mirror of
https://github.com/ROCm/composable_kernel.git
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Gemm multiple d multiple r (#335)
* Imitate XXX_gemm_multiple_d, add XXX_gemm_multiple_d_multiple_r for gemm + reduction * Implement run of kernel * Add example * Fix parameter of typo * Rewrite the reduceMax example * Rewrite the reduceMean + reduceMeanSquare example * Refine naming * Refine folder name * refine naming * Rewrite the gemm + bias + relu + add + layernorm example * Rewrite the gemm + layernorm example * clang-format * Fix bug if sync lds * Fix compile error
This commit is contained in:
rocking5566
@@ -9,7 +9,7 @@
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#include "ck/ck.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_bias_add_reduce_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
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#include "ck/tensor_operation/gpu/device/device_5ary_elementwise.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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@@ -28,57 +28,64 @@ 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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// DataType
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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 BiasDataType = F32;
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using D0DataType = F16;
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using GemmAccDataType = F32;
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using CShuffleDataType = F32;
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using D0DataType = F16;
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using D1DataType = F16;
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using DsDataType = ck::Tuple<D0DataType, D1DataType>;
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using EDataType = F16;
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using ReduceAccDataType = F32;
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using ReduceDataType = F32;
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using ReducePtrsGlobal = ck::Tuple<ReduceDataType*, ReduceDataType*>;
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using R0DataType = F32;
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using R1DataType = F32;
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using RsDataType = ck::Tuple<R0DataType, R1DataType>;
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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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// Layout
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using ALayout = Row;
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using BLayout = Col;
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using D1Layout = Row;
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using ELayout = D1Layout;
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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 D0ElementOp = PassThrough;
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using ReduceSumOp = ck::reduce::Add;
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using ReduceOps = ck::Tuple<ReduceSumOp, ReduceSumOp>;
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// Elementwise op
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using AddReluAdd = ck::tensor_operation::element_wise::AddReluAdd;
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using Square = ck::tensor_operation::element_wise::UnarySquare;
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using Div = ck::tensor_operation::element_wise::UnaryDivide;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = AddReluAdd;
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using QsElementOp = ck::Tuple<PassThrough, Square>;
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using RsElementOp = ck::Tuple<Div, Div>;
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using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
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using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
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using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
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using ReduceInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
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using ReduceOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
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// ReduceOp
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using R0ThreadReduceOp = ck::reduce::Add;
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using R1ThreadReduceOp = ck::reduce::Add;
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using RsThreadReduceOp = ck::Tuple<R0ThreadReduceOp, R1ThreadReduceOp>;
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using ReduceGlobalMemOps =
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ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
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ck::InMemoryDataOperationEnum::AtomicAdd>;
