Merge pull request #260 from ROCm/merge_from_public

Merge from public

example/ck_tile/12_smoothquant/example_smoothquant.cpp

@@ -35,7 +35,8 @@ auto create_args(int argc, char* argv[])
     ck_tile::ArgParser arg_parser;
     arg_parser.insert("m", "3328", "m dimension")
         .insert("n", "4096", "n dimension")
-        .insert("stride", "-1", "stride per row, if -1 then equal to n")
+        .insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
+        .insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
         .insert("e", "1e-5", "epsilon")
         .insert("v", "1", "cpu validation or not")
         .insert("prec", "fp16", "precision")
@@ -49,11 +50,14 @@ auto create_args(int argc, char* argv[])
 template <typename DataType>
 bool run(const ck_tile::ArgParser& arg_parser)
 {
-    ck_tile::index_t m      = arg_parser.get_int("m");
-    ck_tile::index_t n      = arg_parser.get_int("n");
-    ck_tile::index_t stride = arg_parser.get_int("stride");
-    if(stride < 0)
-        stride = n;
+    ck_tile::index_t m        = arg_parser.get_int("m");
+    ck_tile::index_t n        = arg_parser.get_int("n");
+    ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
+    if(x_stride < 0)
+        x_stride = n;
+    ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
+    if(y_stride < 0)
+        y_stride = n;
     std::string data_type = arg_parser.get_str("prec");
     int do_validation     = arg_parser.get_int("v");
     int warmup            = arg_parser.get_int("warmup");
@@ -68,14 +72,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
     using ComputeDataType = float;
 
     // host verify
-    ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
+    ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
     ck_tile::HostTensor<XScaleDataType> xscale_host({n});
 
     ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
     ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
 
-    ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
-    ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
+    ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
+    ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
 
     ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
     ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
@@ -116,7 +120,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
                                       qy_buf.GetDeviceBuffer(),
                                       m,
                                       n,
-                                      stride};
+                                      x_stride,
+                                      y_stride};
 
     auto kargs = Kernel::MakeKargs(args);
 
@@ -133,7 +138,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
     if(do_validation)
     {
         using YDataType = ComputeDataType;
-        ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
+        ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
         // smooth outlier
         {
             auto f = [&](auto n_) {
@@ -183,7 +188,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
             qy_buf.FromDevice(qy_host_dev.data());
             auto [rtol, atol] = get_elimit<QYDataType>();
 
-            if(stride == n)
+            if(y_stride == n)
             {
                 pass = ck_tile::check_err(qy_host_dev,
                                           qy_host_ref,
@@ -195,10 +200,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
             {
                 for(int i_r = 0; i_r < m; i_r++)
                 {
-                    std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
-                                                            qy_host_dev.begin() + i_r * stride + n);
-                    std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
-                                                            qy_host_ref.begin() + i_r * stride + n);
+                    std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
+                                                            qy_host_dev.begin() + i_r * y_stride +
+                                                                n);
+                    std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
+                                                            qy_host_ref.begin() + i_r * y_stride +
+                                                                n);
                     pass &= ck_tile::check_err(qy_host_dev_row,
                                                qy_host_ref_row,
                                                std::string("qy[") + std::to_string(i_r) +
@@ -210,8 +217,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
         }
 
         std::cout << "[" << data_type << "]"
-                  << " m:" << m << ", n:" << n << ", stride:" << stride
-                  << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
+                  << " m:" << m << ", n:" << n << ", x_stride:" << x_stride
+                  << ", y_stride:" << y_stride << ", valid:" << (pass ? "y" : "n") << std::flush
+                  << std::endl;
     }
 
     return pass;

example/ck_tile/12_smoothquant/smoothquant.cpp

@@ -33,7 +33,8 @@ auto create_args(int argc, char* argv[])
     ck_tile::ArgParser arg_parser;
     arg_parser.insert("m", "3328", "m dimension")
         .insert("n", "4096", "n dimension")
-        .insert("stride", "-1", "stride per row, if -1 then equal to n")
+        .insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
+        .insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
         .insert("v", "1", "cpu validation or not")
         .insert("kname", "1", "print kernel name or not")
         .insert("prec", "fp16", "precision")
@@ -47,18 +48,21 @@ auto create_args(int argc, char* argv[])
 template <typename DataType>
 bool run(const ck_tile::ArgParser& arg_parser)
 {
-    ck_tile::index_t m      = arg_parser.get_int("m");
-    ck_tile::index_t n      = arg_parser.get_int("n");
-    ck_tile::index_t stride = arg_parser.get_int("stride");
-    if(stride < 0)
-        stride = n;
+    ck_tile::index_t m        = arg_parser.get_int("m");
+    ck_tile::index_t n        = arg_parser.get_int("n");
+    ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
+    if(x_stride < 0)
+        x_stride = n;
+    ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
+    if(y_stride < 0)
+        y_stride = n;
     std::string data_type = arg_parser.get_str("prec");
     int kname             = arg_parser.get_int("kname");
     int do_validation     = arg_parser.get_int("v");
     int warmup            = arg_parser.get_int("warmup");
     int repeat            = arg_parser.get_int("repeat");
 
