Merge pull request #31 from ROCmSoftwarePlatform/miopen_downstream-dynamic_reduction_pr

[MIOpen Downstream] Dynamic Reduction PR

composable_kernel/src/kernel_wrapper/gridwise_generic_reduction_second_call_blockwise.cpp

+/*******************************************************************************
+ *
+ * MIT License
+ *
+ * Copyright (c) 2021 Advanced Micro Devices, Inc.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ *
+ *******************************************************************************/
+#include "config.hpp"
+#include "number.hpp"
+#include "sequence.hpp"
+#include "tensor_descriptor_helper.hpp"
+#include "data_type_enum_helper.hpp"
+#include "reduction_common.hpp"
+#include "gridwise_generic_2d_reduction_blockwise.hpp"
+
+using namespace ck;
+
+using srcDataType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_SRC_DATATYPE)>::type;
+using dstDataType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_DST_DATATYPE)>::type;
+using compType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_REDUCE_COMPTYPE)>::type;
+
+constexpr index_t BlockSize = CK_PARAM_BLOCKSIZE; // tunable
+
+constexpr index_t srcDims = CK_PARAM_IN_DIMS;
+constexpr index_t dstDims = CK_PARAM_OUT_DIMS;
+
+using toReduceDims  = Sequence<CK_PARAM_TOREDUCE_DIMS>;
+using invariantDims = Sequence<CK_PARAM_INVARIANT_DIMS>; // this could be empty
+
+constexpr ReduceTensorOp_t op          = static_cast<ReduceTensorOp_t>(CK_PARAM_REDUCE_OP);
+constexpr NanPropagation_t nanPropaOpt = CK_PARAM_NAN_PROPAGATE == 0
+                                             ? NanPropagation_t::NOT_PROPAGATE_NAN
+                                             : NanPropagation_t::PROPAGATE_NAN;
+constexpr ReduceTensorIndices_t reduceIndicesOpt = CK_PARAM_REDUCE_INDICES == 0
+                                                       ? ReduceTensorIndices_t::NO_INDICES
+                                                       : ReduceTensorIndices_t::FLATTENED_INDICES;
+
+constexpr bool src2d_need_padding = static_cast<bool>(CK_PARAM_SRC2D_PADDING);
+constexpr bool dst1d_need_padding = static_cast<bool>(CK_PARAM_DST1D_PADDING);
+
+////////////////////////////////////////////////////////////////////////////////////////
+using specDims = typename sequence_merge<invariantDims, toReduceDims>::type;
+
+static_assert(is_valid_sequence_map<specDims>::value && specDims::Size() == srcDims,
+              "Wrong invariant and/or toReduce dimensions!");
+
+// The number of invariant dimensions can be zero if all dimension are to be reduced
+static_assert(invariantDims::Size() > 0 || dstDims == 1,
+              "If all source dimensions are reduced, the dest should have only one dimension !!");
+
+constexpr bool indexable    = reduce_binary_operator<compType, op>::indexable;
+constexpr bool need_indices = indexable && (reduceIndicesOpt != ReduceTensorIndices_t::NO_INDICES);
+
+constexpr index_t GredAccessesPerThreadInBlock = CK_PARAM_ACCESSES_PER_THREAD_INBLOCK; // tunable
+
+// helper functions using variadic template arguments
+template <index_t... Ns>
+__device__ static auto make_tuple_from_array_and_index_seq(const int* lengths, Sequence<Ns...>)
+{
+    return make_tuple(static_cast<index_t>(lengths[Ns])...);
+};
+
+template <index_t arraySize>
+__device__ static auto make_tuple_from_array(const int* lengths, Number<arraySize>)
+{
+    static_assert(arraySize >= 1 && arraySize <= 6, "The tensor should have 1 to 6 dimensions");
+
+    constexpr auto index_seq = typename arithmetic_sequence_gen<0, arraySize, 1>::type{};
+
+    return make_tuple_from_array_and_index_seq(lengths, index_seq);
+};
+
+template <index_t... Ns>
+__device__ static constexpr auto make_tuple_from_seq(Sequence<Ns...>)
+{
+    return make_tuple(Ns...);
+};
+
+extern "C" __global__ void gridwise_generic_reduce_2_prepare(int GridSize,
+                                                             int BlkGroupSize,
+                                                             int outLength0,
+                                                             int outLength1,
+                                                             int outLength2,
+                                                             int outLength3,
+                                                             int outLength4,
+                                                             int outLength5,
+                                                             int outStride0,
+                                                             int outStride1,
+                                                             int outStride2,
+                                                             int outStride3,
+                                                             int outStride4,
+                                                             int outStride5,
