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/op_requires.h" |
25 | #include "tensorflow/core/framework/register_types.h" |
26 | #include "tensorflow/core/framework/tensor.h" |
27 | #include "tensorflow/core/framework/tensor_util.h" |
28 | #include "tensorflow/core/framework/types.h" |
29 | #include "tensorflow/core/util/sparse/sparse_tensor.h" |
30 | |
31 | using tensorflow::gtl::ArraySlice; |
32 | using tensorflow::sparse::SparseTensor; |
33 | |
34 | namespace tensorflow { |
35 | |
36 | using CPUDevice = Eigen::ThreadPoolDevice; |
37 | |
38 | template <typename Device, typename T> |
39 | class SparseSoftmaxOp : public OpKernel { |
40 | public: |
41 | explicit SparseSoftmaxOp(OpKernelConstruction *context) : OpKernel(context) {} |
42 | |
43 | void Compute(OpKernelContext *context) override { |
44 | const Tensor *indices_t, *values_t, *shape_t; |
45 | OP_REQUIRES_OK(context, context->input("sp_indices" , &indices_t)); |
46 | OP_REQUIRES_OK(context, context->input("sp_values" , &values_t)); |
47 | OP_REQUIRES_OK(context, context->input("sp_shape" , &shape_t)); |
48 | |
49 | // Validations. |
50 | OP_REQUIRES(context, TensorShapeUtils::IsMatrix(indices_t->shape()), |
51 | errors::InvalidArgument( |
52 | "Input sp_indices should be a matrix but received shape: " , |
53 | indices_t->shape().DebugString())); |
54 | OP_REQUIRES(context, |
55 | TensorShapeUtils::IsVector(values_t->shape()) && |
56 | TensorShapeUtils::IsVector(shape_t->shape()), |
57 | errors::InvalidArgument( |
58 | "Inputs sp_values and sp_shape should be vectors " |
59 | "but received shapes: " , |
60 | values_t->shape().DebugString(), " and " , |
61 | shape_t->shape().DebugString())); |
62 | OP_REQUIRES(context, shape_t->NumElements() >= 2, |
63 | errors::InvalidArgument( |
64 | "Input should have rank >= 2, but received shape: " , |
65 | shape_t->SummarizeValue(3))); |
66 | TensorShape shape; |
67 | OP_REQUIRES_OK(context, TensorShape::BuildTensorShape( |
68 | shape_t->flat<int64_t>(), &shape)); |
69 | |
70 | const int64_t nnz = indices_t->dim_size(0); |
71 | const int rank = static_cast<int>(indices_t->dim_size(1)); |
72 | SparseTensor st; |
73 | OP_REQUIRES_OK( |
74 | context, SparseTensor::Create(tensor::DeepCopy(*indices_t), |
75 | tensor::DeepCopy(*values_t), shape, &st)); |
76 | |
77 | Tensor *output_values = nullptr; |
78 | OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({nnz}), |
79 | &output_values)); |
80 | typename TTypes<T>::Flat output_flat = output_values->flat<T>(); |
81 | |
82 | Tensor tmp_t; |
83 | OP_REQUIRES_OK(context, context->allocate_temp(DataTypeToEnum<T>::value, |
84 | TensorShape({}), &tmp_t)); |
85 | typename TTypes<T>::Scalar tmp_scalar = tmp_t.scalar<T>(); |
86 | |
87 | gtl::InlinedVector<int64_t, 4> dims(rank); |
88 | std::iota(dims.begin(), dims.end(), 0); |
89 | // { 0, ..., rank-1 }. |
90 | const ArraySlice<int64_t> kReorderDims(dims); |
91 | // All but the last dim -- the class dimension to be max-reduced along. |
92 | const ArraySlice<int64_t> kGroupByDims = kReorderDims.subspan(0, rank - 1); |
93 | st.Reorder<T>(kReorderDims); |
94 | int count = 0; |
95 | |
96 | // The SparseTensor has logical shape [..., b, c], where the |
97 | // innermost size-"c" dimension is the class dimension to be max-reduced. |
98 | // Therefore we group by the first (rank - 1) dimensions. |
99 | const Device &device = context->eigen_device<Device>(); |
100 | for (const auto &g : st.group(kGroupByDims)) { |
101 | const auto group_vals = g.values<T>(); |
102 | const int group_size = group_vals.size(); |
103 | |
104 | // Shifts by max, exponentiates, then renormalizes. |
105 | tmp_scalar.device(context->eigen_device<Device>()) = group_vals.maximum(); |
106 | const T group_max = tmp_scalar(); |
107 | |
108 | Eigen::Tensor<T, 1, Eigen::RowMajor> tmp(group_size); |
109 | tmp.device(device) = (group_vals - tmp.constant(group_max)).exp(); |
110 | |
111 | tmp_scalar.device(device) = tmp.sum().inverse(); |
112 | tmp.device(device) = tmp * tmp.constant(tmp_scalar()); |
113 | |
114 | // Assigns back to output[count, count + group_size). |
115 | Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> output_part( |
116 | output_flat.data() + count, group_size); |
117 | output_part.device(device) = tmp; |
118 | |
119 | count += group_size; |
120 | } |
121 | } |
122 | }; |
123 | |
124 | #define REGISTER_KERNEL(T) \ |
125 | REGISTER_KERNEL_BUILDER( \ |
126 | Name("SparseSoftmax").Device(DEVICE_CPU).TypeConstraint<T>("T"), \ |
127 | SparseSoftmaxOp<CPUDevice, T>) |
128 | |
129 | REGISTER_KERNEL(Eigen::half); |
130 | REGISTER_KERNEL(float); |
131 | REGISTER_KERNEL(double); |
132 | #undef REGISTER_KERNEL |
133 | |
134 | } // namespace tensorflow |
135 | |