mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 02:02:46 +00:00
Grouped Gemm device with multiD grid (#319)
* replace gridwise_v2r3 with multiD
* adjust parameters
* add instances
* fixed test_grouped_gemm
* fix standalone softmax race condition around blockwise reduction
* fixed ci
* fixed comment: remove redundant workspace
* use instanceFactory
* add test layout
* add empty Ds
* add bias example
* use array
* sperate examples
Co-authored-by: Anthony Chang <ac.chang@outlook.com>
[ROCm/composable_kernel commit: 7959dad566]
This commit is contained in:
zjing14
@@ -29,34 +29,39 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ADataType = ck::half_t;
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using BDataType = ck::half_t;
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using CDataType = ck::half_t;
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using AccDataType = float;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F16;
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using DsDataType = ck::Tuple<>;
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using EDataType = F16;
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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 ALayout = Row;
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using BLayout = Col;
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using ELayout = Row;
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using AElementOp = ck::tensor_operation::element_wise::PassThrough;
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using BElementOp = ck::tensor_operation::element_wise::PassThrough;
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using CElementOp = ck::tensor_operation::element_wise::PassThrough;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = PassThrough;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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// static constexpr auto GemmMNPadding =
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// ck::tensor_operation::device::GemmSpecialization::MNPadding;
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// clang-format off
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl
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//######| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer| Num|
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//######| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar| Prefetch|
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//######| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector| |
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1, 1>;
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// clang-format off
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//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| 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|
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//######| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//######| | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, 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<1, 32, 1, 8>, 8>;
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// clang-format on
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using ReferenceGemmInstance = ck::tensor_operation::host::
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ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
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using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
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BDataType,
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EDataType,
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AccDataType,
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AElementOp,
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BElementOp,
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CDEElementOp>;
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int main(int argc, char* argv[])
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{
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@@ -81,11 +86,11 @@ int main(int argc, char* argv[])
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int group_count = rand() % 16 + 1;
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// GEMM shape
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std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes;
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std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
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std::vector<const void*> p_a, p_b;
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std::vector<void*> p_c;
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gemm_shapes.reserve(group_count);
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gemm_descs.reserve(group_count);
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for(int i = 0; i < group_count; i++)
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{
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@@ -93,7 +98,11 @@ int main(int argc, char* argv[])
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int N = 128 + 128 * i;
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int K = 64 + 64 * i;
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gemm_shapes.push_back({M, N, K, K, K, N});
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int stride_A = K;
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int stride_B = K;
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int stride_C = N;
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gemm_descs.push_back({M, N, K, stride_A, stride_B, stride_C, {}});
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}
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auto f_host_tensor_descriptor =
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@@ -111,10 +120,9 @@ int main(int argc, char* argv[])
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};
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std::vector<Tensor<ADataType>> a_tensors;
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;
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std::vector<Tensor<BDataType>> b_tensors;
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std::vector<Tensor<CDataType>> c_host_tensors;
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std::vector<Tensor<CDataType>> c_device_tensors;
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std::vector<Tensor<EDataType>> c_host_tensors;
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std::vector<Tensor<EDataType>> c_device_tensors;
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a_tensors.reserve(group_count);
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b_tensors.reserve(group_count);
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@@ -131,25 +139,25 @@ int main(int argc, char* argv[])
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std::size_t flop = 0, num_btype = 0;
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for(std::size_t i = 0; i < gemm_shapes.size(); i++)
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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{
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a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
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gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
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gemm_descs[i].M_, gemm_descs[i].K_, gemm_descs[i].stride_A_, ALayout{})));
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b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
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gemm_shapes[i].K, gemm_shapes[i].N, gemm_shapes[i].StrideB, BLayout{})));
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c_host_tensors.push_back(Tensor<CDataType>(f_host_tensor_descriptor(
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gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{})));
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c_device_tensors.push_back(Tensor<CDataType>(f_host_tensor_descriptor(
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gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{})));
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gemm_descs[i].K_, gemm_descs[i].N_, gemm_descs[i].stride_B_, BLayout{})));
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c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
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gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
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c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
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gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
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std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
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<< " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << c_device_tensors[i].mDesc
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<< std::endl;
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flop += std::size_t(2) * gemm_shapes[i].M * gemm_shapes[i].K * gemm_shapes[i].N;
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flop += std::size_t(2) * gemm_descs[i].M_ * gemm_descs[i].K_ * gemm_descs[i].N_;
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num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
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sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
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sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSize();
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sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSize();
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switch(init_method)
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{
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@@ -168,14 +176,14 @@ int main(int argc, char* argv[])