+                                                             void* __restrict__ ws_global)
+{
+    (void)GridSize;
+
+    void* p_src2dDesc = ws_global;
+    void* p_dst1dDesc = static_cast<char*>(ws_global) + 2048;
+
+    const int dstLengths[6] = {
+        outLength0, outLength1, outLength2, outLength3, outLength4, outLength5};
+    const int dstStrides[6] = {
+        outStride0, outStride1, outStride2, outStride3, outStride4, outStride5};
+
+    const auto tupleDstLengths = make_tuple_from_array(dstLengths, Number<dstDims>{});
+    const auto tupleDstStrides = make_tuple_from_array(dstStrides, Number<dstDims>{});
+
+    const auto dstDesc = make_naive_tensor_descriptor(tupleDstLengths, tupleDstStrides);
+
+    auto dst1dDesc = transform_tensor_descriptor(
+        dstDesc,
+        make_tuple(make_merge_transform(tupleDstLengths)),
+        make_tuple(typename arithmetic_sequence_gen<0, dstDims, 1>::type{}),
+        make_tuple(Sequence<0>{}));
+
+    const index_t invariantLen = dst1dDesc.GetLength(Number<0>{});
+    const index_t toReduceLen  = BlkGroupSize;
+
+    auto src2dDesc = make_naive_tensor_descriptor_packed(make_tuple(invariantLen, toReduceLen));
+
+    constexpr auto copySliceLen = BlockSize * GredAccessesPerThreadInBlock;
+
+    if constexpr(src2d_need_padding)
+    {
+        const auto srcPad =
+            ((toReduceLen + copySliceLen - 1) / copySliceLen) * copySliceLen - toReduceLen;
+
+        auto src2dDesc_2 =
+            transform_tensor_descriptor(src2dDesc,
+                                        make_tuple(make_pass_through_transform(invariantLen),
+                                                   make_pad_transform(toReduceLen, 0, srcPad)),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}));
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(src2dDesc_2)*>(p_src2dDesc) = src2dDesc_2;
+    }
+    else
+    {
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(src2dDesc)*>(p_src2dDesc) = src2dDesc;
+    }
+
+    if(hipThreadIdx_x == 0)
+        *static_cast<decltype(dst1dDesc)*>(p_dst1dDesc) = dst1dDesc;
+};
+
+template <index_t srcDims, index_t dstDims, typename invariantDims, typename toReduceDims>
+struct get_ref_desc_types
+{
+    static constexpr auto ref_tupleDstLengths =
+        make_tuple_from_seq(typename uniform_sequence_gen<dstDims, 8>::type{});
+    static constexpr auto ref_dstDesc =
+        make_naive_tensor_descriptor(ref_tupleDstLengths, ref_tupleDstLengths);
+
+    static constexpr auto ref_dst1dDesc = transform_tensor_descriptor(
+        ref_dstDesc,
+        make_tuple(make_merge_transform(ref_tupleDstLengths)),
+        make_tuple(typename arithmetic_sequence_gen<0, dstDims, 1>::type{}),
+        make_tuple(Sequence<0>{}));
+
+    static constexpr index_t ref_invariantLen = ref_dst1dDesc.GetLength(Number<0>{});
+    static constexpr index_t ref_toReduceLen  = 8;
+
+    static constexpr auto ref_src2dDesc =
+        make_naive_tensor_descriptor_packed(make_tuple(ref_invariantLen, ref_toReduceLen));
+
+    using refType_src2dDesc = decltype(ref_src2dDesc);
+    using refType_dst1dDesc = decltype(ref_dst1dDesc);
+
+    // used by the BlockWise and MultiBlock method
+    using refType_src2dDesc_padded_34 = decltype(
+        transform_tensor_descriptor(ref_src2dDesc,
+                                    make_tuple(make_pass_through_transform(ref_invariantLen),
+                                               make_pad_transform(ref_toReduceLen, 0, 2)),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                    make_tuple(Sequence<0>{}, Sequence<1>{})));
+
+    using refType_dst1dDesc_padded =
+        decltype(transform_tensor_descriptor(ref_dst1dDesc,
+                                             make_tuple(make_pad_transform(ref_invariantLen, 0, 2)),
+                                             make_tuple(Sequence<0>{}),
+                                             make_tuple(Sequence<0>{})));
+};
+
+using refType_src2dDesc =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::refType_src2dDesc;
+using refType_dst1dDesc =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::refType_dst1dDesc;
+using refType_src2dDesc_padded_34 =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::
+        refType_src2dDesc_padded_34;
+using refType_dst1dDesc_padded =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::
+        refType_dst1dDesc_padded;
+
+template <bool need_padding>
+static __device__ auto get_reduction_src2d_descriptor(const void* p_src2dDesc)
+{
+    if constexpr(need_padding)
+        return (*reinterpret_cast<const refType_src2dDesc_padded_34*>(p_src2dDesc));
+    else
+        return (*reinterpret_cast<const refType_src2dDesc*>(p_src2dDesc));
+};
+
+template <bool need_padding>
+static __device__ auto get_reduction_dst1d_descriptor(const void* p_dst1dDesc)
+{
+    if constexpr(need_padding)
