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Implement device_grouped_gemm_fixed_nk_bias for RDNA4 (#4340)
## Proposed changes Summary: - Modified implementation for grouped_gemm_fixed_nk_bias - FP16 WMMA examples - WMMA instances - Profiler for grouped_gemm_fixed_nk_bias - Add WMMA instances to existing tests **This PR depends on PR https://github.com/ROCm/rocm-libraries/pull/4299 and should be merged after it. Only the last 6 commits are in the scope of this PR.** ## Checklist Please put an `x` into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask. - [x] I have added tests relevant to the introduced functionality, and the unit tests are passing locally - [x] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more than 30 seconds to run. - [x] I have added inline documentation which enables the maintainers with understanding the motivation - [x] I have removed the stale documentation which is no longer relevant after this pull request - [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request - [x] I have run `clang-format` on all changed files - [ ] Any dependent changes have been merged ## Discussion If this is a relatively large or complex change, feel free to start a discussion by explaining why you chose the solution you did and what alternatives you considered ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests. --------- Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
This commit is contained in:
Yung-sheng Tu
@@ -50,6 +50,9 @@ add_example_dependencies(example_grouped_gemm_wmma example_grouped_gemm_multiple
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add_example_executable(example_grouped_gemm_wmma_fixed_nk_fp16 grouped_gemm_wmma_fixed_nk_fp16.cpp)
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add_example_dependencies(example_grouped_gemm_wmma example_grouped_gemm_wmma_fixed_nk_fp16)
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add_example_executable(example_grouped_gemm_wmma_fixed_nk_bias_fp16 grouped_gemm_wmma_fixed_nk_bias_fp16.cpp)
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add_example_dependencies(example_grouped_gemm_wmma example_grouped_gemm_wmma_fixed_nk_bias_fp16)
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list(APPEND gpu_list_tf32 gfx942 gfx950)
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set(target 0)
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406
example/15_grouped_gemm/grouped_gemm_wmma_fixed_nk_bias_fp16.cpp
Normal file
406
example/15_grouped_gemm/grouped_gemm_wmma_fixed_nk_bias_fp16.cpp
Normal file
@@ -0,0 +1,406 @@
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// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include "ck/ck.hpp"
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#include "ck/host_utility/hip_check_error.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.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/stream_config.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_wmma_fixed_nk.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
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#include "ck/utility/data_type.hpp"
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#include "ck/utility/sequence.hpp"
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#include "ck/utility/tuple.hpp"
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#include <array>
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#include <cstddef>
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#include <iostream>
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#include <memory>
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#include <stdexcept>
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#include <string>
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#include <type_traits>
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#include <vector>
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using ck::DeviceMem;
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using ck::hip_check_error;
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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 Row = ck::tensor_layout::gemm::RowMajor;
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using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using SplitKAdd = ck::tensor_operation::element_wise::SplitKAdd;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F32;
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using D0DataType = F16;
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using DsDataType = ck::Tuple<D0DataType>;
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using EDataType = F16;
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using ALayout = Row;
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using BLayout = Row;
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using D0Layout = Row;
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using DsLayout = ck::Tuple<D0Layout>;
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using ELayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = SplitKAdd;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Wmma_Fixed_Nk
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// clang-format off
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//######| ALayout| BLayout| DsLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
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//######| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeat| NRepeat| _MBlock_MRepeat| ScalarPerVector|
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//######| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NRepeat| _NRepeat|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 128, 32, 128, 32, 8, 8, 16, 16, 1, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4>;
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// clang-format on
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struct ProblemSize final
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{
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std::vector<ck::index_t> Ms;
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std::vector<ck::index_t> Ns;
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std::vector<ck::index_t> Ks;
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std::vector<ck::index_t> stride_As;
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std::vector<ck::index_t> stride_Bs;
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std::vector<ck::index_t> stride_Ds;
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std::vector<ck::index_t> stride_Es;
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ck::index_t group_count;
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};
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struct ExecutionConfig final
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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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int k_batch = 1;
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};
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bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
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{
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int group_count = problem_size.group_count;
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// GEMM shape
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std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
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std::vector<const void*> p_a, p_b;
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std::vector<std::array<const void*, 1>> p_ds = {};
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std::vector<void*> p_e;
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gemm_descs.reserve(group_count);
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int sum_of_m = 0;
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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), Row>::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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std::vector<Tensor<ADataType>> a_tensors;