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}
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}
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for(std::size_t i = 0; i < gemm_shapes.size(); i++)
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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{
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a_tensors_device.emplace_back(
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std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
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b_tensors_device.emplace_back(
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std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpace()));
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c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
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sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSpace()));
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sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSpace()));
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a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
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b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
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@@ -187,14 +195,16 @@ int main(int argc, char* argv[])
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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 c_element_op = CDEElementOp{};
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auto gemm = DeviceGemmInstance{};
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auto invoker = gemm.MakeInvoker();
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std::vector<std::array<const void*, 0>> p_Ds = {};
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// do GEMM
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auto argument =
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gemm.MakeArgument(p_a, p_b, p_c, gemm_shapes, a_element_op, b_element_op, c_element_op);
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auto argument = gemm.MakeArgument(
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p_a, p_b, p_Ds, p_c, gemm_descs, a_element_op, b_element_op, c_element_op);
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DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
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@@ -219,7 +229,7 @@ int main(int argc, char* argv[])
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bool pass = true;
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if(do_verification)
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{
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for(std::size_t i = 0; i < gemm_shapes.size(); i++)
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for(std::size_t i = 0; i < gemm_descs.size(); i++)
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{
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c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
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auto ref_gemm = ReferenceGemmInstance{};
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1
example/28_grouped_gemm_bias/CMakeLists.txt
Normal file
1
example/28_grouped_gemm_bias/CMakeLists.txt
Normal file
@@ -0,0 +1 @@
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add_example_executable(example_grouped_gemm_bias_xdl_fp16 grouped_gemm_bias_xdl_fp16.cpp)
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278
example/28_grouped_gemm_bias/grouped_gemm_bias_xdl_fp16.cpp
Normal file
278
example/28_grouped_gemm_bias/grouped_gemm_bias_xdl_fp16.cpp
Normal file
@@ -0,0 +1,278 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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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 "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_grouped_gemm_xdl.hpp"
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#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/host_tensor/device_memory.hpp"
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#include "ck/library/host_tensor/host_tensor.hpp"
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#include "ck/library/host_tensor/host_tensor_generator.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_gemm.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 PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using Add = ck::tensor_operation::element_wise::Add;
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using ADataType = F16;
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using BDataType = F16;
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using AccDataType = F32;
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using CShuffleDataType = F16;
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using D0DataType = F16;
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using DsDataType = ck::Tuple<D0DataType>;
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using EDataType = F16;
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using ALayout = Row;
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using BLayout = Col;
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using ELayout = Row;
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using AElementOp = PassThrough;
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using BElementOp = PassThrough;
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using CDEElementOp = Add;
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static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
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using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl
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// clang-format off
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//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| 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|
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//######| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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| _MBlock_MWaveMPerXdl| ScalarPerVector|
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//######| | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
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//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
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< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, 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<1, 32, 1, 8>, 8>;
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// clang-format on
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int main(int argc, char* argv[])
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{
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bool do_verification = true;
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int init_method = 1;
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bool time_kernel = false;
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if(argc == 4)
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{
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do_verification = std::stoi(argv[1]);
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init_method = std::stoi(argv[2]);
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time_kernel = std::stoi(argv[3]);
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}
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else
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{
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printf("arg1: verification (0=no, 1=yes)\n");
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printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
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printf("arg3: time kernel (0=n0, 1=yes)\n");
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exit(0);
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}
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int group_count = rand() % 16 + 1;
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// GEMM shape
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std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
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std::vector<const void*> p_a, p_b;
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std::vector<std::array<const void*, 1>> p_ds;
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std::vector<void*> p_c;
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gemm_descs.reserve(group_count);
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for(int i = 0; i < group_count; i++)
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{
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int M = 256 + 256 * i;
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int N = 128 + 128 * i;
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int K = 64 + 64 * i;
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int stride_A = K;
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int stride_B = K;
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int stride_C = N;
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std::vector<ck::index_t> stride_Ds = {0};
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gemm_descs.push_back({M, N, K, stride_A, stride_B, stride_C, stride_Ds});
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}
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auto f_host_tensor_descriptor =
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[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
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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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std::vector<Tensor<ADataType>> a_tensors;
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std::vector<Tensor<BDataType>> b_tensors;
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std::vector<Tensor<D0DataType>> d0_tensors;
