/*
 * Copyright (c) 2019-2023, 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 "src/fastertransformer/models/bert/Bert.h"
#include "src/fastertransformer/th_op/th_utils.h"

namespace ft = fastertransformer;
namespace th = torch;
namespace torch_ext {

class IFEncoder {
public:
    virtual ~IFEncoder() {}
    virtual void forward(size_t      batch_size,
                         size_t      seq_len,
                         th::Tensor& input,
                         th::Tensor& sequence_lengths,
                         th::Tensor& output,
                         bool        removing_padding) = 0;
};

template<typename T>
class FTEncoder: public IFEncoder {
public:
    FTEncoder(int                            head_num,
              int                            head_size,
              int                            inter_size,
              int                            layer_num,
              bool                           allow_gemm_test,
              bool                           sparse,
              float                          q_scaling,
              const std::vector<th::Tensor>& w):
        _head_num(head_num),
        _head_size(head_size),
        _inter_size(inter_size),
        _weights(w),
        _layer_num(layer_num),
        _sparse(sparse),
        _q_scaling(q_scaling)
    {
#ifndef SPARSITY_ENABLED
        if (sparse) {
            std::cout << "[WARNING] Sparsity support is not enabled. Will use dense GEMM instead.\n" << std::flush;
        }
#endif
        int hidden_dim = _head_num * _head_size;
        ft::check_cuda_error(cublasLtCreate(&_cublasltHandle));
        sm_ = ft::getSMVersion();
#ifdef SPARSITY_ENABLED
        if (sparse) {
            CHECK_CUSPARSE(cusparseLtInit(&_cusparseLtHandle));
        }
#endif
        std::string sp_config_fname = sparse ? "spgemm_config.in" : "";
        cublas_algo_map_            = new ft::cublasAlgoMap("gemm_config.in", sp_config_fname);
        cublas_wrapper_mutex_       = new std::mutex();
        encoder_weights.bert_layer_weights.clear();
        encoder_weights.bert_layer_weights.resize(_layer_num);

        for (int i = 0; i < _layer_num; i++) {
            encoder_weights.bert_layer_weights[i].attn_layernorm_weights.gamma =
                get_ptr<T>(_weights[0]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attn_layernorm_weights.beta =
                get_ptr<T>(_weights[1]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.query_weight.kernel =
                get_ptr<T>(_weights[2]) + hidden_dim * hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.query_weight.bias =
                get_ptr<T>(_weights[3]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.key_weight.kernel =
                get_ptr<T>(_weights[4]) + hidden_dim * hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.key_weight.bias =
                get_ptr<T>(_weights[5]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.value_weight.kernel =
                get_ptr<T>(_weights[6]) + hidden_dim * hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.value_weight.bias =
                get_ptr<T>(_weights[7]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.attention_output_weight.kernel =
                get_ptr<T>(_weights[8]) + hidden_dim * hidden_dim * i;
            encoder_weights.bert_layer_weights[i].attention_weights.attention_output_weight.bias =
                get_ptr<T>(_weights[9]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].ffn_layernorm_weights.gamma =
                get_ptr<T>(_weights[10]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].ffn_layernorm_weights.beta =
                get_ptr<T>(_weights[11]) + hidden_dim * i;
            encoder_weights.bert_layer_weights[i].ffn_weights.intermediate_weight.kernel =
                get_ptr<T>(_weights[12]) + hidden_dim * _inter_size * i;
            encoder_weights.bert_layer_weights[i].ffn_weights.intermediate_weight.bias =
                get_ptr<T>(_weights[13]) + _inter_size * i;
            encoder_weights.bert_layer_weights[i].ffn_weights.output_weight.kernel =
                get_ptr<T>(_weights[14]) + hidden_dim * _inter_size * i;
            encoder_weights.bert_layer_weights[i].ffn_weights.output_weight.bias =
                get_ptr<T>(_weights[15]) + hidden_dim * i;
        }
        encoder_weights.post_transformer_layernorm_weights.gamma = get_ptr<T>(_weights[16]);
        encoder_weights.post_transformer_layernorm_weights.beta  = get_ptr<T>(_weights[17]);
#ifdef SPARSITY_ENABLED
        if (sparse) {
            auto           stream        = at::cuda::getCurrentCUDAStream().stream();
            cublasHandle_t _cublasHandle = at::cuda::getCurrentCUDABlasHandle();
            cublasSetStream(_cublasHandle, stream);
            ft::cublasMMWrapper cublas_wrapper = ft::cublasMMWrapper(_cublasHandle,
                                                                     _cublasltHandle,
                                                                     _cusparseLtHandle,
                                                                     stream,
                                                                     cublas_algo_map_,
                                                                     cublas_wrapper_mutex_,
                                                                     nullptr);
            for (int i = 0; i < _layer_num; ++i) {
                encoder_weights.bert_layer_weights[i].compress_weights(cublas_wrapper, hidden_dim);
            }
        }
#endif
    }

