1 /* Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 // See docs in ../ops/sparse_ops.cc. 17 18 #define EIGEN_USE_THREADS 19 20 #include <numeric> 21 22 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" 23 #include "tensorflow/core/framework/op_kernel.h" 24 #include "tensorflow/core/framework/register_types.h" 25 #include "tensorflow/core/framework/tensor.h" 26 #include "tensorflow/core/framework/tensor_util.h" 27 #include "tensorflow/core/framework/types.h" 28 #include "tensorflow/core/util/sparse/sparse_tensor.h" 29 30 using tensorflow::gtl::ArraySlice; 31 using tensorflow::sparse::SparseTensor; 32 33 namespace tensorflow { 34 35 using CPUDevice = Eigen::ThreadPoolDevice; 36 37 template <typename Device, typename T> 38 class SparseSoftmaxOp : public OpKernel { 39 public: 40 explicit SparseSoftmaxOp(OpKernelConstruction *context) : OpKernel(context) {} 41 42 void Compute(OpKernelContext *context) override { 43 const Tensor *indices_t, *values_t, *shape_t; 44 OP_REQUIRES_OK(context, context->input("sp_indices", &indices_t)); 45 OP_REQUIRES_OK(context, context->input("sp_values", &values_t)); 46 OP_REQUIRES_OK(context, context->input("sp_shape", &shape_t)); 47 48 // Validations. 49 OP_REQUIRES(context, TensorShapeUtils::IsMatrix(indices_t->shape()), 50 errors::InvalidArgument( 51 "Input sp_indices should be a matrix but received shape: ", 52 indices_t->shape().DebugString())); 53 OP_REQUIRES(context, 54 TensorShapeUtils::IsVector(values_t->shape()) && 55 TensorShapeUtils::IsVector(shape_t->shape()), 56 errors::InvalidArgument( 57 "Inputs sp_values and sp_shape should be vectors " 58 "but received shapes: ", 59 values_t->shape().DebugString(), " and ", 60 shape_t->shape().DebugString())); 61 OP_REQUIRES(context, shape_t->NumElements() >= 2, 62 errors::InvalidArgument( 63 "Input should have rank >= 2, but received shape: ", 64 shape_t->SummarizeValue(3))); 65 OP_REQUIRES(context, 66 indices_t->dim_size(0) < std::numeric_limits<int>::max(), 67 errors::InvalidArgument( 68 "Number of non-zero elements exceeds int32 range")); 69 70 const int nnz = static_cast<int>(indices_t->dim_size(0)); 71 const int rank = static_cast<int>(indices_t->dim_size(1)); 72 SparseTensor st(tensor::DeepCopy(*indices_t), tensor::DeepCopy(*values_t), 73 TensorShape(shape_t->flat<int64>())); 74 75 Tensor *output_values = nullptr; 76 OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({nnz}), 77 &output_values)); 78 typename TTypes<T>::Flat output_flat = output_values->flat<T>(); 79 80 Tensor tmp_t; 81 OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum<T>::value, 82 TensorShape({}), &tmp_t)); 83 typename TTypes<T>::Scalar tmp_scalar = tmp_t.scalar<T>(); 84 85 gtl::InlinedVector<int64, 4> dims(rank); 86 std::iota(dims.begin(), dims.end(), 0); 87 // { 0, ..., rank-1 }. 88 const ArraySlice<int64> kReorderDims(dims); 89 // All but the last dim -- the class dimension to be max-reduced along. 90 const ArraySlice<int64> kGroupByDims(kReorderDims, 0, rank - 1); 91 st.Reorder<T>(kReorderDims); 92 int count = 0; 93 94 // The SparseTensor has logical shape [..., b, c], where the 95 // innermost size-"c" dimension is the class dimension to be max-reduced. 96 // Therefore we group by the first (rank - 1) dimensions. 97 const Device &device = context->eigen_device<Device>(); 98 for (const auto &g : st.group(kGroupByDims)) { 99 const auto group_vals = g.values<T>(); 100 const int group_size = group_vals.size(); 101 102 // Shifts by max, exponentiates, then renormalizes. 103 tmp_scalar.device(context->eigen_device<Device>()) = group_vals.maximum(); 104 const T group_max = tmp_scalar(); 105 106 Eigen::Tensor<T, 1, Eigen::RowMajor> tmp(group_size); 107 tmp.device(device) = (group_vals - tmp.constant(group_max)).exp(); 108 109 tmp_scalar.device(device) = tmp.sum().inverse(); 110 tmp.device(device) = tmp * tmp.constant(tmp_scalar()); 111 112 // Assigns back to output[count, count + group_size). 113 Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> output_part( 114 output_flat.data() + count, group_size); 115 output_part.device(device) = tmp; 116 117 count += group_size; 118 } 119 } 120 }; 121 122 #define REGISTER_KERNEL(T) \ 123 REGISTER_KERNEL_BUILDER( \ 124 Name("SparseSoftmax").Device(DEVICE_CPU).TypeConstraint<T>("T"), \ 125 SparseSoftmaxOp<CPUDevice, T>) 126 127 REGISTER_KERNEL(float); 128 REGISTER_KERNEL(double); 129 #undef REGISTER_KERNEL 130 131 } // namespace tensorflow 132