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std::vector<Tensor<EDataType>> e_host_tensors;
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std::vector<Tensor<EDataType>> e_device_tensors;
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a_tensors.reserve(group_count);
|
||||
b_tensors.reserve(group_count);
|
||||
d0_tensors.reserve(group_count);
|
||||
e_host_tensors.reserve(group_count);
|
||||
e_device_tensors.reserve(group_count);
|
||||
|
||||
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
||||
|
||||
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d0_tensors_device,
|
||||
e_tensors_device;
|
||||
|
||||
a_tensors_device.reserve(group_count);
|
||||
b_tensors_device.reserve(group_count);
|
||||
d0_tensors_device.reserve(group_count);
|
||||
e_tensors_device.reserve(group_count);
|
||||
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].K_, gemm_descs[i].stride_A_, ALayout{})));
|
||||
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].K_, gemm_descs[i].N_, gemm_descs[i].stride_B_, BLayout{})));
|
||||
d0_tensors.push_back(Tensor<D0DataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_Ds_[0], ELayout{})));
|
||||
e_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
||||
e_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
|
||||
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
|
||||
|
||||
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
|
||||
<< " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << e_device_tensors[i].mDesc
|
||||
<< std::endl;
|
||||
|
||||
flop += std::size_t(2) * gemm_descs[i].M_ * gemm_descs[i].K_ * gemm_descs[i].N_;
|
||||
num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
|
||||
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSize();
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d0_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
case 2:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
default:
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
|
||||
d0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
|
||||
}
|
||||
}
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
a_tensors_device.emplace_back(
|
||||
std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
|
||||
b_tensors_device.emplace_back(
|
||||
std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpace()));
|
||||
d0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSpace()));
|
||||
e_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSpace()));
|
||||
|
||||
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
|
||||
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
|
||||
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
|
||||
|
||||
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
|
||||
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
|
||||
p_ds.push_back({d0_tensors_device[i]->GetDeviceBuffer()});
|
||||
p_c.push_back(e_tensors_device[i]->GetDeviceBuffer());
|
||||
}
|
||||
|
||||
auto a_element_op = AElementOp{};
|
||||
auto b_element_op = BElementOp{};
|
||||
auto cde_element_op = CDEElementOp{};
|
||||
|
||||
auto gemm = DeviceGemmInstance{};
|
||||
auto invoker = gemm.MakeInvoker();
|
||||
|
||||
// do GEMM
|
||||
auto argument = gemm.MakeArgument(
|
||||
p_a, p_b, p_ds, p_c, gemm_descs, a_element_op, b_element_op, cde_element_op);
|
||||
|
||||
DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
|
||||
|
||||
gemm.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
|
||||
|
||||
if(!gemm.IsSupportedArgument(argument))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! device_gemm with the specified compilation parameters does "
|
||||
"not support this GEMM problem");
|
||||
}
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
|
||||
float gb_per_sec = num_btype / 1.E6 / ave_time;
|
||||
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< gemm.GetTypeString() << std::endl;
|
||||
|
||||
bool pass = true;
|
||||
if(do_verification)
|
||||
{
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
EDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data());
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
|
||||
b_tensors[i],
|
||||
e_host_tensors[i],
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(int m = 0; m < gemm_descs[i].M_; ++m)
|
||||
{
|
||||
for(int n = 0; n < gemm_descs[i].N_; ++n)
|
||||
{
|
||||
cde_element_op(
|
||||
e_host_tensors[i](m, n), e_host_tensors[i](m, n), d0_tensors[i](m, n));
|
||||
}
|
||||
}
|
||||
|
||||
pass &= ck::utils::check_err(e_device_tensors[i].mData, e_host_tensors[i].mData);
|
||||
}
|
||||
}
|
||||
|
||||
return pass ? 0 : 1;
|
||||
}
|
||||
@@ -46,3 +46,4 @@ add_subdirectory(24_batched_gemm_c_permute)
|
||||
add_subdirectory(25_gemm_bias_c_permute)
|
||||
add_subdirectory(26_contraction)
|
||||
add_subdirectory(27_layernorm)
|
||||
add_subdirectory(28_grouped_gemm_bias)
|
||||
|
||||
@@ -12,12 +12,6 @@ namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
struct GemmShape
|
||||
{
|
||||
ck::index_t M, N, K;
|
||||
ck::index_t StrideA, StrideB, StrideC;
|
||||
};
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout,
|
||||
@@ -65,29 +59,6 @@ using DeviceGemmPtr = std::unique_ptr<DeviceGemm<ALayout,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation>>;
|
||||
|
||||
template <typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation>
|
||||
struct DeviceGroupedGemm : public BaseOperator
|
||||
{
|
||||
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(std::vector<const void*>& p_a,
|
||||
std::vector<const void*>& p_b,
|
||||
std::vector<void*>& p_c,
|
||||
std::vector<GemmShape>& gemm_shapes,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op,
|
||||
ck::index_t KBatch = 1) = 0;
|
||||
|
||||
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
|
||||
};
|
||||
|
||||
template <typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation>
|
||||
using DeviceGroupedGemmPtr = std::unique_ptr<
|
||||
DeviceGroupedGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
|
||||
@@ -0,0 +1,69 @@
|
||||
#pragma once
|
||||
#include <iostream>
|
||||
#include <vector>
|
||||
|
||||
#include "device_base.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
struct GemmDesc
|
||||
{
|
||||
ck::index_t M_, N_, K_;
|
||||
ck::index_t stride_A_, stride_B_, stride_C_;
|
||||
|
||||
std::vector<ck::index_t> stride_Ds_;
|
||||
};
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation>
|
||||
struct DeviceGroupedGemm : public BaseOperator
|
||||
{
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
|
||||
virtual std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(std::vector<const void*>& p_a,
|
||||
std::vector<const void*>& p_b,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_ds,
|
||||
std::vector<void*>& p_e,
|
||||
std::vector<GemmDesc>& gemm_desc,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op) = 0;
|
||||
|
||||
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
|
||||
};
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename DELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation>
|
||||
using DeviceGroupedGemmPtr = std::unique_ptr<DeviceGroupedGemm<ALayout,
|
||||
BLayout,
|
||||
DELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation>>;
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -1,3 +1,4 @@
|
||||
#pragma once
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
@@ -10,9 +11,9 @@
|
||||
#include "ck/tensor_description/tensor_descriptor.hpp"
|
||||
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
|
||||
#include "ck/device_utility/device_prop.hpp"
|
||||
#include "ck/device_utility/kernel_launch.hpp"
|
||||
|
||||
@@ -21,22 +22,20 @@ namespace tensor_operation {
|
||||
namespace device {
|
||||
|
||||
template <typename GridwiseGemm,
|
||||
typename FloatAB,
|
||||
typename FloatC,
|
||||
typename GemmDesc,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
bool HasMainKBlockLoop>
|
||||
__global__ void
|
||||
#if CK_USE_LAUNCH_BOUNDS
|
||||
__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
|
||||
#endif
|
||||
kernel_grouped_gemm_xdlops_v2r3(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
|
||||
const index_t group_count,
|
||||
const AElementwiseOperation a_element_op,
|
||||
const BElementwiseOperation b_element_op,
|
||||
const CElementwiseOperation c_element_op)
|
||||
kernel_grouped_gemm_xdl(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
|
||||
const index_t group_count,
|
||||
const AElementwiseOperation a_element_op,
|
||||
const BElementwiseOperation b_element_op,
|
||||
const CDEElementwiseOperation c_element_op)
|
||||
{
|
||||
#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
|
||||
__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
|
||||
@@ -65,42 +64,48 @@ __global__ void
|
||||
}
|
||||
|
||||
GridwiseGemm::template Run<HasMainKBlockLoop>(
|
||||
gemm_desc_ptr[group_id].a_ptr,
|
||||
gemm_desc_ptr[group_id].b_ptr,
|
||||
gemm_desc_ptr[group_id].c_ptr,
|
||||
gemm_desc_ptr[group_id].a_ptr_,
|
||||
gemm_desc_ptr[group_id].b_ptr_,
|
||||
gemm_desc_ptr[group_id].ds_ptr_,
|
||||
gemm_desc_ptr[group_id].e_ptr_,
|
||||
p_shared,
|
||||
gemm_desc_ptr[group_id].a_grid_desc_k0_m_k1_,
|
||||
gemm_desc_ptr[group_id].b_grid_desc_k0_n_k1_,
|
||||
gemm_desc_ptr[group_id].c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op,
|
||||
gemm_desc_ptr[group_id].grouped_gemm_block_2_ctile_map_);
|
||||
gemm_desc_ptr[group_id].a_grid_desc_k0_m_k1_,
|
||||
gemm_desc_ptr[group_id].b_grid_desc_k0_n_k1_,
|
||||
gemm_desc_ptr[group_id].ds_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
gemm_desc_ptr[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
gemm_desc_ptr[group_id].block_2_ctile_map_);
|
||||
#else
|
||||
ignore = gemm_descs_const;
|
||||
ignore = group_count;
|
||||
ignore = a_element_op;
|
||||
ignore = b_element_op;
|
||||
ignore = c_element_op;
|
||||
#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
|
||||
#endif
|
||||
}
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename AccDataType,
|
||||
typename ALayout,
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout,
|
||||
typename DELayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename GemmAccDataType,
