#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/AccumulateType.h>
#include <ATen/Dispatch.h>
#include <ATen/TensorUtils.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDAApplyUtils.cuh>
#include <c10/macros/Macros.h>

#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/empty.h>
#include <ATen/ops/empty_like.h>
#include <ATen/ops/_thnn_fused_lstm_cell_native.h>
#include <ATen/ops/_thnn_fused_lstm_cell_backward_impl_native.h>
#include <ATen/ops/_thnn_fused_gru_cell_native.h>
#include <ATen/ops/_thnn_fused_gru_cell_backward_native.h>
#endif

namespace at::native {

namespace {

using at::cuda::detail::TensorInfo;
using at::cuda::detail::getTensorInfo;
using at::cuda::detail::IndexToOffset;
using at::cuda::detail::canUse32BitIndexMath;

// Factor will be 3 for GRU and 4 for LSTM
void checkSizes(CheckedFrom c,
                const TensorArg& input_gates, const TensorArg& hidden_gates,
                const TensorArg& input_bias, const TensorArg& hidden_bias,
                int64_t factor, const TensorArg& prev_hidden) {
  checkDim(c, input_gates, 2);
  checkSameSize(c, input_gates, hidden_gates);
  int64_t gates_size = input_gates->size(1);

  if (input_bias->defined()) {
    checkDim(c, input_bias, 1);
    checkNumel(c, input_bias, gates_size);
    checkSameSize(c, input_bias, hidden_bias);
  }

  checkDim(c, prev_hidden, 2);
  checkNumel(c, prev_hidden, input_gates->size(0) * gates_size / factor);

  checkAllSameGPU(c, {input_gates, hidden_gates, input_bias, hidden_bias, prev_hidden});
}

bool allContiguous(at::TensorList tensors) {
  return std::all_of(tensors.begin(), tensors.end(),
                     [](const at::Tensor& t) { return !t.defined() || t.is_contiguous(); });
}

void getLaunchConfig(dim3* block, dim3* grid, int64_t numel) {
  c10::DeviceIndex curDevice = -1;
  AT_CUDA_CHECK(c10::cuda::GetDevice(&curDevice));
  *block = cuda::getApplyBlock();
  TORCH_INTERNAL_ASSERT(cuda::getApplyGrid(numel, *grid, curDevice),
                        "Could not get grid size for pointwise apply.");
}

template<typename T, typename T2>
TensorInfo<T, T2> tryGetTensorInfo(const at::Tensor& t) {
  return t.defined() ? getTensorInfo<T, T2>(t) : TensorInfo<T, T2>{};
}

void collapseDims() {};
template<typename T, typename T2, typename... Args>
void collapseDims(TensorInfo<T, T2>& info, Args&... infos) {
  info.collapseDims();
  collapseDims(infos...);
}

#define DEVICE_LINEAR_GET(D_TENSOR, INDEX)                              \
  D_TENSOR.data[IndexToOffset<scalar_t, index_type, indexing_kind>::get(INDEX, D_TENSOR)]

// Biases are always 1D
#define DEVICE_BIAS_GET(D_TENSOR, INDEX)                              \
  D_TENSOR.data[IndexToOffset<scalar_t, index_type, 1>::get(INDEX, D_TENSOR)]

#define H2F(input) static_cast<accscalar_t>(input)
#define F2H(input) static_cast<scalar_t>(input)

template<typename T>
__device__ __forceinline__
T sigmoid(T in)  {
  T one = static_cast<T>(1.0);
  return one / (one + ::exp(-in));
}

namespace kernel {

template <typename scalar_t, typename accscalar_t, typename index_type, int indexing_kind>
#if __CUDA_ARCH__ >= 350 || defined(USE_ROCM)
C10_LAUNCH_BOUNDS_2(512, 4)
#endif
__global__ void lstm_cell_forward(
            TensorInfo<scalar_t, index_type> input,
            TensorInfo<scalar_t, index_type> hidden,
            TensorInfo<scalar_t, index_type> bias1,
            TensorInfo<scalar_t, index_type> bias2,
            TensorInfo<scalar_t, index_type> _cx,
            TensorInfo<scalar_t, index_type> _hy,
            TensorInfo<scalar_t, index_type> _cy,
            TensorInfo<scalar_t, index_type> workspace,
            index_type hsz,
            index_type totalElements) {
    bool has_bias = bias1.data != nullptr;
    for (index_type linearIndex = blockIdx.x * blockDim.x + threadIdx.x;
       linearIndex < totalElements;
       linearIndex += gridDim.x * blockDim.x) {
      index_type offset = (linearIndex/hsz)*4*hsz+linearIndex%hsz;

