sync from public repo

include/ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_default_policy.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/reduce/block/block_reduce2d_problem.hpp"
+#include "ck_tile/ops/reduce/block/block_reduce2d.hpp"
+
+namespace ck_tile {
+
+struct AddRmsnorm2dRdquantFwdPipelineDefaultPolicy
+{
+    template <typename Problem>
+    CK_TILE_DEVICE static constexpr auto MakeABXBlockTileDistribution()
+    {
+        using S = typename Problem::BlockShape;
+
+        return make_static_tile_distribution(
+            tile_distribution_encoding<
+                sequence<>,
+                tuple<sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M, S::Vector_M>,
+                      sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
+                tuple<sequence<1, 2>, sequence<1, 2>>,
+                tuple<sequence<1, 1>, sequence<2, 2>>,
+                sequence<1, 1, 2, 2>,
+                sequence<0, 3, 0, 3>>{});
+    }
+
+    template <typename Problem>
+    CK_TILE_DEVICE static constexpr auto MakeGammaBlockTileDistribution()
+    {
+        using S = typename Problem::BlockShape;
+
+        return make_static_tile_distribution(
+            tile_distribution_encoding<
+                sequence<S::WarpPerBlock_M, S::ThreadPerWarp_M>,
+                tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
+                tuple<sequence<0, 1>, sequence<0, 1>>,
+                tuple<sequence<0, 1>, sequence<1, 2>>,
+                sequence<1, 1>,
+                sequence<0, 3>>{});
+    }
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2d()
+    {
+        using P_ = BlockReduce2dProblem<typename Problem::ComputeDataType,
+                                        typename Problem::ComputeDataType,
+                                        typename Problem::BlockShape>;
+        return BlockReduce2d<P_>{};
+    }
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dSync()
+    {
+        using P_ = BlockReduce2dProblem<typename Problem::ComputeDataType,
+                                        typename Problem::ComputeDataType,
+                                        typename Problem::BlockShape>;
+        return BlockReduce2dSync<P_>{};
+    }
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dCrossWarpSync()
+    {
+        using P_ = BlockReduce2dProblem<typename Problem::ComputeDataType,
+                                        typename Problem::ComputeDataType,
+                                        typename Problem::BlockShape>;
+        return BlockReduce2dCrossWarpSync<P_>{};
+    }
+
+    template <typename Problem>
+    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
+    {
+        if constexpr(Problem::kNeedCrossWarpSync)
+        {
+            using P_ = BlockReduce2dProblem<typename Problem::ComputeDataType,
+                                            typename Problem::ComputeDataType,
+                                            typename Problem::BlockShape>;
+
+            using block_reduce2d = BlockReduce2d<P_>;
+            using x_block_tile =
+                decltype(make_static_distributed_tensor<typename Problem::ComputeDataType>(
+                    MakeABXBlockTileDistribution<Problem>()));
+            using y_block_tile = decltype(block_reduce2d::template MakeYBlockTile<x_block_tile>());
+
+            return GetBlockReduce2dCrossWarpSync<Problem>().template GetSmemSize<y_block_tile>();
+        }
+        else
+        {
+            return 1; // zero size arrays are an extension
+        }
+    }
+};
+} // namespace ck_tile

include/ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_one_pass.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/rmsnorm2d/pipeline/rmsnorm2d_fwd_pipeline_default_policy.hpp"
+#include <string>
+#include <type_traits>
+
+namespace ck_tile {
+
+template <typename Problem_, typename Policy_ = AddRmsnorm2dRdquantFwdPipelineDefaultPolicy>
+struct AddRmsnorm2dRdquantFwdPipelineOnePass
+{
+    using Problem = ck_tile::remove_cvref_t<Problem_>;
+    using Policy  = ck_tile::remove_cvref_t<Policy_>;
+
+    using ADataType       = ck_tile::remove_cvref_t<typename Problem::ADataType>;
+    using BDataType       = ck_tile::remove_cvref_t<typename Problem::BDataType>;
+    using GammaDataType   = ck_tile::remove_cvref_t<typename Problem::GammaDataType>;
+    using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
+    using XDataType       = ck_tile::remove_cvref_t<typename Problem::XDataType>;
+    using YScaleDataType  = ck_tile::remove_cvref_t<typename Problem::YScaleDataType>;
+    using QYDataType      = ck_tile::remove_cvref_t<typename Problem::QYDataType>;
+
+    static constexpr bool kHasGamma = !std::is_same_v<GammaDataType, ck_tile::null_type>;
+    static constexpr bool kSaveX    = Problem::kSaveX;
+
+    static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
+    static constexpr bool kPadM = false; // TODO - BlockAddRmsnorm2dRdquantFwdProblem::kPadM
+    static constexpr bool kPadN = Problem::kPadN;
+
+    static constexpr const char* name = []() {
+        if constexpr(kNeedCrossWarpSync)
+            return "bpr_op"; // block per row
+        else
+            return "wpr_op"; // warp per row
+    }();
+
+    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
+    {
+        return Policy::template GetSmemSize<Problem>();
+    }
+
+    template <typename AWindow,
+              typename BWindow,
+              typename GammaWindow,
+              typename XWindow,
+              typename YScaleWindow,
+              typename QYWindow>
+    CK_TILE_DEVICE auto operator()(const AWindow& a_window_,
+                                   const BWindow& b_window_,
+                                   const GammaWindow& gamma_window_,
+                                   XWindow& x_window,
+                                   YScaleWindow& yscale_window,
+                                   QYWindow& qy_window,
+                                   ComputeDataType epsilon,
+                                   ck_tile::index_t row_size,
+                                   void* smem) const
+    {
+        const auto a_window =
+            make_tile_window(a_window_, Policy::template MakeABXBlockTileDistribution<Problem>());
+        const auto b_window =
+            make_tile_window(b_window_, Policy::template MakeABXBlockTileDistribution<Problem>());
+        const auto gamma_window = make_tile_window(
+            gamma_window_, Policy::template MakeGammaBlockTileDistribution<Problem>());
+
+        auto reduce_square_sum_func = ReduceOp::SquareAdd{};
+        auto reduce_sum_func        = ReduceOp::Add{};
+        auto reduce_absmax_func     = ReduceOp::AbsMax{};
+        auto reduce_max_func        = ReduceOp::Max{};
+        auto block_reduce2d         = Policy::template GetBlockReduce2d<Problem>();
+        auto block_reduce2d_sync    = Policy::template GetBlockReduce2dSync<Problem>();
+        auto block_reduce2d_cross_warp_sync =
+            Policy::template GetBlockReduce2dCrossWarpSync<Problem>();
+
+        const auto a     = load_tile(a_window);
+        const auto b     = load_tile(b_window);
+        const auto gamma = load_tile(gamma_window);
+
+        auto x = tile_elementwise_in(
+            [&](const auto& a_, const auto& b_) {
+                return type_convert<ComputeDataType>(a_) + type_convert<ComputeDataType>(b_);
+            },
+            a,
+            b);
+
+        if constexpr(kSaveX)
+            store_tile(x_window, cast_tile<XDataType>(x));
+
+        // compute mean square, each-thread->cross-lane->cross-warp
+        auto square_sum = block_reduce2d(
+            x, reduce_square_sum_func.GetIdentityValue<ComputeDataType>(), reduce_square_sum_func);
+        block_reduce2d_sync(square_sum, reduce_sum_func);
+        block_reduce2d_cross_warp_sync(square_sum, smem, reduce_sum_func);
+
+        auto inv_rms = tile_elementwise_in(
+            [&](const auto& v_) {
+                return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ / row_size + epsilon));
+            },
+            square_sum);
+
+        // rmsnorm computation
+        auto y = make_static_distributed_tensor<ComputeDataType>(x.get_tile_distribution());
+        sweep_tile(y, [&, inv_rms_ = inv_rms](auto idx) {
+            constexpr auto i_idx = make_tuple(idx[number<0>{}]);
+            constexpr auto j_idx = make_tuple(idx[number<1>{}]);
+
+            const auto gamma_ = type_convert<ComputeDataType>(gamma[j_idx]);
+
+            const auto x_ = type_convert<ComputeDataType>(x[idx]);
+            auto y_       = x_ * inv_rms_[i_idx] * gamma_;
+
+            y(idx) = type_convert<ComputeDataType>(y_);
+        });
+
+        // compute absmax, each-thread->cross-lane->cross-warp
+        auto absmax = block_reduce2d(
+            y, reduce_absmax_func.GetIdentityValue<ComputeDataType>(), reduce_absmax_func);
+        block_reduce2d_sync(absmax, reduce_max_func);
+        block_reduce2d_cross_warp_sync(absmax, smem, reduce_max_func);
+
+        // ex: yscale = absmax / 127 if int8
+        auto yscale = tile_elementwise_in(
+            [&](const auto& v_) {
+                return v_ / type_convert<ComputeDataType>(numeric<QYDataType>::max());
+            },
+            absmax);
+        store_tile(yscale_window, cast_tile<YScaleDataType>(yscale));
+
+        // quantize y to qy
+        auto qy = make_static_distributed_tensor<QYDataType>(y.get_tile_distribution());
+        sweep_tile(qy, [&, yscale_ = yscale](auto idx) {
+            constexpr auto i_idx = make_tuple(idx[number<0>{}]);
+            auto qy_             = y[idx] / yscale_[i_idx];
+            qy(idx)              = saturates<QYDataType>{}(qy_);
+        });
+        store_tile(qy_window, qy);
+    }
+};
+} // namespace ck_tile

include/ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_problem.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core/utility/type_traits.hpp"
+
+namespace ck_tile {
+
+// X = A + B, Y = Rmsnorm2d(X), QY = RowwiseDynamicQuant(Y) = SaturateCast(Y / YScale)
+template <typename ADataType_,
+          typename BDataType_,
+          typename GammaDataType_,
+          typename ComputeDataType_,
+          typename XDataType_,
+          typename YScaleDataType_,
+          typename QYDataType_,
+          typename BlockShape_,
+          bool kPadN_,
+          bool kSaveX_,
+          bool kThreePass_>
+struct AddRmsnorm2dRdquantFwdPipelineProblem
+{
+    using ADataType       = remove_cvref_t<ADataType_>;
+    using BDataType       = remove_cvref_t<BDataType_>;
+    using GammaDataType   = remove_cvref_t<GammaDataType_>;
+    using ComputeDataType = remove_cvref_t<ComputeDataType_>;
+    using XDataType       = remove_cvref_t<XDataType_>;
+    using YScaleDataType  = remove_cvref_t<YScaleDataType_>;
+    using QYDataType      = remove_cvref_t<QYDataType_>;
+    using BlockShape      = remove_cvref_t<BlockShape_>;
+
+    static constexpr bool kNeedCrossLaneSync = BlockShape::ThreadPerWarp_N > 1;
+    static constexpr bool kNeedCrossWarpSync = BlockShape::WarpPerBlock_N > 1;
+
+    static constexpr bool kPadN      = kPadN_;
+    static constexpr bool kSaveX     = kSaveX_;
+    static constexpr bool kThreePass = kThreePass_;
+};
+
+} // namespace ck_tile