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std::vector<Tensor<BDataType>> b_tensors;
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std::vector<Tensor<D0DataType>> d0_tensors;
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std::vector<Tensor<EDataType>> e_host_tensors;
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std::vector<Tensor<EDataType>> e_device_tensors;
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a_tensors.reserve(group_count);
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b_tensors.reserve(group_count);
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d0_tensors.reserve(group_count);
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e_host_tensors.reserve(group_count);
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e_device_tensors.reserve(group_count);
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using DeviceMemPtr = std::unique_ptr<DeviceMem>;
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std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d0_tensors_device,
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e_tensors_device;
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a_tensors_device.reserve(group_count);
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b_tensors_device.reserve(group_count);
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d0_tensors_device.reserve(group_count);
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e_tensors_device.reserve(group_count);
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std::size_t flop = 0, num_btype = 0;
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for(int i = 0; i < group_count; ++i)
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{
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sum_of_m += problem_size.Ms[i];
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a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], ALayout{})));
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b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
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problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], BLayout{})));
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d0_tensors.push_back(Tensor<D0DataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Ds[i], D0Layout{})));
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e_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Es[i], ELayout{})));
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e_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
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problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Es[i], ELayout{})));
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std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
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<< " b_k_n: " << b_tensors[i].mDesc << " d_m_n: " << d0_tensors[i].mDesc
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<< " e_m_n: " << e_device_tensors[i].mDesc << std::endl;
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flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
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num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
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sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
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sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSize() +
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sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSize();
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switch(config.init_method)
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{
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case 0: break;
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case 1:
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a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d0_tensors[i].GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
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break;
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case 2:
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a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
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break;
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default:
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a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d0_tensors[i].GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
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}
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}
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using GroupedGemmKernelArgument = ck::tensor_operation::device::GroupedGemmKernelArgument<1>;
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std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
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grouped_gemm_kernel_args_.reserve(group_count);
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for(int i = 0; i < group_count; ++i)
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{
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a_tensors_device.emplace_back(std::make_unique<DeviceMem>(
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sizeof(ADataType) * problem_size.Ms[i] * problem_size.Ks[i]));
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b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
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sizeof(BDataType) * problem_size.Ns[i] * problem_size.Ks[i]));
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d0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
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sizeof(D0DataType) * problem_size.Ms[i] * problem_size.Ns[i]));
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e_tensors_device.emplace_back(std::make_unique<DeviceMem>(
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sizeof(EDataType) * problem_size.Ms[i] * problem_size.Ns[i]));
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a_tensors_device[i]->ToDevice(a_tensors[i].mData.data(),
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a_tensors[i].mDesc.GetElementSpaceSize() * sizeof(ADataType));
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b_tensors_device[i]->ToDevice(b_tensors[i].mData.data(),
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b_tensors[i].mDesc.GetElementSpaceSize() * sizeof(BDataType));
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d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data(),
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d0_tensors[i].mDesc.GetElementSpaceSize() *
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sizeof(D0DataType));
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e_tensors_device[i]->SetZero();
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p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
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p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
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p_ds.push_back(std::array<const void*, 1>{d0_tensors_device[i]->GetDeviceBuffer()});
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p_e.push_back(e_tensors_device[i]->GetDeviceBuffer());
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gemm_descs.push_back({sum_of_m,
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problem_size.Ns[i],
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problem_size.Ks[i],
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problem_size.stride_As[i],
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problem_size.stride_Bs[i],
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problem_size.stride_Es[i],
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{problem_size.stride_Ds[i]}});
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grouped_gemm_kernel_args_.push_back(
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{a_tensors_device[i]->GetDeviceBuffer(),
