Add gemm layernorm host code

example/21_gemm_layernorm/gemm_add_add_layernorm_xdl_fp16.cpp

@@ -66,14 +66,6 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMultipleDLayern
         < ALayout, BLayout, DsLayout, HLayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, GammaDataType, BetaDataType, HDataType,  AElementOp,  BElementOp, CDEElementOp,   HElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,              S<64, 4>,               4, S<8, 32>, S<1, 8>, 1, 8, 8, 8, 8, 1>;
 // clang-format on
 
-using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
-                                                                        BDataType,
-                                                                        AccDataType,
-                                                                        AccDataType,
-                                                                        AElementOp,
-                                                                        BElementOp,
-                                                                        PassThrough>;
-
 auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
     return HostTensorDescriptor(std::vector<std::size_t>({len}),
                                 std::vector<std::size_t>({stride}));
@@ -93,6 +85,78 @@ auto f_host_tensor_descriptor2d =
         }
     };
 
+void host_gemm_layernorm(Tensor<HDataType>& e_m_n,
+                         Tensor<HDataType>& h_m_n,
+                         const Tensor<ADataType>& a_m_k,
+                         const Tensor<BDataType>& b_k_n,
+                         const Tensor<D0DataType>& bias_n,
+                         const Tensor<D1DataType>& d1_m_n,
+                         const Tensor<GammaDataType>& gamma_n,
+                         const Tensor<BetaDataType>& beta_n,
+                         AElementOp a_element_op,
+                         BElementOp b_element_op,
+                         CDEElementOp cde_element_op,
+                         int M,
+                         int N,
+                         float epsilon = 1e-5)
+{
+    using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                            BDataType,
+                                                                            AccDataType,
+                                                                            AccDataType,
+                                                                            AElementOp,
+                                                                            BElementOp,
+                                                                            PassThrough>;
+
+    using NormalizeFunctor = ck::tensor_operation::element_wise::Normalize;
+
+    Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});
+
+    auto ref_gemm    = ReferenceGemmInstance{};
+    auto ref_invoker = ref_gemm.MakeInvoker();
+
+    auto ref_argument =
+        ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});
+
+    ref_invoker.Run(ref_argument);
+
+    for(int m = 0; m < M; ++m)
+        for(int n = 0; n < N; ++n)
+        {
+            cde_element_op(e_m_n(m, n), c_m_n(m, n), bias_n(n), d1_m_n(m, n));
+        }
+
+    // LayerNorm
+    Tensor<AccDataType> mean_m(f_host_tensor_descriptor1d(M, 1));
+    Tensor<AccDataType> meanSquare_m(f_host_tensor_descriptor1d(M, 1));
+    auto layerNormInst = NormalizeFunctor{epsilon};
+    for(int m = 0; m < M; ++m)
+    {
+        AccDataType mean       = 0;
+        AccDataType meanSquare = 0;
+
+        for(int n = 0; n < N; ++n)
+        {
+            auto e_val = ck::type_convert<AccDataType>(e_m_n(m, n));
+            mean += e_val;
+            meanSquare += e_val * e_val;
+        }
+
+        mean /= N;
+        meanSquare /= N;
+
+        for(int n = 0; n < N; ++n)
+        {
+            AccDataType h_val     = 0;
+            AccDataType e_val     = ck::type_convert<AccDataType>(e_m_n(m, n));
+            AccDataType gamma_val = ck::type_convert<AccDataType>(gamma_n(n));
+            AccDataType beta_val  = ck::type_convert<AccDataType>(beta_n(n));
+            layerNormInst(h_val, e_val, mean, meanSquare, gamma_val, beta_val);
+            h_m_n(m, n) = ck::type_convert<HDataType>(h_val);
+        }
+    }
+}
+
 int main()
 {
     bool do_verification = true;
@@ -181,28 +245,34 @@ int main()
 
     invoker.Run(argument, StreamConfig{nullptr, false});
 
+    bool pass = true;
+
     if(do_verification)
     {
-        Tensor<AccDataType> c_m_n_host(HostTensorDescriptor{M, N});
         Tensor<HDataType> e_m_n_host(HostTensorDescriptor{M, N});
-
-        auto ref_gemm    = ReferenceGemmInstance{};
-        auto ref_invoker = ref_gemm.MakeInvoker();
-
-        auto ref_argument = ref_gemm.MakeArgument(
-            a_m_k, b_k_n, c_m_n_host, a_element_op, b_element_op, PassThrough{});
-
-        ref_invoker.Run(ref_argument);
-
-        for(int m = 0; m < M; ++m)
-        {
-            for(int n = 0; n < N; ++n)
-            {
-                cde_element_op(e_m_n_host(m, n), c_m_n_host(m, n), d0_n(n), d1_m_n(m, n));
-            }
-        }
+        Tensor<HDataType> h_m_n_host(HostTensorDescriptor{M, N});
+
+        host_gemm_layernorm(e_m_n_host,
+                            h_m_n_host,
+                            a_m_k,
+                            b_k_n,
+                            d0_n,
+                            d1_m_n,
+                            gamma_n,
+                            beta_n,
+                            a_element_op,
+                            b_element_op,
+                            cde_element_op,
+                            M,
+                            N,
+                            epsilon);
 
         e_device_buf.FromDevice(e_m_n.mData.data());
-        return ck::utils::check_err(e_m_n, e_m_n_host) ? 0 : 1;
+        h_device_buf.FromDevice(h_m_n.mData.data());
+
+        pass &= ck::utils::check_err(e_m_n, e_m_n_host);
+        pass &= ck::utils::check_err(h_m_n, h_m_n_host);
     }
+
+    return pass ? 0 : 1;
 }