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Gemm + bias + relu + add + layernorm (#272)
* Copy "gemm reduce" to "gemm bias add reduce" * Implement gemm bias add reduction * Fix compiler error due to merge from develop * Add tensor operation for gemm + bias + add + reduce * Add gemm_bais_add_reduce to ckProfiler * Add c1 functor * Refine type * Use reduceAccDataType instead of explicitly float * Change to use check_err() * Do relu in float32 instead of bhalf_t. Because bhalf_t is unsigned * Refactor relu. using type_trait instead of overloading * Rename DxsReduceAccElementwiseOperation to DxsReduceAccElementwiseOperation * Fix denominator * Refine nameing * Fix denominator in host * Remove useless include header * Use AccDataType * Fix static_cast order * Refine type * [What] Remove tuple type in the base class [Why] External api depend on base class. if base class has relationship with type, we will need many class for different type
This commit is contained in:
rocking5566
@@ -1 +1,2 @@
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add_example_executable(example_gemm_bias_relu_add_layernorm_xdl_fp16 gemm_bias_relu_add_layernorm_xdl_fp16.cpp)
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add_example_executable(example_gemm_layernorm_xdl_fp16 gemm_layernorm_xdl_fp16.cpp)
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@@ -0,0 +1,424 @@
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#include <iostream>
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#include <numeric>
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#include <initializer_list>
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#include <cstdlib>
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#include "check_err.hpp"
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#include "config.hpp"
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#include "device.hpp"
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#include "host_tensor.hpp"
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#include "host_tensor_generator.hpp"
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#include "device_tensor.hpp"
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#include "device_5ary_elementwise.hpp"
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#include "device_gemm_bias_add_reduce_xdl_cshuffle.hpp"
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#include "element_wise_operation.hpp"
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#include "reference_gemm.hpp"
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#include "gemm_specialization.hpp"
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#include "element_wise_reduce_operation.hpp"
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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 ADataType = F16;
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using BDataType = F16;
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using CDataType = F16;
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using C0DataType = F32;
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using C1DataType = F16;
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using GemmAccDataType = F32;
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using ReduceAccDataType = F32;
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using DDataType = F32;
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using DPtrsGlobal = ck::Tuple<DDataType*, DDataType*>;
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using GammaDataType = F16;
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using BetaDataType = F16;
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using LayerNormOutDataType = F16;
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using NormalizeComputeDataType = F32;
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using ALayout = ck::tensor_layout::gemm::RowMajor;
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using BLayout = ck::tensor_layout::gemm::ColumnMajor;
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using CLayout = ck::tensor_layout::gemm::RowMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::Relu;
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using C1ElementOp = PassThrough;
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using ReduceSumOp = ck::reduce::Add<ReduceAccDataType>;
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using DxsReduceOp = ck::Tuple<ReduceSumOp, ReduceSumOp>;
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using UnaryIdenticElementOp =
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ck::tensor_operation::element_wise::UnaryIdentic<ReduceAccDataType, ReduceAccDataType, false>;
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using UnaryDivElementOp =
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ck::tensor_operation::element_wise::UnaryIdentic<ReduceAccDataType, ReduceAccDataType, true>;
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using UnarySquareElementOp =
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ck::tensor_operation::element_wise::UnarySquare<ReduceAccDataType, ReduceAccDataType, false>;
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using DxsInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
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using DxsOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
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using DxsGlobalMemOp =
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ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
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ck::InMemoryDataOperationEnum::AtomicAdd>;
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static constexpr auto GemmSpecialization =
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ck::tensor_operation::device::GemmSpecialization::Default;
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// clang-format off
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using DeviceGemmBiasAddReduceInstance = ck::tensor_operation::device::DeviceGemmBiasAddReduce_Xdl_CShuffle
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//######| ALayout| BLayout| CLayout|AData| BData| CData|C0Data|C1Data| GemmAcc| CShuffle| ReduceAcc| DData| A| B| C| C1| Dxs| DxsInEleOp| DxsAccEleOp| D| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| CReduce| CReduceThreadLds2VGprCopy| CReduceThreadVgpr2GlobalCopy|