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b_tensors_device[i]->GetDeviceBuffer(),
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std::array<const void*, 1>{d0_tensors_device[i]->GetDeviceBuffer()},
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e_tensors_device[i]->GetDeviceBuffer(),
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problem_size.Ms[i],
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problem_size.Ns[i],
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problem_size.Ks[i],
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problem_size.stride_As[i],
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problem_size.stride_Bs[i],
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std::array<ck::index_t, 1>{problem_size.stride_Ds[i]},
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problem_size.stride_Es[i]});
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}
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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{};
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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// do GEMM
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auto argument = gemm.MakeArgument(
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p_a, p_b, p_ds, p_e, gemm_descs, a_element_op, b_element_op, cde_element_op);
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DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
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DeviceMem gemm_workspace_dev(gemm.GetWorkSpaceSize(&argument));
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gemm.SetWorkSpacePointer(&argument, gemm_workspace_dev.GetDeviceBuffer());
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hip_check_error(hipMemcpy(gemm_arg_dev_mem.GetDeviceBuffer(),
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grouped_gemm_kernel_args_.data(),
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gemm.GetDeviceKernelArgSize(&argument),
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hipMemcpyHostToDevice));
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if(!gemm.IsSupportedArgument(argument))
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{
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throw std::runtime_error(
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"wrong! device_gemm with the specified compilation parameters does "
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"not support this GEMM problem");
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}
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gemm.SetDeviceKernelArgs(&argument, gemm_arg_dev_mem.GetDeviceBuffer());
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gemm.SetKBatch(argument, config.k_batch);
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invoker.Run(argument, StreamConfig{nullptr, false});
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if(config.time_kernel)
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{
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
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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.GetTypeString() << std::endl;
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}
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bool pass = true;
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if(config.do_verification)
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{
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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EDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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PassThrough>;
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for(std::size_t i = 0; i < gemm_descs.size(); ++i)
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{
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e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data(),
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e_device_tensors[i].mDesc.GetElementSize() *
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sizeof(EDataType));
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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(a_tensors[i],
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b_tensors[i],
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e_host_tensors[i],
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a_element_op,
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b_element_op,
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PassThrough{});
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ref_invoker.Run(ref_argument);
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for(int m = 0; m < problem_size.Ms[i]; ++m)
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{
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for(int n = 0; n < problem_size.Ns[i]; ++n)
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{
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cde_element_op(
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e_host_tensors[i](m, n), e_host_tensors[i](m, n), d0_tensors[i](m, n));
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}
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}
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pass &= ck::utils::check_err(e_device_tensors[i], e_host_tensors[i]);
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}
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std::cout << "Verification: " << (pass ? "SUCCESS" : "FAILURE") << "!" << std::endl;
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}
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return pass;
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}
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int main(int argc, char* argv[])
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{
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ProblemSize problem_size;
|
||||
ExecutionConfig config;
|
||||
|
||||
problem_size.group_count = 16;
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
// use default cases
|
||||
}
|
||||
else if(argc == 5)
|
||||
{
|
||||
config.do_verification = std::stoi(argv[1]);
|
||||
config.init_method = std::stoi(argv[2]);
|
||||
config.time_kernel = std::stoi(argv[3]);
|
||||
config.k_batch = std::stoi(argv[4]);
|
||||
}
|
||||
else if(argc == 6)
|
||||
{
|
||||
config.do_verification = std::stoi(argv[1]);
|
||||
config.init_method = std::stoi(argv[2]);
|
||||
config.time_kernel = std::stoi(argv[3]);
|
||||
config.k_batch = std::stoi(argv[4]);
|
||||
problem_size.group_count = std::stoi(argv[5]);
|
||||
}
|
||||
else
|
||||
{
|
||||
printf("arg1: verification (0=no, 1=yes)\n");
|
||||
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
|
||||
printf("arg3: time kernel (0=n0, 1=yes)\n");
|
||||
printf("arg4: k_batch (>0)\n");
|
||||
printf("arg5: group count (default=16)");
|
||||
exit(0);
|
||||
}
|
||||
|
||||
// Lambda to get stride based on layout
|
||||
auto get_stride = [](auto layout, auto row_dim, auto col_dim) {
|
||||
if constexpr(std::is_same_v<decltype(layout), Row>)
|
||||
{
|
||||
return col_dim;
|
||||
}
|
||||
else
|
||||
{
|
||||
return row_dim;
|
||||
}
|
||||
};
|
||||
|
||||
for(int i = 0; i < problem_size.group_count; ++i)
|
||||
{
|
||||
problem_size.Ms.push_back(256 + 256 * i);
|
||||
|
||||
problem_size.Ns.push_back(512);
|
||||
problem_size.Ks.push_back(512);
|
||||
|
||||
problem_size.stride_As.push_back(
|
||||
get_stride(ALayout{}, problem_size.Ms[i], problem_size.Ks[i]));
|
||||
problem_size.stride_Bs.push_back(
|
||||
get_stride(BLayout{}, problem_size.Ks[i], problem_size.Ns[i]));
|
||||
problem_size.stride_Ds.push_back(
|
||||
get_stride(D0Layout{}, problem_size.Ms[i], problem_size.Ns[i]));
|
||||
problem_size.stride_Es.push_back(
|
||||
get_stride(ELayout{}, problem_size.Ms[i], problem_size.Ns[i]));
|
||||
}
|
||||
|
||||
return !run_grouped_gemm(problem_size, config);
|
||||
}
|
||||
@@ -7,22 +7,55 @@
|
||||
