| /* Copyright 2015 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. |
| ==============================================================================*/ |
| |
| // See docs in ../ops/random_ops.cc. |
| |
| #include <vector> |
| #include "tensorflow/core/framework/op_kernel.h" |
| #include "tensorflow/core/framework/register_types.h" |
| #include "tensorflow/core/framework/tensor.h" |
| #include "tensorflow/core/framework/tensor_shape.h" |
| #include "tensorflow/core/framework/tensor_util.h" |
| #include "tensorflow/core/lib/random/random_distributions.h" |
| #include "tensorflow/core/platform/logging.h" |
| #include "tensorflow/core/util/guarded_philox_random.h" |
| |
| namespace tensorflow { |
| |
| // TODO(irving): If performance is critical, generate output directly instead |
| // of an in-place shuffle using a pseudorandom permutation like |
| // |
| // https://github.com/otherlab/geode/blob/master/geode/random/permute.cpp |
| // |
| // This is probably also the right thing if we want a GPU version of shuffling. |
| |
| // We use our own version of std::random_shuffle to guarantee that exactly |
| // size - 1 samples are used. |
| template <class Iter, class Random> |
| static inline void RandomShuffle(Iter first, Iter last, Random& uniform) { |
| if (first == last) return; |
| const auto stop = last - 1; |
| for (auto i = first; i != stop; ++i) { |
| using std::iter_swap; |
| iter_swap(i, i + uniform(last - i)); |
| } |
| } |
| |
| template <class IntT, class InT, class OutT, class Random> |
| static void IndexedShuffle(const int64 size, const InT& input_mat, |
| OutT output_mat, Random& uniform) { |
| std::vector<IntT> permutation(size); |
| for (IntT i = 0; i < size; i++) { |
| permutation[i] = i; |
| } |
| RandomShuffle(permutation.begin(), permutation.end(), uniform); |
| for (IntT i = 0; i < size; i++) { |
| output_mat.template chip<0>(i) = input_mat.template chip<0>(permutation[i]); |
| } |
| } |
| |
| template <typename T> |
| class RandomShuffleOp : public OpKernel { |
| public: |
| explicit RandomShuffleOp(OpKernelConstruction* context) : OpKernel(context) { |
| OP_REQUIRES_OK(context, generator_.Init(context)); |
| } |
| |
| void Compute(OpKernelContext* context) override { |
| const Tensor& input = context->input(0); |
| |
| if (input.NumElements() <= 1 || input.dim_size(0) <= 1) { |
| // No shuffling is required, so copy input directly to output |
| context->set_output(0, input); |
| } else { |
| // Reserve enough random samples for shuffling |
| const int64 size = input.dim_size(0); |
| const int64 samples = size - 1; |
| auto local_gen = generator_.ReserveSamples32(samples); |
| random::SingleSampleAdapter<random::PhiloxRandom> single(&local_gen); |
| const auto uniform = [&single](uint32 n) { return single() % n; }; |
| |
| if (input.dims() == 1) { |
| // For 1D data, copy and then shuffle in place |
| context->set_output(0, tensor::DeepCopy(input)); |
| auto vec = context->mutable_output(0)->vec<T>(); |
| RandomShuffle(vec.data(), vec.data() + size, uniform); |
| } else { |
| // For >= 2D, shuffle indices and then copy across |
| Tensor* output = nullptr; |
| OP_REQUIRES_OK(context, |
| context->allocate_output(0, input.shape(), &output)); |
| const auto input_mat = input.flat_outer_dims<T>(); |
| auto output_mat = output->flat_outer_dims<T>(); |
| if (size < kint32max) { |
| IndexedShuffle<int32>(size, input_mat, output_mat, uniform); |
| } else { |
| IndexedShuffle<int64>(size, input_mat, output_mat, uniform); |
| } |
| } |
| } |
| } |
| |
| private: |
| GuardedPhiloxRandom generator_; |
| }; |
| |
| #define REGISTER(T) \ |
| REGISTER_KERNEL_BUILDER( \ |
| Name("RandomShuffle").Device(DEVICE_CPU).TypeConstraint<T>("T"), \ |
| RandomShuffleOp<T>); |
| TF_CALL_ALL_TYPES(REGISTER) |
| |
| } // namespace tensorflow |