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/**
 * @file cudnn_adv.h
 * @brief cuDNN Advanced library - RNN, CTC loss, multi-head attention, and related operations.
 * @since cuDNN 9.0.0
 */

#if !defined(CUDNN_ADV_H_)
#define CUDNN_ADV_H_

#include <stdint.h>

#include "cudnn_version.h"
#include "cudnn_ops.h"

/* These version numbers are autogenerated, do not edit manually. */
#define CUDNN_ADV_MAJOR 9
#define CUDNN_ADV_MINOR 25
#define CUDNN_ADV_PATCH 1

#if (CUDNN_ADV_MAJOR != CUDNN_MAJOR) || (CUDNN_ADV_MINOR != CUDNN_MINOR) || (CUDNN_ADV_PATCH != CUDNN_PATCHLEVEL)
#error Version mismatch in cuDNN ADV INFER!!!
#endif

#if defined(__cplusplus)
extern "C" {
#endif

/* BASIC RNN API */

/**
 * @brief RNN computation algorithm selection.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RNN_ALGO_STANDARD               = 0, /**< Standard cuBLASLt-based algorithm. @since cuDNN 9.0.0 */
    CUDNN_RNN_ALGO_PERSIST_STATIC         = 1, /**< Persistent kernel with static compilation. @since cuDNN 9.0.0 */
    CUDNN_RNN_ALGO_PERSIST_DYNAMIC        = 2, /**< Runtime-compiled persistent kernels via NVRTC. @since cuDNN 9.0.0 */
    CUDNN_RNN_ALGO_PERSIST_STATIC_SMALL_H = 3, /**< Register-based approach for smaller hidden states. @since cuDNN 9.0.0 */
    CUDNN_RNN_ALGO_COUNT                  = 4, /**< Number of RNN algorithms. @since cuDNN 9.0.0 */
} cudnnRNNAlgo_t;

/**
 * @brief Specifies inference or training mode for RNN forward pass.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_FWD_MODE_INFERENCE = 0, /**< Inference mode. @since cuDNN 9.0.0 */
    CUDNN_FWD_MODE_TRAINING  = 1, /**< Training mode (reserves space for backward pass). @since cuDNN 9.0.0 */
} cudnnForwardMode_t;

/**
 * @brief RNN cell type selection.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RNN_RELU = 0, /**< Single-gate RNN cell with ReLU activation. @since cuDNN 9.0.0 */
    CUDNN_RNN_TANH = 1, /**< Single-gate RNN cell with tanh activation. @since cuDNN 9.0.0 */
    CUDNN_LSTM     = 2, /**< Four-gate LSTM with optional recurrent projection and clipping. @since cuDNN 9.0.0 */
    CUDNN_GRU      = 3, /**< Three-gate GRU network. @since cuDNN 9.0.0 */
} cudnnRNNMode_t;

/**
 * @brief Number of bias vectors used in RNN cell formulas.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RNN_NO_BIAS         = 0, /**< No biases used. @since cuDNN 9.0.0 */
    CUDNN_RNN_SINGLE_INP_BIAS = 1, /**< One input bias in input GEMM. @since cuDNN 9.0.0 */
    CUDNN_RNN_DOUBLE_BIAS     = 2, /**< Two bias vectors (default). @since cuDNN 9.0.0 */
    CUDNN_RNN_SINGLE_REC_BIAS = 3  /**< One recurrent bias in recurrent GEMM. @since cuDNN 9.0.0 */
} cudnnRNNBiasMode_t;

/**
 * @brief RNN recurrence direction mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_UNIDIRECTIONAL = 0, /**< Single direction, first input to last. @since cuDNN 9.0.0 */
    CUDNN_BIDIRECTIONAL  = 1, /**< Both directions, outputs concatenated at each layer. @since cuDNN 9.0.0 */
} cudnnDirectionMode_t;

/**
 * @brief RNN first layer input behavior.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_LINEAR_INPUT = 0, /**< Biased matrix multiplication at first layer. @since cuDNN 9.0.0 */
    CUDNN_SKIP_INPUT   = 1, /**< Fixed identity matrix at first layer (no operation). @since cuDNN 9.0.0 */
} cudnnRNNInputMode_t;

/**
 * @brief LSTM cell clipping mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RNN_CLIP_NONE   = 0, /**< Disables LSTM cell clipping. @since cuDNN 9.0.0 */
    CUDNN_RNN_CLIP_MINMAX = 1, /**< Enables LSTM cell clipping. @since cuDNN 9.0.0 */
} cudnnRNNClipMode_t;

/**
 * @brief RNN data memory layout.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_UNPACKED   = 0, /**< Padded, sequence-major layout. @since cuDNN 9.0.0 */
    CUDNN_RNN_DATA_LAYOUT_SEQ_MAJOR_PACKED     = 1, /**< Packed, sequence-major layout. @since cuDNN 9.0.0 */
    CUDNN_RNN_DATA_LAYOUT_BATCH_MAJOR_UNPACKED = 2, /**< Padded, batch-major layout. @since cuDNN 9.0.0 */
} cudnnRNNDataLayout_t;

/* For auxFlags in cudnnSetRNNDescriptor_v8() */
#define CUDNN_RNN_PADDED_IO_DISABLED 0
#define CUDNN_RNN_PADDED_IO_ENABLED (1U << 0)

/** @brief Opaque RNN descriptor. @since cuDNN 9.0.0 */
struct cudnnRNNStruct;
typedef struct cudnnRNNStruct *cudnnRNNDescriptor_t;

/** @brief Opaque RNN data descriptor. @since cuDNN 9.0.0 */
struct cudnnRNNDataStruct;
typedef struct cudnnRNNDataStruct *cudnnRNNDataDescriptor_t;

