/***************************************************************************************************
 * Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 * list of conditions and the following disclaimer.
 *
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 * this list of conditions and the following disclaimer in the documentation
 * and/or other materials provided with the distribution.
 *
 * 3. Neither the name of the copyright holder nor the names of its
 * contributors may be used to endorse or promote products derived from
 * this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
 * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
 * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
 * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
 * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 **************************************************************************************************/

#pragma once

#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/array.h"
#include "cutlass/functional.h"
#include "cutlass/numeric_conversion.h"
#include "cutlass/epilogue/thread/activation.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace epilogue {
namespace thread {

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Applies a linear combination operator to an array of elements.
///
/// D = alpha * accumulator + beta * source + uniform
///
template <
  typename ElementOutput_,                             ///< Data type used to load and store tensors
  int Count,                                           ///< Number of elements computed per operation
  typename ElementAccumulator_ = ElementOutput_,       ///< Accumulator data type
  typename ElementCompute_ = ElementOutput_,           ///< Data type used to compute linear combination
  ScaleType::Kind Scale = ScaleType::Default,          ///< Control Alpha and Beta scaling
  FloatRoundStyle Round = FloatRoundStyle::round_to_nearest
>
class LinearCombinationLeakyRelu {
public:

  using ElementOutput = ElementOutput_;
  using ElementAccumulator = ElementAccumulator_;
  using ElementCompute = ElementCompute_;

  static int const kCount = Count;
  static const ScaleType::Kind kScale = Scale;

  using FragmentOutput = Array<ElementOutput, kCount>;
  using FragmentAccumulator = Array<ElementAccumulator, kCount>;
  using ComputeFragment = Array<ElementCompute, kCount>;

  static FloatRoundStyle const kRound = Round;

  /// Host-constructable parameters structure
  struct Params {

    ElementCompute alpha;                  ///< scales accumulators
    ElementCompute beta_bias;              ///< scales bias tensor
    ElementCompute leaky_alpha;            ///< leaky_alpha
    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Params(): 
      alpha(ElementCompute(1)), 
      beta_bias(ElementCompute(0)),
      leaky_alpha(ElementCompute(1)) 
       { }

    CUTLASS_HOST_DEVICE
    Params(
      ElementCompute alpha,
      ElementCompute beta_bias,
      ElementCompute leaky_alpha = ElementCompute(1)
    ): alpha(alpha), beta_bias(beta_bias), leaky_alpha(leaky_alpha) {

    }

  };

private:

  //
  // Data members
  //

  ElementCompute alpha_;
  ElementCompute beta_bias_;
  ElementCompute leaky_alpha_recip_;

public:

  /// Constructs the function object, possibly loading from pointers in host memory
  CUTLASS_HOST_DEVICE
  LinearCombinationLeakyRelu(Params const &params) {
    alpha_ = (params.alpha);
    beta_bias_ = (params.beta_bias);
    leaky_alpha_recip_ = (ElementCompute(params.leaky_alpha));    
  }

  /// Returns true if source is needed
  CUTLASS_HOST_DEVICE
  bool is_source_needed() const {
    if (Scale == ScaleType::NoBetaScaling) return true;

    if (Scale == ScaleType::OnlyAlphaScaling) return false;

    if (Scale == ScaleType::Nothing) return false;

    return beta_bias_ != ElementCompute(0);
  }

  /// Functionally required for serial reduction in the epilogue
  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition) {
    if (k_partition) {
      beta_bias_ = ElementCompute(1);
    }
  }
  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition, int k_partition_count) {
    if (k_partition) {
      beta_bias_ = ElementCompute(1);
    }
  }
  
  /// Computes linear scaling: D = alpha * accumulator + beta * source
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(
    FragmentAccumulator const &accumulator, 
    FragmentOutput const &source) const {

    // Convert source to interal compute numeric type
    NumericArrayConverter<ElementCompute, ElementOutput, kCount, Round> source_converter;
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;

    ComputeFragment converted_source = source_converter(source);
    ComputeFragment converted_accumulator = accumulator_converter(accumulator);

    // Perform binary operations
    ComputeFragment intermediate;

    multiplies<ComputeFragment> mul_add_source;
    multiply_add<ComputeFragment> mul_add_accumulator;

    LeakyReLU<ComputeFragment> leakyrelu;

    if (Scale == ScaleType::NoBetaScaling) {
      intermediate = converted_source;
      intermediate = mul_add_accumulator(alpha_, converted_accumulator, intermediate);    // D = alpha * Accum + X
    }  else if (Scale == ScaleType::Nothing) {
      intermediate = converted_accumulator;
    } else {
      intermediate = mul_add_source(beta_bias_, converted_source);                        // X =  beta * C + uniform
      intermediate = mul_add_accumulator(alpha_, converted_accumulator, intermediate);    // D = alpha * Accum + X
    }
    // Compute threshold optionally
    intermediate = leakyrelu(intermediate, leaky_alpha_recip_);

    // Convert to destination numeric type
    NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;

    return destination_converter(intermediate);
  }

  /// Computes linear scaling: D = alpha * accumulator
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(
    FragmentAccumulator const &accumulator) const {

    // Convert source to interal compute numeric type
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;
    
    ComputeFragment converted_accumulator = accumulator_converter(accumulator);
    
    // Perform binary operations
    ComputeFragment intermediate;

    multiplies<ComputeFragment> mul_accumulator;
    LeakyReLU<ComputeFragment> leakyrelu;
    //printf("in doing with bias");
    if (Scale == ScaleType::Nothing) {
      intermediate = converted_accumulator;
    } else {
      intermediate = mul_accumulator(alpha_, converted_accumulator);    // D = alpha * Accum
    }
    
    // Compute threshold optionally
    intermediate = leakyrelu(intermediate, leaky_alpha_recip_);
    
    
    // Convert to destination numeric type
    NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round> destination_converter;

    return destination_converter(intermediate);
  }
};

/////////////////////////////////////////////////////////////////////////////////////////////////

} // namespace thread
} // namespace epilogue
} // namespace cutlass

/////////////////////////////////////////////////////////////////////////////////////////////////