|
||||
typename CShuffleDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
GemmSpecialization GemmSpec,
|
||||
ck::index_t NumPrefetch,
|
||||
ck::index_t BlockSize,
|
||||
ck::index_t MPerBlock,
|
||||
ck::index_t NPerBlock,
|
||||
ck::index_t K0PerBlock,
|
||||
ck::index_t K1,
|
||||
ck::index_t KPerBlock,
|
||||
ck::index_t AK1,
|
||||
ck::index_t BK1,
|
||||
ck::index_t MPerXDL,
|
||||
ck::index_t NPerXDL,
|
||||
ck::index_t MXdlPerWave,
|
||||
@@ -119,155 +124,319 @@ template <typename ADataType,
|
||||
ck::index_t BBlockTransferSrcScalarPerVector,
|
||||
ck::index_t BBlockTransferDstScalarPerVector_K1,
|
||||
bool BBlockLdsAddExtraN,
|
||||
ck::index_t CThreadTransferSrcDstVectorDim,
|
||||
ck::index_t CThreadTransferDstScalarPerVector,
|
||||
ck::index_t NumPrefetch = 1,
|
||||
ck::index_t MaxGroupCount = 10>
|
||||
struct DeviceGroupedGemmXdl
|
||||
: public DeviceGroupedGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>
|
||||
index_t CShuffleMXdlPerWavePerShuffle,
|
||||
index_t CShuffleNXdlPerWavePerShuffle,
|
||||
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
index_t CDEBlockTransferScalarPerVector_NPerBlock,
|
||||
LoopScheduler LoopSched = make_default_loop_scheduler()>
|
||||
struct DeviceGroupedGemmXdl : public DeviceGroupedGemm<ALayout,
|
||||
BLayout,
|
||||
DELayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation>
|
||||
{
|
||||
static constexpr index_t NumDTensor = DsDataType::Size();
|
||||
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
static constexpr auto I2 = Number<2>{};
|
||||
|
||||
static constexpr auto K1Number = Number<K1>{};
|
||||
|
||||
static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
|
||||
static auto MakeAGridDescriptor_AK0_M_AK1(index_t MRaw, index_t KRaw, index_t StrideA)
|
||||
{
|
||||
assert(K % K1 == 0);
|
||||
|
||||
const index_t K0 = K / K1;
|
||||
|
||||
const auto a_grid_desc_m_k = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
|
||||
const auto a_grid_desc_mraw_kraw = [&]() {
|
||||
if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
|
||||
make_tuple(StrideA, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
|
||||
else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
|
||||
make_tuple(I1, StrideA));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_right_pad_transform(M, PadM)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
const auto MPad = M - MRaw;
|
||||
const auto KPad = K - KRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad both M and K
|
||||
assert(K % AK1 == 0);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
const auto a_grid_desc_m_k =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
// pad M, but not K
|
||||
assert(KRaw % AK1 == 0);
|
||||
|
||||
const auto AK0 = KRaw / AK1;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_right_pad_transform(MRaw, MPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad K, but not M
|
||||
assert(K % AK1 == 0);
|
||||
|
||||
const auto AK0 = K / AK1;
|
||||
|
||||
const auto a_grid_desc_m_k = transform_tensor_descriptor(
|
||||
a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(MRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
a_grid_desc_m_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_pass_through_transform(M)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
// not pad M or K
|
||||
assert(KRaw % AK1 == 0);
|
||||
|
||||
const auto AK0 = KRaw / AK1;
|
||||
|
||||
const auto a_grid_desc_ak0_m_ak1 =
|
||||
transform_tensor_descriptor(a_grid_desc_mraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
|
||||
make_pass_through_transform(MRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return a_grid_desc_ak0_m_ak1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
|
||||
static auto MakeBGridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
|
||||
{
|
||||
assert(K % K1 == 0);
|
||||
|
||||
const index_t K0 = K / K1;
|
||||
|
||||
const auto b_grid_desc_k_n = [&]() {
|
||||
const auto b_grid_desc_nraw_kraw = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
|
||||
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
|
||||
make_tuple(I1, StrideB));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
|
||||
return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
|
||||
make_tuple(StrideB, I1));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
b_grid_desc_k_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_right_pad_transform(N, PadN)),
|
||||
const auto NPad = N - NRaw;
|
||||
const auto KPad = K - KRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad both N and K
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_right_pad_transform(NRaw, NPad),
|
||||
make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
// pad N, but not K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad K, but not N
|
||||
assert(K % BK1 == 0);
|
||||
|
||||
const auto BK0 = K / BK1;
|
||||
|
||||
const auto b_grid_desc_n_k = transform_tensor_descriptor(
|
||||
b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_pass_through_transform(NRaw), make_right_pad_transform(KRaw, KPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_n_k,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
else
|
||||
{
|
||||
return transform_tensor_descriptor(
|
||||
b_grid_desc_k_n,
|
||||
make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
|
||||
make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
// not pad N or K
|
||||
assert(KRaw % BK1 == 0);
|
||||
|
||||
const auto BK0 = KRaw / BK1;
|
||||
|
||||
const auto b_grid_desc_bk0_n_bk1 =
|
||||
transform_tensor_descriptor(b_grid_desc_nraw_kraw,
|
||||
make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
|
||||
make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<1>{}, Sequence<0>{}),
|
||||
make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
|
||||
|
||||
return b_grid_desc_bk0_n_bk1;
|
||||
}
|
||||
}
|
||||
|
||||
static auto MakeCGridDescriptor_M_N(index_t M, index_t N, index_t StrideC)
|
||||
static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
|
||||
{
|
||||
const auto c_grid_desc_m_n = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
|
||||
const auto c_grid_desc_mraw_nraw = [&]() {
|
||||
if constexpr(is_same<tensor_layout::gemm::RowMajor, DELayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
|
||||
make_tuple(StrideE, I1));
|
||||
}
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
|
||||
else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, DELayout>::value)
|
||||
{
|
||||
return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
|
||||
return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
|
||||
make_tuple(I1, StrideE));
|
||||
}
|
||||
}();
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding)
|
||||
{
|
||||
const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
|
||||
const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
|
||||
const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
|
||||
const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
|
||||
|
||||
const auto MPad = M - MRaw;
|
||||
const auto NPad = N - NRaw;
|
||||
|
||||
if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
|
||||
GemmSpec == GemmSpecialization::MNKPadding)
|
||||
{
|
||||
// pad M and N
|
||||
return transform_tensor_descriptor(c_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad),
|
||||
make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
|
||||
GemmSpec == GemmSpecialization::MKPadding)
|
||||
{
|
||||
// pad M, but not N
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
|
||||
c_grid_desc_mraw_nraw,
|
||||
make_tuple(make_right_pad_transform(MRaw, MPad), make_pass_through_transform(NRaw)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
|
||||
GemmSpec == GemmSpecialization::NKPadding)
|
||||
{
|
||||
// pad N, but not M
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_mraw_nraw,
|
||||
make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(NRaw, NPad)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
}
|
||||
else
|
||||
{
|
||||
|
||||
return transform_tensor_descriptor(
|
||||
c_grid_desc_m_n,
|
||||
make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}),
|
||||
make_tuple(Sequence<0>{}, Sequence<1>{}));
|
||||
// not pad M or N
|
||||
return c_grid_desc_mraw_nraw;
|
||||
}
|
||||
}
|
||||
|
||||
using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_K0_M_K1(1, 1, 1));
|
||||
using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
|
||||
using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
|
||||
using AGridDesc_AK0_M_AK1 = decltype(MakeAGridDescriptor_AK0_M_AK1(1, 1, 1));
|
||||
using BGridDesc_BK0_N_BK1 = decltype(MakeBGridDescriptor_BK0_N_BK1(1, 1, 1));
|
||||
using EGridDesc_M_N = decltype(MakeEGridDescriptor_M_N(1, 1, 1));
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<
|
||||
BlockSize,
|
||||
using GridwiseGemm = GridwiseGemmMultipleD_k0mk1_k0nk1_mn_xdl_cshuffle<
|
||||
ADataType, // TODO: distinguish A/B datatype
|
||||
AccDataType,
|
||||
CDataType,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
AGridDesc_K0_M_K1,
|
||||
BGridDesc_K0_N_K1,
|
||||
CGridDesc_M_N,
|
||||
GemmAccDataType,
|
||||
CShuffleDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
AGridDesc_AK0_M_AK1,
|
||||
BGridDesc_BK0_N_BK1,
|
||||
EGridDesc_M_N,
|
||||
NumPrefetch, // NumGemmKPrefetchStage
|
||||
BlockSize,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
K0PerBlock,
|
||||
KPerBlock,
|
||||
AK1,
|
||||
BK1,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
K1,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_K0_M_K1,
|
||||
@@ -286,30 +455,28 @@ struct DeviceGroupedGemmXdl
|
||||
BBlockTransferDstScalarPerVector_K1,
|
||||
false, // BThreadTransferSrcResetCoordinateAfterRun,
|
||||
BBlockLdsAddExtraN,
|
||||
Sequence<0, 2, 4, 5, 6, 1, 3, 7>, // CThreadTransferSrcDstAccessOrder,
|
||||
CThreadTransferSrcDstVectorDim,
|
||||
CThreadTransferDstScalarPerVector,
|
||||
NumPrefetch>;
|
||||
CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
CDEBlockTransferScalarPerVector_NPerBlock,
|
||||
LoopSched>;
|
||||
|
||||
struct GroupedGemmBlock2CTileMap
