mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-15 18:42:06 +00:00
add g; fixed strides (#355)
[ROCm/composable_kernel commit: 35e49f2de6]
This commit is contained in:
zjing14
@@ -1,2 +1,2 @@
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add_example_executable(example_gemm_bias_e_permute_m3n2_xdl_fp16 gemm_bias_e_permute_m3n2_xdl_fp16.cpp)
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add_example_executable(example_gemm_bias_e_permute_m2n3_xdl_fp16 gemm_bias_e_permute_m2n3_xdl_fp16.cpp)
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add_example_executable(example_gemm_bias_e_permute_g1m3n2k1_xdl_fp16 gemm_bias_e_permute_g1m3n2k1_xdl_fp16.cpp)
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add_example_executable(example_gemm_bias_e_permute_g1m2n3k1_xdl_fp16 gemm_bias_e_permute_g1m2n3k1_xdl_fp16.cpp)
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@@ -16,6 +16,8 @@
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/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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@@ -33,7 +35,7 @@ using DDataType = F16;
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using DsDataType = ck::Tuple<DDataType>;
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using EDataType = F16;
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static constexpr ck::index_t NumDimG = 0;
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static constexpr ck::index_t NumDimG = 1;
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static constexpr ck::index_t NumDimM = 2;
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static constexpr ck::index_t NumDimN = 3;
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static constexpr ck::index_t NumDimK = 1;
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@@ -69,30 +71,31 @@ template <ck::index_t NumDimM,
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typename AElementwiseOperation,
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typename BElementwiseOperation,
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typename CDEElementwiseOperation,
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ck::enable_if_t<NumDimM == 2 && NumDimN == 3 && NumDimK == 1, bool> = false>
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struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::BaseOperator
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ck::enable_if_t<NumDimG == 1 && NumDimM == 2 && NumDimN == 3 && NumDimK == 1, bool> =
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false>
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struct ReferenceContraction_G1_M2_N3_K1 : public ck::tensor_operation::device::BaseOperator
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{
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// Argument
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struct Argument : public ck::tensor_operation::device::BaseArgument
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{
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Argument(const Tensor<ADataType>& a_ms_ks,
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const Tensor<BDataType>& b_ns_ks,
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Tensor<EDataType>& e_ms_ns,
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Argument(const Tensor<ADataType>& a_gs_ms_ks,
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const Tensor<BDataType>& b_gs_ns_ks,
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Tensor<EDataType>& e_gs_ms_ns,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op)
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: a_ms_ks_{a_ms_ks},
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b_ns_ks_{b_ns_ks},
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e_ms_ns_{e_ms_ns},
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: a_gs_ms_ks_{a_gs_ms_ks},
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b_gs_ns_ks_{b_gs_ns_ks},
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e_gs_ms_ns_{e_gs_ms_ns},
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a_element_op_{a_element_op},
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b_element_op_{b_element_op},
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cde_element_op_{cde_element_op}
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{
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}
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const Tensor<ADataType>& a_ms_ks_;
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const Tensor<BDataType>& b_ns_ks_;
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Tensor<EDataType>& e_ms_ns_;
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const Tensor<ADataType>& a_gs_ms_ks_;
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const Tensor<BDataType>& b_gs_ns_ks_;
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Tensor<EDataType>& e_gs_ms_ns_;
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AElementwiseOperation a_element_op_;
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BElementwiseOperation b_element_op_;
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@@ -102,12 +105,12 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
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// Invoker
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struct Invoker : public ck::tensor_operation::device::BaseInvoker
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{
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using Argument = ReferenceContraction_M2_N3_K1::Argument;
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using Argument = ReferenceContraction_G1_M2_N3_K1::Argument;
