beam_search_topk_kernels.h

/*
 * 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.
 */

#include <cuda_runtime.h>

#pragma once

namespace fastertransformer {

// In original beam search implementation, if a beam is finished, we set it as finished
// and only continue to do beam search on remain beams (namely, beam_width - 1 beams in next step)
//
// In this implementation, when a beam is finished, we trace the path and record it in output_ids_tgt,
// and also record the normalized scores. And the beam search continue to use `beam_width` beams in
// next step.
//
// After we collect `beam_width` beams, we will sort them by their norm_scores.
struct BeamHypotheses {
    int*   output_ids_tgt       = nullptr;
    int*   sequence_lengths_tgt = nullptr;
    float* cum_log_probs        = nullptr;  // cum_log
    float* normed_scores        = nullptr;  // cum_log / (length**length_penalty)
    float* log_probs            = nullptr;  // log probs of each generated token
    float* min_normed_scores    = nullptr;  // record the min normed scores for each batch
    int*   num_beams            = nullptr;  // the number of finished beams we collect
    bool*  is_done              = nullptr;

    // Used to set inputs
    const int*   output_ids_src;
    const int*   parent_ids_src;
    const int*   sequence_lengths_src;
    const int*   end_ids;
    const float* log_probs_src;

    // some variables for kernels
    int   step;
    int   ite;
    int   batch_size;
    int   local_batch_size;
    int   max_seq_len;
    float length_penalty;

    bool early_stopping         = true;
    bool is_return_normed_score = true;  // return normed_cum_log_probs or cum_log_probs
};

template<typename T>
void invokeTopkBeamSearch(void*           workspace,
                          size_t&         workspace_size,
                          T*              log_probs,
                          int*            ids,
                          BeamHypotheses* beam_hyps,
                          const bool*     finished,
                          const int*      sequence_lengths,
                          const int       batch_size,
                          const int       beam_width,
                          const int       vocab_size_padded_,
                          const T         diversity_rate,
                          const float     length_penalty,
                          const int*      end_ids,
                          cudaStream_t    stream);

template<typename T>
void invokeTileEncoderResults(T*           tiled_encoder_output,
                              int*         tiled_encoder_sequence_length,
                              const T*     encoder_output,
                              const int*   encoder_sequence_length,
                              const size_t batch_size,
                              const size_t beam_width,
                              const size_t mem_max_seq_len,
                              const size_t d_model,
                              cudaStream_t stream);

void invokeInsertUnfinishedPath(BeamHypotheses beam_hyps,
                                const bool*    finished,
                                const float*   cum_log_probs,
                                const int      batch_size,
                                const int      beam_width,
                                cudaStream_t   stream);

}  // namespace fastertransformer