RNN.cpp

#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/core/Tensor.h>
#include <ATen/Config.h>
#include <ATen/cuda/CUDAConfig.h>
#include <ATen/cuda/CUDAEvent.h>
#include <ATen/cuda/CUDAGraphsUtils.cuh>
#include <ATen/cuda/Exceptions.h>
#include <ATen/MatrixRef.h>
#include <ATen/native/RNN.h>
#include <ATen/TensorUtils.h>
#include <c10/util/accumulate.h>
#include <c10/util/Exception.h>
#include <c10/util/irange.h>

#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#include <ATen/NativeFunctions.h>
#else
#include <ATen/ops/_cudnn_init_dropout_state.h>
#include <ATen/ops/_cudnn_init_dropout_state_native.h>
#include <ATen/ops/_cudnn_rnn.h>
#include <ATen/ops/_cudnn_rnn_backward_native.h>
#include <ATen/ops/_cudnn_rnn_flatten_weight_native.h>
#include <ATen/ops/_cudnn_rnn_native.h>
#include <ATen/ops/empty.h>
#include <ATen/ops/zeros.h>
#include <ATen/ops/zeros_like.h>
#endif

#if !AT_CUDNN_ENABLED()

namespace at { namespace native {

// See Note [ATen preprocessor philosophy]

Tensor _cudnn_rnn_flatten_weight(
    TensorList weight_arr, int64_t weight_stride0,
    int64_t input_size,
    int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_proj_size,
    int64_t fn_num_layers, bool batch_first,
    bool fn_bidirectional
    ) {
  AT_ERROR("_cudnn_rnn_flatten_weight: ATen not compiled with cuDNN support");
}

std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor> _cudnn_rnn(
    const Tensor& input_r,
    TensorList weight, int64_t weight_stride0, const c10::optional<Tensor>& weight_buf_r_opt, const Tensor& hx, const c10::optional<Tensor>& cx_opt,
    int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_proj_size,
    int64_t fn_num_layers, bool batch_first, double fn_dropout,
    bool fn_train, bool fn_bidirectional, IntArrayRef fn_batch_sizes, const c10::optional<Tensor>& fn_dropout_state_opt
    ) {
  AT_ERROR("_cudnn_rnn: ATen not compiled with cuDNN support");
}

std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>> _cudnn_rnn_backward(
    const Tensor& input, TensorList weight, int64_t weight_stride0, const Tensor& weight_buf, const Tensor& hx, const c10::optional<Tensor>& cx_opt,
    const Tensor& output, const c10::optional<Tensor>& grad_output_r_opt, const c10::optional<Tensor>& grad_hy_r_opt, const c10::optional<Tensor>& grad_cy_r_opt,
    int64_t mode, int64_t hidden_size, int64_t proj_size,
    int64_t num_layers, bool batch_first, double dropout,
    bool train, bool bidirectional, IntArrayRef batch_sizes, const c10::optional<Tensor>& dropout_state_opt, const Tensor& reserve,
    std::array<bool, 4> output_mask
    ) {
  AT_ERROR("_cudnn_rnn_backward: ATen not compiled with cuDNN support");
}

Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed,
    c10::optional<ScalarType> dtype,
    c10::optional<Layout> layout,
    c10::optional<Device> device,
    c10::optional<bool> pin_memory) {
  // See [Note: hacky wrapper removal for TensorOptions]
  TensorOptions().dtype(dtype).layout(layout).device(device).pinned_memory(pin_memory);

  AT_ERROR("_cudnn_init_dropout_state: ATen not compiled with cuDNN support");
}

}} // namespace at::native

#else // AT_CUDNN_ENABLED()

#include <ATen/native/cudnn/RNNUtils.h>

namespace at { namespace native {

namespace {
  // DropoutDescriptor

  struct DropoutDescriptorParams {
    bool train;
    double dropout;
    Tensor dropout_state;
    DropoutDescriptorParams() = default;
    void set(bool train_, double dropout_, Tensor dropout_state_) {
      train = train_;
      dropout = dropout_;
      dropout_state = dropout_state_;
    }
    DropoutDescriptor descriptor(cudnnHandle_t handle) const {
      auto dropout_p = train ? dropout : 0;
      DropoutDescriptor dropout_desc;
      if (dropout_p == 0) {
        dropout_desc.set_no_dropout(handle);
      } else {
        dropout_desc.set(handle, dropout_p, dropout_state);
      }
      return dropout_desc;
    }
  };

  // RNNDescriptor

  struct RNNDescriptorParams {
    int64_t hidden_size;
    int64_t proj_size;
    int64_t num_layers;
    cudnnDirectionMode_t bidirectional;
    cudnnRNNMode_t mode;
    cudnnDataType_t datatype;
    cudnnDataType_t input_datatype;
    cudnnRNNAlgo_t algo = CUDNN_RNN_ALGO_STANDARD;
    cudnnRNNInputMode_t input_mode = CUDNN_LINEAR_INPUT;

    int64_t num_directions() const {
      return bidirectional ? 2 : 1;
    }

    void set_mode(int64_t fn_mode) {
      switch (fn_mode) {
        case CUDNN_RNN_RELU:
          mode = CUDNN_RNN_RELU;
          break;
        case CUDNN_RNN_TANH:
          mode = CUDNN_RNN_TANH;
          break;
        case CUDNN_LSTM:
          mode = CUDNN_LSTM;
          break;
        case CUDNN_GRU:
          mode = CUDNN_GRU;
          break;
        default:
        {
          std::ostringstream oss;
          oss << "unrecognized cuDNN RNN mode " << fn_mode;
          AT_ERROR(oss.str());
        }
      }
    }

    void set_bidirectional(bool fn_bidirectional) {
      bidirectional = fn_bidirectional ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL;
    }

    void set_algo(cudnnRNNAlgo_t algo){
      this->algo = algo;
    }

    void set(int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool bidirectional, cudnnDataType_t datatype, cudnnDataType_t input_datatype) {
      this->set_mode(mode);
      this->hidden_size = hidden_size;
      this->proj_size = proj_size;
      this->num_layers = num_layers;
      this->set_bidirectional(bidirectional);
      this->datatype = datatype;
      this->input_datatype = input_datatype;
    }

    RNNDescriptor descriptor(cudnnHandle_t handle, DropoutDescriptor&& dropout_desc) const {
      RNNDescriptor rnn_desc;
      rnn_desc.set(handle, hidden_size, proj_size, num_layers, std::move(dropout_desc), input_mode, bidirectional, mode, datatype, input_datatype, algo, at::globalContext().allowTF32CuDNN());
      return rnn_desc;
    }

    // In some cases, a use of RNNDescriptor does not rely on the
    // DropoutDescriptor.  In this case, we fake up a no-dropout
    // descriptor to make the RNN descriptor initialization go through.
    // This is used by _cudnn_rnn_flatten_weight, which needs an
    // RNNDescriptor for get_parameters(), but does not actually need
    // a fully initialized dropout descriptor.  This lets us avoid
    // having to pass the dropout state to flatten, which has no business
    // knowing what the dropout state is.
    RNNDescriptor descriptor(cudnnHandle_t handle) const {
      DropoutDescriptor dropout_desc;
      dropout_desc.set_no_dropout(handle);
      return descriptor(handle, std::move(dropout_desc));
    }
  };

  // TensorDescriptor list

  std::vector<TensorDescriptor> rnn_descriptor_sequence(const Tensor& tensor, IntArrayRef batch_sizes) {
    std::vector<TensorDescriptor> descriptors(batch_sizes.size());
    size_t i = 0;
    // To be mutated in the loop
    auto batch_tensor_size = tensor.sizes().vec();
    for (auto batch_size : batch_sizes) {
      batch_tensor_size[0] = batch_size;
      // NB: cuDNN RNN API does not support 2d descriptors, so we
      // must pad it out to 3d.
      descriptors[i].set(getCudnnDataType(tensor), batch_tensor_size, tensor.strides(), 3);
      i++;
    }
    return descriptors;
  }

  std::vector<TensorDescriptor> rnn_descriptor(const Tensor& tensor, int64_t N) {
    std::vector<TensorDescriptor> descriptors(N);
    for (const auto i : c10::irange(N)) {
      descriptors[i].set(tensor, 5);
    }
    return descriptors;
  }

  // The best way to understand the meaning of the values stored in
  // this struct is to consider each of the possible ways our
  // input can be structured.
  //
  // Suppose you want to run RNN on the following variable
  // length inputs:
  //
  //    Sequence 1: ABCD
  //    Sequence 2: EF
  //    Sequence 3: G
  //
  // (Let _ be padding when we have non-packed representations.)
  //
  // # Packed input (batch_sizes is non-empty)
  //
  //  input_size
  // +------+                    +
  // | A    |                    |
  // | E    | mini_batch =       |
  // | G    | batch_sizes[0] = 3 |
  // +------+                    |
  // | B    |                    | batch_sizes_sum = 7
  // | F    | batch_sizes[1] = 2 |
  // +------+                    |
  // | C    | batch_sizes[2] = 1 |
  // +------+                    |
  // | D    | batch_sizes[3] = 1 |
  // +------+                    +
  //
  //              (seq_length = 4)
  //
  //    input.size() = batch_sizes_sum x input_size
  //
  // # Unpacked input (batch_first = false)
  //
  //  mini_batch = 3
  // +-------+
  // | A E G |
  // | B F _ | seq_length = 4
  // | C _ _ |
  // | D _ _ |
  // +-------+
  //    ...    input_size
  // +-------+
  //
  //    input.size() = seq_length x mini_batch x input_size
  //
  // # Unpacked input (batch_first = true)
  //
  //  seq_length = 4
  // +---------+
  // | A B C D |
  // | E F _ _ | mini_batch = 3
  // | G _ _ _ |
  // +---------+
  //     ...     input_size
  // +---------+
  //
  //    input.size() = mini_batch x seq_length x input_size
  //
  struct TensorDescriptorListParams {
    IntArrayRef batch_sizes;
    int64_t seq_length;
    int64_t mini_batch;
    // NB: this is not input.size(), which is an IntArrayRef; instead, this
    // size of the inner-most dimension.  In NL applications, this is usually
    // the size of the embedding.  You can also think of this as the size
    // of the "channel" dimension (at risk of confusing vision researchers :)
    int64_t input_size;
    // Only valid when !is_input_packed
    int64_t batch_sizes_sum; // == sum(batch_sizes)