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float Run(const Argument& arg)
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{
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auto f_ms_ns = [&](auto m0, auto m1, auto n0, auto n1, auto n2) {
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const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[2];
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auto f_gs_ms_ns = [&](auto g0, auto m0, auto m1, auto n0, auto n1, auto n2) {
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const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[3];
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AccDataType v_acc = 0;
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@@ -117,9 +120,10 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
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AccDataType v_b;
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arg.a_element_op_(
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v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, k0)));
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v_a, ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, m0, m1, k0)));
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arg.b_element_op_(
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v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, n2, k0)));
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v_b,
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ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, n0, n1, n2, k0)));
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v_acc += v_a * v_b;
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}
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@@ -128,15 +132,16 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
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arg.cde_element_op_(v_c, v_acc);
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arg.e_ms_ns_(m0, m1, n0, n1, n2) = v_c;
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arg.e_gs_ms_ns_(g0, m0, m1, n0, n1, n2) = v_c;
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};
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make_ParallelTensorFunctor(f_ms_ns,
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arg.e_ms_ns_.mDesc.GetLengths()[0],
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arg.e_ms_ns_.mDesc.GetLengths()[1],
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arg.e_ms_ns_.mDesc.GetLengths()[2],
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arg.e_ms_ns_.mDesc.GetLengths()[3],
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arg.e_ms_ns_.mDesc.GetLengths()[4])(
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make_ParallelTensorFunctor(f_gs_ms_ns,
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arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
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arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
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arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
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arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
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arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
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arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
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std::thread::hardware_concurrency());
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return 0;
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@@ -160,14 +165,15 @@ struct ReferenceContraction_M2_N3_K1 : public ck::tensor_operation::device::Base
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return true;
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}
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static auto MakeArgument(const Tensor<ADataType>& a_ms_ks,
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const Tensor<BDataType>& b_ns_ks,
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Tensor<EDataType>& e_ms_ns,
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static auto MakeArgument(const Tensor<ADataType>& a_gs_ms_ks,
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const Tensor<BDataType>& b_gs_ns_ks,
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Tensor<EDataType>& e_gs_ms_ns,
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AElementwiseOperation a_element_op,
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BElementwiseOperation b_element_op,
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CDEElementwiseOperation cde_element_op)
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{
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return Argument{a_ms_ks, b_ns_ks, e_ms_ns, a_element_op, b_element_op, cde_element_op};
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return Argument{
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a_gs_ms_ks, b_gs_ns_ks, e_gs_ms_ns, a_element_op, b_element_op, cde_element_op};
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}
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static auto MakeInvoker() { return Invoker{}; }
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@@ -196,28 +202,31 @@ int main(int argc, char* argv[])
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int init_method = 1;
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bool time_kernel = false;
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ck::index_t G0 = 1;
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ck::index_t M0 = 4;