/**
 * @brief Creates an RNN descriptor.
 *
 * @param[out] rnnDesc  Pointer to the created RNN descriptor.
 *
 * @retval CUDNN_STATUS_SUCCESS  Descriptor created successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnDestroyRNNDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateRNNDescriptor(cudnnRNNDescriptor_t *rnnDesc);

/**
 * @brief Destroys an RNN descriptor.
 *
 * @param[in] rnnDesc  RNN descriptor to destroy.
 *
 * @retval CUDNN_STATUS_SUCCESS  Descriptor destroyed successfully.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCreateRNNDescriptor
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyRNNDescriptor(cudnnRNNDescriptor_t rnnDesc);

/*
 * mathPrec in cudnnSetRNNDescriptor_v8() specifies compute precision.
 * Compute precision is further modified by mathType that sets the
 * preferred option for using NVIDIA Tensor Cores.  dataType specify
 * input/output data type and weight/bias type.
 */

/**
 * @brief Configures an RNN descriptor with network parameters.
 *
 * @param[in,out] rnnDesc     RNN descriptor to configure.
 * @param[in]     algo        RNN computation algorithm.
 * @param[in]     cellMode    RNN cell type (RELU, TANH, LSTM, GRU).
 * @param[in]     biasMode    Bias configuration.
 * @param[in]     dirMode     Unidirectional or bidirectional.
 * @param[in]     inputMode   First layer input behavior.
 * @param[in]     dataType    Input/output and weight data type.
 * @param[in]     mathPrec    Compute precision.
 * @param[in]     mathType    Tensor Core usage preference.
 * @param[in]     inputSize   Input vector size.
 * @param[in]     hiddenSize  Hidden state size.
 * @param[in]     projSize    Recurrent projection size (0 to disable).
 * @param[in]     numLayers   Number of stacked RNN layers.
 * @param[in]     dropoutDesc Dropout descriptor for inter-layer dropout.
 * @param[in]     auxFlags    Auxiliary flags (e.g., CUDNN_RNN_PADDED_IO_ENABLED).
 *
 * @retval CUDNN_STATUS_SUCCESS       Descriptor configured successfully.
 * @retval CUDNN_STATUS_BAD_PARAM     Invalid parameter.
 * @retval CUDNN_STATUS_NOT_SUPPORTED Unsupported configuration.
 *
 * @since cuDNN 9.0.0
 * @see cudnnGetRNNDescriptor_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
                         cudnnRNNAlgo_t algo,
                         cudnnRNNMode_t cellMode,
                         cudnnRNNBiasMode_t biasMode,
                         cudnnDirectionMode_t dirMode,
                         cudnnRNNInputMode_t inputMode,
                         cudnnDataType_t dataType,
                         cudnnDataType_t mathPrec,
                         cudnnMathType_t mathType,
                         int32_t inputSize,
                         int32_t hiddenSize,
                         int32_t projSize,
                         int32_t numLayers,
                         cudnnDropoutDescriptor_t dropoutDesc,
                         uint32_t auxFlags);

/**
 * @brief Retrieves RNN descriptor parameters.
 *
 * @param[in]  rnnDesc     RNN descriptor to query.
 * @param[out] algo        RNN algorithm.
 * @param[out] cellMode    Cell type.
 * @param[out] biasMode    Bias configuration.
 * @param[out] dirMode     Direction mode.
 * @param[out] inputMode   Input mode.
 * @param[out] dataType    Data type.
 * @param[out] mathPrec    Math precision.
 * @param[out] mathType    Math type.
 * @param[out] inputSize   Input size.
 * @param[out] hiddenSize  Hidden size.
 * @param[out] projSize    Projection size.
 * @param[out] numLayers   Number of layers.
 * @param[out] dropoutDesc Dropout descriptor.
 * @param[out] auxFlags    Auxiliary flags.
 *
 * @retval CUDNN_STATUS_SUCCESS  Query succeeded.
 *
 * @since cuDNN 9.0.0
 * @see cudnnSetRNNDescriptor_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetRNNDescriptor_v8(cudnnRNNDescriptor_t rnnDesc,
                         cudnnRNNAlgo_t *algo,
                         cudnnRNNMode_t *cellMode,
                         cudnnRNNBiasMode_t *biasMode,
                         cudnnDirectionMode_t *dirMode,
                         cudnnRNNInputMode_t *inputMode,
                         cudnnDataType_t *dataType,
                         cudnnDataType_t *mathPrec,
                         cudnnMathType_t *mathType,
                         int32_t *inputSize,
                         int32_t *hiddenSize,
                         int32_t *projSize,
                         int32_t *numLayers,
                         cudnnDropoutDescriptor_t *dropoutDesc,
                         uint32_t *auxFlags);

/**
 * @brief Configures LSTM cell clipping parameters.
 *
 * @deprecated Since cuDNN 9.0.0. Use cudnnRNNSetClip_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnRNNSetClip_v8(cudnnRNNDescriptor_t rnnDesc,
                   cudnnRNNClipMode_t clipMode,
                   cudnnNanPropagation_t clipNanOpt,
                   double lclip,
                   double rclip);

/**
 * @brief Configures LSTM cell clipping parameters.
 *
 * @param[in,out] rnnDesc   RNN descriptor.
 * @param[in]     clipMode  Clipping mode (NONE or MINMAX).
 * @param[in]     lclip     Left (minimum) clipping value.
 * @param[in]     rclip     Right (maximum) clipping value.
 *
 * @retval CUDNN_STATUS_SUCCESS  Clipping configured.
 *
 * @since cuDNN 9.0.0
 * @see cudnnRNNGetClip_v9
 */
cudnnStatus_t CUDNNWINAPI
cudnnRNNSetClip_v9(cudnnRNNDescriptor_t rnnDesc, cudnnRNNClipMode_t clipMode, double lclip, double rclip);