    bool is_input_packed() const {
      return batch_sizes.size() != 0;
    }

    void set(IntArrayRef input_sizes, IntArrayRef batch_sizes_, bool batch_first) {
      batch_sizes = batch_sizes_;
      if (is_input_packed()) {
        seq_length = batch_sizes.size();
        mini_batch = batch_sizes[0];
        // NB: When input is packed, the mini_batch size is NOT the size
        // of the outer dimension
        batch_sizes_sum = input_sizes[0];
        input_size = input_sizes[1];
      } else {
        if (batch_first) {
          seq_length = input_sizes[1];
          mini_batch = input_sizes[0];
        } else {
          seq_length = input_sizes[0];
          mini_batch = input_sizes[1];
        }
        input_size = input_sizes[2];
        // TODO: Actually, would this make ASAN's job harder catching
        // an uninitialized access?
        batch_sizes_sum = -1; // something bogus in case we access it
      }
    }

    // TODO: check x for consistency with input_size?
    std::vector<TensorDescriptor> descriptors(Tensor x) const {
      auto is_input_packed = batch_sizes.size() != 0;
      if (is_input_packed) {
        return rnn_descriptor_sequence(x, batch_sizes);
      } else {
        return rnn_descriptor(x[0], seq_length);
      }
    }
  };

  // Everything together

  struct RNNParams {
    DropoutDescriptorParams dropout;
    RNNDescriptorParams rnn;
    TensorDescriptorListParams tensors;
  };

  // NB: Doesn't include the weight descriptor
  struct RNNDescriptors {
    RNNDescriptor rnn_desc;
    // NB: this won't actually lay out the tensor descriptor pointers
    // in the right way, so you'll have to preprocess them
    std::vector<TensorDescriptor> x_descs;
    std::vector<TensorDescriptor> y_descs;
    TensorDescriptor hx_desc;
    TensorDescriptor hy_desc;
    TensorDescriptor cx_desc;
    TensorDescriptor cy_desc;

    RNNDescriptors(const RNNParams& fn, cudnnHandle_t handle, Tensor x, Tensor y, Tensor hx, Tensor cx) {
      rnn_desc = fn.rnn.descriptor(handle, fn.dropout.descriptor(handle));
      x_descs = fn.tensors.descriptors(x);
      y_descs = fn.tensors.descriptors(y);
      hx_desc.set(hx, 5);
      hy_desc.set(hx, 5);
      if (cx.defined()) {
        cx_desc.set(cx, 5);
        cy_desc.set(cx, 5);
      }
    }

    // TODO: This is annoying, having to put the cudnnTensorDescriptor_t
    // in a contiguous array...
    std::vector<cudnnTensorDescriptor_t> get_descs(const std::vector<TensorDescriptor>& descs) {
      std::vector<cudnnTensorDescriptor_t> r;
      r.reserve(descs.size());
      for (auto& desc : descs) {
        r.emplace_back(desc.desc());
      }
      return r;
    }

    std::vector<cudnnTensorDescriptor_t> get_x_descs() {
      return get_descs(x_descs);
    }

    std::vector<cudnnTensorDescriptor_t> get_y_descs() {
      return get_descs(y_descs);
    }
  };

  int64_t get_num_weights(cudnnHandle_t handle, const RNNDescriptor& rnn_desc,
                          const TensorDescriptor& x_desc, cudnnDataType_t datatype) {
    size_t weight_size;
    AT_CUDNN_CHECK(cudnnGetRNNParamsSize(handle, rnn_desc.desc(), x_desc.desc(), &weight_size, datatype));
    auto elem_size = dataSize(datatype);
    TORCH_INTERNAL_ASSERT(weight_size % elem_size == 0, "cudnnGetRNNParamsSize returned nonsensical weight_size");
    return weight_size / elem_size;
  }

  int64_t _num_linear_layers(cudnnRNNMode_t mode) {
    switch(mode) {
      case CUDNN_LSTM:
        return 8;
      case CUDNN_GRU:
        return 6;
      case CUDNN_RNN_RELU:
        return 2;
      case CUDNN_RNN_TANH:
        return 2;
      default:
        AT_ERROR("unknown cuDNN RNN mode ", mode);
    }
  }

  void add_projection_weights(
        cudnnHandle_t handle,
        const RNNDescriptor& rnn_desc,
        const TensorDescriptor& x_desc,
        const FilterDescriptor& w_desc,
        const Tensor& weight_buf,
        int64_t layer,
        std::vector<Tensor>& params
  ) {
    void* matrix_pointer = nullptr;
    // assuming it's LSTM which has 8 "linear layers" (i.e. 4 weights and 4 biases)
    int64_t linear_id = 8;
    FilterDescriptor lin_layer_mat_desc;
    AT_CUDNN_CHECK(cudnnGetRNNLinLayerMatrixParams(
        /*handle=*/handle,
        /*rnnDesc=*/rnn_desc.desc(),
        /*layer=*/layer,
        /*xDesc=*/x_desc.desc(),
        /*wDesc=*/w_desc.desc(),
        /*w=*/weight_buf.data_ptr(),
        /*linLayerID=*/linear_id,
        /*linLayerMatDesc=*/lin_layer_mat_desc.mut_desc(),
        /*linLayerMat=*/&matrix_pointer));

    cudnnDataType_t data_type;
    cudnnTensorFormat_t format;
    int nb_dims;
    constexpr int min_dim = 3;
    int filter_dim_a[min_dim];
    AT_CUDNN_CHECK(cudnnGetFilterNdDescriptor(
          lin_layer_mat_desc.desc(),
          min_dim,
          &data_type,
          &format,
          &nb_dims,
          filter_dim_a
          ));

    TORCH_INTERNAL_ASSERT(nb_dims <= min_dim, "nb_dims = ", nb_dims, "; min_dim  = ", min_dim);
    auto elem_size = dataSize(getCudnnDataType(weight_buf));
    auto offset_bytes = (char*)matrix_pointer - (char*)weight_buf.data_ptr();
    TORCH_INTERNAL_ASSERT(offset_bytes % elem_size == 0, "offset_bytes = ", offset_bytes, "; elem_size = ", elem_size);
    size_t offset = offset_bytes / elem_size;

    int mat_numel = c10::multiply_integers(filter_dim_a, filter_dim_a + nb_dims);
    // Generate a new parameter tensor which is a view into the weight_buf.
    std::initializer_list<int64_t> size = {mat_numel, 1};
    Tensor param = at::empty({0}, weight_buf.options()).set_(weight_buf.storage(), offset, size);
    params.emplace_back(std::move(param));
  }


  /*
    Returns weight and bias tensors for each layer of the RNN. These tensors
    are views on the underlying weight buffer allocated by CuDNN.

    Note: for LSTM and GRU, which have multiple parameters of each type (4 and 3, respectively),
          these parameters are concatenated along the first dimension.
          These parameters are returned in a consistent order by CuDNN:
              (reset, forget, cell, output) for LSTM
              (reset, input, new) for GRU
    Args:
        fn: The RNN function object holding the RNN state
        handle: a CuDNN handle
        weight_buf: a 1D tensor containing the CuDNN-allocated weight (or grad_weight) buffer
    Returns:
        parameters: [(weight_ih, weight_hh, bias_ih, bias_hh)*], with length equal to the num_layers.
            This is represented as a pair of vector, and outer-dimension stride
            (NB: Can't return MatrixRef because we need to allocate the underlying tensor)
  */
  std::pair<std::vector<Tensor>, size_t> // stride0
  get_parameters(
      cudnnHandle_t handle,
      const RNNDescriptorParams& rnn,
      const RNNDescriptor& rnn_desc,
      const TensorDescriptor& x_desc,
      const FilterDescriptor& w_desc,
      const Tensor& weight_buf,
      bool include_bias=true
  ) {
    auto cudnn_methods = { cudnnGetRNNLinLayerMatrixParams, cudnnGetRNNLinLayerBiasParams };
    std::vector<Tensor> params;
    int64_t num_linear_layers = _num_linear_layers(rnn.mode);
    int64_t num_layers = rnn.num_directions() * rnn.num_layers;
    size_t cur_offset = 0;
    size_t global_layer_params_count = 0;
    for (const auto layer : c10::irange(num_layers)) {
      size_t layer_params_count = 0;
      for (auto cudnn_method : cudnn_methods) {
        for (const auto linear_id : c10::irange(num_linear_layers)) {
          FilterDescriptor lin_layer_mat_desc;
          void* matrix_pointer;
          AT_CUDNN_CHECK(cudnn_method(
                handle,
                rnn_desc.desc(),
                layer,
                x_desc.desc(),
                w_desc.desc(),
                weight_buf.data_ptr(),
                linear_id,
                lin_layer_mat_desc.mut_desc(),
                &matrix_pointer
                ));
          cudnnDataType_t data_type;
          cudnnTensorFormat_t format;
          int nb_dims;
          constexpr int min_dim = 3;
          int filter_dim_a[min_dim];
          AT_CUDNN_CHECK(cudnnGetFilterNdDescriptor(
                lin_layer_mat_desc.desc(),
                min_dim,
                &data_type,
                &format,
                &nb_dims,
                filter_dim_a
                ));