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static constexpr auto R0GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
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static constexpr auto R1GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
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using RsGlobalReduceOp = ck::InMemoryDataOperationEnumSequence<R0GlobalReduceOp, R1GlobalReduceOp>;
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static constexpr auto GemmSpecialization =
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ck::tensor_operation::device::GemmSpecialization::Default;
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static constexpr auto GemmDefault = 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| ReduceData| A| B| C| C1| Reduce| ReduceInEleOp| ReduceAccEleOp| Reduce| 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| Operation| | | 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| | 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, ReducePtrsGlobal, AElementOp, BElementOp, CElementOp, D0ElementOp, ReduceOps,ReduceInElementOps, ReduceOutElementOps, ReduceGlobalMemOps, 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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using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleDMultipleR_Xdl_CShuffle
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//######| ALayout| BLayout| ELayout| AData| BData| GemmAccData| CShuffle| DsData| EData| ReduceAccData| RsData| A| B| CDE| Qs| Rs| Thread| Global| 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| CDRThreadTransfer| CDE| RThreadTransfer|
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//######| | | | Type| Type| Type| DataType| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ReduceThreadTransfer| DstScalarPerVector|
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//######| | | | | | | | | | | | Operation| 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| _MPerBlock_NPerBlock| ScalarPerVector| _MPerBlock|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | _NPerBlock| |
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< ALayout, BLayout, ELayout, ADataType, BDataType, GemmAccDataType, CShuffleDataType, DsDataType, EDataType, ReduceAccDataType, RsDataType, AElementOp, BElementOp, CDEElementOp, QsElementOp, RsElementOp, RsThreadReduceOp, RsGlobalReduceOp, GemmDefault, 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<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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EDataType,
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GemmAccDataType,
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AElementOp,
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BElementOp,
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@@ -88,9 +95,9 @@ 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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ReduceDataType,
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ReduceDataType,
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ck::tensor_operation::device::Device5AryElementwise<EDataType,
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R0DataType,
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R1DataType,
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GammaDataType,
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BetaDataType,
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LayerNormOutDataType,
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@@ -124,41 +131,31 @@ auto f_host_tensor_descriptor2d =
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}
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};
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template <typename CDataType,
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typename ReduceDataType,
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typename AccDataType,
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typename BiasDataType,
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typename D0DataType,
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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<BiasDataType>& bias_n,
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const Tensor<D0DataType>& c1_m_n,
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const Tensor<BDataType>& b_k_n,
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const Tensor<D0DataType>& bias_n,
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const Tensor<D1DataType>& d1_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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const Tensor<BetaDataType>& beta_n,
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AElementOp a_element_op,
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BElementOp b_element_op,
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CDEElementOp cde_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<ReduceDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<ReduceDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
