mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 10:09:41 +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)
|
||||
|
||||
Reference in New Issue
Block a user