Merge branch 'develop' into ck_profiler_m_instances

include/ck_tile/host/concat.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
+
+namespace ck_tile {
+
+template <typename T>
+struct IsCharArray : std::false_type
+{
+};
+
+template <std::size_t N>
+struct IsCharArray<char[N]> : std::true_type
+{
+};
+
+template <std::size_t N>
+struct IsCharArray<const char[N]> : std::true_type
+{
+};
+
+template <std::size_t N>
+struct IsCharArray<char (&)[N]> : std::true_type
+{
+};
+
+template <std::size_t N>
+struct IsCharArray<const char (&)[N]> : std::true_type
+{
+};
+
+template <typename... Ts>
+inline constexpr bool AllConvertibleToStringView = ((std::is_convertible_v<Ts, std::string_view> ||
+                                                     IsCharArray<Ts>::value ||
+                                                     std::is_same_v<Ts, char>)&&...);
+
+template <typename... Ts>
+[[nodiscard]] auto concat(const Ts&... xs)
+    -> std::enable_if_t<!AllConvertibleToStringView<Ts...>, std::string>
+{
+    using ::operator<<;
+    thread_local std::ostringstream oss;
+    oss.str("");
+
+    (oss << ... << xs);
+    return oss.str();
+}
+
+template <std::size_t N>
+[[nodiscard]] constexpr inline std::size_t getSize(char (&)[N]) noexcept
+{
+    return N;
+}
+
+template <std::size_t N>
+[[nodiscard]] constexpr inline std::size_t getSize(const char (&)[N]) noexcept
+{
+    return N;
+}
+
+[[nodiscard]] constexpr inline std::size_t getSize(const char* s) noexcept
+{
+    const char* end = s;
+    while(*end++ != 0) {}
+    return end - s - 1;
+}
+
+[[nodiscard]] constexpr inline std::size_t getSize(const char&) noexcept { return 1; }
+
+[[nodiscard]] inline std::size_t getSize(const std::string& s) noexcept { return s.size(); }
+
+[[nodiscard]] constexpr inline std::size_t getSize(const std::string_view& s) noexcept
+{
+    return s.size();
+}
+
+template <typename... Ts>
+auto concatInto(std::string& result, const Ts&... xs)
+    -> std::enable_if_t<AllConvertibleToStringView<Ts...>, void>
+{
+    const std::size_t space = (1 + ... + getSize(xs));
+    result.reserve(result.size() + space);
+    ((result += xs), ...);
+}
+
+template <typename... Ts>
+[[nodiscard]] auto concat(const Ts&... xs)
+    -> std::enable_if_t<AllConvertibleToStringView<Ts...>, std::string>
+{
+    std::string result;
+    concatInto(result, xs...);
+    return result;
+}
+
+// Function for types convertible to std::string_view
+template <typename Sep, typename First, typename... Rest>
+[[nodiscard]] auto concat(Sep sep, const First& first, const Rest&... rest)
+    -> std::enable_if_t<AllConvertibleToStringView<First, Rest...>, std::string>
+{
+    std::string result;
+    result += first;
+    ((result += sep, result += rest), ...);
+    return result;
+}
+
+// Function for other types
+template <typename Sep, typename First, typename... Rest>
+[[nodiscard]] auto concat(Sep sep, const First& first, const Rest&... rest)
+    -> std::enable_if_t<!AllConvertibleToStringView<First, Rest...>, std::string>
+{
+    using ::operator<<;
+    thread_local std::ostringstream oss;
+    oss.str("");
+    oss << first;
+    ((oss << sep << rest), ...);
+    return oss.str();
+}
+
+} // namespace ck_tile

include/ck_tile/host/reference/reference_moe_sorting.hpp

@@ -14,12 +14,15 @@ namespace ck_tile {
 template <typename WeightType, typename IndexType = index_t>
 CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
                                         const HostTensor<WeightType>& weights,
+                                        const HostTensor<IndexType>& local_expert_mask,
                                         HostTensor<IndexType>& p_sorted_token_ids,
                                         HostTensor<WeightType>& sorted_weight,
                                         HostTensor<IndexType>& sorted_expert_ids,
                                         index_t& unit_cnt,
                                         const index_t experts,
-                                        const index_t unit_size)
+                                        const index_t unit_size,
+                                        bool local_expert_masking,
+                                        bool skip_experts_with_zero_token = true)
 {
     const index_t num_token = topk_ids.mDesc.get_lengths()[0];
     const index_t topk      = topk_ids.mDesc.get_lengths()[1];
@@ -33,8 +36,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
 #endif
     std::vector<std::vector<WeightType>> expert_token_weights(
         experts, std::vector<WeightType>(unit_size, 0));
+    // count number of unit-size slices in this expert
     std::vector<IndexType> expert_slices(experts, 1);
+    // count the tokens used in this expert
     std::vector<IndexType> expert_slice_idxs(experts, 0);
+    // TODO: above 2 buffer seems duplicated
 