      scalar_t iig = DEVICE_LINEAR_GET(input, offset+0*hsz);
      scalar_t ifg = DEVICE_LINEAR_GET(input, offset+1*hsz);
      scalar_t icg = DEVICE_LINEAR_GET(input, offset+2*hsz);
      scalar_t iog = DEVICE_LINEAR_GET(input, offset+3*hsz);

      scalar_t hig = DEVICE_LINEAR_GET(hidden, offset+0*hsz);
      scalar_t hfg = DEVICE_LINEAR_GET(hidden, offset+1*hsz);
      scalar_t hcg = DEVICE_LINEAR_GET(hidden,  offset+2*hsz);
      scalar_t hog = DEVICE_LINEAR_GET(hidden,  offset+3*hsz);

      scalar_t* wig = &DEVICE_LINEAR_GET(workspace, offset+0*hsz);
      scalar_t* wfg = &DEVICE_LINEAR_GET(workspace, offset+1*hsz);
      scalar_t* wcg = &DEVICE_LINEAR_GET(workspace, offset+2*hsz);
      scalar_t* wog = &DEVICE_LINEAR_GET(workspace, offset+3*hsz);

      scalar_t cx = DEVICE_LINEAR_GET(_cx, linearIndex);

      scalar_t* hy = &DEVICE_LINEAR_GET(_hy, linearIndex);
      scalar_t* cy = &DEVICE_LINEAR_GET(_cy, linearIndex);

      scalar_t b1i, b1f, b1c, b1o;
      scalar_t b2i, b2f, b2c, b2o;

      if (has_bias) {
        b1i = DEVICE_BIAS_GET(bias1, linearIndex % hsz + 0 * hsz);
        b1f = DEVICE_BIAS_GET(bias1, linearIndex % hsz + 1 * hsz);
        b1c = DEVICE_BIAS_GET(bias1, linearIndex % hsz + 2 * hsz);
        b1o = DEVICE_BIAS_GET(bias1, linearIndex % hsz + 3 * hsz);

        b2i = DEVICE_BIAS_GET(bias2, linearIndex % hsz + 0 * hsz);
        b2f = DEVICE_BIAS_GET(bias2, linearIndex % hsz + 1 * hsz);
        b2c = DEVICE_BIAS_GET(bias2, linearIndex % hsz + 2 * hsz);
        b2o = DEVICE_BIAS_GET(bias2, linearIndex % hsz + 3 * hsz);
      } else {
#ifndef THC_REAL_IS_HALF
        b1i = 0.0; b1f = 0.0; b1c = 0.0; b1o = 0.0;
        b2i = 0.0; b2f = 0.0; b2c = 0.0; b2o = 0.0;
#else
        b1i = F2H(0.0); b1f = F2H(0.0); b1c = F2H(0.0); b1o = F2H(0.0);
        b2i = F2H(0.0); b2f = F2H(0.0); b2c = F2H(0.0); b2o = F2H(0.0);
#endif
      }

      accscalar_t ig, fg, cg, og;
      accscalar_t f_hy, f_cy;

      ig = sigmoid(H2F(iig) + H2F(hig) + H2F(b1i) + H2F(b2i));
      fg = sigmoid(H2F(ifg) + H2F(hfg) + H2F(b1f) + H2F(b2f));
      cg = ::tanh(H2F(icg) + H2F(hcg) + H2F(b1c) + H2F(b2c));
      og = sigmoid(H2F(iog) + H2F(hog) + H2F(b1o) + H2F(b2o));

      f_cy = (fg * H2F(cx)) + (ig * cg);
      f_hy = og * ::tanh(f_cy);

      *hy = F2H(f_hy);
      *cy = F2H(f_cy);

      //SAVE FOR BACKWARDS
      //Also need cy and cx but can be saved easily in python
      *wig = F2H(ig);
      *wfg = F2H(fg);
      *wcg = F2H(cg);
      *wog = F2H(og);
    }
}

