android-16.0.0_r2/s

#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
#include <ATen/native/RNN.h>
#include <ATen/core/Tensor.h>
#include <ATen/Config.h>
#include <ATen/InitialTensorOptions.h>
#include <ATen/MatrixRef.h>
#include <ATen/TensorUtils.h>

#include <ATen/cuda/CUDAConfig.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/cat.h>
#include <ATen/ops/empty.h>
#include <ATen/ops/miopen_rnn.h>
#include <ATen/ops/miopen_rnn_native.h>
#include <ATen/ops/miopen_rnn_backward_native.h>
#include <ATen/ops/zeros.h>
#include <ATen/ops/zeros_like.h>
#endif

#if !AT_ROCM_ENABLED()

namespace at { namespace native {

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

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

}} //namespace at::native

#else // AT_ROCM_ENABLED()

#include <ATen/miopen/miopen-wrapper.h>
#include <ATen/miopen/Descriptors.h>
#include <ATen/miopen/Types.h>
#include <ATen/miopen/Utils.h>

#include <ATen/TensorUtils.h>

#include <functional>
#include <iterator>
#include <sstream>
#include <algorithm>
#include <memory>
#include <mutex>
#include <stdint.h>
#include <unordered_map>

namespace at { namespace native {

//RNNDescriptor.
struct RNNDescriptorParams {
    int64_t hidden_size;
    int64_t num_layers;
    miopenRNNDirectionMode_t direction;
    miopenRNNMode_t rnn_mode;
    miopenDataType_t datatype;
    miopenRNNAlgo_t algo = miopenRNNdefault;
    miopenRNNInputMode_t input_mode = miopenRNNlinear;
    miopenRNNBiasMode_t bias_mode = miopenRNNNoBias;

    int64_t num_directions() const {
        return (direction == miopenRNNbidirection) ? 2 : 1;
    }

    void set_bidirectional(bool fn_bidirectional) {
        direction = fn_bidirectional ? miopenRNNbidirection : miopenRNNunidirection;
    }

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

    void set_mode(int64_t fn_mode) {
        switch (fn_mode) {
            case 0:
                rnn_mode = miopenRNNRELU;
                break;
            case 1:
                rnn_mode = miopenRNNTANH;
                break;
            case 2:
                rnn_mode = miopenLSTM;
                break;
            case 3:
                rnn_mode = miopenGRU;
                break;
            default:
                {
                    std::ostringstream oss;
                    oss << "unrecognized miopen RNN mode " << fn_mode;
                    AT_ERROR(oss.str());
                }
        }
    }

    void set(int64_t mode, int64_t hidden_size, int64_t num_layers, bool bidirectional, miopenDataType_t datatype, miopenRNNBiasMode_t bias_mode) {
        this->set_mode(mode);
        this->hidden_size = hidden_size;
        this->num_layers = num_layers;
        this->set_bidirectional(bidirectional);
        this->datatype = datatype;
        this->bias_mode = bias_mode;
    }

    RNNDescriptor descriptor() const {
        RNNDescriptor rnn_desc;
        rnn_desc.set(hidden_size, num_layers, input_mode, direction, rnn_mode, bias_mode, algo, datatype);
        return rnn_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;

    auto batch_tensor_size = tensor.sizes().vec();
    for (auto batch_size : batch_sizes) {
        batch_tensor_size[0] = batch_size;

        descriptors[i].set(getMiopenDataType(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;
}

struct TensorDescriptorListParams {
    IntArrayRef batch_sizes;
    int64_t seq_length;
    int64_t mini_batch;

    int64_t input_size;
    int64_t batch_sizes_sum;

    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];
            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];
            batch_sizes_sum = -1;
        }
    }

    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);
        }
    }
};

struct RNNParams {
    RNNDescriptorParams rnn;
    TensorDescriptorListParams tensors;
};

struct RNNDescriptors {
    RNNDescriptor rnn_desc;
    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, miopenHandle_t handle, Tensor x, Tensor y, Tensor hx, Tensor cx) {
        rnn_desc = fn.rnn.descriptor();
        x_descs = fn.tensors.descriptors(x);
        y_descs = fn.tensors.descriptors(y);
        hx_desc.set(hx, 5);
        hy_desc.set(hx, 5);
        cx_desc.set(hx, 5);
        cy_desc.set(hx, 5);
    }

    std::vector<miopenTensorDescriptor_t> get_descs(const std::vector<TensorDescriptor>& descs) {
        std::vector<miopenTensorDescriptor_t> r;
        r.reserve(descs.size());
        for (auto& desc : descs) {
            r.emplace_back(desc.desc());
        }
        return r;
    }