-    assert(stride >= n);
+    assert(x_stride >= n);
 
     using TypeConfig = SmoothquantTypeConfig<DataType>;
 
@@ -69,14 +73,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
     using ComputeDataType = typename TypeConfig::ComputeDataType;
 
     // host verify
-    ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
+    ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
     ck_tile::HostTensor<XScaleDataType> xscale_host({n});
 
     ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
     ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
 
-    ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
-    ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
+    ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
+    ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
 
     ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
     ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
@@ -90,7 +94,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
     xscale_buf.ToDevice(xscale_host.data());
 
     std::cout << "[" << data_type << "]"
-              << " m:" << m << ", n:" << n << ", stride:" << stride << std::flush;
+              << " m:" << m << ", n:" << n << ", x_stride:" << x_stride << ", y_stride:" << y_stride
+              << std::flush;
 
     smoothquant_traits traits{data_type};
 
@@ -100,7 +105,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
                           qy_buf.GetDeviceBuffer(),
                           m,
                           n,
-                          stride};
+                          x_stride,
+                          y_stride};
 
     float ave_time = smoothquant(
         traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
@@ -116,7 +122,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
     if(do_validation)
     {
         using YDataType = ComputeDataType;
-        ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
+        ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
         // smooth outlier
         {
             auto f = [&](auto n_) {
@@ -166,7 +172,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
             qy_buf.FromDevice(qy_host_dev.data());
             auto [rtol, atol] = get_elimit<QYDataType>();
 
-            if(stride == n)
+            if(y_stride == n)
             {
                 pass = ck_tile::check_err(qy_host_dev,
                                           qy_host_ref,
@@ -178,10 +184,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
             {
                 for(int i_r = 0; i_r < m; i_r++)
                 {
-                    std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
-                                                            qy_host_dev.begin() + i_r * stride + n);
-                    std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
-                                                            qy_host_ref.begin() + i_r * stride + n);
+                    std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
+                                                            qy_host_dev.begin() + i_r * y_stride +
+                                                                n);
+                    std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
+                                                            qy_host_ref.begin() + i_r * y_stride +
+                                                                n);
                     pass &= ck_tile::check_err(qy_host_dev_row,
                                                qy_host_ref_row,
                                                std::string("qy[") + std::to_string(i_r) +

include/ck/config.h.in

@@ -111,6 +111,22 @@
 #cmakedefine CK_USE_WMMA @CK_USE_WMMA@
 #endif
 
+#ifndef CK_USE_GFX94
+#cmakedefine CK_USE_GFX94 @CK_USE_GFX94@
+#endif
+
+#ifndef DCK_USE_OCP_FP8
+#cmakedefine DCK_USE_OCP_FP8 @DCK_USE_OCP_FP8@
+#endif
+
+#ifndef CK_USE_FNUZ_FP8
+#cmakedefine CK_USE_FNUZ_FP8 @CK_USE_FNUZ_FP8@
+#endif
+
+#ifndef CK_USE_FP8_ON_UNSUPPORTED_ARCH
+#cmakedefine CK_USE_FP8_ON_UNSUPPORTED_ARCH @CK_USE_FP8_ON_UNSUPPORTED_ARCH@
+#endif
+
 // clang-format on
 
 #endif // CK_CONFIG_H_IN

include/ck/tensor_operation/gpu/device/device_base.hpp

@@ -5,6 +5,8 @@
 
 #include <string>
 #include <sstream>
+#include <regex>
+#include <optional>
 
 #include "ck/stream_config.hpp"
 