+        return (*reinterpret_cast<const refType_dst1dDesc_padded*>(p_dst1dDesc));
+    else
+        return (*reinterpret_cast<const refType_dst1dDesc*>(p_dst1dDesc));
+};
+
+extern "C" __global__ void gridwise_generic_reduce_2(int origReduceLen,
+                                                     float alpha,
+                                                     const void* __restrict__ p_src_global,
+                                                     float beta,
+                                                     void* __restrict__ p_dst_global,
+                                                     void* __restrict__ ws_global,
+                                                     long ws_buf2_bytes_offset,
+                                                     void* __restrict__ indices_global)
+{
+    (void)p_src_global;
+
+    const void* p_src2dDesc = ws_global;
+    const void* p_dst1dDesc = static_cast<char*>(ws_global) + 2048;
+    void* ws_buf1_global    = static_cast<char*>(ws_global) + 4096;
+
+    const auto src2dDesc = get_reduction_src2d_descriptor<src2d_need_padding>(p_src2dDesc);
+    const auto dst1dDesc = get_reduction_dst1d_descriptor<dst1d_need_padding>(p_dst1dDesc);
+
+    using gridwise_2d_reduce = GridwiseReduction_xy_to_x_blockwise<BlockSize,
+                                                                   srcDataType,
+                                                                   dstDataType,
+                                                                   compType,
+                                                                   decltype(src2dDesc),
+                                                                   decltype(dst1dDesc),
+                                                                   op,
+                                                                   nanPropaOpt,
+                                                                   reduceIndicesOpt,
+                                                                   false,
+                                                                   true,
+                                                                   GredAccessesPerThreadInBlock>;
+
+    void* const ws_buf2_global =
+        ws_buf2_bytes_offset > 0
+            ? static_cast<void*>(static_cast<char*>(ws_buf1_global) + ws_buf2_bytes_offset)
+            : nullptr;
+
+    constexpr int RunId = need_indices ? 3 : 1;
+    gridwise_2d_reduce::template Run<RunId>(
+        src2dDesc,
+        dst1dDesc,
+        origReduceLen,
+        alpha,
+        static_cast<const srcDataType* const __restrict__>(ws_buf1_global),
+        beta,
+        static_cast<dstDataType* const __restrict__>(p_dst_global),
+        static_cast<const int* const __restrict__>(ws_buf2_global),
+        static_cast<int* const __restrict__>(indices_global));
+};

composable_kernel/src/kernel_wrapper/gridwise_generic_reduction_second_call_threadwise.cpp

+/*******************************************************************************
+ *
+ * MIT License
+ *
+ * Copyright (c) 2021 Advanced Micro Devices, Inc.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ *
+ *******************************************************************************/
+#include "config.hpp"
+#include "number.hpp"
+#include "sequence.hpp"
+#include "tensor_descriptor_helper.hpp"
+#include "data_type_enum_helper.hpp"
+#include "reduction_common.hpp"
+#include "gridwise_generic_2d_reduction_direct_threadwise.hpp"
+
+using namespace ck;
+
+using srcDataType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_SRC_DATATYPE)>::type;
+using dstDataType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_DST_DATATYPE)>::type;
+using compType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_REDUCE_COMPTYPE)>::type;
+
+constexpr index_t BlockSize = CK_PARAM_BLOCKSIZE; // tunable
+
+constexpr index_t srcDims = CK_PARAM_IN_DIMS;
+constexpr index_t dstDims = CK_PARAM_OUT_DIMS;
+
+using toReduceDims  = Sequence<CK_PARAM_TOREDUCE_DIMS>;
+using invariantDims = Sequence<CK_PARAM_INVARIANT_DIMS>; // this could be empty
+
+constexpr ReduceTensorOp_t op          = static_cast<ReduceTensorOp_t>(CK_PARAM_REDUCE_OP);
+constexpr NanPropagation_t nanPropaOpt = CK_PARAM_NAN_PROPAGATE == 0
+                                             ? NanPropagation_t::NOT_PROPAGATE_NAN
+                                             : NanPropagation_t::PROPAGATE_NAN;
+constexpr ReduceTensorIndices_t reduceIndicesOpt = CK_PARAM_REDUCE_INDICES == 0
+                                                       ? ReduceTensorIndices_t::NO_INDICES
+                                                       : ReduceTensorIndices_t::FLATTENED_INDICES;
+
+constexpr bool src2d_need_padding = static_cast<bool>(CK_PARAM_SRC2D_PADDING);
+constexpr bool dst1d_need_padding = static_cast<bool>(CK_PARAM_DST1D_PADDING);
