mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-03 13:11:25 +00:00
Added Multi_ABD support into Gemm and GroupedGemmFixedNK (#978)
* added an example grouped_gemm_multi_abd * fixed ci * add setElementwiseOp * changed API * clean code: add multiA into example * fixed v7r2 copy * add transpose * clean * fixed vector_load check * Update example/15_grouped_gemm/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update example/15_grouped_gemm/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update example/15_grouped_gemm/grouped_gemm_multi_abd_xdl_fixed_nk_bias_fp16.cpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * add reduce * testing * add example_b16_i8 * refactor example * clean * add mpading * disable reduce for kbatch = 1 * seperate reduce device op * add reduce op * add guard for workspace_size * add instances * format * fixed * add client example * add a colmajor * add instances * Update cmake-ck-dev.sh * Update profile_gemm_splitk.cpp * Update gridwise_gemm_xdlops_v2r4r2.hpp * format * Update profile_gemm_splitk.cpp * fixed * fixed * adjust test * adjust precision loss * adjust test * fixed * add bf16_i8 scale bias * fixed scale * fixed scale elementwise_op * revert contraction deviceop changes * fixed * Add AddFastGelu * Revert "Merge branch 'jizhan/gemm_splitk_reduce' into grouped_gemm_multi_abd_fixed_nk_example" This reverts commit3b5d001efd, reversing changes made to943199a991. * add Scales into elementwise * add gemm_multi_abd client example * add client examples * add rcr and crr * add grouped gemm client example * add grouped gemm client example * add instance for rcr crr * format * fixed * fixed cmake * fixed * fixed client_example * format * fixed contraction isSupport * Update include/ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp Co-authored-by: Bartłomiej Kocot <barkocot@amd.com> * Update device_reduce_threadwise.hpp * clean * Fixes * Fix example --------- Co-authored-by: Jing Zhang <jizha@amd.com> Co-authored-by: Bartłomiej Kocot <barkocot@amd.com>
This commit is contained in:
zjing14
@@ -663,7 +663,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
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const bool valid_a_access_dim_k =
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ABlockTransferSrcVectorDim == 2 && arg.as_kz_consecutive_[i];
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const bool valid_a_access_dim = valid_a_access_dim_m || valid_a_access_dim_k;
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if(!(valid_a_vector_size && valid_a_access_dim))
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if(!((valid_a_vector_size && valid_a_access_dim) ||
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ABlockTransferSrcScalarPerVector == 1))
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{
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valid_as_access = false;
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}
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@@ -682,7 +683,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
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const bool valid_b_access_dim_k =
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BBlockTransferSrcVectorDim == 2 && arg.bs_kz_consecutive_[i];
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const bool valid_b_access_dim = valid_b_access_dim_n || valid_b_access_dim_k;
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if(!(valid_b_vector_size && valid_b_access_dim))
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if(!((valid_b_vector_size && valid_b_access_dim) ||
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BBlockTransferSrcScalarPerVector == 1))
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{
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valid_bs_access = false;
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}
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@@ -698,7 +700,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
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arg.ds_max_read_elems_[i] % CDEBlockTransferScalarPerVector_NPerBlock == 0;
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// Vector read of Ds is always on N dimension.
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const bool valid_d_access_dim = arg.ds_nz_consecutive_[i];
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if(!(valid_d_vector_size && valid_d_access_dim))
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if(!((valid_d_vector_size && valid_d_access_dim) ||
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CDEBlockTransferScalarPerVector_NPerBlock == 1))
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{
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valid_ds_access = false;
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}
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@@ -712,7 +715,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
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arg.e_max_write_elems_ % CDEBlockTransferScalarPerVector_NPerBlock == 0;
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// Vector write of E is always on N dimension.
