Add a host mx gemm reference kernel (#1864)

* Add mx gemm reference kernel

* Update copyright year

* Update mx gemm example

* Use element-wise ops in the reference gemm

example/67_gemm_microscaling/gemm_mx_common.hpp

@@ -13,7 +13,7 @@
 #include "ck/utility/blkgemmpipe_scheduler.hpp"
 #include "ck/utility/data_type.hpp"
 #include "ck/utility/sequence.hpp"
-#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_mx_gemm.hpp"
 #include "ck/library/utility/check_err.hpp"
 #include "ck/library/utility/device_memory.hpp"
 #include "ck/library/utility/fill.hpp"
@@ -315,40 +315,27 @@ bool run_mx_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
             std::cout << "Computing GEMM on host..." << std::endl;
         }
 
-        Tensor<CDataType> c({M, N});
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceMXGemm<ADataType,
-        Tensor<float> a({M, K});
+                                                                                  BDataType,
-        Tensor<float> b({K, N});
+                                                                                  CDataType,
-
+                                                                                  AccDataType,
-        for(int m = 0; m < M; m++)
+                                                                                  float,
-        {
+                                                                                  PassThrough,
-            for(int k = 0; k < K; k++)
+                                                                                  PassThrough,
-            {
+                                                                                  PassThrough,
-                a(m, k) = ck::type_convert<float>(a_m_k(m, k)) *
+                                                                                  float,
-                          ck::type_convert<float>(a_m_k_scale(m, k / Scale_Block_K));
+                                                                                  float>;
-            }
-        }
-
-        for(int n = 0; n < N; n++)
-        {
-            for(int k = 0; k < K; k++)
-            {
-                b(k, n) = ck::type_convert<float>(b_k_n(k, n)) *
-                          ck::type_convert<float>(b_k_n_scale(k / Scale_Block_K, n));
-            }
-        }
-
-        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<float,
-                                                                                float,
-                                                                                CShuffleDataType,
-                                                                                CDataType,
-                                                                                PassThrough,
-                                                                                PassThrough,
-                                                                                PassThrough>;
         auto ref_gemm               = ReferenceGemmInstance{};
         auto ref_invoker            = ref_gemm.MakeInvoker();
 
-        auto ref_argument =
+        auto ref_argument = ref_gemm.MakeArgument(a_m_k,
-            ref_gemm.MakeArgument(a, b, c, PassThrough{}, PassThrough{}, PassThrough{});
+                                                  a_m_k_scale,
+                                                  b_k_n,
+                                                  b_k_n_scale,
+                                                  c_m_n_host_result,
+                                                  PassThrough{},
+                                                  PassThrough{},
+                                                  PassThrough{});
 
         ref_invoker.Run(ref_argument);
 
@@ -366,8 +353,9 @@ bool run_mx_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
                       << ((res_verified) ? " (PASSED!)" : " (FAILED!)") << std::endl;
         }
 
-        res_verified = res_verified &&
+        res_verified = res_verified && ck::utils::check_err(c_m_n_device_result,
-                       ck::utils::check_err(c_m_n_device_result, c, "Error: Incorrect results!");
+                                                            c_m_n_host_result,
+                                                            "Error: Incorrect results!");
 
         if(config.verbosity > 0 && res_verified)
             std::cout << "Done." << std::endl;