include/ck_tile/ops/add_rmsnorm2d_rdquant/pipeline/add_rmsnorm2d_rdquant_fwd_pipeline_three_pass.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/rmsnorm2d/pipeline/rmsnorm2d_fwd_pipeline_default_policy.hpp"
+#include <string>
+#include <type_traits>
+
+namespace ck_tile {
+
+template <typename Problem_, typename Policy_ = AddRmsnorm2dRdquantFwdPipelineDefaultPolicy>
+struct AddRmsnorm2dRdquantFwdPipelineThreePass
+{
+    using Problem = ck_tile::remove_cvref_t<Problem_>;
+    using Policy  = ck_tile::remove_cvref_t<Policy_>;
+
+    using ADataType       = ck_tile::remove_cvref_t<typename Problem::ADataType>;
+    using BDataType       = ck_tile::remove_cvref_t<typename Problem::BDataType>;
+    using GammaDataType   = ck_tile::remove_cvref_t<typename Problem::GammaDataType>;
+    using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
+    using XDataType       = ck_tile::remove_cvref_t<typename Problem::XDataType>;
+    using YScaleDataType  = ck_tile::remove_cvref_t<typename Problem::YScaleDataType>;
+    using QYDataType      = ck_tile::remove_cvref_t<typename Problem::QYDataType>;
+
+    static constexpr bool kHasGamma = !std::is_same_v<GammaDataType, ck_tile::null_type>;
+    static constexpr bool kSaveX    = Problem::kSaveX;
+
+    static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
+    static constexpr bool kPadM = false; // TODO - BlockAddRmsnorm2dRdquantFwdProblem::kPadM
+    static constexpr bool kPadN = Problem::kPadN;
+
+    static constexpr const char* name = []() {
+        if constexpr(kNeedCrossWarpSync)
+            return "bpr_tp"; // block per row
+        else
+            return "wpr_tp"; // warp per row
+    }();
+
+    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
+    {
+        return Policy::template GetSmemSize<Problem>();
+    }
+
+    template <typename AWindow,
+              typename BWindow,
+              typename GammaWindow,
+              typename XWindow,
+              typename YScaleWindow,
+              typename QYWindow>
+    CK_TILE_DEVICE auto operator()(const AWindow& a_window_,
+                                   const BWindow& b_window_,
+                                   const GammaWindow& gamma_window_,
+                                   XWindow& x_window_,
+                                   YScaleWindow& yscale_window,
+                                   QYWindow& qy_window,
+                                   ComputeDataType epsilon,
+                                   ck_tile::index_t row_size,
+                                   void* smem) const
+    {
+        auto a_window =
+            make_tile_window(a_window_, Policy::template MakeABXBlockTileDistribution<Problem>());
+        auto b_window =
+            make_tile_window(b_window_, Policy::template MakeABXBlockTileDistribution<Problem>());
+        auto x_window = [&]() {
+            if constexpr(kSaveX)
+                return make_tile_window(x_window_,
+                                        Policy::template MakeABXBlockTileDistribution<Problem>());
+            else
+                return x_window_;
+        }();
+        auto gamma_window = make_tile_window(
+            gamma_window_, Policy::template MakeGammaBlockTileDistribution<Problem>());
+
+        auto reduce_square_sum_func = ReduceOp::SquareAdd{};
+        auto reduce_sum_func        = ReduceOp::Add{};
+        auto reduce_absmax_func     = ReduceOp::AbsMax{};
+        auto reduce_max_func        = ReduceOp::Max{};
+        auto block_reduce2d         = Policy::template GetBlockReduce2d<Problem>();
+        auto block_reduce2d_sync    = Policy::template GetBlockReduce2dSync<Problem>();
+        auto block_reduce2d_cross_warp_sync =
+            Policy::template GetBlockReduce2dCrossWarpSync<Problem>();
+
+        static constexpr index_t Block_N = Problem::BlockShape::Block_N;
+        index_t num_n_tile_iteration =
+            __builtin_amdgcn_readfirstlane(integer_divide_ceil(row_size, Block_N));
+
+        using XTensorType = decltype(cast_tile<ComputeDataType>(load_tile(a_window)));
+        auto square_sum   = block_reduce2d.template MakeYBlockTile<XTensorType>();
+        set_tile(square_sum, reduce_square_sum_func.GetIdentityValue<ComputeDataType>());
+
+        for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
+        {
+            const auto a = load_tile(a_window);
+            const auto b = load_tile(b_window);
+
+            auto x = tile_elementwise_in(
+                [&](const auto& a_, const auto& b_) {
+                    return type_convert<ComputeDataType>(a_) + type_convert<ComputeDataType>(b_);
+                },
+                a,
+                b);
+
+            if constexpr(kSaveX)
+                store_tile(x_window, cast_tile<XDataType>(x));
+
+            block_reduce2d(x, square_sum, reduce_square_sum_func);
+            move_tile_window(x_window, {0, Block_N});
+            move_tile_window(a_window, {0, Block_N});
+            move_tile_window(b_window, {0, Block_N});
+        }
+
+        block_reduce2d_sync(square_sum, reduce_sum_func);
+        block_reduce2d_cross_warp_sync(square_sum, smem, reduce_sum_func);
+
+        auto inv_rms = tile_elementwise_in(
+            [&](const auto& v_) {
+                return type_convert<ComputeDataType>(1.0f) / (sqrt(v_ / row_size + epsilon));
+            },
+            square_sum);
+
+        // reverse read x to reuse cache
+        ck_tile::index_t stride_to_right_most_window =
+            row_size % Block_N == 0 ? row_size - Block_N : row_size - row_size % Block_N;
+
+        if constexpr(kSaveX)
+            move_tile_window(x_window, {0, -Block_N});
+        else
+        {
+            move_tile_window(a_window, {0, -Block_N});
+            move_tile_window(b_window, {0, -Block_N});
+        }
+        move_tile_window(gamma_window, {stride_to_right_most_window});
+
+        using YTensorType = XTensorType;
+        auto absmax       = block_reduce2d.template MakeYBlockTile<YTensorType>();
+        set_tile(absmax, reduce_absmax_func.GetIdentityValue<ComputeDataType>());
+
+        // rmsnorm computation + absmax(threadwise reduce)
+        if constexpr(kSaveX)
+            __syncthreads();
+
+        for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
+        {
+            auto x = [&]() {
+                if constexpr(kSaveX)
+                {
+                    return load_tile(x_window);
+                }
+                else
+                {
+                    const auto a = load_tile(a_window);
+                    const auto b = load_tile(b_window);
+                    return tile_elementwise_in(
+                        [&](const auto& a_, const auto& b_) {
+                            return type_convert<ComputeDataType>(a_) +
+                                   type_convert<ComputeDataType>(b_);
+                        },
+                        a,
+                        b);
+                }
+            }();
+
+            auto gamma = load_tile(gamma_window);
+            auto y     = make_static_distributed_tensor<ComputeDataType>(x.get_tile_distribution());
+
+            sweep_tile(y, [&](auto idx) {
+                constexpr auto i_idx = make_tuple(idx[number<0>{}]);
+                constexpr auto j_idx = make_tuple(idx[number<1>{}]);
+
+                const auto gamma_ = type_convert<ComputeDataType>(gamma[j_idx]);
+
+                const auto x_ = type_convert<ComputeDataType>(x[idx]);
+                auto y_       = x_ * inv_rms[i_idx] * gamma_;
+
+                y(idx) = type_convert<ComputeDataType>(y_);
+            });
+
+            block_reduce2d(y, absmax, reduce_absmax_func);
+
+            if constexpr(kSaveX)
+                move_tile_window(x_window, {0, -Block_N});
+            else
+            {
+                move_tile_window(a_window, {0, -Block_N});
+                move_tile_window(b_window, {0, -Block_N});
+            }
+            move_tile_window(gamma_window, {-Block_N});
+        }
+
+        // compute absmax, cross-lane->cross-warp
+        block_reduce2d_sync(absmax, reduce_max_func);
+        block_reduce2d_cross_warp_sync(absmax, smem, reduce_max_func);
+
+        // ex: yscale = absmax / 127 if int8
+        auto yscale = tile_elementwise_in(
+            [&](const auto& v_) {
+                return v_ / type_convert<ComputeDataType>(numeric<QYDataType>::max());
+            },
+            absmax);
+        store_tile(yscale_window, cast_tile<YScaleDataType>(yscale));
+
+        // quantize y to qy
+        // recompute rmsnorm, try to save y in the future
+        if constexpr(kSaveX)
+            move_tile_window(x_window, {0, Block_N});
+        else
+        {
+            move_tile_window(a_window, {0, Block_N});
+            move_tile_window(b_window, {0, Block_N});
+        }
+        move_tile_window(gamma_window, {Block_N});
+
+        for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
+        {
+            auto x = [&]() {
+                if constexpr(kSaveX)
+                {
+                    return load_tile(x_window);
+                }
+                else
+                {
+                    const auto a = load_tile(a_window);
+                    const auto b = load_tile(b_window);
+                    return tile_elementwise_in(
+                        [&](const auto& a_, const auto& b_) {
+                            return type_convert<ComputeDataType>(a_) +
+                                   type_convert<ComputeDataType>(b_);
+                        },
+                        a,
+                        b);
+                }
+            }();
+
+            auto gamma = load_tile(gamma_window);
+            auto y     = make_static_distributed_tensor<ComputeDataType>(x.get_tile_distribution());
+            auto qy    = make_static_distributed_tensor<QYDataType>(y.get_tile_distribution());
+
+            sweep_tile(y, [&](auto idx) {
+                constexpr auto i_idx = make_tuple(idx[number<0>{}]);
+                constexpr auto j_idx = make_tuple(idx[number<1>{}]);
+
+                const auto gamma_ = type_convert<ComputeDataType>(gamma[j_idx]);
+
+                const auto x_ = type_convert<ComputeDataType>(x[idx]);
+                auto y_       = x_ * inv_rms[i_idx] * gamma_;
+                auto qy_      = y_ / yscale[i_idx];
+                qy(idx)       = saturates<QYDataType>{}(qy_);
+            });
+
+            store_tile(qy_window, qy);
+
+            if constexpr(kSaveX)
+                move_tile_window(x_window, {0, Block_N});
+            else
+            {
+                move_tile_window(a_window, {0, Block_N});
+                move_tile_window(b_window, {0, Block_N});
+            }
+            move_tile_window(gamma_window, {Block_N});
+            move_tile_window(qy_window, {0, Block_N});
+        }
+    }
+};
+} // namespace ck_tile

include/ck_tile/ops/common.hpp

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

include/ck_tile/ops/common/generic_2d_block_shape.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+namespace ck_tile {
+
+/*
+// clang-format off
+
+4-level descriptor: BlockTile-> WarpPerBlock-> WarpTile-> Vector
+
+                         Block_N (Warp_N * WarpPerBlock_N * Repeat_N )
+        +<----------------------< Repeat_N(2)>--------------------->+
+        |                                                           |
+        +<--    <WarpPerBlock_N(2)>  -->+
+            Warp_N
+        +--------------+--------------+--------------+--------------+----+----------------+
+ Warp_M | wrap_0       | wrap_1       |                             |    ^                ^
+        +--------------+--------------+                             |   <WarpPerBlock_M(2)> |
+        | wrap_2       | wrap_3       |                             |    v
+        +--------------+--------------+--------------+--------------+----+           Block_M
+        |                             |                             |
+        +                             +                             |
+        |                             |                             |                     v
+        +--------------+--------------+--------------+--------------+                     +
+
+        each Warp-tile (e.g 16 thrd per row)
+
+         Vector_N (contiguous pixels each thrd holds along N, or vector size)
+        +-----------+-----------+-----------+-----------+-----------+
+        | thrd_0    | thrd_1    | thrd_2    | thrd_3    | ...         Vector_M
+        +-----------+-----------+-----------+-----------+-----------+
+        | thrd_16   | thrd_17   | thrd_18   | thrd_19   | ...
+        +-----------+-----------+-----------+-----------+-----------+
+// clang-format on
+*/
+template <typename BlockTile_,    // block size, seq<M, N>
+          typename WarpPerBlock_, // num warps along seq<M, N>
+          typename WarpTile_,     // warp size, seq<M, N>
+          typename Vector_,       // contiguous pixels(vector size) along seq<M, N>
+          index_t BlockSize_ =
+              warpSize* reduce_on_sequence(WarpPerBlock_{}, multiplies{}, number<1>{})>
+struct Generic2dBlockShape
+{
+    // block size
+    static constexpr index_t Block_M = BlockTile_::at(number<0>{});
+    static constexpr index_t Block_N = BlockTile_::at(number<1>{});
+
+    // num warps along seq<M, N>, within each block
+    static constexpr index_t WarpPerBlock_M = WarpPerBlock_::at(number<0>{});
+    static constexpr index_t WarpPerBlock_N = WarpPerBlock_::at(number<1>{});
+
+    // warp size
+    static constexpr index_t Warp_M = WarpTile_::at(number<0>{});
+    static constexpr index_t Warp_N = WarpTile_::at(number<1>{});
+
+    static_assert(Block_M % (WarpPerBlock_M * Warp_M) == 0);
+    static_assert(Block_N % (WarpPerBlock_N * Warp_N) == 0);
+    // repeat of each thread along seq<M, N>
+    static constexpr index_t Repeat_M = Block_M / (WarpPerBlock_M * Warp_M);
+    static constexpr index_t Repeat_N = Block_N / (WarpPerBlock_N * Warp_N);
+
+    // vector size along seq<M, N>
+    static constexpr index_t Vector_M = Vector_::at(number<0>{});
+    static constexpr index_t Vector_N = Vector_::at(number<1>{});
+
+    static_assert(Warp_M % Vector_M == 0);
+    static_assert(Warp_N % Vector_N == 0);
+    // num of threads along seq<M, N>, within each warp
+    static constexpr index_t ThreadPerWarp_M = Warp_M / Vector_M;
+    static constexpr index_t ThreadPerWarp_N = Warp_N / Vector_N;
+
+    static constexpr index_t BlockSize = BlockSize_;
+};
+
+} // namespace ck_tile