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ck::index_t M1 = 256;
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ck::index_t N0 = 4;
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ck::index_t N1 = 8;
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ck::index_t N2 = 128;
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ck::index_t N1 = 16;
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ck::index_t N2 = 32;
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ck::index_t K0 = 256;
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// A[M0, M1, M2, K0]
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std::vector<ck::index_t> a_ms_ks_lengths{M0, M1, K0};
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std::vector<ck::index_t> a_ms_ks_strides{M1 * K0, K0, 1};
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std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, M0, M1, K0};
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std::vector<ck::index_t> a_gs_ms_ks_strides{M0 * M1 * K0, M1 * K0, K0, 1};
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// B[N0, N1, K0]
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std::vector<ck::index_t> b_ns_ks_lengths{N0, N1, N2, K0};
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std::vector<ck::index_t> b_ns_ks_strides{N1 * N2 * K0, N2 * K0, K0, 1};
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std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, N0, N1, N2, K0};
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std::vector<ck::index_t> b_gs_ns_ks_strides{N0 * N1 * N2 * K0, N1 * N2 * K0, N2 * K0, K0, 1};
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// D[N0, M0, N1, M1, N2]
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std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, N0, N1, N2};
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std::vector<ck::index_t> d_ms_ns_strides{0, 0, N1 * N2, N1, 1};
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std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, M0, M1, N0, N1, N2};
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std::vector<ck::index_t> d_gs_ms_ns_strides{N0 * N1 * N2, 0, 0, N1 * N2, N2, 1};
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// E[N0, M0, N1, M1, N2]
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std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, N0, N1, N2};
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std::vector<ck::index_t> e_ms_ns_strides{N1 * M1 * N2, N2, M0 * N1 * M1 * N2, M1 * N2, 1};
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std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, M0, M1, N0, N1, N2};
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std::vector<ck::index_t> e_gs_ms_ns_strides{
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M0 * M1 * N0 * N1 * N2, N1 * M1 * N2, N2, M0 * N1 * M1 * N2, M1 * N2, 1};
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if(argc == 1)
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{
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@@ -237,50 +246,51 @@ int main(int argc, char* argv[])
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exit(0);
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}
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Tensor<ADataType> a_ms_ks(
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std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
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std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
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Tensor<BDataType> b_ns_ks(
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std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
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std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
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Tensor<DDataType> d_ms_ns(
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std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
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std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
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Tensor<EDataType> e_ms_ns_host_result(
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std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
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std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
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Tensor<EDataType> e_ms_ns_device_result(
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std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
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std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
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Tensor<ADataType> a_gs_ms_ks(
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std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()),
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std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end()));
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Tensor<BDataType> b_gs_ns_ks(
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std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()),
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std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
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Tensor<DDataType> d_gs_ms_ns(
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std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
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std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