/**
 * @brief Retrieves LSTM cell clipping settings.
 *
 * @deprecated Since cuDNN 9.0.0. Use cudnnRNNGetClip_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnRNNGetClip_v8(cudnnRNNDescriptor_t rnnDesc,
                   cudnnRNNClipMode_t *clipMode,
                   cudnnNanPropagation_t *clipNanOpt,
                   double *lclip,
                   double *rclip);

/**
 * @brief Retrieves LSTM cell clipping settings.
 *
 * @param[in]  rnnDesc   RNN descriptor.
 * @param[out] clipMode  Clipping mode.
 * @param[out] lclip     Left clipping value.
 * @param[out] rclip     Right clipping value.
 *
 * @retval CUDNN_STATUS_SUCCESS  Query succeeded.
 *
 * @since cuDNN 9.0.0
 * @see cudnnRNNSetClip_v9
 */
cudnnStatus_t CUDNNWINAPI
cudnnRNNGetClip_v9(cudnnRNNDescriptor_t rnnDesc, cudnnRNNClipMode_t *clipMode, double *lclip, double *rclip);

/**
 * @brief Compiles persistent RNN code using NVRTC for dynamic algorithm.
 *
 * @param[in] handle     cuDNN handle.
 * @param[in] rnnDesc    RNN descriptor (must use PERSIST_DYNAMIC algorithm).
 * @param[in] miniBatch  Exact mini-batch size for compilation.
 *
 * @retval CUDNN_STATUS_SUCCESS       Compilation succeeded.
 * @retval CUDNN_STATUS_NOT_SUPPORTED Unsupported configuration.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnBuildRNNDynamic(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, int miniBatch);

/**
 * @brief Computes workspace and reserve space buffer sizes for RNN.
 *
 * @param[in]  handle          cuDNN handle.
 * @param[in]  rnnDesc         RNN descriptor.
 * @param[in]  fwdMode         Inference or training mode.
 * @param[in]  xDesc           Input data descriptor.
 * @param[out] workSpaceSize   Required workspace size in bytes.
 * @param[out] reserveSpaceSize Required reserve space size in bytes (training only).
 *
 * @retval CUDNN_STATUS_SUCCESS  Sizes computed successfully.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetRNNTempSpaceSizes(cudnnHandle_t handle,
                          cudnnRNNDescriptor_t rnnDesc,
                          cudnnForwardMode_t fwdMode,
                          cudnnRNNDataDescriptor_t xDesc,
                          size_t *workSpaceSize,
                          size_t *reserveSpaceSize);

/**
 * @brief Reports required GPU memory for all RNN weight parameters.
 *
 * @param[in]  handle          cuDNN handle.
 * @param[in]  rnnDesc         RNN descriptor.
 * @param[out] weightSpaceSize Required weight space size in bytes.
 *
 * @retval CUDNN_STATUS_SUCCESS  Size computed.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetRNNWeightSpaceSize(cudnnHandle_t handle, cudnnRNNDescriptor_t rnnDesc, size_t *weightSpaceSize);

/**
 * @brief Obtains start address and shape of RNN weight matrices and bias vectors.
 *
 * @param[in]  handle          cuDNN handle.
 * @param[in]  rnnDesc         RNN descriptor.
 * @param[in]  pseudoLayer     Pseudo-layer index (physical layer and direction).
 * @param[in]  weightSpaceSize Total weight space size.
 * @param[in]  weightSpace     Pointer to weight space.
 * @param[in]  linLayerID      Linear layer ID within the RNN cell.
 * @param[out] mDesc           Tensor descriptor for the weight matrix.
 * @param[out] mAddr           Start address of the weight matrix.
 * @param[out] bDesc           Tensor descriptor for the bias vector.
 * @param[out] bAddr           Start address of the bias vector.
 *
 * @retval CUDNN_STATUS_SUCCESS  Parameters retrieved.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetRNNWeightParams(cudnnHandle_t handle,
                        cudnnRNNDescriptor_t rnnDesc,
                        int32_t pseudoLayer,
                        size_t weightSpaceSize,
                        const void *weightSpace,
                        int32_t linLayerID,
                        cudnnTensorDescriptor_t mDesc,
                        void **mAddr,
                        cudnnTensorDescriptor_t bDesc,
                        void **bAddr);

/**
 * @brief Creates an RNN data descriptor.
 * @param[out] rnnDataDesc  Pointer to created descriptor.
 * @retval CUDNN_STATUS_SUCCESS  Descriptor created.
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateRNNDataDescriptor(cudnnRNNDataDescriptor_t *rnnDataDesc);

/**
 * @brief Destroys an RNN data descriptor.
 * @param[in] rnnDataDesc  Descriptor to destroy.
 * @retval CUDNN_STATUS_SUCCESS  Descriptor destroyed.
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc);

/**
 * @brief Configures an RNN data descriptor with layout and sequence information.
 *
 * @param[in,out] rnnDataDesc     RNN data descriptor.
 * @param[in]     dataType        Data type.
 * @param[in]     layout          Data layout (sequence-major or batch-major).
 * @param[in]     maxSeqLength    Maximum sequence length.
 * @param[in]     batchSize       Batch size.
 * @param[in]     vectorSize      Input vector size.
 * @param[in]     seqLengthArray  Length of each sequence in the batch.
 * @param[in,out] paddingFill     Symbol for filling padding positions.
 *
 * @retval CUDNN_STATUS_SUCCESS  Descriptor configured.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
                          cudnnDataType_t dataType,
                          cudnnRNNDataLayout_t layout,
                          int maxSeqLength,
                          int batchSize,
                          int vectorSize,
                          const int seqLengthArray[], /* length of each sequence in the batch */
                          void *paddingFill);         /* symbol for filling padding position in output */