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//######| | | | Type| Type| Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Elementwise| Reduce| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
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//######| | | | | | | | | | | | | Operation| Operation| Operation| Operation| Operation| | | Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< Row, Col, Row, F16, F16, F16, F32, F16, F32, F32, F32, DPtrsGlobal, AElementOp, BElementOp, CElementOp, C1ElementOp, DxsReduceOp, DxsInElementOps, DxsOutElementOps, DxsGlobalMemOp, GemmSpecialization, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>;
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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CDataType,
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GemmAccDataType,
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AElementOp,
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BElementOp,
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PassThrough>;
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using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
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// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
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using DeviceNormalizeInstance =
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ck::tensor_operation::device::Device5AryElementwise<CDataType,
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DDataType,
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DDataType,
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GammaDataType,
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BetaDataType,
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LayerNormOutDataType,
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NormalizeComputeDataType,
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NormalizeFunctor,
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2,
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8,
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8, // scalarPerVector: gemm_out
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1, // scalarPerVector: reduce_mean
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1, // scalarPerVector: reduce_mean_square
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8, // scalarPerVector: Gamma
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8, // scalarPerVector: Beta
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8>; // scalarPerVector: LayerNorm_out
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auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
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return HostTensorDescriptor(std::vector<std::size_t>({len}),
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std::vector<std::size_t>({stride}));
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};
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auto f_host_tensor_descriptor2d =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({stride, 1}));
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}
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else
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{
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return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
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std::vector<std::size_t>({1, stride}));
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}
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};
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template <typename CDataType,
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typename DDataType,
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typename AccDataType,
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typename C0DataType,
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typename C1DataType,
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typename A_functor,
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typename B_functor,
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typename C_functor,
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typename C1_functor>
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void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
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const Tensor<ADataType>& a_m_k,
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const Tensor<ADataType>& b_k_n,
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const Tensor<C0DataType>& bias_n,
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const Tensor<C1DataType>& c1_m_n,
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const Tensor<GammaDataType>& gamma_n,
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const Tensor<GammaDataType>& beta_n,
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A_functor a_element_op,
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B_functor b_element_op,
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C_functor c_element_op,
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C1_functor c1_element_op,
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int M,
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int N)
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{
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int StrideC = N;
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Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
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Tensor<DDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<DDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
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auto averageOpInst = UnaryDivElementOp{N};
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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 =
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ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
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ref_invoker.Run(ref_argument);
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// c = activation(c + bias) + c1_functor(c1)
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for(int m = 0; m < M; ++m)
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for(int n = 0; n < N; ++n)