template <typename scalar_t, typename accscalar_t, typename index_type, int indexing_kind>
#if __CUDA_ARCH__ >= 350 || defined(USE_ROCM)
C10_LAUNCH_BOUNDS_2(512, 4)
#endif
__global__ void lstm_cell_backward(
              TensorInfo<scalar_t, index_type> storage,
              TensorInfo<scalar_t, index_type> gradInGates,
              TensorInfo<scalar_t, index_type> _cx,
              TensorInfo<scalar_t, index_type> _cy,
              TensorInfo<scalar_t, index_type> gradoutput,
              TensorInfo<scalar_t, index_type> gradoutputcell,
              TensorInfo<scalar_t, index_type> gradInputCx,
              index_type hsz,
              index_type totalElements) {
  bool has_gradoutput = gradoutput.data != nullptr;
  bool has_gradoutputcell = gradoutputcell.data != nullptr;
  for (index_type linearIndex = blockIdx.x * blockDim.x + threadIdx.x;
       linearIndex < totalElements;
       linearIndex += gridDim.x * blockDim.x) {
    index_type offset = (linearIndex/hsz)*4*hsz+linearIndex%hsz;

    scalar_t ig = DEVICE_LINEAR_GET(storage, offset+0*hsz);
    scalar_t fg = DEVICE_LINEAR_GET(storage, offset+1*hsz);
    scalar_t cg = DEVICE_LINEAR_GET(storage, offset+2*hsz);
    scalar_t og = DEVICE_LINEAR_GET(storage, offset+3*hsz);

    scalar_t* ih = &DEVICE_LINEAR_GET(gradInGates, offset+0*hsz);
    scalar_t* fh = &DEVICE_LINEAR_GET(gradInGates, offset+1*hsz);
    scalar_t* ch = &DEVICE_LINEAR_GET(gradInGates, offset+2*hsz);
    scalar_t* oh = &DEVICE_LINEAR_GET(gradInGates, offset+3*hsz);

    //will return hidden grads here
    scalar_t cx = DEVICE_LINEAR_GET(_cx, linearIndex);
    scalar_t cy = DEVICE_LINEAR_GET(_cy, linearIndex);

    scalar_t* gi = &DEVICE_LINEAR_GET(gradInputCx, linearIndex);

    accscalar_t go  = has_gradoutput ? H2F(DEVICE_LINEAR_GET(gradoutput, linearIndex)) : 0.f;
    accscalar_t goc = has_gradoutputcell ? H2F(DEVICE_LINEAR_GET(gradoutputcell, linearIndex)) : 0.f;

    accscalar_t gcx = ::tanh(H2F(cy));

    accscalar_t gog = go * gcx;
    gcx = go * H2F(og) * (1 - gcx*gcx) + goc;

    accscalar_t gig = gcx * H2F(cg);
    accscalar_t gfg = gcx * H2F(cx);
    accscalar_t gcg = gcx * H2F(ig);

    gcx = gcx * H2F(fg);

    gig = gig * (1-H2F(ig)) * H2F(ig);
    gfg = gfg * (1-H2F(fg)) * H2F(fg);
    gcg = gcg * (1-H2F(cg)*H2F(cg));
    gog = gog * (1-H2F(og)) * H2F(og);

    *ih = F2H(gig);
    *fh = F2H(gfg);
    *ch = F2H(gcg);
    *oh = F2H(gog);

    *gi = F2H(gcx);
  }
}

template <typename scalar_t, typename accscalar_t, typename index_type, int indexing_kind>
#if __CUDA_ARCH__ >= 350 || defined(USE_ROCM)
C10_LAUNCH_BOUNDS_2(512, 4)
#endif
__global__ void gru_cell_forward(
            TensorInfo<scalar_t, index_type> Input,
            TensorInfo<scalar_t, index_type> Hidden,
            TensorInfo<scalar_t, index_type> Bias1,
            TensorInfo<scalar_t, index_type> Bias2,
            TensorInfo<scalar_t, index_type> _hx,
            TensorInfo<scalar_t, index_type> _hy,
            TensorInfo<scalar_t, index_type> storage,
            index_type hsz,
            index_type totalElements) {
  bool has_bias = Bias1.data != nullptr;
  for (index_type linearIndex = blockIdx.x * blockDim.x + threadIdx.x;
       linearIndex < totalElements;
       linearIndex += gridDim.x * blockDim.x) {
      index_type offset = (linearIndex/hsz)*3*hsz+linearIndex%hsz;

      scalar_t ir = DEVICE_LINEAR_GET(Input, offset+0*hsz);
      scalar_t ii = DEVICE_LINEAR_GET(Input, offset+1*hsz);
      scalar_t in = DEVICE_LINEAR_GET(Input, offset+2*hsz);
      scalar_t hr = DEVICE_LINEAR_GET(Hidden,offset+0*hsz);
      scalar_t hi = DEVICE_LINEAR_GET(Hidden,offset+1*hsz);
      scalar_t hn = DEVICE_LINEAR_GET(Hidden,  offset+2*hsz);