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

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

Tensor permute_wei_for_miopen(Tensor wei, int64_t mode)
{
    if (mode < 2)
        return wei;

    Tensor permuted_wei;
    if(mode == 2) { // LSTM
        auto sliced_tensor = wei.chunk(4, 0);
        permuted_wei = at::cat({sliced_tensor[0], sliced_tensor[1], sliced_tensor[3], sliced_tensor[2]});
    }
    else if(mode == 3) {    // GRU
        auto sliced_tensor = wei.chunk(3, 0);
        permuted_wei = at::cat({sliced_tensor[1], sliced_tensor[0], sliced_tensor[2]});
    }
    return permuted_wei;
}

void _viewOrCopyParams(MatrixRef<Tensor> params_from, MatrixRef<Tensor> params_to, bool copy) {
    TORCH_CHECK(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.
        for (auto a = layer_params_from.begin(), b = layer_params_to.begin();
                a != layer_params_from.end() && b != layer_params_to.end();
                ++a, ++b) {
            auto param_from = *a, param_to = *b;
            TORCH_CHECK(param_from.type() == param_to.type(), "parameter types mismatch");
            if (copy) {
                param_to.copy_(param_from.view_as(param_to));
            } else {
                param_from.resize_as_(param_to);
            }
        }
    }
}

void _copyParams_and_permute(MatrixRef<Tensor> params_from, MatrixRef<Tensor> params_to, int64_t mode) {
    TORCH_CHECK(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];
        for (auto a = layer_params_from.begin(), b = layer_params_to.begin();
                a != layer_params_from.end() && b != layer_params_to.end();
                ++a, ++b) {
            auto param_from = *a, param_to = *b;
            TORCH_CHECK(param_from.type() == param_to.type(), "parameter types mismatch");
            auto tmp = permute_wei_for_miopen(param_from, mode);
            param_to.copy_(tmp.view_as(param_to));
        }
    }
}

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);
}

int64_t get_num_weights(miopenHandle_t handle, const RNNDescriptor& rnn_desc,
        const TensorDescriptor& x_desc, miopenDataType_t datatype)
{
    size_t weight_size;
    MIOPEN_CHECK(miopenGetRNNParamsSize(handle, rnn_desc.desc(), x_desc.desc(), &weight_size, datatype));
    auto element_size = dataSize(datatype);
    TORCH_CHECK(weight_size % element_size == 0, "miopenGetRNNParamsSize returned nonsensical weight_size.");
    return weight_size / element_size;
}

int64_t _num_linear_layers(miopenRNNMode_t mode) {
    switch(mode) {
        case miopenLSTM:
            return 8;
        case miopenGRU:
            return 6;
        case miopenRNNRELU:
            return 2;
        case miopenRNNTANH:
            return 2;
        default:
            AT_ERROR("Unknown miopen RNN mode : ", mode);
    }
}

std::pair<std::vector<Tensor>, size_t> get_parameters(miopenHandle_t handle, const RNNDescriptorParams& rnn,
                    const RNNDescriptor& rnn_desc, const TensorDescriptor& x_desc, const FilterDescriptor& w_desc,
                    const Tensor& weight_buf)
{
    std::vector<Tensor> params;
    int64_t num_linear_layers = _num_linear_layers(rnn.rnn_mode);
    int64_t num_layers = rnn.num_directions() * rnn.num_layers;
    size_t cur_offset = 0;
    size_t global_layer_params_count = 0;
    auto elem_size = dataSize(getMiopenDataType(weight_buf));
    auto bias_mode = rnn.bias_mode;

    for (const auto layer : c10::irange(num_layers)) {
        size_t layer_params_count = 0;