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auto averageOpInst = UnaryDivElementOp{N};
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int StrideE = N;
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Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
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Tensor<R0DataType> mean_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<R1DataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
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auto averageOpInst = Div{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_gemm.MakeArgument(a_m_k, b_k_n, e_m_n, a_element_op, b_element_op, PassThrough{});
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ref_invoker.Run(ref_argument);
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@@ -166,38 +163,32 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
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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 = ck::type_convert<AccDataType>(c_m_n(m, n)) +
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ck::type_convert<AccDataType>(bias_n(n));
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AccDataType c1 = ck::type_convert<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) = ck::type_convert<CDataType>(acc);
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auto acc = ck::type_convert<GemmAccDataType>(e_m_n(m, n));
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cde_element_op(e_m_n(m, n), acc, bias_n(n), d1_m_n(m, n));
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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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auto r0Op = R0ThreadReduceOp{};
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auto r1Op = R1ThreadReduceOp{};
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for(int m = 0; m < M; ++m)
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{
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auto mean_acc = reduceSumOpInst.GetIdentityValue<AccDataType>();
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auto square_mean_acc = reduceSumOpInst.GetIdentityValue<AccDataType>();
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auto mean_acc = r0Op.GetIdentityValue<ReduceAccDataType>();
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auto mean_square_acc = r1Op.GetIdentityValue<ReduceAccDataType>();
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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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auto e_val = ck::type_convert<ReduceAccDataType>(e_m_n(m, n));
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ReduceAccDataType square_e_val = 0;
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Square{}(square_e_val, e_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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r0Op(mean_acc, e_val);
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r1Op(mean_square_acc, square_e_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<ReduceDataType>(mean_acc);
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meanSquare_m(m) = ck::type_convert<ReduceDataType>(square_mean_acc);
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averageOpInst(mean_square_acc, mean_square_acc);
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mean_m(m) = ck::type_convert<R0DataType>(mean_acc);
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meanSquare_m(m) = ck::type_convert<R1DataType>(mean_square_acc);
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}
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// LayerNorm
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@@ -206,24 +197,25 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
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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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NormalizeComputeDataType out_acc = 0;
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layerNormInst(out_acc,
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ck::type_convert<AccDataType>(c_m_n(m, n)),
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ck::type_convert<AccDataType>(mean_m(m)),
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ck::type_convert<AccDataType>(meanSquare_m(m)),
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ck::type_convert<AccDataType>(gamma_n(n)),
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ck::type_convert<AccDataType>(beta_n(n)));
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out_m_n(m, n) = ck::type_convert<ReduceDataType>(out_acc);
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ck::type_convert<NormalizeComputeDataType>(e_m_n(m, n)),
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ck::type_convert<NormalizeComputeDataType>(mean_m(m)),
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ck::type_convert<NormalizeComputeDataType>(meanSquare_m(m)),
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ck::type_convert<NormalizeComputeDataType>(gamma_n(n)),