include/ck_tile/ops/elementwise.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#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/elementwise/unary_element_wise_operation.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include <type_traits>
+
+namespace ck_tile {
+namespace element_wise {
+
+#if 0
+struct PassThroughPack2
+{
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const;
+
+    CK_TILE_HOST_DEVICE constexpr void operator()(ck_tile::half2_t& y, const ck_tile::f8x2_t& x) const
+    {
+        auto t = type_convert<float2_t>(x);
+        y      = type_convert<half2_t>(t);
+    }
+    constexpr const static bool is_pack2_invocable = true;
+};
+#endif
+
+struct PassThrough
+{
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const;
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<double, double>(double& y, const double& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, double>(float& y, const double& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<double, float>(double& y, const float& x) const
+    {
+        y = type_convert<double>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, float>(float& y, const float& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp16_t, ck_tile::fp16_t>(ck_tile::fp16_t& y, const ck_tile::fp16_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::fp16_t, float>(ck_tile::fp16_t& y,
+                                                                const float& x) const
+    {
+        y = type_convert<ck_tile::fp16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::bf16_t, ck_tile::bf16_t>(ck_tile::bf16_t& y, const ck_tile::bf16_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int32_t, int32_t>(int32_t& y, const int32_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::bf16_t, float>(ck_tile::bf16_t& y,
+                                                                const float& x) const
+    {
+        y = type_convert<ck_tile::bf16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, ck_tile::bf16_t>(float& y,
+                                                                const ck_tile::bf16_t& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::bf16_t, ck_tile::fp16_t>(ck_tile::bf16_t& y, const ck_tile::fp16_t& x) const
+    {
+        y = type_convert<ck_tile::bf16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, ck_tile::fp16_t>(float& y,
+                                                                const ck_tile::fp16_t& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int8_t, int8_t>(int8_t& y, const int8_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::fp16_t, int8_t>(ck_tile::fp16_t& y,
+                                                                 const int8_t& x) const
+    {
+        y = type_convert<ck_tile::fp16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::bf16_t, int8_t>(ck_tile::bf16_t& y,
+                                                                 const int8_t& x) const
+    {
+        y = type_convert<ck_tile::bf16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<uint8_t, uint8_t>(uint8_t& y, const uint8_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int8_t, int32_t>(int8_t& y, const int32_t& x) const
+    {
+        y = type_convert<int8_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int32_t, int8_t>(int32_t& y, const int8_t& x) const
+    {
+        y = type_convert<int32_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int8_t, float>(int8_t& y, const float& x) const
+    {
+        y = type_convert<int8_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, int8_t>(float& y, const int8_t& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
+#ifdef CK_TILE_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int4_t, int4_t>(int4_t& y, const int4_t& x) const
+    {
+        y = x;
+    }
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int4_t, int>(int4_t& y, const int& x) const
+    {
+        y = type_convert<int4_t>(x);
+    }
+#endif
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp8_t, ck_tile::fp8_t>(ck_tile::fp8_t& y, const ck_tile::fp8_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, ck_tile::fp8_t>(float& y,
+                                                               const ck_tile::fp8_t& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::fp8_t, float>(ck_tile::fp8_t& y,
+                                                               const float& x) const
+    {
+        y = type_convert<ck_tile::fp8_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp16_t, ck_tile::fp8_t>(ck_tile::fp16_t& y, const ck_tile::fp8_t& x) const
+    {
+        y = type_convert<ck_tile::fp16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp8_t, ck_tile::fp16_t>(ck_tile::fp8_t& y, const ck_tile::fp16_t& x) const
+    {
+        y = type_convert<ck_tile::fp8_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::bf8_t, ck_tile::bf8_t>(ck_tile::bf8_t& y, const ck_tile::bf8_t& x) const
+    {
+        y = x;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, ck_tile::bf8_t>(float& y,
+                                                               const ck_tile::bf8_t& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::bf8_t, float>(ck_tile::bf8_t& y,
+                                                               const float& x) const
+    {
+        y = type_convert<ck_tile::bf8_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp16_t, ck_tile::bf8_t>(ck_tile::fp16_t& y, const ck_tile::bf8_t& x) const
+    {
+        y = type_convert<ck_tile::fp16_t>(x);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::bf8_t, ck_tile::fp16_t>(ck_tile::bf8_t& y, const ck_tile::fp16_t& x) const
+    {
+        y = ck_tile::type_convert<ck_tile::bf8_t>(x);
+    }
+};
+
+#if 0
+struct UnaryConvert
+{
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const
+    {
+        y = type_convert<Y>(x);
+    }
+};
+
+struct ConvertBF16RTN
+{
+    // convert to bf16 using round to nearest (rtn)
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const
+    {
+        // check Y datatype
+        static_assert(std::is_same_v<Y, ck_tile::bf16_t>, "Data type is not supported by this operation!");
+
+        // check X datatype
+        static_assert(std::is_same_v<X, float> || std::is_same_v<X, ck_tile::fp16_t>,
+                      "Data type is not supported by this operation!");
+
+        y = bf16_convert_rtn<Y>(x);
+    }
+};
+
+struct ConvertF8SR
+{
+    // convert to fp8 using stochastic rounding (SR)
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const
+    {
+        // check Y datatype
+        static_assert(std::is_same_v<Y, ck_tile::fp8_t> || std::is_same_v<Y, ck_tile::bf8_t>,
+                      "Data type is not supported by this operation!");
+
+        // check X datatype
+        static_assert(std::is_same_v<X, float> || std::is_same_v<X, ck_tile::fp16_t>,
+                      "Data type is not supported by this operation!");
+
+        y = f8_convert_sr<Y>(x);
+    }
+};
+
+struct ConvertF8RNE
+{
+    // convert to fp8 using rounding to nearest even
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const
+    {
+        // check Y datatype
+        static_assert(std::is_same_v<Y, ck_tile::fp8_t> || std::is_same_v<Y, ck_tile::bf8_t>,
+                      "Data type is not supported by this operation!");
+
+        // check X datatype
+        static_assert(std::is_same_v<X, float> || std::is_same_v<X, ck_tile::fp16_t>,
+                      "Data type is not supported by this operation!");
+
+        y = f8_convert_rne<Y>(x);
+    }
+};
+#endif
+
+struct Scale
+{
+    CK_TILE_HOST_DEVICE Scale(float scale = 1.f) : scale_(scale) {}
+
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const
+    {
+        y = ck_tile::type_convert<Y>(ck_tile::type_convert<float>(x) * scale_);
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp16_t, ck_tile::fp16_t>(ck_tile::fp16_t& y, const ck_tile::fp16_t& x) const
+    {
+        y = ck_tile::type_convert<ck_tile::fp16_t>(scale_) * x;
+    };
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::bf16_t, ck_tile::bf16_t>(ck_tile::bf16_t& y, const ck_tile::bf16_t& x) const
+    {
+        const float x_tmp = ck_tile::type_convert<float>(x);
+        const float y_tmp = scale_ * x_tmp;
+        y                 = ck_tile::type_convert<ck_tile::bf16_t>(y_tmp);
+    };
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, float>(float& y, const float& x) const
+    {
+        y = scale_ * x;
+    };
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<double, double>(double& y, const double& x) const
+    {
+        y = scale_ * x;
+    };
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<int8_t, int8_t>(int8_t& y, const int8_t& x) const
+    {
+        y = ck_tile::type_convert<int8_t>(scale_ * ck_tile::type_convert<float>(x));
+    };
+
+    float scale_;
+};
+
+struct ScaleAndResetNaNToMinusInfinity
+{
+    CK_TILE_HOST_DEVICE ScaleAndResetNaNToMinusInfinity(float scale) : scale_(scale) {}
+
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const;
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, float>(float& y, const float& x) const
+    {
+        y = ck_tile::isnan(x) ? -numeric<float>::infinity() : scale_ * x;
+    };
+
+    float scale_;
+};
+
+struct UnaryDivide
+{
+    CK_TILE_HOST_DEVICE UnaryDivide(const int32_t divider = 1) : divider_(divider) {}
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = x / type_convert<T>(divider_);
+    };
+
+    int32_t divider_ = 1;
+};
+
+struct UnarySquare
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, ck_tile::fp16_t> ||
+                          std::is_same_v<T, double> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>
+#ifdef CK_TILE_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
+                          || std::is_same_v<T, int4_t>
+#endif
+                      ,
+                      "Data type is not supported by this operation!");
+        y = x * x;
+    };
+};
+
+struct UnaryAbs
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::abs(x);
+    };
+};
+
+struct UnarySqrt
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::sqrt(x);
+    };
+};
+
+struct Relu
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        y = x > 0 ? x : 0;
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()(ck_tile::bf16_t& y, const ck_tile::bf16_t& x) const
+    {
+        float x_f32 = ck_tile::type_convert<float>(x);
+        float y_f32 = x_f32 > 0 ? x_f32 : 0;
+        y           = ck_tile::type_convert<ck_tile::bf16_t>(y_f32);
+    }
+};
+
+// Fast GeLU
+// https://paperswithcode.com/method/gelu
+// y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
+// host code use higher accuracy "exp" and "div"
+// gpu code use lower accuracy "_ocml_exp_f32" and "rcp" function
+struct FastGelu
+{
+    template <typename Y, typename X>
+    CK_TILE_HOST void operator()(Y& y, const X& x) const;
+
+    template <typename Y, typename X>
+    CK_TILE_DEVICE void operator()(Y& y, const X& x) const;
+
+    template <>
+    CK_TILE_HOST void operator()<float, float>(float& y, const float& x) const
+    {
+        // const float u   = -2.f * x * (0.035677f * x * x + 0.797885f);
+        const float c1  = -2.0 * 0.035677f;
+        const float c2  = -2.0 * 0.797885f;
+        const float u   = x * (c1 * x * x + c2);
+        const float emu = exp(u);
+        y               = x / (1.f + emu);
+    }
+
+    // device code, use lower precision "__ocml_exp_f32" and "rcp"
+    template <>
+    CK_TILE_DEVICE void operator()<float, float>(float& y, const float& x) const
+    {
+        // const float u   = 2.f * x * (0.035677f * x * x + 0.797885f);
+        const float c1  = -2.0 * 0.035677f;
+        const float c2  = -2.0 * 0.797885f;
+        const float u   = x * (c1 * x * x + c2);
+        const float emu = __ocml_exp_f32(u);
+
+        y = x * ck_tile::rcp(1.f + emu);
+    }
+
+    template <>
+    CK_TILE_HOST void operator()<ck_tile::fp16_t, ck_tile::fp16_t>(ck_tile::fp16_t& y,
+                                                                   const ck_tile::fp16_t& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, type_convert<float>(x));
+
+        y = type_convert<ck_tile::fp16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_DEVICE void operator()<ck_tile::fp16_t, ck_tile::fp16_t>(ck_tile::fp16_t& y,
+                                                                     const ck_tile::fp16_t& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, type_convert<float>(x));
+
+        y = type_convert<ck_tile::fp16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_HOST void operator()<ck_tile::fp16_t, float>(ck_tile::fp16_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<ck_tile::fp16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_DEVICE void operator()<ck_tile::fp16_t, float>(ck_tile::fp16_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<ck_tile::fp16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_HOST void operator()<ck_tile::bf16_t, float>(ck_tile::bf16_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<ck_tile::bf16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_DEVICE void operator()<ck_tile::bf16_t, float>(ck_tile::bf16_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<ck_tile::bf16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_DEVICE void operator()<ck_tile::bf16_t, ck_tile::bf16_t>(ck_tile::bf16_t& y,
+                                                                     const ck_tile::bf16_t& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, type_convert<float>(x));
+
+        y = type_convert<ck_tile::bf16_t>(y_f);
+    }
+
+    template <>
+    CK_TILE_HOST void operator()<ck_tile::bf16_t, ck_tile::bf16_t>(ck_tile::bf16_t& y,
+                                                                   const ck_tile::bf16_t& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, type_convert<float>(x));
+
+        y = type_convert<ck_tile::bf16_t>(y_f);
+    }
+};
+
+// https://paperswithcode.com/method/gelu
+// y = 0.5*x*(1+erf(x/sqrt(2)))
+struct Gelu
+{
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const;
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<float, float>(float& y, const float& x) const
+    {
+        y = 0.5f * x * (1.f + erf(float(0.70710678118f * x)));
+    }
+
+    template <>
+    CK_TILE_HOST_DEVICE void
+    operator()<ck_tile::fp16_t, ck_tile::fp16_t>(ck_tile::fp16_t& y, const ck_tile::fp16_t& x) const
+    {
+        y = ck_tile::fp16_t(0.5) * x *
+            (ck_tile::fp16_t(1) + ck_tile::fp16_t(erf(float(0.70710678118f * x))));
+    }
+};
+
+struct Sigmoid
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+        constexpr T one = type_convert<T>(1);
+        y               = one / (one + ck_tile::exp(-x));
+    };
+};
+
+struct Silu
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+        constexpr T one = type_convert<T>(1);
+        y               = x * (one / (one + ck_tile::exp(-x)));
+    };
+};
+
+struct TanH
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::tanh(x);
+    };
+};
+
+struct ACos
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::acos(x);
+    };
+};
+
+struct Neg
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::neg(x);
+    };
+};
+
+struct ATan
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::atan(x);
+    };
+};
+
+struct Sin
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::sin(x);
+    };
+};
+
+struct ASinH
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::asinh(x);
+    };
+};
+
+struct Cos
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::cos(x);
+    };
+};
+
+struct ACosH
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::acosh(x);
+    };
+};
+
+struct Tan
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::tan(x);
+    };
+};
+
+struct ATanH
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::atanh(x);
+    };
+};
+
+struct SinH
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::sinh(x);
+    };
+};
+
+struct Ceil
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::ceil(x);
+    };
+};
+
+struct Exp
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::exp(x);
+    };
+};
+
+struct CosH
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::cosh(x);
+    };
+};
+
+struct Floor
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::floor(x);
+    };
+};
+
+struct Log
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::log(x);
+    };
+};
+
+struct ASin
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::asin(x);
+    };
+};
+
+struct Rcp
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int8_t> ||
+                          std::is_same_v<T, int32_t>,
+                      "Data type is not supported by this operation!");
+
+        y = ck_tile::rcp(x);
+    };
+};
+
+struct Swish
+{
+    Swish(float beta = 1.0f) : beta_(beta) {}
+
+    template <typename Y, typename X>
+    CK_TILE_HOST_DEVICE void operator()(Y& y, const X& x) const
+    {
+        static_assert(std::is_same_v<X, float> || std::is_same_v<X, double> ||
+                          std::is_same_v<X, ck_tile::fp16_t>,
+                      "Data type is not supported by this operation!");
+
+        static_assert(std::is_same_v<Y, float> || std::is_same_v<Y, double> ||
+                          std::is_same_v<Y, ck_tile::fp16_t>,
+                      "Data type is not supported by this operation!");
+
+        float bx = -beta_ * type_convert<float>(x);
+        y        = type_convert<Y>(x / (1.f + ck_tile::exp(bx)));
+    };
+
+    const float beta_;
+};
+
+struct SoftRelu
+{
+    SoftRelu(float alpha = 1.f) : alpha_(alpha){};
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        T casted_alpha  = type_convert<T>(alpha_);
+        constexpr T one = type_convert<T>(1);
+        y               = ck_tile::log(one + ck_tile::exp(x * casted_alpha)) / casted_alpha;
+    }
+    const float alpha_;
+};
+
+struct Power
+{
+    Power(float alpha = 0.f, float beta = 1.f, float gamma = 2.f)
+        : alpha_(alpha), beta_(beta), gamma_(gamma){};
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        T casted_alpha     = type_convert<T>(alpha_);
+        T casted_beta      = type_convert<T>(beta_);
+        T casted_gamma     = type_convert<T>(gamma_);
+        T shifted_scaled_x = casted_alpha + casted_beta * x;
+        y                  = ck_tile::pow(shifted_scaled_x, casted_gamma);
+    }
+    const float alpha_;
+    const float beta_;
+    const float gamma_;
+};
+
+struct ClippedRelu
+{
+    ClippedRelu(float alpha = 0.f, float beta = 1.f) : alpha_(alpha), beta_(beta){};
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        T casted_alpha = type_convert<T>(alpha_);
+        T casted_beta  = type_convert<T>(beta_);
+        y              = ck_tile::min(casted_beta, ck_tile::max(casted_alpha, x));
+    }
+    const float alpha_;
+    const float beta_;
+};
+
+struct LeakyRelu
+{
+    LeakyRelu(float alpha = 0.01f) : alpha_(alpha){};
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        T casted_alpha = type_convert<T>(alpha_);
+        y              = x >= 0 ? x : x * casted_alpha;
+    }
+    const float alpha_;
+};
+
+struct Elu
+{
+    Elu(float alpha = 1.f) : alpha_(alpha){};
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        T casted_alpha = type_convert<T>(alpha_);
+        y              = x > 0 ? x : casted_alpha * ck_tile::expm1(x);
+    }
+    const float alpha_;
+};
+
+struct Logistic
+{
+    Logistic(float alpha = 1.f) : alpha_(alpha){};
+
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(T& y, const T& x) const
+    {
+        static_assert(std::is_same_v<T, float> || std::is_same_v<T, double> ||
+                          std::is_same_v<T, ck_tile::fp16_t> || std::is_same_v<T, int32_t> ||
+                          std::is_same_v<T, int8_t>,
+                      "Data type is not supported by this operation!");
+        T casted_alpha  = type_convert<T>(alpha_);
+        constexpr T one = type_convert<T>(1);
+        y               = casted_alpha / (one + ck_tile::exp(-x) * casted_alpha);
+    }
+    const float alpha_;
+};
+
+struct ConvInvscale
+{
+    CK_TILE_HOST_DEVICE
+    ConvInvscale(float scale_in = 1.f, float scale_wei = 1.f, float scale_out = 1.f)
+        : scale_in_(scale_in), scale_wei_(scale_wei), scale_out_(scale_out)
+    {
+    }
+
+    template <typename E, typename C>
+    CK_TILE_HOST_DEVICE void operator()(E& e, const C& c) const;
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::fp8_t, float>(ck_tile::fp8_t& e,
+                                                               const float& c) const
+    {
+        e = type_convert<ck_tile::fp8_t>(c / scale_in_ / scale_wei_ / scale_out_);
+    };
+
+    float scale_in_;
+    float scale_wei_;
+    float scale_out_;
+};
+
+struct ConvScale
+{
+    CK_TILE_HOST_DEVICE
+    ConvScale(float scale_in = 1.f, float scale_wei = 1.f, float scale_out = 1.f)
+        : scale_in_(scale_in), scale_wei_(scale_wei), scale_out_(scale_out)
+    {
+    }
+
+    template <typename E, typename C>
+    CK_TILE_HOST_DEVICE void operator()(E& e, const C& c) const;
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::fp8_t, float>(ck_tile::fp8_t& e,
+                                                               const float& c) const
+    {
+        e = type_convert<ck_tile::fp8_t>(c * scale_in_ * scale_wei_ * scale_out_);
+    };
+
+    float scale_in_;
+    float scale_wei_;
+    float scale_out_;
+};
+
+struct ConvScaleRelu
+{
+    CK_TILE_HOST_DEVICE
+    ConvScaleRelu(float scale_in = 1.f, float scale_wei = 1.f, float scale_out = 1.f)
+        : scale_in_(scale_in), scale_wei_(scale_wei), scale_out_(scale_out)
+    {
+    }
+
+    template <typename E, typename C>
+    CK_TILE_HOST_DEVICE void operator()(E& e, const C& c) const;
+
+    template <>
+    CK_TILE_HOST_DEVICE void operator()<ck_tile::fp8_t, float>(ck_tile::fp8_t& e,
+                                                               const float& c) const
+    {
+        float x;
+        Relu{}.template operator()<float>(x, c * scale_in_ * scale_wei_);
+        e = type_convert<ck_tile::fp8_t>(x * scale_out_);
+    };
+
+    float scale_in_;
+    float scale_wei_;
+    float scale_out_;
+};
+
+template <typename DstType, typename SrcType>
+struct Cast
+{
+    template <typename T>
+    CK_TILE_HOST_DEVICE void operator()(DstType& y, const SrcType& x) const
+    {
+        y = ck_tile::type_convert<DstType>(x);
+    };
+};
+
+// support fastconvert of int8 to fp16
+#if 0
+template <typename InputDataType, typename OutputDataType, index_t RegPackNumber>
+struct FastNumericArrayConverter
+{
+};
+
+template <>
+struct FastNumericArrayConverter<uint8_t, ck_tile::fp16_t, 4>
+{
+    using InputArray  = vector_type<uint8_t, 4>;
+    using OutputArray = vector_type<ck_tile::fp16_t, 4>;
+
+    CK_TILE_DEVICE static OutputArray convert(InputArray const& Input)
+    {
+        OutputArray Output;
+
+        uint32_t* half_2       = reinterpret_cast<uint32_t*>(&Output);
+        uint32_t const uint8_4 = reinterpret_cast<uint32_t const&>(Input);
+
+        static constexpr uint32_t byte_selector_01 = 0x05010500;
+        static constexpr uint32_t byte_selector_23 = 0x05030502;
+        static constexpr uint32_t fp16_adder       = 0x64646464;
+        half_2[0] = __builtin_amdgcn_perm(fp16_adder, uint8_4, byte_selector_01);
+        half_2[1] = __builtin_amdgcn_perm(fp16_adder, uint8_4, byte_selector_23);
+
+        static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64806480;
+        asm volatile("v_pk_add_f16 %0, %1, %2 neg_lo:[0,1] neg_hi:[0,1]"
+                     : "=v"(half_2[0])
+                     : "v"(half_2[0]), "s"(I8s_TO_F16s_MAGIC_NUM));
+        asm volatile("v_pk_add_f16 %0, %1, %2 neg_lo:[0,1] neg_hi:[0,1]"
+                     : "=v"(half_2[1])
+                     : "v"(half_2[1]), "s"(I8s_TO_F16s_MAGIC_NUM));
+
+        return Output;
+    }
+
+    CK_TILE_DEVICE OutputArray operator()(InputArray const& Input) { return convert(Input); }
+};
+
+template <index_t N>
+struct FastNumericArrayConverter<uint8_t, ck_tile::fp16_t, N>
+{
+    static constexpr int VEC_WIDTH = 4;
+    static_assert(!(N % VEC_WIDTH), "N must be multiple of 4.");
+
+    using InputArray  = vector_type<uint8_t, N>;
+    using OutputArray = vector_type<ck_tile::fp16_t, N>;
+
+    CK_TILE_DEVICE static OutputArray convert(InputArray const& Input)
+    {
+        FastNumericArrayConverter<uint8_t, ck_tile::fp16_t, 4> converter;
+
+        OutputArray Output;
+
+        using Vec_InputArray  = vector_type<uint8_t, 4>;
+        using Vec_OutputArray = vector_type<ck_tile::fp16_t, 4>;
+
+        Vec_OutputArray* half_4_ptr       = reinterpret_cast<Vec_OutputArray*>(&Output);
+        Vec_InputArray const* uint8_4_ptr = reinterpret_cast<Vec_InputArray const*>(&Input);
+
+        static_for<0, N / VEC_WIDTH, 1>{}(
+            [&](auto i) { half_4_ptr[i] = converter(uint8_4_ptr[i]); });
+
+        return Output;
+    }
+
+    CK_TILE_DEVICE OutputArray operator()(InputArray const& Input) { return convert(Input); }
+};
+#endif
+} // namespace element_wise
+} // namespace ck_tile