    ~FTEncoder() override
    {
        cublasLtDestroy(_cublasltHandle);
#ifdef SPARSITY_ENABLED
        if (_sparse) {
            cusparseLtDestroy(&_cusparseLtHandle);
        }
#endif
        delete cublas_algo_map_;
        delete cublas_wrapper_mutex_;
    }

    void forward(size_t      batch_size,
                 size_t      seq_len,
                 th::Tensor& input,
                 th::Tensor& sequence_lengths,
                 th::Tensor& output,
                 bool        removing_padding) override
    {
        auto           stream        = at::cuda::getCurrentCUDAStream().stream();
        cublasHandle_t _cublasHandle = at::cuda::getCurrentCUDABlasHandle();
        cublasSetStream(_cublasHandle, stream);
        ft::Allocator<ft::AllocatorType::TH>* allocator = new ft::Allocator<ft::AllocatorType::TH>();
        ft::cublasMMWrapper*                  cublas_wrapper =
#ifdef SPARSITY_ENABLED
            new ft::cublasMMWrapper(_cublasHandle,
                                    _cublasltHandle,
                                    _cusparseLtHandle,
                                    stream,
                                    cublas_algo_map_,
                                    cublas_wrapper_mutex_,
                                    allocator);
#else
            new ft::cublasMMWrapper(
                _cublasHandle, _cublasltHandle, stream, cublas_algo_map_, cublas_wrapper_mutex_, allocator);
#endif

        if (std::is_same<T, half>::value) {
            cublas_wrapper->setFP16GemmConfig();
        }
#ifdef ENABLE_BF16
        else if (std::is_same<T, __nv_bfloat16>::value) {
            cublas_wrapper->setBF16GemmConfig();
        }
#endif
        else if (std::is_same<T, float>::value) {
            cublas_wrapper->setFP32GemmConfig();
        }

        ft::AttentionType attention_type = ft::getAttentionType<T>(_head_size, sm_, removing_padding, seq_len);

        ft::Bert<T>* encoder = new ft::Bert<T>(batch_size,
                                               seq_len,
                                               _head_num,
                                               _head_size,
                                               _inter_size,
                                               _layer_num,
                                               sm_,
                                               _q_scaling,
                                               stream,
                                               cublas_wrapper,
                                               allocator,
                                               false,
                                               attention_type,
                                               _sparse,
                                               ft::ActivationType::Relu,
                                               ft::LayerNormType::pre_layernorm);

        ft::DataType            data_type     = ft::getTensorType<T>();
        std::vector<ft::Tensor> input_tensors = std::vector<ft::Tensor>{
            ft::Tensor{ft::MEMORY_GPU,
                       data_type,
                       std::vector<size_t>{batch_size, seq_len, (size_t)(_head_num * _head_size)},
                       get_ptr<T>(input)},
            ft::Tensor{
                ft::MEMORY_GPU, ft::TYPE_INT32, std::vector<size_t>{batch_size}, get_ptr<int>(sequence_lengths)}};

        std::vector<ft::Tensor> output_tensors = std::vector<ft::Tensor>{
            ft::Tensor{ft::MEMORY_GPU,
                       data_type,
                       std::vector<size_t>{batch_size, seq_len, (size_t)(_head_num * _head_size)},
                       get_ptr<T>(output)}};

        try {
            encoder->forward(&output_tensors, &input_tensors, &encoder_weights);
        }
        catch (std::runtime_error& error) {
            std::cout << error.what();
            exit(-1);
        }
        catch (...) {
            std::cout << "Runtime error";
            exit(-1);
        }
        delete encoder;
        delete cublas_wrapper;
        delete allocator;
    }

private:
    const int               _head_num;
    const int               _head_size;
    const int               _inter_size;
    std::vector<th::Tensor> _weights;
    const int               _layer_num;
    bool                    _sparse;
    const float             _q_scaling;
    int                     sm_;
    cublasLtHandle_t        _cublasltHandle;
#ifdef SPARSITY_ENABLED
    cusparseLtHandle_t _cusparseLtHandle;
#endif
    std::mutex*        cublas_wrapper_mutex_;
    ft::cublasAlgoMap* cublas_algo_map_;
    ft::BertWeight<T>  encoder_weights;
};

class FasterTransformerEncoder: public th::jit::CustomClassHolder {
public:
    FasterTransformerEncoder(th::Tensor pre_attr_layernorm_gamma,
                             th::Tensor pre_attr_layernorm_beta,
                             th::Tensor q_kernel,
                             th::Tensor q_bias,
                             th::Tensor k_kernel,
                             th::Tensor k_bias,
                             th::Tensor v_kernel,
                             th::Tensor v_bias,
                             th::Tensor attr_output_kernel,
                             th::Tensor attr_output_bias,
                             th::Tensor pre_ffn_layernorm_gamma,
                             th::Tensor pre_ffn_layernorm_beta,
                             th::Tensor inter_kernel,
                             th::Tensor inter_bias,
                             th::Tensor output_kernel,
                             th::Tensor output_bias,
                             th::Tensor post_transformer_layernorm_gamma,
                             th::Tensor post_transformer_layernorm_beta,
                             int64_t    head_num,
                             int64_t    head_size,
                             int64_t    inter_size,
                             bool       remove_padding,
                             int64_t    layer_num,
                             bool       allow_gemm_test,
                             bool       sparse,
                             double     q_scaling);

    ~FasterTransformerEncoder();

    th::Tensor forward(th::Tensor input, th::Tensor sequence_lengths);

    std::vector<th::Tensor> get_pickle_info() const;

private:
    const at::ScalarType    _st;
    bool                    _remove_padding;
    IFEncoder*              ftencoder;
    th::Tensor              head_info;
    th::Tensor              scaling_info;
    std::vector<th::Tensor> weights;
    bool                    _allow_gemm_test;
};

}  // namespace torch_ext