+
+////////////////////////////////////////////////////////////////////////////////////////
+using specDims = typename sequence_merge<invariantDims, toReduceDims>::type;
+
+static_assert(is_valid_sequence_map<specDims>::value && specDims::Size() == srcDims,
+              "Wrong invariant and/or toReduce dimensions!");
+
+// The number of invariant dimensions can be zero if all dimension are to be reduced
+static_assert(invariantDims::Size() > 0 || dstDims == 1,
+              "If all source dimensions are reduced, the dest should have only one dimension !!");
+
+constexpr bool indexable    = reduce_binary_operator<compType, op>::indexable;
+constexpr bool need_indices = indexable && (reduceIndicesOpt != ReduceTensorIndices_t::NO_INDICES);
+
+constexpr index_t GredThreadBufferLength = CK_PARAM_THREAD_BUFFER_LENGTH; // tunable
+
+// helper functions using variadic template arguments
+template <index_t... Ns>
+__device__ static auto make_tuple_from_array_and_index_seq(const int* lengths, Sequence<Ns...>)
+{
+    return make_tuple(static_cast<index_t>(lengths[Ns])...);
+};
+
+template <index_t arraySize>
+__device__ static auto make_tuple_from_array(const int* lengths, Number<arraySize>)
+{
+    static_assert(arraySize >= 1 && arraySize <= 6, "The tensor should have 1 to 6 dimensions");
+
+    constexpr auto index_seq = typename arithmetic_sequence_gen<0, arraySize, 1>::type{};
+
+    return make_tuple_from_array_and_index_seq(lengths, index_seq);
+};
+
+template <index_t... Ns>
+__device__ static constexpr auto make_tuple_from_seq(Sequence<Ns...>)
+{
+    return make_tuple(Ns...);
+};
+
+extern "C" __global__ void gridwise_generic_reduce_2_prepare(int GridSize,
+                                                             int BlkGroupSize,
+                                                             int outLength0,
+                                                             int outLength1,
+                                                             int outLength2,
+                                                             int outLength3,
+                                                             int outLength4,
+                                                             int outLength5,
+                                                             int outStride0,
+                                                             int outStride1,
+                                                             int outStride2,
+                                                             int outStride3,
+                                                             int outStride4,
+                                                             int outStride5,
+                                                             void* __restrict__ ws_global)
+{
+    (void)BlkGroupSize;
+
+    void* p_src2dDesc = ws_global;
+    void* p_dst1dDesc = static_cast<char*>(ws_global) + 2048;
+
+    const int dstLengths[6] = {
+        outLength0, outLength1, outLength2, outLength3, outLength4, outLength5};
+    const int dstStrides[6] = {
+        outStride0, outStride1, outStride2, outStride3, outStride4, outStride5};
+
+    const auto tupleDstLengths = make_tuple_from_array(dstLengths, Number<dstDims>{});
+    const auto tupleDstStrides = make_tuple_from_array(dstStrides, Number<dstDims>{});
+
+    const auto dstDesc = make_naive_tensor_descriptor(tupleDstLengths, tupleDstStrides);
+
+    auto dst1dDesc = transform_tensor_descriptor(
+        dstDesc,
+        make_tuple(make_merge_transform(tupleDstLengths)),
+        make_tuple(typename arithmetic_sequence_gen<0, dstDims, 1>::type{}),
+        make_tuple(Sequence<0>{}));
+
+    const index_t invariantLen = dst1dDesc.GetLength(Number<0>{});
+    const index_t toReduceLen  = BlkGroupSize;
+
+    auto src2dDesc = make_naive_tensor_descriptor_packed(make_tuple(invariantLen, toReduceLen));
+
+    constexpr auto copySliceLen = GredThreadBufferLength;
+
+    if constexpr(src2d_need_padding)
+    {
+        const auto srcPad1 = GridSize * BlockSize - invariantLen;
+        const auto srcPad2 =
+            ((toReduceLen + copySliceLen - 1) / copySliceLen) * copySliceLen - toReduceLen;
+        auto src2dDesc_2 =
+            transform_tensor_descriptor(src2dDesc,
+                                        make_tuple(make_pad_transform(invariantLen, 0, srcPad1),
+                                                   make_pad_transform(toReduceLen, 0, srcPad2)),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}));
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(src2dDesc_2)*>(p_src2dDesc) = src2dDesc_2;
+    }
+    else
+    {
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(src2dDesc)*>(p_src2dDesc) = src2dDesc;
+    }
+
+    if constexpr(dst1d_need_padding)
+    {
+        const auto dstPad = GridSize * BlockSize - invariantLen;
