// conv_transpose_op_impl.h is the templated implementation of the
// conv_transpose_op.h file.
#ifndef CAFFE2_OPERATORS_CONV_TRANSPOSE_OP_IMPL_H_
#define CAFFE2_OPERATORS_CONV_TRANSPOSE_OP_IMPL_H_

#include "caffe2/core/context.h"
#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/operators/conv_op_shared.h"
#include "caffe2/operators/conv_transpose_op.h"
#include "caffe2/operators/conv_transpose_unpool_op_base.h"
#include "caffe2/utils/math.h"

C10_DECLARE_bool(caffe2_force_shared_col_buffer);

namespace caffe2 {

template <typename T, class Context>
bool ConvTransposeOp<T, Context>::RunOnDeviceWithOrderNCHW() {
  const Tensor& X = Input(INPUT);
  auto& filter = Input(FILTER);
  const int N = X.dim32(0), M = X.dim32(1), H = X.dim32(2), W = X.dim32(3);
  CAFFE_ENFORCE(filter.dim() == 4, "filter must be 4D tensor");
  CAFFE_ENFORCE(
      filter.dim32(0) == M,
      "filter number must be equal to input channel number");
  const int C = filter.dim32(1);
  CAFFE_ENFORCE(
      filter.dim32(2) == this->kernel_h(),
      "filter height must be equal to kernel height");
  CAFFE_ENFORCE(
      filter.dim32(3) == this->kernel_w(),
      "filter width must be equal to kernel width");
  auto sizes = ConvTransposeUnpoolBase<Context>::GetOutputSize(X, C);
  Tensor* Y = Output(0, sizes, at::dtype<T>());

  const int kernel_dim = C * this->kernel_h() * this->kernel_w();
  const int input_image_size = H * W;
  const int output_image_size = Y->dim32(2) * Y->dim32(3);

  if (InputSize() == 3) {
    auto& bias = Input(BIAS);
    CAFFE_ENFORCE(bias.dim() == 1, "bias must be 1D tensor");
    CAFFE_ENFORCE(
        bias.dim32(0) == C,
        "bias dimension must be equal to output channel number");
    ReinitializeTensor(
        &bias_multiplier_,
        {1, output_image_size},
        at::dtype<T>().device(Context::GetDeviceType()));
      T* bm_data = bias_multiplier_.template mutable_data<T>();
      math::Set<T, Context>(
          output_image_size,
          static_cast<T>(1),
          bm_data,
          &context_);
  }

  const T* Xdata = X.template data<T>();
  const T* filter_data = filter.template data<T>();
  T* Ydata = Y->template mutable_data<T>();

  auto f = [&](Tensor* col_buffer) {
    ReinitializeTensor(col_buffer, vector<int64_t>{C, this->kernel_h(), this->kernel_w(), H, W}, at::dtype<T>().device(Context::GetDeviceType()));
    T* col_buffer_data = col_buffer->template mutable_data<T>();
    for (auto image_id = 0; image_id < N; ++image_id) {
      // Weight term
      math::Gemm<T, Context>(
          CblasTrans,
          CblasNoTrans,
          kernel_dim,
          input_image_size,
          M,
          1,
          filter_data,
          Xdata,
          0,
          col_buffer_data,
          &context_);

      // Col2Im
      math::Col2Im<T, Context, StorageOrder::NCHW>(
          C,
          Y->dim32(2),
          Y->dim32(3),
          this->kernel_h(),
          this->kernel_w(),
          1,
          1,
          this->pad_t(),
          this->pad_l(),
          this->pad_b(),
          this->pad_r(),
          this->stride_h(),
          this->stride_w(),
          col_buffer_data,
          Ydata,
          &context_);