include/ck_tile/ops/epilogue.hpp

@@ -5,4 +5,6 @@
 
 #include "ck_tile/ops/epilogue/cshuffle_epilogue.hpp"
 #include "ck_tile/ops/epilogue/default_2d_epilogue.hpp"
+#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/epilogue/default_2d_epilogue.hpp

@@ -9,23 +9,29 @@ namespace ck_tile {
 
 // this epilogue just store out a M*N matrix, row major
 
-template <typename AccDataType_, typename ODataType_, bool kPadM_, bool kPadN_>
+template <typename AccDataType_,
+          typename ODataType_,
+          bool kPadM_,
+          bool kPadN_,
+          bool UseRawStore_ = true>
 struct Default2DEpilogueProblem
 {
-    using AccDataType           = remove_cvref_t<AccDataType_>;
-    using ODataType             = remove_cvref_t<ODataType_>;
-    static constexpr bool kPadM = kPadM_;
-    static constexpr bool kPadN = kPadN_;
+    using AccDataType                 = remove_cvref_t<AccDataType_>;
+    using ODataType                   = remove_cvref_t<ODataType_>;
+    static constexpr bool kPadM       = kPadM_;
+    static constexpr bool kPadN       = kPadN_;
+    static constexpr bool UseRawStore = UseRawStore_;
 };
 
 template <typename Problem_, typename Policy_ = void>
 struct Default2DEpilogue
 {
-    using Problem               = remove_cvref_t<Problem_>;
-    using AccDataType           = remove_cvref_t<typename Problem::AccDataType>;
-    using ODataType             = remove_cvref_t<typename Problem::ODataType>;
-    static constexpr bool kPadM = Problem::kPadM;
-    static constexpr bool kPadN = Problem::kPadN;
+    using Problem                     = remove_cvref_t<Problem_>;
+    using AccDataType                 = remove_cvref_t<typename Problem::AccDataType>;
+    using ODataType                   = remove_cvref_t<typename Problem::ODataType>;
+    static constexpr bool kPadM       = Problem::kPadM;
+    static constexpr bool kPadN       = Problem::kPadN;
+    static constexpr bool UseRawStore = Problem::UseRawStore;
 
     CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize() { return 0; }
 
@@ -36,7 +42,7 @@ struct Default2DEpilogue
     {
 
         // TODO: this is ugly
-        if constexpr(kPadM || kPadN)
+        if constexpr(UseRawStore && (kPadM || kPadN))
         {
             store_tile_raw(o_dram_window_tmp, cast_tile<ODataType>(o_acc_tile));
             buffer_store_fence();