      scalar_t hx = DEVICE_LINEAR_GET(_hx, linearIndex);
      scalar_t* hy = &DEVICE_LINEAR_GET(_hy, linearIndex);

      scalar_t b1r, b1i, b1n, b2r, b2i, b2n;

      if (has_bias) {
        b1r = DEVICE_BIAS_GET(Bias1, linearIndex%hsz+0*hsz);
        b1i = DEVICE_BIAS_GET(Bias1, linearIndex%hsz+1*hsz);
        b1n = DEVICE_BIAS_GET(Bias1, linearIndex%hsz+2*hsz);

        b2r = DEVICE_BIAS_GET(Bias2, linearIndex%hsz+0*hsz);
        b2i = DEVICE_BIAS_GET(Bias2, linearIndex%hsz+1*hsz);
        b2n = DEVICE_BIAS_GET(Bias2, linearIndex%hsz+2*hsz);
      } else {
#ifndef THC_REAL_IS_HALF
        b1r = 0.0; b1i = 0.0; b1n = 0.0;
        b2r = 0.0; b2i = 0.0; b2n = 0.0;
#else
        b1r = F2H(0.0); b1i = F2H(0.0); b1n = F2H(0.0);
        b2r = F2H(0.0); b2i = F2H(0.0); b2n = F2H(0.0);
#endif
      }

      offset = (linearIndex/hsz)*5*hsz+linearIndex%hsz;

      accscalar_t rg, ig, ng;

      rg = sigmoid(H2F(ir) + H2F(hr) + H2F(b1r) + H2F(b2r));
      ig = sigmoid(H2F(ii) + H2F(hi) + H2F(b1i) + H2F(b2i));

      ng = H2F(in) + H2F(b1n) + rg*( H2F(hn)+H2F(b2n) );
      ng = ::tanh(ng);
      *hy = F2H( ng + ig * ( H2F(hx)-ng ) );

      //SAVE FOR BACKWARDS
      DEVICE_LINEAR_GET(storage, offset+0*hsz) = F2H(rg);
      DEVICE_LINEAR_GET(storage, offset+1*hsz) = F2H(ig);
      DEVICE_LINEAR_GET(storage, offset+2*hsz) = F2H(ng);
      DEVICE_LINEAR_GET(storage, offset+3*hsz) = hx;
      DEVICE_LINEAR_GET(storage, offset+4*hsz) = F2H(H2F(hn) + H2F(b2n));
    }
}

template <typename scalar_t, typename accscalar_t, typename index_type, int indexing_kind>
#if __CUDA_ARCH__ >= 350 || defined(USE_ROCM)
C10_LAUNCH_BOUNDS_2(512, 4)
#endif
__global__ void gru_cell_backward(
             TensorInfo<scalar_t, index_type> gradInInput,
             TensorInfo<scalar_t, index_type> gradInHidden,
             TensorInfo<scalar_t, index_type> gradOutput,
             TensorInfo<scalar_t, index_type> gradInputHx,
             TensorInfo<scalar_t, index_type> storage,
             index_type hsz,
             index_type totalElements) {
  for (index_type linearIndex = blockIdx.x * blockDim.x + threadIdx.x;
       linearIndex < totalElements;
       linearIndex += gridDim.x * blockDim.x) {
    index_type offset = (linearIndex/hsz)*5*hsz+linearIndex%hsz;

    scalar_t rg = DEVICE_LINEAR_GET(storage, offset+0*hsz);
    scalar_t ig = DEVICE_LINEAR_GET(storage, offset+1*hsz);
    scalar_t ng = DEVICE_LINEAR_GET(storage, offset+2*hsz);
    scalar_t hx = DEVICE_LINEAR_GET(storage, offset+3*hsz);
    scalar_t hn = DEVICE_LINEAR_GET(storage, offset+4*hsz);

    scalar_t go = DEVICE_LINEAR_GET(gradOutput, linearIndex);

    offset = (linearIndex/hsz)*3*hsz+linearIndex%hsz;

    accscalar_t gig = H2F(go)*( H2F(hx)-H2F(ng) )*( 1-H2F(ig) )*H2F(ig);
    accscalar_t ghx = H2F(go)*H2F(ig);
    accscalar_t gin = H2F(go)*( 1-H2F(ig) )*( 1-H2F(ng)*H2F(ng) );
    accscalar_t ghn = gin * H2F(rg);
    accscalar_t grg = gin *H2F(hn)*( 1-H2F(rg) )*H2F(rg);