#include <sstream>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/utility/env.hpp"
|
||||
#include "ck/host_utility/hip_check_error.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/utility/env.hpp"
|
||||
#include "ck/utility/scheduler_enum.hpp"
|
||||
#include "ck/utility/tuple.hpp"
|
||||
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_wmma_cshuffle_v3.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace element_wise {
|
||||
struct SplitKAdd
|
||||
{
|
||||
static constexpr const char* name = "SplitKAdd";
|
||||
|
||||
__host__ __device__ void set_kbatch(const index_t& id, const index_t& total)
|
||||
{
|
||||
kbatch_id = id;
|
||||
KBatch = total;
|
||||
}
|
||||
|
||||
template <typename Y, typename X0, typename X1>
|
||||
__host__ __device__ constexpr void operator()(Y& y, const X0& x0, const X1& x1) const
|
||||
{
|
||||
if(kbatch_id == KBatch - 1)
|
||||
{
|
||||
add_op(y, x0, x1);
|
||||
}
|
||||
else
|
||||
{
|
||||
passthrough_op(y, x0);
|
||||
}
|
||||
}
|
||||
|
||||
private:
|
||||
index_t kbatch_id = 0;
|
||||
index_t KBatch = 1;
|
||||
static constexpr auto add_op = Add{};
|
||||
static constexpr auto passthrough_op = PassThrough{};
|
||||
};
|
||||
} // namespace element_wise
|
||||
|
||||
namespace device {
|
||||
|
||||
template <typename GridwiseGemm,
|
||||
@@ -110,6 +143,21 @@ __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
|
||||
|
||||
while(id_local < local_grid_size)
|
||||
{
|
||||
const auto block_2_etile_map =
|
||||
GroupedGemmBlock2ETileMap(local_b2c_tile_map, group_start, id_off);
|
||||
|
||||
const auto tile_index =
|
||||
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
const index_t kbatch_id = __builtin_amdgcn_readfirstlane(tile_index[Number<0>{}]);
|
||||
|
||||
auto c_element_op_copy(c_element_op);
|
||||
if constexpr(std::is_same_v<decltype(c_element_op_copy),
|
||||
ck::tensor_operation::element_wise::SplitKAdd>)
|
||||
{
|
||||
c_element_op_copy.set_kbatch(kbatch_id, k_batch_);
|
||||
}
|
||||
|
||||
KernelArgument kernel_arg{std::array<const void*, 1>{gemmTransKernelArg.p_a_grid},
|
||||
std::array<const void*, 1>{gemmTransKernelArg.p_b_grid},
|
||||
gemmTransKernelArg.p_ds_grid,
|
||||
@@ -124,15 +172,9 @@ __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
|
||||
k_batch_,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
c_element_op_copy,
|
||||
false};
|
||||
|
||||
const auto block_2_etile_map =
|
||||
GroupedGemmBlock2ETileMap(local_b2c_tile_map, group_start, id_off);
|
||||
|
||||
const auto tile_index =
|
||||
block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
|
||||
|
||||
const auto splitk_batch_offset =
|
||||
typename GridwiseGemm::SplitKBatchOffset(kernel_arg, tile_index[Number<0>{}]);
|
||||
|
||||
@@ -813,7 +855,9 @@ struct DeviceGroupedGemm_Wmma_Fixed_Nk : public DeviceGroupedGemmFixedNK<ALayout
|
||||
}
|
||||
|
||||
if constexpr(!std::is_same_v<CDEElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::PassThrough>)
|
||||
ck::tensor_operation::element_wise::PassThrough> &&
|
||||
!std::is_same_v<CDEElementwiseOperation,
|
||||
ck::tensor_operation::element_wise::SplitKAdd>)
|
||||
{
|
||||
if(arg.k_batch_ > 1)
|
||||
{
|
||||
|
||||
@@ -8,27 +8,28 @@
|
||||
#include <ostream>
|
||||
#endif
|
||||
|
||||
#include "ck/utility/env.hpp"
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/tensor_description/multi_index_transform_helper.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_wave_tiles.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_wave_tiles_interleave.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_thread_tiles.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_thread_tiles_preshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_wmma_selector.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_global.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r2.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7r3.hpp"
|
||||
#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_global.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_direct_store.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_cshuffle_v3_wmma.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_cshuffle_v3_welford_wmma.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_cshuffle_v3_reduce_wmma.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_cshuffle_v3_welford_wmma.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_cshuffle_v3_wmma.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/epilogue_direct_store.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_thread_tiles.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_thread_tiles_preshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_wave_tiles.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_ab_transfer_wave_tiles_interleave.hpp"
|
||||
#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
|
||||
#include "ck/utility/common_header.hpp"
|
||||
#include "ck/utility/env.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
|
||||
@@ -3,13 +3,14 @@
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <vector>
|
||||
#include <memory>
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_wmma_fixed_nk.hpp"
|
||||
#include "ck/utility/type.hpp"
|
||||
|
||||
#include <memory>
|
||||
#include <vector>
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
@@ -70,20 +71,52 @@ void add_device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f32_mk_nk_mn_instances(
|
||||
PassThrough,
|
||||
Add>>>& instances);
|
||||
|
||||
void add_device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<
|
||||
std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
|
||||
Row,
|
||||
Row_Tuple,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
F16_Tuple,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
ck::tensor_operation::element_wise::SplitKAdd>>>&
|
||||
instances);
|
||||
|
||||
void add_device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<
|
||||
std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
|
||||
Col,
|
||||
Row_Tuple,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
F16_Tuple,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
ck::tensor_operation::element_wise::SplitKAdd>>>&
|
||||
instances);
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType>
|
||||
struct DeviceOperationInstanceFactory<
|
||||
ck::tensor_operation::device::DeviceGroupedGemmFixedNK<ALayout,
|
||||
BLayout,
|
||||
Row_Tuple,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
F32_Tuple,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
@@ -91,11 +124,11 @@ struct DeviceOperationInstanceFactory<
|
||||
{
|
||||
using DeviceOp = DeviceGroupedGemmFixedNK<ALayout,
|
||||
BLayout,
|
||||
Row_Tuple,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
F32_Tuple,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
@@ -112,28 +145,96 @@ struct DeviceOperationInstanceFactory<
|
||||
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
{
|
||||
#if defined(CK_USE_XDL)
|
||||
add_device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
|
||||
#endif
|
||||
}
|
||||
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
{
|
||||
#if defined(CK_USE_XDL)
|
||||
add_device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
// fp32_output
|
||||
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
|
||||
is_same_v<EDataType, float>)
|
||||
else if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
|
||||
is_same_v<EDataType, float>)
|
||||
{
|
||||
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
{
|
||||
#ifdef CK_USE_XDL
|
||||
add_device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f32_mk_kn_mn_instances(op_ptrs);
|
||||
#endif
|
||||
}
|
||||
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
|
||||
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
{
|
||||
#ifdef CK_USE_XDL
|
||||
add_device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f32_mk_nk_mn_instances(op_ptrs);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
return op_ptrs;
|
||||
}
|
||||
};
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType>
|
||||
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedGemmFixedNK<
