mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-11 17:00:18 +00:00
cec69bc3bc
* Add host API * manually rebase on develop * clean * manually rebase on develop * exclude tests from all target * address review comments * update client app name * fix missing lib name * clang-format update * refactor * refactor * refactor * refactor * refactor * fix test issue * refactor * refactor * refactor * upate cmake and readme Co-authored-by: Chao Liu <chao.liu2@amd.com>
429 lines
20 KiB
C++
429 lines
20 KiB
C++
#pragma once
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#include <memory>
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#include "check_err.hpp"
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#include "config.hpp"
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#include "element_wise_operation.hpp"
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#include "tensor_layout.hpp"
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#include "device.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_gemm.hpp"
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#include "reference_batched_gemm.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_batched_gemm_instance {
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using DeviceGemmNoOpPtr =
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ck::tensor_operation::device::DeviceGemmPtr<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::PassThrough>;
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void add_device_batched_gemm_xdl_bf16_bf16_bf16_gmk_gkn_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_bf16_bf16_bf16_gmk_gnk_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gkn_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gnk_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances(std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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void add_device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_instances(
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std::vector<DeviceGemmNoOpPtr>&);
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} // namespace device_batched_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 CDataType,
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typename ALayout,
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typename BLayout,
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typename CLayout>
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bool profile_batched_gemm_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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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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int BatchCount)
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{
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bool pass = true;
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auto f_host_tensor_descriptor = [](std::size_t batch_count,
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std::size_t row,
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std::size_t col,
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std::size_t stride,
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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>({batch_count, row, col}),
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std::vector<std::size_t>({row * stride, 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>({batch_count, row, col}),
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std::vector<std::size_t>({col * stride, 1, stride}));
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}
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};
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Tensor<ADataType> a_g_m_k(f_host_tensor_descriptor(BatchCount, M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_g_k_n(f_host_tensor_descriptor(BatchCount, K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_g_m_n_host_result(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
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Tensor<CDataType> c_g_m_n_device_result(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
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std::unique_ptr<Tensor<float>> c_f32_g_m_n_host_result = nullptr;
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std::unique_ptr<Tensor<float>> c_f32_g_m_n_device_result = nullptr;
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std::cout << "a_g_m_k: " << a_g_m_k.mDesc << std::endl;
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std::cout << "b_g_k_n: " << b_g_k_n.mDesc << std::endl;
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std::cout << "c_g_m_n: " << c_g_m_n_host_result.mDesc << std::endl;
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std::size_t num_thread = 1;
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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_g_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5}, num_thread);
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b_g_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5}, num_thread);
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break;
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default:
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a_g_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0}, num_thread);
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b_g_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-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_g_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::PassThrough;
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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{};
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if(do_verification)
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{
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if constexpr(is_same<ADataType, ck::bhalf_t>::value &&
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is_same<BDataType, ck::bhalf_t>::value &&
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is_same<CDataType, ck::bhalf_t>::value)
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{
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Tensor<float> a_f32_g_m_k(
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f_host_tensor_descriptor(BatchCount, M, K, StrideA, ALayout{}));
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Tensor<float> b_f32_g_k_n(
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f_host_tensor_descriptor(BatchCount, K, N, StrideB, BLayout{}));
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c_f32_g_m_n_host_result = std::make_unique<Tensor<float>>(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
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c_f32_g_m_n_device_result = std::make_unique<Tensor<float>>(
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f_host_tensor_descriptor(BatchCount, M, N, StrideC, CLayout{}));
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bf16_to_f32_(a_g_m_k, a_f32_g_m_k);
