1 | /* Copyright 2015 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 | #define EIGEN_USE_THREADS |
17 | |
18 | #include "tensorflow/core/kernels/sparse_slice_op.h" |
19 | |
20 | #include <vector> |
21 | |
22 | #include "tensorflow/core/framework/op_kernel.h" |
23 | #include "tensorflow/core/framework/register_types.h" |
24 | #include "tensorflow/core/framework/tensor_shape.h" |
25 | #include "tensorflow/core/util/sparse/sparse_tensor.h" |
26 | |
27 | namespace tensorflow { |
28 | |
29 | typedef Eigen::ThreadPoolDevice CPUDevice; |
30 | |
31 | namespace functor { |
32 | |
33 | template <typename T> |
34 | struct SparseSliceFunctor<CPUDevice, T> { |
35 | void operator()(OpKernelContext* context, const Tensor& input_indices, |
36 | const Tensor& input_values, const Tensor& input_shape, |
37 | const Tensor& input_start, const Tensor& input_size, |
38 | typename AsyncOpKernel::DoneCallback done) const { |
39 | (void)done; // Unused (only used in GPU implementation) |
40 | const int input_dims = input_shape.NumElements(); |
41 | |
42 | sparse::SparseTensor sparse_tensor; |
43 | TensorShape sparse_tensor_shape; |
44 | OP_REQUIRES_OK(context, |
45 | TensorShapeBase<TensorShape>::BuildTensorShapeBase( |
46 | input_shape.vec<int64_t>(), &sparse_tensor_shape)); |
47 | OP_REQUIRES_OK(context, sparse::SparseTensor::Create( |
48 | input_indices, input_values, |
49 | sparse_tensor_shape, &sparse_tensor)); |
50 | |
51 | const gtl::ArraySlice<int64_t> start(input_start.flat<int64_t>().data(), |
52 | input_dims); |
53 | const gtl::ArraySlice<int64_t> size(input_size.flat<int64_t>().data(), |
54 | input_dims); |
55 | |
56 | const StatusOr<sparse::SparseTensor> output_or = |
57 | sparse::SparseTensor::Slice<T>(sparse_tensor, start, size); |
58 | OP_REQUIRES_OK(context, output_or.status()); |
59 | auto output = output_or.value(); |
60 | |
61 | context->set_output(0, output.indices()); |
62 | context->set_output(1, output.values()); |
63 | |
64 | TensorShape output_shape; |
65 | OP_REQUIRES_OK(context, TensorShapeBase<TensorShape>::BuildTensorShapeBase( |
66 | output.shape(), &output_shape)); |
67 | |
68 | TensorShape allocated_shape; |
69 | OP_REQUIRES_OK(context, TensorShapeBase<TensorShape>::BuildTensorShapeBase( |
70 | {output_shape.dims()}, &allocated_shape)); |
71 | |
72 | Tensor* shape = nullptr; |
73 | OP_REQUIRES_OK(context, |
74 | context->allocate_output(2, allocated_shape, &shape)); |
75 | for (int dim = 0; dim < output_shape.dims(); ++dim) { |
76 | shape->vec<int64_t>()(dim) = output_shape.dim_size(dim); |
77 | } |
78 | } |
79 | }; |
80 | |
81 | } // namespace functor |
82 | |
83 | namespace { |
84 | |
85 | template <typename Device, typename T> |
86 | void SparseSliceOpImpl(OpKernelContext* context, |
87 | typename AsyncOpKernel::DoneCallback done = nullptr) { |
88 | // Note that setting this empty lambda as the default parameter value directly |
89 | // can cause strange compiler/linker errors, so we do it like this instead. |
90 | if (!done) { |
91 | done = [] {}; |
92 | } |
93 | |
94 | const Tensor& input_indices = context->input(0); |
95 | const Tensor& input_values = context->input(1); |
96 | const Tensor& input_shape = context->input(2); |
97 | const Tensor& input_start = context->input(3); |
98 | const Tensor& input_size = context->input(4); |
99 | |
100 | OP_REQUIRES_ASYNC(context, TensorShapeUtils::IsMatrix(input_indices.shape()), |
101 | errors::InvalidArgument( |
102 | "Input indices should be a matrix but received shape " , |
