/*
 * Copyright (c) 2019-2024, 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 "tensorrt_llm/common/cudaUtils.h"
#include "tensorrt_llm/common/memoryUtils.h"
#include "tensorrt_llm/layers/baseBeamSearchLayer.h"

#include <algorithm>

using namespace tensorrt_llm::common;
using namespace tensorrt_llm::kernels;

namespace tensorrt_llm
{
namespace layers
{

__global__ void update_indir_cache_kernel(int* tgt_indir_cache, int const* src_indir_cache, int const** parent_ids,
    FinishedState const* finished, int const* sequence_lengths, int const* input_lengths, int batch_dim,
    int local_batch_size, int beam_width, int max_attention_window, int sink_token_length, int max_seq_len)
{
    int time_step = threadIdx.x + blockIdx.x * blockDim.x;
    int bb_id = threadIdx.y + blockIdx.y * blockDim.y;   // should be just blockIdx.y?
    int const current_step{sequence_lengths[bb_id] - 1}; // the sequence_lengths is updated, need to minus 1
    int const input_length{input_lengths == nullptr ? 0 : input_lengths[bb_id]};
    int const batch_id = bb_id / beam_width;
    int const beam_id = bb_id % beam_width;
    // Exit when the batch_beam or timestep is out of the bound.
    // Assume that KV Cache is shared and fixed for context part,
    //  so we don't need to update the indices for context part.
    if (bb_id >= beam_width * local_batch_size || time_step >= max_seq_len || time_step < input_length
        || time_step < (max_seq_len - max_attention_window) || finished[bb_id].isFinished())
    {
        return;
    }
    int time_step_circ = time_step;
    if (time_step_circ >= sink_token_length)
    {
        time_step_circ
            = sink_token_length + (time_step - sink_token_length) % (max_attention_window - sink_token_length);
    }

    // for the parent_ids, we will still keep it for all past tokens (i.e. max_seq_len)
    int const src_beam = parent_ids[batch_id][beam_id * max_seq_len + current_step];

    // for the indir tables, we have the cyclic kv cache.
    const uint32_t tgt_offset
        = batch_id * beam_width * max_attention_window + beam_id * max_attention_window + time_step_circ;
    const uint32_t src_offset
        = batch_id * beam_width * max_attention_window + src_beam * max_attention_window + time_step_circ;

    tgt_indir_cache[tgt_offset] = (time_step == current_step) ? beam_id : src_indir_cache[src_offset];
}

void update_indir_cache_kernelLauncher(int* tgt_indir_cache, int const* src_indir_cache, int const** parent_ids,
    FinishedState const* finished, int const* sequence_lengths, int const* input_lengths, int batch_dim,
    int local_batch_size, int beam_width, int max_seq_len, int max_attention_window, int sink_token_length,
    cudaStream_t stream)
{
    const dim3 block(32);
    // Update indirections steps [input_length[bb_id], sequence_lengths[bb_id]], included
    const dim3 grid((max_seq_len + block.x - 1) / block.x, local_batch_size * beam_width);
    update_indir_cache_kernel<<<grid, block, 0, stream>>>(tgt_indir_cache, src_indir_cache, parent_ids, finished,
        sequence_lengths, input_lengths, batch_dim, local_batch_size, beam_width, max_attention_window,
        sink_token_length, max_seq_len);
}

template <typename T>
BaseBeamSearchLayer<T>::BaseBeamSearchLayer(runtime::SizeType vocab_size, runtime::SizeType vocab_size_padded,
    cudaStream_t stream, std::shared_ptr<IAllocator> allocator)
    : BaseLayer(stream, std::move(allocator), nullptr)
    , vocab_size_(vocab_size)
    , vocab_size_padded_(vocab_size_padded)
{
}

template <typename T>
BaseBeamSearchLayer<T>::BaseBeamSearchLayer(BaseBeamSearchLayer<T> const& beam_search_layer)
    : BaseLayer(beam_search_layer)
    , vocab_size_(beam_search_layer.vocab_size_)
    , vocab_size_padded_(beam_search_layer.vocab_size_padded_)
    , topk_softmax_workspace_size_(beam_search_layer.topk_softmax_workspace_size_)
{
}

template <typename T>
BaseBeamSearchLayer<T>::~BaseBeamSearchLayer()
{
    TLLM_LOG_TRACE(__PRETTY_FUNCTION__);
    freeBuffer();
}

template <typename T>
void BaseBeamSearchLayer<T>::freeBuffer()
{
    TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
    if (mIsAllocateBuffer)
    {
        mIsAllocateBuffer = false;
    }
    TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
}

template <typename T>
void BaseBeamSearchLayer<T>::allocateBuffer(runtime::SizeType batch_size)
{
    TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
    mIsAllocateBuffer = true;
    TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
}

template <typename T>
void BaseBeamSearchLayer<T>::setupBase(runtime::SizeType batch_size, SetupParams const& setupParams)
{
    TLLM_LOG_TRACE("%s start", __PRETTY_FUNCTION__);
    allocateBuffer(batch_size);
    TLLM_LOG_TRACE("%s stop", __PRETTY_FUNCTION__);
}

template <typename T>
void BaseBeamSearchLayer<T>::forward(BeamSearchOutputParams& outputs, ForwardParams const& params)
{
    TLLM_LOG_TRACE("%s", __PRETTY_FUNCTION__);
    Tensor& output_ids_ptr = outputs.output_ids_ptr;

    auto const batch_size = static_cast<std::int32_t>(output_ids_ptr.shape[0]);
    auto const beam_width = static_cast<std::int32_t>(output_ids_ptr.shape[1]);
    auto const max_seq_len = static_cast<std::int32_t>(output_ids_ptr.shape[2]);

    TLLM_CHECK_WITH_INFO(params.ite == 0, "Pipeline Parallelism is not supported yet !");

    int const ite = params.ite;
    auto* const input_lengths = params.input_lengths ? params.input_lengths->template getPtr<int const>() : nullptr;
    int* sequence_length = (outputs.sequence_length) ? outputs.sequence_length->template getPtr<int>() : nullptr;
    Tensor const& logits = params.logits;
    auto const local_batch_size = logits.shape[0];

    invokeSoftMax(outputs, params);
    sync_check_cuda_error();

    if (beam_width > 1)
    {
        update_indir_cache_kernelLauncher(outputs.tgt_cache_indirection.template getPtr<int>(),
            params.src_cache_indirection.template getPtr<int const>(),
            outputs.parent_ids_ptr.template getPtr<int const*>(),
            reinterpret_cast<FinishedState const*>(
                outputs.finished->template getPtr<const FinishedState::UnderlyingType>()),
            sequence_length, input_lengths, batch_size, local_batch_size, beam_width, max_seq_len,
            params.max_attention_window, params.sink_token_length, mStream);
        sync_check_cuda_error();
    }
}

template class BaseBeamSearchLayer<float>;
template class BaseBeamSearchLayer<half>;

} // namespace layers
} // namespace tensorrt_llm