arm_compute/core/NEON/kernels/assembly/NEGEMMInterleavedPrepareBWrapperKernel.h

/*
 * Copyright (c) 2018-2019 ARM Limited.
 *
 * SPDX-License-Identifier: MIT
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 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
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#ifndef __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__
#define __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__

#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/NEON/INEKernel.h"
#include "arm_compute/core/NEON/kernels/assembly/Helpers.h"
#include "arm_compute/core/NEON/kernels/assembly/INEGEMMWrapperKernel.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"

namespace arm_compute
{
/** Unit of work for @ref NEGEMMInterleavedPrepareBWrapperKernel to process */
struct PrepareBWorkload
{
    /** Constructor
     *
     * @param[in] offset_b             Offset from the start of b's allocation
     * @param[in] offset_transformed_b Offset from the start of transformed_b's allocation.
     * @param[in] x0                   First value to process along the X dimension (N).
     * @param[in] xmax                 Last value to process along the X dimension (N).
     * @param[in] k0                   First value to process along the K dimension.
     * @param[in] kmax                 Last value to process along the K dimension.
     */
    PrepareBWorkload(unsigned int offset_b, unsigned int offset_transformed_b, unsigned int x0, unsigned int xmax, unsigned int k0, unsigned int kmax)
        : _offset_b(offset_b), _offset_transformed_b(offset_transformed_b), _x0(x0), _xmax(xmax), _k0(k0), _kmax(kmax)
    {
    }
    unsigned int _offset_b;             /**< Offset from the start of b's allocation.*/
    unsigned int _offset_transformed_b; /**< Offset from the start of transformed_b's allocation.*/
    unsigned int _x0;                   /**< First value to process along the X dimension (N). */
    unsigned int _xmax;                 /**< Last value to process along the X dimension (N). */
    unsigned int _k0;                   /**< First value to process along the K dimension. */
    unsigned int _kmax;                 /**< Last value to process along the K dimension. */
};

namespace detail
{
// Call the lambda function for each workload generated by the passed window.
template <typename strategy, bool use_buffer_manager, typename Lambda>
void for_each_element_in_window(const Window &window, const ITensor *b, ITensor *transformed_b, unsigned int N, unsigned int K, Lambda &&lambda)
{
    unsigned int wl_index    = 0;
    unsigned int num_buffers = 0, reshaped_block_size = 0;

    if(use_buffer_manager)
    {
        num_buffers         = transformed_b->info()->tensor_shape()[1];
        reshaped_block_size = transformed_b->info()->strides_in_bytes().y();
    }

    unsigned int offset_transformed_b = transformed_b->info()->offset_first_element_in_bytes();
    execute_window_loop(window, [&](const Coordinates & coordinates)
    {
        const unsigned int x0    = coordinates.x();
        const unsigned int k0    = coordinates.y();
        const unsigned int multi = coordinates.z();

        const unsigned int offset_b = b->info()->offset_element_in_bytes(Coordinates(0, 0, multi));
        const unsigned int xmax     = std::min(x0 + window.x().step(), N);
        const unsigned int kmax     = std::min(k0 + window.y().step(), K);

        /* Figure out the size of each block. */
        unsigned int x_size = (xmax - x0);
        unsigned int k_size = (kmax - k0);

        /* Round sizes up as needed. */
        x_size = ceil_to_multiple(x_size, strategy::out_width());
        k_size = ceil_to_multiple(k_size, strategy::k_unroll());

        lambda(PrepareBWorkload(offset_b, offset_transformed_b, x0, xmax, k0, kmax));

        //Each workload represents one block:
        if(use_buffer_manager)
        {
            // Rotate through the BufferManager's buffers:
            wl_index++;
            offset_transformed_b = (wl_index % num_buffers) * reshaped_block_size;
        }
        else
        {
            offset_transformed_b += (x_size * k_size * sizeof(typename strategy::operand_type));
        }
    });
}

// Calculate the size of transformed_b:
template <typename strategy>
unsigned int get_B_pretransposed_array_size(unsigned int N, unsigned int K, const BlockSizes &bs, unsigned int multis)
{
    // How many full blocks do N / K contain ?
    size_t num_full_k = K / bs.k_block;
    size_t num_full_x = N / bs.x_block;

    ARM_COMPUTE_ERROR_ON(bs.x_block % strategy::out_width() != 0);
    ARM_COMPUTE_ERROR_ON(bs.k_block % strategy::k_unroll() != 0);

    size_t normal_x_size = bs.x_block;
    size_t normal_k_size = bs.k_block;

    // Round up the leftovers to be a multiple of the strategy processing size:
    size_t left_over_x_size = ceil_to_multiple(N % bs.x_block, strategy::out_width());
    size_t left_over_k_size = ceil_to_multiple(K % bs.k_block, strategy::k_unroll());