|
||||
struct GroupedGemmBlock2ETileMap
|
||||
{
|
||||
using UnderlyingBlock2CTileMap = typename GridwiseGemm::DefaultBlock2CTileMap;
|
||||
using UnderlyingBlock2CTileMap = typename GridwiseGemm::DefaultBlock2ETileMap;
|
||||
static_assert(
|
||||
std::is_same<decltype(GridwiseGemm::MakeDefaultBlock2CTileMap(CGridDesc_M_N{}, 1, 1)),
|
||||
typename GridwiseGemm::DefaultBlock2CTileMap>::value,
|
||||
std::is_same<decltype(GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{})),
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap>::value,
|
||||
"Wrong! Should be the same type name");
|
||||
GroupedGemmBlock2CTileMap()
|
||||
GroupedGemmBlock2ETileMap()
|
||||
{
|
||||
block_2_ctile_map_ = GridwiseGemm::MakeDefaultBlock2CTileMap(CGridDesc_M_N{}, 1, 1);
|
||||
block_2_ctile_map_ = GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{});
|
||||
BlockStart_ = -1;
|
||||
}
|
||||
|
||||
GroupedGemmBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n,
|
||||
index_t M01,
|
||||
index_t N01,
|
||||
ck::index_t BlockStart)
|
||||
GroupedGemmBlock2ETileMap(const EGridDesc_M_N& c_grid_desc_m_n, ck::index_t BlockStart)
|
||||
{
|
||||
block_2_ctile_map_ = GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n, M01, N01);
|
||||
block_2_ctile_map_ = GridwiseGemm::MakeDefaultBlock2ETileMap(c_grid_desc_m_n);
|
||||
BlockStart_ = BlockStart;
|
||||
}
|
||||
|
||||
@@ -327,29 +494,35 @@ struct DeviceGroupedGemmXdl
|
||||
return block_2_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
|
||||
}
|
||||
|
||||
__host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
|
||||
__host__ bool CheckValidity(const EGridDesc_M_N& c_grid_desc_m_n) const
|
||||
{
|
||||
return block_2_ctile_map_.CheckValidity(c_grid_desc_m_n);
|
||||
}
|
||||
|
||||
typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
|
||||
typename GridwiseGemm::DefaultBlock2ETileMap block_2_ctile_map_;
|
||||
ck::index_t BlockStart_;
|
||||
};
|
||||
|
||||
struct GemmDescKernelArg
|
||||
struct GemmBiasTransKernelArg
|
||||
{
|
||||
AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
|
||||
BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
|
||||
CGridDesc_M_N c_grid_desc_m_n_;
|
||||
AGridDesc_AK0_M_AK1 a_grid_desc_k0_m_k1_;
|
||||
BGridDesc_BK0_N_BK1 b_grid_desc_k0_n_k1_;
|
||||
EGridDesc_M_N e_grid_desc_m_n_;
|
||||
|
||||
typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_;
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_;
|
||||
|
||||
GroupedGemmBlock2CTileMap grouped_gemm_block_2_ctile_map_;
|
||||
StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of different
|
||||
|
||||
const ADataType* a_ptr;
|
||||
const BDataType* b_ptr;
|
||||
CDataType* c_ptr;
|
||||
GroupedGemmBlock2ETileMap block_2_ctile_map_;
|
||||
|
||||
const ADataType* a_ptr_;
|
||||
const BDataType* b_ptr_;
|
||||
typename GridwiseGemm::DsGridPointer ds_ptr_;
|
||||
EDataType* e_ptr_;
|
||||
|
||||
ck::index_t BlockStart_, BlockEnd_;
|
||||
};
|
||||
@@ -357,97 +530,112 @@ struct DeviceGroupedGemmXdl
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
Argument(std::vector<const void*>& p_a,
|
||||
std::vector<const void*>& p_b,
|
||||
std::vector<void*>& p_c,
|
||||
std::vector<GemmShape>& gemm_shapes,
|
||||
index_t M01,
|
||||
index_t N01,
|
||||
Argument(std::vector<const void*>& p_As,
|
||||
std::vector<const void*>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmDesc>& gemm_descs,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
: M01_{M01},
|
||||
N01_{N01},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
c_element_op_{c_element_op}
|
||||
CDEElementwiseOperation c_element_op)
|
||||
: a_element_op_{a_element_op}, b_element_op_{b_element_op}, c_element_op_{c_element_op}
|
||||
{
|
||||
grid_size_ = 0;
|
||||
|
||||
p_workspace_ = nullptr;
|
||||
group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
|
||||
|
||||
group_count_ = ck::type_convert<ck::index_t>(gemm_shapes.size());
|
||||
|
||||
if(!(group_count_ == ck::type_convert<ck::index_t>(p_a.size()) &&
|
||||
group_count_ == ck::type_convert<ck::index_t>(p_b.size()) &&
|
||||
group_count_ == ck::type_convert<ck::index_t>(p_c.size())))
|
||||
if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) &&
|
||||
group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) &&
|
||||
group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
|
||||
{
|
||||
throw std::runtime_error("wrong! group_count_ != P_a/b/c.size");
|
||||
throw std::runtime_error("wrong! group_count_ != p_As/b/c.size");
|
||||
}
|
||||
|
||||
gemm_desc_kernel_arg_.reserve(group_count_);
|
||||
|
||||
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
const index_t M = gemm_shapes[i].M;
|
||||
const index_t N = gemm_shapes[i].N;
|
||||
const index_t K = gemm_shapes[i].K;
|
||||
const index_t M = gemm_descs[i].M_;
|
||||
const index_t N = gemm_descs[i].N_;
|
||||
const index_t K = gemm_descs[i].K_;
|
||||
|
||||
const index_t StrideA = gemm_shapes[i].StrideA;
|
||||
const index_t StrideB = gemm_shapes[i].StrideB;
|
||||
const index_t StrideC = gemm_shapes[i].StrideC;
|
||||
const index_t StrideA = gemm_descs[i].stride_A_;
|
||||
const index_t StrideB = gemm_descs[i].stride_B_;
|
||||
const index_t StrideC = gemm_descs[i].stride_C_;
|
||||
|
||||
const auto a_grid_desc_k0_m_k1_ =
|
||||
DeviceGroupedGemmXdl::MakeAGridDescriptor_K0_M_K1(M, K, StrideA);
|
||||
DeviceGroupedGemmXdl::MakeAGridDescriptor_AK0_M_AK1(M, K, StrideA);
|
||||
const auto b_grid_desc_k0_n_k1_ =
|
||||
DeviceGroupedGemmXdl::MakeBGridDescriptor_K0_N_K1(K, N, StrideB);
|
||||
const auto c_grid_desc_m_n_ =
|
||||
DeviceGroupedGemmXdl::MakeCGridDescriptor_M_N(M, N, StrideC);
|
||||
DeviceGroupedGemmXdl::MakeBGridDescriptor_BK0_N_BK1(K, N, StrideB);
|
||||
|
||||
const auto e_grid_desc_m_n_ =
|
||||
DeviceGroupedGemmXdl::MakeEGridDescriptor_M_N(M, N, StrideC);
|
||||
|
||||
const index_t grid_size_grp =
|
||||
GroupedGemmBlock2CTileMap(c_grid_desc_m_n_, M01, N01, 0)
|
||||
.block_2_ctile_map_.CalculateGridSize(c_grid_desc_m_n_);
|
||||
GroupedGemmBlock2ETileMap(e_grid_desc_m_n_, 0)
|
||||
.block_2_ctile_map_.CalculateGridSize(e_grid_desc_m_n_);
|
||||
|
||||
const index_t BlockStart = grid_size_;
|
||||
const index_t BlockEnd = grid_size_ + grid_size_grp;
|
||||
|
||||
grid_size_ += grid_size_grp;
|
||||
|
||||
const auto grouped_gemm_block_2_ctile_map_ =
|
||||
GroupedGemmBlock2CTileMap(c_grid_desc_m_n_, M01, N01, BlockStart);
|
||||
const auto block_2_ctile_map_ =
|
||||
GroupedGemmBlock2ETileMap(e_grid_desc_m_n_, BlockStart);
|
||||
|
||||
if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
|
||||
b_grid_desc_k0_n_k1_,
|
||||
c_grid_desc_m_n_,
|
||||
grouped_gemm_block_2_ctile_map_))
|
||||
e_grid_desc_m_n_,
|
||||
block_2_ctile_map_))
|
||||
{
|
||||
const auto c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
|
||||
GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
|
||||
auto e_grid_desc_mblock_mperblock_nblock_nperblock_ =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
e_grid_desc_m_n_);
|
||||
StaticallyIndexedArray<
|
||||
typename GridwiseGemm::EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
NumDTensor>
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_; // FIXME: Ds desc may be of
|
||||
// different
|
||||
|
||||
typename GridwiseGemm::DsGridPointer p_ds_grid_{};
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto j) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<j.value, DsDataType>>;
|
||||
|
||||
p_ds_grid_(j) = static_cast<const DDataType*>(p_Ds[i][j]);
|
||||
|
||||
const auto d_grid_desc_m_n = DeviceGroupedGemmXdl::MakeEGridDescriptor_M_N(
|
||||
M, N, gemm_descs[i].stride_Ds_[j]);
|
||||
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_(j) =
|
||||
GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
|
||||
d_grid_desc_m_n);
|
||||
});
|
||||
|
||||
gemm_desc_kernel_arg_.push_back(
|
||||
GemmDescKernelArg{a_grid_desc_k0_m_k1_,
|
||||
b_grid_desc_k0_n_k1_,
|
||||
c_grid_desc_m_n_,
|
||||
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
|
||||
grouped_gemm_block_2_ctile_map_,
|
||||
static_cast<const ADataType*>(p_a[i]),
|
||||
static_cast<const BDataType*>(p_b[i]),
|
||||
static_cast<CDataType*>(p_c[i]),
|
||||
BlockStart,
|
||||
BlockEnd});
|
||||
GemmBiasTransKernelArg{a_grid_desc_k0_m_k1_,
|
||||
b_grid_desc_k0_n_k1_,
|
||||
e_grid_desc_m_n_,
|
||||
e_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
ds_grid_desc_mblock_mperblock_nblock_nperblock_,
|
||||
block_2_ctile_map_,
|
||||
static_cast<const ADataType*>(p_As[i]),
|
||||
static_cast<const BDataType*>(p_Bs[i]),
|
||||
p_ds_grid_,
|
||||
static_cast<EDataType*>(p_Es[i]),
|
||||
BlockStart,
|
||||
BlockEnd});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// private:
|
||||
index_t M01_;
|
||||
index_t N01_;
|
||||
index_t group_count_;
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CElementwiseOperation c_element_op_;
|
||||
CDEElementwiseOperation c_element_op_;
|
||||
|
||||
std::vector<GemmDescKernelArg> gemm_desc_kernel_arg_;
|
||||
std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
|
||||
|
||||
index_t grid_size_;
|
||||
};
|
||||
@@ -473,16 +661,15 @@ struct DeviceGroupedGemmXdl
|
||||
<< arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
|
||||
<< arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_.GetLength(I2) << "}";
|
||||
|
||||
std::cout << ", arg.c_grid_desc_m_n_{ "
|
||||
<< arg.gemm_desc_kernel_arg_[i].c_grid_desc_m_n_.GetLength(I0) << ", "
|
||||
<< arg.gemm_desc_kernel_arg_[i].c_grid_desc_m_n_.GetLength(I1) << "}"
|
||||
std::cout << ", arg.e_grid_desc_m_n_{ "
|
||||
<< arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I0) << ", "
|
||||
<< arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I1) << "}"
|
||||
<< std::endl;
|
||||
|
||||
if(!GridwiseGemm::CheckValidity(
|
||||
arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_,
|
||||
arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_,
|
||||
arg.gemm_desc_kernel_arg_[i].c_grid_desc_m_n_,
|
||||
arg.gemm_desc_kernel_arg_[i].grouped_gemm_block_2_ctile_map_))
|
||||
if(!GridwiseGemm::CheckValidity(arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_,