include/ck_tile/ops/epilogue/dynamic_quant_epilogue.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck_tile/core.hpp"
+#include "ck_tile/ops/reduce.hpp"
+
+namespace ck_tile {
+
+template <bool kPadM_,
+          bool kPadN_,
+          bool UseSmoothInputScale_,
+          bool UseRawStore_ = true,
+          bool UseMax3_     = false>
+struct DynamicQuantEpilogueTraits
+{
+    static constexpr bool kPadM               = kPadM_;
+    static constexpr bool kPadN               = kPadN_;
+    static constexpr bool UseSmoothInputScale = UseSmoothInputScale_;
+    static constexpr bool UseRawStore         = UseRawStore_;
+    static constexpr bool UseMax3             = UseMax3_;
+};
+
+// this epilogue just store out a M*N matrix, row major
+template <typename AccDataType_,
+          typename XScaleDataType_,
+          typename YScaleDataType_,
+          typename ODataType_,
+          typename BlockShape_,
+          typename Traits_>
+struct DynamicQuantEpilogueProblem
+{
+    using AccDataType    = remove_cvref_t<AccDataType_>;
+    using XScaleDataType = remove_cvref_t<XScaleDataType_>;
+    using YScaleDataType = remove_cvref_t<YScaleDataType_>;
+    using ODataType      = remove_cvref_t<ODataType_>;
+    using BlockShape     = remove_cvref_t<BlockShape_>; // can consum generic 2d shape
+    using Traits         = remove_cvref_t<Traits_>;
+};
+
+// TODO: we should put descriptor creation function into policy
+template <typename Problem_, typename Policy_ = void>
+struct DynamicQuantEpilogue
+{
+    using Problem                     = remove_cvref_t<Problem_>;
+    using AccDataType                 = remove_cvref_t<typename Problem::AccDataType>;
+    using XScaleDataType              = remove_cvref_t<typename Problem::XScaleDataType>;
+    using YScaleDataType              = remove_cvref_t<typename Problem::YScaleDataType>;
+    using ODataType                   = remove_cvref_t<typename Problem::ODataType>;
+    using BlockShape                  = remove_cvref_t<typename Problem::BlockShape>;
+    static constexpr bool kPadM       = Problem::Traits::kPadM;
+    static constexpr bool kPadN       = Problem::Traits::kPadN;
+    static constexpr bool UseRawStore = Problem::Traits::UseRawStore;
+    static constexpr bool UseMax3     = Problem::Traits::UseMax3;
+
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2d()
+    {
+        using P_ = BlockReduce2dProblem<AccDataType, AccDataType, BlockShape>;
+        return BlockReduce2d<P_>{};
+    }
+
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dSync()
+    {
+        using P_ = BlockReduce2dProblem<AccDataType, AccDataType, BlockShape>;
+        return BlockReduce2dSync<P_>{};
+    }
+
+    CK_TILE_HOST_DEVICE static constexpr auto GetBlockReduce2dCrossWarpSync()
+    {
+        using P_ = BlockReduce2dProblem<AccDataType, AccDataType, BlockShape>;
+        return BlockReduce2dCrossWarpSync<P_>{};
+    }
+
+    CK_TILE_DEVICE static constexpr auto MakeSmoothInputScaleTileDistribution()
+    {
+        using S = BlockShape;
+#if 0
+        // don't remove this
+        // Note that if we set encoding purposely like this, you will result in compile fail
+        // TODO: x_scale create local-scratch to accept arbitrary acc input (with same length)
+        return make_static_tile_distribution(
+            tile_distribution_encoding<
+                sequence<S::Repeat_M, S::WarpPerBlock_M, S::ThreadPerWarp_M>,
+                tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
+                tuple<sequence<0, 1>, sequence<0, 1>>,
+                tuple<sequence<1, 1>, sequence<2, 2>>,
+                sequence<0, 1, 1>,
+                sequence<0, 0, 3>>{});
+#else
+        return make_static_tile_distribution(
+            tile_distribution_encoding<
+                sequence<S::WarpPerBlock_M, S::ThreadPerWarp_M>,
+                tuple<sequence<S::Repeat_N, S::WarpPerBlock_N, S::ThreadPerWarp_N, S::Vector_N>>,
+                tuple<sequence<0, 1>, sequence<0, 1>>,
+                tuple<sequence<0, 1>, sequence<1, 2>>,
+                sequence<1, 1>,
+                sequence<0, 3>>{});
+#endif
+    }
+
+    CK_TILE_HOST_DEVICE static constexpr index_t GetSmemSize()
+    {
+        auto reduce_crosswarp_sync = GetBlockReduce2dCrossWarpSync();
+        return reduce_crosswarp_sync.GetSmemSize();
+    }
+
+    // TODO: this function assume store out vector size is the same as OAccTile last dimension size
+    //       how do we fix this ?
+    template <typename ODramWindowTmp,
+              typename XScaleWindow,
+              typename YScaleWindow,
+              typename OAccTile>
+    CK_TILE_DEVICE auto operator()(ODramWindowTmp& o_dram_window_tmp,
+                                   const XScaleWindow& x_scale_window_,
+                                   YScaleWindow& y_scale_window,
+                                   const OAccTile& o_acc_tile,
+                                   void* smem)
+    {
+        auto reduce                = GetBlockReduce2d();
+        auto reduce_sync           = GetBlockReduce2dSync();
+        auto reduce_crosswarp_sync = GetBlockReduce2dCrossWarpSync();
+        const auto x_scale_window =
+            make_tile_window(x_scale_window_, MakeSmoothInputScaleTileDistribution());
+
+        auto x_scale = load_tile(x_scale_window);
+
+        auto o_acc_tmp = o_acc_tile;
+
+        sweep_tile(o_acc_tmp, [&](auto idx) {
+            constexpr auto j_idx = make_tuple(idx[number<1>{}]);
+            const auto xs_       = type_convert<AccDataType>(x_scale[j_idx]);
+            o_acc_tmp(idx)       = o_acc_tmp(idx) * xs_;
+        });
+
+        const auto f_absmax = [](auto acc_, auto v_0_) { return max(acc_, abs(v_0_)); };
+
+        auto row_absmax = [&]() {
+            constexpr auto y_size_per_row =
+                OAccTile{}.get_tile_distribution().get_ys_to_d_descriptor().get_lengths().at(
+                    number<1>{});
+            if constexpr(UseMax3 && std::is_same_v<AccDataType, float> && y_size_per_row % 2 == 0)
+            {
+                // fast max3+abs implementation
+                const auto f_max3 = [](auto acc_, auto v_0_, auto v_1_) {
+                    float rtn;
+                    asm volatile("v_max3_f32 %0, %1, abs(%2), abs(%3)"
+                                 : "=v"(rtn)
+                                 : "v"(acc_), "v"(v_0_), "v"(v_1_));
+                    return rtn;
+                };
+                return reduce(o_acc_tmp, type_convert<AccDataType>(0), f_max3, sequence<1, 2>{});
+            }
+            else
+            {
+                return reduce(o_acc_tmp, type_convert<AccDataType>(0), f_absmax);
+            }
+        }();
+        reduce_sync(row_absmax, f_absmax);
+        reduce_crosswarp_sync(row_absmax, smem, f_absmax);
+
+        // here y_scale is Acc TYpe, need convert to YScale type later
+        auto y_scale = tile_elementwise_in(
+            [&](const auto& v_) {
+                return v_ / type_convert<AccDataType>(numeric<ODataType>::max());
+            },
+            row_absmax);
+
+        store_tile(y_scale_window, cast_tile<YScaleDataType>(y_scale));
+
+        sweep_tile(o_acc_tmp, [&](auto idx) {
+            constexpr auto row_id = make_tuple(idx[number<0>{}]);
+            o_acc_tmp(idx)        = o_acc_tmp[idx] / y_scale(row_id);
+        });
+
+        // TODO: this is ugly
+        if constexpr(UseRawStore && (kPadM || kPadN))
+        {
+            store_tile_raw(o_dram_window_tmp, cast_tile<ODataType>(o_acc_tmp));
+            buffer_store_fence();
+        }
+        else
+        {
+            store_tile(o_dram_window_tmp, cast_tile<ODataType>(o_acc_tmp));
+        }
+    }
+};
+} // namespace ck_tile

include/ck_tile/ops/fmha.hpp

@@ -43,4 +43,5 @@
 #include "ck_tile/ops/fmha/pipeline/block_fmha_pipeline_qx_ks_vs_custom_policy.hpp"
 #include "ck_tile/ops/fmha/pipeline/tile_fmha_shape.hpp"
 #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/fmha/kernel/fmha_fwd_kernel.hpp

@@ -69,7 +69,8 @@ struct FmhaFwdKernel
         // sync with generate.py
         // clang-format off
         using bfs = typename FmhaPipeline::BlockFmhaShape;
-        using gbr = typename bfs::Gemm0BlockWarps;
+        using g0br = typename bfs::Gemm0BlockWarps;
+        using g1br = typename bfs::Gemm1BlockWarps;
         using gwt = typename bfs::Gemm0WarpTile;
         #define _SS_  std::string
         #define _TS_  std::to_string
@@ -81,11 +82,12 @@ struct FmhaFwdKernel
             if (kPadHeadDimV) n += "dv";
             return n.empty() ? n : std::string("p") + n; }();
         return
-            _SS_("fmha_fwd_d") + _TS_(bfs::kK0BlockLength) + "_" + _SS_(t2s<QDataType>::name) +
+            _SS_("fmha_fwd_d") + _TS_(bfs::kQKHeaddim) + "_" + _SS_(t2s<QDataType>::name) +
             "_" + (kIsGroupMode ? "group" : "batch") + "_" + _SS_(TilePartitioner::name) + "_"
             "b" + _TS_(bfs::kM0) + "x" + _TS_(bfs::kN0) + "x" + _TS_(bfs::kK0) + "x" +
-                    _TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kK0BlockLength) + "_" +
-            "r" + _TS_(gbr::at(ck_tile::number<0>{})) + "x" + _TS_(gbr::at(ck_tile::number<1>{})) + "x" + _TS_(gbr::at(ck_tile::number<2>{})) + "_" +
+                    _TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kQKHeaddim) + "_" +
+            "r" + _TS_(g0br::at(ck_tile::number<0>{})) + "x" + _TS_(g0br::at(ck_tile::number<1>{})) + "x" + _TS_(g0br::at(ck_tile::number<2>{})) + "_" +
+            "r" + _TS_(g1br::at(ck_tile::number<0>{})) + "x" + _TS_(g1br::at(ck_tile::number<1>{})) + "x" + _TS_(g1br::at(ck_tile::number<2>{})) + "_" +
             "w" + _TS_(gwt::at(ck_tile::number<0>{})) + "x" + _TS_(gwt::at(ck_tile::number<1>{})) + "x" + _TS_(gwt::at(ck_tile::number<2>{})) + "_" +
             (kBlockPerCuInput == -1 ? "" : ("o" + _TS_(kBlockPerCu) + "_")) + _SS_(FmhaPipeline::name) + "_" +
             "v" + (std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor> ? "r" : "c") + (pn.empty() ? "" : "_" + pn) +
@@ -655,7 +657,7 @@ struct FmhaFwdKernel
             {
                 return pad_tensor_view(
                     q_dram_naive,
-                    make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0BlockLength>{}),
+                    make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kSubQKHeaddim>{}),
                     sequence<kPadSeqLenQ, kPadHeadDimQ>{});
             }
             else
@@ -722,7 +724,7 @@ struct FmhaFwdKernel
             [&]() {
                 if constexpr(FmhaPipeline::kQLoadOnce)
                     return make_tuple(number<FmhaPipeline::kM0>{},
-                                      number<FmhaPipeline::kK0BlockLength>{});
+                                      number<FmhaPipeline::kSubQKHeaddim>{});
                 else
                     return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0>{});
             }(),

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

@@ -65,7 +65,8 @@ struct FmhaFwdSplitKVKernel
         // sync with generate.py
         // clang-format off
         using bfs = typename FmhaPipeline::BlockFmhaShape;
-        using gbr = typename bfs::Gemm0BlockWarps;
+        using g0br = typename bfs::Gemm0BlockWarps;
+        using g1br = typename bfs::Gemm1BlockWarps;
         using gwt = typename bfs::Gemm0WarpTile;
         #define _SS_  std::string
         #define _TS_  std::to_string
@@ -77,11 +78,12 @@ struct FmhaFwdSplitKVKernel
             if (kPadHeadDimV) n += "dv";
             return n.empty() ? n : std::string("p") + n; }();
         return
-            _SS_("fmha_fwd_splitkv_d") + _TS_(bfs::kK0BlockLength) + "_" + _SS_(t2s<QDataType>::name) +
+            _SS_("fmha_fwd_splitkv_d") + _TS_(bfs::kQKHeaddim) + "_" + _SS_(t2s<QDataType>::name) +
             "_" + (kIsGroupMode ? "group" : "batch") + "_"
             "b" + _TS_(bfs::kM0) + "x" + _TS_(bfs::kN0) + "x" + _TS_(bfs::kK0) + "x" +
-                    _TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kK0BlockLength) + "_" +
-            "r" + _TS_(gbr::at(ck_tile::number<0>{})) + "x" + _TS_(gbr::at(ck_tile::number<1>{})) + "x" + _TS_(gbr::at(ck_tile::number<2>{})) + "_" +
+                    _TS_(bfs::kN1) + "x" + _TS_(bfs::kK1) + "x" + _TS_(bfs::kQKHeaddim) + "_" +
+            "r" + _TS_(g0br::at(ck_tile::number<0>{})) + "x" + _TS_(g0br::at(ck_tile::number<1>{})) + "x" + _TS_(g0br::at(ck_tile::number<2>{})) + "_" +
+            "r" + _TS_(g1br::at(ck_tile::number<0>{})) + "x" + _TS_(g1br::at(ck_tile::number<1>{})) + "x" + _TS_(g1br::at(ck_tile::number<2>{})) + "_" +
             "w" + _TS_(gwt::at(ck_tile::number<0>{})) + "x" + _TS_(gwt::at(ck_tile::number<1>{})) + "x" + _TS_(gwt::at(ck_tile::number<2>{})) + "_" +
             (kBlockPerCuInput == -1 ? "" : ("o" + _TS_(kBlockPerCu) + "_")) + _SS_(FmhaPipeline::name) + "_" +
             "v" + (std::is_same_v<VLayout, ck_tile::tensor_layout::gemm::RowMajor> ? "r" : "c") + (pn.empty() ? "" : "_" + pn) +
@@ -584,7 +586,7 @@ struct FmhaFwdSplitKVKernel
             {
                 return pad_tensor_view(
                     q_dram_naive,
-                    make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0BlockLength>{}),
+                    make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kSubQKHeaddim>{}),
                     sequence<kPadSeqLenQ, kPadHeadDimQ>{});
             }
             else
@@ -733,7 +735,7 @@ struct FmhaFwdSplitKVKernel
             [&]() {
                 if constexpr(FmhaPipeline::kQLoadOnce)
                     return make_tuple(number<FmhaPipeline::kM0>{},
-                                      number<FmhaPipeline::kK0BlockLength>{});
+                                      number<FmhaPipeline::kSubQKHeaddim>{});
                 else
                     return make_tuple(number<FmhaPipeline::kM0>{}, number<FmhaPipeline::kK0>{});
             }(),
@@ -894,7 +896,7 @@ struct FmhaFwdSplitKVKernel
                 o_acc_ptr,
                 make_tuple(kargs.seqlen_q, kargs.hdim_v),
                 make_tuple(kargs.stride_o_acc, 1),
-                number<1>{},
+                number<FmhaPipeline::kAlignmentOacc>{},
                 number<1>{});
 
             return pad_tensor_view(

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

@@ -26,8 +26,8 @@ struct FmhaFwdSplitKVTilePartitioner
     {
         // TODO: this may need tuning
         return dim3(ck_tile::integer_divide_ceil(max_seqlen_q, kM0) *
-                        ck_tile::integer_divide_ceil(hdim_v, kN1),
-                    nhead * num_splits,
+                        ck_tile::integer_divide_ceil(hdim_v, kN1) * num_splits,
+                    nhead,
                     batch_size);
     }
 