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
ck::tensor_operation::element_wise::SplitKAdd>>
|
||||
{
|
||||
using DeviceOp = DeviceGroupedGemmFixedNK<ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
ck::tensor_operation::element_wise::SplitKAdd>;
|
||||
|
||||
static auto GetInstances()
|
||||
{
|
||||
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
|
||||
|
||||
// fp16_output
|
||||
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
|
||||
is_same_v<EDataType, half_t>)
|
||||
{
|
||||
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
{
|
||||
add_device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f32_mk_nk_mn_instances(op_ptrs);
|
||||
#if defined(CK_USE_WMMA)
|
||||
add_device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
|
||||
#endif
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
|
||||
is_same_v<ELayout, Row>)
|
||||
{
|
||||
#if defined(CK_USE_WMMA)
|
||||
add_device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
|
||||
#endif
|
||||
}
|
||||
}
|
||||
return op_ptrs;
|
||||
|
||||
@@ -1,11 +1,14 @@
|
||||
# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
||||
# SPDX-License-Identifier: MIT
|
||||
|
||||
# ONLY XDL_KERNELS
|
||||
# ONLY XDL_AND_WMMA_KERNELS
|
||||
add_instance_library(device_grouped_gemm_bias_instance
|
||||
device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instance.cpp
|
||||
device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instance.cpp
|
||||
|
||||
device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f32_mk_kn_mn_instance.cpp
|
||||
device_grouped_gemm_xdl_fixed_nk_bias_f16_f16_f32_mk_nk_mn_instance.cpp
|
||||
|
||||
device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instance.cpp
|
||||
device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instance.cpp
|
||||
)
|
||||
|
||||
@@ -0,0 +1,95 @@
|
||||
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
||||
// SPDX-License-Identifier: MIT
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_wmma_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/utility/data_type.hpp"
|
||||
#include "ck/utility/sequence.hpp"
|
||||
#include "ck/utility/tuple.hpp"
|
||||
|
||||
#include <memory>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using D0DataType = F16;
|
||||
using DsDataType = ck::Tuple<D0DataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Row;
|
||||
using D0Layout = Row;
|
||||
using DsLayout = ck::Tuple<D0Layout>;
|
||||
using ELayout = Row;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using SplitKAdd = ck::tensor_operation::element_wise::SplitKAdd;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = SplitKAdd;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
using device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeat| NRepeat| _MBlock_MRepeat| ScalarPerVector|
|
||||
//#############################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NRepeat| _NRepeat|
|
||||
//#############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 256, 128, 32, 8, 8, 16, 16, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 128, 128, 32, 8, 8, 16, 16, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 128, 64, 32, 8, 2, 16, 16, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<16, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 128, 64, 32, 8, 8, 16, 16, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 64, 128, 32, 8, 2, 16, 16, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 64, 128, 32, 8, 8, 16, 16, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 128, 64, 32, 8, 2, 16, 16, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 128, 64, 32, 8, 8, 16, 16, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 64, 128, 32, 8, 2, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 64, 128, 32, 8, 8, 16, 16, 2, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S< 4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 4>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CDEElementOp>>>& instances)
|
||||
{
|
||||
add_device_operation_instances(
|
||||
instances, device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_kn_mn_instances{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,98 @@
|
||||
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
||||
// SPDX-License-Identifier: MIT
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_wmma_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/utility/data_type.hpp"
|
||||
#include "ck/utility/sequence.hpp"
|
||||
#include "ck/utility/tuple.hpp"
|
||||
|
||||
#include <memory>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
using F16 = ck::half_t;
|
||||
using F32 = float;
|
||||
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using AccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using D0DataType = F16;
|
||||
using DsDataType = ck::Tuple<D0DataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using D0Layout = Row;
|
||||
using DsLayout = ck::Tuple<D0Layout>;
|
||||
using ELayout = Row;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using SplitKAdd = ck::tensor_operation::element_wise::SplitKAdd;
|
||||
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = SplitKAdd;
|
||||
|
||||
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
|
||||
|
||||
using device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#############################| A| B| Ds| E| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
|
||||
//#############################| Layout| Layout| Layout| Layout| Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Size| Block| Block| Block| | | Wmma| Wmma| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeat| NRepeat| _MBlock_MRepeat| ScalarPerVector|
|
||||
//#############################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NRepeat| _NRepeat|
|
||||
//#############################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 128, 256, 64, 8, 8, 16, 16, 4, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 128, 128, 64, 8, 8, 16, 16, 4, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 128, 64, 64, 8, 8, 16, 16, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 256, 64, 128, 64, 8, 8, 16, 16, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 128, 128, 64, 8, 8, 16, 16, 4, 4, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 128, 64, 64, 8, 8, 16, 16, 4, 2, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 64, 128, 64, 8, 8, 16, 16, 2, 4, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 128, 32, 64, 8, 8, 16, 16, 4, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 32, 128, 64, 8, 8, 16, 16, 1, 4, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 128, 32, 256, 64, 8, 8, 16, 16, 2, 4, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 64, 64, 64, 64, 8, 8, 16, 16, 4, 2, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 64, 64, 32, 64, 8, 8, 16, 16, 2, 2, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 4>,
|
||||
DeviceGroupedGemm_Wmma_Fixed_Nk<ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmSpec, 64, 32, 64, 64, 8, 8, 16, 16, 2, 2, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CDEElementOp>>>& instances)
|
||||
{
|
||||
add_device_operation_instances(
|
||||
instances, device_grouped_gemm_wmma_fixed_nk_bias_f16_f16_f16_mk_nk_mn_instances{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -0,0 +1,411 @@
|
||||
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
||||
// SPDX-License-Identifier: MIT
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_bias.hpp"
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/device_memory.hpp"
|
||||
#include "ck/library/utility/fill.hpp"
|
||||
#include "ck/library/utility/host_tensor.hpp"
|
||||
#include "ck/library/utility/literals.hpp"
|
||||
#include "ck/stream_config.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_wmma_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/utility/env.hpp"
|
||||
#include "ck/utility/tuple.hpp"
|
||||
#include "ck/utility/type.hpp"
|
||||
|
||||
#include <array>
|
||||
#include <cstddef>
|
||||
#include <iomanip>
|
||||
#include <iostream>
|
||||
#include <memory>
|
||||
