imag_host_result,
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"Verification error: incorrect results in imaginary part!",
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1e-2f,
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1e-1f);
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}
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return result;
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}
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return true;
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}
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