+        auto dst1dDesc_2 =
+            transform_tensor_descriptor(dst1dDesc,
+                                        make_tuple(make_pad_transform(invariantLen, 0, dstPad)),
+                                        make_tuple(Sequence<0>{}),
+                                        make_tuple(Sequence<0>{}));
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(dst1dDesc_2)*>(p_dst1dDesc) = dst1dDesc_2;
+    }
+    else
+    {
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(dst1dDesc)*>(p_dst1dDesc) = dst1dDesc;
+    }
+};
+
+template <index_t srcDims, index_t dstDims, typename invariantDims, typename toReduceDims>
+struct get_ref_desc_types
+{
+    static constexpr auto ref_tupleDstLengths =
+        make_tuple_from_seq(typename uniform_sequence_gen<dstDims, 8>::type{});
+    static constexpr auto ref_dstDesc =
+        make_naive_tensor_descriptor(ref_tupleDstLengths, ref_tupleDstLengths);
+
+    static constexpr auto ref_dst1dDesc = transform_tensor_descriptor(
+        ref_dstDesc,
+        make_tuple(make_merge_transform(ref_tupleDstLengths)),
+        make_tuple(typename arithmetic_sequence_gen<0, dstDims, 1>::type{}),
+        make_tuple(Sequence<0>{}));
+
+    static constexpr index_t ref_invariantLen = ref_dst1dDesc.GetLength(Number<0>{});
+    static constexpr index_t ref_toReduceLen  = 8;
+
+    static constexpr auto ref_src2dDesc =
+        make_naive_tensor_descriptor_packed(make_tuple(ref_invariantLen, ref_toReduceLen));
+
+    using refType_src2dDesc = decltype(ref_src2dDesc);
+    using refType_dst1dDesc = decltype(ref_dst1dDesc);
+
+    // used by the DirectThreadWise and DirectWarpWise method
+    using refType_src2dDesc_padded_12 =
+        decltype(transform_tensor_descriptor(ref_src2dDesc,
+                                             make_tuple(make_pad_transform(ref_invariantLen, 0, 2),
+                                                        make_pad_transform(ref_toReduceLen, 0, 2)),
+                                             make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                             make_tuple(Sequence<0>{}, Sequence<1>{})));
+
+    using refType_dst1dDesc_padded =
+        decltype(transform_tensor_descriptor(ref_dst1dDesc,
+                                             make_tuple(make_pad_transform(ref_invariantLen, 0, 2)),
+                                             make_tuple(Sequence<0>{}),
+                                             make_tuple(Sequence<0>{})));
+};
+
+using refType_src2dDesc =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::refType_src2dDesc;
+using refType_dst1dDesc =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::refType_dst1dDesc;
+using refType_src2dDesc_padded_12 =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::
+        refType_src2dDesc_padded_12;
+using refType_dst1dDesc_padded =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::
+        refType_dst1dDesc_padded;
+
+template <bool need_padding>
+static __device__ auto get_reduction_src2d_descriptor(const void* p_src2dDesc)
+{
+    if constexpr(need_padding)
+        return (*reinterpret_cast<const refType_src2dDesc_padded_12*>(p_src2dDesc));
+    else
+        return (*reinterpret_cast<const refType_src2dDesc*>(p_src2dDesc));
+};
+
+template <bool need_padding>
+static __device__ auto get_reduction_dst1d_descriptor(const void* p_dst1dDesc)
+{
+    if constexpr(need_padding)
+        return (*reinterpret_cast<const refType_dst1dDesc_padded*>(p_dst1dDesc));
+    else
+        return (*reinterpret_cast<const refType_dst1dDesc*>(p_dst1dDesc));
+};
+
+extern "C" __global__ void gridwise_generic_reduce_2(int origReduceLen,
+                                                     float alpha,
+                                                     const void* __restrict__ p_src_global,
+                                                     float beta,
+                                                     void* __restrict__ p_dst_global,
+                                                     void* __restrict__ ws_global,
+                                                     long ws_buf2_bytes_offset,
+                                                     void* __restrict__ indices_global)
+{
+    (void)p_src_global;
+
+    const void* p_src2dDesc = ws_global;
+    const void* p_dst1dDesc = static_cast<char*>(ws_global) + 2048;
+    void* ws_buf1_global    = static_cast<char*>(ws_global) + 4096;
+
+    const auto src2dDesc = get_reduction_src2d_descriptor<src2d_need_padding>(p_src2dDesc);
+    const auto dst1dDesc = get_reduction_dst1d_descriptor<dst1d_need_padding>(p_dst1dDesc);
+
+    using gridwise_2d_reduce = GridwiseReduction_xy_to_x_direct_threadwise<BlockSize,
+                                                                           srcDataType,
+                                                                           dstDataType,