          TORCH_INTERNAL_ASSERT(nb_dims <= min_dim, "nb_dims = ", nb_dims, "; min_dim  = ", min_dim);
          auto elem_size = dataSize(getCudnnDataType(weight_buf));
          auto offset_bytes = (char*)matrix_pointer - (char*)weight_buf.data_ptr();
          TORCH_INTERNAL_ASSERT(offset_bytes % elem_size == 0, "offset_bytes = ", offset_bytes, "; elem_size = ", elem_size);
          size_t offset = offset_bytes / elem_size;

          // for all the RNN types provided by CUDNN, all the ih weights
          // are the same size and are allocated in a contiguous chunk
          // (same for the hh weights, and the ih and hh biases).
          // Since we're storing all the weights in a single tensor anyway,
          // might as well merge the CUDNN ones into a single tensor as well
          int mat_numel = c10::multiply_integers(filter_dim_a, filter_dim_a + nb_dims);
          if (linear_id == 0 || linear_id == num_linear_layers / 2) {
            // We could also exclude bias params by restricting cudnn_methods to just { cudnnGetRNNLinLayerMatrixParams }
            // at the very top.  However, to do so would throw off the cur_offset account, which is currently a strict
            // and informative check that all params are laid out the way we think they are.  If include_bias is false,
            // I'd rather keep full cur_offset checks rather than save some CPU overhead by skipping the cudnn_method =
            // cudnnGetRNNLinLayerBiasParams iteration.
            if (include_bias || cudnn_method != cudnnGetRNNLinLayerBiasParams) {
              // Generate a new parameter tensor which is a view into the weight_buf.
              std::initializer_list<int64_t> size = {
                mat_numel * num_linear_layers / 2, 1};
              Tensor param = at::empty({0}, weight_buf.options()).set_(weight_buf.storage(), offset, size);
              params.emplace_back(std::move(param));
              layer_params_count++;
            }
          } else {
            TORCH_INTERNAL_ASSERT(cur_offset == offset, "cur_offset = ", cur_offset, "; offset = ", offset);
          }
          cur_offset = offset + mat_numel;
        }
      } // for cudnn_method
      if (rnn.proj_size != 0) {
        add_projection_weights(handle, rnn_desc, x_desc, w_desc, weight_buf, layer, params);
        layer_params_count++;
      }

      if (layer == 0) {
        global_layer_params_count = layer_params_count;
      } else {
        TORCH_INTERNAL_ASSERT(global_layer_params_count == layer_params_count,
                   "global_layer_params_count = ", global_layer_params_count,
                   "; layer_params_count = ", layer_params_count);
      }
    } // for layer
    return std::make_pair(params, global_layer_params_count);
  }

  // This is a lightweight version of the method above used to quickly get the expected
  // parameter offsets.
  std::vector<void*> get_expected_data_ptrs(
        const Tensor& weight_buf, cudnnHandle_t handle, const RNNDescriptorParams& rnn,
        const RNNDescriptor& rnn_desc, const TensorDescriptor& x_desc, cudnnDataType_t datatype) {
    FilterDescriptor w_desc;
    w_desc.set(weight_buf, 3);

    int64_t num_linear_layers = _num_linear_layers(rnn.mode);
    int64_t num_dir_layers = rnn.num_directions() * rnn.num_layers;
    const auto cudnn_methods = { cudnnGetRNNLinLayerMatrixParams, cudnnGetRNNLinLayerBiasParams };
    std::vector<void*> data_ptrs;
    if (rnn.proj_size != 0) {
      data_ptrs.reserve(num_dir_layers * (2 * 2 + 1));
    } else {
      data_ptrs.reserve(num_dir_layers * 2 * 2);
    }
    for (const auto layer : c10::irange(num_dir_layers)) {
      for (auto cudnn_method : cudnn_methods) {
        // This API returns a separate pointer for weight of every gate,
        // but we represent them as a single tensor, so we're only interested
        // in a very limited subset of possible values.
        const std::array<int64_t, 2> linear_offsets = { 0, num_linear_layers / 2 };
        for (int64_t linear_id : linear_offsets) {
          FilterDescriptor lin_layer_mat_desc;
          void* matrix_pointer;
          AT_CUDNN_CHECK(cudnn_method(
                handle,
                rnn_desc.desc(),
                layer,
                x_desc.desc(),
                w_desc.desc(),
                weight_buf.data_ptr(),
                linear_id,
                lin_layer_mat_desc.mut_desc(),
                &matrix_pointer
                ));
          data_ptrs.push_back(matrix_pointer);
        }
      }
      if (rnn.proj_size != 0) {
        // assuming it's LSTM which has 8 "linear layers" (i.e. 4 weights and 4 biases)
        int64_t linear_id = 8;
        FilterDescriptor lin_layer_mat_desc;
        void* matrix_pointer;
        AT_CUDNN_CHECK(cudnnGetRNNLinLayerMatrixParams(
              handle,
              rnn_desc.desc(),
              layer,
              x_desc.desc(),
              w_desc.desc(),
              weight_buf.data_ptr(),
              linear_id,
              lin_layer_mat_desc.mut_desc(),
              &matrix_pointer
              ));
        data_ptrs.push_back(matrix_pointer);
      }
    }
    return data_ptrs;
  }

  void _viewOrCopyOneParam(const Tensor& param_from, const Tensor& param_to,
                          bool copy, bool allow_type_change=false) {
    // if copying, allow_type_change may be true or false.
    // if viewing, allow_type_change must be false.
    TORCH_INTERNAL_ASSERT(copy || !allow_type_change,
                          "if viewing, type change is not allowed.");
    TORCH_INTERNAL_ASSERT(allow_type_change || (param_from.scalar_type() == param_to.scalar_type()),
                          "parameter types mismatch");
    if (copy) {
        param_to.copy_(param_from.view_as(param_to));
    } else {
        param_from.resize_as_(param_to);
    }
  }

  void _viewOrCopyParams(MatrixRef<Tensor> params_from, MatrixRef<Tensor> params_to,
                         bool copy, bool allow_type_change=false) {
    TORCH_INTERNAL_ASSERT(params_from.size(0) == params_to.size(0), "number of layers mismatch");
    for (const auto i : c10::irange(params_from.size(0))) {
      auto layer_params_from = params_from[i];
      auto layer_params_to = params_to[i];
      // NOTE: these lists have all weights before all biases, so if the layer
      // doesn't use biases, iteration will terminate once layer_params_from ends
      // and ignore them.

      // NOTE: there is an exception from the above statement. If LSTMs with projections
      // are used, weights layout will be w_ih, w_hh, b_ih, b_hh, w_hr. So need to handle no-bias
      // case specially, because will need to copy 0->0, 1->1, 2->4. This case can be uniquely
      // identified by checking if number of defined parameters for each layer is 3.
      if (layer_params_from.size() == 3 && layer_params_to.size() != 3) {
        _viewOrCopyOneParam(layer_params_from[0], layer_params_to[0], copy, allow_type_change);
        _viewOrCopyOneParam(layer_params_from[1], layer_params_to[1], copy, allow_type_change);
        _viewOrCopyOneParam(layer_params_from[2], layer_params_to[4], copy, allow_type_change);
        continue;
      }
      if (layer_params_to.size() == 3 && layer_params_from.size() != 3) {
        _viewOrCopyOneParam(layer_params_from[0], layer_params_to[0], copy, allow_type_change);
        _viewOrCopyOneParam(layer_params_from[1], layer_params_to[1], copy, allow_type_change);
        _viewOrCopyOneParam(layer_params_from[4], layer_params_to[2], copy, allow_type_change);
        continue;
      }
      for (auto a = layer_params_from.begin(), b = layer_params_to.begin();
            a != layer_params_from.end() && b != layer_params_to.end();
            ++a, ++b) {
        _viewOrCopyOneParam(*a, *b, copy, allow_type_change);
      }
    }
  }

  void _copyParams(MatrixRef<Tensor> params_from, MatrixRef<Tensor> params_to) {
    _viewOrCopyParams(params_from, params_to, true);
  }

  void _viewParams(MatrixRef<Tensor> params_from, MatrixRef<Tensor> params_to) {
    _viewOrCopyParams(params_from, params_to, false);
  }


  std::vector<int64_t> _input_size(const TensorDescriptorListParams& tensors) {
    if (tensors.is_input_packed()) {
      return {tensors.batch_sizes_sum, tensors.input_size};
    } else {
      return {tensors.seq_length, tensors.mini_batch, tensors.input_size};
    }
  }

  std::vector<int64_t> _hidden_size(const RNNDescriptorParams& rnn, const TensorDescriptorListParams& tensors) {
    if (rnn.proj_size != 0) {
      return {rnn.num_layers * rnn.num_directions(), tensors.mini_batch, rnn.proj_size};
    } else {
      return {rnn.num_layers * rnn.num_directions(), tensors.mini_batch, rnn.hidden_size};
    }
  }

  std::vector<int64_t> _cell_size(const RNNDescriptorParams& rnn, const TensorDescriptorListParams& tensors) {
    return {rnn.num_layers * rnn.num_directions(), tensors.mini_batch, rnn.hidden_size};
  }

  std::vector<int64_t> _output_size(const RNNDescriptorParams& rnn, const TensorDescriptorListParams& tensors) {
    auto out_size = rnn.hidden_size;
    if (rnn.proj_size != 0) {
      out_size = rnn.proj_size;
    }
    if (tensors.is_input_packed()) {
      return {tensors.batch_sizes_sum, out_size * rnn.num_directions()};
    } else {
      return {tensors.seq_length, tensors.mini_batch, out_size * rnn.num_directions()};
    }
  }

