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/*! \file
  \brief Functor performing linear combination with a maximum operation used by epilogues.
*/

#pragma once

#include "cutlass/half.h"
#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"
#include "cutlass/epilogue/thread/scale_type.h"

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

namespace cutlass {
namespace epilogue {
namespace thread {

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

namespace detail {

/// Single source of truth for whether to unroll for `LinearCombinationClamp()`
constexpr bool LinearCombinationReluIsHeavy() {
  return false;
}

}

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

/// 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
                                                       ///< Usually it is 128/sizeof_bits<ElementOutput_>,
                                                       ///< but we use 64 or 32 sometimes when there are not enough data to store
  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 LinearCombinationRelu {
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 FragmentCompute = Array<ElementCompute, kCount>;
  using FragmentScaleBias = Array<ElementCompute, kCount>;
  using FragmentSource = Array<ElementOutput, kCount>;

  static FloatRoundStyle const kRound = Round;

  static bool const kIsHeavy = detail::LinearCombinationReluIsHeavy();

  /// Host-constructable parameters structure
  struct Params {

    ElementCompute alpha;                  ///< scales accumulators
    ElementCompute beta;                   ///< scales source tensor
    ElementCompute threshold;              ///< minimum value that is output 
    ElementCompute const *alpha_ptr;       ///< pointer to accumulator scalar - if not null, loads it from memory
    ElementCompute const *beta_ptr;        ///< pointer to source scalar - if not null, loads it from memory
    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Params(): 
      alpha(ElementCompute(1)), 
      beta(ElementCompute(0)),
      threshold(ElementCompute(0)), 
      alpha_ptr(nullptr), 
      beta_ptr(nullptr) { }

    CUTLASS_HOST_DEVICE
    Params(
      ElementCompute alpha,
      ElementCompute beta = ElementCompute(0),
      ElementCompute threshold = ElementCompute(0)
    ): alpha(alpha), beta(beta), threshold(threshold), alpha_ptr(nullptr), beta_ptr(nullptr) {

    }

    CUTLASS_HOST_DEVICE
    Params(
      ElementCompute const *alpha_ptr,
      ElementCompute const *beta_ptr = nullptr,
      ElementCompute threshold = ElementCompute(0)
    ): alpha(0), beta(0), threshold(threshold), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {

    }
  };

private:

  //
  // Data members
  //

  ElementCompute alpha_;
  ElementCompute beta_;
  ElementCompute threshold_;

public:

  /// Constructs the function object, possibly loading from pointers in host memory
  CUTLASS_HOST_DEVICE
  LinearCombinationRelu(Params const &params) {

    alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
    beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
    threshold_ = params.threshold;
  }

  /// 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::OnlyAlphaPerChannelScaling) return false;

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

    return beta_ != ElementCompute(0);
  }

  /// Functionally required for serial reduction in the epilogue
  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition, int k_partition_count) {
    if (k_partition) {
      beta_ = ElementCompute(1);
    }

    if (k_partition != k_partition_count - 1) {
      // set to NaN to make ReLU no-op for all except last k partitions
      int64_t allones = -1;
      threshold_ = reinterpret_cast<ElementCompute const &>(allones);
    }
  }
  
  /// 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;

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

    // Perform binary operations
    FragmentCompute intermediate;

    multiplies<FragmentCompute> mul_add_source;
    multiply_add<FragmentCompute> mul_add_accumulator;
    ReLu<FragmentCompute> relu;

    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_, converted_source);                             // X =  beta * C + uniform
      intermediate = mul_add_accumulator(alpha_, converted_accumulator, intermediate);    // D = alpha * Accum + X
    }

    // Compute threshold optionally
    intermediate = relu(threshold_, intermediate);

    // 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;

    FragmentCompute converted_accumulator = accumulator_converter(accumulator);

    // Perform binary operations
    FragmentCompute intermediate;

    multiplies<FragmentCompute> mul_accumulator;
    ReLu<FragmentCompute> relu;

    if (Scale == ScaleType::Nothing) {
      intermediate = converted_accumulator;
    } else {
      intermediate = mul_accumulator(alpha_, converted_accumulator);    // D = alpha * Accum
    }

    // Compute threshold optionally
    intermediate = relu(threshold_, intermediate);