@@ -12,6 +14,34 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
+#define GET_OBJECT_NAME_IMLP                                                  \
+    std::optional<std::string> GetObjectName() const override                 \
+    {                                                                         \
+        std::string str = __PRETTY_FUNCTION__;                                \
+        static std::regex obj_name_expr{"<std::string> (.*)::GetObjectName"}; \
+        std::smatch match;                                                    \
+        if(!std::regex_search(str, match, obj_name_expr))                     \
+        {                                                                     \
+            return str;                                                       \
+        }                                                                     \
+        return std::string(match[1]) + ';';                                   \
+    }
+
+#define GET_TEMPLATE_INFO_IMPL                                  \
+    std::optional<std::string> GetTemplateInfo() const override \
+    {                                                           \
+        std::string str = __PRETTY_FUNCTION__;                  \
+        static std::regex template_expr{"\\[(.*)\\]"};          \
+        std::smatch match;                                      \
+        if(!std::regex_search(str, match, template_expr))       \
+        {                                                       \
+            return std::nullopt;                                \
+        }                                                       \
+        return std::string(match[1]);                           \
+    }
+
+#define REGISTER_EXTRA_PRINTING_METHODS GET_OBJECT_NAME_IMLP GET_TEMPLATE_INFO_IMPL
+
 struct BaseArgument
 {
     BaseArgument()                    = default;
@@ -48,6 +78,10 @@ struct BaseOperator
 
     virtual std::string GetTypeIdName() const { return typeid(*this).name(); }
 
+    virtual std::optional<std::string> GetObjectName() const { return std::nullopt; }
+
+    virtual std::optional<std::string> GetTemplateInfo() const { return std::nullopt; }
+
     virtual std::string GetTypeIdHashCode() const
     {
         std::ostringstream oss;

include/ck/tensor_operation/gpu/device/device_batched_gemm_multi_d.hpp

@@ -89,7 +89,8 @@ struct DeviceBatchedGemmV2MultiD : public BaseOperator
                         index_t BatchStrideE,
                         AElementwiseOperation a_element_op,
                         BElementwiseOperation b_element_op,
-                        CDEElementwiseOperation cde_element_op) = 0;
+                        CDEElementwiseOperation cde_element_op,
+                        index_t KBatch) = 0;
 
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
 };

include/ck/tensor_operation/gpu/device/impl/device_batched_gemm_multiple_d_xdl_cshuffle_v3.hpp

@@ -41,12 +41,15 @@ __global__ void
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     const index_t g_idx = blockIdx.z % karg.Batch;
+    const index_t k_idx = blockIdx.z / karg.Batch;
 
     const auto a_batch_offset  = karg.compute_ptr_offset_of_batch.GetAPtrOffset(g_idx);
     const auto b_batch_offset  = karg.compute_ptr_offset_of_batch.GetBPtrOffset(g_idx);
     const auto ds_batch_offset = karg.compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
     const auto c_batch_offset  = karg.compute_ptr_offset_of_batch.GetCPtrOffset(g_idx);
 
+    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, k_idx);
+
     // populate pointer, desc for Ds
     static_for<0, GridwiseGemm::NumDTensor, 1>{}([&](auto i) {
         // D pointer
@@ -54,8 +57,8 @@ __global__ void
     });
 
     GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
-        karg.p_a_grid + a_batch_offset,
-        karg.p_b_grid + b_batch_offset,
+        karg.p_a_grid + a_batch_offset + splitk_batch_offset.a_k_split_offset,
+        karg.p_b_grid + b_batch_offset + splitk_batch_offset.b_k_split_offset,
         karg.p_ds_grid,
         karg.p_c_grid + c_batch_offset,
         p_shared,
@@ -87,12 +90,15 @@ __global__ void
     __shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     const index_t g_idx = blockIdx.z % karg.Batch;
+    const index_t k_idx = blockIdx.z / karg.Batch;
 
     const auto a_batch_offset  = karg.compute_ptr_offset_of_batch.GetAPtrOffset(g_idx);
     const auto b_batch_offset  = karg.compute_ptr_offset_of_batch.GetBPtrOffset(g_idx);
     const auto ds_batch_offset = karg.compute_ptr_offset_of_batch.GetDsPtrOffset(g_idx);
     const auto c_batch_offset  = karg.compute_ptr_offset_of_batch.GetCPtrOffset(g_idx);
 
+    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, k_idx);
+
     // populate pointer, desc for Ds
     static_for<0, GridwiseGemm::NumDTensor, 1>{}([&](auto i) {
         // D pointer
@@ -100,8 +106,8 @@ __global__ void
     });
 