  inline bool use_persist_common_heuristics(const RNNDescriptorParams& rnn,
                                            const TensorDescriptorListParams& tensors) {
    return rnn.num_layers == 1 &&
           rnn.hidden_size <= 1024 &&
           rnn.num_directions() == 1 &&
           rnn.hidden_size % 128 == 0 &&
           tensors.input_size % 128 == 0;
  }

  inline bool use_persist_device_heuristics(const RNNDescriptorParams& rnn,
                                            const TensorDescriptorListParams& tensors) {
    auto bsize = tensors.mini_batch;
    cudaDeviceProp* prop = at::cuda::getCurrentDeviceProperties();
    if (prop->major == 7) {
      if (prop->minor == 5) {
        // Excludes Turing from using persistent rnn.
        return false;
      } else {
        // technically, batch size should be multiple of 8, but there are quite a few multiple-of-8 batchsizes that give bad perf,
        // weed them out
        return ((bsize % 16 == 0 && bsize != 80 && bsize !=112) || bsize == 8) &&
               ((tensors.seq_length >=40 && bsize <=128) ||
                (tensors.seq_length >=20 && bsize <=96) ||
                (tensors.seq_length >=10 && bsize <=32));
      }
    } else if (prop->major >= 8 && prop->multiProcessorCount >= 98) {
      // SM count check excludes A30 (similar issue to A40)
      if (prop->minor == 6) {
        // Excludes sm_86 GPU devices from using persistent rnn.
        // This is because there are some edge cases that will throw exceptions with cudnn 8.0.5 on Nvidia A40 GPU.
        return false;
      }
      // Based on tests by Vasily Volkov and xwang233.  Vasily only tried bsize <= 128,
      // so conservatively enable persistence for bsize <= 128 only.
      // TODO:  Run more tests for bsize > 128.
      if (rnn.mode == CUDNN_GRU) {
        // Persistent GRU performance is flakier than other RNN types.  Exclude them for now.
        // TODO:  Write a more refined GRU heuristic.
        return false;
      } else if (rnn.mode == CUDNN_LSTM) {
        // Persistent LSTMs are comparable to or better than non-persistent for bsize <= 128.
        return (bsize % 8 == 0) && (bsize <= 128);
      } else {
        // Persistent RNN_RELU and TANH show poor performance when bsize >= 96 AND hidden size >= 896.
        return (bsize % 8 == 0) && (bsize <= 128) && (bsize < 96 || rnn.hidden_size < 896);
      }
    } else {
      return false;
    }
  }

  inline bool use_rnn_persist_small_h(const RNNDescriptorParams& rnn,
                                            const TensorDescriptorListParams& tensors,
                                            bool forward) {
#if CUDNN_VERSION >= 8201 // 8.2.1
    cudaDeviceProp* prop = at::cuda::getCurrentDeviceProperties();
    if (prop->major < 6) return false;

    if (forward) {
      if (rnn.mode == CUDNN_RNN_RELU || rnn.mode == CUDNN_RNN_TANH) {
        return rnn.hidden_size <= 384;
      }
      if (rnn.mode == CUDNN_LSTM || rnn.mode == CUDNN_GRU) {
        return rnn.hidden_size <= 192;
      }
    } else /* backward */ {
      if (rnn.mode == CUDNN_RNN_RELU || rnn.mode == CUDNN_RNN_TANH) {
        return rnn.hidden_size <= 256;
      }
      if (rnn.mode == CUDNN_LSTM || rnn.mode == CUDNN_GRU) {
        return rnn.hidden_size <= 128;
      }
    }

    return false;
#else
    return false;
#endif
  }

  cudnnRNNAlgo_t get_algo(const RNNDescriptorParams& rnn, const TensorDescriptorListParams& tensors, const Tensor input, bool forward) {
    // LSTM with projections only works with standard algorithm
    if (rnn.proj_size != 0) {
      return CUDNN_RNN_ALGO_STANDARD;
    }

    // Persistent algos typically don't work for packed inputs with sequence lengths that vary
    // across batch elements, and will return CUDNN_STATUS_NOT_SUPPORTED if attempted. See
    // https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html#features-of-rnn-functions
    if (!tensors.is_input_packed()) {
      auto cudnnDataType = getCudnnDataType(input);
#if CUDNN_VERSION >= 8201 // 8.2.1
      if (cudnnDataType != CUDNN_DATA_DOUBLE) {
        if (use_rnn_persist_small_h(rnn, tensors, forward)) {
          return CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H;
        }
      }
#endif
      if (cudnnDataType == CUDNN_DATA_HALF) {
        if (use_persist_common_heuristics(rnn, tensors) &&
            use_persist_device_heuristics(rnn, tensors)) {
          return CUDNN_RNN_ALGO_PERSIST_STATIC;
        }
      }
    }

    return CUDNN_RNN_ALGO_STANDARD;
  }

  cudnnDataType_t promote_rnn_math_type(cudnnDataType_t dtype) {
    if (dtype == CUDNN_DATA_HALF) {
      return CUDNN_DATA_FLOAT;
    }
    return dtype;
  }

} // anonymous namespace

// Utilities exposed in RNNUtils.h
namespace cudnn_rnn {

TORCH_CUDA_CPP_API std::tuple<Tensor, std::vector<Tensor>>
copy_weights_to_flat_buf_views(
    TensorList weight_arr,
    int64_t weight_stride0,
    int64_t input_size,
    int64_t mode,
    int64_t hidden_size,
    int64_t proj_size,
    int64_t num_layers,
    bool batch_first,
    bool bidirectional,
    const cudnnDataType_t flat_buf_datatype,
    const TensorOptions& flat_buf_options,
    bool set_orig_weights_to_flat_buf,
    bool allow_type_change /*=false*/,
    bool include_bias /*=true*/) {
  // flat_buf_datatype is accepted as a separate argument (rather than extracted
  // from flat_buf_options) because to extract flat_buf_datatype from
  // flat_buf_options, we'd need to say auto flat_buf_datatype =
  // getCudnnDataTypeFromScalarType(typeMetaToScalarType(options.dtype()));
  // typeMetaToScalarType is a surprisingly nontrivial function.  We should
  // avoid it if we can.
  TORCH_CHECK(
      weight_arr.size() > 0,
      "copy_weights_to_flat_buf_views: cannot flatten empty weight list");

  RNNDescriptorParams rnn;
  rnn.set(
      mode,
      hidden_size,
      proj_size,
      num_layers,
      bidirectional,
      promote_rnn_math_type(flat_buf_datatype),
      flat_buf_datatype);

  auto handle = getCudnnHandle();
  RNNDescriptor rnn_desc = rnn.descriptor(handle);

  TensorGeometry x_geom({1, input_size});
  TensorDescriptor x_desc;
  // Why do we pad to 5 dims here (and elsewhere)?
  // https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html#cudnnRNNForwardTraining
  // expects descriptors padded to 3 dimensions.
  x_desc.set(flat_buf_datatype, x_geom.sizes(), x_geom.strides(), 5);

  auto num_weights =
      get_num_weights(handle, rnn_desc, x_desc, flat_buf_datatype);
  auto weight_buf = at::zeros(num_weights, flat_buf_options);

  FilterDescriptor w_desc;
  w_desc.set(weight_buf, 3);

  // Slice off views into weight_buf
  std::vector<Tensor> params_arr;
  size_t params_stride0;
  std::tie(params_arr, params_stride0) = get_parameters(
      handle, rnn, rnn_desc, x_desc, w_desc, weight_buf, include_bias);
  MatrixRef<Tensor> weight{weight_arr, static_cast<size_t>(weight_stride0)},
      params{params_arr, params_stride0};

  // Copy weights
  _viewOrCopyParams(weight, params, /*copy=*/true, allow_type_change);
  if (set_orig_weights_to_flat_buf) {
    // Update the storage
    for (const auto i : c10::irange(weight.size(0))) {
      // There is a special case for LSTM with projections and no bias,
      // where weight copy is done in 0->0, 1->1, 2->4 layout
      if (weight[i].size() == 3 && params[i].size() == 5) {
        weight[i][0].set_(params[i][0].view_as(weight[i][0]));
        weight[i][1].set_(params[i][1].view_as(weight[i][1]));
        weight[i][2].set_(params[i][4].view_as(weight[i][2]));
      } else {
        for (auto orig_param_it = weight[i].begin(),
                  new_param_it = params[i].begin();
             orig_param_it != weight[i].end() &&
             new_param_it != params[i].end();
             orig_param_it++, new_param_it++) {
          auto orig_param = *orig_param_it, new_param = *new_param_it;
          orig_param.set_(new_param.view_as(orig_param));
        }
      }
    }
  }

  return std::make_tuple(weight_buf, params_arr);
}

} // namespace cudnn_rnn

using namespace cudnn_rnn;

// NB: does inplace update into TensorList
// It would be a relatively simple matter to refactor this into multiple
// functions, only one of which does an inplace update, but we leave this
// for future work
Tensor _cudnn_rnn_flatten_weight(
    TensorList weight_arr, int64_t weight_stride0,
    int64_t input_size,
    int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_proj_size,
    int64_t fn_num_layers, bool batch_first,
    bool fn_bidirectional
    ) {
  // returns flat weight_buf
  return std::get<0>(copy_weights_to_flat_buf_views(
      weight_arr,
      weight_stride0,
      input_size,
      fn_mode,
      fn_hidden_size,
      fn_proj_size,
      fn_num_layers,
      batch_first,
      fn_bidirectional,
      /*flat_buf_datatype=*/getCudnnDataType(weight_arr[0]),
      /*flat_buf_options=*/weight_arr[0].options(),
      /*set_orig_weights_to_flat_buf=*/true));
}

const char * WEIGHT_FORMAT_WARN = "RNN module weights are not part of single contiguous "
                                  "chunk of memory. This means they need to be compacted "
                                  "at every call, possibly greatly increasing memory usage. "
                                  "To compact weights again call flatten_parameters().";