@@ -42,8 +42,9 @@ struct FmhaFwdSplitKVTilePartitioner
             return ck_tile::make_tuple(quotient, modulus);
         };
 
-        const auto [i_tile_m, i_tile_n] = f(blockIdx.x, num_tile_n1);
-        const auto [i_nhead, i_split]   = f(blockIdx.y, num_splits);
+        const auto [mn, i_split]        = f(blockIdx.x, num_splits);
+        const auto [i_tile_m, i_tile_n] = f(mn, num_tile_n1);
+        const index_t i_nhead           = blockIdx.y;
         const index_t i_batch           = blockIdx.z;
 
         return ck_tile::make_tuple(i_tile_m, i_tile_n, i_split, i_nhead, i_batch);

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

@@ -178,13 +178,13 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             k_lds_ptr, Policy::template MakeKLdsWriteBlockDescriptor<Problem>());
 
         auto k_lds_write_window =
-            make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
+            make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto k_lds_read_window =
             make_tile_window(k_lds_write_window.get_bottom_tensor_view(),
                              make_tuple(number<kN0>{}, number<kK0>{}),
                              k_lds_write_window.get_window_origin(),
-                             Policy::template MakeKRegSliceBlockDescriptor<Problem>());
+                             Policy::template MakeKRegBlockDescriptor<Problem>());
 
         auto k_reg_tensor = make_static_distributed_tensor<KDataType>(
             Policy::template MakeKRegBlockDescriptor<Problem>());
@@ -204,16 +204,13 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             v_lds_ptr, Policy::template MakeVLdsWriteBlockDescriptor<Problem>());
 
         auto v_lds_write_window =
-            make_tile_window(v_lds, make_tuple(number<kN0>{}, number<kK2>{}), {0, 0});
+            make_tile_window(v_lds, make_tuple(number<kN0>{}, number<kVHeaddim>{}), {0, 0});
 
         auto v_lds_read_window =
             make_tile_window(v_lds_write_window.get_bottom_tensor_view(),
                              make_tuple(number<kN0>{}, number<kK2>{}),
                              v_lds_write_window.get_window_origin(),
-                             Policy::template MakeVRegSliceBlockDescriptor<Problem>());
-
-        auto v_reg_tensor = make_static_distributed_tensor<VDataType>(
-            Policy::template MakeVRegBlockDescriptor<Problem>());
+                             Policy::template MakeVRegBlockDescriptor<Problem>());
 
         //------------------------------------------------------------------
         // KT, Reg ->LDS ->Reg
@@ -227,7 +224,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             kt_lds_ptr, Policy::template MakeShuffledKLdsWriteBlockDescriptor<Problem>());
 
         auto shuffled_k_lds_write_window = make_tile_window(
-            shuffled_k_lds_write, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
+            shuffled_k_lds_write, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto kt_lds_read = make_tensor_view<address_space_enum::lds>(
             kt_lds_ptr, Policy::template MakeKTLdsReadBlockDescriptor<Problem>());
@@ -257,7 +254,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
 
         block_sync_lds();
 
-        v_reg_tensor = load_tile(v_lds_read_window);
+        auto v_reg_tensor = load_tile(v_lds_read_window);
         block_sync_lds();
         //---------------------------- Loop Load in ----------------------------//
         // Q: HBM ->Reg ->LDS
@@ -276,7 +273,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
 
         auto q_lds_window =
-            make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
+            make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto q_lds_read_window =
             make_tile_window(q_lds_window.get_bottom_tensor_view(),
@@ -297,7 +294,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             qt_lds_ptr, Policy::template MakeShuffledQLdsWriteBlockDescriptor<Problem>());
 
         auto shuffled_q_lds_write_window = make_tile_window(
-            shuffled_q_lds_write, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
+            shuffled_q_lds_write, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto qt_lds_read = make_tensor_view<address_space_enum::lds>(
             qt_lds_ptr, Policy::template MakeQTLdsReadBlockDescriptor<Problem>());
@@ -322,7 +319,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
 
         auto do_lds_window =
-            make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
+            make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kVHeaddim>{}), {0, 0});
 
         auto do_lds_read_window =
             make_tile_window(do_lds_window.get_bottom_tensor_view(),
@@ -341,7 +338,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
             dot_lds_ptr, Policy::template MakeShuffledOGradLdsWriteBlockDescriptor<Problem>());
 
         auto shuffled_do_lds_write_window = make_tile_window(
-            shuffled_do_lds_write, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
+            shuffled_do_lds_write, make_tuple(number<kM0>{}, number<kVHeaddim>{}), {0, 0});
 
         auto dot_read_lds = make_tensor_view<address_space_enum::lds>(
             dot_lds_ptr, Policy::template MakeOGradTLdsReadBlockDescriptor<Problem>());
@@ -483,9 +480,9 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVR
 
         index_t i_total_loops = 0;
         index_t seqlen_q_step = seqlen_q_start;
-        static_assert(kQKHeaddim == kK0, "kQKHeaddim should equal to kK0");
+        static_assert(kQKHeaddim >= kK0, "kQKHeaddim should be equal or greater than kK0");
         static_assert(kM0 == kK1, "kM0 should equal to kK1");
-        static_assert(kVHeaddim == kK2, "kVHeaddim should equal to kK2");
+        static_assert(kVHeaddim >= kK2, "kVHeaddim should be equal or greater than kK2");
         static_assert(kM0 == kK3, "kM0 should equal to kK3");
         constexpr index_t k4_loops = kN0 / kK4;
 

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

@@ -178,13 +178,13 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             k_lds_ptr, Policy::template MakeKLdsWriteBlockDescriptor<Problem>());
 
         auto k_lds_write_window =
-            make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
+            make_tile_window(k_lds, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto k_lds_read_window =
             make_tile_window(k_lds_write_window.get_bottom_tensor_view(),
                              make_tuple(number<kN0>{}, number<kK0>{}),
                              k_lds_write_window.get_window_origin(),
-                             Policy::template MakeKRegSliceBlockDescriptor<Problem>());
+                             Policy::template MakeKRegBlockDescriptor<Problem>());
 
         auto k_reg_tensor = make_static_distributed_tensor<KDataType>(
             Policy::template MakeKRegBlockDescriptor<Problem>());
@@ -204,16 +204,13 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             v_lds_ptr, Policy::template MakeVLdsWriteBlockDescriptor<Problem>());
 
         auto v_lds_write_window =
-            make_tile_window(v_lds, make_tuple(number<kN0>{}, number<kK2>{}), {0, 0});
+            make_tile_window(v_lds, make_tuple(number<kN0>{}, number<kVHeaddim>{}), {0, 0});
 
         auto v_lds_read_window =
             make_tile_window(v_lds_write_window.get_bottom_tensor_view(),
                              make_tuple(number<kN0>{}, number<kK2>{}),
                              v_lds_write_window.get_window_origin(),
-                             Policy::template MakeVRegSliceBlockDescriptor<Problem>());
-
-        auto v_reg_tensor = make_static_distributed_tensor<VDataType>(
-            Policy::template MakeVRegBlockDescriptor<Problem>());
+                             Policy::template MakeVRegBlockDescriptor<Problem>());
 
         //------------------------------------------------------------------
         // KT, Reg ->LDS ->Reg
@@ -227,7 +224,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             kt_lds_ptr, Policy::template MakeShuffledKLdsWriteBlockDescriptor<Problem>());
 
         auto shuffled_k_lds_write_window = make_tile_window(
-            shuffled_k_lds_write, make_tuple(number<kN0>{}, number<kK0>{}), {0, 0});
+            shuffled_k_lds_write, make_tuple(number<kN0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto kt_lds_read = make_tensor_view<address_space_enum::lds>(
             kt_lds_ptr, Policy::template MakeKTLdsReadBlockDescriptor<Problem>());
@@ -257,7 +254,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
 
         block_sync_lds();
 
-        v_reg_tensor = load_tile(v_lds_read_window);
+        auto v_reg_tensor = load_tile(v_lds_read_window);
         //---------------------------- Loop Load in ----------------------------//
         // Q: HBM ->Reg ->LDS
         auto q_dram_window =
@@ -275,7 +272,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             q_lds_ptr, Policy::template MakeQLdsBlockDescriptor<Problem>());
 
         auto q_lds_window =
-            make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
+            make_tile_window(q_lds, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto q_lds_read_window =
             make_tile_window(q_lds_window.get_bottom_tensor_view(),
@@ -296,7 +293,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             qt_lds_ptr, Policy::template MakeShuffledQLdsWriteBlockDescriptor<Problem>());
 
         auto shuffled_q_lds_write_window = make_tile_window(
-            shuffled_q_lds_write, make_tuple(number<kM0>{}, number<kK0>{}), {0, 0});
+            shuffled_q_lds_write, make_tuple(number<kM0>{}, number<kQKHeaddim>{}), {0, 0});
 
         auto qt_lds_read = make_tensor_view<address_space_enum::lds>(
             qt_lds_ptr, Policy::template MakeQTLdsReadBlockDescriptor<Problem>());
@@ -321,7 +318,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             do_lds_ptr, Policy::template MakeOGradLdsBlockDescriptor<Problem>());
 
         auto do_lds_window =
-            make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
+            make_tile_window(do_lds, make_tuple(number<kM0>{}, number<kVHeaddim>{}), {0, 0});
 
         auto do_lds_read_window =
             make_tile_window(do_lds_window.get_bottom_tensor_view(),
@@ -340,7 +337,7 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
             dot_lds_ptr, Policy::template MakeShuffledOGradLdsWriteBlockDescriptor<Problem>());
 
         auto shuffled_do_lds_write_window = make_tile_window(
-            shuffled_do_lds_write, make_tuple(number<kM0>{}, number<kK2>{}), {0, 0});
+            shuffled_do_lds_write, make_tuple(number<kM0>{}, number<kVHeaddim>{}), {0, 0});
 
         auto dot_read_lds = make_tensor_view<address_space_enum::lds>(
             dot_lds_ptr, Policy::template MakeOGradTLdsReadBlockDescriptor<Problem>());
@@ -482,9 +479,9 @@ struct BlockFmhaBwdDQDKDVPipelineKRKTRVRIGLP
 
         index_t i_total_loops = 0;
         index_t seqlen_q_step = seqlen_q_start;
-        static_assert(kQKHeaddim == kK0, "kQKHeaddim should equal to kK0");
+        static_assert(kQKHeaddim >= kK0, "kQKHeaddim should be equal or greater than kK0");
         static_assert(kM0 == kK1, "kM0 should equal to kK1");
-        static_assert(kVHeaddim == kK2, "kVHeaddim should equal to kK2");
+        static_assert(kVHeaddim >= kK2, "kVHeaddim should be equal or greater than kK2");
         static_assert(kM0 == kK3, "kM0 should equal to kK3");
         constexpr index_t k4_loops = kN0 / kK4;
 