      // Bias term
      if (InputSize() == 3) {
        const T* bias_data = Input(BIAS).template data<T>();
        const T* bm_data = bias_multiplier_.template data<T>();
#if !defined(__ARM_NEON__) && !defined(__ARM_NEON)
        math::Gemm<T, Context>(
            CblasNoTrans,
            CblasNoTrans,
            C,
            output_image_size,
            1,
            1,
            bias_data,
            bm_data,
            1,
            Ydata,
            &context_);
#else
        math::BiasCHW<T, Context>(
            bias_data,
            bm_data,
            C,
            output_image_size,
            Ydata,
            &context_);
#endif // !defined(__ARM_NEON__) && !defined(__ARM_NEON)
      }

      Xdata += M * H * W;
      Ydata += Y->numel() / Y->dim32(0);
    }
  };
  if (FLAGS_caffe2_force_shared_col_buffer || shared_buffer_) {
    runWithSharedBuffer<Context>(ws_, f);
  } else {
    f(&col_buffer_);
  }
  return true;
}

template <typename T, class Context>
bool ConvTransposeOp<T, Context>::RunOnDeviceWithOrderNHWC() {
  const Tensor& X = Input(INPUT);
  auto& filter = Input(FILTER);
  const auto N = X.dim32(0), H = X.dim32(1), W = X.dim32(2), M = X.dim32(3);
  CAFFE_ENFORCE(filter.dim() == 4, "filter must be 4D tensor");
  CAFFE_ENFORCE(
      filter.dim32(0) == M,
      "filter number must be equal to input channel number");
  CAFFE_ENFORCE(
      filter.dim32(1) == this->kernel_h(),
      "filter height must be equal to kernel height");
  CAFFE_ENFORCE(
      filter.dim32(2) == this->kernel_w(),
      "filter width must be equal to kernel width");
  const int C = filter.dim32(3);
  auto sizes = ConvTransposeUnpoolBase<Context>::GetOutputSize(X, C);
  Tensor* Y = Output(0, sizes, at::dtype<T>());

  const auto kernel_dim = C * this->kernel_h() * this->kernel_w();
  const auto input_image_size = H * W;
  const auto output_image_size = Y->dim32(1) * Y->dim32(2);

  if (InputSize() == 3) {
    auto& bias = Input(BIAS);
    CAFFE_ENFORCE(bias.dim() == 1, "bias must be 1D tensor");
    CAFFE_ENFORCE(
        bias.dim32(0) == C,
        "bias dimension must be equal to output channel number");
    // TODO(jerryzh): is it OK to remove the check of whether numel is output_image_size
    ReinitializeTensor(
        &bias_multiplier_,
        {1, output_image_size},
        at::dtype<T>().device(Context::GetDeviceType()));
      T* bm_data = bias_multiplier_.template mutable_data<T>();
      math::Set<T, Context>(
          output_image_size,
          static_cast<T>(1),
          bm_data,
          &context_);
  }
  const T* Xdata = X.template data<T>();
  const T* filter_data = filter.template data<T>();
  T* Ydata = Y->template mutable_data<T>();

  auto f = [&](Tensor* /*col_buffer*/) {
    ReinitializeTensor(
        &col_buffer_,
        vector<int64_t>{H, W, this->kernel_h(), this->kernel_w(), C},
        at::dtype<T>().device(Context::GetDeviceType()));
    T* col_buffer_data = col_buffer_.template mutable_data<T>();
    for (auto image_id = 0; image_id < N; ++image_id) {
      // Weight term
      math::Gemm<T, Context>(
          CblasNoTrans,
          CblasNoTrans,
          input_image_size,
          kernel_dim,
          M,
          1,
          Xdata,
          filter_data,
          0,
          col_buffer_data,
          &context_);
      // Col2Im
      math::Col2Im<T, Context, StorageOrder::NHWC>(
          C,
          Y->dim32(1),
          Y->dim32(2),
          this->kernel_h(),
          this->kernel_w(),
          1,
          1,
          this->pad_t(),
          this->pad_l(),
          this->pad_b(),
          this->pad_r(),
          this->stride_h(),
          this->stride_w(),
          col_buffer_data,
          Ydata,
          &context_);
      // Bias term
      if (InputSize() == 3) {
        const T* bm_data = bias_multiplier_.template data<T>();
        const T* bias_data = Input(BIAS).template data<T>();
        math::Gemm<T, Context>(
            CblasNoTrans,
            CblasNoTrans,
            output_image_size,
            C,
            1,
            1,
            bm_data,
            bias_data,
            1,
            Ydata,
            &context_);
      }
      Xdata += M * H * W;
      Ydata += Y->numel() / Y->dim32(0);
    }
  };
  if (FLAGS_caffe2_force_shared_col_buffer || shared_buffer_) {
    runWithSharedBuffer<Context>(ws_, f);
  } else {
    f(&col_buffer_);
  }
  return true;
}