#include <stdexcept>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
namespace ck {
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename AccDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename DsLayout,
|
||||
typename ELayout>
|
||||
bool profile_grouped_gemm_fixed_nk_bias_impl(int do_verification,
|
||||
int init_method,
|
||||
bool do_log,
|
||||
bool time_kernel,
|
||||
const std::vector<int>& Ms,
|
||||
const std::vector<int>& Ns,
|
||||
const std::vector<int>& Ks,
|
||||
const std::vector<int>& StrideAs,
|
||||
const std::vector<int>& StrideBs,
|
||||
const std::vector<int>& StrideDs,
|
||||
const std::vector<int>& StrideEs,
|
||||
const std::vector<int>& kbatches = {1},
|
||||
int n_warmup = 1,
|
||||
int n_iter = 10)
|
||||
{
|
||||
bool pass = true;
|
||||
using ComputeDataType = ADataType;
|
||||
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
using namespace ck::literals;
|
||||
|
||||
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor({row, col}, {stride, 1_uz}, layout);
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor({row, col}, {1_uz, stride}, layout);
|
||||
}
|
||||
};
|
||||
|
||||
std::size_t group_count = Ms.size();
|
||||
|
||||
if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
|
||||
group_count == StrideBs.size() && group_count == StrideDs.size() &&
|
||||
group_count == StrideEs.size()))
|
||||
{
|
||||
throw std::runtime_error("wrong! inconsistent M/N/Ks, StrideA/B/Cs size\n");
|
||||
}
|
||||
|
||||
using D0DataType = remove_cvref_t<ck::tuple_element_t<Number<0>{}, DsDataType>>;
|
||||
|
||||
std::vector<Tensor<ADataType>> a_tensors;
|
||||
std::vector<Tensor<BDataType>> b_tensors;
|
||||
std::vector<Tensor<D0DataType>> d0_tensors;
|
||||
std::vector<Tensor<EDataType>> e_host_tensors;
|
||||
std::vector<Tensor<EDataType>> e_device_tensors;
|
||||
|
||||
a_tensors.reserve(group_count);
|
||||
b_tensors.reserve(group_count);
|
||||
d0_tensors.reserve(group_count);
|
||||
e_host_tensors.reserve(group_count);
|
||||
e_device_tensors.reserve(group_count);
|
||||
|
||||
double max_abs_in_val = 0.f;
|
||||
int sum_of_m = 0;
|
||||
|
||||
using D0Layout = remove_cvref_t<ck::tuple_element_t<Number<0>{}, DsLayout>>;
|
||||
|
||||
for(std::size_t i = 0; i < group_count; ++i)
|
||||
{
|
||||
sum_of_m += Ms[i];
|
||||
a_tensors.push_back(
|
||||
Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
|
||||
b_tensors.push_back(
|
||||
Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{})));
|
||||
d0_tensors.push_back(
|
||||
Tensor<D0DataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideDs[i], D0Layout{})));
|
||||
e_host_tensors.push_back(
|
||||
Tensor<EDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideEs[i], ELayout{})));
|
||||
e_device_tensors.push_back(
|
||||
Tensor<EDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideEs[i], ELayout{})));
|
||||
|
||||
if(ck::EnvIsEnabled(CK_ENV(CK_LOGGING)))
|
||||
{
|
||||
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_tensors[i].mDesc
|
||||
<< ", b_k_n[" << i << "]:" << b_tensors[i].mDesc << ", d_m_n[" << i
|
||||
<< "]:" << d0_tensors[i].mDesc << ", e_m_n_device_results[" << i
|
||||
<< "]:" << e_device_tensors[i].mDesc << std::endl;
|
||||
}
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_tensors[i]);
|
||||
ck::utils::FillUniformDistributionIntegerValue<BDataType>{-5.f, 5.f}(b_tensors[i]);
|
||||
max_abs_in_val = 10.f;
|
||||
break;
|
||||
default:
|
||||
ck::utils::FillUniformDistribution<ADataType>{0.0f, 1.0f}(a_tensors[i]);
|
||||
ck::utils::FillUniformDistribution<BDataType>{-0.5f, 0.5f}(b_tensors[i]);
|
||||
max_abs_in_val = 1.0f;
|
||||
}
|
||||
ck::utils::FillUniformDistribution<D0DataType>{-0.5, 0.5}(d0_tensors[i]);
|
||||
}
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CDEElementOp = ck::tensor_operation::element_wise::SplitKAdd;
|
||||
|
||||
constexpr auto a_element_op = AElementOp{};
|
||||
constexpr auto b_element_op = BElementOp{};
|
||||
constexpr auto cde_element_op = CDEElementOp{};
|
||||
|
||||
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
||||
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d0_tensors_device,
|
||||
e_tensors_device;
|
||||
|
||||
a_tensors_device.reserve(group_count);
|
||||
b_tensors_device.reserve(group_count);
|
||||
d0_tensors_device.reserve(group_count);
|
||||
e_tensors_device.reserve(group_count);
|
||||
|
||||
std::vector<const void*> p_a, p_b;
|
||||
std::vector<std::array<const void*, 1>> p_ds;
|
||||
std::vector<void*> p_e;
|
||||
|
||||
p_a.reserve(group_count);
|
||||
p_b.reserve(group_count);
|
||||
p_ds.reserve(group_count);
|
||||
p_e.reserve(group_count);
|
||||
|
||||
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
|
||||
gemm_descs.reserve(group_count);
|
||||
|
||||
std::vector<ck::tensor_operation::device::GroupedGemmKernelArgument<1>>
|
||||
grouped_gemm_kernel_args_;
|
||||
grouped_gemm_kernel_args_.reserve(group_count);
|
||||
|
||||
for(std::size_t i = 0; i < group_count; ++i)
|
||||
{
|
||||
a_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
d0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
e_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSpaceSize()));
|
||||
|
||||
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
|
||||
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
|
||||
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
|
||||
|
||||
gemm_descs.push_back(
|
||||
{sum_of_m, Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideEs[i], {StrideDs[i]}});
|
||||
|
||||
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
|
||||
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
|
||||
p_ds.push_back(std::array<const void*, 1>{d0_tensors_device[i]->GetDeviceBuffer()});
|
||||
p_e.push_back(e_tensors_device[i]->GetDeviceBuffer());
|
||||
|
||||
grouped_gemm_kernel_args_.push_back(
|
||||
{a_tensors_device[i]->GetDeviceBuffer(),
|
||||
b_tensors_device[i]->GetDeviceBuffer(),
|
||||
std::array<const void*, 1>{d0_tensors_device[i]->GetDeviceBuffer()},
|
||||
e_tensors_device[i]->GetDeviceBuffer(),
|
||||
Ms[i],
|
||||
Ns[i],
|
||||
Ks[i],
|
||||
StrideAs[i],
|
||||
StrideBs[i],
|
||||
std::array<ck::index_t, 1>{StrideDs[i]},
|
||||
StrideEs[i]});
|
||||
}
|
||||
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmFixedNK<ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CDEElementOp>;
|
||||
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
if(op_ptrs.size() <= 0)
|
||||
{
|
||||
std::cerr << "Skip! no device GEMM instance found" << std::endl;
|
||||
return true;
|
||||
}
|
||||
|
||||
std::string best_gemm_name;
|
||||
float best_ave_time = 0;
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
float best_kbatch = 0;
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); ++i)
|
||||
{
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<
|
||||
ADataType,
|
||||
BDataType,
|
||||
EDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
ck::tensor_operation::element_wise::PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_tensors[i],
|
||||
b_tensors[i],
|
||||
e_host_tensors[i],
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < Ms[i]; ++m)
|
||||
{
|
||||
for(int n = 0; n < Ns[i]; ++n)
|
||||
{
|
||||
cde_element_op(
|
||||
e_host_tensors[i](m, n), e_host_tensors[i](m, n), d0_tensors[i](m, n));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// profile device GEMM instances
|
||||
for(auto& gemm_ptr : op_ptrs)
|
||||
{
|
||||
auto argument_ptr = gemm_ptr->MakeArgumentPointer(
|
||||
p_a, p_b, p_ds, p_e, gemm_descs, a_element_op, b_element_op, cde_element_op);
|
||||
|
||||
auto invoker_ptr = gemm_ptr->MakeInvokerPointer();
|
||||
|
||||
DeviceMem gemm_desc_workspace(gemm_ptr->GetWorkSpaceSize(argument_ptr.get()));
|
||||
|
||||
DeviceMem grouped_gemm_kernel_args_dev(
|
||||
gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
|
||||
|
||||
hipGetErrorString(hipMemcpy(grouped_gemm_kernel_args_dev.GetDeviceBuffer(),
|
||||
grouped_gemm_kernel_args_.data(),
|
||||
gemm_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
|
||||
hipMemcpyHostToDevice));
|
||||
|
||||
gemm_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
|
||||
|
||||