/**
 * @brief Retrieves RNN data descriptor parameters.
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetRNNDataDescriptor(cudnnRNNDataDescriptor_t rnnDataDesc,
                          cudnnDataType_t *dataType,
                          cudnnRNNDataLayout_t *layout,
                          int *maxSeqLength,
                          int *batchSize,
                          int *vectorSize,
                          int arrayLengthRequested,
                          int seqLengthArray[],
                          void *paddingFill);

/**
 * @brief Computes the forward pass of an RNN network.
 *
 * @param[in]     handle           cuDNN handle.
 * @param[in]     rnnDesc          RNN descriptor.
 * @param[in]     fwdMode          Inference or training mode.
 * @param[in]     devSeqLengths    Per-batch sequence lengths (device memory).
 * @param[in]     xDesc            Input data descriptor.
 * @param[in]     x                Input data pointer.
 * @param[in]     yDesc            Output data descriptor.
 * @param[out]    y                Output data pointer.
 * @param[in]     hDesc            Hidden state descriptor.
 * @param[in]     hx               Initial hidden state (NULL for zero).
 * @param[out]    hy               Final hidden state (NULL to discard).
 * @param[in]     cDesc            Cell state descriptor (LSTM only).
 * @param[in]     cx               Initial cell state (NULL for zero).
 * @param[out]    cy               Final cell state (NULL to discard).
 * @param[in]     weightSpaceSize  Weight space size in bytes.
 * @param[in]     weightSpace      Weight space pointer.
 * @param[in]     workSpaceSize    Workspace size in bytes.
 * @param[in,out] workSpace        Workspace pointer.
 * @param[in]     reserveSpaceSize Reserve space size (training only).
 * @param[in,out] reserveSpace     Reserve space pointer (training only).
 *
 * @retval CUDNN_STATUS_SUCCESS        Forward pass completed.
 * @retval CUDNN_STATUS_BAD_PARAM      Invalid parameter.
 * @retval CUDNN_STATUS_EXECUTION_FAILED  Execution failed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnRNNBackwardData_v8, cudnnRNNBackwardWeights_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnRNNForward(cudnnHandle_t handle,
                cudnnRNNDescriptor_t rnnDesc,
                cudnnForwardMode_t fwdMode,
                const int32_t devSeqLengths[],
                cudnnRNNDataDescriptor_t xDesc,
                const void *x,
                cudnnRNNDataDescriptor_t yDesc,
                void *y,
                cudnnTensorDescriptor_t hDesc,
                const void *hx,
                void *hy,
                cudnnTensorDescriptor_t cDesc,
                const void *cx,
                void *cy,
                size_t weightSpaceSize,
                const void *weightSpace,
                size_t workSpaceSize,
                void *workSpace,
                size_t reserveSpaceSize,
                void *reserveSpace);

/* Sequence data descriptor */

/**
 * @brief Sequence data dimension indices.
 * @deprecated Since cuDNN 9.0.0. Use RNN data descriptors instead.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_SEQDATA_TIME_DIM  = 0, /**< Time/sequence length dimension. @since cuDNN 9.0.0 */
    CUDNN_SEQDATA_BATCH_DIM = 1, /**< Batch dimension. @since cuDNN 9.0.0 */
    CUDNN_SEQDATA_BEAM_DIM  = 2, /**< Beam dimension. @since cuDNN 9.0.0 */
    CUDNN_SEQDATA_VECT_DIM  = 3  /**< Vector dimension. @since cuDNN 9.0.0 */
} cudnnSeqDataAxis_t;

/** @brief Opaque sequence data descriptor. @deprecated Since cuDNN 9.0.0. @since cuDNN 9.0.0 */
struct cudnnSeqDataStruct;
typedef struct cudnnSeqDataStruct *cudnnSeqDataDescriptor_t CUDNN_DEPRECATED;

#define CUDNN_SEQDATA_DIM_COUNT 4 /* dimension count */

/**
 * @brief Creates a sequence data descriptor.
 * @deprecated Since cuDNN 9.0.0. Use RNN data descriptors instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateSeqDataDescriptor(cudnnSeqDataDescriptor_t *seqDataDesc);

/**
 * @brief Destroys a sequence data descriptor.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroySeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc);

/**
 * @brief Configures a sequence data descriptor.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetSeqDataDescriptor(cudnnSeqDataDescriptor_t seqDataDesc,
                          cudnnDataType_t dataType,
                          int nbDims,
                          const int dimA[],
                          const cudnnSeqDataAxis_t axes[],
                          size_t seqLengthArraySize,
                          const int seqLengthArray[],
                          void *paddingFill);

/**
 * @brief Retrieves sequence data descriptor parameters.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetSeqDataDescriptor(const cudnnSeqDataDescriptor_t seqDataDesc,
                          cudnnDataType_t *dataType,
                          int *nbDims,
                          int nbDimsRequested,
                          int dimA[],
                          cudnnSeqDataAxis_t axes[],
                          size_t *seqLengthArraySize,
                          size_t seqLengthSizeRequested,
                          int seqLengthArray[],
                          void *paddingFill);

