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#include "caffe2/operators/lengths_top_k_op.h"
namespace caffe2 {
template <typename T, class Context>
bool LengthsTopKOp<T, Context>::RunOnDevice() {
auto& X = Input(X_IN);
auto& Y = Input(Y_IN);
int N = Y.dim32(0);
const T* X_data = X.template data<T>();
const int* input_len = Y.template data<int>();
auto output_dims = std::vector<int64_t>({N, k_});
auto* output_topk_values = Output(TOPK_VALUES_OUT, output_dims, at::dtype<T>());
auto* output_topk_indices =
Output(TOPK_INDICES_OUT, output_dims, at::dtype<int>());
T* output_topk_values_data = output_topk_values->template mutable_data<T>();
int* output_topk_indices_data =
output_topk_indices->template mutable_data<int>();
auto cmp = [](std::pair<T, int64_t>& lhs, std::pair<T, int64_t>& rhs) {
return lhs.first > rhs.first ||
(lhs.first == rhs.first && lhs.second < rhs.second);
};
// Sort preserving indices
int next_index = 0;
for (int64_t i = 0; i < N; ++i) {
// Build a min-heap, the heap element is pair of (value, idx)
// the top of the heap is the smallest value
std::priority_queue<
std::pair<T, int64_t>,
std::vector<std::pair<T, int64_t>>,
decltype(cmp)>
p_queue(cmp);
// Maintain the size of heap to be less or equal to k_, so the
// heap will hold the k_ largest values
for (int64_t j = 0; j < input_len[i]; ++j) {
const auto value = X_data[next_index++];
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
if (p_queue.size() < k_ || value > p_queue.top().first) {
p_queue.push(std::make_pair(value, j));
}
// NOLINTNEXTLINE(clang-diagnostic-sign-compare)
if (p_queue.size() > k_) {
p_queue.pop();
}
}
int last_index = p_queue.size();
for (int64_t j = 0; j < k_; ++j) {
if (p_queue.size() > 0) {
auto& pqElem = p_queue.top();
output_topk_values_data[i * k_ + last_index - j - 1] = pqElem.first;
output_topk_indices_data[i * k_ + last_index - j - 1] = pqElem.second;
p_queue.pop();
} else {
output_topk_values_data[i * k_ + j] = 0;
output_topk_indices_data[i * k_ + j] = -1;
}
}
}
return true;
}
template <typename T, class Context>
bool LengthsTopKGradientOp<T, Context>::RunOnDevice() {
auto& input_len = Input(LENGTH_IN);
int N = input_len.numel();
auto& input_indices = Input(INDICES_IN);
CAFFE_ENFORCE_GE(input_indices.dim(), 2, "input dim must be >= 2");
CAFFE_ENFORCE_EQ(
input_indices.numel(), N * k_, "input_indices shape is not correct");
auto& input_topk = Input(DER_TOPK_IN);
CAFFE_ENFORCE_EQ(
input_topk.numel(), N * k_, "input_topk shape is not correct");
const int* input_len_data = input_len.template data<int>();
const int* input_indices_data = input_indices.template data<int>();
const T* input_topk_data = input_topk.template data<T>();
int num_indices = 0;
for (int i = 0; i < N; i++) {
num_indices += input_len_data[i];
}
auto* X_out = Output(DER_X_OUT, {num_indices}, at::dtype<T>());
T* X_out_data = X_out->template mutable_data<T>();
math::Set<T, Context>(num_indices, 0.0, X_out_data, &context_);
int index_offset = 0;
for (int i = 0; i < N; i++) {
for (int j = 0; j < std::min(input_len_data[i], k_); j++) {
int cur_index = index_offset + input_indices_data[i * k_ + j];
CAFFE_ENFORCE_LT(
cur_index, num_indices, "cur_index should be less than num_indices");
X_out_data[cur_index] = input_topk_data[i * k_ + j];
}
index_offset += input_len_data[i];
}
return true;
}
REGISTER_CPU_OPERATOR(LengthsTopK, LengthsTopKOp<float, CPUContext>);
REGISTER_CPU_OPERATOR(
LengthsTopKGradient,
LengthsTopKGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(LengthsTopK)
.NumInputs(2)
.NumOutputs(2)
.SetDoc(R"DOC(
Apply TopK to each segment of the input tensor, where segments are defined by
their LENGTHS, and concatenate them in an output tensor of
shape=(SIZE(LENGTHs), k). In case there's less than k values in a segment,
the output value will be padded by 0, and the corresponding output indices will
be padded by -1.
)DOC")
.Input(
0,
"DATA",
"Tensor of rank 1. First dimension must be equal to the sum of "
"lengths")
.Input(1, "LENGTHS", "Tensor of int32 lengths of rank 1")
.Output(
0,
"TopKValue",
"Output top k elements for each segment, with"
"shape=(SIZE(lengths), k)")
.Output(
1,
"TopKIndices",
"Output indices in DATA corresponding to value in TopKValue")
.Arg(
"k",
"the number of top values to return for each segment, if the number "
"of values is smaller than k, the values would be padded with 0 and "
"indices would be padded with -1.");
OPERATOR_SCHEMA(LengthsTopKGradient).NumInputs(3).NumOutputs(1);
namespace {
class GetLengthsTopKGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
return SingleGradientDef(
"LengthsTopKGradient",
"",
vector<string>{I(1), O(1), GO(0)},
vector<string>{GI(0)});
}
};
} // namespace
REGISTER_GRADIENT(LengthsTopK, GetLengthsTopKGradient);
} // namespace caffe2
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