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https://github.com/ROCm/composable_kernel.git
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Gemm alpha beta profiler (fp32 & fp16) (#91)
* [What] Refactor verification of gemm alpha_beta, move to reference operation [Why] Sync with other verification * Profile mk_nk for gemm bias 2d * Support bias 2d with mn * kn in profiler * Support bias 2d with km*kn and km*nk in profiler * Support fp32 bias 2d in profiler * format * format Co-authored-by: rocking <chunylai@amd.com> Co-authored-by: Chao Liu <chao.liu2@amd.com>
This commit is contained in:
rocking5566
311
profiler/include/profile_gemm_bias_2d_impl.hpp
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311
profiler/include/profile_gemm_bias_2d_impl.hpp
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#pragma once
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#include "config.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "host_conv.hpp"
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#include "tensor_layout.hpp"
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#include "device_tensor.hpp"
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#include "element_wise_operation.hpp"
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#include "device_gemm.hpp"
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#include "reference_gemm_bias_2d.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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namespace device_gemm_instance {
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using DeviceGemmAlphaBetaPtr = ck::tensor_operation::device::DeviceGemmBiasPtr<
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::PassThrough,
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ck::tensor_operation::element_wise::AlphaBetaAdd>;
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_km_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_km_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_km_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_km_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_kn_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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void add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_nk_mn_instances(
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std::vector<DeviceGemmAlphaBetaPtr>&);
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} // namespace device_gemm_instance
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} // namespace device
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} // namespace tensor_operation
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} // namespace ck
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namespace ck {
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namespace profiler {
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template <typename ADataType,
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typename BDataType,
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typename C0DataType,
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typename CDataType,
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typename AccDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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void profile_gemm_bias_2d_impl(int do_verification,
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int init_method,
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bool do_log,
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int nrepeat,
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int M,
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int N,
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int K,
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int StrideA,
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int StrideB,
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int StrideC,
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float alpha,
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float beta)
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{
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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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if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
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Tensor<C0DataType> c0_m_n(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
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std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
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std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
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std::cout << "c0_m_n: " << c0_m_n.mDesc << std::endl;
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std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
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std::size_t num_thread = std::thread::hardware_concurrency();
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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.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
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c0_m_n.GenerateTensorValue(GeneratorTensor_2<C0DataType>{-5, 5}, num_thread);
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break;
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default:
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
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c0_m_n.GenerateTensorValue(GeneratorTensor_3<C0DataType>{-0.5, 0.5}, num_thread);
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}
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// set zero to c_device_buf
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c_m_n_device_result.GenerateTensorValue(GeneratorTensor_0<CDataType>{}, num_thread);
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::AlphaBetaAdd;
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const auto a_element_op = AElementOp{};
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const auto b_element_op = BElementOp{};
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const auto c_element_op = CElementOp{alpha, beta};
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if(do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemmBias2D<ADataType,
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BDataType,
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C0DataType,
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CDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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auto ref_gemm = ReferenceGemmInstance{};
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auto ref_invoker = ref_gemm.MakeInvoker();
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auto ref_argument = ref_gemm.MakeArgument(
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a_m_k, b_k_n, c0_m_n, c_m_n_host_result, a_element_op, b_element_op, c_element_op);
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ref_invoker.Run(ref_argument);
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
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DeviceMem c0_device_buf(sizeof(C0DataType) * c0_m_n.mDesc.GetElementSpace());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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c0_device_buf.ToDevice(c0_m_n.mData.data());
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c_device_buf.ToDevice(c_m_n_device_result.mData.data());
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// add device GEMM instances
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std::vector<ck::tensor_operation::device::device_gemm_instance::DeviceGemmAlphaBetaPtr>
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gemm_ptrs;
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if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
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is_same<CDataType, half_t>::value)
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{
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if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
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}
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}
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else if constexpr(is_same<ADataType, float>::value && is_same<BDataType, float>::value &&
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is_same<CDataType, float>::value)
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{
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if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_kn_mn_instances(gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_mk_nk_mn_instances(gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_km_kn_mn_instances(gemm_ptrs);
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}
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else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
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is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
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{
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ck::tensor_operation::device::device_gemm_instance::
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add_device_gemm_xdl_c_shuffle_bias_2d_f32_f32_f32_km_nk_mn_instances(gemm_ptrs);
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}
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}
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if(gemm_ptrs.size() <= 0)
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{
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throw std::runtime_error("wrong! no device GEMM instance found");
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}
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std::string best_gemm_name;
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float best_ave_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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// profile device GEMM instances
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for(auto& gemm_ptr : gemm_ptrs)
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{
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auto argument_ptr =
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gemm_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<C0DataType*>(c0_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_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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auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
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if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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std::string gemm_name = gemm_ptr->GetTypeString();
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float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
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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 * M + 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
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<< " GB/s, " << gemm_name << std::endl;
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if(tflops > best_tflops)
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{
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best_gemm_name = gemm_name;
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best_tflops = tflops;
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best_ave_time = ave_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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c_device_buf.FromDevice(c_m_n_device_result.mData.data());
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check_error(c_m_n_host_result, c_m_n_device_result);
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if(do_log)
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{
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LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c0 : ", c0_m_n.mData, ",") << std::endl;
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LogRangeAsType<float>(std::cout << "c_host : ", c_m_n_host_result.mData, ",")
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<< std::endl;
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LogRangeAsType<float>(std::cout << "c_device: ", c_m_n_device_result.mData, ",")
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<< std::endl;
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}
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}
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}
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else
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{
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std::cout << "does not support this GEMM problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
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<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
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}
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} // namespace profiler
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} // namespace ck
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