// Copyright (c) 2025 PaddlePaddle Authors. 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 "helper.h"
#include "paddle/extension.h"

template <paddle::DataType D>
void SwapCacheImplAllLayers(const std::vector<paddle::Tensor>& cache_gpu_tensors, // gpu
                   const std::vector<int64_t>& cache_cpu_ptrs, // cpu
                   const int64_t& max_block_num_cpu,
                   const std::vector<int64_t>& swap_block_ids_gpu,
                   const std::vector<int64_t>& swap_block_ids_cpu,
                   int mode) {
    typedef PDTraits<D> traits_;
    typedef typename traits_::DataType DataType_;
    typedef typename traits_::data_t data_t;
    auto stream = cache_gpu_tensors[0].stream();
    for(int layer_idx=0; layer_idx < cache_gpu_tensors.size(); layer_idx++){
        const paddle::Tensor& cache_gpu = cache_gpu_tensors[layer_idx];
        const int64_t& cache_cpu_pointer = cache_cpu_ptrs[layer_idx];
        data_t* cache_gpu_ptr = const_cast<data_t*>(cache_gpu.data<data_t>());
        auto* cache_cpu_ptr = reinterpret_cast<data_t*>(cache_cpu_pointer);
        auto cache_shape = cache_gpu.shape();
        const int64_t max_block_num_gpu = cache_shape[0];
        const int64_t num_heads = cache_shape[1];
        const int64_t block_size = cache_shape[2];
        const int64_t head_dim = cache_shape[3];
        const int64_t cache_stride = num_heads * block_size * head_dim;

        auto stream = cache_gpu.stream();
        if (swap_block_ids_gpu.size() == 0) {
            return;
        }
        int i = 0;
        int64_t consecutive_block_count = 1;
        int64_t last_gpu_block_id = swap_block_ids_gpu[i];
        int64_t last_cpu_block_id = swap_block_ids_cpu[i];
        int64_t first_gpu_block_id = last_gpu_block_id;  // first block id in a consecutive block ids
        int64_t first_cpu_block_id = last_cpu_block_id;
        i += 1;
        while(true){
            if (i >= swap_block_ids_gpu.size()) {
                break;
            }
            int64_t gpu_block_id = swap_block_ids_gpu[i];
            int64_t cpu_block_id = swap_block_ids_cpu[i];
            assert(gpu_block_id >= 0 && gpu_block_id < max_block_num_gpu);
            assert(cpu_block_id >= 0 && cpu_block_id < max_block_num_cpu);
            if (gpu_block_id == last_gpu_block_id + 1 && cpu_block_id == last_cpu_block_id + 1){ // consecutive
                consecutive_block_count += 1;
                last_gpu_block_id = gpu_block_id;
                last_cpu_block_id = cpu_block_id;
            } else{
                // end of a consecutive block ids
                auto *cache_gpu_ptr_now = cache_gpu_ptr + first_gpu_block_id * cache_stride;
                auto *cache_cpu_ptr_now = cache_cpu_ptr + first_cpu_block_id * cache_stride;
                if (mode == 0) { // copy from device to host
                    cudaMemcpyAsync(cache_cpu_ptr_now, cache_gpu_ptr_now, cache_stride * sizeof(DataType_) * consecutive_block_count, cudaMemcpyDeviceToHost, stream);
                } else { // copy from host to device
                    cudaMemcpyAsync(cache_gpu_ptr_now, cache_cpu_ptr_now, cache_stride * sizeof(DataType_) * consecutive_block_count, cudaMemcpyHostToDevice, stream);
                }
                first_gpu_block_id = gpu_block_id;
                first_cpu_block_id = cpu_block_id;
                last_gpu_block_id = gpu_block_id;
                last_cpu_block_id = cpu_block_id;
                consecutive_block_count = 1;
            }
            i += 1;
        }
        // last batch
        auto *cache_gpu_ptr_now = cache_gpu_ptr + first_gpu_block_id * cache_stride;
        auto *cache_cpu_ptr_now = cache_cpu_ptr + first_cpu_block_id * cache_stride;
        if (mode == 0) { // copy from device to host
            cudaMemcpyAsync(cache_cpu_ptr_now, cache_gpu_ptr_now, cache_stride * sizeof(DataType_) * consecutive_block_count, cudaMemcpyDeviceToHost, stream);
        } else { // copy from host to device
            cudaMemcpyAsync(cache_gpu_ptr_now, cache_cpu_ptr_now, cache_stride * sizeof(DataType_) * consecutive_block_count, cudaMemcpyHostToDevice, stream);
        }
    }
    cudaStreamSynchronize(stream);
}

void SwapCacheAllLayers(const std::vector<paddle::Tensor>&  cache_gpu_tensors, // gpu
               const std::vector<int64_t>& cache_cpu_ptrs, // cpu memory pointer
               int64_t max_block_num_cpu, // cpu max block num
               const std::vector<int64_t>& swap_block_ids_gpu,
               const std::vector<int64_t>& swap_block_ids_cpu,
               int rank,
               int mode) {
    cudaSetDevice(rank); // used for distributed launch
    assert(cache_gpu_tensors.size() > 0 && cache_gpu_tensors.size() == cache_cpu_ptrs.size());
    switch (cache_gpu_tensors[0].dtype()) {
        case paddle::DataType::BFLOAT16:
            return SwapCacheImplAllLayers<paddle::DataType::BFLOAT16>(
                        cache_gpu_tensors,
                        cache_cpu_ptrs,
                        max_block_num_cpu,
                        swap_block_ids_gpu,
                        swap_block_ids_cpu,
                        mode);
        case paddle::DataType::FLOAT16:
            return SwapCacheImplAllLayers<paddle::DataType::FLOAT16>(
                        cache_gpu_tensors,
                        cache_cpu_ptrs,
                        max_block_num_cpu,
                        swap_block_ids_gpu,
                        swap_block_ids_cpu,
                        mode);
        case paddle::DataType::UINT8:
            return SwapCacheImplAllLayers<paddle::DataType::UINT8>(
                        cache_gpu_tensors,
                        cache_cpu_ptrs,
                        max_block_num_cpu,
                        swap_block_ids_gpu,
                        swap_block_ids_cpu,
                        mode);
        default:
            PD_THROW("Unsupported data type.");
    }
}

PD_BUILD_STATIC_OP(swap_cache_all_layers)
    .Inputs({paddle::Vec("cache_gpu_tensors")})
    .Attrs({"cache_cpu_ptrs: std::vector<int64_t>",
            "max_block_num_cpu: int64_t",
            "swap_block_ids_gpu: std::vector<int64_t>",
            "swap_block_ids_cpu: std::vector<int64_t>",
            "rank: int",
            "mode: int",})
    .Outputs({paddle::Vec("cache_dst_outs")})
    .SetInplaceMap({{paddle::Vec("cache_gpu_tensors"), paddle::Vec("cache_dst_outs")}})
    .SetKernelFn(PD_KERNEL(SwapCacheAllLayers));