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ck::type_convert<NormalizeComputeDataType>(beta_n(n)));
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out_m_n(m, n) = ck::type_convert<LayerNormOutDataType>(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 BiasDataType,
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typename EDataType,
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typename D0DataType,
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typename ReduceDataType,
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typename D1DataType,
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typename R0DataType,
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typename R1DataType,
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typename GammaDataType,
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typename BetaDataType,
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typename NormalizeDataType>
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@@ -231,12 +223,12 @@ void DumpGemmLayerNormPerf(float gemm_reduce_time, float normalize_time, int M,
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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(BiasDataType) * M * N +
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sizeof(D0DataType) * M * N + sizeof(ReduceDataType) * M +
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sizeof(ReduceDataType) * M;
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sizeof(EDataType) * M * N + sizeof(D0DataType) * M * N +
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sizeof(D0DataType) * M * N + sizeof(R0DataType) * M +
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sizeof(R1DataType) * M;
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std::size_t normalize_num_byte = sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
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sizeof(ReduceDataType) * M + sizeof(GammaDataType) * N +
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std::size_t normalize_num_byte = sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
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sizeof(R1DataType) * 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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@@ -259,37 +251,37 @@ int main()
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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 StrideD0 = 1024;
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ck::index_t StrideD0 = 0;
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ck::index_t StrideD1 = 1024;
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ck::index_t StrideE = 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<BiasDataType> bias_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<D0DataType> c1_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
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Tensor<ReduceDataType> reduceMean_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<ReduceDataType> reduceMeanSquare_m(f_host_tensor_descriptor1d(M, 1));
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Tensor<D0DataType> bias_n(f_host_tensor_descriptor1d(N, 1));
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Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor2d(M, N, StrideD1, ELayout{}));
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> r0_Mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> r1_MeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<LayerNormOutDataType> layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
|
||||
bias_n.GenerateTensorValue(GeneratorTensor_3<BiasDataType>{-1, 1});
|
||||
c1_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-5, 5});
|
||||
bias_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{-1, 1});
|
||||
d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{-5, 5});
|
||||
gamma_n.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{-1, 1});
|
||||
beta_n.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{-1, 1});
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem bias_device_buf(sizeof(BiasDataType) * bias_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d0_device_buf(sizeof(D0DataType) * c1_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMean_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMeanSquare_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem bias_device_buf(sizeof(D0DataType) * bias_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r0_Mean_device_buf(sizeof(R0DataType) * r0_Mean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r1_MeanSquare_device_buf(sizeof(R1DataType) *
|
||||
r1_MeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) *
|
||||
@@ -298,60 +290,51 @@ int main()
|
||||
a_device_buf.ToDevice(a_m_k.mData.data());
|
||||
b_device_buf.ToDevice(b_k_n.mData.data());
|
||||
bias_device_buf.ToDevice(bias_n.mData.data());
|
||||
d0_device_buf.ToDevice(c1_m_n.mData.data());
|
||||
d1_device_buf.ToDevice(d1_m_n.mData.data());
|
||||
gamma_device_buf.ToDevice(gamma_n.mData.data());
|
||||