// NB: when fn_batch_sizes is empty, that means no batch sizes was specified
std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor> _cudnn_rnn(
    const Tensor& input_r,
    TensorList weight, int64_t weight_stride0, const c10::optional<Tensor>& weight_buf_r_opt, const Tensor& hx, const c10::optional<Tensor>& cx_opt,
    int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_proj_size,
    int64_t fn_num_layers, bool batch_first, double fn_dropout,
    bool fn_train, bool fn_bidirectional, IntArrayRef fn_batch_sizes, const c10::optional<Tensor>& fn_dropout_state_opt
    ) {
  // See [Note: hacky wrapper removal for optional tensor]
  c10::MaybeOwned<Tensor> weight_buf_r_maybe_owned = at::borrow_from_optional_tensor(weight_buf_r_opt);
  const Tensor& weight_buf_r = *weight_buf_r_maybe_owned;
  const Tensor& cx = c10::value_or_else(cx_opt, [] {return Tensor();});
  const Tensor& fn_dropout_state = c10::value_or_else(fn_dropout_state_opt, [] {return Tensor();});

  check_attributes(input_r, weight, {hx, cx}, /*check_dtype=*/true);
  auto input = input_r;
  auto weight_buf = weight_buf_r;
  if (!weight_buf.defined()) {
    TORCH_WARN(WEIGHT_FORMAT_WARN);
  }
  if (fn_dropout_state.defined()) {
      auto input_arg = TensorArg(input, "input", 1);
      auto dropout_state_arg = TensorArg(fn_dropout_state, "dropout_states", 15);
      checkSameGPU("cudnn_rnn", input_arg, dropout_state_arg);
  }
  RNNParams fn;
  auto datatype = getCudnnDataType(input);
  fn.rnn.set(fn_mode, fn_hidden_size, fn_proj_size, fn_num_layers, fn_bidirectional, promote_rnn_math_type(datatype), datatype);
  fn.dropout.set(fn_train, fn_dropout, fn_dropout_state);
  fn.tensors.set(input.sizes(), fn_batch_sizes, batch_first);

  // TODO: Set device to input

  if (fn.rnn.mode != CUDNN_LSTM) {
    TORCH_CHECK(!cx.defined(),
             "rnn: illegal defined cx for non-LSTM RNN");
  }

  // TODO: can batch_first be a wrapper around this function?
  auto is_input_packed = fn.tensors.batch_sizes.size() != 0;
  if (batch_first && !is_input_packed) {
    input = input.transpose(0, 1);
  }

  auto hidden_size = _hidden_size(fn.rnn, fn.tensors);
  auto cell_size = _cell_size(fn.rnn, fn.tensors);
  auto output_size = _output_size(fn.rnn, fn.tensors);

  TORCH_CHECK(hx.is_contiguous(),
           "rnn: hx is not contiguous");
  TORCH_CHECK(!cx.defined() || cx.is_contiguous(),
           "rnn: cx is not contiguous");

  auto x = input.contiguous();
  auto output = at::empty(output_size, input.options());
  auto hy = at::empty(hidden_size, hx.options());
  Tensor cy;
  if (cx.defined()) {
    cy = at::empty(cell_size, cx.options());
  } else {
    cy = at::empty({0}, hx.options()); // NB: Not allowed to return undefined tensors
  }
  auto y = output;

  auto handle = getCudnnHandle();
  cudnnRNNAlgo_t algo = get_algo(fn.rnn, fn.tensors, input, true);
  fn.rnn.set_algo(algo);
  RNNDescriptors descs(fn, handle, x, y, hx, cx);

  FilterDescriptor w_desc;
  if (!weight_buf.defined()) {
    auto num_weights = get_num_weights(handle, descs.rnn_desc, descs.x_descs[0], datatype);
    weight_buf = at::empty(num_weights, x.options());
    w_desc.set(weight_buf, 3);
    weight_buf.zero_();
    std::vector<Tensor> params;
    size_t params_stride0;
    std::tie(params, params_stride0) = get_parameters(handle, fn.rnn, descs.rnn_desc, descs.x_descs[0], w_desc, weight_buf);
    _copyParams(MatrixRef<Tensor>{weight, static_cast<size_t>(weight_stride0)},
                MatrixRef<Tensor>{params, params_stride0});
  } else {
    w_desc.set(weight_buf, 3);
  }

  TORCH_CHECK(!cx.defined() || cx.sizes().equals(cell_size),
          "Expected cell size ", IntArrayRef{cell_size}, ", got ", cx.sizes());
  size_t workspace_size;
  auto x_descs_arr = descs.get_x_descs();
  auto y_descs_arr = descs.get_y_descs();
  AT_CUDNN_CHECK(cudnnGetRNNWorkspaceSize(
        handle,
        descs.rnn_desc.desc(),
        fn.tensors.seq_length,
        x_descs_arr.data(),
        &workspace_size
        ));
  Tensor workspace = at::empty(workspace_size, input.options().dtype(kByte));
  Tensor reserve;
  // NB: Previously, the test was for fn.requires_grad, but we don't have
  // this information.  Use 'train' as a proxy.
  if (fn_train) {
    size_t reserve_size;
    AT_CUDNN_CHECK(cudnnGetRNNTrainingReserveSize(
          handle,
          descs.rnn_desc.desc(),
          fn.tensors.seq_length,
          x_descs_arr.data(),
          &reserve_size
          ));
    reserve = at::empty(reserve_size, input.options().dtype(kByte));
    AT_CUDNN_CHECK(cudnnRNNForwardTraining(
          handle,
          descs.rnn_desc.desc(),
          fn.tensors.seq_length,
          x_descs_arr.data(), x.data_ptr(),
          descs.hx_desc.desc(), hx.data_ptr(),
          descs.cx_desc.desc(), cx.defined() ? cx.data_ptr() : nullptr,
          w_desc.desc(), weight_buf.data_ptr(),
          y_descs_arr.data(), y.data_ptr(),
          descs.hy_desc.desc(), hy.data_ptr(),
          descs.cy_desc.desc(), cy.defined() ? cy.data_ptr() : nullptr,
          workspace.data_ptr(), workspace.size(0),
          reserve.mutable_data_ptr(), reserve.size(0)
          ));
  } else { // inference
    reserve = at::empty({0}, input.options().dtype(kByte));
    AT_CUDNN_CHECK(cudnnRNNForwardInference(
          handle,
          descs.rnn_desc.desc(),
          fn.tensors.seq_length,
          x_descs_arr.data(), x.data_ptr(),
          descs.hx_desc.desc(), hx.data_ptr(),
          descs.cx_desc.desc(), cx.defined() ? cx.data_ptr() : nullptr,
          w_desc.desc(), weight_buf.data_ptr(),
          y_descs_arr.data(), y.data_ptr(),
          descs.hy_desc.desc(), hy.data_ptr(),
          descs.cy_desc.desc(), cy.defined() ? cy.data_ptr() : nullptr,
          workspace.data_ptr(), workspace.size(0)
          ));

  }

  if (batch_first && !is_input_packed) {
    output.transpose_(0, 1);
  }

  return std::make_tuple(output, hy, cy, reserve, weight_buf);
}

std::tuple<Tensor, Tensor, Tensor> _cudnn_rnn_backward_input(
    const Tensor& input_r, const Tensor& weight_buf, const Tensor& hx, const Tensor& cx,
    const Tensor& output_r, const Tensor& grad_output_r, const Tensor& grad_hy,
    const Tensor& grad_cy,
    int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_proj_size,
    int64_t fn_num_layers, bool batch_first, double fn_dropout,
    bool fn_train, bool fn_bidirectional, IntArrayRef fn_batch_sizes,
    const Tensor& fn_dropout_state, const Tensor& fn_reserve,
    std::array<bool, 3> output_mask
    ) {

  auto input = input_r;
  auto grad_output = grad_output_r;
  auto output = output_r;

  RNNParams fn;
  auto datatype = getCudnnDataType(input);
  fn.rnn.set(fn_mode, fn_hidden_size, fn_proj_size, fn_num_layers, fn_bidirectional, promote_rnn_math_type(datatype), datatype);
  fn.dropout.set(fn_train, fn_dropout, fn_dropout_state);
  fn.tensors.set(input.sizes(), fn_batch_sizes, batch_first);

  // TODO: Set device to input
  auto handle = getCudnnHandle();

  if (fn.rnn.mode != CUDNN_LSTM) {
    TORCH_CHECK(!cx.defined(),
             "rnn: illegal defined cx for non-LSTM RNN");
  }

  auto is_input_packed = fn_batch_sizes.size() != 0;
  if (batch_first && !is_input_packed) {
    input = input.transpose(0, 1);
    grad_output = grad_output.transpose(0, 1);
    output = output.transpose(0, 1);
  }

  auto input_size = _input_size(fn.tensors);
  auto hidden_size = _hidden_size(fn.rnn, fn.tensors);
  auto cell_size = _cell_size(fn.rnn, fn.tensors);
  auto output_size = _output_size(fn.rnn, fn.tensors);

  TORCH_CHECK(hx.is_contiguous(),
           "rnn: hx is not contiguous");
  TORCH_CHECK(!cx.defined() || cx.is_contiguous(),
           "rnn: cx is not contiguous");

  auto x = input.contiguous();
  auto dy = grad_output.contiguous();
  auto y = output;
  auto w = weight_buf;
  auto dx = at::empty(input.sizes(), input.options()); // TODO: more compact way of saying this
  auto dhy = grad_hy.contiguous().view(hidden_size);
  auto dcy = grad_cy.defined() ? grad_cy.contiguous().view(cell_size) : Tensor();
  auto dhx = at::empty(hidden_size, hx.options());
  TORCH_INTERNAL_ASSERT(cx.defined() || !output_mask[2], "illegally required grad of cx for non-LSTM RNN");
  auto dcx = cx.defined() ? at::empty(cell_size, cx.options()) : Tensor();

