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 // See docs in ../ops/array_ops.cc.
17
18 #include <vector>
19
20 #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
21 #include "tensorflow/core/framework/op_kernel.h"
22 #include "tensorflow/core/framework/register_types.h"
23 #include "tensorflow/core/framework/tensor.h"
24 #include "tensorflow/core/framework/tensor_types.h"
25 #include "tensorflow/core/framework/types.h"
26
27 #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
28
29 #include "tensorflow/core/kernels/concat_lib_gpu.h"
30 #include "tensorflow/core/kernels/gpu_device_array.h"
31
32 namespace tensorflow {
33 namespace {
34
35 template <typename T, typename IntType>
ConcatGPUCall(OpKernelContext * c,const std::vector<std::unique_ptr<typename TTypes<T,2>::ConstMatrix>> & inputs_flat,typename TTypes<T,2>::Tensor * output_flat)36 void ConcatGPUCall(
37 OpKernelContext* c,
38 const std::vector<std::unique_ptr<typename TTypes<T, 2>::ConstMatrix>>&
39 inputs_flat,
40 typename TTypes<T, 2>::Tensor* output_flat) {
41 GpuDeviceArrayOnHost<const T*> input_ptrs(c, inputs_flat.size());
42 OP_REQUIRES_OK(c, input_ptrs.Init());
43 for (int i = 0; i < inputs_flat.size(); ++i) {
44 input_ptrs.Set(i, inputs_flat[i]->data());
45 }
46 OP_REQUIRES_OK(c, input_ptrs.Finalize());
47
48 GpuDeviceArrayOnHost<IntType> output_scan(c, inputs_flat.size() + 1);
49 OP_REQUIRES_OK(c, output_scan.Init());
50 IntType scan = 0;
51 output_scan.Set(0, scan);
52 bool one_size_input = true;
53 for (int i = 0; i < inputs_flat.size(); ++i) {
54 if (one_size_input && i < inputs_flat.size() - 1 &&
55 inputs_flat[i]->dimension(1) != inputs_flat[i + 1]->dimension(1)) {
56 one_size_input = false;
57 }
58 scan += inputs_flat[i]->dimension(1);
59 output_scan.Set(i + 1, scan);
60 }
61 if (!one_size_input) OP_REQUIRES_OK(c, output_scan.Finalize());
62
63 ConcatGPUImpl<T, IntType>(c->eigen_gpu_device(), input_ptrs.data(),
64 output_scan.data(), one_size_input,
65 inputs_flat[0]->dimension(1), output_flat);
66 }
67
68 } // end namespace
69
70 template <typename T>
ConcatGPU(OpKernelContext * c,const std::vector<std::unique_ptr<typename TTypes<T,2>::ConstMatrix>> & inputs_flat,Tensor * output,typename TTypes<T,2>::Tensor * output_flat)71 void ConcatGPU(
72 OpKernelContext* c,
73 const std::vector<std::unique_ptr<typename TTypes<T, 2>::ConstMatrix>>&
74 inputs_flat,
75 Tensor* output, typename TTypes<T, 2>::Tensor* output_flat) {
76 if (inputs_flat.size() < 16) {
77 if (output->NumElements() < std::numeric_limits<int32>::max()) {
78 ConcatGPUSlice<T, int32>(c->eigen_gpu_device(), inputs_flat, output_flat);
79 } else {
80 ConcatGPUSlice<T, int64_t>(c->eigen_gpu_device(), inputs_flat,
81 output_flat);
82 }
83 } else {
84 // Switching indexing to int64 might cause performance issues.
85 // Hence, we keep int32 indexing in the GPU kernel unless we need to
86 // switch to int64.
87 if (output->NumElements() < std::numeric_limits<int32>::max()) {
88 ConcatGPUCall<T, int32>(c, inputs_flat, output_flat);
89 } else {
90 ConcatGPUCall<T, int64_t>(c, inputs_flat, output_flat);
91 }
92 }
93 }
94
95 #define REGISTER(T) \
96 template void ConcatGPU<T>( \
97 OpKernelContext * c, \
98 const std::vector<std::unique_ptr<typename TTypes<T, 2>::ConstMatrix>>& \
99 inputs_flat, \
100 Tensor* output, typename TTypes<T, 2>::Tensor* output_flat);
101
102 TF_CALL_INTEGRAL_TYPES(REGISTER); // int32 Needed for TensorLists.
103 TF_CALL_bfloat16(REGISTER);
104 TF_CALL_GPU_ALL_TYPES(REGISTER);
105
106 #undef REGISTER
107
108 } // namespace tensorflow
109
110 #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
111