| /* 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. |
| ==============================================================================*/ |
| #include "tensorflow/core/framework/partial_tensor_shape.h" |
| #include "tensorflow/core/framework/tensor.h" |
| #include "tensorflow/core/framework/tensor_util.h" |
| #include "tensorflow/core/kernels/data/dataset.h" |
| #include "tensorflow/core/util/batch_util.h" |
| |
| namespace tensorflow { |
| namespace data { |
| namespace { |
| |
| // See documentation in ../ops/dataset_ops.cc for a high-level |
| // description of the following op. |
| |
| class PaddedBatchDatasetOp : public UnaryDatasetOpKernel { |
| public: |
| explicit PaddedBatchDatasetOp(OpKernelConstruction* ctx) |
| : UnaryDatasetOpKernel(ctx), |
| op_version_(ctx->def().op() == "PaddedBatchDataset" ? 1 : 2) {} |
| |
| void MakeDataset(OpKernelContext* ctx, DatasetBase* input, |
| DatasetBase** output) override { |
| int64 batch_size; |
| OP_REQUIRES_OK(ctx, |
| ParseScalarArgument<int64>(ctx, "batch_size", &batch_size)); |
| OP_REQUIRES( |
| ctx, batch_size > 0, |
| errors::InvalidArgument("Batch size must be greater than zero.")); |
| |
| bool drop_remainder = false; |
| if (op_version_ > 1) { |
| OP_REQUIRES_OK(ctx, ParseScalarArgument<bool>(ctx, "drop_remainder", |
| &drop_remainder)); |
| } |
| |
| OpInputList padded_shape_tensors; |
| OP_REQUIRES_OK(ctx, |
| ctx->input_list("padded_shapes", &padded_shape_tensors)); |
| std::vector<PartialTensorShape> padded_shapes; |
| padded_shapes.reserve(padded_shape_tensors.size()); |
| OP_REQUIRES(ctx, |
| padded_shape_tensors.size() == input->output_shapes().size(), |
| errors::InvalidArgument("Number of padded shapes (", |
| padded_shape_tensors.size(), |
| ") must match the number of components " |
| "in the input dataset's elements (", |
| input->output_shapes().size(), ")")); |
| for (const Tensor& padded_shape_t : padded_shape_tensors) { |
| OP_REQUIRES(ctx, TensorShapeUtils::IsVector(padded_shape_t.shape()), |
| errors::InvalidArgument("All padded shapes must be vectors")); |
| PartialTensorShape padded_shape; |
| OP_REQUIRES_OK(ctx, PartialTensorShape::MakePartialShape( |
| padded_shape_t.vec<int64>().data(), |
| padded_shape_t.NumElements(), &padded_shape)); |
| padded_shapes.push_back(std::move(padded_shape)); |
| } |
| OpInputList padding_values_list; |
| OP_REQUIRES_OK(ctx, |
| ctx->input_list("padding_values", &padding_values_list)); |
| std::vector<Tensor> padding_values; |
| OP_REQUIRES(ctx, |
| padding_values_list.size() == input->output_shapes().size(), |
| errors::InvalidArgument( |
| "Number of padding values (", padding_values_list.size(), |
| ") must match the number of components in the input " |
| "dataset's elements (", |
| input->output_shapes().size(), ")")); |
| for (int i = 0; i < padding_values_list.size(); ++i) { |
| const Tensor& padding_value_t = padding_values_list[i]; |
| OP_REQUIRES( |
| ctx, TensorShapeUtils::IsScalar(padding_value_t.shape()), |
| errors::InvalidArgument("All padding values must be scalars")); |
| OP_REQUIRES(ctx, padding_value_t.dtype() == input->output_dtypes()[i], |
| errors::InvalidArgument( |
| "Mismatched type between padding value ", i, |
| " and input dataset's component ", i, ": ", |
| DataTypeString(padding_value_t.dtype()), " vs. ", |
| DataTypeString(input->output_dtypes()[i]))); |