  TORCH_CHECK(fn_train,
           "cudnn RNN backward can only be called in training mode");

  TORCH_CHECK(input.sizes().equals(input_size),
           "Expected input size ", IntArrayRef{input_size}, ", got ", input.sizes());
  TORCH_CHECK(output.sizes().equals(output_size),
           "Expected output size ", IntArrayRef{output_size}, ", got ", output.sizes());

  TORCH_CHECK(!hx.defined() || hx.sizes().equals(hidden_size),
           "Expected hidden size ", IntArrayRef{hidden_size}, ", got ", hx.sizes());
  TORCH_CHECK(!cx.defined() || cx.sizes().equals(cell_size),
           "Expected cell size ", IntArrayRef{cell_size}, ", got ", cx.sizes());
  TORCH_CHECK(!dhy.defined() || dhy.sizes().equals(hidden_size),
           "Expected d_hidden size ", IntArrayRef{hidden_size}, ", got ", dhy.sizes());
  TORCH_CHECK(!dcy.defined() || dcy.sizes().equals(cell_size),
           "Expected d_cell size ", IntArrayRef{cell_size}, ", got ", dcy.sizes());

  TORCH_CHECK(dhy.is_cuda() && dy.is_cuda() && (!dcy.defined() || dcy.is_cuda()),
           "Gradients aren't CUDA tensors");

  cudnnRNNAlgo_t algo = get_algo(fn.rnn, fn.tensors, input, false);
  fn.rnn.set_algo(algo);
  RNNDescriptors descs(fn, handle, x, y, hx, cx);

  FilterDescriptor w_desc;
  w_desc.set(weight_buf, 3);

  size_t workspace_size;
  auto x_descs_arr = descs.get_x_descs();
  auto y_descs_arr = descs.get_y_descs();
  AT_CUDNN_CHECK(cudnnGetRNNWorkspaceSize(
        handle,
        descs.rnn_desc.desc(),
        fn.tensors.seq_length,
        x_descs_arr.data(),
        &workspace_size
        ));
  // TODO: put this in the correct device???
  Tensor workspace = at::empty(workspace_size, input.options().dtype(kByte));
  AT_CUDNN_CHECK(cudnnRNNBackwardData(
        handle,
        descs.rnn_desc.desc(),
        fn.tensors.seq_length,
        y_descs_arr.data(), y.data_ptr(),
        y_descs_arr.data(), dy.data_ptr(),
        descs.hy_desc.desc(), dhy.data_ptr(),
        descs.cy_desc.desc(), cx.defined() ? dcy.data_ptr() : nullptr,
        w_desc.desc(), w.data_ptr(),
        descs.hx_desc.desc(), hx.data_ptr(),
        descs.cx_desc.desc(), cx.defined() ? cx.data_ptr() : nullptr,
        x_descs_arr.data(), dx.data_ptr(),
        descs.hx_desc.desc(), dhx.data_ptr(),
        descs.cx_desc.desc(), cx.defined() ? dcx.data_ptr() : nullptr,
        workspace.data_ptr(), workspace.size(0),
        fn_reserve.data_ptr(), fn_reserve.size(0)
        ));

  if (batch_first && !is_input_packed) {
    dx = dx.transpose_(0, 1);
  }

  return std::make_tuple(dx, dhx, dcx);
}

// NB: This MUST BE CALLED AFTER _cudnn_rnn_backward_input.
// We'll give a user friendly combined function...
std::vector<Tensor> _cudnn_rnn_backward_weight(
    // TODO: I think tensor geometry sufficient for weight_buf/weight
    const Tensor& input_r, TensorList weight_arr, int64_t weight_stride0,
    const Tensor& weight_buf, const Tensor& hx, const Tensor& cx,
    const Tensor& output_r,
    int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_proj_size,
    int64_t fn_num_layers, bool batch_first, double fn_dropout,
    bool fn_train, bool fn_bidirectional, IntArrayRef fn_batch_sizes,
    const Tensor& fn_dropout_state, const Tensor& fn_reserve
    ) {

  MatrixRef<Tensor> weight{ weight_arr, static_cast<size_t>(weight_stride0) };
  auto input = input_r;
  auto output = output_r;

  RNNParams fn;
  auto datatype = getCudnnDataType(input);
  fn.rnn.set(fn_mode, fn_hidden_size, fn_proj_size, fn_num_layers, fn_bidirectional, promote_rnn_math_type(datatype), datatype);
  fn.dropout.set(fn_train, fn_dropout, fn_dropout_state);
  fn.tensors.set(input.sizes(), fn_batch_sizes, batch_first);

  auto handle = getCudnnHandle();

  if (fn.rnn.mode != CUDNN_LSTM) {
    TORCH_CHECK(!cx.defined(),
             "rnn: illegal defined cx for non-LSTM RNN");
  }

  auto is_input_packed = fn_batch_sizes.size() != 0;
  if (batch_first && !is_input_packed) {
    input = input.transpose(0, 1);
    output = output.transpose(0, 1);
  }

  auto input_size = _input_size(fn.tensors);
  auto hidden_size = _hidden_size(fn.rnn, fn.tensors);

  TORCH_CHECK(fn_train,
           "cudnn RNN backward can only be called in training mode");

  TORCH_CHECK(input.sizes().equals(input_size),
           "Expected input size ", IntArrayRef{input_size}, ", got ", input.sizes());
  TORCH_CHECK(!hx.defined() || hx.sizes().equals(hidden_size),
           "Expected hidden size ", IntArrayRef{hidden_size}, ", got ", hx.sizes());

  // TODO: the above were the only checks in rnn.py, but it doesn't seem
  // like these checks are enough

  TORCH_CHECK(hx.is_contiguous(),
           "rnn: hx is not contiguous");
  TORCH_CHECK(!cx.defined() || cx.is_contiguous(),
           "rnn: cx is not contiguous");

  auto x = input.contiguous();
  const auto& y = output;
  auto dw = at::zeros(weight_buf.sizes(), weight_buf.options());

  cudnnRNNAlgo_t algo = get_algo(fn.rnn, fn.tensors, input, false);
  fn.rnn.set_algo(algo);
  RNNDescriptors descs(fn, handle, x, y, hx, cx);

  FilterDescriptor w_desc;
  w_desc.set(weight_buf, 3);

  size_t workspace_size;
  auto x_descs_arr = descs.get_x_descs();
  auto y_descs_arr = descs.get_y_descs();
  AT_CUDNN_CHECK(cudnnGetRNNWorkspaceSize(
        handle,
        descs.rnn_desc.desc(),
        fn.tensors.seq_length,
        x_descs_arr.data(),
        &workspace_size
        ));
  Tensor workspace = at::empty(workspace_size, input.options().dtype(kByte));
  AT_CUDNN_CHECK(cudnnRNNBackwardWeights(
        handle,
        descs.rnn_desc.desc(),
        fn.tensors.seq_length,
        x_descs_arr.data(), x.data_ptr(),
        descs.hx_desc.desc(), hx.data_ptr(),
        y_descs_arr.data(), y.data_ptr(),
        workspace.data_ptr(), workspace.size(0),
        w_desc.desc(), dw.data_ptr(),
        fn_reserve.data_ptr(), fn_reserve.size(0)
        ));


  std::vector<Tensor> grad_params_arr;
  size_t grad_params_stride0;
  std::tie(grad_params_arr, grad_params_stride0) = get_parameters(handle, fn.rnn, descs.rnn_desc, descs.x_descs[0], w_desc, dw);
  if (grad_params_stride0 == static_cast<size_t>(weight_stride0)) {
     _viewParams(MatrixRef<Tensor>{grad_params_arr, grad_params_stride0},
              MatrixRef<Tensor>{weight_arr, static_cast<size_t>(weight_stride0)});
      return grad_params_arr;
  } else {
     std::vector<Tensor> grad_weight_arr;
     grad_weight_arr.reserve( weight.numel() );
     for (const auto& w : weight_arr) {
        grad_weight_arr.emplace_back(at::empty(w.sizes(), w.options()));
     }
     _copyParams(MatrixRef<Tensor>{grad_params_arr, grad_params_stride0},
              MatrixRef<Tensor>{grad_weight_arr, static_cast<size_t>(weight_stride0)});
     return grad_weight_arr;
  }
}

// We need this dispatcher because _cudnn_rnn_backward_weight has a stringent
// ordering requirement with _cudnn_rnn_backward_input
std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>> _cudnn_rnn_backward(
    const Tensor& input, TensorList weight, int64_t weight_stride0, const Tensor& weight_buf, const Tensor& hx, const c10::optional<Tensor>& cx_opt,
    const Tensor& output, const c10::optional<Tensor>& grad_output_r_opt, const c10::optional<Tensor>& grad_hy_r_opt, const c10::optional<Tensor>& grad_cy_r_opt,
    int64_t mode, int64_t hidden_size, int64_t proj_size,
    int64_t num_layers, bool batch_first, double dropout,
    bool train, bool bidirectional, IntArrayRef batch_sizes, const c10::optional<Tensor>& dropout_state_opt, const Tensor& reserve,
    std::array<bool, 4> output_mask
    ) {
  // See [Note: hacky wrapper removal for optional tensor]
  c10::MaybeOwned<Tensor> cx_maybe_owned = at::borrow_from_optional_tensor(cx_opt);
  const Tensor& cx = *cx_maybe_owned;
  const Tensor& grad_output_r = c10::value_or_else(grad_output_r_opt, [] {return Tensor();});
  const Tensor& grad_hy_r = c10::value_or_else(grad_hy_r_opt, [] {return Tensor();});
  const Tensor& grad_cy_r = c10::value_or_else(grad_cy_r_opt, [] {return Tensor();});
  const Tensor& dropout_state = c10::value_or_else(dropout_state_opt, [] {return Tensor();});

  if (!grad_output_r.defined() && !grad_hy_r.defined() && !grad_cy_r.defined()) {
    return std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>>(Tensor(), Tensor(), Tensor(), std::vector<Tensor>(weight.size()));
  }

  auto grad_output = grad_output_r.defined() ? grad_output_r : at::zeros_like(output, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  auto grad_hy = grad_hy_r.defined() ? grad_hy_r : at::zeros_like(hx, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
  auto grad_cy = cx.defined() ? (grad_cy_r.defined() ? grad_cy_r : at::zeros_like(cx, LEGACY_CONTIGUOUS_MEMORY_FORMAT)) : grad_cy_r;

