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
|
#define EIGEN_USE_THREADS
#include <algorithm>
#include <unordered_map>
#include <utility>
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor_util.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/public/tensor.h"
#include "tensorflow/core/util/sparse/sparse_tensor.h"
namespace tensorflow {
template <typename T>
class SparseConcatOp : public OpKernel {
public:
explicit SparseConcatOp(OpKernelConstruction* context) : OpKernel(context) {
OP_REQUIRES_OK(context, context->GetAttr("concat_dim", &concat_dim_));
}
void Compute(OpKernelContext* context) override {
OpInputList inds;
OP_REQUIRES_OK(context, context->input_list("indices", &inds));
const int N = inds.size();
for (int i = 0; i < N; i++) {
OP_REQUIRES(context, TensorShapeUtils::IsMatrix(inds[i].shape()),
errors::InvalidArgument(
"Input indices should be a matrix but received shape ",
inds[i].shape().DebugString(), " at position ", i));
}
OpInputList vals;
OP_REQUIRES_OK(context, context->input_list("values", &vals));
OP_REQUIRES(context, vals.size() == N,
errors::InvalidArgument("Expected ", N, " input values, got ",
vals.size()));
for (int i = 0; i < N; i++) {
OP_REQUIRES(context, TensorShapeUtils::IsVector(vals[i].shape()),
errors::InvalidArgument(
"Input values should be a vector but received shape ",
vals[i].shape().DebugString(), " at position ", i));
}
OpInputList shapes;
OP_REQUIRES_OK(context, context->input_list("shapes", &shapes));
OP_REQUIRES(context, shapes.size() == N,
errors::InvalidArgument("Expected ", N, " input shapes, got ",
shapes.size()));
for (int i = 0; i < N; i++) {
OP_REQUIRES(context, TensorShapeUtils::IsVector(shapes[i].shape()),
errors::InvalidArgument(
"Input shapes should be a vector but received shape ",
shapes[i].shape().DebugString(), " at position ", i));
}
const TensorShape input_shape(shapes[0].vec<int64>());
OP_REQUIRES(
context, concat_dim_ >= 0 && concat_dim_ < input_shape.dims(),
errors::InvalidArgument("Concat dimension must be between 0 and rank (",
input_shape.dims(), "), got ", concat_dim_));
for (int i = 1; i < N; ++i) {
const TensorShape current_shape(shapes[i].vec<int64>());
OP_REQUIRES(context, current_shape.dims() == input_shape.dims(),
errors::InvalidArgument(
"Ranks of all input tensors must match: expected ",
input_shape.dims(), " but got ", current_shape.dims(),
" at position ", i));
for (int j = 0; j < input_shape.dims(); ++j) {
if (j != concat_dim_) {
OP_REQUIRES(
context, input_shape.dim_size(j) == current_shape.dim_size(j),
errors::InvalidArgument(
"Input shapes must match: expected ", input_shape.dim_size(j),
" for dimension ", j, " but got ", current_shape.dim_size(j),
" at position ", i));
}
}
}
// The input and output sparse tensors are assumed to be ordered along
// increasing dimension number. But in order for concat to work properly,
// order[0] must be concat_dim. So we will reorder the inputs to the
// concat ordering, concatenate, then reorder back to the standard order.
// We make a deep copy of the input tensors to ensure that the in-place
// reorder doesn't create race conditions for other ops that may be
// concurrently reading the indices and values tensors.
gtl::InlinedVector<int64, 8> std_order(input_shape.dims());
std::iota(std_order.begin(), std_order.end(), 0);
std::vector<int64> concat_order;
concat_order.reserve(input_shape.dims());
concat_order.push_back(concat_dim_);
for (int j = 0; j < input_shape.dims(); ++j) {
if (j != concat_dim_) {
concat_order.push_back(j);
}
}
std::vector<sparse::SparseTensor> sp_inputs;
for (int i = 0; i < N; ++i) {
const TensorShape current_shape(shapes[i].vec<int64>());
sp_inputs.emplace_back(tensor::DeepCopy(inds[i]),
tensor::DeepCopy(vals[i]), current_shape,
std_order);
sp_inputs[i].Reorder<T>(concat_order);
}
sparse::SparseTensor concat = sparse::SparseTensor::Concat<T>(sp_inputs);
concat.Reorder<T>(std_order);
context->set_output(0, concat.indices());
context->set_output(1, concat.values());
Tensor* output_shape_out = nullptr;
OP_REQUIRES_OK(context, context->allocate_output(
2, TensorShape({concat.shape().dims()}),
&output_shape_out));
auto output_shape = output_shape_out->vec<int64>();
for (int j = 0; j < concat.shape().dims(); ++j) {
output_shape(j) = concat.shape().dim_size(j);
}
}
private:
int concat_dim_;
};
#define REGISTER_KERNELS(type) \
REGISTER_KERNEL_BUILDER( \
Name("SparseConcat").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
SparseConcatOp<type>)
TF_CALL_ALL_TYPES(REGISTER_KERNELS);
#undef REGISTER_KERNELS
} // namespace tensorflow
|