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const bool valid_e_access_dim = arg.e_nz_consecutive_;
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if(!(valid_e_vector_size && valid_e_access_dim))
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if(!((valid_e_vector_size && valid_e_access_dim) ||
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CDEBlockTransferScalarPerVector_NPerBlock == 1))
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{
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return false;
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}
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@@ -169,78 +169,6 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
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static constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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#if 0
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static constexpr auto matrix_padder =
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MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
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static auto MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideAs)
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{
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const auto a_grid_desc_mraw_kraw = [&]() {
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if constexpr(is_same_v<tensor_layout::gemm::RowMajor, AsLayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(StrideAs, I1));
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}
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else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, AsLayout>)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
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make_tuple(I1, StrideAs));
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}
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}();
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return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
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}
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static auto MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideBs)
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{
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const auto b_grid_desc_nraw_kraw = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, BsLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
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make_tuple(I1, StrideBs));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BsLayout>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
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make_tuple(StrideBs, I1));
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}
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}();
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return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
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}
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template <typename ELay>
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static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
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{
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const auto e_grid_desc_mraw_nraw = [&]() {
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if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
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make_tuple(StrideE, I1));
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}
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else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
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{
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return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
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make_tuple(I1, StrideE));
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}
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}();
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return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
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}
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static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
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const std::array<index_t, NumDTensor>& NRaws,
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const std::array<index_t, NumDTensor>& DsStride)
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{
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return generate_tuple(
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[&](auto i) {
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using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
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return DeviceOp::MakeEGridDescriptor_M_N<DLayout>(MRaws[i], NRaws[i], DsStride[i]);
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},
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Number<NumDTensor>{});
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}
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#endif
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using ComputeDataType = EDataType;
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// GridwiseGemm
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@@ -384,7 +312,7 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
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// B desc
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bs_grid_desc_n_k_(i) =
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GridwiseGemm::template MakeBGridDescriptor_N_K<BLayout, GemmSpec>(
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KRaw, NRaw, StrideBs[i]);
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NRaw, KRaw, StrideBs[i]);
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});
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// populate pointer, desc for Ds
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@@ -424,15 +352,6 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