gemm_ptr->SetDeviceKernelArgs(argument_ptr.get(),
|
||||
grouped_gemm_kernel_args_dev.GetDeviceBuffer());
|
||||
|
||||
std::string gemm_name = gemm_ptr->GetTypeString();
|
||||
|
||||
for(std::size_t j = 0; j < kbatches.size(); ++j)
|
||||
{
|
||||
|
||||
auto kbatch_curr = kbatches[j];
|
||||
|
||||
gemm_ptr->SetKBatch(argument_ptr.get(), kbatch_curr);
|
||||
|
||||
if(gemm_ptr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); ++i)
|
||||
{
|
||||
e_tensors_device[i]->SetZero();
|
||||
}
|
||||
|
||||
invoker_ptr->Run(argument_ptr.get(),
|
||||
StreamConfig{nullptr, false, 0, n_warmup, n_iter});
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
bool instance_pass = true;
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); ++i)
|
||||
{
|
||||
e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data());
|
||||
auto atol = ck::utils::get_absolute_threshold<ComputeDataType, EDataType>(
|
||||
max_abs_in_val, gemm_descs[i].K_);
|
||||
auto rtol = ck::utils::get_relative_threshold<ComputeDataType, EDataType>(
|
||||
gemm_descs[i].K_);
|
||||
|
||||
instance_pass =
|
||||
instance_pass && ck::utils::check_err(e_device_tensors[i],
|
||||
e_host_tensors[i],
|
||||
"Error: Incorrect results!",
|
||||
rtol,
|
||||
atol);
|
||||
|
||||
if(do_log)
|
||||
{
|
||||
LogRangeAsType<float>(std::cout << "a : ", a_tensors[i].mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(std::cout << "b: ", b_tensors[i].mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(std::cout << "d0: ", d0_tensors[i].mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(
|
||||
std::cout << "e_device: ", e_device_tensors[i].mData, ",")
|
||||
<< std::endl;
|
||||
LogRangeAsType<float>(
|
||||
std::cout << "e_host : ", e_host_tensors[i].mData, ",")
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "Instance: " << gemm_name << "; KBatch: " << kbatch_curr << " "
|
||||
<< (instance_pass ? "SUCCEED" : "FAILED") << std::endl;
|
||||
|
||||
pass = pass && instance_pass;
|
||||
}
|
||||
|
||||
float ave_time = invoker_ptr->Run(
|
||||
argument_ptr.get(), StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter});
|
||||
|
||||
if(time_kernel)
|
||||
{
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
|
||||
|
||||
num_btype +=
|
||||
sizeof(ADataType) * Ms[i] * Ks[i] + sizeof(BDataType) * Ks[i] * Ns[i] +
|
||||
sizeof(D0DataType) * Ms[i] * Ns[i] + sizeof(EDataType) * Ms[i] * Ns[i];
|
||||
}
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops
|
||||
<< " TFlops, " << gb_per_sec << " GB/s, " << gemm_name << ", KBatch "
|
||||
<< kbatch_curr << 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;
|
||||
best_kbatch = kbatch_curr;
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "Instance: " << gemm_name
|
||||
<< ", does not support this GEMM problem (KBatch: " << kbatch_curr << ")"
|
||||
<< std::endl;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if(time_kernel)
|
||||
{
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_gemm_name << ", KBatch = " << best_kbatch
|
||||
<< std::endl;
|
||||
}
|
||||
return pass;
|
||||
}
|
||||
|
||||
} // namespace profiler
|
||||
} // namespace ck
|
||||
@@ -25,6 +25,12 @@ if (CK_USE_XDL OR CK_USE_WMMA)
|
||||
add_dependencies(test_grouped_gemm test_grouped_gemm_fixed_nk)
|
||||
endif()
|
||||
|
||||
add_gtest_executable(test_grouped_gemm_fixed_nk_bias test_grouped_gemm_fixed_nk_bias.cpp)
|
||||
if(result EQUAL 0)
|
||||
target_link_libraries(test_grouped_gemm_fixed_nk_bias PRIVATE utility device_grouped_gemm_bias_instance)
|
||||
add_dependencies(test_grouped_gemm test_grouped_gemm_fixed_nk_bias)
|
||||
endif()
|
||||
|
||||
add_gtest_executable(test_grouped_gemm_multi_abd_fixed_nk test_grouped_gemm_multi_abd_fixed_nk.cpp)
|
||||
if(result EQUAL 0)
|
||||
target_link_libraries(test_grouped_gemm_multi_abd_fixed_nk PRIVATE utility device_grouped_gemm_fixed_nk_multi_abd_instance)
|
||||
|
||||
306
test/grouped_gemm/test_grouped_gemm_fixed_nk_bias.cpp
Normal file
306
test/grouped_gemm/test_grouped_gemm_fixed_nk_bias.cpp
Normal file
@@ -0,0 +1,306 @@
|
||||
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
||||
// SPDX-License-Identifier: MIT
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/host_utility/device_prop.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_wmma_fixed_nk.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
|
||||
#include "ck/utility/data_type.hpp"
|
||||
#include "ck/utility/number.hpp"
|
||||
#include "ck/utility/tuple.hpp"
|
||||
#include "profiler/profile_grouped_gemm_fixed_nk_bias_impl.hpp"
|
||||
#include "gtest/gtest.h"
|
||||
|
||||
#include <iostream>
|
||||
#include <tuple>
|
||||
#include <type_traits>
|
||||
#include <vector>
|
||||
|
||||
static ck::index_t param_mask = 0xffffff;
|
||||
static ck::index_t instance_index = -1;
|
||||
|
||||
using FP32 = float;
|
||||
using FP16 = ck::half_t;
|
||||
using BF16 = ck::bhalf_t;
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using SplitKAdd = ck::tensor_operation::element_wise::SplitKAdd;
|
||||
|
||||
using CDEElementOp = SplitKAdd;
|
||||
|
||||
// clang-format off
|
||||
using KernelTypes = ::testing::Types<
|
||||
std::tuple<FP16, FP16, ck::Tuple<FP16>, FP16, Row, Row, ck::Tuple<Row>, Row>,
|
||||
std::tuple<FP16, FP16, ck::Tuple<FP16>, FP16, Row, Col, ck::Tuple<Row>, Row>
|
||||
>;
|
||||
// clang-format on
|
||||
|
||||
template <typename Tuple>
|
||||
class TestGroupedGemmFixedNKBias : public testing::Test
|
||||
{
|
||||
protected:
|
||||
using ADataType = std::tuple_element_t<0, Tuple>;
|
||||
using BDataType = std::tuple_element_t<1, Tuple>;
|
||||
using DsDataType = std::tuple_element_t<2, Tuple>;
|
||||
using EDataType = std::tuple_element_t<3, Tuple>;
|
||||
using ALayout = std::tuple_element_t<4, Tuple>;
|
||||
using BLayout = std::tuple_element_t<5, Tuple>;
|
||||
using DsLayout = std::tuple_element_t<6, Tuple>;
|
||||
using ELayout = std::tuple_element_t<7, Tuple>;
|
||||
using AccDataType = FP32;
|
||||
|
||||
public:
|
||||
static constexpr bool verify_ = true;
|
||||
static constexpr int init_method_ = 1; // integer value initialization
|
||||
static constexpr bool log_ = false;
|
||||
static constexpr bool bench_ = false; // measure kernel performance
|
||||
static constexpr int n_warmup_ = 0;
|
||||
static constexpr int n_iter_ = 1;
|
||||
|
||||
std::vector<int> k_batches_;
|
||||
|
||||
bool IsSplitKSupported()
|
||||
{
|
||||
// gfx11 does not support split-K due to missing atomic add for fp16/bf16
|
||||
// Technically, we could still use split-K for fp32, but we currently don't have
|
||||
// instances for it so we disable it entirely
|
||||
constexpr bool require_16bit_atomic_add =
|
||||
std::is_same_v<EDataType, FP16> || std::is_same_v<EDataType, BF16>;
|
||||
bool missing_atomic_add = require_16bit_atomic_add && ck::is_gfx11_supported();
|
||||
|
||||
// CDE element operators are not supported in combination with split K
|
||||
constexpr bool has_cde_element_operator =
|
||||
!std::is_same_v<CDEElementOp, PassThrough> && !std::is_same_v<CDEElementOp, SplitKAdd>;
|
||||
|
||||
return !missing_atomic_add && !has_cde_element_operator;
|
||||
}
|
||||
|
||||
void SetUp() override
|
||||
{
|
||||
if(!IsSplitKSupported())
|
||||
{
|
||||
k_batches_ = {1};
|
||||
}
|
||||
else
|
||||
{
|
||||
k_batches_ = {1, 2, 3, 4, 8};
|
||||
}
|
||||
}
|
||||
|
||||
private:
|
||||
template <typename Layout>
|
||||
void SetStrides(std::vector<int>& strides,
|
||||
const std::vector<int>& rows,
|
||||
const std::vector<int>& cols) const
|
||||
{
|
||||
if(std::is_same_v<Layout, Row>)
|
||||
{
|
||||
for(const auto c : cols)
|
||||
{
|
||||
strides.emplace_back(c);
|
||||
}
|
||||
}
|
||||
else if(std::is_same_v<Layout, Col>)
|
||||
{
|
||||
for(const auto r : rows)
|
||||
{
|
||||
strides.emplace_back(r);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename Layouts>
|
||||
void SetTupleStrides(std::vector<int>& strides,
|
||||
const std::vector<int>& rows,
|
||||
const std::vector<int>& cols) const
|
||||
{
|
||||
if constexpr(Layouts::Size() > 0)
|
||||
{
|
||||
// As of now multi ABD implementation supports only tensors with matching layouts.