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Tensor<EDataType> e_gs_ms_ns_host_result(
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std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
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std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
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Tensor<EDataType> e_gs_ms_ns_device_result(
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std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
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std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
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std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
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std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
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std::cout << "d_ms_ns: " << d_ms_ns.mDesc << std::endl;
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std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
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std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
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std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
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std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
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std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
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b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
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break;
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default:
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a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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d_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
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b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
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break;
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}
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DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpaceSize());
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DeviceMem d_device_buf(sizeof(DDataType) * d_ms_ns.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpaceSize());
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DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
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DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
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DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
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DeviceMem e_device_buf(sizeof(EDataType) *
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e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
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a_device_buf.ToDevice(a_ms_ks.mData.data());
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b_device_buf.ToDevice(b_ns_ks.mData.data());
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d_device_buf.ToDevice(d_ms_ns.mData.data());
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a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
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b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
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d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
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// set zero
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e_device_buf.SetZero();
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@@ -296,14 +306,14 @@ int main(int argc, char* argv[])
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b_device_buf.GetDeviceBuffer(),
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std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
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e_device_buf.GetDeviceBuffer(),
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a_ms_ks_lengths,
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a_ms_ks_strides,
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b_ns_ks_lengths,
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b_ns_ks_strides,
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std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
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std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
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e_ms_ns_lengths,
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e_ms_ns_strides,
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a_gs_ms_ks_lengths,
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a_gs_ms_ks_strides,
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b_gs_ns_ks_lengths,
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b_gs_ns_ks_strides,
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std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
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std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
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e_gs_ms_ns_lengths,
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e_gs_ms_ns_strides,
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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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@@ -317,18 +327,18 @@ int main(int argc, char* argv[])