        // Get layer params
        for (const auto linear_id : c10::irange(num_linear_layers)) {
            FilterDescriptor lin_layer_mat_desc;
            size_t offset;
            MIOPEN_CHECK(miopenGetRNNLayerParamOffset(
                rnn_desc.desc(),
                layer,
                x_desc.desc(),
                linear_id,
                lin_layer_mat_desc.mut_desc(),
                &offset));

            size_t param_size;
            MIOPEN_CHECK(miopenGetRNNLayerParamSize(
                handle,
                rnn_desc.desc(),
                layer,
                x_desc.desc(),
                linear_id,
                &param_size));
            param_size /= elem_size;

            if(linear_id == 0 || linear_id == num_linear_layers / 2) {
                std::initializer_list<int64_t> size = { static_cast<int64_t>(param_size * num_linear_layers / 2), 1L};
                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 + param_size;
        }

        // Get bias params
        if (bias_mode == miopenRNNwithBias) {
            for (const auto linear_id : c10::irange(num_linear_layers)) {
                FilterDescriptor lin_layer_mat_desc;
                size_t offset;
                MIOPEN_CHECK(miopenGetRNNLayerBiasOffset(
                    rnn_desc.desc(),
                    layer,
                    x_desc.desc(),
                    linear_id,
                    lin_layer_mat_desc.mut_desc(),
                    &offset));

                size_t bias_size;
                MIOPEN_CHECK(miopenGetRNNLayerBiasSize(
                    handle,
                    rnn_desc.desc(),
                    layer,
                    linear_id,
                    &bias_size));
                bias_size /= elem_size;

                if(linear_id == 0 || linear_id == num_linear_layers / 2) {
                    std::initializer_list<int64_t> size = { static_cast<int64_t>(bias_size * num_linear_layers / 2), 1L};
                    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 + bias_size;
            }
        }

        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);
        }
    } // layer
    return std::make_pair(params, global_layer_params_count);
}

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) {
    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) {
    if (tensors.is_input_packed()) {
        return {tensors.batch_sizes_sum, rnn.hidden_size * rnn.num_directions()};
    } else {
        return {tensors.seq_length, tensors.mini_batch, rnn.hidden_size * rnn.num_directions()};
    }
}

std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor> miopen_rnn(
        const Tensor& input_r, TensorList weight, int64_t weight_stride0,
        const Tensor& hx, const std::optional<Tensor>& cx_opt,
        int64_t fn_mode, int64_t fn_hidden_size, int64_t fn_num_layers,
        bool batch_first, double fn_dropout, bool fn_train, bool fn_bidirectional,
        IntArrayRef fn_batch_sizes, const std::optional<Tensor>& fn_dropout_state_opt
        ) {
    // 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& fn_dropout_state = c10::value_or_else(fn_dropout_state_opt, [] {return Tensor();});

    check_attributes(input_r, weight, {hx, cx});
    auto input = input_r;

    RNNParams fn;
    auto datatype = getMiopenDataType(input);
    miopenRNNBiasMode_t bias_mode = (weight_stride0 == 4) ? miopenRNNwithBias : miopenRNNNoBias;
    fn.rnn.set(fn_mode, fn_hidden_size, fn_num_layers, fn_bidirectional, datatype, bias_mode);
    fn.tensors.set(input.sizes(), fn_batch_sizes, batch_first);

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

    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 output_size = _output_size(fn.rnn, fn.tensors);

    TORCH_CHECK(hx.is_contiguous(), "miopen_rnn : hx is not contiguous.");
    TORCH_CHECK(!cx.defined() || cx.is_contiguous(), "miopen_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(hidden_size, cx.options());
    } else {
        cy = at::empty({0}, hx.options());
    }

    auto y = output;
    auto handle = getMiopenHandle();
    miopenRNNAlgo_t algo = miopenRNNdefault;
    fn.rnn.set_algo(algo);

    RNNDescriptors descs(fn, handle, x, y, hx, cx);

    FilterDescriptor w_desc;
    auto num_weights = get_num_weights(handle, descs.rnn_desc, descs.x_descs[0], datatype);
    auto weight_buf = at::empty(num_weights, x.options());
    w_desc.set(weight_buf, 3);
    weight_buf.zero_();
    auto [params, params_stride0] = get_parameters(handle, fn.rnn, descs.rnn_desc, descs.x_descs[0], w_desc, weight_buf);
    if (fn_mode < 2)
        _copyParams(MatrixRef<Tensor>{weight, static_cast<size_t>(weight_stride0)},
                MatrixRef<Tensor>{params, params_stride0});
    else
        _copyParams_and_permute(MatrixRef<Tensor>{weight, static_cast<size_t>(weight_stride0)},
                    MatrixRef<Tensor>{params, params_stride0}, fn_mode);