beta_device_buf.ToDevice(beta_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto c_element_op = CElementOp{};
|
||||
auto d_element_op = D0ElementOp{};
|
||||
std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
auto qs_element_op = QsElementOp{};
|
||||
auto rs_element_op = RsElementOp{N, N};
|
||||
|
||||
auto passthrough = UnaryIdenticElementOp{};
|
||||
auto square = UnarySquareElementOp{};
|
||||
auto div = UnaryDivElementOp{N};
|
||||
std::array<void*, 2> reduce_in_element_ops = {&passthrough, &square};
|
||||
std::array<void*, 2> reduce_out_element_ops = {&div, &div};
|
||||
|
||||
std::array<void*, 2> p_reduces = {reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer()};
|
||||
|
||||
// Prepare GEMM, reduce_mean, reduce_mean_square
|
||||
auto gemmReduce = DeviceGemmBiasAddReduceInstance{};
|
||||
// Prepare GEMM, mean, mean_square
|
||||
auto gemmReduce = DeviceOpInstance{};
|
||||
auto gemmReduce_invoker = gemmReduce.MakeInvoker();
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
bias_device_buf.GetDeviceBuffer(),
|
||||
{d0_device_buf.GetDeviceBuffer()},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
p_reduces,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
{StrideD0},
|
||||
gemm_element_ops,
|
||||
{&d_element_op},
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops);
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(
|
||||
a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{bias_device_buf.GetDeviceBuffer(), d1_device_buf.GetDeviceBuffer()},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
{r0_Mean_device_buf.GetDeviceBuffer(), r1_MeanSquare_device_buf.GetDeviceBuffer()},
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
{StrideD0, StrideD1},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op);
|
||||
|
||||
if(!gemmReduce.IsSupportedArgument(gemmReduce_argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
throw std::runtime_error("wrong! this device_op instance does not support this problem");
|
||||
}
|
||||
|
||||
reduceMean_device_buf.SetZero();
|
||||
reduceMeanSquare_device_buf.SetZero();
|
||||
// init reducetion buffer to 0
|
||||
r0_Mean_device_buf.SetZero();
|
||||
r1_MeanSquare_device_buf.SetZero();
|
||||
|
||||
// Prepare LayerNorm
|
||||
std::array<const void*, 5> input = {c_device_buf.GetDeviceBuffer(),
|
||||
reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 5> input = {e_device_buf.GetDeviceBuffer(),
|
||||
r0_Mean_device_buf.GetDeviceBuffer(),
|
||||
r1_MeanSquare_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {layerNorm_device_buf.GetDeviceBuffer()};
|
||||
@@ -361,12 +344,12 @@ int main()
|
||||
auto normalize_argument = normalize.MakeArgument(input,
|
||||
output,
|
||||
{M, N},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
{1, 0},
|
||||
{1, 0},
|
||||
{0, 1},
|
||||
{0, 1},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
NormalizeFunctor{});
|
||||
|
||||
if(!normalize.IsSupportedArgument(normalize_argument))
|
||||
@@ -383,21 +366,20 @@ int main()
|
||||
{
|
||||
// verification
|
||||
Tensor<LayerNormOutDataType> host_layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
host_gemm_layernorm<CDataType, ReduceDataType, 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,
|
||||
d_element_op,
|
||||
M,
|
||||
N);
|
||||
host_gemm_layernorm(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
bias_n,
|
||||
d1_m_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
M,
|
||||
N);
|
||||
|
||||
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
|
||||
pass &= ck::utils::check_err(layerNorm_m_n.mData,
|
||||
@@ -419,10 +401,11 @@ int main()
|
||||
if(time_kernel)
|
||||
DumpGemmLayerNormPerf<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
BiasDataType,
|
||||
EDataType,
|
||||
D0DataType,
|
||||
ReduceDataType,
|
||||
D1DataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType>(
|
||||
|
||||
@@ -9,7 +9,7 @@
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_reduce_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d_multiple_r_xdl_cshuffle.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_5ary_elementwise.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
@@ -28,65 +28,73 @@ using F32 = float;
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
// DataType
|
||||
using ADataType = F16;
|
||||
using BDataType = F16;
|
||||
using CDataType = F16;
|
||||
using GemmAccDataType = F32;
|
||||
using CShuffleDataType = F32;
|
||||
using DsDataType = ck::Tuple<>;
|
||||
using EDataType = F16;
|
||||
using ReduceAccDataType = F32;
|
||||
using ReduceDataType = F32;
|
||||
using ReducePtrsGlobal = ck::Tuple<ReduceDataType*, ReduceDataType*>;
|
||||
using R0DataType = F32;
|
||||
using R1DataType = F32;
|
||||
using RsDataType = ck::Tuple<R0DataType, R1DataType>;
|
||||
using GammaDataType = F16;
|
||||
using BetaDataType = F16;
|
||||
using LayerNormOutDataType = F16;
|
||||
using NormalizeComputeDataType = F32;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
// Layout
|
||||
using ALayout = Row;
|
||||
using BLayout = Col;
|
||||
using D1Layout = Row;
|
||||
using ELayout = D1Layout;