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{
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AccDataType acc =
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static_cast<AccDataType>(c_m_n(m, n)) + static_cast<AccDataType>(bias_n(n));
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AccDataType c1 = static_cast<AccDataType>(c1_m_n(m, n));
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c_element_op(acc, acc);
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c1_element_op(c1, c1);
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acc += c1;
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c_m_n(m, n) = static_cast<CDataType>(acc);
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}
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// reduce_mean and reduce_square_mean
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auto reduceSumOpInst = ReduceSumOp{};
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for(int m = 0; m < M; ++m)
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{
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AccDataType mean_acc = reduceSumOpInst.GetIdentityValue();
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AccDataType square_mean_acc = reduceSumOpInst.GetIdentityValue();
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for(int n = 0; n < N; ++n)
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{
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AccDataType c_val = ck::type_convert<AccDataType>(c_m_n(m, n));
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AccDataType square_c_val = 0;
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UnarySquareElementOp{}(square_c_val, c_val);
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reduceSumOpInst(mean_acc, c_val);
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reduceSumOpInst(square_mean_acc, square_c_val);
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}
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averageOpInst(mean_acc, mean_acc);
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averageOpInst(square_mean_acc, square_mean_acc);
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mean_m(m) = ck::type_convert<DDataType>(mean_acc);
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meanSquare_m(m) = ck::type_convert<DDataType>(square_mean_acc);
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}
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// LayerNorm
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auto layerNormInst = NormalizeFunctor{};
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for(int m = 0; m < M; ++m)
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{
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for(int n = 0; n < N; ++n)
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{
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AccDataType out_acc = 0;
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layerNormInst(out_acc, c_m_n(m, n), mean_m(m), meanSquare_m(m), gamma_n(n), beta_n(n));
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out_m_n(m, n) = static_cast<DDataType>(out_acc);
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}
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}
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}
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template <typename ADataType,
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typename BDataType,
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typename CDataType,
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typename C0DataType,
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typename C1DataType,
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typename DDataType,
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typename GammaDataType,
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typename BetaDataType,
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typename NormalizeDataType>
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void DumpGemmLayerNormPerf(float gemm_reduce_time, float normalize_time, int M, int N, int K)
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{
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std::size_t gemm_flop = std::size_t(2) * M * N * K + std::size_t(2) * M * N;
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std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
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sizeof(CDataType) * M * N + sizeof(C0DataType) * M * N +
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sizeof(C1DataType) * M * N + sizeof(DDataType) * M +
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sizeof(DDataType) * M;
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std::size_t normalize_num_byte = sizeof(CDataType) * M * N + sizeof(DDataType) * M +
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sizeof(DDataType) * M + sizeof(GammaDataType) * N +
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sizeof(BetaDataType) * N + sizeof(NormalizeDataType) * M * N;
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float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
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float gemm_gb_per_sec = gemm_num_byte / 1.E6 / gemm_reduce_time;
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float normalize_gb_per_sec = normalize_num_byte / 1.E6 / normalize_time;
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std::cout << "gemm + reduce_mean + reduce_square_mean Perf: " << gemm_reduce_time << " ms, "
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<< tflops << " TFlops, " << gemm_gb_per_sec << " GB/s, " << std::endl;
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std::cout << "5-ary elementwise Perf: " << normalize_time << " ms, " << normalize_gb_per_sec
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<< " GB/s, " << std::endl;
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}
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int main()
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{
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// GEMM shape
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ck::index_t M = 1024;
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ck::index_t N = 1024;
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ck::index_t K = 1024;
