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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);
|
||||
}
|
||||
Reference in New Issue
Block a user