| /* 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 <algorithm> |
| #include <cmath> |
| |
| #include "tensorflow/lite/c/builtin_op_data.h" |
| #include "tensorflow/lite/c/c_api_internal.h" |
| #include "tensorflow/lite/kernels/internal/tensor_utils.h" |
| #include "tensorflow/lite/kernels/kernel_util.h" |
| #include "tensorflow/lite/kernels/op_macros.h" |
| |
| namespace tflite { |
| namespace ops { |
| namespace builtin { |
| |
| namespace { |
| |
| TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { |
| TF_LITE_ENSURE_EQ(context, NumInputs(node), 5); |
| TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); |
| |
| const TfLiteTensor* ids = GetInput(context, node, 0); |
| TF_LITE_ENSURE_EQ(context, NumDimensions(ids), 1); |
| TF_LITE_ENSURE_EQ(context, ids->type, kTfLiteInt32); |
| |
| const TfLiteTensor* indices = GetInput(context, node, 1); |
| TF_LITE_ENSURE_EQ(context, NumDimensions(indices), 2); |
| TF_LITE_ENSURE_EQ(context, indices->type, kTfLiteInt32); |
| |
| const TfLiteTensor* shape = GetInput(context, node, 2); |
| TF_LITE_ENSURE_EQ(context, NumDimensions(shape), 1); |
| TF_LITE_ENSURE_EQ(context, shape->type, kTfLiteInt32); |
| |
| const TfLiteTensor* weights = GetInput(context, node, 3); |
| TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 1); |
| TF_LITE_ENSURE_EQ(context, weights->type, kTfLiteFloat32); |
| |
| TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0), |
| SizeOfDimension(ids, 0)); |
| TF_LITE_ENSURE_EQ(context, SizeOfDimension(indices, 0), |
| SizeOfDimension(weights, 0)); |
| |
| const TfLiteTensor* value = GetInput(context, node, 4); |
| TF_LITE_ENSURE(context, NumDimensions(value) >= 2); |
| |
| // Mark the output as a dynamic tensor. |
| TfLiteTensor* output = GetOutput(context, node, 0); |
| TF_LITE_ENSURE_EQ(context, output->type, kTfLiteFloat32); |
| output->allocation_type = kTfLiteDynamic; |
| |
| return kTfLiteOk; |
| } |
| |
| void FinalizeAggregation(TfLiteCombinerType combiner, int num_elements, |
| float current_total_weight, |
| float current_squares_weight, int embedding_size, |
| float* output) { |
| if (combiner != kTfLiteCombinerTypeSum && num_elements > 0) { |
| float multiplier = 1.0; |
| switch (combiner) { |
| case kTfLiteCombinerTypeMean: |
| multiplier = current_total_weight; |
| break; |
| case kTfLiteCombinerTypeSqrtn: |
| multiplier = std::sqrt(current_squares_weight); |
| break; |
| default: |
| break; |
| } |
| for (int k = 0; k < embedding_size; k++) { |
| output[k] /= multiplier; |
| } |
| } |
| } |
| |
| TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { |
| auto* params = |
| reinterpret_cast<TfLiteEmbeddingLookupSparseParams*>(node->builtin_data); |
| TfLiteTensor* output = GetOutput(context, node, 0); |
| const TfLiteTensor* ids = GetInput(context, node, 0); |
| const TfLiteTensor* indices = GetInput(context, node, 1); |
| const TfLiteTensor* dense_shape = GetInput(context, node, 2); |
| const TfLiteTensor* weights = GetInput(context, node, 3); |
| const TfLiteTensor* value = GetInput(context, node, 4); |
| |
| const int lookup_rank = SizeOfDimension(indices, 1); |
| const int embedding_rank = NumDimensions(value); |
| const int num_lookups = SizeOfDimension(ids, 0); |
| const int num_rows = SizeOfDimension(value, 0); |
| |
| // The last dimension gets replaced by the embedding. |