template <typename T, class Context>
bool ConvTransposeGradientOp<T, Context>::RunOnDeviceWithOrderNCHW() {
  auto& X = Input(INPUT);
  auto& filter = Input(FILTER);
  auto& dY = Input(OUTPUT_GRAD);

  const int N = X.dim32(0), M = X.dim32(1), H = X.dim32(2), W = X.dim32(3);
  // We only handle LegacyPadding::NOTSET case and ignore cases of
  // LegacyPadding::VALID and LegacyPadding::SAME
  // Thus, we don't need to manually compute padding values
  // We simply use the values from the user
  CAFFE_ENFORCE(filter.dim() == 4);
  const int C = filter.dim32(1);
  CAFFE_ENFORCE(
      filter.dim32(2) == this->kernel_h(),
      "filter height must be equal to kernel height");
  CAFFE_ENFORCE(
      filter.dim32(3) == this->kernel_w(),
      "filter width must be equal to kernel width");
  auto* dfilter = Output(FILTER_GRAD, filter.sizes(), at::dtype<T>());

  const int kernel_dim = C * this->kernel_h() * this->kernel_w();
  const int output_image_size = dY.dim32(2) * dY.dim32(3);
  // The col buffer is stored in CHW order as well
  ReinitializeTensor(
      &col_buffer_,
      vector<int64_t>{C, this->kernel_h(), this->kernel_w(), H, W},
      at::dtype<T>().device(Context::GetDeviceType()));
  if (!no_bias_) {
    auto* dbias = Output(BIAS_OR_INPUT_GRAD);
    dbias->Resize(C);
    // TODO(jerryzh): is it OK to remove the check of whether numel is output_image_size
    ReinitializeTensor(
        &bias_multiplier_,
        {1, output_image_size},
        at::dtype<T>().device(Context::GetDeviceType()));
    T* bm_data = bias_multiplier_.template mutable_data<T>();
    math::Set<T, Context>(
        output_image_size,
        static_cast<T>(1),
        bm_data,
        &context_);
  }
  T* col_buffer_data = col_buffer_.template mutable_data<T>();
  const T* Xdata = X.template data<T>();
  const T* filter_data = filter.template data<T>();
  const T* dYdata = dY.template data<T>();
  T* dfilter_data = dfilter->template mutable_data<T>();
  // Pre-setting the gradients to zero
  math::Set<T, Context>(dfilter->numel(), 0, dfilter_data, &context_);
  if (!no_bias_) {
    auto* dbias = Output(BIAS_OR_INPUT_GRAD);
    T* dbias_data = dbias->template mutable_data<T>();
    math::Set<T, Context>(dbias->numel(), 0, dbias_data, &context_);
  }
  for (auto image_id = 0; image_id < N; ++image_id) {
    // gradient w.r.t. filters. Im2Col followed by Gemm
    // Im2Col.
    math::Im2Col<T, Context, StorageOrder::NCHW>(
        C,
        dY.dim32(2),
        dY.dim32(3),
        this->kernel_h(),
        this->kernel_w(),
        1,
        1,
        this->pad_t(),
        this->pad_l(),
        this->pad_b(),
        this->pad_r(),
        this->stride_h(),
        this->stride_w(),
        dYdata,
        col_buffer_data,
        &context_);
    // Gemm
    math::Gemm<T, Context>(
        CblasNoTrans,
        CblasTrans,
        M,
        kernel_dim,
        H * W,
        1,
        Xdata,
        col_buffer_data,
        1,
        dfilter_data,
        &context_);
    // gradient w.r.t. bias
    if (!no_bias_) {
      const T* bm_data = bias_multiplier_.template data<T>();
      T* input_grad_data = Output(BIAS_OR_INPUT_GRAD)->template mutable_data<T>();
      math::Gemm<T, Context>(
          CblasNoTrans,
          CblasNoTrans,
          C,
          1,
          output_image_size,
          1,
          dYdata,
          bm_data,
          1,
          input_grad_data,
          &context_);
    }
    dYdata += dY.numel() / dY.dim32(0);
    Xdata += X.numel() / X.dim32(0);
  }
  if (OutputSize() == 3 || (no_bias_ && (OutputSize() == 2))) {
    // Compute gradients w.r.t. the input
    // Since we have changed dYdata in the above loop, we will need to reset.
    dYdata = dY.template data<T>();

