1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
|
/* 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/nn_ops.cc.
#define EIGEN_USE_THREADS
#include <algorithm>
#include <numeric>
#include <vector>
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#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/lib/gtl/top_n.h"
#include "tensorflow/core/util/work_sharder.h"
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;
template <typename T>
class TopK : public OpKernel {
public:
explicit TopK(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("sorted", &sorted_));
if (num_inputs() < 2) { // k is an attr (TopK).
OP_REQUIRES_OK(context, context->GetAttr("k", &k_));
} else { // k is an input (TopKV2), so we won't know it until Compute.
k_ = -1;
}
}
void Compute(OpKernelContext* context) override {
int k = k_;
if (num_inputs() >= 2) {
const auto& k_in = context->input(1);
OP_REQUIRES(context, TensorShapeUtils::IsScalar(k_in.shape()),
errors::InvalidArgument("k must be scalar, got shape ",
k_in.shape().DebugString()));
k = k_in.scalar<int32>()();
}
OP_REQUIRES(context, k >= 0,
errors::InvalidArgument("Need k >= 0, got ", k));
const auto& input_in = context->input(0);
OP_REQUIRES(context, input_in.dims() >= 1,
errors::InvalidArgument("input must be >= 1-D, got shape ",
input_in.shape().DebugString()));
OP_REQUIRES(context, input_in.dim_size(input_in.dims() - 1) >= k,
errors::InvalidArgument("input must have at least k columns"));
const auto& input = input_in.flat_inner_dims<T>();
const int64 num_rows = input.dimension(0); // generally batch_size
const int64 num_cols = input.dimension(1);
TensorShape output_shape = input_in.shape();
output_shape.set_dim(input_in.dims() - 1, k);
Tensor* values_out = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(0, output_shape, &values_out));
Tensor* indices_out = nullptr;
OP_REQUIRES_OK(context,
context->allocate_output(1, output_shape, &indices_out));
// Nothing to do for top-nothing.
if (k == 0) return;
auto values = values_out->flat_inner_dims<T>();
auto indices = indices_out->flat_inner_dims<int32>();
const CPUDevice& d = context->eigen_device<CPUDevice>();
// Special case for k == 1.
if (k == 1) {
#ifdef EIGEN_HAS_INDEX_LIST
typename Eigen::IndexList<Eigen::type2index<1>> reduce_on_cols;
typename Eigen::IndexList<int, Eigen::type2index<1>> rows_by_one;
rows_by_one.set(0, num_rows);
#else
Eigen::array<int, 1> reduce_on_cols = {1};
Eigen::array<int, 2> rows_by_one = {static_cast<int>(num_rows), 1};
#endif
values.device(d) =
input.maximum(/*dims=*/reduce_on_cols).eval().reshape(rows_by_one);
// Get the indices of the maximum values.
for (int r = 0; r < num_rows; ++r) {
for (int c = 0; c < num_cols; ++c) {
if (values(r, 0) == input(r, c)) {
indices(r, 0) = c;
break;
}
}
}
return;
}
auto SortIndices = [&, context](int start_batch, int limit_batch) {
for (int32 b = start_batch; b < limit_batch; ++b) {
const T* input_data = &input(b, 0);
const auto comp = [input_data](const int32 a, const int32 b) {
return input_data[a] > input_data[b];
};
gtl::TopN<int32, decltype(comp)> filter(k, comp);
// TODO(ebrevdo): For large k < num_cols, instead of using
// TopN, it may be faster to create a temporary vector of
// values 0..num_cols - 1 and then use std::partial_sort_copy
// of this into indices. Choosing the appropriate minimum k or
// ratio of k/num_cols will require some experimentation.
if (k == num_cols) {
// Set the initial array of indices 0 ... k - 1.
std::iota(&indices(b, 0), &indices(b, k), 0);
// Use an in-place sort.
std::sort(&indices(b, 0), &indices(b, k), comp);
} else {
// Use the TopN heap object to sort.
filter.reserve(num_cols);
for (int32 c = 0; c < num_cols; ++c) {
filter.push(c);
}
int32 i = 0;
if (sorted_) {
std::unique_ptr<std::vector<int32>> top_k(filter.Extract());
for (auto top_k_it = top_k->begin(); top_k_it != top_k->end();
++top_k_it, ++i) {
indices(b, i) = *top_k_it;
}
} else {
for (auto top_k_it = filter.unsorted_begin();
top_k_it != filter.unsorted_end(); ++top_k_it, ++i) {
indices(b, i) = *top_k_it;
}
}
}
// Now that the indices are sorted, copy the values over in
// sorted order.
std::transform(&indices(b, 0), &indices(b, k), &values(b, 0),
[b, &input](const int32 loc) { return input(b, loc); });
} // for (int32 b = ...
};
// Guesstimate of cost; 4*N*log(K) where N == num_cols.
// If K == N, assume the cost is N*log(K + 1).
const int64 cmp_cost = 3 * Eigen::TensorOpCost::AddCost<int32>() +
Eigen::TensorOpCost::AddCost<T>();
const int64 base_cost =
cmp_cost *
static_cast<int64>(num_cols *
Eigen::numext::log2(static_cast<float>(k + 1)));
const int64 sort_cost = (k == num_cols) ? base_cost : 4 * base_cost;
const int64 copy_cost = 2 * k * Eigen::TensorOpCost::AddCost<T>();
const int64 total_cost = sort_cost + copy_cost;
auto worker_threads = *(context->device()->tensorflow_cpu_worker_threads());
Shard(worker_threads.num_threads, worker_threads.workers, num_rows,
total_cost, SortIndices);
}
private:
int k_;
bool sorted_;
};
#define REGISTER_KERNELS_NAME(name, type) \
REGISTER_KERNEL_BUILDER( \
Name(#name).Device(DEVICE_CPU).TypeConstraint<type>("T"), TopK<type>)
#define REGISTER_KERNELS(type) \
REGISTER_KERNELS_NAME(TopK, type); \
REGISTER_KERNELS_NAME(TopKV2, type)
TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNELS);
#undef REGISTER_KERNELS_TO_NAME
#undef REGISTER_KERNELS
} // namespace tensorflow
|