+                                                                           compType,
+                                                                           decltype(src2dDesc),
+                                                                           decltype(dst1dDesc),
+                                                                           op,
+                                                                           nanPropaOpt,
+                                                                           reduceIndicesOpt,
+                                                                           false,
+                                                                           true,
+                                                                           GredThreadBufferLength>;
+
+    void* const ws_buf2_global =
+        ws_buf2_bytes_offset > 0
+            ? static_cast<void*>(static_cast<char*>(ws_buf1_global) + ws_buf2_bytes_offset)
+            : nullptr;
+
+    constexpr int RunId = need_indices ? 3 : 1;
+    gridwise_2d_reduce::template Run<RunId>(
+        src2dDesc,
+        dst1dDesc,
+        origReduceLen,
+        alpha,
+        static_cast<const srcDataType* const __restrict__>(ws_buf1_global),
+        beta,
+        static_cast<dstDataType* const __restrict__>(p_dst_global),
+        static_cast<const int* const __restrict__>(ws_buf2_global),
+        static_cast<int* const __restrict__>(indices_global));
+};

composable_kernel/src/kernel_wrapper/gridwise_generic_reduction_second_call_warpwise.cpp

+/*******************************************************************************
+ *
+ * MIT License
+ *
+ * Copyright (c) 2021 Advanced Micro Devices, Inc.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ *
+ *******************************************************************************/
+#include "config.hpp"
+#include "number.hpp"
+#include "sequence.hpp"
+#include "tensor_descriptor_helper.hpp"
+#include "data_type_enum_helper.hpp"
+#include "reduction_common.hpp"
+#include "gridwise_generic_2d_reduction_direct_warpwise.hpp"
+
+using namespace ck;
+
+using srcDataType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_SRC_DATATYPE)>::type;
+using dstDataType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_DST_DATATYPE)>::type;
+using compType =
+    typename get_datatype_from_enum<static_cast<DataTypeEnum_t>(CK_PARAM_REDUCE_COMPTYPE)>::type;
+
+constexpr index_t BlockSize = CK_PARAM_BLOCKSIZE; // tunable
+
+constexpr index_t srcDims = CK_PARAM_IN_DIMS;
+constexpr index_t dstDims = CK_PARAM_OUT_DIMS;
+
+using toReduceDims  = Sequence<CK_PARAM_TOREDUCE_DIMS>;
+using invariantDims = Sequence<CK_PARAM_INVARIANT_DIMS>; // this could be empty
+
+constexpr ReduceTensorOp_t op          = static_cast<ReduceTensorOp_t>(CK_PARAM_REDUCE_OP);
+constexpr NanPropagation_t nanPropaOpt = CK_PARAM_NAN_PROPAGATE == 0
+                                             ? NanPropagation_t::NOT_PROPAGATE_NAN
+                                             : NanPropagation_t::PROPAGATE_NAN;
+constexpr ReduceTensorIndices_t reduceIndicesOpt = CK_PARAM_REDUCE_INDICES == 0
+                                                       ? ReduceTensorIndices_t::NO_INDICES
+                                                       : ReduceTensorIndices_t::FLATTENED_INDICES;
+
+constexpr bool src2d_need_padding = static_cast<bool>(CK_PARAM_SRC2D_PADDING);
+constexpr bool dst1d_need_padding = static_cast<bool>(CK_PARAM_DST1D_PADDING);
+
+////////////////////////////////////////////////////////////////////////////////////////
+using specDims = typename sequence_merge<invariantDims, toReduceDims>::type;
+
+static_assert(is_valid_sequence_map<specDims>::value && specDims::Size() == srcDims,
+              "Wrong invariant and/or toReduce dimensions!");
+
+// The number of invariant dimensions can be zero if all dimension are to be reduced
+static_assert(invariantDims::Size() > 0 || dstDims == 1,
+              "If all source dimensions are reduced, the dest should have only one dimension !!");
+
+constexpr bool indexable    = reduce_binary_operator<compType, op>::indexable;
+constexpr bool need_indices = indexable && (reduceIndicesOpt != ReduceTensorIndices_t::NO_INDICES);
+
+constexpr index_t GredAccessesPerThreadInWarp = CK_PARAM_ACCESSES_PER_THREAD_INWARP; // tunable
+
+// helper functions using variadic template arguments
+template <index_t... Ns>
+__device__ static auto make_tuple_from_array_and_index_seq(const int* lengths, Sequence<Ns...>)
+{
+    return make_tuple(static_cast<index_t>(lengths[Ns])...);
+};
+
+template <index_t arraySize>
+__device__ static auto make_tuple_from_array(const int* lengths, Number<arraySize>)
+{
+    static_assert(arraySize >= 1 && arraySize <= 6, "The tensor should have 1 to 6 dimensions");
+