|
||||
arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_,
|
||||
arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_,
|
||||
arg.gemm_desc_kernel_arg_[i].block_2_ctile_map_))
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
|
||||
@@ -500,58 +687,39 @@ struct DeviceGroupedGemmXdl
|
||||
hipGetErrorString(
|
||||
hipMemcpy(arg.p_workspace_,
|
||||
arg.gemm_desc_kernel_arg_.data(),
|
||||
arg.gemm_desc_kernel_arg_.size() * sizeof(GemmDescKernelArg),
|
||||
arg.gemm_desc_kernel_arg_.size() * sizeof(GemmBiasTransKernelArg),
|
||||
hipMemcpyHostToDevice));
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop_) {
|
||||
const auto kernel = kernel_grouped_gemm_xdl<GridwiseGemm,
|
||||
GemmBiasTransKernelArg,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
has_main_k_block_loop_>;
|
||||
|
||||
return launch_and_time_kernel(
|
||||
stream_config,
|
||||
kernel,
|
||||
dim3(arg.grid_size_),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
cast_pointer_to_constant_address_space(arg.p_workspace_),
|
||||
arg.gemm_desc_kernel_arg_.size(),
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_);
|
||||
};
|
||||
|
||||
if(has_main_k_block_loop)
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_grouped_gemm_xdlops_v2r3<GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
GemmDescKernelArg,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
true>;
|
||||
|
||||
ave_time =
|
||||
launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(arg.grid_size_),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
cast_pointer_to_constant_address_space(arg.p_workspace_),
|
||||
arg.gemm_desc_kernel_arg_.size(),
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_);
|
||||
ave_time = launch_kernel(integral_constant<bool, true>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
const auto kernel =
|
||||
kernel_grouped_gemm_xdlops_v2r3<GridwiseGemm,
|
||||
ADataType, // TODO: distiguish A/B datatype
|
||||
CDataType,
|
||||
GemmDescKernelArg,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CElementwiseOperation,
|
||||
false>;
|
||||
|
||||
ave_time =
|
||||
launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(arg.grid_size_),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
cast_pointer_to_constant_address_space(arg.p_workspace_),
|
||||
arg.gemm_desc_kernel_arg_.size(),
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_);
|
||||
ave_time = launch_kernel(integral_constant<bool, false>{});
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
@@ -585,31 +753,34 @@ struct DeviceGroupedGemmXdl
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(std::vector<const void*>& p_a,
|
||||
std::vector<const void*>& p_b,
|
||||
std::vector<void*>& p_c,
|
||||
std::vector<GemmShape> gemm_shapes,
|
||||
static auto MakeArgument(std::vector<const void*>& p_As,
|
||||
std::vector<const void*>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmDesc> gemm_descs,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op)
|
||||
CDEElementwiseOperation c_element_op)
|
||||
{
|
||||
return Argument{p_a, p_b, p_c, gemm_shapes, 1, 1, a_element_op, b_element_op, c_element_op};
|
||||
return Argument{
|
||||
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseArgument> MakeArgumentPointer(std::vector<const void*>& p_a,
|
||||
std::vector<const void*>& p_b,
|
||||
std::vector<void*>& p_c,
|
||||
std::vector<GemmShape>& gemm_shapes,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CElementwiseOperation c_element_op,
|
||||
index_t /* KBatch */ = 1) override
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(std::vector<const void*>& p_As,
|
||||
std::vector<const void*>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmDesc>& gemm_descs,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation c_element_op) override
|
||||
{
|
||||
return std::make_unique<Argument>(
|
||||
p_a, p_b, p_c, gemm_shapes, 1, 1, a_element_op, b_element_op, c_element_op);
|
||||
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
@@ -629,8 +800,9 @@ struct DeviceGroupedGemmXdl
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< K0PerBlock << ", "
|
||||
<< K1 << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1 << ", "
|
||||
<< MPerXDL << ", "
|
||||
<< NPerXDL << ", "
|
||||
<< MXdlPerWave << ", "
|
||||
@@ -643,7 +815,7 @@ struct DeviceGroupedGemmXdl
|
||||
|
||||
size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
|
||||
{
|
||||
return dynamic_cast<const Argument*>(p_arg)->group_count_ * sizeof(GemmDescKernelArg);
|
||||
return dynamic_cast<const Argument*>(p_arg)->group_count_ * sizeof(GemmBiasTransKernelArg);
|
||||
}
|
||||
};
|
||||
|
||||
|
||||
@@ -0,0 +1,134 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <cstdlib>
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
using DsType = Tuple<>;
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
|
||||
Row,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances);
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
|
||||
Col,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances);
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
|
||||
Row,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances);
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
|
||||
Col,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances);
|
||||
|
||||
template <typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout,
|
||||
typename ADataType,
|
||||
typename BDataType,
|
||||
typename DsDataType,
|
||||
typename EDataType>
|
||||
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedGemm<
|
||||
ALayout,
|
||||
BLayout,
|
||||
CLayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough>>
|
||||
{
|
||||
using DeviceOp = DeviceGroupedGemm<ALayout,
|
||||
BLayout,
|
||||
CLayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough>;
|
||||
|
||||
static auto GetInstances()
|
||||
{
|
||||
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
|
||||
|
||||
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
|
||||
is_same_v<EDataType, half_t>)
|
||||
{
|
||||
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
|
||||
is_same_v<CLayout, Row>)
|
||||
{
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
|
||||
is_same_v<CLayout, Row>)
|
||||
{
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
|
||||
is_same_v<CLayout, Row>)
|
||||
{
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
|
||||
}
|
||||
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
|
||||
is_same_v<CLayout, Row>)
|
||||
{
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
|
||||
}
|
||||
}
|
||||
return op_ptrs;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -23,6 +23,8 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using DsType = ck::Tuple<>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
@@ -30,23 +32,40 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
|
||||
// Compilation parameters for a[k, m] * b[k, n] = c[m, n]
|
||||
using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>
|
||||
//##################| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| 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|
|
||||
//##################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//##################| | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 2, 2, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 2, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 2, 2, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 2, 2, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
|
||||
std::vector<DeviceGroupedGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
|
||||
Row,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances{});
|
||||
|
||||
@@ -23,30 +23,48 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using DsType = ck::Tuple<>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
|
||||
using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>
|
||||
//##################| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| 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|
|
||||
//##################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//##################| | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 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>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 2, 8, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 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>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 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>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 2, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 2, 8, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 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>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 2, 8, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 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>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
|
||||
std::vector<DeviceGroupedGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
|
||||
Col,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances{});
|
||||
|
||||
@@ -23,6 +23,8 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using DsType = ck::Tuple<>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
@@ -30,32 +32,40 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
|
||||
// Compilation parameters for a[m, k] * b[k, n] = c[m, n]
|
||||
using device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//#################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 256, 4, 8, 32, 32, 1, 4, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 64, 4, 8, 32, 32, 1, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 32, 32, 4, 8, 32, 32, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 16, 256, 4, 8, 16, 16, 1, 8, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 16, 128, 4, 8, 16, 16, 1, 4, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 16, 64, 4, 8, 16, 16, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 16, 32, 4, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 16, 16, 4, 8, 16, 16, 1, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>
