/*
 * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
#pragma once

#include <curand_kernel.h>

namespace turbomind {

void invokeTopPInitialize(int*         topp_id_val_buf,
                          int*         topp_offset_buf,
                          int*         begin_topp_offset_buf_,
                          const size_t batch_size,
                          const int    n,
                          cudaStream_t stream);

template<typename T>
void invokeTopPSampling(void*           workspace,
                        size_t&         workspace_size,
                        size_t&         cub_temp_storage_size,
                        int*            output_ids,
                        int*            sequence_length,
                        bool*           finished_buf,
                        float*          cum_log_probs,
                        float*          output_log_probs,
                        const T*        log_probs,
                        const int*      id_vals,
                        int*            offset_buf,
                        int*            begin_offset_buf,
                        curandState_t*  curandstate,
                        const int       batch_size,
                        const size_t    vocab_size_padded,
                        const int*      end_ids,
                        const float     top_p,
                        cudaStream_t    stream,
                        cudaDeviceProp* cuda_device_prop,
                        const bool*     skip_decode);

template<typename T>
void invokeBatchTopPSampling(void*           workspace,
                             size_t&         workspace_size,
                             size_t&         cub_temp_storage_size,
                             int*            output_ids,
                             int*            sequence_length,
                             bool*           finished_buf,
                             float*          cum_log_probs,
                             float*          output_log_probs,
                             const T*        log_probs,
                             const int*      id_vals,
                             int*            offset_buf,
                             int*            begin_offset_buf,
                             curandState_t*  curandstate,
                             const int       batch_size,
                             const size_t    vocab_size_padded,
                             const int*      end_ids,
                             const float     max_top_p,
                             const float*    top_ps,
                             cudaStream_t    stream,
                             cudaDeviceProp* cuda_device_prop,
                             const bool*     skip_decode);

template<typename T>
void invokeAddBiasSoftMax(T*           logits,
                          const T*     bias,
                          const int*   end_ids,
                          const bool*  finished,
                          const int    m,
                          const int    n_padded,
                          const int    n,
                          cudaStream_t stream);

namespace segmented_topp_impl {
enum DType_t
{
    kFLOAT,
    kHALF,
    kINT8
};

template<typename Key_Data_Type_   = float,
         typename Value_Data_Type_ = int32_t,
         int BLOCK_THREADS_        = 256,
         int KEYS_PER_LDG_         = 1>
struct Segmented_topk_kernel_params {
    typedef Key_Data_Type_   Key_Data_Type;
    typedef Value_Data_Type_ Value_Data_Type;
    enum
    {
        BLOCK_THREADS = BLOCK_THREADS_
    };
    enum
    {
        ITEMS_INCREMENT = 32
    };
    // enum { KEYS_PER_LDG = 2 * 4 / sizeof(Key_Data_Type_) };
    enum
    {
        KEYS_PER_LDG = KEYS_PER_LDG_
    };
};

struct TopKPerSegmentContext {
    TopKPerSegmentContext(): sm_count(0), sm_shared_size(0), sm_version(0){};
    int sm_count;
    int sm_shared_size;
    int sm_version;
};

struct TopKPerSegmentParams {
    // input/output keys and values
    void *gmem_src_keys, *gmem_dst_keys, *gmem_dst_vals;
    // not used in the custom implementation
    void* gmem_src_vals;
    // int array of size num_segments
    int* gmem_active_count_per_segment;
    int* gmem_active_count_total;
    int* gmem_begin_offsets;
    // gmem_end_offsets will be populated
    int*  gmem_end_offsets;
    void* workspace;
    // total number of items for all segments
    int num_items;
    int num_segments;
    // top_k per segment
    int   num_top_k;
    float top_p;
    float confidence_threshold;
};

int topPPerSegment(const TopKPerSegmentContext& context,
                   TopKPerSegmentParams&        params,
                   const DType_t                DT_SCORE,
                   void*                        temp_storage,
                   size_t&                      temp_storage_bytes,
                   cudaStream_t                 stream);
}  // namespace segmented_topp_impl

void invokeComputeToppDecay(float*         runtime_top_p,
                            const float*   runtime_initial_top_p,
                            const int*     output_ids,
                            const float*   top_p_decay,
                            const float*   top_p_min,
                            const int32_t* top_p_reset_ids,
                            const int      local_batch_size,
                            cudaStream_t   stream);

}  // namespace turbomind