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}
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}
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void Print() const
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{
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// std::cout << "A[M, K]: " << as_grid_desc_m_k_ << std::endl;
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// std::cout << "B[N, K]: " << bs_grid_desc_n_k_ << std::endl;
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// static_for<0, NumDTensor, 1>{}(
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//[&](auto i) { std::cout << "Ds[M, N]: " << ds_grid_desc_m_n_[i] << std::endl; });
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// std::cout << "E[M, N]: " << e_grid_desc_m_n_ << std::endl;
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}
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// private:
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// pointers
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typename GridwiseGemm::AsGridPointer p_as_grid_;
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@@ -578,7 +497,10 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
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}
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else
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{
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all_valid = false;
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if(ABlockTransferSrcScalarPerVector != 1)
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{
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all_valid = false;
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}
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}
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});
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@@ -602,13 +524,15 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
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}
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else
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{
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all_valid = false;
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if(BBlockTransferSrcScalarPerVector != 1)
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{
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all_valid = false;
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}
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}
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});
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// check vector load of Ds
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// only support RowMajor for now
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static_for<0, NumDTensor, 1>{}([&](auto i) {
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using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
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@@ -618,21 +542,21 @@ struct DeviceGemmMultipleABD_Xdl_CShuffle : public DeviceGemmMultipleABD<AsLayou
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}
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});
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if(!all_valid)
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{
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return false;
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}
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// check vector store of E
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// only support RowMajor for now
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if constexpr(is_same_v<ELayout, Row>)
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{
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if(arg.NRaw_ % CDEBlockTransferScalarPerVector_NPerBlock != 0)
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{
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return false;
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all_valid = false;
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}
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}
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else
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{
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all_valid = false;
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}
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if(!all_valid)
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{
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return false;
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}
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@@ -0,0 +1,851 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/utility/common_header.hpp"
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_grouped_gemm_multi_abd_fixed_nk.hpp"
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#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_abd_xdl_cshuffle.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename GridwiseGemm,
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typename GemmDesc,
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GemmSpecialization GemmSpec,
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typename AsLayout,
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typename BsLayout,
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typename DsLayout,
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typename ELayout,
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typename Block2ETileMap,
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typename GroupedGemmBlock2ETileMap,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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InMemoryDataOperationEnum EGlobalMemoryDataOperation,
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bool HasMainKBlockLoop>
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__global__ void
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#if CK_USE_LAUNCH_BOUNDS
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__launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