| padding_values.push_back(tensor::DeepCopy(padding_value_t)); |
| } |
| |
| *output = |
| new Dataset(ctx, batch_size, drop_remainder, std::move(padded_shapes), |
| std::move(padding_values), input); |
| } |
| |
| private: |
| class Dataset : public DatasetBase { |
| public: |
| Dataset(OpKernelContext* ctx, int64 batch_size, bool drop_remainder, |
| std::vector<PartialTensorShape> padded_shapes, |
| std::vector<Tensor> padding_values, const DatasetBase* input) |
| : DatasetBase(DatasetContext(ctx)), |
| batch_size_(batch_size), |
| drop_remainder_(drop_remainder), |
| padded_shapes_(std::move(padded_shapes)), |
| padding_values_(std::move(padding_values)), |
| input_(input) { |
| input_->Ref(); |
| |
| // NOTE(mrry): Currently we implement "batch up to" |
| // semantics. If we could tell statically that the input dataset |
| // is infinite, then we could always report `batch_size` as the |
| // 0th dimension. |
| // TODO(mrry): Need to validate that the input shape and the |
| // padded shape are "compatible" (i.e. that padded shape is >= |
| // input shape, with both static and dynamic checks as appropriate). |
| const auto& input_shapes = input_->output_shapes(); |
| output_shapes_.reserve(input_shapes.size()); |
| for (size_t i = 0; i < input_shapes.size(); ++i) { |
| if (drop_remainder_) { |
| output_shapes_.push_back( |
| PartialTensorShape({batch_size_}).Concatenate(padded_shapes_[i])); |
| } else { |
| output_shapes_.push_back( |
| PartialTensorShape({-1}).Concatenate(padded_shapes_[i])); |
| } |
| } |
| } |
| |
| ~Dataset() override { input_->Unref(); } |
| |
| std::unique_ptr<IteratorBase> MakeIteratorInternal( |
| const string& prefix) const override { |
| return std::unique_ptr<IteratorBase>( |
| new Iterator({this, strings::StrCat(prefix, "::PaddedBatch")})); |
| } |
| |
| const DataTypeVector& output_dtypes() const override { |
| return input_->output_dtypes(); |
| } |
| |
| const std::vector<PartialTensorShape>& output_shapes() const override { |
| return output_shapes_; |
| } |
| |
| string DebugString() const override { |
| return strings::StrCat("PaddedBatchDatasetOp(", batch_size_, |
| ")::Dataset"); |
| } |
| |
| protected: |
| Status AsGraphDefInternal(SerializationContext* ctx, |
| DatasetGraphDefBuilder* b, |
| Node** output) const override { |
| Node* input_graph_node = nullptr; |
| TF_RETURN_IF_ERROR(b->AddInputDataset(ctx, input_, &input_graph_node)); |
| Node* batch_size = nullptr; |
| TF_RETURN_IF_ERROR(b->AddScalar(batch_size_, &batch_size)); |
| |
| std::vector<Node*> padded_shapes; |
| padded_shapes.reserve(padded_shapes_.size()); |
| for (int i = 0; i < padded_shapes_.size(); i++) { |
| Node* node; |
| Tensor t(DT_INT64, TensorShape({padded_shapes_[i].dims()})); |
| for (int j = 0; j < padded_shapes_[i].dims(); j++) { |
| t.vec<int64>()(j) = padded_shapes_[i].dim_size(j); |
| } |
| TF_RETURN_IF_ERROR(b->AddTensor(t, &node)); |
| padded_shapes.emplace_back(node); |
| } |
| |
| std::vector<Node*> padding_values; |
| padding_values.reserve(padding_values_.size()); |
| for (const Tensor& t : padding_values_) { |
| Node* node; |
| TF_RETURN_IF_ERROR(b->AddTensor(t, &node)); |
| padding_values.emplace_back(node); |
| } |
| |
| Node* drop_remainder = nullptr; |
| TF_RETURN_IF_ERROR(b->AddScalar(drop_remainder_, &drop_remainder)); |
| |
| AttrValue output_types; |
| b->BuildAttrValue(output_dtypes(), &output_types); |