103 | input_indices.shape().DebugString()), |
104 | done); |
105 | OP_REQUIRES_ASYNC(context, TensorShapeUtils::IsVector(input_values.shape()), |
106 | errors::InvalidArgument( |
107 | "Input values should be a vector but received shape " , |
108 | input_values.shape().DebugString()), |
109 | done); |
110 | OP_REQUIRES_ASYNC(context, TensorShapeUtils::IsVector(input_shape.shape()), |
111 | errors::InvalidArgument( |
112 | "Input shape should be a vector but received shape " , |
113 | input_shape.shape().DebugString()), |
114 | done); |
115 | OP_REQUIRES_ASYNC(context, TensorShapeUtils::IsVector(input_start.shape()), |
116 | errors::InvalidArgument( |
117 | "Input start should be a vector but received shape " , |
118 | input_start.shape().DebugString()), |
119 | done); |
120 | OP_REQUIRES_ASYNC(context, TensorShapeUtils::IsVector(input_size.shape()), |
121 | errors::InvalidArgument( |
122 | "Input size should be a vector but received shape " , |
123 | input_size.shape().DebugString()), |
124 | done); |
125 | |
126 | const int input_dims = input_shape.NumElements(); |
127 | OP_REQUIRES_ASYNC(context, input_dims == input_start.NumElements(), |
128 | errors::InvalidArgument( |
129 | "Expected start to be a vector of length " , input_dims, |
130 | " but got length " , input_start.NumElements()), |
131 | done); |
132 | |
133 | OP_REQUIRES_ASYNC(context, input_dims == input_size.NumElements(), |
134 | errors::InvalidArgument( |
135 | "Expected size to be a vector of length " , input_dims, |
136 | " but got length " , input_size.NumElements()), |
137 | done); |
138 | |
139 | functor::SparseSliceFunctor<Device, T>()(context, input_indices, input_values, |
140 | input_shape, input_start, input_size, |
141 | done); |
142 | } |
143 | |
144 | } // namespace |
145 | |
146 | template <typename Device, typename T> |
147 | class SparseSliceOp : public OpKernel { |
148 | public: |
149 | explicit SparseSliceOp(OpKernelConstruction* context) : OpKernel(context) {} |
150 | |
151 | void Compute(OpKernelContext* context) override { |
152 | SparseSliceOpImpl<Device, T>(context); |
153 | } |
154 | }; |
155 | |
156 | #define REGISTER_KERNELS(type) \ |
157 | REGISTER_KERNEL_BUILDER( \ |
158 | Name("SparseSlice").Device(DEVICE_CPU).TypeConstraint<type>("T"), \ |
159 | SparseSliceOp<CPUDevice, type>) |
160 | |
161 | TF_CALL_ALL_TYPES(REGISTER_KERNELS); |
162 | #undef REGISTER_KERNELS |
163 | |
164 | #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM |
165 | |
166 | typedef Eigen::GpuDevice GPUDevice; |
167 | |
168 | template <typename T> |
169 | class SparseSliceGPUOp : public AsyncOpKernel { |
170 | public: |
171 | explicit SparseSliceGPUOp(OpKernelConstruction* context) |
172 | : AsyncOpKernel(context) {} |
173 | |
174 | void ComputeAsync(OpKernelContext* context, DoneCallback done) override { |
175 | SparseSliceOpImpl<GPUDevice, T>(context, done); |
176 | } |
177 | }; |
178 | |
179 | #define REGISTER_KERNELS(type) \ |
180 | REGISTER_KERNEL_BUILDER(Name("SparseSlice") \ |
181 | .Device(DEVICE_GPU) \ |
182 | .HostMemory("shape") \ |
183 | .HostMemory("start") \ |
184 | .HostMemory("size") \ |
185 | .HostMemory("output_shape") \ |
186 | .TypeConstraint<type>("T"), \ |
187 | SparseSliceGPUOp<type>) |
188 | |
189 | TF_CALL_POD_TYPES(REGISTER_KERNELS); |
190 | #undef REGISTER_KERNELS |
191 | |
192 | #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM |
193 | |
194 | } // namespace tensorflow |
195 | |