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float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
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ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
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e_ms_ns_lengths.begin() + NumDimM,
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std::size_t M = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG,
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e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
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e_ms_ns_lengths.begin() + NumDimM + NumDimN,
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std::size_t N = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM,
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e_gs_ms_ns_lengths.begin() + NumDimG + NumDimM + NumDimN,
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ck::index_t{1},
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std::multiplies<ck::index_t>{});
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ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
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a_ms_ks_lengths.begin() + NumDimM + NumDimK,
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std::size_t K = std::accumulate(a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM,
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a_gs_ms_ks_lengths.begin() + NumDimG + NumDimM + NumDimK,
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||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
@@ -343,53 +353,63 @@ int main(int argc, char* argv[])
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< op.GetTypeString() << std::endl;
|
||||
|
||||
e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
|
||||
e_device_buf.FromDevice(e_gs_ms_ns_device_result.mData.data());
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
Tensor<CShuffleDataType> c_gs_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_M2_N3_K1<NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
using ReferenceOpInstance = ReferenceContraction_G1_M2_N3_K1<NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceOpInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
auto ref_argument = ref_gemm.MakeArgument(a_gs_ms_ks,
|
||||
b_gs_ns_ks,
|
||||
c_gs_ms_ns_host_result,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
|
||||
for(size_t g0 = 0; g0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[0]; ++g0)
|
||||
{
|
||||
for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
|
||||
for(size_t m0 = 0; m0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[1]; ++m0)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++n0)
|
||||
for(size_t m1 = 0; m1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[2]; ++m1)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n1)
|
||||
for(size_t n0 = 0; n0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[3]; ++n0)
|
||||
{
|
||||
for(size_t n2 = 0; n2 < e_ms_ns_host_result.mDesc.GetLengths()[4]; ++n2)
|
||||
for(size_t n1 = 0; n1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[4]; ++n1)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, n0, n1, n2),
|
||||
c_ms_ns_host_result(m0, m1, n0, n1, n2),
|
||||
d_ms_ns(m0, m1, n0, n1, n2));
|
||||
for(size_t n2 = 0; n2 < e_gs_ms_ns_host_result.mDesc.GetLengths()[5];
|
||||
++n2)
|
||||
{
|
||||
cde_element_op(e_gs_ms_ns_host_result(g0, m0, m1, n0, n1, n2),
|
||||
c_gs_ms_ns_host_result(g0, m0, m1, n0, n1, n2),
|
||||
d_gs_ms_ns(g0, m0, m1, n0, n1, n2));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
||||
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData)
|
||||
? 0
|
||||
: 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
@@ -33,7 +33,7 @@ using DDataType = F16;
|
||||
using DsDataType = ck::Tuple<DDataType>;
|
||||
using EDataType = F16;
|
||||
|
||||
static constexpr ck::index_t NumDimG = 0;
|
||||
static constexpr ck::index_t NumDimG = 1;
|
||||
static constexpr ck::index_t NumDimM = 3;
|
||||
static constexpr ck::index_t NumDimN = 2;
|
||||
static constexpr ck::index_t NumDimK = 1;
|
||||
@@ -53,13 +53,13 @@ using DeviceOpInstanceKKNN = ck::tensor_operation::device::
|
||||
//############################################| | | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Spacialization| Spacialization| 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|
|
||||
//############################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
|
||||
DeviceBatchedContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 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, 4>, 1>;
|
||||
DeviceBatchedContractionMultipleD_Xdl_CShuffle< NumDimG, NumDimM, NumDimN, NumDimK, F16, F16, F32, F16, DsDataType, F16, AElementOp, BElementOp, CDEElementOp, GemmSpec, ABSpec, ABSpec, DESpec, 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, 4>, 8>;
|
||||
// clang-format on
|
||||
|
||||
using DeviceOpInstance = DeviceOpInstanceKKNN;
|
||||
|
||||
// hardcoded for NumDimM == NumDimN == NumDimK == 2
|
||||
template <ck::index_t NumDimM,
|
||||
template <ck::index_t NumDimG,
|
||||
ck::index_t NumDimM,
|
||||
ck::index_t NumDimN,
|
||||
ck::index_t NumDimK,
|
||||
typename ADataType,
|
||||
@@ -69,30 +69,31 @@ template <ck::index_t NumDimM,
|
||||
typename AElementwiseOperation,
|
||||
typename BElementwiseOperation,
|
||||
typename CDEElementwiseOperation,
|
||||
ck::enable_if_t<NumDimM == 3 && NumDimN == 2 && NumDimK == 1, bool> = false>
|
||||
struct ReferenceContraction_M3_N2_K1 : public ck::tensor_operation::device::BaseOperator
|
||||
ck::enable_if_t<NumDimG == 1 && NumDimM == 3 && NumDimN == 2 && NumDimK == 1, bool> =
|
||||
false>
|
||||