     for(index_t t = 0; t < num_token; t++)
     {
@@ -72,8 +78,23 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
     IndexType* out_tokens    = p_sorted_token_ids.data();
     WeightType* out_weights  = sorted_weight.data();
     IndexType* out_expert_id = sorted_expert_ids.data();
+    int curr_expert_id       = 0;
     for(index_t e = 0; e < experts; e++)
     {
+        if(local_expert_masking)
+        {
+            if(local_expert_mask(e) == 0)
+                continue;
+        }
+        if(skip_experts_with_zero_token)
+        {
+            if(expert_slice_idxs[e] == 0)
+            {
+                curr_expert_id++;
+                continue;
+            }
+        }
+
         memcpy(out_tokens, expert_tokens[e].data(), sizeof(index_t) * expert_slices[e] * unit_size);
         out_tokens += expert_slices[e] * unit_size;
         memcpy(out_weights,
@@ -83,10 +104,11 @@ CK_TILE_HOST void reference_moe_sorting(const HostTensor<IndexType>& topk_ids,
 
         for(index_t s = 0; s < expert_slices[e]; s++)
         {
-            out_expert_id[s] = e;
+            out_expert_id[s] = curr_expert_id;
             unit_cnt++;
         }
         out_expert_id += expert_slices[e];
+        curr_expert_id++;
     }
     unit_cnt *= unit_size;
     return;

include/ck_tile/ops/add_rmsnorm2d_rdquant.hpp

@@ -10,3 +10,4 @@
 #include "ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_three_pass.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/batched_transpose.hpp

@@ -9,3 +9,4 @@
 #include "ck_tile/ops/batched_transpose/pipeline/batched_transpose_problem.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/common.hpp

@@ -5,3 +5,4 @@
 
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/common/utils.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <string>
+
+#include "ck_tile/core.hpp"
+
+namespace ck_tile {
+
+// clang-format off
+template <typename T> struct typeToStr;
+template <> struct typeToStr<float> { static constexpr const char * name = "fp32"; };
+template <> struct typeToStr<fp16_t> { static constexpr const char * name = "fp16"; };
+template <> struct typeToStr<bf16_t> { static constexpr const char * name = "bf16"; };
+template <> struct typeToStr<fp8_t> { static constexpr const char * name = "fp8"; };
+template <> struct typeToStr<bf8_t> { static constexpr const char * name = "bf8"; };
+template <> struct typeToStr<int8_t> { static constexpr const char * name = "int8"; };
+// clang-format on
+
+template <typename ADataType_, typename BDataType_>
+std::string gemm_prec_str()
+{
+    std::string base_str = std::string(typeToStr<ADataType_>::name);
+    if(!std::is_same_v<ADataType_, BDataType_>)
+    {
+        base_str += "_" + std::string(typeToStr<BDataType_>::name);
+    }
+    return base_str;
+}
+
+} // namespace ck_tile

include/ck_tile/ops/elementwise.hpp

@@ -6,3 +6,4 @@
 #include "ck_tile/ops/elementwise/unary_element_wise_operation.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/epilogue.hpp

@@ -8,3 +8,4 @@
 #include "ck_tile/ops/epilogue/dynamic_quant_epilogue.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/flatmm.hpp

@@ -9,3 +9,4 @@
 #include "ck_tile/ops/flatmm/block/flatmm_uk_config.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/fmha.hpp

@@ -44,3 +44,4 @@
 #include "ck_tile/ops/fmha/pipeline/tile_fmha_traits.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/fmha/block/block_masking.hpp

@@ -310,7 +310,7 @@ struct SimplifiedGenericAttentionMask
 
         const index_t x_per_split = ck_tile::max(1, integer_divide_ceil(x_total, num_splits));
         const index_t split_start = x_per_split * i_split;
-        const index_t split_end   = split_start + x_per_split;
+        const index_t split_end   = ck_tile::min(x_total, split_start + x_per_split);
 
         return ck_tile::make_tuple(ck_tile::max(origin_start, split_start),
                                    ck_tile::min(origin_end, split_end));

include/ck_tile/ops/fmha/kernel/fmha_fwd_splitkv_kernel.hpp

@@ -742,7 +742,7 @@ struct FmhaFwdSplitKVKernel
                 return pad_tensor_view(
                     v_dram_transposed,
                     make_tuple(number<FmhaPipeline::kN1>{}, number<FmhaPipeline::kK1>{}),
-                    sequence<kPadHeadDimV, false>{});
+                    sequence<kPadHeadDimV, kPadSeqLenK>{});
             }
             else
             {

include/ck_tile/ops/fmha/pipeline/block_fmha_fwd_splitkv_pipeline_nwarp_sshuffle_qr_ks_vs.hpp