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#endif
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kernel_grouped_gemm_xdl_fixed_nk(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
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const index_t group_count,
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const index_t grid_size_grp,
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const AElementwiseOperation a_element_op,
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const BElementwiseOperation b_element_op,
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const CDEElementwiseOperation cde_element_op)
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{
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#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
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defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
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__shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
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const index_t KBatch = 1;
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const index_t block_id = get_block_1d_id();
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const auto gemm_desc_ptr =
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reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
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const index_t group_id = block_id / grid_size_grp;
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if(group_id >= group_count)
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return;
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const index_t M = gemm_desc_ptr[group_id].M;
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const index_t N = gemm_desc_ptr[group_id].N;
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const index_t K = gemm_desc_ptr[group_id].K;
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if(M * N * K == 0)
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return;
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const auto StrideAs = gemm_desc_ptr[group_id].StrideAs;
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const auto StrideBs = gemm_desc_ptr[group_id].StrideBs;
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const auto StrideDs = gemm_desc_ptr[group_id].StrideDs;
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const auto StrideE = gemm_desc_ptr[group_id].StrideE;
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const auto e_grid_desc_m_n =
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GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
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const index_t BlockStart = group_id * grid_size_grp;
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const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n, KBatch};
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const auto local_grid_size = local_b2e_tile_map.CalculateGridSize(e_grid_desc_m_n);
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constexpr auto NumATensor = GridwiseGemm::AsGridPointer::Size();
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constexpr auto NumBTensor = GridwiseGemm::BsGridPointer::Size();
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constexpr auto NumDTensor = GridwiseGemm::DsGridPointer::Size();
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typename GridwiseGemm::AsGridPointer p_as_grid_;
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typename GridwiseGemm::BsGridPointer p_bs_grid_;
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typename GridwiseGemm::DsGridPointer p_ds_grid_;
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static_for<0, NumATensor, 1>{}([&](auto i) {
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using ADataType = remove_cvref_t<decltype(p_as_grid_(i))>;
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p_as_grid_(i) = static_cast<ADataType>(gemm_desc_ptr[group_id].p_as_grid[i]);
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});
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static_for<0, NumBTensor, 1>{}([&](auto i) {
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using BDataType = remove_cvref_t<decltype(p_bs_grid_(i))>;
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p_bs_grid_(i) = static_cast<BDataType>(gemm_desc_ptr[group_id].p_bs_grid[i]);
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});
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static_for<0, NumDTensor, 1>{}([&](auto i) {
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using DDataType = remove_cvref_t<decltype(p_ds_grid_(i))>;
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p_ds_grid_(i) = static_cast<DDataType>(gemm_desc_ptr[group_id].p_ds_grid[i]);
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});
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index_t id_off = 0;
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index_t id_local = get_block_1d_id() - BlockStart;
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while(id_local < local_grid_size)
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{
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const auto block_2_etile_map =
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GroupedGemmBlock2ETileMap(local_b2e_tile_map, BlockStart, id_off);
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GridwiseGemm::
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template Run<HasMainKBlockLoop, GemmSpec, AsLayout, BsLayout, DsLayout, ELayout>(
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p_as_grid_,
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p_bs_grid_,
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p_ds_grid_,
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gemm_desc_ptr[group_id].p_e_grid,
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p_shared,
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a_element_op,
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b_element_op,
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cde_element_op,