    // Calculate the total size of the buffer:
    size_t total = num_full_k * normal_k_size * (num_full_x * normal_x_size + left_over_x_size);
    total += left_over_k_size * (left_over_x_size + num_full_x * normal_x_size);

    total *= multis;

    return total;
}
} // namespace detail

/** Common interface for the templated wrappers around the B reshape NEON assembly implementations */
class NEGEMMInterleavedPrepareBWrapperKernel : public INEKernel
{
public:
    /** Transform the block at the given coordinates
     *
     * @param[in] wl   Workload to process.
     * @param[in] info Information about the current thread.
     */
    virtual void transform(const PrepareBWorkload &wl, const ThreadInfo &info) = 0;
    /** Generate an array of workloads
     *
     * @param[out] workloads Container to store the generated workloads.
     */
    virtual void create_workloads(std::vector<PrepareBWorkload> &workloads) = 0;
    /** Return the block_sizes used to resape B
     *
     * The same block sizes must be used to reshape A and for the matrix multiplication
     *
     * @return The block sizes used to reshape B.
     */
    virtual BlockSizes block_sizes() const = 0;

    // Inherited methods overridden:
    const char *name() const override
    {
        return "NEGEMMInterleavedPrepareBWrapperKernel";
    }

    bool is_parallelisable() const override
    {
        return false; // Can't run on arbitrary windows but can be parallelised using an array of workloads
    }
};

/** Equivalent to arm_gemm::GemmInterleaved's strategy::transforms::PrepareB() but using Compute Library types.
 */
template <typename strategy>
class NEGEMMInterleavedPrepareBWrapperKernelTemplate : public NEGEMMInterleavedPrepareBWrapperKernel
{
public:
    /** Configure the reshape B routine.
     *
     * @param[in]  b             Input matrix B.
     * @param[out] transformed_b Reshaped matrix B.
     * @param[in]  transpose_b   Also transpose B ?
     * @param[in]  ci            CPU information
     * @param[in]  params        M, N, K sizes.
     */
    void configure(const ITensor *b, ITensor *transformed_b, bool transpose_b, const CPUInfo &ci, const INEGEMMWrapperKernel::Params &params)
    {
        const unsigned int multis = b->info()->tensor_shape().z();
        _Nsize                    = b->info()->tensor_shape().x();
        _Ksize                    = b->info()->tensor_shape().y();
        _b                        = b;
        _transformed_b            = transformed_b;
        _transpose_b              = transpose_b;

        _block_sizes = calculate_block_sizes<strategy>(ci, params.M, params.N, params.K);

        auto_init_if_empty(*transformed_b->info(), b->info()->clone()->set_tensor_shape(TensorShape{ detail::get_B_pretransposed_array_size<strategy>(_Nsize, _Ksize, _block_sizes, multis) }));

        Window window;
        window.set(Window::DimX, Window::Dimension(0, ceil_to_multiple(_Nsize, _block_sizes.x_block), _block_sizes.x_block));
        window.set(Window::DimY, Window::Dimension(0, ceil_to_multiple(_Ksize, _block_sizes.k_block), _block_sizes.k_block));
        window.set(Window::DimZ, Window::Dimension(0, multis));

        INEKernel::configure(window);
    }

    // Inherited methods overridden:
    void transform(const PrepareBWorkload &wl, const ThreadInfo &info) override
    {
        strategy strat(info.cpu_info);
        strat.transforms.PrepareB(reinterpret_cast<typename strategy::operand_type *>(_transformed_b->buffer() + wl._offset_transformed_b),
                                  reinterpret_cast<typename strategy::operand_type *>(_b->buffer() + wl._offset_b),
                                  _b->info()->strides_in_bytes().y() / sizeof(typename strategy::operand_type),
                                  wl._x0, wl._xmax, wl._k0, wl._kmax, _transpose_b);
    }
    void create_workloads(std::vector<PrepareBWorkload> &workloads) override
    {
        detail::for_each_element_in_window<strategy, true>(window(), _b, _transformed_b, _Nsize, _Ksize, [&workloads](PrepareBWorkload && wl)
        {
            workloads.push_back(std::move(wl));
        });
    }
    void run(const Window &window, const ThreadInfo &info) override
    {
        ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(window, INEKernel::window());
        detail::for_each_element_in_window<strategy, false>(window, _b, _transformed_b, _Nsize, _Ksize, [&](PrepareBWorkload && wl)
        {
            this->transform(wl, info);
        });
    }
    BlockSizes block_sizes() const override
    {
        return _block_sizes;
    }

private:
    const ITensor *_b
    {
        nullptr
    };
    ITensor     *_transformed_b{ nullptr };
    unsigned int _Nsize{ 0 };
    unsigned int _Ksize{ 0 };
    bool         _transpose_b{ false };
    BlockSizes   _block_sizes{};
};

} // namespace arm_compute
#endif /* __ARM_COMPUTE_NEGEMMINTERLEAVEDPREPAREBWRAPPERKERNEL_H__ */