|
||||
//##################| ALayout| BLayout| CLayout| AData| BData| AccData| CShuffle| DsType| EData| A| B| C| 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|
|
||||
//##################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//##################| | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, 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<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 2, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 2, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 2, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 2, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 2, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<DeviceGroupedGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
|
||||
Row,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances{});
|
||||
|
||||
@@ -23,53 +23,49 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
template <ck::index_t... Is>
|
||||
using S = ck::Sequence<Is...>;
|
||||
|
||||
using DsType = Tuple<>;
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
static constexpr auto GemmMNPadding = ck::tensor_operation::device::GemmSpecialization::MNPadding;
|
||||
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
|
||||
|
||||
// Compilation parameters for a[m, k] * b[n, k] = c[m, n]
|
||||
using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 64, 4, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 32, 4, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 32, 128, 4, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 64, 32, 4, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 64, 32, 64, 4, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
// irregular tile size
|
||||
using device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_irregular_tile_instances = std::tuple<
|
||||
// clang-format off
|
||||
//##################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
|
||||
//##################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar|
|
||||
//##################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector|
|
||||
//##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmMNPadding, 256, 128, 144, 8, 8, 16, 16, 2, 9, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<8, 8, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>,
|
||||
DeviceGroupedGemmXdl< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmMNPadding, 256, 128, 144, 4, 8, 16, 16, 2, 9, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 16, 4>, S<1, 0, 2>, S<1, 0, 2>, 2, 2, 2, true, 7, 1>
|
||||
//##################| ALayout| BLayout| CLayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| 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|
|
||||
//##################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| 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| _MBlock_MWaveMPerXdl| ScalarPerVector|
|
||||
//##################| | | | | | | | | | 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_NWaveNPerXdl| _NWaveNPerXdl|
|
||||
//##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, 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<1, 32, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, 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>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 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>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 64, 64, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, 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>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 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>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 32, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 64, 64, 32, 32, 8, 8, 32, 32, 2, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>,
|
||||
DeviceGroupedGemmXdl< Row, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 64, 32, 64, 32, 8, 8, 32, 32, 1, 2, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 4>, 8>
|
||||
// clang-format on
|
||||
>;
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<DeviceGroupedGemmPtr<PassThrough, PassThrough, PassThrough>>& instances)
|
||||
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
|
||||
Col,
|
||||
Row,
|
||||
F16,
|
||||
F16,
|
||||
DsType,
|
||||
F16,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>>>& instances)
|
||||
{
|
||||
add_device_operation_instances(instances,
|
||||
device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances{});
|
||||
add_device_operation_instances(
|
||||
instances, device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_irregular_tile_instances{});
|
||||
}
|
||||
|
||||
} // namespace instance
|
||||
|
||||
@@ -7,9 +7,11 @@
|
||||
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/utility/conv_util.hpp"
|
||||
#include "ck/library/host_tensor/device_memory.hpp"
|
||||
@@ -17,41 +19,17 @@
|
||||
#include "ck/library/host_tensor/host_tensor_generator.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
|
||||
using DeviceGroupedGemmNoOpPtr = ck::tensor_operation::device::DeviceGroupedGemmPtr<
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough>;
|
||||
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
|
||||
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
|
||||
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
|
||||
std::vector<DeviceGroupedGemmNoOpPtr>&);
|
||||
|
||||
} // namespace instance
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
|
||||
namespace ck {
|
||||
namespace profiler {
|
||||
|
||||
template <typename ADataType,
|
||||
typename BDataType,
|
||||
typename CDataType,
|
||||
typename EDataType,
|
||||
typename AccDataType,
|
||||
typename ALayout,
|
||||
typename BLayout,
|
||||
typename CLayout>
|
||||
void profile_grouped_gemm_impl(int do_verification,
|
||||
bool profile_grouped_gemm_impl(int do_verification,
|
||||
int init_method,
|
||||
bool do_log,
|
||||
bool time_kernel,
|
||||
@@ -62,6 +40,9 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
const std::vector<int>& StrideBs,
|
||||
const std::vector<int>& StrideCs)
|
||||
{
|
||||
|
||||
bool pass = true;
|
||||
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
|
||||
@@ -86,7 +67,7 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
|
||||
std::vector<Tensor<ADataType>> a_m_k;
|
||||
std::vector<Tensor<BDataType>> b_k_n;
|
||||
std::vector<Tensor<CDataType>> c_m_n_device_results;
|
||||
std::vector<Tensor<EDataType>> c_m_n_device_results;
|
||||
|
||||
for(std::size_t i = 0; i < group_count; i++)
|
||||
{
|
||||
@@ -96,7 +77,7 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{})));
|
||||
|
||||
c_m_n_device_results.push_back(
|
||||
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
|
||||
Tensor<EDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
|
||||
|
||||
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
|
||||
<< "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
|
||||
@@ -115,7 +96,7 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
b_k_n[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
|
||||
}
|
||||
|
||||
c_m_n_device_results[i].GenerateTensorValue(GeneratorTensor_0<CDataType>{}, num_thread);
|
||||
c_m_n_device_results[i].GenerateTensorValue(GeneratorTensor_0<EDataType>{}, num_thread);
|
||||
}
|
||||
|
||||
using AElementOp = ck::tensor_operation::element_wise::PassThrough;
|
||||
@@ -145,9 +126,9 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
p_b.reserve(group_count);
|
||||
p_c.reserve(group_count);
|
||||
|
||||
std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes;
|
||||
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
|
||||
|
||||
gemm_shapes.reserve(group_count);
|
||||
gemm_descs.reserve(group_count);
|
||||
|
||||
for(std::size_t i = 0; i < group_count; i++)
|
||||
{
|
||||
@@ -157,56 +138,34 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
std::make_unique<DeviceMem>(sizeof(BDataType) * b_k_n[i].mDesc.GetElementSpace()));
|
||||
|
||||
c_device_buf.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(CDataType) * c_m_n_device_results[i].mDesc.GetElementSpace()));
|
||||
sizeof(EDataType) * c_m_n_device_results[i].mDesc.GetElementSpace()));
|
||||
|
||||
a_device_buf[i]->ToDevice(a_m_k[i].mData.data());
|
||||
b_device_buf[i]->ToDevice(b_k_n[i].mData.data());
|
||||
c_device_buf[i]->ToDevice(c_m_n_device_results[i].mData.data());
|
||||
|
||||
gemm_shapes.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i]});
|
||||
gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
|
||||
|
||||
p_a.push_back(a_device_buf[i]->GetDeviceBuffer());
|
||||
p_b.push_back(b_device_buf[i]->GetDeviceBuffer());
|
||||
p_c.push_back(c_device_buf[i]->GetDeviceBuffer());
|
||||
}
|
||||
|
||||
// add device GEMM instances
|
||||
std::vector<ck::tensor_operation::device::instance::DeviceGroupedGemmNoOpPtr> gemm_ptrs;
|
||||
using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemm<ALayout,
|
||||
BLayout,
|
||||
CLayout,
|
||||
ADataType,
|
||||
BDataType,
|
||||
ck::Tuple<>,
|
||||
EDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
CElementOp>;
|
||||
|
||||
if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value &&
|
||||
is_same<CDataType, half_t>::value)
|
||||
{
|
||||
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
|
||||
}
|
||||
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
|
||||
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
|
||||
}
|
||||
}
|
||||
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
|
||||
DeviceOp>::GetInstances();
|
||||
|
||||
if(gemm_ptrs.size() <= 0)
|
||||
if(op_ptrs.size() <= 0)
|
||||
{
|
||||
throw std::runtime_error("wrong! no device GEMM instance found");
|
||||
}
|
||||
@@ -216,14 +175,17 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
float best_tflops = 0;
|
||||
float best_gb_per_sec = 0;
|
||||
|
||||
auto p_ds = std::vector<std::array<const void*, 0>>{};
|
||||
|
||||
// profile device GEMM instances
|
||||
for(auto& gemm_ptr : gemm_ptrs)