@@ -343,6 +343,8 @@ struct BlockFmhaFwdSplitKVPipelineNWarpSShuffleQRKSVS
         // moving k_dram_window is an in-page-block operation, so there is
         // no need to invoke k_page_block_navigator.move_tile_window() here.
         move_tile_window(k_dram_window, {0, kK0});
+        // ensure LDS access by Q is done before the over-writting by K
+        block_sync_lds();
         store_tile(k_lds_window, tile_elementwise_in(k_element_func, k_block_tile));
 
         do

include/ck_tile/ops/fused_moe.hpp

@@ -7,6 +7,7 @@
 #include "ck_tile/ops/fused_moe/kernel/fused_moegemm_shape.hpp"
 #include "ck_tile/ops/fused_moe/kernel/fused_moegemm_tile_partitioner.hpp"
 #include "ck_tile/ops/fused_moe/kernel/moe_sorting_kernel.hpp"
+#include "ck_tile/ops/fused_moe/kernel/moe_sorting_problem.hpp"
 #include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_ex.hpp"
 #include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_policy.hpp"
 #include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_uk.hpp"
@@ -14,6 +15,6 @@
 #include "ck_tile/ops/fused_moe/pipeline/fused_moegemm_traits.hpp"
 #include "ck_tile/ops/fused_moe/pipeline/moe_sorting_pipeline.hpp"
 #include "ck_tile/ops/fused_moe/pipeline/moe_sorting_policy.hpp"
-#include "ck_tile/ops/fused_moe/pipeline/moe_sorting_problem.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/fused_moe/kernel/fused_moegemm_kernel.hpp

@@ -22,7 +22,7 @@
 //  (only for reference)    exp-0  exp-1     exp-2   exp-3          exp-4  exp-5
 // weight_id_per_expert is: [[a], [g, j, m], [d, k], [b, e, h, l, n], [], [c, f, i, o]]
 //
-// max_num_tokens_padded : topk * input_tokens + num_experts * (M_a - 1)
+// max_num_tokens_padded : topk * input_tokens + num_experts * M_a - topk (updated)
 // * this could be larger than actual, since actual tokens are on GPU
 //
 // sorted_token_ids_ptr   : [0, 6, 6, 6, 2, 3, 4, 6, 1, 3, 6, 6, 0, 1, 2, 3, 4, 6, 6, 6, 6, 6, 6, 6, 0, 1, 2, 5]

include/ck_tile/ops/fused_moe/pipeline/moe_sorting_problem.hpp → include/ck_tile/ops/fused_moe/kernel/moe_sorting_problem.hpp

@@ -25,4 +25,28 @@ struct MoeSortingProblem
         InternalLoadUnroll_;                           // TODO: need better design(like tile size)
     static constexpr index_t ExpertTile = ExpertTile_; // TODO: only used in store out
 };
+
+template <typename IndexType_,
+          typename WeightType_,
+          index_t SubTokenTile_,    // 1,2,4,8, or 0 in the future
+          bool SubTokenOneShot_,    // if we only loop over once or not
+          bool LocalExpertMasking_, // used in EP case
+          bool SkipExpertsWithZeroTokens_ = true,
+          index_t ExpertTile_             = 0>
+struct MoeSortingProblemEx
+{
+    // TODO: this kernel only support warp per row
+    using WeightType = remove_cvref_t<WeightType_>;
+    using IndexType  = remove_cvref_t<IndexType_>;
+
+    static constexpr index_t WarpSize               = get_warp_size();
+    static constexpr index_t WarpsPerBlock          = 1;
+    static constexpr index_t SubTokenTile           = SubTokenTile_;
+    static constexpr bool SubTokenOneShot           = SubTokenOneShot_;
+    static constexpr bool LocalExpertMasking        = LocalExpertMasking_;
+    static constexpr bool SkipExpertsWithZeroTokens = SkipExpertsWithZeroTokens_;
+    static_assert(SubTokenTile == 1 || SubTokenTile == 2 || SubTokenTile == 4 || SubTokenTile == 8);
+    static constexpr index_t ExpertTile = ExpertTile_; // TODO: only used in store out
+};
+
 } // namespace ck_tile