  Tensor dx, dhx, dcx;
  // NB: unconditionally compute this gradient, because it mutates reserve
  std::tie(dx, dhx, dcx) = at::native::_cudnn_rnn_backward_input(input, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, {output_mask[0], output_mask[1], output_mask[2]});
  std::vector<Tensor> dw;
  if (output_mask[3]) {
    dw = at::native::_cudnn_rnn_backward_weight(input, weight, weight_stride0, weight_buf, hx, cx, output, mode, hidden_size, proj_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve);
  }
  return std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>>{dx, dhx, dcx, dw};
}

// TODO: I am not sure if we actually need the 'dropout' and 'train' parameters
// to initialize just the state tensor
//
// NB: You can have any color you like, as long as it's a CUDA byte
// tensor.  Why does this function take a TensorOptions at all in that case?
// This is a factory function: it produces tensors but takes no tensors
// as input.  The codegen currently assumes that ALL factory functions
// take TensorOptions, so it's just a lot easier for this function to
// be bound if it also does it.
Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed,
    c10::optional<ScalarType> dtype,
    c10::optional<Layout> layout,
    c10::optional<Device> device,
    c10::optional<bool> pin_memory) {
  // See [Note: hacky wrapper removal for TensorOptions]
  TensorOptions options = TensorOptions().dtype(dtype).layout(layout).device(device).pinned_memory(pin_memory);

  auto handle = getCudnnHandle();
  DropoutDescriptor dropout_desc;
  auto dropout_p = train ? dropout : 0;
  dropout_desc.initialize_rng(handle, dropout_p, dropout_seed, options);
  return dropout_desc.state;
}

////////////////////////////////////////////////////////////////////////////////
// CUDA dispatch for the generic RNN ops (at::lstm, at::gru, ...)
////////////////////////////////////////////////////////////////////////////////

namespace {

// Helpers for working with different hidden types.
std::tuple<Tensor, Tensor> unpack_hidden(const Tensor& hidden) {
  return std::make_tuple(hidden, at::Tensor{});
}

std::tuple<Tensor, Tensor> unpack_hidden(const std::tuple<Tensor, Tensor>& hidden) {
  return hidden;
}

template<typename hidden_type>
hidden_type pack_hidden(const Tensor& hx, const Tensor& cx) {
  static_assert(std::is_same<hidden_type, void>::value, "pack_hidden not implemented for this type");
  AT_ERROR("NOT IMPLEMENTED");
}

template<>
Tensor pack_hidden<Tensor>(const Tensor& hx, const Tensor& cx) {
  AT_ASSERT(cx.numel() == 0);
  return hx;
}

template<>
std::tuple<Tensor, Tensor> pack_hidden<std::tuple<Tensor, Tensor>>(const Tensor& hx, const Tensor& cx) {
  return std::make_tuple(hx, cx);
}

/**
 * Note [DropoutState and CUDA graph capture]
 * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 * (1) Telling a capturing stream to wait on an event recorded in a non-capturing stream is an error.
 * (2) Telling a non-capturing stream to wait on an event recorded during capture is also an error.
 *
 * So DropoutState's usage syncs could error if an RNN with dropout is called in an uncaptured region
 * then called in a captured region (triggering 1), or called in a captured region then called
 # in an uncaptured region (triggering 2).
 *
 * To prevent 1 and 2, lock() only syncs on the last usage event if it was recorded in the same
 * capture state as the current state (which also means the same graph, if capture is in progress).
 *
 * The solution should be safe as long as capture obeys the following restrictions:
 *  - Only one capture may be underway at a time in a given process.
 *  - While a capture is underway, no calls to eager ops on noncapturing streams (on any thread)
 *    may interleave with the captured ops.
 *
 * TODO: As people experiment with capture, keep an eye out for use cases that might need to
 * relax those restrictions.
 *
 * See https://github.com/pytorch/pytorch/pull/56433 for more discussion.
 */

struct DropoutState {
  // Both buffer and event are lazily instantiated when a dropout state is needed
  // for the first time. Note that in this case needed != used, as we don't need
  // a buffer to e.g. run RNNs in test mode.
  at::Tensor buffer;
  c10::optional<cuda::CUDAEvent> event;
  std::mutex mutex;
#if !defined(USE_ROCM)
  // cudaStreamGetCaptureInfo will never give back a capture id of 0, so 0 can serve
  // as a sentinel value that capture was not underway.
  cuda::CaptureId_t capture_id_last_lock = 0;
  cuda::CaptureId_t capture_id_last_unlock = 0;
#endif

  // Every time we use a dropout state, we need to synchronize with its event,
  // to make sure all previous uses finish running before this one starts. Once
  // we're done, we record the event to allow others to synchronize with this kernel.
  // Those events are really needed only for inter-stream sync on a single GPU.
  // I doubt anyone will want to run cuDNN RNNs in parallel on a single GPU, so
  // they should end up being complete no-ops.
  void lock() {
    // NB: We can't ignore the lock even when event is undefined, because someone
    // could then define it before we get to unlock().
    mutex.lock();
    if (event) {
#if !defined(USE_ROCM)
      // See Note [DropoutState and CUDA graph capture]
      cudaStreamCaptureStatus status;
      AT_CUDA_CHECK(cudaStreamGetCaptureInfo(cuda::getCurrentCUDAStream(),
                                             &status,
                                             &capture_id_last_lock));
      if (status == cudaStreamCaptureStatus::cudaStreamCaptureStatusNone) {
        capture_id_last_lock = 0;
      }
      if (capture_id_last_lock == capture_id_last_unlock) {
        event->block(cuda::getCurrentCUDAStream());
      }
#else
      event->block(cuda::getCurrentCUDAStream());
#endif
    }
  }

  void unlock() {
    if (event) {
      event->record();
#if !defined(USE_ROCM)
      // See Note [DropoutState and CUDA graph capture]
      cudaStreamCaptureStatus status;
      AT_CUDA_CHECK(cudaStreamGetCaptureInfo(cuda::getCurrentCUDAStream(),
                                             &status,
                                             &capture_id_last_unlock));
      if (status == cudaStreamCaptureStatus::cudaStreamCaptureStatusNone) {
        capture_id_last_unlock = 0;
      }
      TORCH_INTERNAL_ASSERT(capture_id_last_unlock == capture_id_last_lock);
#endif
    }
    mutex.unlock();
  }
};

DropoutState& get_dropout_state(double dropout_p, bool train, TensorOptions options) {
  // Each state is slightly over 2MB and initialized lazily, so it's fine to cache them.
  static std::vector<DropoutState> dropout_state_cache { static_cast<size_t>(cuda::getNumGPUs()) };
  static std::mutex state_cache_mut;

  AT_ASSERT(options.device().is_cuda());
  int device = options.device().index();

  std::unique_lock<std::mutex> lock {state_cache_mut};
  auto& state = dropout_state_cache.at(device);
  if (train && dropout_p > 0) {
    const auto &gen = at::detail::getCUDAHooks().getDefaultCUDAGenerator(device);
    auto gen_impl = gen.get<at::CUDAGeneratorImpl>();
    bool reset_rnn_state = gen_impl->reset_rnn_state();
    if (!state.buffer.defined() || reset_rnn_state) {
      std::unique_lock<std::mutex> lock {state.mutex};
      int64_t seed = at::empty({}, options.dtype(at::kLong)).random_(gen).item<int64_t>();
      state.buffer = at::_cudnn_init_dropout_state(
          dropout_p, train, seed, options.dtype(at::kByte));
      // NB: CUDA binds the event to a device at creation time, so we can initialize it
      // only now, when we know we're on the correct device.
      if (!state.event.has_value()) {
        state.event.emplace();
      }
    }
  }
  return state;
}

Tensor try_get_weight_buf(
      const Tensor& input, TensorList parameters, bool has_biases,
      cudnnRNNMode_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool bidirectional) {

  // Prepare all relevant descriptors
  auto handle = getCudnnHandle();
  auto & any_param = parameters.at(0);
  auto datatype = getCudnnDataType(any_param);

  // Something very naughty is happening here.  try_get_weight_buf
  // is called from _cudnn_impl, which is a *composite*.  In other words,
  // inside the composite function we need to query cudnn to figure out how big
  // the weight buf actually is going to be.  This clearly cannot be done
  // symbolically.  For now, we insert guards here; but once we have the black
  // box handling for dynamic shapes, we could also hypothetically infer out
  // the relationships
  RNNDescriptorParams rnn;
  rnn.set(mode, hidden_size.guard_int(__FILE__, __LINE__), proj_size.guard_int(__FILE__, __LINE__), num_layers, bidirectional, promote_rnn_math_type(datatype), datatype);
  RNNDescriptor rnn_desc = rnn.descriptor(handle);