library/include/ck/library/reference_tensor_operation/cpu/reference_mx_gemm.hpp

@@ -0,0 +1,178 @@
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace host {
+
+template <typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename AccDataType,
+          typename ScaleDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename ComputeTypeA = CDataType,
+          typename ComputeTypeB = ComputeTypeA>
+struct ReferenceMXGemm : public device::BaseOperator
+{
+    // Argument
+    struct Argument : public device::BaseArgument
+    {
+        Argument(const Tensor<ADataType>& a_m_k,
+                 const Tensor<ScaleDataType>& a_m_kblock_scales,
+                 const Tensor<BDataType>& b_k_n,
+                 const Tensor<ScaleDataType>& b_kblock_n_scales,
+                 Tensor<CDataType>& c_m_n,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+            : a_m_k_{a_m_k},
+              a_m_kblock_scales_{a_m_kblock_scales},
+              b_k_n_{b_k_n},
+              b_kblock_n_scales_{b_kblock_n_scales},
+              c_m_n_{c_m_n},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              c_element_op_{c_element_op}
+        {
+        }
+
+        const Tensor<ADataType>& a_m_k_;
+        const Tensor<ScaleDataType>& a_m_kblock_scales_;
+        const Tensor<BDataType>& b_k_n_;
+        const Tensor<ScaleDataType>& b_kblock_n_scales_;
+        Tensor<CDataType>& c_m_n_;
+
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CElementwiseOperation c_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public device::BaseInvoker
+    {
+        using Argument = ReferenceMXGemm::Argument;
+
+        float Run(const Argument& arg)
+        {
+            using GemmInstance = ck::tensor_operation::host::ReferenceGemm<ComputeTypeA,
+                                                                           ComputeTypeB,
+                                                                           CDataType,
+                                                                           AccDataType,
+                                                                           AElementwiseOperation,
+                                                                           BElementwiseOperation,
+                                                                           CElementwiseOperation,
+                                                                           ComputeTypeA,
+                                                                           ComputeTypeB>;
+
+            Tensor<ComputeTypeA> a_m_k_scaled(arg.a_m_k_.mDesc);
+            Tensor<ComputeTypeB> b_k_n_scaled(arg.b_k_n_.mDesc);
+
+            const auto M           = arg.a_m_k_.mDesc.GetLengths()[0];
+            const auto N           = arg.b_k_n_.mDesc.GetLengths()[1];
+            const auto K           = arg.a_m_k_.mDesc.GetLengths()[1];
+            const auto SCALE_BLOCK = K / arg.a_m_kblock_scales_.mDesc.GetLengths()[1];
+
+            for(size_t m = 0; m < M; m++)
+            {
+                for(size_t k = 0; k < K; k++)
+                {
+                    a_m_k_scaled(m, k) =
+                        type_convert<ComputeTypeA>(arg.a_m_k_(m, k)) *
+                        type_convert<ComputeTypeA>(arg.a_m_kblock_scales_(m, k / SCALE_BLOCK));
+                }
+            }
+
+            for(size_t n = 0; n < N; n++)
+            {
+                for(size_t k = 0; k < K; k++)
+                {
+                    b_k_n_scaled(k, n) =
+                        type_convert<ComputeTypeB>(arg.b_k_n_(k, n)) *
+                        type_convert<ComputeTypeB>(arg.b_kblock_n_scales_(k / SCALE_BLOCK, n));
+                }
+            }
+
+            auto ref_gemm     = GemmInstance{};
+            auto ref_invoker  = ref_gemm.MakeInvoker();
+            auto ref_argument = ref_gemm.MakeArgument(a_m_k_scaled,
+                                                      b_k_n_scaled,
+                                                      arg.c_m_n_,
+                                                      arg.a_element_op_,
+                                                      arg.b_element_op_,
+                                                      arg.c_element_op_);
+
+            ref_invoker.Run(ref_argument);
+
+            return 0;
+        }
+
+        float Run(const device::BaseArgument* p_arg,
+                  const StreamConfig& /* stream_config */ = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg));
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
+
+    static auto MakeArgument(const Tensor<ADataType>& a_m_k,
+                             const Tensor<ScaleDataType>& a_m_kblock_scales,
+                             const Tensor<BDataType>& b_k_n,
+                             const Tensor<ScaleDataType>& b_kblock_n_scales,
+                             Tensor<CDataType>& c_m_n,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CElementwiseOperation c_element_op)
+    {
+        return Argument{a_m_k,
+                        a_m_kblock_scales,
+                        b_k_n,
+                        b_kblock_n_scales,
+                        c_m_n,
+                        a_element_op,
+                        b_element_op,
+                        c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "ReferenceMXGemm"
+            << std::endl;
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace host
+} // namespace tensor_operation
+} // namespace ck