include/ck_tile/ops/gemm.hpp

@@ -29,6 +29,8 @@
 #include "ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp"
 #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_base.hpp"
 #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v3.hpp"
+#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4.hpp"
+#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_comp_v4_default_policy.hpp"
 #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_mem.hpp"
 #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_ag_bg_cr_scheduler.hpp"
 #include "ck_tile/ops/gemm/pipeline/gemm_pipeline_agmem_bgmem_creg_v1.hpp"
@@ -46,3 +48,4 @@
 #include "ck_tile/ops/gemm/warp/warp_gemm_impl.hpp"
 #include "ck_tile/ops/common/generic_2d_block_shape.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
+#include "ck_tile/ops/common/utils.hpp"

include/ck_tile/ops/gemm/block/block_gemm_areg_breg_creg_v1.hpp

@@ -14,24 +14,54 @@ namespace ck_tile {
 template <typename Problem_, typename Policy_ = BlockGemmARegBRegCRegV1DefaultPolicy>
 struct BlockGemmARegBRegCRegV1
 {
-    using Problem        = remove_cvref_t<Problem_>;
-    using Policy         = remove_cvref_t<Policy_>;
-    using ADataType      = remove_cvref_t<typename Problem::ADataType>;
-    using BDataType      = remove_cvref_t<typename Problem::BDataType>;
-    using CDataType      = remove_cvref_t<typename Problem::CDataType>;
-    using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
-
-    static constexpr index_t kBlockSize   = Problem::kBlockSize;
-    static constexpr index_t MPerBlock    = BlockGemmShape::kM;
-    static constexpr index_t NPerBlock    = BlockGemmShape::kN;
-    static constexpr index_t KPerBlock    = BlockGemmShape::kK;
-    static constexpr auto config          = Policy::template GetWarpGemmMWarpNWarp<Problem>();
-    using WG                              = remove_cvref_t<decltype(config.template at<0>())>;
-    static constexpr index_t MWarp        = config.template at<1>();
-    static constexpr index_t NWarp        = config.template at<2>();
-    static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WG::kM);
-    static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WG::kN);
-    static constexpr index_t KIterPerWarp = KPerBlock / WG::kK;
+    private:
+    template <typename PipelineProblem_, typename GemmPolicy_>
+    struct GemmTraits_
+    {
+        using Problem        = remove_cvref_t<PipelineProblem_>;
+        using Policy         = remove_cvref_t<GemmPolicy_>;
+        using ADataType      = remove_cvref_t<typename Problem::ADataType>;
+        using BDataType      = remove_cvref_t<typename Problem::BDataType>;
+        using CDataType      = remove_cvref_t<typename Problem::CDataType>;
+        using BlockGemmShape = remove_cvref_t<typename Problem::BlockGemmShape>;
+
+        static constexpr index_t kBlockSize = Problem::kBlockSize;
+
+        static constexpr index_t MPerBlock = BlockGemmShape::kM;
+        static constexpr index_t NPerBlock = BlockGemmShape::kN;
+        static constexpr index_t KPerBlock = BlockGemmShape::kK;
+
+        static constexpr auto config = Policy::template GetWarpGemmMWarpNWarp<Problem>();
+        using WarpGemm               = remove_cvref_t<decltype(config.template at<0>())>;
+
+        static constexpr index_t MWarp        = config.template at<1>();
+        static constexpr index_t NWarp        = config.template at<2>();
+        static constexpr index_t MIterPerWarp = MPerBlock / (MWarp * WarpGemm::kM);
+        static constexpr index_t NIterPerWarp = NPerBlock / (NWarp * WarpGemm::kN);
+        static constexpr index_t KIterPerWarp = KPerBlock / WarpGemm::kK;
+
+        static constexpr index_t KPack = WarpGemm::kKPerThread;
+    };
+
+    public:
+    using Problem = remove_cvref_t<Problem_>;
+    using Policy  = remove_cvref_t<Policy_>;
+
+    using Traits = GemmTraits_<Problem, Policy>;
+
+    using WarpGemm       = typename Traits::WarpGemm;
+    using BlockGemmShape = typename Traits::BlockGemmShape;
+
+    using ADataType = remove_cvref_t<typename Traits::ADataType>;
+    using BDataType = remove_cvref_t<typename Traits::BDataType>;
+    using CDataType = remove_cvref_t<typename Traits::CDataType>;
+
+    static constexpr index_t KIterPerWarp = Traits::KIterPerWarp;
+    static constexpr index_t MIterPerWarp = Traits::MIterPerWarp;
+    static constexpr index_t NIterPerWarp = Traits::NIterPerWarp;
+
+    static constexpr index_t MWarp = Traits::MWarp;
+    static constexpr index_t NWarp = Traits::NWarp;
 