| const int output_rank = (lookup_rank - 1) + (embedding_rank - 1); |
| |
| // Make sure that the actual dense shape of the sparse tensor represented by |
| // (loopkup, indices, dense_shape) is consistent. |
| TF_LITE_ENSURE_EQ(context, SizeOfDimension(dense_shape, 0), lookup_rank); |
| |
| // Resize output tensor. |
| TfLiteIntArray* output_shape = TfLiteIntArrayCreate(output_rank); |
| int k = 0; |
| int embedding_size = 1; |
| int lookup_size = 1; |
| for (int i = 0; i < lookup_rank - 1; i++, k++) { |
| const int dim = dense_shape->data.i32[i]; |
| lookup_size *= dim; |
| output_shape->data[k] = dim; |
| } |
| for (int i = 1; i < embedding_rank; i++, k++) { |
| const int dim = SizeOfDimension(value, i); |
| embedding_size *= dim; |
| output_shape->data[k] = dim; |
| } |
| TF_LITE_ENSURE_STATUS(context->ResizeTensor(context, output, output_shape)); |
| const int output_size = lookup_size * embedding_size; |
| TfLiteTensorRealloc(output_size * sizeof(float), output); |
| |
| std::fill_n(output->data.f, output_size, 0.0f); |
| |
| // Keep track of the current bucket for aggregation/combination. |
| int current_output_offset = 0; |
| float current_total_weight = 0.0; |
| float current_squares_weight = 0.0; |
| int num_elements = 0; |
| |
| for (int i = 0; i < num_lookups; i++) { |
| int idx = ids->data.i32[i]; |
| if (idx >= num_rows || idx < 0) { |
| context->ReportError(context, |
| "Embedding Lookup Sparse: index out of bounds. " |
| "Got %d, and bounds are [0, %d]", |
| idx, num_rows - 1); |
| return kTfLiteError; |
| } |
| |
| // Check where we need to aggregate. |
| const int example_indices_offset = i * lookup_rank; |
| int output_bucket = 0; |
| int stride = 1; |
| for (int k = (lookup_rank - 1) - 1; k >= 0; k--) { |
| output_bucket += indices->data.i32[example_indices_offset + k] * stride; |
| stride *= dense_shape->data.i32[k]; |
| } |
| const int output_offset = output_bucket * embedding_size; |
| |
| // If we are in a new aggregation bucket and the combiner is not the sum, |
| // go back and finalize the result of the previous bucket. |
| if (output_offset != current_output_offset) { |
| FinalizeAggregation(params->combiner, num_elements, current_total_weight, |
| current_squares_weight, embedding_size, |
| &output->data.f[current_output_offset]); |
| |
| // Track next bucket. |
| num_elements = 0; |
| current_total_weight = 0.0; |
| current_squares_weight = 0.0; |
| current_output_offset = output_offset; |
| } |
| |
| // Add element to aggregation. |
| ++num_elements; |
| const int example_embedding_offset = idx * embedding_size; |
| const float w = weights->data.f[i]; |
| current_squares_weight += w * w; |
| current_total_weight += w; |
| for (int k = 0; k < embedding_size; k++) { |
| output->data.f[current_output_offset + k] += |
| (value->data.f[example_embedding_offset + k] * w); |
| } |
| } |
| |
| // Finalize last bucket. |
| FinalizeAggregation(params->combiner, num_elements, current_total_weight, |
| current_squares_weight, embedding_size, |
| &output->data.f[current_output_offset]); |
| |
| return kTfLiteOk; |
| } |
| |
| } // namespace |
| |
| TfLiteRegistration* Register_EMBEDDING_LOOKUP_SPARSE() { |
| static TfLiteRegistration r = {nullptr, nullptr, Prepare, Eval}; |
| return &r; |
| } |
| |
| } // namespace builtin |
| } // namespace ops |
| } // namespace tflite |