     GridwiseGemm::template Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
-        karg.p_a_grid + a_batch_offset,
-        karg.p_b_grid + b_batch_offset,
+        karg.p_a_grid + a_batch_offset + splitk_batch_offset.a_k_split_offset,
+        karg.p_b_grid + b_batch_offset + splitk_batch_offset.b_k_split_offset,
         karg.p_ds_grid,
         karg.p_c_grid + c_batch_offset,
         p_shared_0,
@@ -303,7 +309,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                  index_t Batch_,
                  AElementwiseOperation a_element_op_,
                  BElementwiseOperation b_element_op_,
-                 CElementwiseOperation c_element_op_)
+                 CElementwiseOperation c_element_op_,
+                 index_t KBatch_)
             : GridwiseGemm::Argument{p_a_grid_,
                                      p_b_grid_,
                                      p_ds_grid_,
@@ -315,7 +322,7 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                                      StrideB_,
                                      StrideDs_,
                                      StrideE_,
-                                     1,
+                                     KBatch_,
                                      a_element_op_,
                                      b_element_op_,
                                      c_element_op_},
@@ -336,13 +343,14 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                 arg.Print();
             }
 
-            if(!GridwiseGemm::CheckValidity(arg) || arg.KBatch > 1)
+            if(!GridwiseGemm::CheckValidity(arg))
             {
                 throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
             }
 
             index_t gdx, gdy, gdz;
-            std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.Batch);
+            std::tie(gdx, gdy, gdz) =
+                GridwiseGemm::CalculateGridSize(arg.M, arg.N, arg.Batch * arg.KBatch);
 
             float ave_time = 0;
 
@@ -387,10 +395,11 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                         rotating_mem.Next();
                         // clear c mem
                         if(arg_.KBatch > 1)
-                            hipGetErrorString(hipMemsetAsync(arg_.p_c_grid,
-                                                             0,
-                                                             arg_.M * arg_.N * sizeof(CDataType),
-                                                             stream_config.stream_id_));
+                            hipGetErrorString(
+                                hipMemsetAsync(arg_.p_c_grid,
+                                               0,
+                                               arg.Batch * arg_.M * arg_.N * sizeof(CDataType),
+                                               stream_config.stream_id_));
                     };
 
                     ave_time = ck::utility::launch_and_time_kernel_with_preprocess<false>(
@@ -889,7 +898,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                              index_t BatchStrideE,
                              AElementwiseOperation a_element_op,
                              BElementwiseOperation b_element_op,
-                             CElementwiseOperation c_element_op)
+                             CElementwiseOperation c_element_op,
+                             index_t KBatch = 1)
     {
         return Argument{static_cast<const ADataType*>(p_a),
                         static_cast<const BDataType*>(p_b),
@@ -909,7 +919,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                         Batch,
                         a_element_op,
                         b_element_op,
-                        c_element_op};
+                        c_element_op,
+                        KBatch};
     }
 
     static auto MakeInvoker() { return Invoker{}; }
@@ -934,7 +945,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                         index_t BatchStrideE,
                         AElementwiseOperation a_element_op,
                         BElementwiseOperation b_element_op,
-                        CElementwiseOperation c_element_op) override
+                        CElementwiseOperation c_element_op,
+                        index_t KBatch = 1) override
     {
         return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
                                           static_cast<const BDataType*>(p_b),
@@ -954,7 +966,8 @@ struct DeviceBatchedGemmMultiD_Xdl_CShuffle_V3
                                           Batch,
                                           a_element_op,
                                           b_element_op,
-                                          c_element_op);
+                                          c_element_op,
+                                          KBatch);
     }
 
     // polymorphic

include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp

@@ -729,6 +729,7 @@ struct DeviceGemm_Xdl_CShuffleV3 : public DeviceGemmV2<ALayout,
 
         return str.str();
     }
+    REGISTER_EXTRA_PRINTING_METHODS
 };
 
 } // namespace device

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3_multi_d.hpp

@@ -41,7 +41,7 @@ __global__ void
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx9__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
-    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);
+    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
 
     GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
         karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
@@ -76,7 +76,7 @@ __global__ void
     __shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];
     __shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
-    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg);
+    auto splitk_batch_offset = typename GridwiseGemm::SplitKBatchOffset(karg, blockIdx.z);
 