    DEVICE_LINEAR_GET(gradInInput, offset+0*hsz) = F2H(grg);
    DEVICE_LINEAR_GET(gradInInput, offset+1*hsz) = F2H(gig);
    DEVICE_LINEAR_GET(gradInInput, offset+2*hsz) = F2H(gin);

    DEVICE_LINEAR_GET(gradInHidden, offset+0*hsz) = F2H(grg);
    DEVICE_LINEAR_GET(gradInHidden, offset+1*hsz) = F2H(gig);
    DEVICE_LINEAR_GET(gradInHidden, offset+2*hsz) = F2H(ghn);
    DEVICE_LINEAR_GET(gradInputHx, linearIndex) = F2H(ghx);
  }
}

#undef DEVICE_LINEAR_GET
#undef DEVICE_BIAS_GET
#undef H2F
#undef F2H

} // namespace kernel

template<typename scalar_t, typename index_type>
void lstm_forward_impl(const Tensor& input_gates, const Tensor& hidden_gates,
                       const Tensor& input_bias, const Tensor& hidden_bias,
                       const Tensor& cx,
                       const Tensor& hy, const Tensor& cy, const Tensor& workspace) {
  using accscalar_t = acc_type<scalar_t, /*is_cuda=*/true>;

  dim3 block, grid;
  int64_t numel = cx.numel();
  if (numel == 0) return;
  getLaunchConfig(&block, &grid, numel);

  auto input_gatesI = getTensorInfo<scalar_t, index_type>(input_gates);
  auto hidden_gatesI = getTensorInfo<scalar_t, index_type>(hidden_gates);
  auto input_biasI = tryGetTensorInfo<scalar_t, index_type>(input_bias);
  auto hidden_biasI = tryGetTensorInfo<scalar_t, index_type>(hidden_bias);
  auto cxI = getTensorInfo<scalar_t, index_type>(cx);
  auto hyI = getTensorInfo<scalar_t, index_type>(hy);
  auto cyI = getTensorInfo<scalar_t, index_type>(cy);
  auto workspaceI = getTensorInfo<scalar_t, index_type>(workspace);
  index_type hidden_size = cxI.sizes[cxI.dims-1];

  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  if (allContiguous({input_gates, hidden_gates, input_bias, hidden_bias, cx, hy, cy, workspace})) {
    collapseDims(input_gatesI, hidden_gatesI, input_biasI, hidden_biasI, cxI, hyI, cyI, workspaceI);
    kernel::lstm_cell_forward<scalar_t, accscalar_t, index_type, 1>
      <<<grid, block, 0, stream>>>
        (input_gatesI, hidden_gatesI, input_biasI, hidden_biasI, cxI, hyI, cyI, workspaceI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  } else {
    kernel::lstm_cell_forward<scalar_t, accscalar_t, index_type, 2>
      <<<grid, block, 0, stream>>>
        (input_gatesI, hidden_gatesI, input_biasI, hidden_biasI, cxI, hyI, cyI, workspaceI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  }
}

template<typename scalar_t, typename index_type>
void lstm_backward_impl(const Tensor& grad_hy, const Tensor& grad_cy,
                        const Tensor& cx, const Tensor& cy,
                        const Tensor& workspace,
                        const Tensor& grad_gates, const Tensor& grad_cx) {
  using accscalar_t = acc_type<scalar_t, /*is_cuda=*/true>;

  dim3 block, grid;
  int64_t numel = cx.numel();
  getLaunchConfig(&block, &grid, numel);
  if (numel == 0) return;

  auto grad_hyI = tryGetTensorInfo<scalar_t, index_type>(grad_hy);
  auto grad_cyI = tryGetTensorInfo<scalar_t, index_type>(grad_cy);
  auto cxI = getTensorInfo<scalar_t, index_type>(cx);
  auto cyI = getTensorInfo<scalar_t, index_type>(cy);
  auto workspaceI = getTensorInfo<scalar_t, index_type>(workspace);
  auto grad_gatesI = getTensorInfo<scalar_t, index_type>(grad_gates);
  auto grad_cxI = getTensorInfo<scalar_t, index_type>(grad_cx);
  index_type hidden_size = cxI.sizes[cxI.dims-1];