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ck::index_t StrideA = 1024;
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ck::index_t StrideB = 1024;
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ck::index_t StrideC = 1024;
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ck::index_t StrideC1 = 1024;
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Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
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Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
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Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
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Tensor<C0DataType> bias_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<C1DataType> c1_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
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Tensor<DDataType> reduceMean_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<DDataType> reduceMeanSquare_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<LayerNormOutDataType> layerNorm_m_n(
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f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
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a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
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b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
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bias_n.GenerateTensorValue(GeneratorTensor_3<C0DataType>{-1, 1});
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c1_m_n.GenerateTensorValue(GeneratorTensor_3<C1DataType>{-5, 5});
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gamma_n.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-1, 1});
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beta_n.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-1, 1});
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DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
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DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
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DeviceMem c_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpace());
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DeviceMem bias_device_buf(sizeof(C0DataType) * bias_n.mDesc.GetElementSpace());
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DeviceMem c1_device_buf(sizeof(C1DataType) * c1_m_n.mDesc.GetElementSpace());
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DeviceMem reduceMean_device_buf(sizeof(DDataType) * reduceMean_m.mDesc.GetElementSpace());
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DeviceMem reduceMeanSquare_device_buf(sizeof(DDataType) *
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reduceMeanSquare_m.mDesc.GetElementSpace());
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DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpace());
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DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpace());
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DeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) *
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layerNorm_m_n.mDesc.GetElementSpace());
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a_device_buf.ToDevice(a_m_k.mData.data());
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b_device_buf.ToDevice(b_k_n.mData.data());
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bias_device_buf.ToDevice(bias_n.mData.data());
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c1_device_buf.ToDevice(c1_m_n.mData.data());
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gamma_device_buf.ToDevice(gamma_n.mData.data());
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beta_device_buf.ToDevice(beta_n.mData.data());
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auto a_element_op = AElementOp{};
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auto b_element_op = BElementOp{};
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auto c_element_op = CElementOp{};
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auto c1_element_op = C1ElementOp{};
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auto dxs_global =
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ck::make_tuple(static_cast<DDataType*>(reduceMean_device_buf.GetDeviceBuffer()),
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static_cast<DDataType*>(reduceMeanSquare_device_buf.GetDeviceBuffer()));
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auto dxs_in_element_op = DxsInElementOps{};
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auto dxs_out_element_op = DxsOutElementOps{N, N};
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// Prepare GEMM, reduce_mean, reduce_mean_square
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auto gemmReduce = DeviceGemmBiasAddReduceInstance{};
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auto gemmReduce_invoker = gemmReduce.MakeInvoker();
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auto gemmReduce_argument =
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gemmReduce.MakeArgument(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
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static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
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static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
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static_cast<C0DataType*>(bias_device_buf.GetDeviceBuffer()),
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static_cast<C1DataType*>(c1_device_buf.GetDeviceBuffer()),
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dxs_global,
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M,
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N,
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K,
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StrideA,
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StrideB,
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StrideC,
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StrideC1,
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a_element_op,
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b_element_op,
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c_element_op,
