/* Copyright (c) 2018 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/operators/fused/fusion_seqpool_cvm_concat_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/operators/jit/kernels.h"

namespace paddle {
namespace operators {

void FusionSeqPoolCVMConcatOp::InferShape(
    framework::InferShapeContext* ctx) const {
  PADDLE_ENFORCE_GE(
      ctx->Inputs("X").size(), 1UL,
      "Inputs(X) of FusionSeqPoolCVMConcatOp should not be empty.");
  PADDLE_ENFORCE(ctx->HasOutput("Out"),
                 "Output(Out) of FusionSeqPoolCVMConcatOp should not be null.");
  int axis = ctx->Attrs().Get<int>("axis");
  PADDLE_ENFORCE_EQ(
      axis, 1, "FusionSeqPoolCVMConcatOp only supports concat axis=1 yet.");
  bool use_cvm = ctx->Attrs().Get<bool>("use_cvm");
  PADDLE_ENFORCE_EQ(
      use_cvm, true,
      "FusionSeqPoolCVMConcatOp only supports use_cvm is true yet.");

  auto ins_dims = ctx->GetInputsDim("X");
  const size_t n = ins_dims.size();
  PADDLE_ENFORCE_GT(n, 0UL, "Input tensors count should > 0.");
  if (n == 1) {
    LOG(WARNING) << "Only have one input, may waste memory";
  }

  // The output height should be confirmed in Compute,
  // since input lod is not accessible here.
  PADDLE_ENFORCE_EQ(ins_dims[0].size(), 2,
                    "The dims size of first input should be 2.");
  ctx->SetOutputDim("Out", {-1, ins_dims[0][axis] * static_cast<int>(n)});
}

framework::OpKernelType FusionSeqPoolCVMConcatOp::GetExpectedKernelType(
    const framework::ExecutionContext& ctx) const {
  return framework::OpKernelType(
      framework::GetDataTypeOfVar(ctx.MultiInputVar("X")[0]), ctx.GetPlace());
}

void FusionSeqPoolCVMConcatOpMaker::Make() {
  AddInput("X", "(LoDTensor) Input tensors of this operator.").AsDuplicable();
  AddInput("CVM",
           "(Tensor),  a 2-D Tensor with shape [N x 2], where N is the batch "
           "size, 2 is show and click.");
  AddOutput("Out", "(LoDTensor) Output tensor of concat operator.");
  AddAttr<std::string>("pooltype",
                       "(string, default 'SUM') some of the pooling "
                       "pooltype of SequencePoolOp.")
      .SetDefault("SUM")
      .InEnum({"AVERAGE", "SUM", "SQRT"});
  AddAttr<bool>("use_cvm", "bool, use cvm or not").SetDefault(true);
  AddAttr<int>("axis",
               "The axis along which the input tensors will be concatenated. "
               "Only supports concat axis=1 yet.")
      .SetDefault(1);
  AddComment(R"DOC(
Fusion Sequence Pool of pooltype(sum, average and sqrt) and Concat Operator.
)DOC");
}

template <typename T>
class FusionSeqPoolCVMConcatKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& ctx) const override {
    auto ins = ctx.MultiInput<LoDTensor>("X");
    auto* out = ctx.Output<LoDTensor>("Out");
    std::string pooltype = ctx.Attr<std::string>("pooltype");
    auto x0_lod = ins[0]->lod();
    auto x0_dims = ins[0]->dims();
    auto y_dims = out->dims();
    size_t bs = x0_lod[0].size() - 1;
    out->Resize({static_cast<int64_t>(bs), y_dims[1]});
    framework::LoD y_lod(1);
    y_lod[0].resize(bs + 1);
    for (size_t i = 0; i <= bs; ++i) {
      y_lod[0][i] = i;
    }
    out->set_lod(y_lod);
    auto place = ctx.GetPlace();
    T* y_data = out->mutable_data<T>(place);

    int w = ins[0]->numel() / x0_dims[0];
    PADDLE_ENFORCE_EQ(y_dims[1] % w, 0,
                      "The output of dims[1] should be dividable of w");
    jit::seq_pool_attr_t attr(w, jit::SeqPoolType::kSum);
    if (pooltype == "AVERAGE") {
      attr.type = jit::SeqPoolType::kAvg;
    } else if (pooltype == "SQRT") {
      attr.type = jit::SeqPoolType::kSqrt;
    }
    auto seqpool =
        jit::KernelFuncs<jit::SeqPoolTuple<T>, platform::CPUPlace>::Cache().At(
            attr);
    size_t n = ins.size();
    size_t dst_step_size = n * w;
    for (size_t i = 0; i < n; ++i) {
      auto x_dims = ins[i]->dims();
      auto x_lod = ins[i]->lod()[0];
      const T* src = ins[i]->data<T>();
      T* dst = y_data + i * w;
      PADDLE_ENFORCE_EQ(static_cast<int>(ins[i]->numel() / x_dims[0]), w,
                        "Width of all inputs should be equal.");
      PADDLE_ENFORCE_EQ(x_lod.size(), bs + 1,
                        "Batchsize of all inputs should be equal.");
      for (size_t j = 0; j < bs; ++j) {
        attr.h = static_cast<int>(x_lod[j + 1] - x_lod[j]);
        seqpool(src, dst, &attr);

        // Currently only use_cvm is true.
        dst[0] = log(dst[0] + 1);
        dst[1] = log(dst[1] + 1) - dst[0];

        dst += dst_step_size;
        src += attr.h * attr.w;
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
REGISTER_OPERATOR(fusion_seqpool_cvm_concat, ops::FusionSeqPoolCVMConcatOp,
                  ops::FusionSeqPoolCVMConcatOpMaker,
                  paddle::framework::EmptyGradOpMaker);

REGISTER_OP_CPU_KERNEL(fusion_seqpool_cvm_concat,
                       ops::FusionSeqPoolCVMConcatKernel<float>,
                       ops::FusionSeqPoolCVMConcatKernel<double>);