    auto* dX = Output(
        no_bias_ ? BIAS_OR_INPUT_GRAD : INPUT_GRAD, X.sizes(), at::dtype<T>());
    T* dXdata = dX->template mutable_data<T>();
    for (auto image_id = 0; image_id < N; ++image_id) {
      // Im2Col.
      // TODO(zyan3): Probably duplicate work as in gradient computation
      // w.r.t filters
      math::Im2Col<T, Context, StorageOrder::NCHW>(
          C,
          dY.dim32(2),
          dY.dim32(3),
          this->kernel_h(),
          this->kernel_w(),
          1,
          1,
          this->pad_t(),
          this->pad_l(),
          this->pad_b(),
          this->pad_r(),
          this->stride_h(),
          this->stride_w(),
          dYdata,
          col_buffer_data,
          &context_);
      // Gemm
      math::Gemm<T, Context>(
          CblasNoTrans,
          CblasNoTrans,
          M,
          H * W,
          kernel_dim,
          1,
          filter_data,
          col_buffer_data,
          0,
          dXdata,
          &context_);
      dYdata += dY.numel() / dY.dim32(0);
      dXdata += X.numel() / X.dim32(0);
    }
  }
  return true;
}

template <typename T, class Context>
bool ConvTransposeGradientOp<T, Context>::RunOnDeviceWithOrderNHWC() {
  auto& X = Input(INPUT);
  auto& filter = Input(FILTER);
  auto& dY = Input(OUTPUT_GRAD);

  const int N = X.dim32(0), H = X.dim32(1), W = X.dim32(2), M = X.dim32(3);
  // We only handle LegacyPadding::NOTSET case and ignore cases of
  // LegacyPadding::VALID and LegacyPadding::SAME
  // Thus, we don't need to manually compute padding values
  // We simply use the values from the user
  CAFFE_ENFORCE(filter.dim() == 4, "filter must be 4D tensor");
  CAFFE_ENFORCE(
      filter.dim32(1) == this->kernel_h(),
      "filter height must be equal to kernel height");
  CAFFE_ENFORCE(
      filter.dim32(2) == this->kernel_w(),
      "filter width must be equal to kernel width");
  const int C = filter.dim32(3);
  auto* dfilter = Output(FILTER_GRAD, filter.sizes(), at::dtype<T>());