     CK_TILE_DEVICE static constexpr auto MakeABlockDistributionEncode()
     {
@@ -43,7 +73,7 @@ struct BlockGemmARegBRegCRegV1
                                        sequence<1, 2>,
                                        sequence<0, 0>>{};
         constexpr auto a_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
-            a_block_outer_dstr_encoding, typename WG::AWarpDstrEncoding{});
+            a_block_outer_dstr_encoding, typename WarpGemm::AWarpDstrEncoding{});
 
         return a_block_dstr_encode;
     }
@@ -58,7 +88,7 @@ struct BlockGemmARegBRegCRegV1
                                        sequence<1, 2>,
                                        sequence<0, 0>>{};
         constexpr auto b_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
-            b_block_outer_dstr_encoding, typename WG::BWarpDstrEncoding{});
+            b_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
 
         return b_block_dstr_encode;
     }
@@ -73,7 +103,7 @@ struct BlockGemmARegBRegCRegV1
             sequence<1, 2>,
             sequence<0, 0>>{};
         constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
-            c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
+            c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
 
         return c_block_dstr_encode;
     }
@@ -112,13 +142,13 @@ struct BlockGemmARegBRegCRegV1
                                                        .get_static_tile_distribution_encoding())>>,
             "C distribution is wrong!");
 
-        using AWarpDstr = typename WG::AWarpDstr;
-        using BWarpDstr = typename WG::BWarpDstr;
-        using CWarpDstr = typename WG::CWarpDstr;
+        using AWarpDstr = typename WarpGemm::AWarpDstr;
+        using BWarpDstr = typename WarpGemm::BWarpDstr;
+        using CWarpDstr = typename WarpGemm::CWarpDstr;
 
-        using AWarpTensor = typename WG::AWarpTensor;
-        using BWarpTensor = typename WG::BWarpTensor;
-        using CWarpTensor = typename WG::CWarpTensor;
+        using AWarpTensor = typename WarpGemm::AWarpTensor;
+        using BWarpTensor = typename WarpGemm::BWarpTensor;
+        using CWarpTensor = typename WarpGemm::CWarpTensor;
 
         constexpr auto a_warp_y_lengths =
             to_sequence(AWarpDstr{}.get_ys_to_d_descriptor().get_lengths());
@@ -157,7 +187,7 @@ struct BlockGemmARegBRegCRegV1
                         merge_sequences(sequence<1, 1>{}, c_warp_y_lengths));
 
                     // warp GEMM
-                    WG{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
+                    WarpGemm{}(c_warp_tensor, a_warp_tensor, b_warp_tensor);
 
                     // write C warp tensor into C block tensor
                     c_block_tensor.set_y_sliced_thread_data(
@@ -180,7 +210,7 @@ struct BlockGemmARegBRegCRegV1
             sequence<0, 0>>{};
 
         constexpr auto c_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
-            c_block_outer_dstr_encoding, typename WG::CWarpDstrEncoding{});
+            c_block_outer_dstr_encoding, typename WarpGemm::CWarpDstrEncoding{});
         constexpr auto c_block_dstr = make_static_tile_distribution(c_block_dstr_encode);
         auto c_block_tensor         = make_static_distributed_tensor<CDataType>(c_block_dstr);
         return c_block_tensor;

include/ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
 #include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
+#include "ck_tile/ops/common.hpp"
+#include "ck_tile/host/concat.hpp"
 
 namespace ck_tile {
 
@@ -57,6 +59,18 @@ struct BatchedGemmKernel : public GemmKernel<TilePartitioner_, GemmPipeline_, Ep
     using BLayout          = typename Base::BLayout;
     using CLayout          = typename Base::CLayout;
 
+    [[nodiscard]] CK_TILE_HOST static const std::string GetName()
+    {
+        // clang-format off
+        using P_ = GemmPipeline;
+
+        return concat('_', "gemm_batched", gemm_prec_str<ADataType, BDataType>,
+                      concat('x', P_::kMPerBlock, P_::kNPerBlock, P_::kKPerBlock), 
+                      concat('x', P_::GetVectorSizeA(), P_::GetVectorSizeB(), P_::GetVectorSizeC()),
+                      concat('x', P_::kPadM, P_::kPadN, P_::kPadK));
+        // clang-format on
+    }
+
     struct BatchedGemmKernelArgs : GemmKernelArgs
     {
         index_t batch_stride_A;