  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  if (allContiguous({grad_hy, grad_cy, cx, cy, workspace, grad_gates, grad_cx})) {
    collapseDims(grad_hyI, grad_cyI, cxI, cyI, workspaceI, grad_gatesI, grad_cxI);
    kernel::lstm_cell_backward<scalar_t, accscalar_t, index_type, 1>
      <<<grid, block, 0, stream>>>
        (workspaceI, grad_gatesI, cxI, cyI, grad_hyI, grad_cyI, grad_cxI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  } else {
    kernel::lstm_cell_backward<scalar_t, accscalar_t, index_type, 2>
      <<<grid, block, 0, stream>>>
        (workspaceI, grad_gatesI, cxI, cyI, grad_hyI, grad_cyI, grad_cxI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  }
}

template<typename scalar_t, typename index_type>
void gru_forward_impl(const Tensor& input_gates, const Tensor& hidden_gates,
                      const Tensor& input_bias, const Tensor& hidden_bias,
                      const Tensor& hx,
                      const Tensor& hy, const Tensor& workspace) {
  using accscalar_t = acc_type<scalar_t, /*is_cuda=*/true>;

  dim3 block, grid;
  int64_t numel = hx.numel();
  if (numel == 0) return;
  getLaunchConfig(&block, &grid, numel);

  auto input_gatesI = getTensorInfo<scalar_t, index_type>(input_gates);
  auto hidden_gatesI = getTensorInfo<scalar_t, index_type>(hidden_gates);
  auto input_biasI = tryGetTensorInfo<scalar_t, index_type>(input_bias);
  auto hidden_biasI = tryGetTensorInfo<scalar_t, index_type>(hidden_bias);
  auto hxI = getTensorInfo<scalar_t, index_type>(hx);
  auto hyI = getTensorInfo<scalar_t, index_type>(hy);
  auto workspaceI = getTensorInfo<scalar_t, index_type>(workspace);
  index_type hidden_size = hxI.sizes[hxI.dims-1];

  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  if (allContiguous({input_gates, hidden_gates, input_bias, hidden_bias, hx, hy, workspace})) {
    collapseDims(input_gatesI, hidden_gatesI, input_biasI, hidden_biasI, hxI, hyI, workspaceI);
    kernel::gru_cell_forward<scalar_t, accscalar_t, index_type, 1>
      <<<grid, block, 0, stream>>>
        (input_gatesI, hidden_gatesI, input_biasI, hidden_biasI, hxI, hyI, workspaceI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  } else {
    kernel::gru_cell_forward<scalar_t, accscalar_t, index_type, 2>
      <<<grid, block, 0, stream>>>
        (input_gatesI, hidden_gatesI, input_biasI, hidden_biasI, hxI, hyI, workspaceI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  }
}

template<typename scalar_t, typename index_type>
void gru_backward_impl(const Tensor& grad_hy, const Tensor& workspace,
                       const Tensor& grad_input_gates, const Tensor& grad_hidden_gates, const Tensor& grad_hx) {
  using accscalar_t = acc_type<scalar_t, /*is_cuda=*/true>;

  dim3 block, grid;
  int64_t numel = grad_hy.numel();
  if (numel == 0) return;
  getLaunchConfig(&block, &grid, numel);

  auto grad_hyI = getTensorInfo<scalar_t, index_type>(grad_hy);
  auto workspaceI = getTensorInfo<scalar_t, index_type>(workspace);
  auto grad_input_gatesI = getTensorInfo<scalar_t, index_type>(grad_input_gates);
  auto grad_hidden_gatesI = getTensorInfo<scalar_t, index_type>(grad_hidden_gates);
  auto grad_hxI = getTensorInfo<scalar_t, index_type>(grad_hx);
  index_type hidden_size = grad_hyI.sizes[grad_hyI.dims-1];

  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  if (allContiguous({grad_hy, workspace, grad_input_gates, grad_hidden_gates, grad_hx})) {
    collapseDims(grad_hyI, workspaceI, grad_input_gatesI, grad_hidden_gatesI, grad_hxI);
    kernel::gru_cell_backward<scalar_t, accscalar_t, index_type, 1>
      <<<grid, block, 0, stream>>>
        (grad_input_gatesI, grad_hidden_gatesI, grad_hyI, grad_hxI, workspaceI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  } else {
    kernel::gru_cell_backward<scalar_t, accscalar_t, index_type, 2>
      <<<grid, block, 0, stream>>>
        (grad_input_gatesI, grad_hidden_gatesI, grad_hyI, grad_hxI, workspaceI, hidden_size, numel);
    C10_CUDA_KERNEL_LAUNCH_CHECK();
  }
}