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M,
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N,
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K,
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StrideAs,
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StrideBs,
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StrideDs,
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StrideE,
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block_2_etile_map);
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id_off += grid_size_grp;
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id_local += grid_size_grp;
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}
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#else
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ignore = gemm_descs_const;
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ignore = group_count;
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ignore = grid_size_grp;
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ignore = a_element_op;
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ignore = b_element_op;
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ignore = cde_element_op;
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#endif
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}
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template <typename AsLayout,
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typename BsLayout,
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typename DsLayout,
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typename ELayout,
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typename AsDataType,
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typename BsDataType,
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typename AccDataType,
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typename CShuffleDataType,
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typename DsDataType,
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typename EDataType,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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GemmSpecialization GemmSpec,
|
||||
ck::index_t NumPrefetch,
|
||||
ck::index_t BlockSize,
|
||||
ck::index_t MPerBlock,
|
||||
ck::index_t NPerBlock,
|
||||
ck::index_t KPerBlock,
|
||||
ck::index_t AK1,
|
||||
ck::index_t BK1,
|
||||
ck::index_t MPerXDL,
|
||||
ck::index_t NPerXDL,
|
||||
ck::index_t MXdlPerWave,
|
||||
ck::index_t NXdlPerWave,
|
||||
typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
typename ABlockTransferThreadClusterArrangeOrder,
|
||||
typename ABlockTransferSrcAccessOrder,
|
||||
ck::index_t ABlockTransferSrcVectorDim,
|
||||
ck::index_t ABlockTransferSrcScalarPerVector,
|
||||
ck::index_t ABlockTransferDstScalarPerVector_AK1,
|
||||
bool ABlockLdsExtraM,
|
||||
typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
typename BBlockTransferThreadClusterArrangeOrder,
|
||||
typename BBlockTransferSrcAccessOrder,
|
||||
ck::index_t BBlockTransferSrcVectorDim,
|
||||
ck::index_t BBlockTransferSrcScalarPerVector,
|
||||
ck::index_t BBlockTransferDstScalarPerVector_BK1,
|
||||
bool BBlockLdsExtraN,
|
||||
index_t CShuffleMXdlPerWavePerShuffle,
|
||||
index_t CShuffleNXdlPerWavePerShuffle,
|
||||
typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
index_t CDEBlockTransferScalarPerVector_NPerBlock,
|
||||
typename ComputeType = EDataType,
|
||||
LoopScheduler LoopSched = make_default_loop_scheduler()>
|
||||
struct DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK
|
||||
: public DeviceGroupedGemmMultiABDFixedNK<AsLayout,
|
||||
BsLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
AsDataType,
|
||||
BsDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation>
|
||||
{
|
||||
using DeviceOp = DeviceGroupedGemm_Xdl_Multi_ABD_Fixed_NK;
|
||||
|
||||
static constexpr index_t NumATensor = AsDataType::Size();
|
||||
static constexpr index_t NumBTensor = BsDataType::Size();
|
||||
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 index_t NumGemmKPrefetchStage = 1;
|
||||
|
||||
// GridwiseGemm
|
||||
using GridwiseGemm = GridwiseGemmMultipleABD_xdl_cshuffle<
|
||||
AsDataType,
|
||||
BsDataType,
|
||||
ComputeType,
|
||||
AccDataType,
|
||||
CShuffleDataType,
|
||||
DsDataType,
|
||||
EDataType,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
NumGemmKPrefetchStage,
|
||||
BlockSize,
|
||||
MPerBlock,
|
||||
NPerBlock,
|
||||
KPerBlock,
|
||||
AK1,
|
||||
BK1,
|
||||
MPerXDL,
|
||||
NPerXDL,
|
||||
MXdlPerWave,
|
||||
NXdlPerWave,
|
||||
ABlockTransferThreadClusterLengths_AK0_M_AK1,
|
||||
ABlockTransferThreadClusterArrangeOrder,
|
||||
ABlockTransferSrcAccessOrder,
|
||||
ABlockTransferSrcVectorDim,
|
||||
ABlockTransferSrcScalarPerVector,
|
||||
ABlockTransferDstScalarPerVector_AK1,
|
||||
false,
|
||||
ABlockLdsExtraM,
|
||||
BBlockTransferThreadClusterLengths_BK0_N_BK1,
|
||||
BBlockTransferThreadClusterArrangeOrder,
|
||||
BBlockTransferSrcAccessOrder,
|
||||
BBlockTransferSrcVectorDim,
|
||||
BBlockTransferSrcScalarPerVector,
|
||||
BBlockTransferDstScalarPerVector_BK1,
|
||||
false,
|
||||
BBlockLdsExtraN,
|
||||
CShuffleMXdlPerWavePerShuffle,
|
||||
CShuffleNXdlPerWavePerShuffle,
|
||||
CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
|
||||
CDEBlockTransferScalarPerVector_NPerBlock,
|
||||
LoopSched>;
|
||||
|
||||
template <typename UnderlyingBlockToCTileMap>
|
||||
struct OffsettedBlockToCTileMapMLoops
|
||||
{
|
||||
using underlying_type = UnderlyingBlockToCTileMap;
|
||||
|
||||
__host__ __device__ OffsettedBlockToCTileMapMLoops(
|
||||
UnderlyingBlockToCTileMap block_to_ctile_map, index_t block_start, index_t id_off = 0)
|
||||
{
|
||||
block_to_ctile_map_ = block_to_ctile_map;
|
||||
block_start_ = block_start;
|
||||
id_off_ = id_off;
|
||||
}
|
||||
|
||||
template <typename TopIdx>
|
||||
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
|
||||
{
|
||||
auto idx_bot = block_to_ctile_map_.CalculateBottomIndex(
|
||||
make_multi_index(idx_top[Number<0>{}] - block_start_ + id_off_));
|
||||
|
||||
return make_tuple(
|
||||
// idx_bot[Number<0>{}],
|
||||
idx_bot[Number<1>{}],
|
||||
idx_bot[Number<2>{}]);
|
||||
}
|
||||
|
||||
