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Speed up torch.unique_consecutive() #64835

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Speed up torch.unique_consecutive() #64835

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a526fac
Speed up torch.unique_consecutive()
oToToT Sep 10, 2021
4e7da18
Add assertion in _unique_dim_cpu_impl()
oToToT Sep 11, 2021
2d204dc
Revert unexpected modification
oToToT Sep 11, 2021
89e6648
Use dim() instead of sizes().size()
oToToT Sep 12, 2021
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32 changes: 18 additions & 14 deletions aten/src/ATen/native/Unique.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -135,24 +135,28 @@ ForwardIt _unique_dim_cpu_impl(ForwardIt first, ForwardIt last,
if (first == last) {
return last;
}
// save to calculate distance to iterators
ForwardIt begin = first;

// set first inverse index and count
inverse_indices_vec[indices[0]] = 0;
counts[0] += 1;
TORCH_INTERNAL_ASSERT(inverse_indices_vec.is_contiguous(),
"_unique_dim_cpu_impl only support contiguous inverse_indices_vec");
TORCH_INTERNAL_ASSERT(counts.is_contiguous(),
"_unique_dim_cpu_impl only support contiguous counts");

int64_t *indices_data = indices.data();
int64_t *inverse_data = inverse_indices_vec.data_ptr<int64_t>();
int64_t *counts_data = counts.data_ptr<int64_t>();

ForwardIt result = first;
while (++first != last) {
if (!at::equal(*result, *first) && ++result != first) {
*result = std::move(*first);
ForwardIt previous = first;
int64_t *current_counts = counts_data;
for (ForwardIt current = first; current != last; current++) {
if (!at::equal(*current, *result)) {
*(++result) = std::move(*current);
*(current_counts++) = std::distance(previous, current);
previous = current;
}
int64_t idx_result = std::distance(begin, result);
int64_t idx_first = std::distance(begin, first);
inverse_indices_vec[indices[idx_first]] = idx_result;
counts[idx_result] += 1;
inverse_data[*(indices_data++)] = std::distance(first, result);
}

*current_counts = std::distance(previous, last);
return ++result;
}

Expand Down Expand Up @@ -275,7 +279,7 @@ unique_dim_consecutive_cpu(const Tensor& self, const int64_t dim, const bool ret

std::tuple<Tensor, Tensor, Tensor>
unique_consecutive_cpu(const Tensor& self, const bool return_inverse, const bool return_counts, c10::optional<int64_t> dim) {
if (!dim.has_value()) {
if (!dim.has_value() || (dim.value() == 0 && self.dim() == 1)) {
return AT_DISPATCH_ALL_TYPES_AND2(at::ScalarType::BFloat16, at::ScalarType::Bool, self.scalar_type(), "unique", [&] {
return unique_consecutive_cpu_template<scalar_t>(self, return_inverse, return_counts);
});
Expand Down