// Copyright (c) 2019 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 <paddle/fluid/platform/device_context.h>

#include <algorithm>
#include <cstdint>
#include <type_traits>

#include "paddle/fluid/framework/convert_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/operators/math/bert_encoder_functor.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"

namespace paddle {
namespace operators {

template <typename T, typename DeviceContext>
class EmbeddingEltWiseLayerNormKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &context) const override {
    auto &device_ctx = context.template device_context<DeviceContext>();
    auto ids = context.MultiInput<phi::DenseTensor>("Ids");
    auto embs = context.MultiInput<phi::DenseTensor>("Embs");
    int input_num = static_cast<int>(ids.size());

    phi::DenseTensor in_ids_(
        framework::TransToPhiDataType(framework::proto::VarType::INT64)),
        in_embs_(
            framework::TransToPhiDataType(framework::proto::VarType::INT64));
    framework::DDim in_dim{input_num};
    int device_id;
#ifdef PADDLE_WITH_HIP
    hipGetDevice(&device_id);
#else
    cudaGetDevice(&device_id);
#endif

    auto &dev_ctx = context.template device_context<phi::GPUContext>();

    in_ids_.Resize(in_dim);
    in_embs_.Resize(in_dim);

    int64_t *in_ids_d = dev_ctx.template Alloc<int64_t>(
        &in_ids_, in_ids_.numel() * sizeof(int64_t));
    int64_t *in_embs_d = dev_ctx.template Alloc<int64_t>(
        &in_embs_, in_embs_.numel() * sizeof(int64_t));

    std::vector<int64_t> in1s, in2s;
    for (int i = 0; i < input_num; ++i) {
      in1s.push_back(reinterpret_cast<uintptr_t>(ids[i]->data<int64_t>()));
      in2s.push_back(reinterpret_cast<uintptr_t>(embs[i]->data<T>()));
    }
#ifdef PADDLE_WITH_HIP
    hipMemcpyAsync(in_ids_d,
                   in1s.data(),
                   sizeof(int64_t) * input_num,
                   hipMemcpyHostToDevice,
                   device_ctx.stream());
    hipMemcpyAsync(in_embs_d,
                   in2s.data(),
                   sizeof(int64_t) * input_num,
                   hipMemcpyHostToDevice,
                   device_ctx.stream());
#else
    cudaMemcpyAsync(in_ids_d,
                    in1s.data(),
                    sizeof(int64_t) * input_num,
                    cudaMemcpyHostToDevice,
                    device_ctx.stream());
    cudaMemcpyAsync(in_embs_d,
                    in2s.data(),
                    sizeof(int64_t) * input_num,
                    cudaMemcpyHostToDevice,
                    device_ctx.stream());
#endif

    auto *bias = context.Input<phi::DenseTensor>("Bias");
    auto *scale = context.Input<phi::DenseTensor>("Scale");
    auto *out = context.Output<phi::DenseTensor>("Out");

    // should be (B * S * hidden)
    auto id0_dims = ids[0]->dims();
    auto emb0_dims = embs[0]->dims();

    int batch = id0_dims[0];
    int seq_len = id0_dims[1];
    int hidden = emb0_dims[1];

    auto *bias_d = bias->data<T>();
    auto *scale_d = scale->data<T>();
    auto *output_d = dev_ctx.template Alloc<T>(out, out->numel() * sizeof(T));

    float eps = context.Attr<float>("epsilon");

    if (std::is_same<T, paddle::platform::float16>::value) {
      const half *scale_new = reinterpret_cast<const half *>(scale_d);
      const half *bias_new = reinterpret_cast<const half *>(bias_d);
      half *output_new = reinterpret_cast<half *>(output_d);

      math::EmbEltwiseLayerNormFunctor<half> emb_eltwise_layernorm_func;
      emb_eltwise_layernorm_func(batch,
                                 seq_len,
                                 hidden,
                                 in_ids_d,
                                 scale_new,
                                 bias_new,
                                 in_embs_d,
                                 output_new,
                                 eps,
                                 input_num,
                                 device_ctx.stream());
    } else {
      math::EmbEltwiseLayerNormFunctor<T> emb_eltwise_layernorm_func;
      emb_eltwise_layernorm_func(batch,
                                 seq_len,
                                 hidden,
                                 in_ids_d,
                                 scale_d,
                                 bias_d,
                                 in_embs_d,
                                 output_d,
                                 eps,
                                 input_num,
                                 device_ctx.stream());
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
namespace plat = paddle::platform;
#if defined(PADDLE_WITH_CUDA) && CUDA_VERSION >= 10000
PD_REGISTER_STRUCT_KERNEL(fused_embedding_eltwise_layernorm,
                          GPU,
                          ALL_LAYOUT,
                          ops::EmbeddingEltWiseLayerNormKernel,
                          float,
                          plat::float16) {}
#else
PD_REGISTER_STRUCT_KERNEL(fused_embedding_eltwise_layernorm,
                          GPU,
                          ALL_LAYOUT,
                          ops::EmbeddingEltWiseLayerNormKernel,
                          float) {}
#endif