    TORCH_CHECK(!cx.defined() || cx.sizes().equals(hidden_size), "Expected cell size ", IntArrayRef{hidden_size}, ", got", cx.sizes());

    size_t workspace_size;
    auto x_descs_arr = descs.get_x_descs();
    auto y_descs_arr = descs.get_y_descs();

    //Allocate workspace size.
    MIOPEN_CHECK(miopenGetRNNWorkspaceSize(handle, descs.rnn_desc.desc(), fn.tensors.seq_length, x_descs_arr.data(), &workspace_size));
    auto workspace = at::empty(workspace_size, input.options().dtype(kByte));

    //Train or inference.
    Tensor reserve;
    if (fn_train) { //Train.
        size_t reserver_size;
        MIOPEN_CHECK(miopenGetRNNTrainingReserveSize(handle, descs.rnn_desc.desc(), fn.tensors.seq_length, x_descs_arr.data(), &reserver_size));
        reserve = at::empty(reserver_size, input.options().dtype(kByte));
        MIOPEN_CHECK(miopenRNNForwardTraining(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, reserve.mutable_data_ptr(), reserver_size ));
    } else { //Inference.
        reserve = at::empty({0}, input.options().dtype(kByte));
        MIOPEN_CHECK(miopenRNNForwardInference(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));
    }

    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, Tensor> miopen_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_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 = getMiopenDataType(input);
    fn.rnn.set(fn_mode, fn_hidden_size, fn_num_layers, fn_bidirectional, datatype, miopenRNNwithBias);
    fn.tensors.set(input.sizes(), fn_batch_sizes, batch_first);

    auto handle = getMiopenHandle();

    if(fn.rnn.rnn_mode != miopenLSTM) {
        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 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());
    auto dhy = grad_hy.contiguous().view(hidden_size);
    auto dcy = grad_cy.defined() ? grad_cy.contiguous().view(hidden_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(hidden_size, cx.options()) : Tensor();

    TORCH_CHECK(fn_train, "miopen 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(hidden_size),
        "Expected cell size ", IntArrayRef{hidden_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(hidden_size),
        "Expected d_cell size ", IntArrayRef{hidden_size}, ", got ", dcy.sizes());

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

    miopenRNNAlgo_t algo = miopenRNNdefault;
    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();

    MIOPEN_CHECK(miopenGetRNNWorkspaceSize(
        handle,
        descs.rnn_desc.desc(),
        fn.tensors.seq_length,
        x_descs_arr.data(),
        &workspace_size
        ));
    auto workspace = at::empty(workspace_size, input.options().dtype(kByte));

    MIOPEN_CHECK(miopenRNNBackwardData(
        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, workspace);
}

std::vector<Tensor> miopen_rnn_backward_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_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, const Tensor& fn_workspace
        ) {
    MatrixRef<Tensor> weight{ weight_arr, static_cast<size_t>(weight_stride0) };

    auto input = input_r;
    auto output = output_r;

    RNNParams fn;
    auto datatype = getMiopenDataType(input);
    miopenRNNBiasMode_t bias_mode = (weight_stride0 == 4) ? miopenRNNwithBias : miopenRNNNoBias;
    fn.rnn.set(fn_mode, fn_hidden_size, fn_num_layers, fn_bidirectional, datatype, bias_mode);
    fn.tensors.set(input.sizes(), fn_batch_sizes, batch_first);

    auto handle = getMiopenHandle();

    if (fn.rnn.rnn_mode != miopenLSTM) {
        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, "miopen 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());

    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());

    miopenRNNAlgo_t algo = miopenRNNdefault;
    fn.rnn.set_algo(algo);
    RNNDescriptors descs(fn, handle, x, y, hx, cx);

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

    auto x_descs_arr = descs.get_x_descs();
    auto y_descs_arr = descs.get_y_descs();