} // anonymous namespace

// Note [64-bit index math check elision]
// It's enough to perform the check for 64-bit math on the largest tensor only.
// If 32-bit is enough for it, it will suffice for all other tensors too, and we
// can save some work using this trick.

std::tuple<Tensor, Tensor, Tensor> _thnn_fused_lstm_cell_cuda(
      const Tensor& input_gates, const Tensor& hidden_gates,
      const Tensor& cx, const std::optional<Tensor>& input_bias_opt, const std::optional<Tensor>& hidden_bias_opt) {
  // See [Note: hacky wrapper removal for optional tensor]
  c10::MaybeOwned<Tensor> input_bias_maybe_owned = at::borrow_from_optional_tensor(input_bias_opt);
  const Tensor& input_bias = *input_bias_maybe_owned;
  const Tensor& hidden_bias = hidden_bias_opt.value_or(Tensor());

  checkSizes("_thnn_fused_lstm_cell_cuda",
             {input_gates, "input_gates", 1}, {hidden_gates, "hidden_gates", 2},
             {input_bias, "input_bias", 3}, {hidden_bias, "hidden_bias", 4},
             /*factor=*/4, {cx, "prev_hidden", 5});

  auto workspace = at::empty_like(input_gates, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  auto hy = at::empty_like(cx, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  auto cy = at::empty_like(cx, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  AT_DISPATCH_FLOATING_TYPES_AND2(
    at::ScalarType::Half,
    at::ScalarType::BFloat16,
    input_gates.scalar_type(),
    "_thnn_fused_lstm_cell_cuda",
    [&] {
      if (canUse32BitIndexMath(workspace)) { // See Note [64-bit index math check elision]
        lstm_forward_impl<scalar_t, int32_t>(input_gates, hidden_gates, input_bias, hidden_bias, cx, hy, cy, workspace);
      } else {
        lstm_forward_impl<scalar_t, int64_t>(input_gates, hidden_gates, input_bias, hidden_bias, cx, hy, cy, workspace);
      }
  });
  return std::make_tuple(std::move(hy), std::move(cy), std::move(workspace));
}

void checkLSTMBackwardSizes(const TensorArg& grad_hy, const TensorArg& grad_cy,
                            const TensorArg& cx, const TensorArg& cy,
                            const TensorArg& workspace) {
  CheckedFrom c = "fused_lstm_cell_backward";
  const TensorArg& defined_grad = grad_hy->defined() ? grad_hy : grad_cy;
  checkDim(c, defined_grad, 2);
  auto exp_size = defined_grad->sizes();
  if (grad_hy->defined()) {
    checkSize(c, grad_hy, exp_size);
  }
  if (grad_cy->defined()) {
    checkSize(c, grad_cy, exp_size);
  }
  checkSize(c, cx, exp_size);
  checkSize(c, cy, exp_size);
  checkDim(c, workspace, 2);
  checkNumel(c, workspace, exp_size[0] * exp_size[1] * 4);
}

std::tuple<Tensor, Tensor, Tensor> _thnn_fused_lstm_cell_backward_impl_cuda( const std::optional<Tensor>& grad_hy_opt, const std::optional<Tensor>& grad_cy_opt,
      const Tensor& cx, const Tensor& cy,
      const Tensor& workspace, bool has_bias) {
  // See [Note: hacky wrapper removal for optional tensor]
  c10::MaybeOwned<Tensor> grad_hy_maybe_owned = at::borrow_from_optional_tensor(grad_hy_opt);
  const Tensor& grad_hy = *grad_hy_maybe_owned;
  const Tensor& grad_cy = grad_cy_opt.value_or(Tensor());

  if (!grad_hy.defined() && !grad_cy.defined()) {
    return std::tuple<Tensor, Tensor, Tensor>();
  }
  checkLSTMBackwardSizes({grad_hy, "grad_hy", 1}, {grad_cy, "grad_cy", 2},
                         {cx, "cx", 3}, {cy, "cy", 4},
                         {workspace, "workspace", 5});

  auto grad_gates = at::empty_like(workspace, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  auto grad_cx = at::empty_like(cx, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  AT_DISPATCH_FLOATING_TYPES_AND2(
    at::ScalarType::Half,
    at::ScalarType::BFloat16,
    workspace.scalar_type(),
    "_thnn_fused_lstm_cell_cuda_backward",
    [&] {
      if (canUse32BitIndexMath(workspace)) { // See Note [64-bit index math check elision]
        lstm_backward_impl<scalar_t, int32_t>(grad_hy, grad_cy, cx, cy, workspace, grad_gates, grad_cx);
      } else {
        lstm_backward_impl<scalar_t, int64_t>(grad_hy, grad_cy, cx, cy, workspace, grad_gates, grad_cx);
      }
  });