     GridwiseGemm::template Run_2Lds<HasMainKBlockLoop, CGlobalMemoryDataOperation, TailNum>(
         karg.p_a_grid + splitk_batch_offset.a_k_split_offset,
@@ -639,27 +639,27 @@ struct GridwiseGemmMultiD_xdl_cshuffle_v3
 
     struct SplitKBatchOffset
     {
-        __device__ SplitKBatchOffset(Argument& karg)
+        __device__ SplitKBatchOffset(Argument& karg, index_t k_id)
         {
             if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
-                a_k_split_offset = blockIdx.z * karg.KRead;
+                a_k_split_offset = k_id * karg.KRead;
             }
             else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
-                a_k_split_offset = blockIdx.z * karg.KRead * karg.StrideA;
+                a_k_split_offset = k_id * karg.KRead * karg.StrideA;
             }
 
             if constexpr(is_same_v<tensor_layout::gemm::RowMajor, BLayout>)
             {
-                b_k_split_offset = blockIdx.z * karg.KRead * karg.StrideB;
+                b_k_split_offset = k_id * karg.KRead * karg.StrideB;
             }
             else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
             {
-                b_k_split_offset = blockIdx.z * karg.KRead;
+                b_k_split_offset = k_id * karg.KRead;
             }
 
-            if(blockIdx.z < static_cast<uint32_t>(karg.KBatch - 1))
+            if(k_id < karg.KBatch - 1)
             {
                 karg.K = karg.KRead;
             }

include/ck/utility/amd_ck_fp8.hpp

@@ -18,6 +18,20 @@
 #define CK_USE_OCP_FP8 0
 #endif
 
+namespace {
+// https://en.cppreference.com/w/cpp/types/conditional
+template <bool B, class T, class F>
+struct conditional
+{
+    using type = T;
+};
+template <class T, class F>
+struct conditional<false, T, F>
+{
+    using type = F;
+};
+} // namespace
+
 namespace ck {
 
 using f8_fnuz_t  = _BitInt(8);
@@ -191,11 +205,10 @@ __host__ __device__ static inline T cast_from_f8(fp8_storage_t x)
         }
     }
 
-    typename __hip_internal::conditional<
+    typename conditional<
         sizeof(T) == 2,
         unsigned short int,
-        typename __hip_internal::conditional<sizeof(T) == 4, unsigned int, unsigned long long>::
-            type>::type retval;
+        typename conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type retval;
 
     if constexpr(we == 5 && is_half && !is_fnuz)
     {
@@ -538,11 +551,10 @@ __host__ __device__ static inline fp8_storage_t cast_to_f8(T _x, unsigned int rn
 
     constexpr int mfmt = (sizeof(T) == 8) ? 52 : ((sizeof(T) == 4) ? 23 : 10);
 
-    using T_bitwise = typename __hip_internal::conditional<
+    using T_bitwise = typename conditional<
         sizeof(T) == 2,
         unsigned short int,
-        typename __hip_internal::conditional<sizeof(T) == 4, unsigned int, unsigned long long>::
-            type>::type;
+        typename conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type;
     T_bitwise x_bitwise = bit_cast<T_bitwise>(_x);
 
     unsigned long long x{x_bitwise};

include/ck/utility/math_v2.hpp

@@ -611,7 +611,7 @@ inline __device__ int8_t neg<int8_t>(int8_t x)
 template <>
 inline __device__ half_t neg<half_t>(half_t x)
 {
-    return __hneg(x);
+    return __hneg(static_cast<__half>(x));
 };
 
 template <typename T>

include/ck_tile/README.md

@@ -45,5 +45,8 @@ our implementation of different device operators.
 **[ops/epilogue]**  
 epilogue part of our kernel. We may extend this epilogue part to let users to build their own cutomized epilogues.
 
+**[ref]**  
+reference implementation of cpu or gpu. This folder is supposed to include a specific header on demand.
+
 ## examples
 currently we put all ck_tile related example under [/example/ck_tile](/example/ck_tile/) folder. Please check each example's subfolder.

include/ck_tile/core.hpp

@@ -54,6 +54,7 @@
 #include "ck_tile/core/tensor/tile_window_linear.hpp"
 #include "ck_tile/core/tensor/tile_window_utils.hpp"
 #include "ck_tile/core/tensor/update_tile.hpp"
+#include "ck_tile/core/utility/amd_address_space.hpp"
 #include "ck_tile/core/utility/bit_cast.hpp"
 #include "ck_tile/core/utility/functional.hpp"
 #include "ck_tile/core/utility/functional_with_tuple.hpp"

include/ck_tile/ops/flatmm.hpp

@@ -5,6 +5,7 @@
 
 #include "ck_tile/ops/flatmm/block/flatmm_32x512x128_1x4x1_16x16x32.hpp"
 #include "ck_tile/ops/flatmm/block/flatmm_sn_32x128x512_1x4x1_16x16x32.hpp"
+#include "ck_tile/ops/flatmm/block/flatmm_sn_32x128x512_1x4x1_16x16x32_itl.hpp"
 #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/fused_moe/pipeline/fused_moegemm_pipeline_flatmm_policy.hpp