    MIOPEN_CHECK(miopenRNNBackwardWeights(
        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(),
        w_desc.desc(), dw.data_ptr(),
        fn_workspace.data_ptr(), fn_workspace.size(0),
        fn_reserve.data_ptr(), fn_reserve.size(0)
        ));

    auto [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;
    }
}

std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>> miopen_rnn_backward(
        const Tensor& input, TensorList weight, int64_t weight_stride0, const Tensor& weight_buf, const Tensor& hx, const std::optional<Tensor>& cx_opt,
        const Tensor& output, const std::optional<Tensor>& grad_output_r_opt, const std::optional<Tensor>& grad_hy_r_opt, const std::optional<Tensor>& grad_cy_r_opt, int64_t mode, int64_t hidden_size, int64_t num_layers, bool batch_first,
        double dropout, bool train, bool bidirectional, IntArrayRef batch_sizes, const std::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;

    auto [dx, dhx, dcx, ws] = at::native::miopen_rnn_backward_input(input, weight_buf, hx, cx, output, grad_output, grad_hy, grad_cy, mode, hidden_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::miopen_rnn_backward_weight(input, weight, weight_stride0, weight_buf, hx, cx, output, mode, hidden_size, num_layers, batch_first, dropout, train, bidirectional, batch_sizes, dropout_state, reserve, ws);
        if (mode > 1) {
            for (const auto i : c10::irange(dw.size())) {
                dw[i] = permute_wei_for_miopen(dw[i], mode);
            }
        }
    }
    return std::tuple<Tensor, Tensor, Tensor, std::vector<Tensor>>{dx, dhx, dcx, dw};
}

namespace {

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);
}

template<typename hidden_type>
std::pair<Tensor, hidden_type> _miopen_impl(
    const Tensor& input, const Tensor& _batch_sizes, const hidden_type& hidden,
    TensorList params, bool has_biases, miopenRNNMode_t mode,
    int64_t num_layers, double dropout_p, bool train, bool bidirectional) {
    auto [hx, cx] = unpack_hidden(hidden);
    int64_t hidden_size = hx.size(2);

    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)) };

    Tensor dropout_state = at::empty({0}, input.options());

    auto miopen_output = at::miopen_rnn(
        input, params, has_biases ? 4 : 2,
        hx, cx, static_cast<int>(mode), hidden_size, num_layers, /*batch_first=*/false,
        dropout_p, train, bidirectional, batch_sizes, dropout_state);

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

template<typename hidden_type>
std::pair<Tensor, hidden_type> _miopen_impl(
    const Tensor& input, const hidden_type& hidden,
    TensorList params, bool has_biases, miopenRNNMode_t mode,
    int64_t num_layers, double dropout_p, bool train, bool bidirectional, bool batch_first) {
    auto [hx, cx] = unpack_hidden(hidden);
    int64_t hidden_size = hx.size(2);

    Tensor dropout_state = at::empty({0}, input.options());

    auto miopen_output = at::miopen_rnn(
        input, params, has_biases ? 4 : 2,
        hx, cx, static_cast<int>(mode), hidden_size, num_layers, batch_first, dropout_p,
        train, bidirectional, /*batch_sizes=*/{}, dropout_state);

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

#define ONE_HIDDEN_RNN(NAME, MODE)                                             \
void NAME##_miopen(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) = _miopen_impl(input, hx, params, has_biases,           \
      MODE, num_layers, dropout_p, train, bidirectional, batch_first);         \
}                                                                              \
                                                                               \
void NAME##_packed_miopen(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) = _miopen_impl(data, batch_sizes, hx, params,           \
      has_biases, MODE, num_layers, dropout_p, train, bidirectional);          \
}                                                                              \
                                                                               \
REGISTER_CUDA_DISPATCH(NAME##_miopen_stub, &NAME##_miopen);                    \
REGISTER_CUDA_DISPATCH(NAME##_packed_miopen_stub, &NAME##_packed_miopen);

ONE_HIDDEN_RNN(gru, miopenGRU)
ONE_HIDDEN_RNN(rnn_tanh, miopenRNNTANH)
ONE_HIDDEN_RNN(rnn_relu, miopenRNNRELU)

void lstm_miopen(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 = _miopen_impl(input, std::make_tuple(hx[0], hx[1]), params, has_biases,
        miopenLSTM, 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_miopen(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 = _miopen_impl(data, batch_sizes, std::make_tuple(hx[0], hx[1]),
        params, has_biases, miopenLSTM, 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_miopen_stub, &lstm_miopen);
REGISTER_CUDA_DISPATCH(lstm_packed_miopen_stub, &lstm_packed_miopen);

} // anonymous namespace
}} //namespace native.

#endif