  TensorGeometry x_geom ({1, input.sym_size(-1).guard_int(__FILE__, __LINE__)});
  TensorDescriptor x_desc;
  // datatype for x_desc comes from any_param, not input.
  // try_get_weight_buf's job is to check "is the weight buffer correctly laid out
  // for us to run it with input of the same datatype?"
  x_desc.set(datatype, x_geom.sizes(), x_geom.strides(), 5);

  auto num_params = get_num_weights(handle, rnn_desc, x_desc, datatype);

  // Try to get parameter storage
  auto param_storage = any_param.storage();
  auto weight_buf = at::empty({0}, any_param.options()).set_(param_storage);
  if (weight_buf.size(0) < num_params) {
    return {};
  } else if (weight_buf.size(0) > num_params) {
    weight_buf = weight_buf.narrow(0, 0, num_params);
  }

  // Get and check data pointers
  auto expected_data_ptrs = get_expected_data_ptrs(
      weight_buf, handle, rnn, rnn_desc, x_desc, datatype);

  int64_t num_parameters = parameters.size();
  int64_t num_ptrs = expected_data_ptrs.size();
  if (proj_size != 0) {
    AT_ASSERT(num_parameters % (has_biases ? 5 : 3) == 0);
    AT_ASSERT(num_ptrs % 5 == 0);
    if (has_biases) {
      AT_ASSERT(num_ptrs == num_parameters);
      for (const auto i : c10::irange(num_parameters)) {
        if (expected_data_ptrs[i] != parameters[i].data_ptr()) return {};
      }
    } else {
      AT_ASSERT(num_parameters % 3 == 0);
      AT_ASSERT(num_ptrs == num_parameters * 5 / 3);
      for (int64_t param_i = 0, ptr_i = 0;
          ptr_i < num_ptrs;
          ptr_i += 5, param_i += 3) {
        if (expected_data_ptrs[ptr_i] != parameters[param_i].data_ptr()) return {};
        if (expected_data_ptrs[ptr_i + 1] != parameters[param_i + 1].data_ptr()) return {};
        if (expected_data_ptrs[ptr_i + 4] != parameters[param_i + 2].data_ptr()) return {};
      }
    }
  } else {
    AT_ASSERT(num_ptrs == (num_parameters * (has_biases ? 1 : 2)));
    AT_ASSERT(num_parameters % (has_biases ? 4 : 2) == 0);
    for (int64_t param_i = 0, ptr_i = 0;
        ptr_i < num_ptrs;
        ptr_i += (has_biases ? 2 : 4), param_i += 2) {
      if (expected_data_ptrs[ptr_i] != parameters[param_i].data_ptr()) return {};
      if (expected_data_ptrs[ptr_i + 1] != parameters[param_i + 1].data_ptr()) return {};
    }
  }
  if (!parameters[num_parameters - 1].is_contiguous()) return {};
  return weight_buf;
}

template<typename hidden_type>
std::pair<Tensor, hidden_type> _cudnn_impl(
      const Tensor& input, const Tensor& _batch_sizes, const hidden_type& hidden,
      TensorList params, bool has_biases, cudnnRNNMode_t mode,
      int64_t num_layers, double dropout_p, bool train, bool bidirectional) {
  Tensor hx, cx;
  std::tie(hx, cx) = unpack_hidden(hidden);
  auto hidden_size = hx.sym_size(2);
  SymInt proj_size = 0;
  // For LSTM models with projections hidden size could be different
  if (cx.defined() && cx.sym_size(2) != hx.sym_size(2)) {
    hidden_size = cx.sym_size(2);
    proj_size = hx.sym_size(2);
  }

  // TODO:  try_get_weight_buf returns a Tensor, but _cudnn_rnn below takes a c10::optional<Tensor>
  // in weight_buf's slot.  Do we want try_get_weight_buf to return a c10::optional<Tensor>
  // instead of a defined or undefined Tensor?
  at::cuda::OptionalCUDAGuard guard(input.get_device());
  auto weight_buf = try_get_weight_buf(
      input, params, has_biases, mode, hidden_size, proj_size, num_layers, bidirectional);

  TORCH_CHECK(_batch_sizes.dim() == 1, "batch_sizes tensor should be 1D");
  IntArrayRef batch_sizes { _batch_sizes.data_ptr<int64_t>(), static_cast<size_t>(_batch_sizes.size(0)) };

  auto & dropout_state = get_dropout_state(dropout_p, train, input.options());
  std::unique_lock<DropoutState> lock { dropout_state };
  int64_t num_params = has_biases ? 4 : 2;
  if (proj_size != 0) {
    ++num_params;
  }
  auto sym_batch_sizes = c10::SymIntArrayRef(reinterpret_cast<const c10::SymInt*>(batch_sizes.data()), batch_sizes.size());
  // cudnn_output = std::tuple<output, hy, cy, reserve, new_weight_buf>
  auto cudnn_output = at::_cudnn_rnn_symint(
      input, params, num_params, weight_buf,
      hx, cx, static_cast<int>(mode), hidden_size, proj_size, num_layers, /*batch_first=*/false,
      dropout_p, train, bidirectional, sym_batch_sizes, dropout_state.buffer);

  return {std::get<0>(cudnn_output),
          pack_hidden<hidden_type>(std::get<1>(cudnn_output), std::get<2>(cudnn_output))};
}

template<typename hidden_type>
std::pair<Tensor, hidden_type> _cudnn_impl(
      const Tensor& input, const hidden_type& hidden,
      TensorList params, bool has_biases, cudnnRNNMode_t mode,
      int64_t num_layers, double dropout_p, bool train, bool bidirectional, bool batch_first) {
  Tensor hx, cx;
  std::tie(hx, cx) = unpack_hidden(hidden);
  auto hidden_size = hx.sym_size(2);
  c10::SymInt proj_size = 0;
  // For LSTM models with projections hidden size could be different
  if (cx.defined() && cx.sym_size(2) != hx.sym_size(2)) {
    hidden_size = cx.sym_size(2);
    proj_size = hx.sym_size(2);
  }
  at::cuda::OptionalCUDAGuard guard(input.get_device());
  auto weight_buf = try_get_weight_buf(
      input, params, has_biases, mode, hidden_size, proj_size, num_layers, bidirectional);
  auto & dropout_state = get_dropout_state(dropout_p, train, input.options());
  std::unique_lock<DropoutState> lock { dropout_state };
  int64_t num_params = has_biases ? 4 : 2;
  if (proj_size != 0) {
    ++num_params;
  }
  // cudnn_output = std::tuple<output, hy, cy, reserve, new_weight_buf>
  auto cudnn_output = at::_cudnn_rnn_symint(
      input, params, num_params, weight_buf,
      hx, cx, static_cast<int>(mode), hidden_size, proj_size, num_layers, batch_first, dropout_p,
      train, bidirectional, /*batch_sizes=*/{}, dropout_state.buffer);

  return {std::get<0>(cudnn_output),
          pack_hidden<hidden_type>(std::get<1>(cudnn_output), std::get<2>(cudnn_output))};
}

#define ONE_HIDDEN_RNN(NAME, MODE)                                             \
void NAME##_cudnn(Tensor& output, Tensor& hy,                                  \
      const Tensor& input, const Tensor& hx,                                   \
      TensorList params, bool has_biases,                                      \
      int64_t num_layers, double dropout_p, bool train, bool bidirectional, bool batch_first) { \
  std::tie(output, hy) = _cudnn_impl(input, hx, params, has_biases,            \
      MODE, num_layers, dropout_p, train, bidirectional, batch_first);         \
}                                                                              \
                                                                               \
void NAME##_packed_cudnn(Tensor& output, Tensor& hy,                           \
      const Tensor& data, const Tensor& batch_sizes, const Tensor& hx,         \
      TensorList params, bool has_biases,                                      \
      int64_t num_layers, double dropout_p, bool train, bool bidirectional) {  \
  std::tie(output, hy) = _cudnn_impl(data, batch_sizes, hx, params,            \
      has_biases, MODE, num_layers, dropout_p, train, bidirectional);          \
}                                                                              \
                                                                               \
REGISTER_CUDA_DISPATCH(NAME##_cudnn_stub, &NAME##_cudnn);                      \
REGISTER_CUDA_DISPATCH(NAME##_packed_cudnn_stub, &NAME##_packed_cudnn);

ONE_HIDDEN_RNN(gru, CUDNN_GRU)
ONE_HIDDEN_RNN(rnn_tanh, CUDNN_RNN_TANH)
ONE_HIDDEN_RNN(rnn_relu, CUDNN_RNN_RELU)

void lstm_cudnn(Tensor& output, Tensor& hy, Tensor& cy,
      const Tensor& input, TensorList hx,
      TensorList params, bool has_biases,
      int64_t num_layers, double dropout_p, bool train, bool bidirectional, bool batch_first) {
  auto result = _cudnn_impl(input, std::make_tuple(hx[0], hx[1]), params, has_biases,
      CUDNN_LSTM, num_layers, dropout_p, train, bidirectional, batch_first);
  output = result.first;
  hy = std::get<0>(result.second);
  cy = std::get<1>(result.second);
}

void lstm_packed_cudnn(Tensor& output, Tensor& hy, Tensor& cy,
      const Tensor& data, const Tensor& batch_sizes, TensorList hx,
      TensorList params, bool has_biases,
      int64_t num_layers, double dropout_p, bool train, bool bidirectional) {
  auto result = _cudnn_impl(data, batch_sizes, std::make_tuple(hx[0], hx[1]),
      params, has_biases, CUDNN_LSTM, num_layers, dropout_p, train, bidirectional);
  output = result.first;
  hy = std::get<0>(result.second);
  cy = std::get<1>(result.second);
}

REGISTER_CUDA_DISPATCH(lstm_cudnn_stub, &lstm_cudnn);
REGISTER_CUDA_DISPATCH(lstm_packed_cudnn_stub, &lstm_packed_cudnn);

} // anonymous namepsace

}} // namespace at::native

#endif // AT_CUDNN_ENABLED()