struct ReferenceContraction_G1_M3_N2_K1 : public ck::tensor_operation::device::BaseOperator
|
||||
{
|
||||
// Argument
|
||||
struct Argument : public ck::tensor_operation::device::BaseArgument
|
||||
{
|
||||
Argument(const Tensor<ADataType>& a_ms_ks,
|
||||
const Tensor<BDataType>& b_ns_ks,
|
||||
Tensor<EDataType>& e_ms_ns,
|
||||
Argument(const Tensor<ADataType>& a_gs_ms_ks,
|
||||
const Tensor<BDataType>& b_gs_ns_ks,
|
||||
Tensor<EDataType>& e_gs_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
: a_ms_ks_{a_ms_ks},
|
||||
b_ns_ks_{b_ns_ks},
|
||||
e_ms_ns_{e_ms_ns},
|
||||
: a_gs_ms_ks_{a_gs_ms_ks},
|
||||
b_gs_ns_ks_{b_gs_ns_ks},
|
||||
e_gs_ms_ns_{e_gs_ms_ns},
|
||||
a_element_op_{a_element_op},
|
||||
b_element_op_{b_element_op},
|
||||
cde_element_op_{cde_element_op}
|
||||
{
|
||||
}
|
||||
|
||||
const Tensor<ADataType>& a_ms_ks_;
|
||||
const Tensor<BDataType>& b_ns_ks_;
|
||||
Tensor<EDataType>& e_ms_ns_;
|
||||
const Tensor<ADataType>& a_gs_ms_ks_;
|
||||
const Tensor<BDataType>& b_gs_ns_ks_;
|
||||
Tensor<EDataType>& e_gs_ms_ns_;
|
||||
|
||||
AElementwiseOperation a_element_op_;
|
||||
BElementwiseOperation b_element_op_;
|
||||
@@ -102,12 +103,12 @@ struct ReferenceContraction_M3_N2_K1 : public ck::tensor_operation::device::Base
|
||||
// Invoker
|
||||
struct Invoker : public ck::tensor_operation::device::BaseInvoker
|
||||
{
|
||||
using Argument = ReferenceContraction_M3_N2_K1::Argument;
|
||||
using Argument = ReferenceContraction_G1_M3_N2_K1::Argument;
|
||||
|
||||
float Run(const Argument& arg)
|
||||
{
|
||||
auto f_ms_ns = [&](auto m0, auto m1, auto m2, auto n0, auto n1) {
|
||||
const int K0 = arg.a_ms_ks_.mDesc.GetLengths()[3];
|
||||
auto f_gs_ms_ns = [&](auto g0, auto m0, auto m1, auto m2, auto n0, auto n1) {
|
||||
const int K0 = arg.a_gs_ms_ks_.mDesc.GetLengths()[4];
|
||||
|
||||
AccDataType v_acc = 0;
|
||||
|
||||
@@ -117,9 +118,10 @@ struct ReferenceContraction_M3_N2_K1 : public ck::tensor_operation::device::Base
|
||||
AccDataType v_b;
|
||||
|
||||
arg.a_element_op_(
|
||||
v_a, ck::type_convert<const AccDataType>(arg.a_ms_ks_(m0, m1, m2, k0)));
|
||||
v_a,
|
||||
ck::type_convert<const AccDataType>(arg.a_gs_ms_ks_(g0, m0, m1, m2, k0)));
|
||||
arg.b_element_op_(
|
||||
v_b, ck::type_convert<const AccDataType>(arg.b_ns_ks_(n0, n1, k0)));
|
||||
v_b, ck::type_convert<const AccDataType>(arg.b_gs_ns_ks_(g0, n0, n1, k0)));
|
||||
|
||||
v_acc += v_a * v_b;
|
||||
}
|
||||
@@ -128,15 +130,16 @@ struct ReferenceContraction_M3_N2_K1 : public ck::tensor_operation::device::Base
|
||||
|
||||
arg.cde_element_op_(v_c, v_acc);
|
||||
|
||||
arg.e_ms_ns_(m0, m1, m2, n0, n1) = v_c;
|
||||
arg.e_gs_ms_ns_(g0, m0, m1, m2, n0, n1) = v_c;
|
||||
};
|
||||
|
||||
make_ParallelTensorFunctor(f_ms_ns,
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[0],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[1],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[2],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[3],
|
||||
arg.e_ms_ns_.mDesc.GetLengths()[4])(
|
||||
make_ParallelTensorFunctor(f_gs_ms_ns,
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[0],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[1],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[2],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[3],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[4],
|
||||
arg.e_gs_ms_ns_.mDesc.GetLengths()[5])(
|
||||
std::thread::hardware_concurrency());
|
||||
|
||||
return 0;
|
||||
@@ -160,14 +163,15 @@ struct ReferenceContraction_M3_N2_K1 : public ck::tensor_operation::device::Base
|
||||
return true;
|
||||
}
|
||||
|
||||
static auto MakeArgument(const Tensor<ADataType>& a_ms_ks,
|
||||
const Tensor<BDataType>& b_ns_ks,
|
||||
Tensor<EDataType>& e_ms_ns,
|
||||
static auto MakeArgument(const Tensor<ADataType>& a_gs_ms_ks,
|
||||
const Tensor<BDataType>& b_gs_ns_ks,
|
||||
Tensor<EDataType>& e_gs_ms_ns,
|
||||
AElementwiseOperation a_element_op,
|
||||
BElementwiseOperation b_element_op,
|
||||
CDEElementwiseOperation cde_element_op)
|
||||
{
|
||||
return Argument{a_ms_ks, b_ns_ks, e_ms_ns, a_element_op, b_element_op, cde_element_op};
|
||||
return Argument{
|
||||
a_gs_ms_ks, b_gs_ns_ks, e_gs_ms_ns, a_element_op, b_element_op, cde_element_op};
|
||||
}
|
||||
|
||||
static auto MakeInvoker() { return Invoker{}; }
|
||||
@@ -182,7 +186,7 @@ struct ReferenceContraction_M3_N2_K1 : public ck::tensor_operation::device::Base
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "ReferenceContraction_M3_N2_K1"
|
||||
str << "ReferenceContraction_G1_M3_N2_K1"
|
||||
<< std::endl;
|
||||
// clang-format on
|
||||
|
||||
@@ -196,36 +200,33 @@ int main(int argc, char* argv[])
|
||||
int init_method = 1;
|
||||
bool time_kernel = false;
|
||||
|
||||
ck::index_t G0 = 1;
|
||||
|
||||
ck::index_t M0 = 4;
|
||||
ck::index_t M1 = 32;
|
||||
ck::index_t M2 = 128;
|
||||
ck::index_t M1 = 8;
|
||||
ck::index_t M2 = 256;
|
||||
|
||||
ck::index_t N0 = 16;
|
||||
ck::index_t N1 = 256;
|
||||
ck::index_t N0 = 32;
|
||||
ck::index_t N1 = 128;
|
||||
|
||||
ck::index_t K0 = 256;
|
||||
ck::index_t K0 = 1024;
|
||||
|
||||
// A[M0, M1, M2, K0]
|
||||
std::vector<ck::index_t> a_ms_ks_lengths{M0, M1, M2, K0};
|
||||
std::vector<ck::index_t> a_ms_ks_strides{M1 * M2 * K0, M2 * K0, K0, 1};
|
||||
std::vector<ck::index_t> a_gs_ms_ks_lengths{G0, M0, M1, M2, K0};
|
||||
std::vector<ck::index_t> a_gs_ms_ks_strides{M0 * M1 * M2 * K0, M1 * M2 * K0, M2 * K0, K0, 1};
|
||||
|
||||
// B[N0, N1, K0]
|
||||
std::vector<ck::index_t> b_ns_ks_lengths{N0, N1, K0};
|
||||
std::vector<ck::index_t> b_ns_ks_strides{N1 * K0, K0, 1};
|
||||
#if 1
|
||||
std::vector<ck::index_t> b_gs_ns_ks_lengths{G0, N0, N1, K0};
|
||||