|
||||
for(auto& gemm_ptr : op_ptrs)
|
||||
{
|
||||
auto argument_ptr =
|
||||
gemm_ptr->MakeArgumentPointer(p_a,
|
||||
p_b,
|
||||
p_ds,
|
||||
p_c,
|
||||
gemm_shapes,
|
||||
gemm_descs,
|
||||
ck::tensor_operation::element_wise::PassThrough{},
|
||||
ck::tensor_operation::element_wise::PassThrough{},
|
||||
ck::tensor_operation::element_wise::PassThrough{});
|
||||
@@ -242,12 +204,12 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
|
||||
|
||||
std::size_t flop = 0, num_btype = 0;
|
||||
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
|
||||
|
||||
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] + sizeof(BDataType) * Ks[i] * Ns[i] +
|
||||
sizeof(CDataType) * Ms[i] * Ns[i];
|
||||
sizeof(EDataType) * Ms[i] * Ns[i];
|
||||
}
|
||||
|
||||
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
|
||||
@@ -266,18 +228,18 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
|
||||
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
|
||||
|
||||
Tensor<CDataType> c_m_n_host_result(
|
||||
Tensor<EDataType> c_m_n_host_result(
|
||||
f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{}));
|
||||
|
||||
using ReferenceGemmInstance =
|
||||
ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
EDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
@@ -294,7 +256,8 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
c_element_op);
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
ck::utils::check_err(c_m_n_device_results[i].mData, c_m_n_host_result.mData);
|
||||
pass = pass && ck::utils::check_err(c_m_n_device_results[i].mData,
|
||||
c_m_n_host_result.mData);
|
||||
|
||||
if(do_log)
|
||||
{
|
||||
@@ -319,6 +282,8 @@ void profile_grouped_gemm_impl(int do_verification,
|
||||
|
||||
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
|
||||
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
|
||||
|
||||
return pass;
|
||||
} // namespace profiler
|
||||
|
||||
} // namespace profiler
|
||||
|
||||
@@ -2,39 +2,8 @@
|
||||
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#include <iostream>
|
||||
#include <numeric>
|
||||
#include <initializer_list>
|
||||
#include <cstdlib>
|
||||
|
||||
#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_grouped_gemm_xdl.hpp"
|
||||
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
|
||||
|
||||
#include "ck/library/utility/check_err.hpp"
|
||||
#include "ck/library/host_tensor/device_memory.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor.hpp"
|
||||
#include "ck/library/host_tensor/host_tensor_generator.hpp"
|
||||
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
|
||||
|
||||
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
|
||||
|
||||
using DeviceGroupedGemmPtr_ = ck::tensor_operation::device::DeviceGroupedGemmPtr<
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough,
|
||||
ck::tensor_operation::element_wise::PassThrough>;
|
||||
|
||||
namespace ck {
|
||||
namespace tensor_operation {
|
||||
namespace device {
|
||||
namespace instance {
|
||||
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
|
||||
std::vector<DeviceGroupedGemmPtr_>&);
|
||||
}
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
#include "profiler/include/profile_grouped_gemm_impl.hpp"
|
||||
|
||||
namespace {
|
||||
|
||||
@@ -43,169 +12,52 @@ using BDataType = ck::half_t;
|
||||
using CDataType = ck::half_t;
|
||||
using AccDataType = float;
|
||||
|
||||
using ALayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
|
||||
using CLayout = ck::tensor_layout::gemm::RowMajor;
|
||||
using Row = ck::tensor_layout::gemm::RowMajor;
|
||||
using Col = ck::tensor_layout::gemm::ColumnMajor;
|
||||
|
||||
bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
|
||||
template <typename ALayout, typename BLayout, typename CLayout>
|
||||
bool TestGroupedGemm()
|
||||
{
|
||||
int group_count = rand() % 10 + 1;
|
||||
|
||||
// GEMM shape
|
||||
std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes;
|
||||
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
|
||||
std::vector<const void*> p_a, p_b;
|
||||
std::vector<void*> p_c;
|
||||
|
||||
gemm_shapes.reserve(group_count);
|
||||
std::vector<int> Ms, Ns, Ks, StrideAs, StrideBs, StrideCs;
|
||||
|
||||
for(int i = 0; i < group_count; i++)
|
||||
{
|
||||
int M = 256 + 256 * (rand() % 10);
|
||||
int N = 256 + 256 * (rand() % 10);
|
||||
int K = 128 + 128 * (rand() % 10);
|
||||
Ms.push_back(256 + 256 * (rand() % 10));
|
||||
Ns.push_back(256 + 256 * (rand() % 10));
|
||||
Ks.push_back(128 + 128 * (rand() % 10));
|
||||
|
||||
int AStride = std::is_same<ck::tensor_layout::gemm::RowMajor, ALayout>::value ? K : M;
|
||||
int BStride = std::is_same<ck::tensor_layout::gemm::RowMajor, BLayout>::value ? N : K;
|
||||
int CStride = std::is_same<ck::tensor_layout::gemm::RowMajor, CLayout>::value ? N : M;
|
||||
|
||||
gemm_shapes.push_back({M, N, K, AStride, BStride, CStride});
|
||||
StrideAs.push_back(std::is_same<Row, ALayout>::value ? Ks[i] : Ms[i]);
|
||||
StrideBs.push_back(std::is_same<Row, BLayout>::value ? Ns[i] : Ks[i]);
|
||||
StrideCs.push_back(std::is_same<Row, CLayout>::value ? Ns[i] : Ms[i]);
|
||||
}
|
||||
|
||||
auto f_host_tensor_descriptor =
|
||||
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
|
||||
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({stride, 1}));
|
||||
}
|
||||
else
|
||||
{
|
||||
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
|
||||
std::vector<std::size_t>({1, stride}));
|
||||
}
|
||||
};
|
||||
|
||||
std::vector<Tensor<ADataType>> a_tensors;
|
||||
;
|
||||
std::vector<Tensor<BDataType>> b_tensors;
|
||||
std::vector<Tensor<CDataType>> c_host_tensors;
|
||||
std::vector<Tensor<CDataType>> c_device_tensors;
|
||||
|
||||
a_tensors.reserve(group_count);
|
||||
b_tensors.reserve(group_count);
|
||||
c_host_tensors.reserve(group_count);
|
||||
c_device_tensors.reserve(group_count);
|
||||
|
||||
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
|
||||
|
||||
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
|
||||
|
||||
a_tensors_device.reserve(group_count);
|
||||
b_tensors_device.reserve(group_count);
|
||||
c_tensors_device.reserve(group_count);
|
||||
|
||||
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
||||
{
|
||||
a_tensors.emplace_back(Tensor<ADataType>(f_host_tensor_descriptor(
|
||||
gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
|
||||
b_tensors.emplace_back(Tensor<BDataType>(f_host_tensor_descriptor(
|
||||
gemm_shapes[i].K, gemm_shapes[i].N, gemm_shapes[i].StrideB, BLayout{})));
|
||||
c_host_tensors.emplace_back(Tensor<CDataType>(f_host_tensor_descriptor(
|
||||
gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{})));
|
||||
c_device_tensors.emplace_back(Tensor<CDataType>(f_host_tensor_descriptor(
|
||||
gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{})));
|
||||
|
||||
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
}
|
||||
|
||||
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
||||
{
|
||||
a_tensors_device.emplace_back(
|
||||
std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize()));
|
||||
b_tensors_device.emplace_back(
|
||||
std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize()));
|
||||
c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
|
||||
sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSize()));
|
||||
|
||||
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
|
||||
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
|
||||
|
||||
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
|
||||
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
|
||||
p_c.push_back(c_tensors_device[i]->GetDeviceBuffer());
|
||||
}
|
||||
|
||||
auto a_element_op = PassThrough{};
|
||||
auto b_element_op = PassThrough{};
|
||||
auto c_element_op = PassThrough{};
|
||||
|
||||
// do GEMM
|
||||
auto invoker_ptr = groupedGemmPtr->MakeInvokerPointer();
|
||||
|
||||
auto argument_ptr = groupedGemmPtr->MakeArgumentPointer(
|
||||
p_a, p_b, p_c, gemm_shapes, a_element_op, b_element_op, c_element_op);
|
||||
|
||||
DeviceMem gemm_desc_workspace(groupedGemmPtr->GetWorkSpaceSize(argument_ptr.get()));
|
||||
|
||||
groupedGemmPtr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
|
||||
|
||||
invoker_ptr->Run(argument_ptr.get());
|
||||
|
||||
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
|
||||
{
|
||||
c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
|
||||
|
||||
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
AccDataType,
|
||||
PassThrough,
|
||||
PassThrough,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceGemmInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
|
||||
b_tensors[i],
|
||||
c_host_tensors[i],
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
c_element_op);
|
||||
|
||||
if(!groupedGemmPtr->IsSupportedArgument(argument_ptr.get()))
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
bool res = ck::utils::check_err(c_host_tensors[i].mData, c_device_tensors[i].mData);
|
||||
|
||||
std::cout << "group_id: " << i << (res ? " SUCCESS" : " FAILURE") << std::endl;
|
||||
|
||||
if(!res)
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
return ck::profiler::profile_grouped_gemm_impl<ADataType,
|
||||
BDataType,
|
||||
CDataType,
|
||||
AccDataType,
|
||||
ALayout,
|
||||
BLayout,
|
||||
CLayout>(
|
||||
true, 1, false, 1, Ms, Ns, Ks, StrideAs, StrideBs, StrideCs);
|
||||
}
|
||||
|
||||
} // anonymous namespace
|
||||
|
||||
int main()
|
||||
{
|
||||
std::vector<DeviceGroupedGemmPtr_> groupedGemmPtrs;
|
||||
ck::tensor_operation::device::instance::
|
||||
add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(groupedGemmPtrs);
|
||||
|
||||
bool res = true;
|
||||
|
||||
for(auto& gemmPtr : groupedGemmPtrs)
|
||||
{
|
||||
res &= TestGroupedGemm(gemmPtr);
|
||||
}
|
||||
res = res && TestGroupedGemm<Row, Row, Row>();
|
||||
res = res && TestGroupedGemm<Row, Col, Row>();
|
||||
res = res && TestGroupedGemm<Col, Row, Row>();
|
||||
res = res && TestGroupedGemm<Col, Col, Row>();
|
||||
|
||||
std::cout << "TestGroupedGemm ..... " << (res ? "SUCCESS" : "FAILURE") << std::endl;
|
||||
|
||||
|
||||
Reference in New Issue
Block a user