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c1_element_op,
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dxs_in_element_op,
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dxs_out_element_op);
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if(!gemmReduce.IsSupportedArgument(gemmReduce_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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reduceMean_device_buf.SetZero();
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reduceMeanSquare_device_buf.SetZero();
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|
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// Prepare LayerNorm
|
||||
auto normalize = DeviceNormalizeInstance{};
|
||||
auto normalize_invoker = normalize.MakeInvoker();
|
||||
auto normalize_argument = normalize.MakeArgument(
|
||||
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
|
||||
static_cast<DDataType*>(reduceMean_device_buf.GetDeviceBuffer()),
|
||||
static_cast<DDataType*>(reduceMeanSquare_device_buf.GetDeviceBuffer()),
|
||||
static_cast<GammaDataType*>(gamma_device_buf.GetDeviceBuffer()),
|
||||
static_cast<BetaDataType*>(beta_device_buf.GetDeviceBuffer()),
|
||||
static_cast<LayerNormOutDataType*>(layerNorm_device_buf.GetDeviceBuffer()),
|
||||
{M, N},
|
||||
{StrideC, 1},
|
||||
{1, 0},
|
||||
{1, 0},
|
||||
{0, 1},
|
||||
{0, 1},
|
||||
{StrideC, 1},
|
||||
NormalizeFunctor{});
|
||||
|
||||
if(!normalize.IsSupportedArgument(normalize_argument))
|
||||
{
|
||||
throw std::runtime_error("The runtime parameters seems not supported by the "
|
||||
"Device5AryElementwise instance, exiting!");
|
||||
}
|
||||
|
||||
// run kernel
|
||||
gemmReduce_invoker.Run(gemmReduce_argument, StreamConfig{nullptr, false});
|
||||
normalize_invoker.Run(normalize_argument, StreamConfig{nullptr, false});
|
||||
|
||||
bool pass = true;
|
||||
{
|
||||
// verification
|
||||
Tensor<LayerNormOutDataType> host_layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
|
||||
host_gemm_layernorm<CDataType, DDataType, ReduceAccDataType>(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
bias_n,
|
||||
c1_m_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
c1_element_op,
|
||||
M,
|
||||
N);
|
||||
|
||||
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
|
||||
pass &= ck::utils::check_err(layerNorm_m_n.mData,
|
||||
host_layerNorm_m_n.mData,
|
||||
"Error: Incorrect results layerNorm_m_n",
|
||||
1e-2,
|
||||
1e-2);
|
||||
}
|
||||
|
||||
{
|
||||
// evaluate kernel perf
|
||||
bool time_kernel = true;
|
||||
|
||||
float gemm_reduce_mean_reduce_square_mean_ave_time =
|
||||
gemmReduce_invoker.Run(gemmReduce_argument, StreamConfig{nullptr, time_kernel});
|
||||
float normalize_ave_time =
|
||||
normalize_invoker.Run(normalize_argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
if(time_kernel)
|
||||
DumpGemmLayerNormPerf<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
C0DataType,
|
||||
C1DataType,
|
||||
DDataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType>(
|
||||
gemm_reduce_mean_reduce_square_mean_ave_time, normalize_ave_time, M, N, K);
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -2,7 +2,6 @@
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
#include <cstdlib>
|
||||
#include <stdlib.h>
|
||||
|
||||
#include "check_err.hpp"
|
||||
#include "config.hpp"
|
||||
@@ -54,8 +53,8 @@ using UnaryDivElementOp =
|
||||
ck::tensor_operation::element_wise::UnaryIdentic<ReduceAccDataType, ReduceAccDataType, true>;
|
||||
using UnarySquareElementOp =
|
||||
ck::tensor_operation::element_wise::UnarySquare<ReduceAccDataType, ReduceAccDataType, false>;
|
||||
using DxsInElementOp = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
|
||||
using DxsOutElementOp = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
|
||||
using DxsInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
|
||||
using DxsOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
|
||||
|
||||
using DxsGlobalMemOp =
|
||||
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
|
||||
@@ -70,7 +69,7 @@ using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_
|
||||
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Reduce| | | MemoryData| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| ExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MPerBlock| ScalarPerVector| ThreadClusterLengths| SrcDstScalarPerVector| SrcDstScalarPerVector|
|
||||
//######| | | | | | | | | | | Operation| Operation| Operation| Operation| | | Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock| _MPerBlock_NPerBlock| _NPerBlock| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
< Row, Col, Row, F16, F16, F16, F32, F32, F32, DPtrsGlobal, AElementOp, BElementOp, CElementOp, DxsReduceOp, DxsInElementOp, DxsOutElementOp, DxsGlobalMemOp, GemmSpecialization, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>;
|
||||
< Row, Col, Row, F16, F16, F16, F32, F32, F32, DPtrsGlobal, AElementOp, BElementOp, CElementOp, DxsReduceOp, DxsInElementOps, DxsOutElementOps, DxsGlobalMemOp, GemmSpecialization, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8, S<64, 4>, 4, 1>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
@@ -143,7 +142,7 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<DDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<DDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = UnaryDivElementOp{M};
|
||||
auto averageOpInst = UnaryDivElementOp{N};
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
@@ -162,7 +161,7 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
ReduceAccDataType c_val = ck::type_convert<float>(c_m_n(m, n));
|
||||
ReduceAccDataType c_val = ck::type_convert<ReduceAccDataType>(c_m_n(m, n));
|
||||
ReduceAccDataType square_c_val = 0;
|
||||
UnarySquareElementOp{}(square_c_val, c_val);
|
||||
|
||||
@@ -267,8 +266,8 @@ int main()
|
||||
ck::make_tuple(static_cast<DDataType*>(reduceMean_device_buf.GetDeviceBuffer()),
|
||||
static_cast<DDataType*>(reduceMeanSquare_device_buf.GetDeviceBuffer()));
|
||||
|
||||
auto dxs_in_element_op = DxsInElementOp{};
|
||||
auto dxs_out_element_op = DxsOutElementOp{M, M};
|
||||
auto dxs_in_element_op = DxsInElementOps{};
|
||||
auto dxs_out_element_op = DxsOutElementOps{N, N};
|
||||
|
||||
// Prepare GEMM, reduce_mean, reduce_mean_square
|
||||
auto gemmReduce = DeviceGemmReduceInstance{};
|
||||
|
||||
Reference in New Issue
Block a user