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bf16_to_f32_(b_g_k_n, b_f32_g_k_n);
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using ReferenceBatchedGemmInstance = ck::tensor_operation::host::
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ReferenceBatchedGemm<float, float, float, AElementOp, BElementOp, CElementOp>;
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auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
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auto ref_invoker = ref_batched_gemm.MakeInvoker();
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auto ref_argument = ref_batched_gemm.MakeArgument(a_f32_g_m_k,
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b_f32_g_k_n,
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*c_f32_g_m_n_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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}
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else
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{
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using ReferenceBatchedGemmInstance =
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ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
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BDataType,
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CDataType,
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AElementOp,
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BElementOp,
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CElementOp>;
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auto ref_batched_gemm = ReferenceBatchedGemmInstance{};
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auto ref_invoker = ref_batched_gemm.MakeInvoker();
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auto ref_argument = ref_batched_gemm.MakeArgument(
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a_g_m_k, b_g_k_n, c_g_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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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_g_m_k.mDesc.GetElementSpace());
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DeviceMem b_device_buf(sizeof(BDataType) * b_g_k_n.mDesc.GetElementSpace());
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DeviceMem c_device_buf(sizeof(CDataType) * c_g_m_n_device_result.mDesc.GetElementSpace());
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a_device_buf.ToDevice(a_g_m_k.mData.data());
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b_device_buf.ToDevice(b_g_k_n.mData.data());
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c_device_buf.ToDevice(c_g_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_batched_gemm_instance::DeviceGemmNoOpPtr>
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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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f16_f16_f16_gmk_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f16_f16_f16_gmk_gnk_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f16_f16_f16_gkm_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f16_f16_f16_gkm_gnk_gmn_instances(gemm_ptrs);
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}
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}
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else if constexpr(is_same<ADataType, bhalf_t>::value && is_same<BDataType, bhalf_t>::value &&
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is_same<CDataType, bhalf_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_bf16_bf16_bf16_gmk_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_bf16_bf16_bf16_gmk_gnk_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_bf16_bf16_bf16_gkm_gnk_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f32_f32_f32_gmk_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f32_f32_f32_gmk_gnk_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f32_f32_f32_gkm_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_f32_f32_f32_gkm_gnk_gmn_instances(gemm_ptrs);
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}
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}
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else if constexpr(is_same<ADataType, int8_t>::value && is_same<BDataType, int8_t>::value &&
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is_same<CDataType, int8_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_int8_int8_int8_gmk_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_int8_int8_int8_gmk_gnk_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_int8_int8_int8_gkm_gkn_gmn_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_batched_gemm_instance::
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add_device_batched_gemm_xdl_int8_int8_int8_gkm_gnk_gmn_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<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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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::PassThrough{},
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BatchCount);
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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 =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = std::size_t(2) * BatchCount * M * N * K;
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std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(CDataType) * M * N) *
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BatchCount;
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float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
|
|
|
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
|
|
|
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
|
|
<< " GB/s, " << gemm_name << std::endl;
|
|
|
|
if(tflops > best_tflops)
|
|
{
|
|
best_gemm_name = gemm_name;
|
|
best_tflops = tflops;
|
|
best_ave_time = ave_time;
|
|
best_gb_per_sec = gb_per_sec;
|
|
}
|
|
|
|
if(do_verification)
|
|
{
|
|
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
|
|
|
|
if constexpr(is_same<ADataType, ck::bhalf_t>::value &&
|
|
is_same<BDataType, ck::bhalf_t>::value &&
|
|
is_same<CDataType, ck::bhalf_t>::value)
|
|
{
|
|
|
|
bf16_to_f32_(c_g_m_n_device_result, *c_f32_g_m_n_device_result);
|
|
float err = check_error(*c_f32_g_m_n_host_result, *c_f32_g_m_n_device_result);
|
|
pass = pass && (err < 1E-6);
|
|
}
|
|
else
|
|
{
|
|
float err = check_error(c_g_m_n_host_result, c_g_m_n_device_result);
|
|
pass = pass && (err < 1E-6);
|
|
}
|
|
|
|
if(do_log)
|
|
{
|
|
LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
|
|
LogRangeAsType<float>(std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
|
|
<< std::endl;
|
|
LogRangeAsType<float>(
|
|
std::cout << "c_device: ", c_g_m_n_device_result.mData, ",")
|
|
<< std::endl;
|
|
}
|
|
}
|
|
}
|
|
else
|
|
{
|
|
std::cout << "this device GEMM instance does not support this GEMM problem"
|
|
<< std::endl;
|
|
}
|
|
}
|
|
|
|
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
|
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
|
|
|
|
return pass;
|
|
}
|
|
|
|
} // namespace profiler
|
|
} // namespace ck
|