lookup_table_op.h

/* Copyright (c) 2016 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 "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/selected_rows.h"

namespace paddle {
namespace operators {

using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;

template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    auto* table_t = context.Input<LoDTensor>("W");  // float tensor
    auto* ids_var = context.InputVar("Ids");        // int tensor

    int64_t* ids;
    int64_t ids_numel;
    Tensor* output_t;

    // ids_var_types also can be LOD_TENSOR_ARRAY, it's used as concat_rows.
    // Maybe near future we will add concat_rows op.
    if (ids_var->IsType<LoDTensor>()) {
      auto* ids_t = context.Input<LoDTensor>("Ids");
      output_t = context.Output<LoDTensor>("Out");
      ids = const_cast<int64_t*>(ids_t->data<int64_t>());
      ids_numel = ids_t->numel();
    } else if (ids_var->IsType<SelectedRows>()) {
      auto* ids_t = context.Input<SelectedRows>("Ids");
      output_t =
          const_cast<Tensor*>(&(context.Output<SelectedRows>("Out")->value()));
      ids = const_cast<int64_t*>(ids_t->rows().data());
      ids_numel = ids_t->rows().size();
      output_t->Resize({ids_numel, table_t->dims()[1]});
    } else {
      PADDLE_THROW("Unsupported Variable Type of Ids");
    }

    int64_t padding_idx = context.Attr<int64_t>("padding_idx");

    int N = table_t->dims()[0];
    int D = table_t->dims()[1];
    auto* table = table_t->data<T>();
    auto* output = output_t->mutable_data<T>(context.GetPlace());

    if (padding_idx == -1) {
      for (int64_t i = 0; i < ids_numel; ++i) {
        PADDLE_ENFORCE_LT(ids[i], N);
        PADDLE_ENFORCE_GE(ids[i], 0);
        memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
      }
    } else {
      for (int64_t i = 0; i < ids_numel; ++i) {
        if (ids[i] == padding_idx) {
          memset(output + i * D, 0, D * sizeof(T));
        } else {
          PADDLE_ENFORCE_LT(ids[i], N);
          PADDLE_ENFORCE_GE(ids[i], 0);
          memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
        }
      }
    }
  }
};

template <typename T>
class LookupTableGradKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext& context) const override {
    bool is_sparse = context.Attr<bool>("is_sparse");
    // Since paddings are not trainable and fixed in forward, the gradient of
    // paddings makes no sense and we don't deal with it in backward.
    if (is_sparse) {
      auto* ids = context.Input<LoDTensor>("Ids");
      auto* table = context.Input<LoDTensor>("W");
      auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
      auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));

      auto* ids_data = ids->data<int64_t>();
      auto ids_dim = ids->dims();

      framework::Vector<int64_t> new_rows;
      new_rows.reserve(ids_dim[0]);
      for (int64_t i = 0; i < ids_dim[0]; i++) {
        new_rows.push_back(ids_data[i]);
      }
      d_table->set_rows(new_rows);

      auto* d_table_value = d_table->mutable_value();
      d_table_value->Resize({ids_dim[0], table->dims()[1]});
      d_table_value->mutable_data<T>(context.GetPlace());

      d_table->set_height(table->dims()[0]);

      auto* d_output_data = d_output->data<T>();
      auto* d_table_data = d_table_value->data<T>();

      PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
      memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
    } else {
      auto* ids = context.Input<LoDTensor>("Ids");
      auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
      auto* d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
      auto* table = context.Input<LoDTensor>("W");

      auto* ids_data = ids->data<int64_t>();
      auto ids_dim = ids->dims();

      int N = table->dims()[0];
      int D = d_output->dims()[1];

      auto* d_output_data = d_output->data<T>();
      auto* d_table_data = d_table->mutable_data<T>(context.GetPlace());

      memset(d_table_data, 0, d_table->numel() * sizeof(T));

      for (int64_t i = 0; i < ids->numel(); ++i) {
        PADDLE_ENFORCE_LT(ids_data[i], N);
        PADDLE_ENFORCE_GE(ids_data[i], 0);
        for (int j = 0; j < D; ++j) {
          d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
        }
      }
    }
  }
};

}  // namespace operators
}  // namespace paddle