template <typename CTileIdx, typename CTileDim>
|
||||
__host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
|
||||
const CTileDim& c_tile_dim) const
|
||||
{
|
||||
return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
|
||||
{
|
||||
return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
|
||||
{
|
||||
return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
|
||||
}
|
||||
|
||||
UnderlyingBlockToCTileMap block_to_ctile_map_;
|
||||
index_t block_start_;
|
||||
index_t id_off_;
|
||||
};
|
||||
|
||||
template <index_t MPerBlock_, index_t NPerBlock_>
|
||||
struct BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops
|
||||
{
|
||||
static constexpr auto I0 = Number<0>{};
|
||||
static constexpr auto I1 = Number<1>{};
|
||||
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops() = default;
|
||||
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
|
||||
const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
|
||||
BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
|
||||
operator=(const BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&) = default;
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&
|
||||
operator=(BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops&&) = default;
|
||||
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(index_t M,
|
||||
index_t N,
|
||||
index_t KBatch,
|
||||
index_t M01 = 8)
|
||||
: M_(M), N_(N), KBatch_(KBatch), M01_(M01)
|
||||
{
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ __device__ BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
|
||||
const CGridDesc_M_N& c_grid_desc_m_n, index_t KBatch, index_t M01 = 8)
|
||||
: BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops(
|
||||
c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), KBatch, M01)
|
||||
{
|
||||
}
|
||||
|
||||
__host__ __device__ constexpr index_t CalculateGridSize(index_t M, index_t N) const
|
||||
{
|
||||
const auto M0 = math::integer_divide_ceil(M, MPerBlock);
|
||||
const auto N0 = math::integer_divide_ceil(N, NPerBlock);
|
||||
|
||||
return M0 * N0 * KBatch_;
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ __device__ constexpr index_t
|
||||
CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
|
||||
{
|
||||
return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
|
||||
}
|
||||
|
||||
template <typename CGridDesc_M_N>
|
||||
__host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
|
||||
{
|
||||
return true;
|
||||
}
|
||||
|
||||
template <typename TopIdx>
|
||||
__host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
|
||||
{
|
||||
auto block_1d_id = idx_top[I0];
|
||||
|
||||
const auto M0 = math::integer_divide_ceil(M_, MPerBlock_);
|
||||
const auto N0 = math::integer_divide_ceil(N_, NPerBlock_);
|
||||
|
||||
block_1d_id = block_1d_id % (M0 * N0 * KBatch_); // hide groups
|
||||
|
||||
const index_t idx_ksplit = block_1d_id / (M0 * N0);
|
||||
block_1d_id = block_1d_id % (M0 * N0);
|
||||
|
||||
index_t idx_N0 = block_1d_id % N0;
|
||||
index_t idx_M0 = block_1d_id / N0;
|
||||
|
||||
const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;
|
||||
|
||||
index_t idx_M00 = idx_M0 / M01_;
|
||||
index_t idx_M01 = idx_M0 % M01_;
|
||||
index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
|
||||
|
||||
return make_tuple(idx_ksplit,
|
||||
idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
|
||||
idx_N0_M01_local / M01_adapt);
|
||||
}
|
||||
|
||||
template <typename CTileIdx, typename CTileDim>
|
||||
__host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
|
||||
const CTileDim& /* c_tile_dim */) const
|
||||
{
|
||||
return true; // always valid provided that user gets grid size from CalculateGridSize()
|
||||
}
|
||||
|
||||
private:
|
||||
index_t M_;
|
||||
index_t N_;
|
||||
index_t KBatch_;
|
||||
index_t M01_;
|
||||
};
|
||||
|
||||
using Block2ETileMap = BlockToCTileMap_KBatch_M00_N0_M01Adapt_MLoops<MPerBlock, NPerBlock>;
|
||||
using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMapMLoops<Block2ETileMap>;
|
||||
|
||||
struct GemmBiasTransKernelArg
|
||||
{
|
||||
// pointers
|
||||
std::array<const void*, NumATensor> as_ptr_;
|
||||
std::array<const void*, NumBTensor> bs_ptr_;
|
||||
std::array<const void*, NumDTensor> ds_ptr_;
|
||||
void* e_ptr_;
|
||||
|
||||
index_t M_, N_, K_;
|
||||
std::array<index_t, NumATensor> StrideAs_;
|
||||
std::array<index_t, NumBTensor> StrideBs_;
|
||||
std::array<index_t, NumDTensor> StrideDs_;
|
||||
index_t StrideE_;
|
||||
};
|
||||
|
||||
// Argument
|
||||
struct Argument : public BaseArgument
|
||||
{
|
||||
|
||||
void UpdateKBatch(index_t) {}
|
||||
|
||||
Argument(std::vector<std::array<const void*, NumATensor>>&,
|
||||
std::vector<std::array<const void*, NumBTensor>>&,
|
||||
std::vector<std::array<const void*, NumDTensor>>&,
|
||||
std::vector<void*>&,
|
||||
std::vector<GemmMultiABDDesc>& gemm_descs,
|
||||
AElementwiseOperation a_element_op = AElementwiseOperation{},
|
||||
BElementwiseOperation b_element_op = BElementwiseOperation{},
|
||||
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{})
|
||||
: a_element_op_{a_element_op}, b_element_op_{b_element_op}, c_element_op_{c_element_op}
|
||||
{
|
||||
grid_size_ = 0;
|
||||
|
||||
k_batch_ = 1;
|
||||
|
||||
grouped_gemm_kernel_args_dev = nullptr;
|
||||
|
||||
group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
|
||||
|
||||
gemm_desc_kernel_arg_.reserve(group_count_);
|
||||
|
||||
index_t group_id = 0;
|
||||
|
||||
sum_of_m = gemm_descs[0].M_;
|
||||
const index_t AverM = math::integer_divide_ceil(sum_of_m, group_count_);
|
||||
const index_t N = gemm_descs[0].N_;
|
||||
const index_t K = gemm_descs[0].K_;
|
||||
|
||||
for(std::size_t i = 0; i < gemm_descs.size(); i++)
|
||||
{
|
||||
if(sum_of_m != gemm_descs[i].M_ || N != gemm_descs[i].N_ || K != gemm_descs[i].K_)
|
||||
{
|
||||
throw std::runtime_error("wrong! M/N/K is not identical");
|
||||
}
|
||||
|
||||
a_mtx_mraw_kraw_.emplace_back(sum_of_m, K);
|
||||
b_mtx_nraw_kraw_.emplace_back(N, K);
|
||||
|
||||
// pointer
|
||||
std::array<const void*, NumATensor> p_as_grid;
|
||||
std::array<const void*, NumBTensor> p_bs_grid;
|
||||
std::array<const void*, NumDTensor> p_ds_grid;
|
||||
|
||||