|
||||
using Layout = ck::remove_cvref_t<ck::tuple_element_t<ck::Number<0>{}, Layouts>>;
|
||||
SetStrides<Layout>(strides, rows, cols);
|
||||
}
|
||||
}
|
||||
|
||||
public:
|
||||
void Run(const std::vector<int>& Ms,
|
||||
const std::vector<int>& Ns,
|
||||
const std::vector<int>& Ks,
|
||||
const std::vector<int>& StrideAs = {},
|
||||
const std::vector<int>& StrideBs = {},
|
||||
const std::vector<int>& StrideDs = {},
|
||||
const std::vector<int>& StrideEs = {})
|
||||
{
|
||||
std::vector<int> stride_as = StrideAs;
|
||||
std::vector<int> stride_bs = StrideBs;
|
||||
std::vector<int> stride_ds = StrideDs;
|
||||
std::vector<int> stride_es = StrideEs;
|
||||
|
||||
if(stride_as.empty())
|
||||
{
|
||||
SetStrides<ALayout>(stride_as, Ms, Ks);
|
||||
}
|
||||
if(stride_bs.empty())
|
||||
{
|
||||
SetStrides<BLayout>(stride_bs, Ks, Ns);
|
||||
}
|
||||
if(stride_ds.empty())
|
||||
{
|
||||
SetTupleStrides<DsLayout>(stride_ds, Ms, Ns);
|
||||
}
|
||||
if(stride_es.empty())
|
||||
{
|
||||
SetStrides<ELayout>(stride_es, Ms, Ns);
|
||||
}
|
||||
|
||||
std::vector<int> k_batches;
|
||||
for(size_t i = 0; i < k_batches_.size(); i++)
|
||||
{
|
||||
if(param_mask & (1 << i))
|
||||
{
|
||||
k_batches.push_back(k_batches_[i]);
|
||||
}
|
||||
}
|
||||
|
||||
RunSingle(Ms, Ns, Ks, stride_as, stride_bs, stride_ds, stride_es, k_batches);
|
||||
}
|
||||
|
||||
void RunSingle(const std::vector<int>& Ms,
|
||||
const std::vector<int>& Ns,
|
||||
const std::vector<int>& Ks,
|
||||
const std::vector<int>& StrideAs,
|
||||
const std::vector<int>& StrideBs,
|
||||
const std::vector<int>& StrideDs,
|
||||
const std::vector<int>& StrideEs,
|
||||
const std::vector<int>& kbatches)
|
||||
{
|
||||
bool pass = ck::profiler::profile_grouped_gemm_fixed_nk_bias_impl<ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AccDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
DsLayout,
|
||||
ELayout>(verify_,
|
||||
init_method_,
|
||||
log_,
|
||||
bench_,
|
||||
Ms,
|
||||
Ns,
|
||||
Ks,
|
||||
StrideAs,
|
||||
StrideBs,
|
||||
StrideDs,
|
||||
StrideEs,
|
||||
kbatches,
|
||||
n_warmup_,
|
||||
n_iter_);
|
||||
EXPECT_TRUE(pass);
|
||||
}
|
||||
};
|
||||
|
||||
TYPED_TEST_SUITE(TestGroupedGemmFixedNKBias, KernelTypes);
|
||||
|
||||
TYPED_TEST(TestGroupedGemmFixedNKBias, TinyCases)
|
||||
{
|
||||
const std::vector<int> Ms{2, 1};
|
||||
constexpr int N = 768;
|
||||
constexpr int K = 544;
|
||||
|
||||
const std::vector<int> Ns(Ms.size(), N);
|
||||
const std::vector<int> Ks(Ms.size(), K);
|
||||
|
||||
this->Run(Ms, Ns, Ks);
|
||||
}
|
||||
|
||||
TYPED_TEST(TestGroupedGemmFixedNKBias, SmallCases)
|
||||
{
|
||||
const std::vector<int> Ms{2, 1, 3, 4, 5};
|
||||
constexpr int N = 768;
|
||||
constexpr int K = 544;
|
||||
|
||||
const std::vector<int> Ns(Ms.size(), N);
|
||||
const std::vector<int> Ks(Ms.size(), K);
|
||||
|
||||
this->Run(Ms, Ns, Ks);
|
||||
}
|
||||
|
||||
TYPED_TEST(TestGroupedGemmFixedNKBias, MidCases)
|
||||
{
|
||||
const std::vector<int> Ms{167, 183, 177, 153, 139, 204};
|
||||
constexpr int N = 768;
|
||||
constexpr int K = 544;
|
||||
|
||||
const std::vector<int> Ns(Ms.size(), N);
|
||||
const std::vector<int> Ks(Ms.size(), K);
|
||||
|
||||
this->Run(Ms, Ns, Ks);
|
||||
}
|
||||
|
||||
TYPED_TEST(TestGroupedGemmFixedNKBias, Regular)
|
||||
{
|
||||
const std::vector<int> Ms{64, 128, 256};
|
||||
constexpr int N = 768;
|
||||
constexpr int K = 320;
|
||||
|
||||
const std::vector<int> Ns(Ms.size(), N);
|
||||
const std::vector<int> Ks(Ms.size(), K);
|
||||
|
||||
this->Run(Ms, Ns, Ks);
|
||||
}
|
||||
|
||||
TYPED_TEST(TestGroupedGemmFixedNKBias, MNKPadded)
|
||||
{
|
||||
const std::vector<int> Ms{127, 150, 188, 210};
|
||||
constexpr int N = 136;
|
||||
constexpr int K = 280;
|
||||
|
||||
const std::vector<int> Ns(Ms.size(), N);
|
||||
const std::vector<int> Ks(Ms.size(), K);
|
||||
|
||||
this->Run(Ms, Ns, Ks);
|
||||
}
|
||||
|
||||
TYPED_TEST(TestGroupedGemmFixedNKBias, TestLargeKBatch)
|
||||
{
|
||||
// In some cases Split K is not supported. Running this test would fail since no instance will
|
||||
// be supported, so we skip the test
|
||||
if(!this->IsSplitKSupported())
|
||||
{
|
||||
GTEST_SKIP() << "Split-K not supported for for the current configuration (FP16/BF16 on "
|
||||
"GFX11, or using CDE element-wise operation)";
|
||||
}
|
||||
|
||||
const std::vector<int> Ms{188, 210};
|
||||
constexpr int N = 768;
|
||||
constexpr int K = 4096;
|
||||
|
||||
const std::vector<int> Ns(Ms.size(), N);
|
||||
const std::vector<int> Ks(Ms.size(), K);
|
||||
|
||||
this->k_batches_ = {32, 64};
|
||||
|
||||
this->Run(Ms, Ns, Ks);
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
testing::InitGoogleTest(&argc, argv);
|
||||
if(argc == 1) {}
|
||||
else if(argc == 3)
|
||||
{
|
||||
param_mask = strtol(argv[1], nullptr, 0);
|
||||
instance_index = atoi(argv[2]);
|
||||
}
|
||||
else
|
||||
{
|
||||
std::cout << "Usage of " << argv[0] << std::endl;
|
||||
std::cout << "Arg1,2: param_mask instance_index(-1 means all)" << std::endl;
|
||||
}
|
||||
return RUN_ALL_TESTS();
|
||||
}
|
||||
Reference in New Issue
Block a user