std::vector<ck::index_t> b_gs_ns_ks_strides{N0 * N1 * K0, N1 * K0, K0, 1};
|
||||
|
||||
// D[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_ms_ns_strides{0, 0, 0, N1, 1};
|
||||
// E[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{N0 * M1 * N1 * M2, N1 * M2, 1, M1 * N1 * M2, M2};
|
||||
#else
|
||||
// D[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> d_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_ms_ns_strides{0, 0, 0, N1, 1};
|
||||
// E[M0, N0, M1, N1, M2]
|
||||
std::vector<ck::index_t> e_ms_ns_lengths{M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_ms_ns_strides{M1 * M2 * N0 * N1, M2 * N0 * N1, N0 * N1, N1, 1};
|
||||
#endif
|
||||
std::vector<ck::index_t> d_gs_ms_ns_lengths{G0, M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> d_gs_ms_ns_strides{N0 * N1, 0, 0, 0, N1, 1};
|
||||
|
||||
// E[M1, M0, N0, M1, N1]
|
||||
std::vector<ck::index_t> e_gs_ms_ns_lengths{G0, M0, M1, M2, N0, N1};
|
||||
std::vector<ck::index_t> e_gs_ms_ns_strides{
|
||||
M0 * M1 * M2 * N1 * N0, N0 * M1 * N1, N1, M0 * N0 * M1 * N1, M1 * N1, 1};
|
||||
|
||||
if(argc == 1)
|
||||
{
|
||||
@@ -245,50 +246,51 @@ int main(int argc, char* argv[])
|
||||
exit(0);
|
||||
}
|
||||
|
||||
Tensor<ADataType> a_ms_ks(
|
||||
std::vector<std::size_t>(a_ms_ks_lengths.begin(), a_ms_ks_lengths.end()),
|
||||
std::vector<std::size_t>(a_ms_ks_strides.begin(), a_ms_ks_strides.end()));
|
||||
Tensor<BDataType> b_ns_ks(
|
||||
std::vector<std::size_t>(b_ns_ks_lengths.begin(), b_ns_ks_lengths.end()),
|
||||
std::vector<std::size_t>(b_ns_ks_strides.begin(), b_ns_ks_strides.end()));
|
||||
Tensor<DDataType> d_ms_ns(
|
||||
std::vector<std::size_t>(d_ms_ns_lengths.begin(), d_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(d_ms_ns_strides.begin(), d_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_ms_ns_device_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
Tensor<ADataType> a_gs_ms_ks(
|
||||
std::vector<std::size_t>(a_gs_ms_ks_lengths.begin(), a_gs_ms_ks_lengths.end()),
|
||||
std::vector<std::size_t>(a_gs_ms_ks_strides.begin(), a_gs_ms_ks_strides.end()));
|
||||
Tensor<BDataType> b_gs_ns_ks(
|
||||
std::vector<std::size_t>(b_gs_ns_ks_lengths.begin(), b_gs_ns_ks_lengths.end()),
|
||||
std::vector<std::size_t>(b_gs_ns_ks_strides.begin(), b_gs_ns_ks_strides.end()));
|
||||
Tensor<DDataType> d_gs_ms_ns(
|
||||
std::vector<std::size_t>(d_gs_ms_ns_lengths.begin(), d_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(d_gs_ms_ns_strides.begin(), d_gs_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_gs_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
Tensor<EDataType> e_gs_ms_ns_device_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
|
||||
std::cout << "a_ms_ks: " << a_ms_ks.mDesc << std::endl;
|
||||
std::cout << "b_ns_ks: " << b_ns_ks.mDesc << std::endl;
|
||||
std::cout << "d_ms_ns: " << d_ms_ns.mDesc << std::endl;
|
||||
std::cout << "e_ms_ns: " << e_ms_ns_host_result.mDesc << std::endl;
|
||||
std::cout << "a_gs_ms_ks: " << a_gs_ms_ks.mDesc << std::endl;
|
||||
std::cout << "b_gs_ns_ks: " << b_gs_ns_ks.mDesc << std::endl;
|
||||
std::cout << "d_gs_ms_ns: " << d_gs_ms_ns.mDesc << std::endl;
|
||||
std::cout << "e_gs_ms_ns: " << e_gs_ms_ns_host_result.mDesc << std::endl;
|
||||
|
||||
switch(init_method)
|
||||
{
|
||||
case 0: break;
|
||||
case 1:
|
||||
a_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
|
||||
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
|
||||
break;
|
||||
default:
|
||||
a_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
a_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
|
||||
b_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
d_gs_ms_ns.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
|
||||
break;
|
||||
}
|
||||
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_ms_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_ns_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_ms_ns.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) * e_ms_ns_device_result.mDesc.GetElementSpaceSize());
|
||||
DeviceMem a_device_buf(sizeof(ADataType) * a_gs_ms_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem b_device_buf(sizeof(BDataType) * b_gs_ns_ks.mDesc.GetElementSpaceSize());
|
||||
DeviceMem d_device_buf(sizeof(DDataType) * d_gs_ms_ns.mDesc.GetElementSpaceSize());
|
||||
DeviceMem e_device_buf(sizeof(EDataType) *
|
||||
e_gs_ms_ns_device_result.mDesc.GetElementSpaceSize());
|
||||
|
||||
a_device_buf.ToDevice(a_ms_ks.mData.data());
|
||||
b_device_buf.ToDevice(b_ns_ks.mData.data());
|
||||
d_device_buf.ToDevice(d_ms_ns.mData.data());
|
||||
a_device_buf.ToDevice(a_gs_ms_ks.mData.data());
|
||||
b_device_buf.ToDevice(b_gs_ns_ks.mData.data());
|
||||
d_device_buf.ToDevice(d_gs_ms_ns.mData.data());
|
||||
|
||||
// set zero
|
||||
e_device_buf.SetZero();
|
||||
@@ -304,14 +306,14 @@ int main(int argc, char* argv[])
|
||||
b_device_buf.GetDeviceBuffer(),
|
||||
std::array<const void*, 1>{d_device_buf.GetDeviceBuffer()},
|
||||
e_device_buf.GetDeviceBuffer(),
|
||||
a_ms_ks_lengths,
|
||||
a_ms_ks_strides,
|
||||
b_ns_ks_lengths,
|
||||
b_ns_ks_strides,
|
||||
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_lengths},
|
||||
std::array<std::vector<ck::index_t>, 1>{d_ms_ns_strides},
|
||||
e_ms_ns_lengths,
|
||||
e_ms_ns_strides,
|
||||
a_gs_ms_ks_lengths,
|
||||
a_gs_ms_ks_strides,
|
||||
b_gs_ns_ks_lengths,
|
||||
b_gs_ns_ks_strides,
|
||||
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_lengths},
|
||||
std::array<std::vector<ck::index_t>, 1>{d_gs_ms_ns_strides},
|
||||
e_gs_ms_ns_lengths,
|
||||
e_gs_ms_ns_strides,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
cde_element_op);
|
||||
