/*
 * Copyright (c) 2018-2023 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifndef ARM_COMPUTE_CPU_INTERNAL_CPU_GEMM_ASSEMBLY_DISPATCH_H
#define ARM_COMPUTE_CPU_INTERNAL_CPU_GEMM_ASSEMBLY_DISPATCH_H

#include "arm_compute/core/ActivationLayerInfo.h"
#include "src/core/common/Macros.h"
#include "src/cpu/ICpuOperator.h"

namespace arm_compute
{
namespace cpu
{
/* Convolution method supported by the assembly gemm interface */
enum class AsmConvMethod
{
    Im2Col,
    Indirect,
    Conv
};

struct AsmGemmInfo
{
    AsmConvMethod             method{ AsmConvMethod::Im2Col };
    PadStrideInfo             ps_info{};
    ActivationLayerInfo       activation_info{};
    GEMMLowpOutputStageInfo   output_stage{};
    bool                      negated_offsets{ true };
    bool                      reinterpret_input_as_3d{ false };
    bool                      depth_output_gemm3d{ false };
    int64_t                   padding_top{ 0 };
    int64_t                   padding_left{ 0 };
    float                     padding_value{ 0.f };
    bool                      fast_mode{ false };
    bool                      fixed_format{ false };
    arm_compute::WeightFormat weight_format{ arm_compute::WeightFormat::UNSPECIFIED };
    bool                      reshape_b_only_on_first_run{ true };
};

/** Assembly kernel glue */
class CpuGemmAssemblyDispatch : public ICpuOperator
{
public:
    /** Constructor */
    CpuGemmAssemblyDispatch();
    /** Defautl destructor */
    ~CpuGemmAssemblyDispatch() = default;

    ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuGemmAssemblyDispatch);

    class IFallback
    {
    public:
        virtual void run(ITensorPack &tensors)                              = 0;
        virtual void prepare(ITensorPack &tensors)                          = 0;
        virtual experimental::MemoryRequirements workspace() const          = 0;
        virtual bool                             is_configured() const      = 0;
        virtual bool                             isVarWeightsKernel() const = 0;
        virtual ~IFallback()                                                = default;
    };

public:
    /** If supported create a Compute Library function else fallback to the arm_gemm function.
     *
     * @note Configuring "batches"
     * The shapes of @p a @p b and @p d are arranged as follows:
     *     Lowest dimension <-> Highest dimension
     * a: [K, M, Batch, Multi]
     * b: [N, K, Multi]
     * d: [N, M, Batch, Multi]
     *
     * The "Batch" refers to where "Batch" number of MxK slices of tensor a multiplies with a single KxN slice of b
     * The "Multi" refers to where "Multi" number of individual multiplication of a with b
     *
     * E.g. the following are some example input shape configurations
     *
     * (1) Normal 2D gemm
     * a: [K=3, M=4]
     * b: [N=5, K=3]
     * d: [N=5, M=4]
     *
     * (2) Batches of a sharing b (e.g. gemm-based batched convolution where b is the shared )
     * a: [K=3, M=4, Batch=9]
     * b: [N=5, K=3]
     * d: [N=5, M=4, Batch=9]
     *
     * (3) "Batches" of independent gemm (e.g. batched matmul)
     * a: [K=3, M=4, Batch=1, Multi=7]
     * b: [N=5, K=3, Multi=7]
     * d: [N=5, M=4, Batch=1, Multi=7]
     *
     * (4) "Batches" of independent gemm where b is also shared
     * a: [K=3, M=4, Batch=4, Multi=7]
     * b: [N=5, K=3, Multi=7]
     * d: [N=5, M=4, Batch=4, Multi=7]
     *
     * @param[in]  a    Input tensor (Matrix A)
     * @param[in]  b    Input tensor (Matrix B)
     * @param[in]  c    Input tensor (Matrix C) used to pass the bias for quantized calculations
     * @param[out] d    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0.
     * @param[in]  info GEMM meta-data
     */
    void configure(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, ITensorInfo *d, const AsmGemmInfo &info);

    /** Indicates whether or not this function can be used to process the given parameters.
     *
     * @param[in] a    Input tensor info (Matrix A)
     * @param[in] b    Input tensor info (Matrix B)
     * @param[in] c    Input tensor info (Matrix C) used to pass the bias for quantized calculations
     * @param[in] d    Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0.
     * @param[in] info GEMM meta-data
     *
     * @return a status.
     */
    static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *d, const AsmGemmInfo &info);

    /** Indicates whether or not there is an optimal assembly implementation that can be used to process the given parameters.
     *
     * This method has the same use of @ref
     * NEGEMMConvolutionLayer::has_opt_impl, with the only caveat that
     * the value of arm_compute::WeightFormat need to be passed via the
     * parameter info.
     *
     * @return a status.
     */
    static Status has_opt_impl(arm_compute::WeightFormat &weight_format, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *d, const AsmGemmInfo &info);
    /** Checks if activation is supported by the gemm assembly dispatcher
     *
     * @param[in] activation Activation to check
     *
     * @return True if activation is supported else false
     */
    static bool is_activation_supported(const ActivationLayerInfo &activation);
    /** Was the function successfully configured ?
     *
     * @return True if the function is configured and ready to run
     */
    bool is_configured() const;
    /** Indicates if the convolution executes in variable weights mode.
     *
     * Similar to @ref CpuGemm::isVarWeightsKernel
     */
    bool isVarWeightsKernel() const
    {
        return _arm_gemm && _arm_gemm->isVarWeightsKernel();
    }

    // Inherited methods overridden:
    void prepare(ITensorPack &tensors) override;
    void run(ITensorPack &tensors) override;
    experimental::MemoryRequirements workspace() const override;

private:
    std::unique_ptr<IFallback> _arm_gemm; /**< Interface for the arm_gemm fallback */
};
} // namespace cpu
} // namespace arm_compute
#endif /* ARM_COMPUTE_CPU_INTERNAL_CPU_GEMM_ASSEMBLY_DISPATCH_H */