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

@@ -5,9 +5,8 @@
 
 #include "ck_tile/core.hpp"
 #include "ck_tile/ops/common/tensor_layout.hpp"
-#include "ck_tile/ops/gemm/pipeline/gemm_pipeline_problem.hpp"
+#include "ck_tile/ops/gemm/block/block_gemm_problem.hpp"
 #include "ck_tile/ops/gemm/pipeline/tile_gemm_shape.hpp"
-#include "ck_tile/ops/gemm/pipeline/tile_gemm_traits.hpp"
 #include "ck_tile/ops/gemm/warp/warp_gemm.hpp"
 #include "ck_tile/ops/gemm/warp/warp_gemm_dispatcher.hpp"
 #include "ck_tile/ops/gemm/block/block_gemm_areg_bsmem_creg_v1_custom_policy.hpp"
@@ -27,20 +26,15 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto GetQKBlockGemm()
     {
         using GemmProblem =
-            GemmPipelineProblem<typename Problem::QDataType,
-                                typename Problem::KDataType,
-                                typename Problem::AccDataType,
-                                TileGemmShape<sequence<Problem::BlockFmhaShape::kM0,
-                                                       Problem::BlockFmhaShape::kN0,
-                                                       Problem::BlockFmhaShape::kK0>,
-                                              typename Problem::BlockFmhaShape::Gemm0BlockWarps,
-                                              typename Problem::BlockFmhaShape::Gemm0WarpTile>,
-                                TileGemmTraits<Problem::kPadSeqLenQ,
-                                               Problem::kPadSeqLenK,
-                                               Problem::kPadHeadDimQ,
-                                               typename tensor_layout::gemm::RowMajor,
-                                               typename tensor_layout::gemm::ColumnMajor,
-                                               typename tensor_layout::gemm::RowMajor>>;
+            BlockGemmProblem<typename Problem::QDataType,
+                             typename Problem::KDataType,
+                             typename Problem::AccDataType,
+                             Problem::kBlockSize,
+                             TileGemmShape<sequence<Problem::BlockFmhaShape::kM0,
+                                                    Problem::BlockFmhaShape::kN0,
+                                                    Problem::BlockFmhaShape::kK0>,
+                                           typename Problem::BlockFmhaShape::Gemm0BlockWarps,
+                                           typename Problem::BlockFmhaShape::Gemm0WarpTile>>;
 
         using WarpGemm = WarpGemmMfmaDispatcher<
             typename Problem::QDataType,
@@ -66,20 +60,15 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto GetPTOGradTBlockGemm()
     {
         using GemmProblem =
-            GemmPipelineProblem<typename Problem::GemmDataType,
-                                typename Problem::OGradDataType,
-                                typename Problem::AccDataType,
-                                TileGemmShape<sequence<Problem::BlockFmhaShape::kN0,
-                                                       Problem::BlockFmhaShape::kVHeaddim,
-                                                       Problem::BlockFmhaShape::kK1>,
-                                              typename Problem::BlockFmhaShape::Gemm1BlockWarps,
-                                              typename Problem::BlockFmhaShape::Gemm1WarpTile>,
-                                TileGemmTraits<Problem::kPadSeqLenQ,
-                                               Problem::kPadHeadDimV,
-                                               Problem::kPadHeadDimV,
-                                               typename tensor_layout::gemm::RowMajor,
-                                               typename tensor_layout::gemm::ColumnMajor,
-                                               typename tensor_layout::gemm::RowMajor>>;
+            BlockGemmProblem<typename Problem::GemmDataType,
+                             typename Problem::OGradDataType,
+                             typename Problem::AccDataType,
+                             Problem::kBlockSize,
+                             TileGemmShape<sequence<Problem::BlockFmhaShape::kN0,
+                                                    Problem::BlockFmhaShape::kVHeaddim,
+                                                    Problem::BlockFmhaShape::kK1>,
+                                           typename Problem::BlockFmhaShape::Gemm1BlockWarps,
+                                           typename Problem::BlockFmhaShape::Gemm1WarpTile>>;
 
         using WarpGemm =
             WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
@@ -104,20 +93,15 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto GetOGradVBlockGemm()
     {
         using GemmProblem =
-            GemmPipelineProblem<typename Problem::OGradDataType,
-                                typename Problem::VDataType,
-                                typename Problem::AccDataType,
-                                TileGemmShape<sequence<Problem::BlockFmhaShape::kM0,
-                                                       Problem::BlockFmhaShape::kN0,
-                                                       Problem::BlockFmhaShape::kK2>,
-                                              typename Problem::BlockFmhaShape::Gemm2BlockWarps,
-                                              typename Problem::BlockFmhaShape::Gemm2WarpTile>,
-                                TileGemmTraits<Problem::kPadSeqLenQ,
-                                               Problem::kPadSeqLenK,
-                                               Problem::kPadHeadDimQ,
-                                               typename tensor_layout::gemm::RowMajor,
-                                               typename tensor_layout::gemm::ColumnMajor,
-                                               typename tensor_layout::gemm::RowMajor>>;
+            BlockGemmProblem<typename Problem::OGradDataType,
+                             typename Problem::VDataType,
+                             typename Problem::AccDataType,
+                             Problem::kBlockSize,
+                             TileGemmShape<sequence<Problem::BlockFmhaShape::kM0,
+                                                    Problem::BlockFmhaShape::kN0,
+                                                    Problem::BlockFmhaShape::kK2>,
+                                           typename Problem::BlockFmhaShape::Gemm2BlockWarps,
+                                           typename Problem::BlockFmhaShape::Gemm2WarpTile>>;
 
         using WarpGemm = WarpGemmMfmaDispatcher<
             typename Problem::OGradDataType,
@@ -143,20 +127,15 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto GetSGradTQTBlockGemm()
     {
         using GemmProblem =
-            GemmPipelineProblem<typename Problem::GemmDataType,
-                                typename Problem::QDataType,
-                                typename Problem::AccDataType,
-                                TileGemmShape<sequence<Problem::BlockFmhaShape::kN0,
-                                                       Problem::BlockFmhaShape::kQKHeaddim,
-                                                       Problem::BlockFmhaShape::kK3>,
-                                              typename Problem::BlockFmhaShape::Gemm3BlockWarps,
-                                              typename Problem::BlockFmhaShape::Gemm3WarpTile>,
-                                TileGemmTraits<Problem::kPadSeqLenK,
-                                               Problem::kPadHeadDimQ,
-                                               Problem::kPadSeqLenK,
-                                               typename tensor_layout::gemm::RowMajor,
-                                               typename tensor_layout::gemm::ColumnMajor,
-                                               typename tensor_layout::gemm::RowMajor>>;
+            BlockGemmProblem<typename Problem::GemmDataType,
+                             typename Problem::QDataType,
+                             typename Problem::AccDataType,
+                             Problem::kBlockSize,
+                             TileGemmShape<sequence<Problem::BlockFmhaShape::kN0,
+                                                    Problem::BlockFmhaShape::kQKHeaddim,
+                                                    Problem::BlockFmhaShape::kK3>,
+                                           typename Problem::BlockFmhaShape::Gemm3BlockWarps,
+                                           typename Problem::BlockFmhaShape::Gemm3WarpTile>>;
 
         using WarpGemm =
             WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
@@ -181,20 +160,15 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto GetSGradKTBlockGemm()
     {
         using GemmProblem =
-            GemmPipelineProblem<typename Problem::GemmDataType,
-                                typename Problem::KDataType,
-                                typename Problem::AccDataType,
-                                TileGemmShape<sequence<Problem::BlockFmhaShape::kM0,
-                                                       Problem::BlockFmhaShape::kQKHeaddim,
-                                                       Problem::BlockFmhaShape::kK4>,
-                                              typename Problem::BlockFmhaShape::Gemm4BlockWarps,
-                                              typename Problem::BlockFmhaShape::Gemm4WarpTile>,
-                                TileGemmTraits<Problem::kPadSeqLenQ,
-                                               Problem::kPadHeadDimQ,
-                                               Problem::kPadSeqLenK,
-                                               typename tensor_layout::gemm::RowMajor,
-                                               typename tensor_layout::gemm::ColumnMajor,
-                                               typename tensor_layout::gemm::RowMajor>>;
+            BlockGemmProblem<typename Problem::GemmDataType,
+                             typename Problem::KDataType,
+                             typename Problem::AccDataType,
+                             Problem::kBlockSize,
+                             TileGemmShape<sequence<Problem::BlockFmhaShape::kM0,
+                                                    Problem::BlockFmhaShape::kQKHeaddim,
+                                                    Problem::BlockFmhaShape::kK4>,
+                                           typename Problem::BlockFmhaShape::Gemm4BlockWarps,
+                                           typename Problem::BlockFmhaShape::Gemm4WarpTile>>;
 
         using WarpGemm =
             WarpGemmMfmaDispatcher<typename Problem::GemmDataType,
@@ -222,7 +196,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         using QDataType               = remove_cvref_t<typename Problem::QDataType>;
         constexpr index_t kBlockSize  = Problem::kBlockSize;
         constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock  = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock  = Problem::BlockFmhaShape::kQKHeaddim;
         constexpr index_t kMaxVecLoad = 16 / sizeof(QDataType);
         constexpr index_t kMinVecLoad = 4 / sizeof(QDataType);
 
@@ -241,7 +215,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         using KDataType               = remove_cvref_t<typename Problem::KDataType>;
         constexpr index_t kBlockSize  = Problem::kBlockSize;
         constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock  = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock  = Problem::BlockFmhaShape::kQKHeaddim;
         constexpr index_t kMaxVecLoad = 16 / sizeof(KDataType);
         constexpr index_t kMinVecLoad = 4 / sizeof(KDataType);
 
@@ -260,7 +234,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         using VDataType                = remove_cvref_t<typename Problem::VDataType>;
         constexpr index_t kBlockSize   = Problem::kBlockSize;
         constexpr index_t kMNPerBlock  = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock   = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock   = Problem::BlockFmhaShape::kVHeaddim;
         constexpr index_t kMaxVecLoad  = 16 / sizeof(VDataType);
         constexpr index_t total_pixels = kMNPerBlock * kKPerBlock / kBlockSize;
 
@@ -280,7 +254,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         using OGradDataType           = remove_cvref_t<typename Problem::OGradDataType>;
         constexpr index_t kBlockSize  = Problem::kBlockSize;
         constexpr index_t kMNPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock  = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock  = Problem::BlockFmhaShape::kVHeaddim;
         constexpr index_t kMaxVecLoad = 16 / sizeof(OGradDataType);
         constexpr index_t kMinVecLoad = 4 / sizeof(OGradDataType);
 
@@ -341,7 +315,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         constexpr index_t kBlockSize = Problem::kBlockSize;
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
 
         constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
 
@@ -353,7 +327,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         constexpr index_t kBlockSize   = Problem::kBlockSize;
         constexpr index_t kNPerBlock   = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock   = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock   = Problem::BlockFmhaShape::kQKHeaddim;
         constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
 
         return total_pixels / GetAlignmentK<Problem>();
@@ -364,7 +338,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         constexpr index_t kBlockSize = Problem::kBlockSize;
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
 
         constexpr index_t total_pixels = kNPerBlock * kKPerBlock / kBlockSize;
 
@@ -402,7 +376,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
 
         constexpr index_t K1 = GetAlignmentK<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -425,7 +399,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
 
         constexpr index_t K1 = GetAlignmentV<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -448,7 +422,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
         constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
 
         constexpr index_t K1 = GetAlignmentQ<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -471,7 +445,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
         constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
 
         constexpr index_t K1 = GetAlignmentOGrad<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -842,44 +816,12 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto MakeKLdsWriteBlockDescriptor()
     {
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
         constexpr index_t kKPack     = GetSmemKPackK<Problem>();
 
         return MakeXLdsBlockDescriptor<kNPerBlock, kKPerBlock, kKPack>();
     }
 
-    template <typename Problem>
-    CK_TILE_HOST_DEVICE static constexpr auto MakeKRegSliceBlockDescriptor()
-    {
-        using BlockGemm       = remove_cvref_t<decltype(GetQKBlockGemm<Problem>())>;
-        constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
-        using WarpGemm        = remove_cvref_t<decltype(config.template at<0>())>;
-
-        constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<0>{});
-        constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<1>{});
-
-        constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
-
-        constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
-        constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
-
-        constexpr auto k_block_outer_dstr_encoding =
-            tile_distribution_encoding<sequence<MWarp>,
-                                       tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
-                                       tuple<sequence<0, 1>>,
-                                       tuple<sequence<0, 1>>,
-                                       sequence<1, 2>,
-                                       sequence<0, 0>>{};
-
-        constexpr auto k_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
-            k_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
-
-        constexpr auto k_block_dstr = make_static_tile_distribution(k_block_dstr_encode);
-
-        return k_block_dstr;
-    }
-
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeKRegBlockDescriptor()
     {
@@ -891,7 +833,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm0BlockWarps::at(number<1>{});
 
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
 
         constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
         constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
@@ -916,45 +858,13 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto MakeVLdsWriteBlockDescriptor()
     {
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
 
         constexpr index_t kVPack = GetSmemKPackV<Problem>();
 
         return MakeXLdsBlockDescriptor<kNPerBlock, kKPerBlock, kVPack>();
     }
 
-    template <typename Problem>
-    CK_TILE_HOST_DEVICE static constexpr auto MakeVRegSliceBlockDescriptor()
-    {
-        using BlockGemm       = remove_cvref_t<decltype(GetOGradVBlockGemm<Problem>())>;
-        constexpr auto config = BlockGemm::Policy::template GetWarpGemmMWarpNWarp<Problem>();
-        using WarpGemm        = remove_cvref_t<decltype(config.template at<0>())>;
-
-        constexpr index_t MWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<0>{});
-        constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<1>{});
-
-        constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
-
-        constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
-        constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
-
-        constexpr auto v_block_outer_dstr_encoding =
-            tile_distribution_encoding<sequence<MWarp>,
-                                       tuple<sequence<NIterPerWarp, NWarp>, sequence<KIterPerWarp>>,
-                                       tuple<sequence<0, 1>>,
-                                       tuple<sequence<0, 1>>,
-                                       sequence<1, 2>,
-                                       sequence<0, 0>>{};
-
-        constexpr auto v_block_dstr_encode = detail::make_embed_tile_distribution_encoding(
-            v_block_outer_dstr_encoding, typename WarpGemm::BWarpDstrEncoding{});
-
-        constexpr auto v_block_dstr = make_static_tile_distribution(v_block_dstr_encode);
-
-        return v_block_dstr;
-    }
-
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeVRegBlockDescriptor()
     {
@@ -966,7 +876,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         constexpr index_t NWarp = Problem::BlockFmhaShape::Gemm2BlockWarps::at(number<1>{});
 
         constexpr index_t kNPerBlock = Problem::BlockFmhaShape::kN0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
 
         constexpr index_t NIterPerWarp = kNPerBlock / (NWarp * WarpGemm::kN);
         constexpr index_t KIterPerWarp = kKPerBlock / WarpGemm::kK;
@@ -992,7 +902,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
 
         constexpr index_t K1 = GetAlignmentK<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -1074,7 +984,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     CK_TILE_HOST_DEVICE static constexpr auto MakeQLdsBlockDescriptor()
     {
         constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
 
         constexpr index_t kKPack = GetSmemKPackQ<Problem>();
 
