fc_op.h

/* 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. */

#pragma once

#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/fc.h"

namespace paddle {
namespace operators {
enum { kFCMKLDNNFP32 = 1, kFCMKLDNNINT8 = 2 };

using Tensor = framework::Tensor;

inline void FCOutputSize(const framework::DDim& in_dims,
                         const framework::DDim& w_dims,
                         std::vector<int64_t>& out_dims,  // NOLINT
                         int in_num_col_dims, bool padding_weights) {
  auto in_mat_dims = framework::flatten_to_2d(in_dims, in_num_col_dims);
  auto w_dims0 = padding_weights ? w_dims[0] - 4 : w_dims[0];
  auto w_dims1 = padding_weights ? w_dims[1] - 4 : w_dims[1];
  PADDLE_ENFORCE_EQ(in_mat_dims[1], w_dims0,
                    platform::errors::InvalidArgument(
                        "Fully Connected input and weigth size do not match. "
                        "input width: %d,weight height: %d",
                        in_mat_dims[1], w_dims0));

  out_dims.reserve(static_cast<size_t>(in_num_col_dims + 1));
  for (int i = 0; i < in_num_col_dims; ++i) {
    out_dims.push_back(in_dims[i]);
  }
  out_dims.push_back(w_dims1);
}

template <typename DeviceContext, typename T>
class FCOpKernel : public framework::OpKernel<T> {
 public:
  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
    auto* input = ctx.Input<framework::LoDTensor>("Input");
    auto* w = ctx.Input<Tensor>("W");
    auto* bias = ctx.Input<Tensor>("Bias");
    auto* output = ctx.Output<framework::LoDTensor>("Out");
    int in_num_col_dims = ctx.Attr<int>("in_num_col_dims");
    bool with_relu =
        (ctx.Attr<std::string>("activation_type") == "relu") ? true : false;

    auto w_dims = w->dims();
    bool padding_weights = ctx.Attr<bool>("padding_weights");

    std::vector<int64_t> output_dims;
    FCOutputSize(input->dims(), w_dims, output_dims, in_num_col_dims,
                 padding_weights);
    output->Resize(framework::make_ddim(output_dims));
    output->set_lod(input->lod());

    auto out_dims = output->dims();
    auto w_dims0 = padding_weights ? w_dims[0] - 4 : w_dims[0];
    auto w_dims1 = padding_weights ? w_dims[1] - 4 : w_dims[1];
    int M = framework::product(out_dims) / w_dims1;

    const T* input_data = input->data<T>();
    const T* w_data = w->data<T>();
    T* output_data = output->mutable_data<T>(ctx.GetPlace());

    auto& dev_ctx = ctx.template device_context<DeviceContext>();
    math::FCFunctor<DeviceContext, T> fc;
    fc(dev_ctx, M, w_dims1, w_dims0, input_data, w_data, output_data,
       bias ? bias->data<T>() : NULL, with_relu, padding_weights);
  }
};

}  // namespace operators
}  // namespace paddle