static_for<0, NumATensor, 1>{}([&](auto j) { p_as_grid[j] = nullptr; });
|
||||
static_for<0, NumBTensor, 1>{}([&](auto j) { p_bs_grid[j] = nullptr; });
|
||||
static_for<0, NumDTensor, 1>{}([&](auto j) { p_ds_grid[j] = nullptr; });
|
||||
|
||||
std::array<index_t, NumATensor> StrideAs;
|
||||
std::array<index_t, NumBTensor> StrideBs;
|
||||
std::array<index_t, NumDTensor> StrideDs;
|
||||
|
||||
const index_t StrideE = gemm_descs[i].stride_C_;
|
||||
|
||||
if(gemm_descs[i].stride_As_.size() != NumATensor)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! gemm_descs[i].stride_As_.size() does not match NumATensor");
|
||||
}
|
||||
|
||||
static_for<0, NumATensor, 1>{}(
|
||||
[&](auto j) { StrideAs[j] = gemm_descs[i].stride_As_[j]; });
|
||||
|
||||
if(gemm_descs[i].stride_Bs_.size() != NumBTensor)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! gemm_descs[i].stride_Bs_.size() does not match NumBTensor");
|
||||
}
|
||||
|
||||
static_for<0, NumBTensor, 1>{}(
|
||||
[&](auto j) { StrideBs[j] = gemm_descs[i].stride_Bs_[j]; });
|
||||
|
||||
if(gemm_descs[i].stride_Ds_.size() != NumDTensor)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"wrong! gemm_descs[i].stride_Ds_.size() does not match NumDTensor");
|
||||
}
|
||||
|
||||
static_for<0, NumDTensor, 1>{}(
|
||||
[&](auto j) { StrideDs[j] = gemm_descs[i].stride_Ds_[j]; });
|
||||
|
||||
const auto e_grid_desc_m_n =
|
||||
GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
|
||||
AverM, N, StrideE);
|
||||
|
||||
// block-to-e-tile map
|
||||
const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n, k_batch_};
|
||||
|
||||
grid_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);
|
||||
|
||||
if(group_id * grid_size_grp_ != grid_size_)
|
||||
{
|
||||
throw std::runtime_error("wrong! grid_size_grp_ is not identical!");
|
||||
}
|
||||
|
||||
grid_size_ += grid_size_grp_;
|
||||
|
||||
// check block-to-E-tile
|
||||
if(!local_b2c_tile_map.CheckValidity(e_grid_desc_m_n))
|
||||
{
|
||||
throw std::runtime_error("wrong! block_2_etile_map validation failed");
|
||||
}
|
||||
|
||||
gemm_desc_kernel_arg_.push_back(GemmBiasTransKernelArg{
|
||||
p_as_grid,
|
||||
p_bs_grid,
|
||||
p_ds_grid,
|
||||
nullptr,
|
||||
AverM,
|
||||
N,
|
||||
K,
|
||||
StrideAs,
|
||||
StrideBs,
|
||||
StrideDs,
|
||||
StrideE,
|
||||
});
|
||||
|
||||
group_id++;
|
||||
}
|
||||
|
||||
const auto e_grid_desc_sum_m_n =
|
||||
GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(
|
||||
sum_of_m, gemm_desc_kernel_arg_[0].N_, gemm_desc_kernel_arg_[0].StrideE_);
|
||||
|
||||
const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_sum_m_n, 1};
|
||||
|
||||
barrier_size_grp_ = local_b2c_tile_map.CalculateGridSize(e_grid_desc_sum_m_n);
|
||||
}
|
||||
|
||||
// private:
|
||||
index_t group_count_;
|
||||
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
CDEElementwiseOperation c_element_op_;
|
||||
|
||||
std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
|
||||
std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
|
||||
std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
|
||||
|
||||
const void* grouped_gemm_kernel_args_dev;
|
||||
|
||||
index_t grid_size_;
|
||||
index_t grid_size_grp_;
|
||||
index_t barrier_size_grp_;
|
||||
index_t sum_of_m;
|
||||
|
||||
index_t k_batch_ = 1;
|
||||
};
|
||||
|
||||
// Invoker
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
using Argument = DeviceOp::Argument;
|
||||
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
bool has_main_k_block_loop = true;
|
||||
|
||||
for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
|
||||
{
|
||||
if(GridwiseGemm::CalculateHasMainKBlockLoop(arg.gemm_desc_kernel_arg_[i].K_) !=
|
||||
has_main_k_block_loop)
|
||||
{
|
||||
throw std::runtime_error("wrong! not all gemm has_main_k_block_loop");
|
||||
}
|
||||
}
|
||||
|
||||
if(arg.grouped_gemm_kernel_args_dev == nullptr)
|
||||
{
|
||||
throw std::runtime_error("wrong! grouped_gemm_kernel_args_dev is nullpr");
|
||||
}
|
||||
|
||||
float ave_time = 0;
|
||||
|
||||
auto launch_kernel = [&](auto has_main_k_block_loop_, auto e_global_memory_operation_) {
|
||||
const auto kernel = kernel_grouped_gemm_xdl_fixed_nk<
|
||||
GridwiseGemm,
|
||||
GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>,
|
||||
GemmSpec,
|
||||
AsLayout,
|
||||
BsLayout,
|
||||
DsLayout,
|
||||
ELayout,
|
||||
Block2ETileMap,
|
||||
GroupedGemmBlock2ETileMap,
|
||||
AElementwiseOperation,
|
||||
BElementwiseOperation,
|
||||
CDEElementwiseOperation,
|
||||
e_global_memory_operation_,
|
||||
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.grouped_gemm_kernel_args_dev),
|
||||
arg.gemm_desc_kernel_arg_.size(),
|
||||
arg.grid_size_grp_,
|
||||
arg.a_element_op_,
|
||||
arg.b_element_op_,
|
||||
arg.c_element_op_);
|
||||
};
|
||||
|
||||
constexpr auto AtomicAdd = InMemoryDataOperationEnum::AtomicAdd;
|
||||
constexpr auto Set = InMemoryDataOperationEnum::Set;
|
||||
|
||||
if(arg.k_batch_ > 1)
|
||||
{
|
||||
if(has_main_k_block_loop)
|
||||
{
|
||||
ave_time =
|
||||
launch_kernel(integral_constant<bool, true>{},
|
||||
integral_constant<InMemoryDataOperationEnum, AtomicAdd>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time =
|
||||
launch_kernel(integral_constant<bool, false>{},
|
||||
integral_constant<InMemoryDataOperationEnum, AtomicAdd>{});
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
if(has_main_k_block_loop)
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, true>{},
|
||||
integral_constant<InMemoryDataOperationEnum, Set>{});
|
||||
}
|
||||
else
|
||||
{
|
||||
ave_time = launch_kernel(integral_constant<bool, false>{},
|
||||
integral_constant<InMemoryDataOperationEnum, Set>{});
|
||||
}
|
||||
}
|
||||
|
||||
return ave_time;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
static bool IsSupportedArgument(const Argument& arg)
|
||||
{
|
||||
if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
bool supported = true;
|
||||
|
||||
// If we use padding we do not support vector loads for dimensions not divisible by vector
|
||||
// load size.
|
||||
if constexpr(GemmSpec != GemmSpecialization::Default)
|
||||
{
|
||||
// [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
|
||||
// thus we have to adapt it to the {M,K} or {N,K} layout.