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using BElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using CElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using ReduceSumOp = ck::reduce::Add;
|
||||
using ReduceOps = ck::Tuple<ReduceSumOp, ReduceSumOp>;
|
||||
// Elementwise op
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
using Square = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using Div = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using AElementOp = PassThrough;
|
||||
using BElementOp = PassThrough;
|
||||
using CDEElementOp = PassThrough;
|
||||
using QsElementOp = ck::Tuple<PassThrough, Square>;
|
||||
using RsElementOp = ck::Tuple<Div, Div>;
|
||||
|
||||
using UnaryIdenticElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
using UnaryDivElementOp = ck::tensor_operation::element_wise::UnaryDivide;
|
||||
using UnarySquareElementOp = ck::tensor_operation::element_wise::UnarySquare;
|
||||
using ReduceInElementOps = ck::Tuple<UnaryIdenticElementOp, UnarySquareElementOp>;
|
||||
using ReduceOutElementOps = ck::Tuple<UnaryDivElementOp, UnaryDivElementOp>;
|
||||
// ReduceOp
|
||||
using R0ThreadReduceOp = ck::reduce::Add;
|
||||
using R1ThreadReduceOp = ck::reduce::Add;
|
||||
using RsThreadReduceOp = ck::Tuple<R0ThreadReduceOp, R1ThreadReduceOp>;
|
||||
|
||||
using ReduceGlobalMemOps =
|
||||
ck::InMemoryDataOperationEnumSequence<ck::InMemoryDataOperationEnum::AtomicAdd,
|
||||
ck::InMemoryDataOperationEnum::AtomicAdd>;
|
||||
static constexpr auto R0GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
static constexpr auto R1GlobalReduceOp = ck::InMemoryDataOperationEnum::AtomicAdd;
|
||||
using RsGlobalReduceOp = ck::InMemoryDataOperationEnumSequence<R0GlobalReduceOp, R1GlobalReduceOp>;
|
||||
|
||||
static constexpr auto GemmSpecialization =
|
||||
ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// clang-format off
|
||||
using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| CLayout|AData| BData| CData| GemmAcc| CShuffle| ReduceAcc| ReduceData| A| B| C| Reduce| ReduceInEleOp| ReduceAccEleOp| Reduce| 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|
|
||||
//######| | | | Type| Type| Type| DataType| DataType| DataType| Type Tuple| Elementwise| Elementwise| Elementwise| Operation| | | 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| | 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, ReducePtrsGlobal, AElementOp, BElementOp, CElementOp, ReduceOps,ReduceInElementOps, ReduceOutElementOps, ReduceGlobalMemOps, 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>;
|
||||
using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleDMultipleR_Xdl_CShuffle
|
||||
//######| ALayout| BLayout| ELayout| AData| BData| GemmAccData| CShuffle| DsData| EData| ReduceAccData| RsData| A| B| CDE| Qs| Rs| Thread| Global| 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| CDRThreadTransfer| CDE| RThreadTransfer|
|
||||
//######| | | | Type| Type| Type| DataType| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Elementwise| Elementwise| Reduce| Reduce| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ReduceThreadTransfer| DstScalarPerVector|
|
||||
//######| | | | | | | | | | | | Operation| 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| _MPerBlock_NPerBlock| ScalarPerVector| _MPerBlock|
|
||||
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | _NPerBlock| |
|
||||
< ALayout, BLayout, ELayout, ADataType, BDataType, GemmAccDataType, CShuffleDataType, DsDataType, EDataType, ReduceAccDataType, RsDataType, AElementOp, BElementOp, CDEElementOp, QsElementOp, RsElementOp, RsThreadReduceOp, RsGlobalReduceOp, GemmDefault, 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<64, 4>, 4, 1>;
|
||||
// clang-format on
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
EDataType,
|
||||
GemmAccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
PassThrough>;
|
||||
|
||||
using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
|
||||
|
||||
// A:x, B:E[x], C:E[x^2], D:Gamma, E:Beta , F:y
|
||||
using DeviceNormalizeInstance =
|
||||
ck::tensor_operation::device::Device5AryElementwise<CDataType,
|
||||
ReduceDataType,
|
||||
ReduceDataType,
|
||||
ck::tensor_operation::device::Device5AryElementwise<EDataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType,
|
||||
@@ -120,60 +128,54 @@ auto f_host_tensor_descriptor2d =
|
||||
}
|
||||
};
|
||||
|
||||
template <typename CDataType,
|
||||
typename ReduceDataType,
|
||||
typename A_functor,
|
||||
typename B_functor,
|
||||
typename C_functor>
|
||||
void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
const Tensor<ADataType>& a_m_k,
|
||||
const Tensor<ADataType>& b_k_n,
|
||||
const Tensor<BDataType>& b_k_n,
|
||||
const Tensor<GammaDataType>& gamma_n,
|
||||
const Tensor<BetaDataType>& beta_n,
|
||||
A_functor a_element_op,
|
||||
B_functor b_element_op,
|
||||
C_functor c_element_op,
|
||||
AElementOp a_element_op,
|
||||
BElementOp b_element_op,
|
||||
CDEElementOp c_element_op,
|
||||
int M,
|
||||
int N)
|
||||
{
|
||||
using out_type = ck::remove_reference_t<decltype(out_m_n(0, 0))>;
|
||||
|
||||
int StrideC = N;
|
||||