  const int kernel_dim = C * this->kernel_h() * this->kernel_w();
  const int output_image_size = dY.dim32(1) * dY.dim32(2);
  // The col buffer is stored in HWC order as well
  ReinitializeTensor(
      &col_buffer_,
      vector<int64_t>{H, W, this->kernel_h(), this->kernel_w(), C},
      at::dtype<T>().device(Context::GetDeviceType()));
  if (!no_bias_) {
    auto* dbias = Output(BIAS_OR_INPUT_GRAD);
    dbias->Resize(C);
    // TODO(jerryzh): is it OK to remove the check of whether numel is output_image_size
    ReinitializeTensor(
        &bias_multiplier_,
        {1, output_image_size},
        at::dtype<T>().device(Context::GetDeviceType()));
    T* bm_data = bias_multiplier_.template mutable_data<T>();
    math::Set<T, Context>(
        output_image_size,
        static_cast<T>(1),
        bm_data,
        &context_);
  }
  T* col_buffer_data = col_buffer_.template mutable_data<T>();
  const T* Xdata = X.template data<T>();
  const T* filter_data = filter.template data<T>();
  const T* dYdata = dY.template data<T>();
  T* dfilter_data = dfilter->template mutable_data<T>();
  // Pre-setting the gradients to zero
  math::Set<T, Context>(dfilter->numel(), 0, dfilter_data, &context_);
  if (!no_bias_) {
    auto* dbias = Output(BIAS_OR_INPUT_GRAD);
    T* dbias_data = dbias->template mutable_data<T>();
    math::Set<T, Context>(dbias->numel(), 0, dbias_data, &context_);
  }
  for (auto image_id = 0; image_id < N; ++image_id) {
    // gradient w.r.t. filters. Im2Col followed by Gemm
    // Im2Col.
    math::Im2Col<T, Context, StorageOrder::NHWC>(
        C,
        dY.dim32(1),
        dY.dim32(2),
        this->kernel_h(),
        this->kernel_w(),
        1,
        1,
        this->pad_t(),
        this->pad_l(),
        this->pad_b(),
        this->pad_r(),
        this->stride_h(),
        this->stride_w(),
        dYdata,
        col_buffer_data,
        &context_);
    // Gemm
    math::Gemm<T, Context>(
        CblasTrans,
        CblasNoTrans,
        M,
        kernel_dim,
        H * W,
        1,
        Xdata,
        col_buffer_data,
        1,
        dfilter_data,
        &context_);
    // gradients w.r.t. bias
    if (!no_bias_) {
      const T* bm_data = bias_multiplier_.template data<T>();
      T* input_grad_data = Output(BIAS_OR_INPUT_GRAD)->template mutable_data<T>();
      math::Gemm<T, Context>(
          CblasTrans,
          CblasNoTrans,
          C,
          1,
          output_image_size,
          1,
          dYdata,
          bm_data,
          1,
          input_grad_data,
          &context_);
    }
    dYdata += dY.numel() / dY.dim32(0);
    Xdata += X.numel() / X.dim32(0);
  }
  if (OutputSize() == 3 || (no_bias_ && (OutputSize() == 2))) {
    // Compute gradients w.r.t. the input
    // Since we have changed dYdata in the above loop, we will need to reset.
    dYdata = dY.template data<T>();

    auto* dX = Output(
        no_bias_ ? BIAS_OR_INPUT_GRAD : INPUT_GRAD, X.sizes(), at::dtype<T>());
    T* dXdata = dX->template mutable_data<T>();
    for (auto image_id = 0; image_id < N; ++image_id) {
      // Im2Col.
      // TODO(zyan3): Probably duplicate work as in gradient computation
      // w.r.t filters
      math::Im2Col<T, Context, StorageOrder::NHWC>(
          C,
          dY.dim32(1),
          dY.dim32(2),
          this->kernel_h(),
          this->kernel_w(),
          1,
          1,
          this->pad_t(),
          this->pad_l(),
          this->pad_b(),
          this->pad_r(),
          this->stride_h(),
          this->stride_w(),
          dYdata,
          col_buffer_data,
          &context_);
      // Gemm
      math::Gemm<T, Context>(
          CblasNoTrans,
          CblasTrans,
          H * W,
          M,
          kernel_dim,
          1,
          col_buffer_data,
          filter_data,
          0,
          dXdata,
          &context_);
      dYdata += dY.numel() / dY.dim32(0);
      dXdata += X.numel() / X.dim32(0);
    }
  }
  return true;
}

} // namespace caffe2
#endif // CAFFE2_OPERATORS_CONV_TRANSPOSE_OP_IMPL_H_