|
||||
const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
|
||||
const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;
|
||||
|
||||
for(index_t i = 0; i < arg.group_count_; ++i)
|
||||
{
|
||||
const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
|
||||
const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});
|
||||
|
||||
supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
|
||||
supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
|
||||
}
|
||||
}
|
||||
|
||||
return supported;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
|
||||
}
|
||||
|
||||
static auto MakeArgument(std::vector<std::array<const void*, NumATensor>>& p_As,
|
||||
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmMultiABDDesc> gemm_descs,
|
||||
AElementwiseOperation a_element_op = AElementwiseOperation{},
|
||||
BElementwiseOperation b_element_op = BElementwiseOperation{},
|
||||
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{})
|
||||
{
|
||||
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<std::array<const void*, NumATensor>>& p_As,
|
||||
std::vector<std::array<const void*, NumBTensor>>& p_Bs,
|
||||
std::vector<std::array<const void*, NumDTensor>>& p_Ds,
|
||||
std::vector<void*>& p_Es,
|
||||
std::vector<GemmMultiABDDesc>& gemm_descs,
|
||||
AElementwiseOperation a_element_op = AElementwiseOperation{},
|
||||
BElementwiseOperation b_element_op = BElementwiseOperation{},
|
||||
CDEElementwiseOperation c_element_op = CDEElementwiseOperation{}) override
|
||||
{
|
||||
return std::make_unique<Argument>(
|
||||
p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DeviceGroupedGemm_Xdl_Fixed_NK"
|
||||
<< "<"
|
||||
<< BlockSize << ", "
|
||||
<< MPerBlock << ", "
|
||||
<< NPerBlock << ", "
|
||||
<< KPerBlock << ", "
|
||||
<< AK1 << ", "
|
||||
<< BK1 << ", "
|
||||
<< MPerXDL << ", "
|
||||
<< NPerXDL << ", "
|
||||
<< MXdlPerWave << ", "
|
||||
<< NXdlPerWave << ", "
|
||||
<< ABlockTransferSrcScalarPerVector << ", "
|
||||
<< BBlockTransferSrcScalarPerVector << ", "
|
||||
<< CShuffleMXdlPerWavePerShuffle << ", "
|
||||
<< CShuffleNXdlPerWavePerShuffle << ", "
|
||||
<< getGemmSpecializationString(GemmSpec)
|
||||
<< ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
|
||||
static void SetElementwiseOps(Argument& arg,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation c_element_op)
|
||||
{
|
||||
arg.a_element_op_ = a_element_op;
|
||||
arg.b_element_op_ = b_element_op;
|
||||
arg.c_element_op_ = c_element_op;
|
||||
}
|
||||
|
||||
static void SetDeviceKernelArgs(Argument& arg, const void* kernel_args)
|
||||
{
|
||||
arg.grouped_gemm_kernel_args_dev = kernel_args;
|
||||
}
|
||||
|
||||
// polymorphic
|
||||
void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const override
|
||||
{
|
||||
return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), kernel_args);
|
||||
}
|
||||
|
||||
void SetElementwiseOps(BaseArgument* p_arg,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation c_element_op) const override
|
||||
{
|
||||
|
||||
SetElementwiseOps(
|
||||
*dynamic_cast<Argument*>(p_arg), a_element_op, b_element_op, c_element_op);
|
||||
}
|
||||
|
||||
size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const override
|
||||
{
|
||||
auto arg = *dynamic_cast<const Argument*>(p_arg);
|
||||
|
||||
return arg.group_count_ *
|
||||
sizeof(GroupedGemmMultiABDKernelArgument<NumATensor, NumBTensor, NumDTensor>);
|
||||
}
|
||||
|
||||
#if 0
|
||||
size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
|
||||
{
|
||||
auto arg = *dynamic_cast<const Argument*>(p_arg);
|
||||
|
||||
return arg.group_count_ * arg.barrier_size_grp_ * sizeof(uint32_t);
|
||||
}
|
||||
|
||||
void SetWorkSpacePointer(BaseArgument* p_arg,
|
||||
void* p_workspace,
|
||||
const StreamConfig& stream_config = StreamConfig{}) const override
|
||||
{
|
||||
auto p_arg_ = dynamic_cast<Argument*>(p_arg);
|
||||
p_arg_->p_workspace_ = p_workspace;
|
||||
|
||||
hip_check_error(
|
||||
hipMemsetAsync(p_workspace, 0, GetWorkSpaceSize(p_arg), stream_config.stream_id_));
|
||||
}
|
||||
#endif
|
||||
|
||||
static void SetKBatch(Argument& arg, index_t k_batch) { arg.UpdateKBatch(k_batch); }
|
||||
|
||||
// polymorphic
|
||||
void SetKBatch(BaseArgument* p_arg, index_t k_batch) const override
|
||||
{
|
||||
return SetKBatch(*dynamic_cast<Argument*>(p_arg), k_batch);
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -11,7 +11,6 @@
|
||||
#include "ck/host_utility/kernel_launch.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/device_reduce.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/impl/device_reduce_common.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_multiblock.hpp"
|
||||
#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
|
||||
|
||||
namespace ck {
|
||||
|
||||
Reference in New Issue
Block a user