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

    return destination_converter(intermediate);
  }

  /// Computes per-channel linear scaling and bias : D = scale * accumulator + bias
  /// Scale and Bias are from input Fragment
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(
    FragmentAccumulator const &accumulator,
    FragmentScaleBias const &scale,
    FragmentScaleBias const &bias) const {
    
    // Convert source to interal compute numeric type
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;

    FragmentCompute converted_accumulator = accumulator_converter(accumulator);

    // Perform per-channel scale and bias
    FragmentCompute intermediate;

    multiply_add<FragmentCompute> mul_add_accumulator;

    if(Scale == ScaleType::OnlyAlphaPerChannelScaling)
      intermediate = mul_add_accumulator(scale, converted_accumulator, bias);    // D = scale * Accum + bias
    else
      intermediate = mul_add_accumulator(alpha_, converted_accumulator, bias);   // D = alpha * Accum + bias

    ReLu<FragmentCompute> relu;

    // Compute threshold optionally
    intermediate = relu(threshold_, intermediate);

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

    return destination_converter(intermediate);
  }
};

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

// Conditional guards to enable partial specialization for packed integers
#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 720) && ((__CUDACC_VER_MAJOR__ > 10) || ((__CUDACC_VER_MAJOR__ >= 10) && (__CUDACC_VER_MINOR__ >= 2)))

/// Applies a linear combination operator to an array of elements.
///
/// D = alpha * accumulator + beta * source + uniform
///
/// Special handling for int types

template <
  typename ElementOutput_,                             ///< Data type used to load and store tensors
  int Count,                                           ///< Number of elements computed per operation
  ScaleType::Kind Scale,                               ///< Control Alpha and Beta scaling
  FloatRoundStyle Round
>
class LinearCombinationRelu <ElementOutput_, Count, int, float, Scale, Round> {
public:

  using ElementOutput = ElementOutput_;
  using ElementAccumulator = int;
  using ElementCompute = float;

  static bool const kIsHeavy = detail::LinearCombinationReluIsHeavy();

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

  using FragmentOutput = Array<ElementOutput, kCount>;
  using FragmentAccumulator = Array<ElementAccumulator, kCount>;
  using FragmentCompute = Array<ElementCompute, kCount>;
  using FragmentScaleBias = Array<ElementCompute, kCount>;
  using FragmentSource = Array<ElementOutput, kCount>;

  static FloatRoundStyle const kRound = Round;

  /// Host-constructable parameters structure
  struct Params {

    ElementCompute alpha;                  ///< scales accumulators
    ElementCompute beta;                   ///< scales source tensor
    ElementCompute threshold;              ///< minimum value that is output 
    ElementCompute const *alpha_ptr;       ///< pointer to accumulator scalar - if not null, loads it from memory
    ElementCompute const *beta_ptr;        ///< pointer to source scalar - if not null, loads it from memory
    //
    // Methods
    //

    CUTLASS_HOST_DEVICE
    Params(): 
      alpha(ElementCompute(1)), 
      beta(ElementCompute(0)),
      threshold(ElementCompute(0)), 
      alpha_ptr(nullptr), 
      beta_ptr(nullptr) { }

    CUTLASS_HOST_DEVICE
    Params(
      ElementCompute alpha,
      ElementCompute beta = ElementCompute(0),
      ElementCompute threshold = ElementCompute(0)
    ): alpha(alpha), beta(beta), threshold(threshold), alpha_ptr(nullptr), beta_ptr(nullptr) {

    }

    CUTLASS_HOST_DEVICE
    Params(
      ElementCompute const *alpha_ptr,
      ElementCompute const *beta_ptr = nullptr,
      ElementCompute threshold = ElementCompute(0)
    ): alpha(0), beta(0), threshold(threshold), alpha_ptr(alpha_ptr), beta_ptr(beta_ptr) {

    }
  };

private:

  //
  // Data members
  //

  ElementCompute alpha_;
  ElementCompute beta_;
  ElementCompute threshold_;

public:

  /// Constructs the function object, possibly loading from pointers in host memory
  CUTLASS_HOST_DEVICE
  LinearCombinationRelu(Params const &params) {

    alpha_ = (params.alpha_ptr ? *params.alpha_ptr : params.alpha);
    beta_ = (params.beta_ptr ? *params.beta_ptr : params.beta);
    threshold_ = params.threshold;
  }