/* Multihead Attention */

/*
 * Multi-head attention options passed via 'attnMode' in cudnnSetAttnDescriptor().
 * Use the bitwise OR operator to combine several settings listed below.  Additional
 * minor options can be added here w/o changing or introducing new API functions.
 */
#define CUDNN_ATTN_QUERYMAP_ALL_TO_ONE 0         /* multiple Q-s map to a single (K,V) set when beam size > 1 */
#define CUDNN_ATTN_QUERYMAP_ONE_TO_ONE (1U << 0) /* multiple Q-s map to multiple (K,V) sets when beam size > 1 */
#define CUDNN_ATTN_DISABLE_PROJ_BIASES 0         /* no biases in attention input and output projections */
#define CUDNN_ATTN_ENABLE_PROJ_BIASES (1U << 1)  /* use biases in attention input and output projections */

/** @brief Opaque multi-head attention descriptor. @deprecated Since cuDNN 9.0.0. @since cuDNN 9.0.0 */
struct cudnnAttnStruct;
typedef struct cudnnAttnStruct *cudnnAttnDescriptor_t CUDNN_DEPRECATED;

/**
 * @brief Creates a multi-head attention descriptor.
 * @deprecated Since cuDNN 9.0.0. Use graph API SDPA operations instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnCreateAttnDescriptor(cudnnAttnDescriptor_t *attnDesc);

/**
 * @brief Destroys a multi-head attention descriptor.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnDestroyAttnDescriptor(cudnnAttnDescriptor_t attnDesc);

/**
 * @brief Configures a multi-head attention descriptor.
 * @deprecated Since cuDNN 9.0.0. Use graph API SDPA operations instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
                       unsigned attnMode,
                       int nHeads,
                       double smScaler,
                       cudnnDataType_t dataType,
                       cudnnDataType_t computePrec,
                       cudnnMathType_t mathType,
                       cudnnDropoutDescriptor_t attnDropoutDesc,
                       cudnnDropoutDescriptor_t postDropoutDesc,
                       int qSize,
                       int kSize,
                       int vSize,
                       int qProjSize,
                       int kProjSize,
                       int vProjSize,
                       int oProjSize,
                       int qoMaxSeqLength,
                       int kvMaxSeqLength,
                       int maxBatchSize,
                       int maxBeamSize);

/**
 * @brief Retrieves multi-head attention descriptor parameters.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetAttnDescriptor(cudnnAttnDescriptor_t attnDesc,
                       unsigned *attnMode,
                       int *nHeads,
                       double *smScaler,
                       cudnnDataType_t *dataType,
                       cudnnDataType_t *computePrec,
                       cudnnMathType_t *mathType,
                       cudnnDropoutDescriptor_t *attnDropoutDesc,
                       cudnnDropoutDescriptor_t *postDropoutDesc,
                       int *qSize,
                       int *kSize,
                       int *vSize,
                       int *qProjSize,
                       int *kProjSize,
                       int *vProjSize,
                       int *oProjSize,
                       int *qoMaxSeqLength,
                       int *kvMaxSeqLength,
                       int *maxBatchSize,
                       int *maxBeamSize);

/**
 * @brief Computes weight, workspace, and reserve space sizes for multi-head attention.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetMultiHeadAttnBuffers(cudnnHandle_t handle,
                             const cudnnAttnDescriptor_t attnDesc,
                             size_t *weightSizeInBytes,
                             size_t *workSpaceSizeInBytes,
                             size_t *reserveSpaceSizeInBytes);

/**
 * @brief Specifies weight/bias groups in multi-head attention layers.
 * @deprecated Since cuDNN 9.0.0. Use graph API SDPA operations instead.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_MH_ATTN_Q_WEIGHTS = 0, /**< Query projection weights. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_K_WEIGHTS = 1, /**< Key projection weights. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_V_WEIGHTS = 2, /**< Value projection weights. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_O_WEIGHTS = 3, /**< Output projection weights. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_Q_BIASES  = 4, /**< Query projection biases. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_K_BIASES  = 5, /**< Key projection biases. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_V_BIASES  = 6, /**< Value projection biases. @since cuDNN 9.0.0 */
    CUDNN_MH_ATTN_O_BIASES  = 7, /**< Output projection biases. @since cuDNN 9.0.0 */
} cudnnMultiHeadAttnWeightKind_t;

#define CUDNN_ATTN_WKIND_COUNT 8 /* Number of attention weight/bias tensors */

/**
 * @brief Obtains shape and start address of attention weight/bias tensors.
 * @deprecated Since cuDNN 9.0.0.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetMultiHeadAttnWeights(cudnnHandle_t handle,
                             const cudnnAttnDescriptor_t attnDesc,
                             cudnnMultiHeadAttnWeightKind_t wKind,
                             size_t weightSizeInBytes,
                             const void *weights,
                             cudnnTensorDescriptor_t wDesc,
                             void **wAddr);

/**
 * @brief Computes multi-head attention forward pass.
 * @deprecated Since cuDNN 9.0.0. Use graph API SDPA operations instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnMultiHeadAttnForward(cudnnHandle_t handle,
                          const cudnnAttnDescriptor_t attnDesc,
                          int currIdx,
                          const int loWinIdx[],
                          const int hiWinIdx[],
                          const int devSeqLengthsQO[],
                          const int devSeqLengthsKV[],
                          const cudnnSeqDataDescriptor_t qDesc,
                          const void *queries,
                          const void *residuals,
                          const cudnnSeqDataDescriptor_t kDesc,
                          const void *keys,
                          const cudnnSeqDataDescriptor_t vDesc,
                          const void *values,
                          const cudnnSeqDataDescriptor_t oDesc,
                          void *out,
                          size_t weightSizeInBytes,
                          const void *weights,
                          size_t workSpaceSizeInBytes,
                          void *workSpace,
                          size_t reserveSpaceSizeInBytes,
                          void *reserveSpace);

