embedding_lookup_sparse.cc | Explore in Territory

/* Copyright 2017 The TensorFlow 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.
==============================================================================*/

// Op that looks up items from a sparse tensor in an embedding matrix.
// The sparse lookup tensor is represented by three individual tensors: lookup,
// indices, and dense_shape. The representation assume that the corresponding
// dense tensor would satisfy:
//   * dense.shape = dense_shape
//   * dense[tuple(indices[i])] = lookup[i]
//
// By convention, indices should be sorted.
//
// Options:
//   combiner: The reduction op (SUM, MEAN, SQRTN).
//     * SUM computes the weighted sum of the embedding results.
//     * MEAN is the weighted sum divided by the total weight.
//     * SQRTN is the weighted sum divided by the square root of the sum of the
//       squares of the weights.
//
// Input:
//     Tensor[0]: Ids to lookup, dim.size == 1, int32.
//     Tensor[1]: Indices, int32.
//     Tensor[2]: Dense shape, int32.
//     Tensor[3]: Weights to use for aggregation, float.
//     Tensor[4]: Params, a matrix of multi-dimensional items,
//                dim.size >= 2, float.
//
// Output:
//   A (dense) tensor representing the combined embeddings for the sparse ids.
//   For each row in the sparse tensor represented by (lookup, indices, shape)
//   the op looks up the embeddings for all ids in that row, multiplies them by
//   the corresponding weight, and combines these embeddings as specified in the
//   last dimension.
//
//   Output.dim = [l0, ... , ln-1, e1, ..., em]
//   Where dense_shape == [l0, ..., ln] and Tensor[4].dim == [e0, e1, ..., em]
//
//   For instance, if params is a 10x20 matrix and ids, weights are:
//
//   [0, 0]: id 1, weight 2.0
//   [0, 1]: id 3, weight 0.5
//   [1, 0]: id 0, weight 1.0
//   [2, 3]: id 1, weight 3.0
//
//   with combiner=MEAN, then the output will be a (3, 20) tensor where:
//
//   output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
//   output[1, :] = (params[0, :] * 1.0) / 1.0
//   output[2, :] = (params[1, :] * 3.0) / 3.0
//
//   When indices are out of bound, the op will not succeed.

#include <stdint.h>

#include <algorithm>
#include <cmath>

#include "tensorflow/lite/core/c/builtin_op_data.h"
#include "tensorflow/lite/core/c/common.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/internal/tensor_utils.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/util.h"

namespace tflite {
namespace ops {
namespace builtin {

namespace {

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { … }

void FinalizeAggregation(TfLiteCombinerType combiner, int num_elements,
                         float current_total_weight,
                         float current_squares_weight, int embedding_size,
                         float* output) { … }

TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { … }

}  // namespace

TfLiteRegistration* Register_EMBEDDING_LOOKUP_SPARSE() { … }

}  // namespace builtin
}  // namespace ops
}  // namespace tflite
chromium/third_party/tflite/src/tensorflow/lite/kernels/embedding_lookup_sparse.cc