@@ -325,18 +327,18 @@ int main(int argc, char* argv[])
|
||||
|
||||
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
|
||||
|
||||
ck::index_t M = std::accumulate(e_ms_ns_lengths.begin(),
|
||||
e_ms_ns_lengths.begin() + NumDimM,
|
||||
ck::index_t M = std::accumulate(e_gs_ms_ns_lengths.begin(),
|
||||
e_gs_ms_ns_lengths.begin() + NumDimM,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t N = std::accumulate(e_ms_ns_lengths.begin() + NumDimM,
|
||||
e_ms_ns_lengths.begin() + NumDimM + NumDimN,
|
||||
ck::index_t N = std::accumulate(e_gs_ms_ns_lengths.begin() + NumDimM,
|
||||
e_gs_ms_ns_lengths.begin() + NumDimM + NumDimN,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
ck::index_t K = std::accumulate(a_ms_ks_lengths.begin() + NumDimM,
|
||||
a_ms_ks_lengths.begin() + NumDimM + NumDimK,
|
||||
ck::index_t K = std::accumulate(a_gs_ms_ks_lengths.begin() + NumDimM,
|
||||
a_gs_ms_ks_lengths.begin() + NumDimM + NumDimK,
|
||||
ck::index_t{1},
|
||||
std::multiplies<ck::index_t>{});
|
||||
|
||||
@@ -351,53 +353,64 @@ int main(int argc, char* argv[])
|
||||
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
|
||||
<< op.GetTypeString() << std::endl;
|
||||
|
||||
e_device_buf.FromDevice(e_ms_ns_device_result.mData.data());
|
||||
e_device_buf.FromDevice(e_gs_ms_ns_device_result.mData.data());
|
||||
|
||||
if(do_verification)
|
||||
{
|
||||
Tensor<CShuffleDataType> c_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_ms_ns_lengths.begin(), e_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_ms_ns_strides.begin(), e_ms_ns_strides.end()));
|
||||
Tensor<CShuffleDataType> c_gs_ms_ns_host_result(
|
||||
std::vector<std::size_t>(e_gs_ms_ns_lengths.begin(), e_gs_ms_ns_lengths.end()),
|
||||
std::vector<std::size_t>(e_gs_ms_ns_strides.begin(), e_gs_ms_ns_strides.end()));
|
||||
|
||||
using ReferenceOpInstance = ReferenceContraction_M3_N2_K1<NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
using ReferenceOpInstance = ReferenceContraction_G1_M3_N2_K1<NumDimG,
|
||||
NumDimM,
|
||||
NumDimN,
|
||||
NumDimK,
|
||||
ADataType,
|
||||
BDataType,
|
||||
CShuffleDataType,
|
||||
AccDataType,
|
||||
AElementOp,
|
||||
BElementOp,
|
||||
PassThrough>;
|
||||
|
||||
auto ref_gemm = ReferenceOpInstance{};
|
||||
auto ref_invoker = ref_gemm.MakeInvoker();
|
||||
|
||||
auto ref_argument = ref_gemm.MakeArgument(
|
||||
a_ms_ks, b_ns_ks, c_ms_ns_host_result, a_element_op, b_element_op, PassThrough{});
|
||||
auto ref_argument = ref_gemm.MakeArgument(a_gs_ms_ks,
|
||||
b_gs_ns_ks,
|
||||
c_gs_ms_ns_host_result,
|
||||
a_element_op,
|
||||
b_element_op,
|
||||
PassThrough{});
|
||||
|
||||
ref_invoker.Run(ref_argument);
|
||||
|
||||
for(size_t m0 = 0; m0 < e_ms_ns_host_result.mDesc.GetLengths()[0]; ++m0)
|
||||
for(size_t g0 = 0; g0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[0]; ++g0)
|
||||
{
|
||||
for(size_t m1 = 0; m1 < e_ms_ns_host_result.mDesc.GetLengths()[1]; ++m1)
|
||||
for(size_t m0 = 0; m0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[1]; ++m0)
|
||||
{
|
||||
for(size_t m2 = 0; m2 < e_ms_ns_host_result.mDesc.GetLengths()[2]; ++m2)
|
||||
for(size_t m1 = 0; m1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[2]; ++m1)
|
||||
{
|
||||
for(size_t n0 = 0; n0 < e_ms_ns_host_result.mDesc.GetLengths()[3]; ++n0)
|
||||
for(size_t m2 = 0; m2 < e_gs_ms_ns_host_result.mDesc.GetLengths()[3]; ++m2)
|
||||
{
|
||||
for(size_t n1 = 0; n1 < e_ms_ns_host_result.mDesc.GetLengths()[4]; ++n1)
|
||||
for(size_t n0 = 0; n0 < e_gs_ms_ns_host_result.mDesc.GetLengths()[4]; ++n0)
|
||||
{
|
||||
cde_element_op(e_ms_ns_host_result(m0, m1, m2, n0, n1),
|
||||
c_ms_ns_host_result(m0, m1, m2, n0, n1),
|
||||
d_ms_ns(m0, m1, m2, n0, n1));
|
||||
for(size_t n1 = 0; n1 < e_gs_ms_ns_host_result.mDesc.GetLengths()[5];
|
||||
++n1)
|
||||
{
|
||||
cde_element_op(e_gs_ms_ns_host_result(g0, m0, m1, m2, n0, n1),
|
||||
c_gs_ms_ns_host_result(g0, m0, m1, m2, n0, n1),
|
||||
d_gs_ms_ns(g0, m0, m1, m2, n0, n1));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return ck::utils::check_err(e_ms_ns_device_result.mData, e_ms_ns_host_result.mData) ? 0 : 1;
|
||||
return ck::utils::check_err(e_gs_ms_ns_device_result.mData, e_gs_ms_ns_host_result.mData)
|
||||
? 0
|
||||
: 1;
|
||||
}
|
||||
|
||||
return 0;
|
||||
@@ -500,11 +500,8 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
|
||||
std::array<long_index_t, NumDTensor> ds_offset;
|
||||
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
if constexpr(NumDimG > 0)
|
||||
ds_offset[i] =
|
||||
ds_grid_desc_g_m_n_[i].CalculateOffset(make_multi_index(g_idx, 0, 0));
|
||||
else
|
||||
ds_offset[i] = 0;
|
||||
ds_offset[i] =
|
||||
ds_grid_desc_g_m_n_[i].CalculateOffset(make_multi_index(g_idx, 0, 0));
|
||||
});
|
||||
|
||||
return ds_offset;
|
||||
@@ -512,10 +509,7 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
|
||||
|
||||
__host__ __device__ constexpr long_index_t GetEPtrOffset(index_t g_idx) const
|
||||
{
|
||||
if constexpr(NumDimG > 0)
|
||||
return e_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
|
||||
else
|
||||
return 0;
|
||||
return e_grid_desc_g_m_n_.CalculateOffset(make_multi_index(g_idx, 0, 0));
|
||||
}
|
||||
|
||||
private:
|
||||
@@ -634,6 +628,8 @@ struct DeviceBatchedContractionMultipleD_Xdl_CShuffle
|
||||
compute_ptr_offset_of_batch_{
|
||||
a_batch_stride_, b_batch_stride_, ds_grid_desc_g_m_n_, e_grid_desc_g_m_n_}
|
||||
{
|
||||
static_assert(NumDimG > 0 && NumDimM > 0 && NumDimN > 0 && NumDimK > 0, "");
|
||||
|
||||
// populate pointer, batch stride, desc for Ds
|
||||
static_for<0, NumDTensor, 1>{}([&](auto i) {
|
||||
using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
|
||||
|
||||
Reference in New Issue
Block a user