/*
 * \brief Cross-library version checker.
 * This function is implemented differently in each sub-library. Each sublib
 * checks whether its own version matches that of its dependencies.
 * \returns CUDNN_STATUS_SUCCESS if the version check passes,
 *          CUDNN_STATUS_SUBLIBRARY_VERSION_MISMATCH if the versions are inconsistent.
 */
cudnnStatus_t CUDNNWINAPI
cudnnAdvVersionCheck(void);

/**
 * @brief Weight gradient accumulation mode.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_WGRAD_MODE_ADD = 0, /**< Add partial gradients to existing buffer. @since cuDNN 9.0.0 */
    CUDNN_WGRAD_MODE_SET = 1, /**< Overwrite buffer with partial gradients. @since cuDNN 9.0.0 */
} cudnnWgradMode_t;

/**
 * @brief Computes RNN data gradients (backward pass with respect to inputs).
 *
 * @param[in]     handle           cuDNN handle.
 * @param[in]     rnnDesc          RNN descriptor.
 * @param[in]     devSeqLengths    Per-batch sequence lengths (device memory).
 * @param[in]     yDesc            Output data descriptor.
 * @param[in]     y                Forward output data.
 * @param[in]     dy               Output gradient data.
 * @param[in]     xDesc            Input data descriptor.
 * @param[out]    dx               Computed input gradient.
 * @param[in]     hDesc            Hidden state descriptor.
 * @param[in]     hx               Initial hidden state from forward pass.
 * @param[in]     dhy              Hidden state gradient (from upstream).
 * @param[out]    dhx              Computed initial hidden state gradient.
 * @param[in]     cDesc            Cell state descriptor (LSTM only).
 * @param[in]     cx               Initial cell state from forward pass.
 * @param[in]     dcy              Cell state gradient (from upstream).
 * @param[out]    dcx              Computed initial cell state gradient.
 * @param[in]     weightSpaceSize  Weight space size.
 * @param[in]     weightSpace      Weight space pointer.
 * @param[in]     workSpaceSize    Workspace size.
 * @param[in,out] workSpace        Workspace pointer.
 * @param[in]     reserveSpaceSize Reserve space size.
 * @param[in,out] reserveSpace     Reserve space (from forward training pass).
 *
 * @retval CUDNN_STATUS_SUCCESS  Backward data pass completed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnRNNForward, cudnnRNNBackwardWeights_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnRNNBackwardData_v8(cudnnHandle_t handle,
                        cudnnRNNDescriptor_t rnnDesc,
                        const int32_t devSeqLengths[],
                        cudnnRNNDataDescriptor_t yDesc,
                        const void *y,
                        const void *dy,
                        cudnnRNNDataDescriptor_t xDesc,
                        void *dx,
                        cudnnTensorDescriptor_t hDesc,
                        const void *hx,
                        const void *dhy,
                        void *dhx,
                        cudnnTensorDescriptor_t cDesc,
                        const void *cx,
                        const void *dcy,
                        void *dcx,
                        size_t weightSpaceSize,
                        const void *weightSpace,
                        size_t workSpaceSize,
                        void *workSpace,
                        size_t reserveSpaceSize,
                        void *reserveSpace);

/**
 * @brief Computes RNN weight gradients (backward pass with respect to parameters).
 *
 * @param[in]     handle           cuDNN handle.
 * @param[in]     rnnDesc          RNN descriptor.
 * @param[in]     addGrad          Accumulate (ADD) or overwrite (SET) gradients.
 * @param[in]     devSeqLengths    Per-batch sequence lengths (device memory).
 * @param[in]     xDesc            Input data descriptor.
 * @param[in]     x                Input data.
 * @param[in]     hDesc            Hidden state descriptor.
 * @param[in]     hx               Initial hidden state.
 * @param[in]     yDesc            Output data descriptor.
 * @param[in]     y                Forward output data.
 * @param[in]     weightSpaceSize  Weight space size.
 * @param[in,out] dweightSpace     Computed weight gradients.
 * @param[in]     workSpaceSize    Workspace size.
 * @param[in,out] workSpace        Workspace pointer.
 * @param[in]     reserveSpaceSize Reserve space size.
 * @param[in,out] reserveSpace     Reserve space (from forward training pass).
 *
 * @retval CUDNN_STATUS_SUCCESS  Weight gradients computed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnRNNForward, cudnnRNNBackwardData_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnRNNBackwardWeights_v8(cudnnHandle_t handle,
                           cudnnRNNDescriptor_t rnnDesc,
                           cudnnWgradMode_t addGrad,
                           const int32_t devSeqLengths[],
                           cudnnRNNDataDescriptor_t xDesc,
                           const void *x,
                           cudnnTensorDescriptor_t hDesc,
                           const void *hx,
                           cudnnRNNDataDescriptor_t yDesc,
                           const void *y,
                           size_t weightSpaceSize,
                           void *dweightSpace,
                           size_t workSpaceSize,
                           void *workSpace,
                           size_t reserveSpaceSize,
                           void *reserveSpace);

/**
 * @brief Computes multi-head attention data gradients.
 * @deprecated Since cuDNN 9.0.0. Use graph API SDPA operations instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnMultiHeadAttnBackwardData(cudnnHandle_t handle,
                               const cudnnAttnDescriptor_t attnDesc,
                               const int loWinIdx[],
                               const int hiWinIdx[],
                               const int devSeqLengthsDQDO[],
                               const int devSeqLengthsDKDV[],
                               const cudnnSeqDataDescriptor_t doDesc,
                               const void *dout,
                               const cudnnSeqDataDescriptor_t dqDesc,
                               void *dqueries,
                               const void *queries,
                               const cudnnSeqDataDescriptor_t dkDesc,
                               void *dkeys,
                               const void *keys,
                               const cudnnSeqDataDescriptor_t dvDesc,
                               void *dvalues,
                               const void *values,
                               size_t weightSizeInBytes,
                               const void *weights,
                               size_t workSpaceSizeInBytes,
                               void *workSpace,
                               size_t reserveSpaceSizeInBytes,
                               void *reserveSpace);