| |
| AttrValue N; |
| b->BuildAttrValue<int64>(padded_shapes_.size(), &N); |
| |
| TF_RETURN_IF_ERROR(b->AddDataset( |
| this, {{0, input_graph_node}, {1, batch_size}, {4, drop_remainder}}, |
| {{2, padded_shapes}, {3, padding_values}}, |
| {{"Toutput_types", output_types}, {"N", N}}, output)); |
| return Status::OK(); |
| } |
| |
| private: |
| // Copies element into the index^th slice of parent (in the 0th dimension). |
| // |
| |
| class Iterator : public DatasetIterator<Dataset> { |
| public: |
| explicit Iterator(const Params& params) |
| : DatasetIterator<Dataset>(params) {} |
| |
| Status Initialize(IteratorContext* ctx) override { |
| AddConstantParameter(ctx, "batch_size", dataset()->batch_size_); |
| return dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_); |
| } |
| |
| Status GetNextInternal(IteratorContext* ctx, |
| std::vector<Tensor>* out_tensors, |
| bool* end_of_sequence) override { |
| // Each row of `batch_elements` is a tuple of tensors from the |
| // input iterator. |
| std::vector<std::vector<Tensor>> batch_elements; |
| { |
| mutex_lock l(mu_); |
| if (!input_impl_) { |
| *end_of_sequence = true; |
| return Status::OK(); |
| } else { |
| *end_of_sequence = false; |
| batch_elements.reserve(dataset()->batch_size_); |
| for (int i = 0; i < dataset()->batch_size_ && !*end_of_sequence; |
| ++i) { |
| std::vector<Tensor> batch_element_tuple; |
| TF_RETURN_IF_ERROR(input_impl_->GetNext(ctx, &batch_element_tuple, |
| end_of_sequence)); |
| if (!*end_of_sequence) { |
| batch_elements.push_back(std::move(batch_element_tuple)); |
| } |
| } |
| if (*end_of_sequence) { |
| input_impl_.reset(); |
| } |
| } |
| } |
| |
| if (batch_elements.empty()) { |
| DCHECK(*end_of_sequence); |
| return Status::OK(); |
| } |
| |
| if (dataset()->drop_remainder_ && |
| batch_elements.size() < dataset()->batch_size_) { |
| *end_of_sequence = true; |
| return Status::OK(); |
| } |
| |
| // Copy the retrieved batch elements into one output tensor |
| // per tuple component. |
| // NOTE(mrry): If the input or output sizes are statically |
| // known, we could potentially read the input values in-place |
| // into their respective slice locations. This would require a |
| // different GetNext() overload that supports zero-copy, and might |
| // make sense in an optimization pass. |
| const size_t num_tuple_components = batch_elements[0].size(); |
| const int64 num_batch_elements = batch_elements.size(); |
| for (size_t component_index = 0; component_index < num_tuple_components; |
| ++component_index) { |
| // 1. Determine the shape of the padded tensor. |
| TensorShape batch_component_shape({num_batch_elements}); |
| const PartialTensorShape& padded_shape = |
| dataset()->padded_shapes_[component_index]; |
| |
| for (int dim = 0; dim < padded_shape.dims(); ++dim) { |
| if (padded_shape.dim_size(dim) == -1) { |
| batch_component_shape.AddDim(0); |
| } else { |
| batch_component_shape.AddDim(padded_shape.dim_size(dim)); |
| } |
| } |
| |
| for (int64 i = 0; i < num_batch_elements; ++i) { |
| const TensorShape& element_shape = |
| batch_elements[i][component_index].shape(); |
| // TODO(mrry): Perform this check in the shape function if |
| // enough static information is available to do so. |
| if (element_shape.dims() != padded_shape.dims()) { |
| return errors::InvalidArgument( |
| "All elements in a batch must have the same rank as the " |