  auto grad_bias = has_bias ? grad_gates.sum(0, /*keepdim=*/false) : at::Tensor{};
  return std::make_tuple(std::move(grad_gates), std::move(grad_cx), std::move(grad_bias));
}

static constexpr int64_t GRU_WORKSPACE_MULTIPLIER = 5;

std::tuple<Tensor, Tensor> _thnn_fused_gru_cell_cuda(
      const Tensor& input_gates, const Tensor& hidden_gates,
      const Tensor& hx, const std::optional<Tensor>& input_bias_opt, const std::optional<Tensor>& hidden_bias_opt) {
  // See [Note: hacky wrapper removal for optional tensor]
  c10::MaybeOwned<Tensor> input_bias_maybe_owned = at::borrow_from_optional_tensor(input_bias_opt);
  const Tensor& input_bias = *input_bias_maybe_owned;
  const Tensor& hidden_bias = hidden_bias_opt.value_or(Tensor());

  checkSizes("_thnn_fused_gru_cell_cuda",
             {input_gates, "input_gates", 1}, {hidden_gates, "hidden_gates", 2},
             {input_bias, "input_bias", 3}, {hidden_bias, "hidden_bias", 4},
             /*factor=*/3, {hx, "prev_hidden", 5});

  auto workspace = at::empty({hx.size(0), hx.size(1) * GRU_WORKSPACE_MULTIPLIER}, hx.options());
  auto hy = at::empty_like(hx, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  AT_DISPATCH_FLOATING_TYPES_AND2(
    at::ScalarType::Half,
    at::ScalarType::BFloat16,
    input_gates.scalar_type(),
    "_thnn_fused_gru_cell_cuda",
    [&] {
      if (canUse32BitIndexMath(workspace)) { // See Note [64-bit index math check elision]
        gru_forward_impl<scalar_t, int32_t>(input_gates, hidden_gates, input_bias, hidden_bias, hx, hy, workspace);
      } else {
        gru_forward_impl<scalar_t, int64_t>(input_gates, hidden_gates, input_bias, hidden_bias, hx, hy, workspace);
      }
  });
  return std::make_tuple(std::move(hy), std::move(workspace));
}

void checkGRUBackwardSizes(const TensorArg& grad_hy, const TensorArg& workspace) {
  CheckedFrom c = "fused_gru_cell_backward";
  checkDim(c, grad_hy, 2);
  checkSize(c, workspace, {grad_hy->size(0), grad_hy->size(1) * GRU_WORKSPACE_MULTIPLIER});
}

std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor> _thnn_fused_gru_cell_backward_cuda(
      const Tensor& grad_hy, const Tensor& workspace, bool has_bias) {
  checkGRUBackwardSizes({grad_hy, "grad_hy", 1}, {workspace, "workspace", 2});

  int64_t hidden_size = workspace.size(1) / GRU_WORKSPACE_MULTIPLIER;
  auto grad_input_gates = at::empty({workspace.size(0), hidden_size * 3}, workspace.options());
  auto grad_hidden_gates = at::empty({workspace.size(0), hidden_size * 3}, workspace.options());
  auto grad_hx = at::empty_like(grad_hy, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  AT_DISPATCH_FLOATING_TYPES_AND2(
    at::ScalarType::Half,
    at::ScalarType::BFloat16,
    grad_hy.scalar_type(),
    "_thnn_fused_gru_cell_cuda_backward",
    [&] {
      if (canUse32BitIndexMath(workspace)) { // See Note [64-bit index math check elision]
        gru_backward_impl<scalar_t, int32_t>(grad_hy, workspace, grad_input_gates, grad_hidden_gates, grad_hx);
      } else {
        gru_backward_impl<scalar_t, int64_t>(grad_hy, workspace, grad_input_gates, grad_hidden_gates, grad_hx);
      }
  });

  at::Tensor grad_input_bias, grad_hidden_bias;
  if (has_bias) {
    grad_input_bias = grad_input_gates.sum(0, /*keepdim=*/false);
    grad_hidden_bias = grad_hidden_gates.sum(0, /*keepdim=*/false);
  }

  return std::make_tuple(
    std::move(grad_input_gates),
    std::move(grad_hidden_gates),
    std::move(grad_hx),
    std::move(grad_input_bias),
    std::move(grad_hidden_bias)
  );
}

} // namespace at::native