+    constexpr auto index_seq = typename arithmetic_sequence_gen<0, arraySize, 1>::type{};
+
+    return make_tuple_from_array_and_index_seq(lengths, index_seq);
+};
+
+template <index_t... Ns>
+__device__ static constexpr auto make_tuple_from_seq(Sequence<Ns...>)
+{
+    return make_tuple(Ns...);
+};
+
+extern "C" __global__ void gridwise_generic_reduce_2_prepare(int GridSize,
+                                                             int BlkGroupSize,
+                                                             int outLength0,
+                                                             int outLength1,
+                                                             int outLength2,
+                                                             int outLength3,
+                                                             int outLength4,
+                                                             int outLength5,
+                                                             int outStride0,
+                                                             int outStride1,
+                                                             int outStride2,
+                                                             int outStride3,
+                                                             int outStride4,
+                                                             int outStride5,
+                                                             void* __restrict__ ws_global)
+{
+    (void)BlkGroupSize;
+
+    void* p_src2dDesc = ws_global;
+    void* p_dst1dDesc = static_cast<char*>(ws_global) + 2048;
+
+    const int dstLengths[6] = {
+        outLength0, outLength1, outLength2, outLength3, outLength4, outLength5};
+    const int dstStrides[6] = {
+        outStride0, outStride1, outStride2, outStride3, outStride4, outStride5};
+
+    const auto tupleDstLengths = make_tuple_from_array(dstLengths, Number<dstDims>{});
+    const auto tupleDstStrides = make_tuple_from_array(dstStrides, Number<dstDims>{});
+
+    const auto dstDesc = make_naive_tensor_descriptor(tupleDstLengths, tupleDstStrides);
+
+    auto dst1dDesc = transform_tensor_descriptor(
+        dstDesc,
+        make_tuple(make_merge_transform(tupleDstLengths)),
+        make_tuple(typename arithmetic_sequence_gen<0, dstDims, 1>::type{}),
+        make_tuple(Sequence<0>{}));
+
+    const index_t invariantLen = dst1dDesc.GetLength(Number<0>{});
+    const index_t toReduceLen  = BlkGroupSize;
+
+    auto src2dDesc = make_naive_tensor_descriptor_packed(make_tuple(invariantLen, toReduceLen));
+
+    constexpr auto copySliceLen = warpSize * GredAccessesPerThreadInWarp;
+
+    if constexpr(src2d_need_padding)
+    {
+        const auto srcPad1 = GridSize * BlockSize / warpSize - invariantLen;
+        const auto srcPad2 =
+            ((toReduceLen + copySliceLen - 1) / copySliceLen) * copySliceLen - toReduceLen;
+
+        auto src2dDesc_2 =
+            transform_tensor_descriptor(src2dDesc,
+                                        make_tuple(make_pad_transform(invariantLen, 0, srcPad1),
+                                                   make_pad_transform(toReduceLen, 0, srcPad2)),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}));
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(src2dDesc_2)*>(p_src2dDesc) = src2dDesc_2;
+    }
+    else
+    {
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(src2dDesc)*>(p_src2dDesc) = src2dDesc;
+    }
+
+    if constexpr(dst1d_need_padding)
+    {
+        const auto dstPad = GridSize * BlockSize / warpSize - invariantLen;
+        auto dst1dDesc_2 =
+            transform_tensor_descriptor(dst1dDesc,
+                                        make_tuple(make_pad_transform(invariantLen, 0, dstPad)),
+                                        make_tuple(Sequence<0>{}),
+                                        make_tuple(Sequence<0>{}));
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(dst1dDesc_2)*>(p_dst1dDesc) = dst1dDesc_2;
+    }
+    else
+    {
+        if(hipThreadIdx_x == 0)
+            *static_cast<decltype(dst1dDesc)*>(p_dst1dDesc) = dst1dDesc;
+    }
+};
+
+template <index_t srcDims, index_t dstDims, typename invariantDims, typename toReduceDims>
+struct get_ref_desc_types
+{
+    static constexpr auto ref_tupleDstLengths =
+        make_tuple_from_seq(typename uniform_sequence_gen<dstDims, 8>::type{});
+    static constexpr auto ref_dstDesc =
+        make_naive_tensor_descriptor(ref_tupleDstLengths, ref_tupleDstLengths);
+
+    static constexpr auto ref_dst1dDesc = transform_tensor_descriptor(
+        ref_dstDesc,
+        make_tuple(make_merge_transform(ref_tupleDstLengths)),
+        make_tuple(typename arithmetic_sequence_gen<0, dstDims, 1>::type{}),
+        make_tuple(Sequence<0>{}));
+
+    static constexpr index_t ref_invariantLen = ref_dst1dDesc.GetLength(Number<0>{});