| "padded shape for component", |
| component_index, ": expected rank ", padded_shape.dims(), |
| " but got element with rank ", element_shape.dims()); |
| } |
| for (int dim = 0; dim < padded_shape.dims(); ++dim) { |
| if (padded_shape.dim_size(dim) == -1) { |
| // Take the max of all batch elements in this dimension. |
| if (batch_elements[i][component_index].shape().dim_size(dim) > |
| batch_component_shape.dim_size(dim + 1)) { |
| batch_component_shape.set_dim( |
| dim + 1, |
| batch_elements[i][component_index].shape().dim_size(dim)); |
| } |
| } else { |
| if (batch_elements[i][component_index].shape().dim_size(dim) > |
| batch_component_shape.dim_size(dim + 1)) { |
| return errors::DataLoss( |
| "Attempted to pad to a smaller size than the input " |
| "element."); |
| } |
| } |
| } |
| } |
| |
| // 2. Copy each batch element to the appropriate location in |
| // the output component tensor. |
| Tensor batch_component(ctx->allocator({}), |
| output_dtypes()[component_index], |
| batch_component_shape); |
| TF_RETURN_IF_ERROR(batch_util::SetElementZero( |
| &batch_component, dataset()->padding_values_[component_index])); |
| |
| // Build the output tuple component by copying one slice |
| // from each input element in the batch. |
| TensorShape component_shape({}); |
| for (int i = 1; i < batch_component_shape.dims(); ++i) { |
| component_shape.AddDim(batch_component_shape.dim_size(i)); |
| } |
| for (int64 i = 0; i < num_batch_elements; ++i) { |
| // Take the fast path if possible. |
| if (batch_elements[i][component_index].shape() == component_shape) { |
| TF_RETURN_IF_ERROR(batch_util::CopyElementToSlice( |
| batch_elements[i][component_index], &batch_component, i)); |
| } else { |
| TF_RETURN_IF_ERROR(batch_util::CopyElementToLargerSlice( |
| batch_elements[i][component_index], &batch_component, i)); |
| } |
| } |
| out_tensors->push_back(std::move(batch_component)); |
| } |
| *end_of_sequence = false; |
| return Status::OK(); |
| } |
| |
| protected: |
| Status SaveInternal(IteratorStateWriter* writer) override { |
| mutex_lock l(mu_); |
| if (input_impl_) |
| TF_RETURN_IF_ERROR(SaveInput(writer, input_impl_)); |
| else |
| TF_RETURN_IF_ERROR(writer->WriteScalar(full_name("exhausted"), "")); |
| return Status::OK(); |
| } |
| |
| Status RestoreInternal(IteratorContext* ctx, |
| IteratorStateReader* reader) override { |
| mutex_lock l(mu_); |
| if (reader->Contains(full_name("exhausted"))) { |
| input_impl_.reset(); |
| } else { |
| TF_RETURN_IF_ERROR( |
| dataset()->input_->MakeIterator(ctx, prefix(), &input_impl_)); |
| TF_RETURN_IF_ERROR(RestoreInput(ctx, reader, input_impl_)); |
| } |
| return Status::OK(); |
| } |
| |
| private: |
| mutex mu_; |
| std::unique_ptr<IteratorBase> input_impl_ GUARDED_BY(mu_); |
| }; |
| |
| const int64 batch_size_; |
| const bool drop_remainder_; |
| const std::vector<PartialTensorShape> padded_shapes_; |
| const std::vector<Tensor> padding_values_; |
| const DatasetBase* const input_; |
| std::vector<PartialTensorShape> output_shapes_; |
| }; |
| |
| const int op_version_; |
| }; |
| |
| REGISTER_KERNEL_BUILDER(Name("PaddedBatchDataset").Device(DEVICE_CPU), |
| PaddedBatchDatasetOp); |
| |
| REGISTER_KERNEL_BUILDER(Name("PaddedBatchDatasetV2").Device(DEVICE_CPU), |
| PaddedBatchDatasetOp); |
| |
| } // namespace |
| } // namespace data |
| } // namespace tensorflow |