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ReduceDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = UnaryDivElementOp{N};
|
||||
int StrideE = N;
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
auto averageOpInst = Div{N};
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument =
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, c_element_op);
|
||||
ref_gemm.MakeArgument(a_m_k, b_k_n, e_m_n, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
// reduce_mean and reduce_square_mean
|
||||
auto reduceSumOpInst = ReduceSumOp{};
|
||||
auto r0Op = R0ThreadReduceOp{};
|
||||
auto r1Op = R1ThreadReduceOp{};
|
||||
for(int m = 0; m < M; ++m)
|
||||
{
|
||||
auto mean_acc = reduceSumOpInst.GetIdentityValue<ReduceAccDataType>();
|
||||
auto square_mean_acc = reduceSumOpInst.GetIdentityValue<ReduceAccDataType>();
|
||||
auto mean_acc = r0Op.GetIdentityValue<ReduceAccDataType>();
|
||||
auto mean_square_acc = r1Op.GetIdentityValue<ReduceAccDataType>();
|
||||
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
auto c_val = ck::type_convert<ReduceAccDataType>(c_m_n(m, n));
|
||||
auto square_c_val = reduceSumOpInst.GetIdentityValue<ReduceAccDataType>();
|
||||
auto e_val = ck::type_convert<ReduceAccDataType>(e_m_n(m, n));
|
||||
ReduceAccDataType square_e_val = 0;
|
||||
Square{}(square_e_val, e_val);
|
||||
|
||||
UnarySquareElementOp{}(square_c_val, c_val);
|
||||
|
||||
reduceSumOpInst(mean_acc, c_val);
|
||||
reduceSumOpInst(square_mean_acc, square_c_val);
|
||||
r0Op(mean_acc, e_val);
|
||||
r1Op(mean_square_acc, square_e_val);
|
||||
}
|
||||
|
||||
averageOpInst(mean_acc, mean_acc);
|
||||
averageOpInst(square_mean_acc, square_mean_acc);
|
||||
mean_m(m) = ck::type_convert<ReduceDataType>(mean_acc);
|
||||
meanSquare_m(m) = ck::type_convert<ReduceDataType>(square_mean_acc);
|
||||
averageOpInst(mean_square_acc, mean_square_acc);
|
||||
mean_m(m) = ck::type_convert<R0DataType>(mean_acc);
|
||||
meanSquare_m(m) = ck::type_convert<R1DataType>(mean_square_acc);
|
||||
}
|
||||
|
||||
// LayerNorm
|
||||
@@ -182,22 +184,23 @@ void host_gemm_layernorm(Tensor<LayerNormOutDataType>& out_m_n,
|
||||
{
|
||||
for(int n = 0; n < N; ++n)
|
||||
{
|
||||
float out_f32 = 0;
|
||||
layerNormInst(out_f32,
|
||||
static_cast<float>(c_m_n(m, n)),
|
||||
static_cast<float>(mean_m(m)),
|
||||
static_cast<float>(meanSquare_m(m)),
|
||||
static_cast<float>(gamma_n(n)),
|
||||
static_cast<float>(beta_n(n)));
|
||||
out_m_n(m, n) = static_cast<out_type>(out_f32);
|
||||
NormalizeComputeDataType out_acc = 0;
|
||||
layerNormInst(out_acc,
|
||||
ck::type_convert<NormalizeComputeDataType>(e_m_n(m, n)),
|
||||
ck::type_convert<NormalizeComputeDataType>(mean_m(m)),
|
||||
ck::type_convert<NormalizeComputeDataType>(meanSquare_m(m)),
|
||||
ck::type_convert<NormalizeComputeDataType>(gamma_n(n)),
|
||||
ck::type_convert<NormalizeComputeDataType>(beta_n(n)));
|
||||
out_m_n(m, n) = ck::type_convert<LayerNormOutDataType>(out_acc);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename ReduceDataType,
|
||||
typename EDataType,
|
||||
typename R0DataType,
|
||||
typename R1DataType,
|
||||
typename GammaDataType,
|
||||
typename BetaDataType,
|
||||
typename NormalizeDataType>
|
||||
@@ -205,11 +208,11 @@ void DumpGemmLayerNormPerf(float gemm_reduce_time, float normalize_time, int M,
|
||||
{
|
||||
std::size_t gemm_flop = std::size_t(2) * M * N * K;
|
||||
std::size_t gemm_num_byte = sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
|
||||
sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M;
|
||||
sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M;
|
||||
|
||||
std::size_t normalize_num_btye = sizeof(CDataType) * M * N + sizeof(ReduceDataType) * M +
|
||||
sizeof(ReduceDataType) * M + sizeof(GammaDataType) * N +
|
||||
std::size_t normalize_num_btye = sizeof(EDataType) * M * N + sizeof(R0DataType) * M +
|
||||
sizeof(R1DataType) * M + sizeof(GammaDataType) * N +
|
||||
sizeof(BetaDataType) * N + sizeof(NormalizeDataType) * M * N;
|
||||
|
||||
float tflops = static_cast<float>(gemm_flop) / 1.E9 / gemm_reduce_time;
|
||||
@@ -232,17 +235,17 @@ int main()
|
||||
|
||||
ck::index_t StrideA = 1024;
|
||||
ck::index_t StrideB = 1024;
|
||||
ck::index_t StrideC = 1024;
|
||||
ck::index_t StrideE = 1024;
|
||||
|
||||
Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
|
||||
Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
|
||||
Tensor<CDataType> c_m_n(f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
Tensor<ReduceDataType> reduceMean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<ReduceDataType> reduceMeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<EDataType> e_m_n(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
Tensor<R0DataType> r0_Mean_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<R1DataType> r1_MeanSquare_m(f_host_tensor_descriptor1d(M, 1));
|
||||
Tensor<GammaDataType> gamma_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<BetaDataType> beta_n(f_host_tensor_descriptor1d(N, 1));
|
||||
Tensor<LayerNormOutDataType> layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{-1, 1});
|
||||