/**
 * @brief Computes multi-head attention weight gradients.
 * @deprecated Since cuDNN 9.0.0. Use graph API SDPA operations instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnMultiHeadAttnBackwardWeights(cudnnHandle_t handle,
                                  const cudnnAttnDescriptor_t attnDesc,
                                  cudnnWgradMode_t addGrad,
                                  const cudnnSeqDataDescriptor_t qDesc,
                                  const void *queries,
                                  const cudnnSeqDataDescriptor_t kDesc,
                                  const void *keys,
                                  const cudnnSeqDataDescriptor_t vDesc,
                                  const void *values,
                                  const cudnnSeqDataDescriptor_t doDesc,
                                  const void *dout,
                                  size_t weightSizeInBytes,
                                  const void *weights,
                                  void *dweights,
                                  size_t workSpaceSizeInBytes,
                                  void *workSpace,
                                  size_t reserveSpaceSizeInBytes,
                                  void *reserveSpace);

/*
 * CTC (Connectionist Temporal Classification) loss descriptor create/destory/set/get functions
 */
/**
 * @brief Input normalization mode for loss functions.
 * @since cuDNN 9.0.0
 */
typedef enum {
    CUDNN_LOSS_NORMALIZATION_NONE    = 0, /**< Input treated as normalized probability. @since cuDNN 9.0.0 */
    CUDNN_LOSS_NORMALIZATION_SOFTMAX = 1, /**< Input treated as unnormalized activation (softmax applied). @since cuDNN 9.0.0 */
} cudnnLossNormalizationMode_t;

/**
 * @brief Creates a CTC loss descriptor.
 * @param[out] ctcLossDesc  Pointer to created descriptor.
 * @retval CUDNN_STATUS_SUCCESS  Descriptor created.
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnCreateCTCLossDescriptor(cudnnCTCLossDescriptor_t *ctcLossDesc);

/**
 * @brief Configures a CTC loss descriptor with compute type.
 * @deprecated Since cuDNN 9.0.0. Use cudnnSetCTCLossDescriptor_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t compType);

/**
 * @brief Configures CTC loss with normalization mode.
 * @deprecated Since cuDNN 9.0.0. Use cudnnSetCTCLossDescriptor_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
                            cudnnDataType_t compType,
                            cudnnLossNormalizationMode_t normMode,
                            cudnnNanPropagation_t gradMode);

/**
 * @brief Configures CTC loss with normalization, gradient mode, and max label length.
 * @deprecated Since cuDNN 9.0.0. Use cudnnSetCTCLossDescriptor_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnSetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
                             cudnnDataType_t compType,
                             cudnnLossNormalizationMode_t normMode,
                             cudnnNanPropagation_t gradMode,
                             int maxLabelLength);

/**
 * @brief Configures CTC loss with normalization, CTC gradient mode, and max label length.
 *
 * @param[in,out] ctcLossDesc    CTC loss descriptor.
 * @param[in]     compType       Compute data type.
 * @param[in]     normMode       Loss normalization mode.
 * @param[in]     ctcGradMode    Gradient mode for out-of-bounds samples.
 * @param[in]     maxLabelLength Maximum label length.
 *
 * @retval CUDNN_STATUS_SUCCESS  Descriptor configured.
 *
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnSetCTCLossDescriptor_v9(cudnnCTCLossDescriptor_t ctcLossDesc,
                             cudnnDataType_t compType,
                             cudnnLossNormalizationMode_t normMode,
                             cudnnCTCGradMode_t ctcGradMode,
                             int maxLabelLength);

/**
 * @brief Retrieves CTC loss compute type.
 * @deprecated Since cuDNN 9.0.0. Use cudnnGetCTCLossDescriptor_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc, cudnnDataType_t *compType);

/**
 * @brief Retrieves CTC loss extended parameters.
 * @deprecated Since cuDNN 9.0.0. Use cudnnGetCTCLossDescriptor_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetCTCLossDescriptorEx(cudnnCTCLossDescriptor_t ctcLossDesc,
                            cudnnDataType_t *compType,
                            cudnnLossNormalizationMode_t *normMode,
                            cudnnNanPropagation_t *gradMode);

/**
 * @brief Retrieves CTC loss v8 parameters.
 * @deprecated Since cuDNN 9.0.0. Use cudnnGetCTCLossDescriptor_v9 instead.
 * @since cuDNN 9.0.0
 */
CUDNN_DEPRECATED cudnnStatus_t CUDNNWINAPI
cudnnGetCTCLossDescriptor_v8(cudnnCTCLossDescriptor_t ctcLossDesc,
                             cudnnDataType_t *compType,
                             cudnnLossNormalizationMode_t *normMode,
                             cudnnNanPropagation_t *gradMode,
                             int *maxLabelLength);

/**
 * @brief Retrieves CTC loss v9 parameters.
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetCTCLossDescriptor_v9(cudnnCTCLossDescriptor_t ctcLossDesc,
                             cudnnDataType_t *compType,
                             cudnnLossNormalizationMode_t *normMode,
                             cudnnCTCGradMode_t *ctcGradMode,
                             int *maxLabelLength);

/**
 * @brief Destroys a CTC loss descriptor.
 * @param[in] ctcLossDesc  Descriptor to destroy.
 * @retval CUDNN_STATUS_SUCCESS  Descriptor destroyed.
 * @since cuDNN 9.0.0
 */
cudnnStatus_t CUDNNWINAPI
cudnnDestroyCTCLossDescriptor(cudnnCTCLossDescriptor_t ctcLossDesc);