  /// 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::OnlyAlphaPerChannelScaling) return false;

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

    return beta_ != ElementCompute(0);
  }

  /// Functionally required for serial reduction in the epilogue
  CUTLASS_HOST_DEVICE
  void set_k_partition(int k_partition, int k_partition_count) {
    if (k_partition) {
      beta_ = ElementCompute(1);
    }

    if (k_partition != k_partition_count - 1) {
      // set to NaN to make ReLU no-op for all except last k partitions
      int64_t allones = -1;
      threshold_ = reinterpret_cast<ElementCompute const &>(allones);
    }
  }
  
  /// 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;

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

    // Perform binary operations
    FragmentCompute intermediate;

    multiplies<FragmentCompute> mul_add_source;
    multiply_add<FragmentCompute> mul_add_accumulator;
    ReLu<FragmentCompute> relu;

    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_, converted_source);                             // X =  beta * C + uniform
      intermediate = mul_add_accumulator(alpha_, converted_accumulator, intermediate);    // D = alpha * Accum + X
    }

    // Compute threshold optionally
    intermediate = relu(threshold_, intermediate);

    if (cutlass::platform::numeric_limits<ElementOutput>::is_integer) {
      // Convert floats back to INT
      FragmentAccumulator scaled_accumulator;

      NumericArrayConverter<int, ElementCompute, kCount, Round> compute_converter;

      scaled_accumulator = compute_converter(intermediate);

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

      return destination_converter(scaled_accumulator);
    } else {
      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;

    FragmentCompute converted_accumulator = accumulator_converter(accumulator);

    // Perform binary operations
    FragmentCompute intermediate;

    multiplies<FragmentCompute> mul_accumulator;
    ReLu<FragmentCompute> relu;

    if (Scale == ScaleType::Nothing) {
      intermediate = converted_accumulator;
    } else {
      intermediate = mul_accumulator(alpha_, converted_accumulator);    // D = alpha * Accum
    }

    // Compute threshold optionally
    intermediate = relu(threshold_, intermediate);

    if (cutlass::platform::numeric_limits<ElementOutput>::is_integer) {
      // Convert floats back to INT
      FragmentAccumulator scaled_accumulator;

      NumericArrayConverter<int, ElementCompute, kCount, Round> compute_converter;

      scaled_accumulator = compute_converter(intermediate);

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

      return destination_converter(scaled_accumulator);
    } else {
      NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
          destination_converter;
      return destination_converter(intermediate);
    }
  }

  /// Computes per-channel linear scaling and bias : D = scale * accumulator + bias
  /// Scale and Bias are from input Fragment
  CUTLASS_HOST_DEVICE
  FragmentOutput operator()(
    FragmentAccumulator const &accumulator,
    FragmentScaleBias const &scale,
    FragmentScaleBias const &bias) const {
    
    // Convert source to interal compute numeric type
    NumericArrayConverter<ElementCompute, ElementAccumulator, kCount, Round> accumulator_converter;

    FragmentCompute converted_accumulator = accumulator_converter(accumulator);

    // Perform per-channel scale and bias
    FragmentCompute intermediate;

    multiply_add<FragmentCompute> mul_add_accumulator;

    if(Scale == ScaleType::OnlyAlphaPerChannelScaling)
      intermediate = mul_add_accumulator(scale, converted_accumulator, bias);    // D = scale * Accum + bias
    else
      intermediate = mul_add_accumulator(alpha_, converted_accumulator, bias);   // D = alpha * Accum + bias

    ReLu<FragmentCompute> relu;

    // Compute threshold optionally
    intermediate = relu(threshold_, intermediate);

    if (cutlass::platform::numeric_limits<ElementOutput>::is_integer) {
      // Convert floats back to INT
      FragmentAccumulator scaled_accumulator;

      NumericArrayConverter<int, ElementCompute, kCount, Round> compute_converter;

      scaled_accumulator = compute_converter(intermediate);

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

      return destination_converter(scaled_accumulator);
    } else {
      NumericArrayConverter<ElementOutput, ElementCompute, kCount, Round>
          destination_converter;
      return destination_converter(intermediate);
    }
  }
};

#endif // Conditional guards to enable partial specialization for packed integers

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

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

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