b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-1, 1});
|
||||
@@ -251,11 +254,10 @@ int main()
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem c_device_buf(sizeof(CDataType) * c_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMean_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem reduceMeanSquare_device_buf(sizeof(ReduceDataType) *
|
||||
reduceMeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_m_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r0_Mean_device_buf(sizeof(R0DataType) * r0_Mean_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem r1_MeanSquare_device_buf(sizeof(R1DataType) *
|
||||
r1_MeanSquare_m.mDesc.GetElementSpaceSize());
|
||||
DeviceMem gamma_device_buf(sizeof(GammaDataType) * gamma_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem beta_device_buf(sizeof(BetaDataType) * beta_n.mDesc.GetElementSpaceSize());
|
||||
DeviceMem layerNorm_device_buf(sizeof(LayerNormOutDataType) *
|
||||
@@ -266,40 +268,33 @@ int main()
|
||||
gamma_device_buf.ToDevice(gamma_n.mData.data());
|
||||
beta_device_buf.ToDevice(beta_n.mData.data());
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto c_element_op = CElementOp{};
|
||||
std::array<void*, 3> gemm_element_ops = {&a_element_op, &b_element_op, &c_element_op};
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
auto qs_element_op = QsElementOp{};
|
||||
auto rs_element_op = RsElementOp{N, N};
|
||||
|
||||
auto passthrough = UnaryIdenticElementOp{};
|
||||
auto square = UnarySquareElementOp{};
|
||||
auto div = UnaryDivElementOp{N};
|
||||
std::array<void*, 2> reduce_in_element_ops = {&passthrough, &square};
|
||||
std::array<void*, 2> reduce_out_element_ops = {&div, &div};
|
||||
|
||||
std::array<void*, 2> p_reduces = {reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer()};
|
||||
|
||||
// Prepare GEMM, reduce_mean, reduce_mean_square
|
||||
auto gemmReduce = DeviceGemmReduceInstance{};
|
||||
// Prepare GEMM, mean, mean_square
|
||||
auto gemmReduce = DeviceOpInstance{};
|
||||
auto gemmReduce_invoker = gemmReduce.MakeInvoker();
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
nullptr,
|
||||
{},
|
||||
c_device_buf.GetDeviceBuffer(),
|
||||
p_reduces,
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
StrideC,
|
||||
{},
|
||||
gemm_element_ops,
|
||||
{},
|
||||
reduce_in_element_ops,
|
||||
reduce_out_element_ops);
|
||||
auto gemmReduce_argument = gemmReduce.MakeArgument(
|
||||
a_device_buf.GetDeviceBuffer(),
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
{},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
{r0_Mean_device_buf.GetDeviceBuffer(), r1_MeanSquare_device_buf.GetDeviceBuffer()},
|
||||
M,
|
||||
N,
|
||||
K,
|
||||
StrideA,
|
||||
StrideB,
|
||||
{},
|
||||
StrideE,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
qs_element_op,
|
||||
rs_element_op);
|
||||
|
||||
if(!gemmReduce.IsSupportedArgument(gemmReduce_argument))
|
||||
{
|
||||
@@ -308,13 +303,13 @@ int main()
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
reduceMean_device_buf.SetZero();
|
||||
reduceMeanSquare_device_buf.SetZero();
|
||||
r0_Mean_device_buf.SetZero();
|
||||
r1_MeanSquare_device_buf.SetZero();
|
||||
|
||||
// Prepare LayerNorm
|
||||
std::array<const void*, 5> input = {c_device_buf.GetDeviceBuffer(),
|
||||
reduceMean_device_buf.GetDeviceBuffer(),
|
||||
reduceMeanSquare_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 5> input = {e_device_buf.GetDeviceBuffer(),
|
||||
r0_Mean_device_buf.GetDeviceBuffer(),
|
||||
r1_MeanSquare_device_buf.GetDeviceBuffer(),
|
||||
gamma_device_buf.GetDeviceBuffer(),
|
||||
beta_device_buf.GetDeviceBuffer()};
|
||||
std::array<void*, 1> output = {layerNorm_device_buf.GetDeviceBuffer()};
|
||||
@@ -324,12 +319,12 @@ int main()
|
||||
auto normalize_argument = normalize.MakeArgument(input,
|
||||
output,
|
||||
{M, N},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
{1, 0},
|
||||
{1, 0},
|
||||
{0, 1},
|
||||
{0, 1},
|
||||
{StrideC, 1},
|
||||
{StrideE, 1},
|
||||
NormalizeFunctor{});
|
||||
|
||||
if(!normalize.IsSupportedArgument(normalize_argument))
|
||||
@@ -346,18 +341,18 @@ int main()
|
||||
{
|
||||
// verification
|
||||
Tensor<LayerNormOutDataType> host_layerNorm_m_n(
|
||||
f_host_tensor_descriptor2d(M, N, StrideC, CLayout{}));
|
||||
f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
|
||||
|
||||
host_gemm_layernorm<CDataType, ReduceDataType>(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
M,
|
||||
N);
|
||||
host_gemm_layernorm(host_layerNorm_m_n,
|
||||
a_m_k,
|
||||
b_k_n,
|
||||
gamma_n,
|
||||
beta_n,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op,
|
||||
M,
|
||||
N);
|
||||
|
||||
layerNorm_device_buf.FromDevice(layerNorm_m_n.mData.data());
|
||||
pass &= ck::utils::check_err(layerNorm_m_n.mData,
|
||||
@@ -379,8 +374,9 @@ int main()
|
||||
if(time_kernel)
|
||||
DumpGemmLayerNormPerf<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
ReduceDataType,
|
||||
EDataType,
|
||||
R0DataType,
|
||||
R1DataType,
|
||||
GammaDataType,
|
||||
BetaDataType,
|
||||
LayerNormOutDataType>(
|
||||
|
||||
Reference in New Issue
Block a user