@@ -810,21 +810,46 @@ struct FusedMoeGemmPipelineFlatmmPolicy
     CK_TILE_HOST_DEVICE static constexpr auto GetUK_1()
     {
         using S_ = typename Problem::BlockShape;
+        using T_ = typename Problem::Traits;
         if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::bf16_t> &&
                      std::is_same_v<typename Problem::DDataType, ck_tile::bf16_t> &&
                      std::is_same_v<typename Problem::TopkWeightDataType, float> &&
                      S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
-                     S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32)
+                     S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
+                     T_::PipeInterleave == false)
         {
             return FlatmmSn_32x128x512_1x4x1_16x16x32_BF16{};
+            // return FlatmmSn_32x128x512_1x4x1_16x16x32_BF16_itl{};
         }
         else if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::fp16_t> &&
                           std::is_same_v<typename Problem::DDataType, ck_tile::fp16_t> &&
                           std::is_same_v<typename Problem::TopkWeightDataType, float> &&
                           S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
-                          S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32)
+                          S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
+                          T_::PipeInterleave == false)
         {
             return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16{};
+            // return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16_itl{};
+        }
+        else if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::bf16_t> &&
+                          std::is_same_v<typename Problem::DDataType, ck_tile::bf16_t> &&
+                          std::is_same_v<typename Problem::TopkWeightDataType, float> &&
+                          S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
+                          S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
+                          T_::PipeInterleave == true)
+        {
+            // return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16{};
+            return FlatmmSn_32x128x512_1x4x1_16x16x32_BF16_itl{};
+        }
+        else if constexpr(std::is_same_v<typename Problem::YDataType, ck_tile::fp16_t> &&
+                          std::is_same_v<typename Problem::DDataType, ck_tile::fp16_t> &&
+                          std::is_same_v<typename Problem::TopkWeightDataType, float> &&
+                          S_::Block_M1 == 32 && S_::Block_N1 == 128 && S_::Block_K1 == 512 &&
+                          S_::Warp_M0 == 16 && S_::Warp_N0 == 16 && S_::Warp_K0 == 32 &&
+                          T_::PipeInterleave == true)
+        {
+            // return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16{};
+            return FlatmmSn_32x128x512_1x4x1_16x16x32_FP16_itl{};
         }
     }
 };

include/ck_tile/ops/fused_moe/pipeline/fused_moegemm_traits.hpp

@@ -22,7 +22,8 @@ template <bool IsGateOnly_,
           FusedMoeGemmWeightPermuteEnum PermuteEnum_ =
               FusedMoeGemmWeightPermuteEnum::b_nr_kr_waveflatten,
           bool PadHiddenSize_       = false,
-          bool PadIntermediateSize_ = false>
+          bool PadIntermediateSize_ = false,
+          bool PipeInterleave_      = true>
 struct FusedMoeGemmTraits
 {
     // Gate+Up or Gate only
@@ -32,6 +33,7 @@ struct FusedMoeGemmTraits
     static constexpr FusedMoeGemmWeightPermuteEnum PermuteEnum = PermuteEnum_;
     static constexpr bool PadHiddenSize                        = PadHiddenSize_;
     static constexpr bool PadIntermediateSize                  = PadIntermediateSize_;
+    static constexpr bool PipeInterleave                       = PipeInterleave_;
 };
 
 // Note: this need to be a bit mask

include/ck_tile/ops/gemm.hpp

@@ -23,10 +23,10 @@
 #include "ck_tile/ops/gemm/block/block_gemm_asmem_bsmem_creg_v1_default_policy.hpp"
 #include "ck_tile/ops/gemm/block/block_gemm_problem.hpp"
 #include "ck_tile/ops/gemm/block/block_universal_gemm_as_bs_cr.hpp"
+#include "ck_tile/ops/gemm/kernel/batched_gemm_kernel.hpp"
 #include "ck_tile/ops/gemm/kernel/gemm_kernel.hpp"
 #include "ck_tile/ops/gemm/kernel/gemm_tile_partitioner.hpp"
 #include "ck_tile/ops/gemm/kernel/grouped_gemm_kernel.hpp"
-#include "ck_tile/ops/gemm/kernel/batched_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_mem.hpp"