+    static constexpr index_t ref_toReduceLen  = 8;
+
+    static constexpr auto ref_src2dDesc =
+        make_naive_tensor_descriptor_packed(make_tuple(ref_invariantLen, ref_toReduceLen));
+
+    using refType_src2dDesc = decltype(ref_src2dDesc);
+    using refType_dst1dDesc = decltype(ref_dst1dDesc);
+
+    // used by the DirectThreadWise and DirectWarpWise method
+    using refType_src2dDesc_padded_12 =
+        decltype(transform_tensor_descriptor(ref_src2dDesc,
+                                             make_tuple(make_pad_transform(ref_invariantLen, 0, 2),
+                                                        make_pad_transform(ref_toReduceLen, 0, 2)),
+                                             make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                             make_tuple(Sequence<0>{}, Sequence<1>{})));
+
+    using refType_dst1dDesc_padded =
+        decltype(transform_tensor_descriptor(ref_dst1dDesc,
+                                             make_tuple(make_pad_transform(ref_invariantLen, 0, 2)),
+                                             make_tuple(Sequence<0>{}),
+                                             make_tuple(Sequence<0>{})));
+};
+
+using refType_src2dDesc =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::refType_src2dDesc;
+using refType_dst1dDesc =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::refType_dst1dDesc;
+using refType_src2dDesc_padded_12 =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::
+        refType_src2dDesc_padded_12;
+using refType_dst1dDesc_padded =
+    typename get_ref_desc_types<srcDims, dstDims, invariantDims, toReduceDims>::
+        refType_dst1dDesc_padded;
+
+template <bool need_padding>
+static __device__ auto get_reduction_src2d_descriptor(const void* p_src2dDesc)
+{
+    if constexpr(need_padding)
+        return (*reinterpret_cast<const refType_src2dDesc_padded_12*>(p_src2dDesc));
+    else
+        return (*reinterpret_cast<const refType_src2dDesc*>(p_src2dDesc));
+};
+
+template <bool need_padding>
+static __device__ auto get_reduction_dst1d_descriptor(const void* p_dst1dDesc)
+{
+    if constexpr(need_padding)
+        return (*reinterpret_cast<const refType_dst1dDesc_padded*>(p_dst1dDesc));
+    else
+        return (*reinterpret_cast<const refType_dst1dDesc*>(p_dst1dDesc));
+};
+
+extern "C" __global__ void gridwise_generic_reduce_2(int origReduceLen,
+                                                     float alpha,
+                                                     const void* __restrict__ p_src_global,
+                                                     float beta,
+                                                     void* __restrict__ p_dst_global,
+                                                     void* __restrict__ ws_global,
+                                                     long ws_buf2_bytes_offset,
+                                                     void* __restrict__ indices_global)
+{
+    (void)p_src_global;
+
+    const void* p_src2dDesc = ws_global;
+    const void* p_dst1dDesc = static_cast<char*>(ws_global) + 2048;
+    void* ws_buf1_global    = static_cast<char*>(ws_global) + 4096;
+
+    const auto src2dDesc = get_reduction_src2d_descriptor<src2d_need_padding>(p_src2dDesc);
+    const auto dst1dDesc = get_reduction_dst1d_descriptor<dst1d_need_padding>(p_dst1dDesc);
+
+    using gridwise_2d_reduce =
+        GridwiseReduction_xy_to_x_direct_warpwise<BlockSize,
+                                                  srcDataType,
+                                                  dstDataType,
+                                                  compType,
+                                                  decltype(src2dDesc),
+                                                  decltype(dst1dDesc),
+                                                  op,
+                                                  nanPropaOpt,
+                                                  reduceIndicesOpt,
+                                                  false,
+                                                  true,
+                                                  GredAccessesPerThreadInWarp>;
+
+    void* const ws_buf2_global =
+        ws_buf2_bytes_offset > 0
+            ? static_cast<void*>(static_cast<char*>(ws_buf1_global) + ws_buf2_bytes_offset)
+            : nullptr;
+
+    constexpr int RunId = need_indices ? 3 : 1;
+    gridwise_2d_reduce::template Run<RunId>(
+        src2dDesc,
+        dst1dDesc,
+        origReduceLen,
+        alpha,
+        static_cast<const srcDataType* const __restrict__>(ws_buf1_global),
+        beta,
+        static_cast<dstDataType* const __restrict__>(p_dst_global),
+        static_cast<const int* const __restrict__>(ws_buf2_global),
+        static_cast<int* const __restrict__>(indices_global));
+};