/**
 * @brief Computes CTC loss and gradients given probabilities and labels.
 *
 * Labels and sequence lengths are in CPU memory. For GPU-memory variant, use cudnnCTCLoss_v8.
 * Target label values must be in [1, A - 1], where A is the alphabet size, including the CTC
 * blank label, from probsDesc. Label 0 is reserved for the CTC blank label.
 *
 * @param[in]  handle              cuDNN handle.
 * @param[in]  probsDesc           Tensor descriptor for probabilities (T x N x A).
 * @param[in]  probs               Probabilities after softmax (GPU memory).
 * @param[in]  hostLabels           Labels (CPU memory).
 * @param[in]  hostLabelLengths     Length of each label (CPU memory).
 * @param[in]  hostInputLengths     Timing step lengths per batch (CPU memory).
 * @param[out] costs               CTC costs (GPU memory).
 * @param[in]  gradientsDesc       Tensor descriptor for gradients (T x N x A).
 * @param[out] gradients           CTC gradients (GPU memory, NULL for costs only).
 * @param[in]  algo                CTC loss algorithm.
 * @param[in]  ctcLossDesc         CTC loss descriptor.
 * @param[in]  workspace           Workspace (GPU memory).
 * @param[in]  workSpaceSizeInBytes Workspace size.
 *
 * @retval CUDNN_STATUS_SUCCESS  CTC loss computed.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCTCLoss_v8, cudnnGetCTCLossWorkspaceSize
 */
cudnnStatus_t CUDNNWINAPI
cudnnCTCLoss(
    cudnnHandle_t handle,
    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
                                                timing steps, N is the mini batch size, A is the alphabet size)  */
    const void *probs,                       /* probabilities after softmax, in GPU memory */
    const int hostLabels[],                  /* labels, in CPU memory */
    const int hostLabelLengths[],            /* the length of each label, in CPU memory */
    const int hostInputLengths[],            /* the lengths of timing steps in each batch, in CPU memory */
    void *costs,                             /* the returned costs of CTC, in GPU memory */
    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
    void *gradients,         /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
    cudnnCTCLossDescriptor_t ctcLossDesc,
    void *workspace,              /* pointer to the workspace, in GPU memory */
    size_t workSpaceSizeInBytes); /* size of the workspace */

/**
 * @brief Computes CTC loss and gradients (v8, supports CUDA graphs with GPU memory labels).
 *
 * Labels and sequence lengths are in GPU memory (unlike cudnnCTCLoss which uses CPU memory).
 * Target label values must be in [1, A - 1], where A is the alphabet size, including the CTC
 * blank label, from probsDesc. Label 0 is reserved for the CTC blank label.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCTCLoss, cudnnGetCTCLossWorkspaceSize_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnCTCLoss_v8(
    cudnnHandle_t handle,
    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
    cudnnCTCLossDescriptor_t ctcLossDesc,
    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
                                                timing steps, N is the mini batch size, A is the alphabet size)  */
    const void *probs,                       /* probabilities after softmax, in GPU memory */
    const int labels[],                      /* labels, in GPU memory */
    const int labelLengths[],                /* the length of each label, in GPU memory */
    const int inputLengths[],                /* the lengths of timing steps in each batch, in GPU memory */
    void *costs,                             /* the returned costs of CTC, in GPU memory */
    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the dimensions are T,N,A */
    void *gradients,             /* the returned CTC gradients, in GPU memory, to compute costs only, set it to NULL */
    size_t workSpaceSizeInBytes, /* size of the workspace */
    void *workspace);            /* pointer to the workspace, in GPU memory */

/**
 * @brief Returns the GPU workspace size required for CTC loss computation.
 *
 * Target label values must be in [1, A - 1], where A is the alphabet size, including the CTC
 * blank label, from probsDesc. Label 0 is reserved for the CTC blank label.
 *
 * @since cuDNN 9.0.0
 * @see cudnnCTCLoss
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetCTCLossWorkspaceSize(
    cudnnHandle_t handle,
    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
                                                timing steps, N is the mini batch size, A is the alphabet size) */
    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
                                                    dimensions are T,N,A. To compute costs
                                                    only, set it to NULL */
    const int *labels,                           /* labels, in CPU memory */
    const int *labelLengths,                     /* the length of each label, in CPU memory */
    const int *inputLengths,                     /* the lengths of timing steps in each batch, in CPU memory */
    cudnnCTCLossAlgo_t algo,                     /* algorithm selected, supported now 0 and 1 */
    cudnnCTCLossDescriptor_t ctcLossDesc,
    size_t *sizeInBytes); /* pointer to the returned workspace size */

/**
 * @brief Returns the GPU workspace size required for CTC loss v8 computation.
 * @since cuDNN 9.0.0
 * @see cudnnCTCLoss_v8
 */
cudnnStatus_t CUDNNWINAPI
cudnnGetCTCLossWorkspaceSize_v8(
    cudnnHandle_t handle,
    cudnnCTCLossAlgo_t algo, /* algorithm selected, supported now 0 and 1 */
    cudnnCTCLossDescriptor_t ctcLossDesc,
    const cudnnTensorDescriptor_t probsDesc, /* Tensor descriptor for probabilities, the dimensions are T,N,A (T is the
                                                timing steps, N is the mini batch size, A is the alphabet size) */
    const cudnnTensorDescriptor_t gradientsDesc, /* Tensor descriptor for gradients, the
                                                    dimensions are T,N,A. To compute costs
                                                    only, set it to NULL */
    size_t *sizeInBytes);                        /* pointer to the returned workspace size */

#if defined(__cplusplus)
}
#endif

#endif /* CUDNN_ADV_H_ */