@@ -1118,7 +1028,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK0;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kQKHeaddim;
 
         constexpr index_t K1 = GetAlignmentQ<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -1281,7 +1191,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         // Hold full block data
         constexpr index_t kMPerBlock = Problem::BlockFmhaShape::kM0;
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
 
         constexpr index_t kKPack = GetSmemKPackOGrad<Problem>();
 
@@ -1325,7 +1235,7 @@ struct BlockFmhaBwdPipelineDefaultPolicy
     {
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
-        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kK2;
+        constexpr index_t kKPerBlock = Problem::BlockFmhaShape::kVHeaddim;
 
         constexpr index_t K1 = GetAlignmentOGrad<Problem>();
         constexpr index_t K0 = kKPerBlock / K1;
@@ -1885,6 +1795,8 @@ struct BlockFmhaBwdPipelineDefaultPolicy
         static constexpr index_t kN0        = Problem::BlockFmhaShape::kN0;
         static constexpr index_t kQKHeaddim = Problem::BlockFmhaShape::kQKHeaddim;
         static constexpr index_t kVHeaddim  = Problem::BlockFmhaShape::kVHeaddim;
+        static constexpr index_t kK0        = Problem::BlockFmhaShape::kK0;
+        static constexpr index_t kK2        = Problem::BlockFmhaShape::kK2;
         static constexpr index_t kK4        = Problem::BlockFmhaShape::kK4;
 
         static constexpr index_t WarpGemmM =
@@ -1899,14 +1811,12 @@ struct BlockFmhaBwdPipelineDefaultPolicy
 
         // Compute
         static constexpr index_t Gemm0MFMA =
-            kM0 * kN0 * kQKHeaddim /
-            (kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
+            kM0 * kN0 * kK0 / (kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
         static constexpr index_t Gemm1MFMA =
-            kM0 * kN0 * kVHeaddim /
-            (kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
-        static constexpr index_t Gemm2MFMA =
             kN0 * kVHeaddim * kM0 /
             (kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
+        static constexpr index_t Gemm2MFMA =
+            kM0 * kN0 * kK2 / (kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
         static constexpr index_t Gemm3MFMA =
             kN0 * kQKHeaddim * kM0 /
             (kBlockSize / get_warp_size() * WarpGemmM * WarpGemmN * WarpGemmK);
@@ -1929,13 +1839,12 @@ struct BlockFmhaBwdPipelineDefaultPolicy
             kM0 * kQKHeaddim / get_warp_size() / GetTransposedAlignmentQ<Problem>();
         static constexpr index_t SGradT_LDS_READ_P1 =
             kM0 * kK4 / (get_warp_size() * Gemm4MWarp) / GetSmemKPackSGrad<Problem>();
-        static constexpr index_t Q_LDS_READ =
-            kM0 * kQKHeaddim / kBlockSize / GetAlignmentQ<Problem>();
+        static constexpr index_t Q_LDS_READ   = kM0 * kK0 / kBlockSize / GetAlignmentQ<Problem>();
         static constexpr index_t LSE_LDS_READ = WarpGemmM == 16 ? kM0 / (4 * 4) : kM0 / (2 * 4);
         static constexpr index_t SGradT_LDS_READ_P2 =
             kM0 * (kN0 - kK4) / (get_warp_size() * Gemm4MWarp) / GetSmemKPackSGrad<Problem>();
         static constexpr index_t OGrad_LDS_READ =
-            kM0 * kVHeaddim / kBlockSize / GetAlignmentOGrad<Problem>();
+            kM0 * kK2 / kBlockSize / GetAlignmentOGrad<Problem>();
         static constexpr index_t D_LDS_READ = WarpGemmM == 16 ? kM0 / (4 * 4) : kM0 / (2 * 4);
 
         // LDS Write

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

@@ -12,6 +12,16 @@ namespace detail {
 template <index_t N>
 struct log2;
 
+template <>
+struct log2<4> : std::integral_constant<index_t, 2>
+{
+};
+
+template <>
+struct log2<8> : std::integral_constant<index_t, 3>
+{
+};
+
 template <>
 struct log2<16> : std::integral_constant<index_t, 4>
 {
@@ -72,18 +82,18 @@ struct BlockFmhaFwdSplitKVCombinePipeline
         {
             if constexpr(kHeadDimV <= 32)
             {
-                constexpr std::array<int, 4> occupancy{3, 3, 3, 1};
-                return occupancy[detail::log2<kMaxSplits>::value - 4];
+                constexpr std::array occupancy{3, 3, 3, 3, 3, 1};
+                return occupancy[detail::log2<kMaxSplits>::value - 2];
             }
             else if constexpr(kHeadDimV <= 128)
             {
-                constexpr std::array<int, 4> occupancy{3, 3, 2, 1};
-                return occupancy[detail::log2<kMaxSplits>::value - 4];
+                constexpr std::array occupancy{3, 3, 3, 3, 2, 1};
+                return occupancy[detail::log2<kMaxSplits>::value - 2];
             }
             else if constexpr(kHeadDimV <= 256)
             {
-                constexpr std::array<int, 4> occupancy{2, 2, 2, 1};
-                return occupancy[detail::log2<kMaxSplits>::value - 4];
+                constexpr std::array occupancy{2, 2, 2, 2, 2, 1};
+                return occupancy[detail::log2<kMaxSplits>::value - 2];
             }
         }
     }();
@@ -138,9 +148,8 @@ struct BlockFmhaFwdSplitKVCombinePipeline
         auto lse_accum = make_static_distributed_tensor<LSEDataType>(
             Policy::template MakeLSEaccRegTileDistribution<Problem>());
 
-        // copy LDS (shape=[kM0, kMaxSplits]) to lse_accum (shape=[kM0, max(kMaxSplits, warp_size)])
-        // this will extend the distributed tensor width so that each thread in wave have data to
-        // reduce.
+        // copy LDS (shape=[kM0, kMaxSplits]) to lse_accum (shape=[kM0, kMaxSplits])
+        // and fill up -INF values outside the [kM0, num_splits] region.
         {
             constexpr auto spans = decltype(lse_accum)::get_distributed_spans();
             sweep_tile_span(spans[number<0>{}], [&](auto idx0) {

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

@@ -10,11 +10,26 @@ namespace ck_tile {
 
 struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
 {
+    template <index_t BlockSize, index_t M, index_t N, typename DataType>
+    CK_TILE_HOST_DEVICE static constexpr auto GetVectorSizeForTile()
+    {
+        constexpr index_t PixelsPerThread = (M * N) / BlockSize;
+        static_assert(0 < PixelsPerThread);
+
+        constexpr index_t MaxNPerThread = 16 / sizeof(DataType);
+        constexpr index_t NPerThread    = min(MaxNPerThread, PixelsPerThread);
+
+        return NPerThread;
+    }
+
+    // alignment for dram lse tile (shape=[kMaxSplits, kM0])
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto GetAlignmentLSE()
     {
-        using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
-        return 16 / sizeof(LSEDataType);
+        return GetVectorSizeForTile<Problem::kBlockSize,
+                                    Problem::kMaxSplits,
+                                    Problem::kM0,
+                                    typename Problem::LSEDataType>();
     }
 
     template <typename Problem>
@@ -47,29 +62,31 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
                MakeLSEaccLdsBlockDescriptor<Problem>().get_element_space_size();
     }
 
+    // shape=[kMaxSplits, kM0]
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccDramTileDistribution()
     {
         using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
 
         constexpr index_t kBlockSize = Problem::kBlockSize;
+        constexpr index_t kNumWarps  = Problem::kNumWarps;
 
         constexpr index_t kNPerBlock = Problem::kM0;
         constexpr index_t kMPerBlock = Problem::kMaxSplits;
 
-        constexpr index_t NPerThread = 16 / sizeof(LSEDataType);
-        constexpr index_t NThreads   = kNPerBlock / NPerThread;
+        constexpr index_t NPerThread =
+            GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
+        constexpr index_t NThreads = kNPerBlock / NPerThread;
 
         constexpr index_t MThreadsPerWarp = get_warp_size() / NThreads;
-        constexpr index_t TotalWarps      = kBlockSize / get_warp_size();
-        constexpr index_t MPerThread      = kMPerBlock / (TotalWarps * MThreadsPerWarp);
+        constexpr index_t MPerThread      = kMPerBlock / (kNumWarps * MThreadsPerWarp);
 
         static_assert(NThreads * NPerThread == kNPerBlock);
-        static_assert(MPerThread * TotalWarps * MThreadsPerWarp == kMPerBlock);
+        static_assert(MPerThread * kNumWarps * MThreadsPerWarp == kMPerBlock);
 
         return make_static_tile_distribution(
             tile_distribution_encoding<sequence<1>,
-                                       tuple<sequence<MPerThread, TotalWarps, MThreadsPerWarp>,
+                                       tuple<sequence<MPerThread, kNumWarps, MThreadsPerWarp>,
                                              sequence<NThreads, NPerThread>>,
                                        tuple<sequence<1>, sequence<1, 2>>,
                                        tuple<sequence<1>, sequence<2, 0>>,
@@ -77,15 +94,18 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
                                        sequence<0, 1>>{});
     }
 
-    // 3d + padding, [kMaxSplits, kM0]
+    // 3d + padding, shape=[kMaxSplits, kM0]
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccLdsStoreBlockDescriptor()
     {
         using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
 
+        constexpr index_t kBlockSize = Problem::kBlockSize;
+
         constexpr index_t kMPerBlock = Problem::kMaxSplits;
         constexpr index_t kNPerBlock = Problem::kM0;
-        constexpr index_t NPack      = 16 / sizeof(LSEDataType);
+        constexpr index_t NPack =
+            GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
 
         constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
             make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
@@ -103,15 +123,18 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
         return lse_acc_lds_block_desc;
     }
 
-    // 3d + padding, [kM0, kMaxSplits]
+    // 3d + padding, shape=[kM0, kMaxSplits]
     template <typename Problem>
     CK_TILE_HOST_DEVICE static constexpr auto MakeLSEaccLdsBlockDescriptor()
     {
         using LSEDataType = remove_cvref_t<typename Problem::LSEDataType>;
 
+        constexpr index_t kBlockSize = Problem::kBlockSize;
+
         constexpr index_t kMPerBlock = Problem::kMaxSplits;
         constexpr index_t kNPerBlock = Problem::kM0;
-        constexpr index_t NPack      = 16 / sizeof(LSEDataType);
+        constexpr index_t NPack =
+            GetVectorSizeForTile<kBlockSize, kMPerBlock, kNPerBlock, LSEDataType>();
 
         constexpr auto lse_acc_lds_block_desc_0 = make_naive_tensor_descriptor(
             make_tuple(number<kNPerBlock / NPack>{}, number<kMPerBlock>{}, number<NPack>{}),
@@ -134,26 +157,28 @@ struct BlockFmhaFwdSplitKVCombinePipelineDefaultPolicy
     {
         constexpr index_t kBlockSize = Problem::kBlockSize;
 
-        constexpr index_t kNPerBlock = max(Problem::kMaxSplits, get_warp_size());
+        constexpr index_t kNPerBlock = Problem::kMaxSplits;
         constexpr index_t kMPerBlock = Problem::kM0;
 
-        constexpr index_t NThreads   = get_warp_size();
+        constexpr index_t NThreads   = 4;
         constexpr index_t NPerThread = kNPerBlock / NThreads;
 
-        constexpr index_t MThreads   = kBlockSize / NThreads;
-        constexpr index_t MPerThread = kMPerBlock / MThreads;
+        constexpr index_t MThreads       = kBlockSize / NThreads;
+        constexpr index_t MPerThread     = kMPerBlock / MThreads;
+        constexpr index_t MWarps         = kBlockSize / get_warp_size();
+        constexpr index_t MThreadPerWarp = get_warp_size() / NThreads;
 
         static_assert(NThreads * NPerThread == kNPerBlock);
-        static_assert(MThreads * MPerThread == kMPerBlock);
+        static_assert(MWarps * MThreadPerWarp * MPerThread == kMPerBlock);
 
         return make_static_tile_distribution(
             tile_distribution_encoding<
                 sequence<1>,
-                tuple<sequence<MThreads, MPerThread>, sequence<NThreads, NPerThread>>,
-                tuple<sequence<1>, sequence<2>>,
-                tuple<sequence<0>, sequence<0>>,
+                tuple<sequence<MWarps, MThreadPerWarp, MPerThread>, sequence<NThreads, NPerThread>>,
+                tuple<sequence<1>, sequence<2, 1>>,
+                tuple<sequence<0>, sequence<0, 1>>,
                 sequence<1, 2>,
-                sequence<1, 1>>{});
+                sequence<2, 1>>{});
     }
 
     template <typename Problem>