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Small cleanup to gradient index and hist. (#7668)

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* Code comments.
* Const accessor to index.
* Remove some weird variables in the `Index` class.
* Simplify the `MemStackAllocator`.
This commit is contained in:
Jiaming Yuan
2022-02-23 11:37:21 +08:00
committed by GitHub
parent 49c74a5369
commit 6762c45494
12 changed files with 149 additions and 148 deletions
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47
src/data/gradient_index.cc
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@@ -10,6 +10,7 @@
#include "../common/column_matrix.h"
#include "../common/hist_util.h"
#include "../common/threading_utils.h"
namespace xgboost {
@@ -34,7 +35,6 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
std::max(static_cast<size_t>(1), std::min(batch.Size(), static_cast<size_t>(n_threads)));
auto page = batch.GetView();
common::MemStackAllocator<size_t, 128> partial_sums(batch_threads);
size_t *p_part = partial_sums.Get();
size_t block_size = batch.Size() / batch_threads;
@@ -48,10 +48,10 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
size_t iend = (tid == (batch_threads - 1) ? batch.Size()
: (block_size * (tid + 1)));
size_t sum = 0;
for (size_t i = ibegin; i < iend; ++i) {
sum += page[i].size();
row_ptr[rbegin + 1 + i] = sum;
size_t running_sum = 0;
for (size_t ridx = ibegin; ridx < iend; ++ridx) {
running_sum += page[ridx].size();
row_ptr[rbegin + 1 + ridx] = running_sum;
}
});
}
@@ -59,9 +59,9 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
#pragma omp single
{
exc.Run([&]() {
p_part[0] = prev_sum;
partial_sums[0] = prev_sum;
for (size_t i = 1; i < batch_threads; ++i) {
p_part[i] = p_part[i - 1] + row_ptr[rbegin + i * block_size];
partial_sums[i] = partial_sums[i - 1] + row_ptr[rbegin + i * block_size];
}
});
}
@@ -74,55 +74,52 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
: (block_size * (tid + 1)));
for (size_t i = ibegin; i < iend; ++i) {
row_ptr[rbegin + 1 + i] += p_part[tid];
row_ptr[rbegin + 1 + i] += partial_sums[tid];
}
});
}
}
exc.Rethrow();
const size_t n_offsets = cut.Ptrs().size() - 1;
const size_t n_index = row_ptr[rbegin + batch.Size()];
const size_t n_index = row_ptr[rbegin + batch.Size()]; // number of entries in this page
ResizeIndex(n_index, isDense_);
CHECK_GT(cut.Values().size(), 0U);
uint32_t *offsets = nullptr;
if (isDense_) {
index.ResizeOffset(n_offsets);
offsets = index.Offset();
for (size_t i = 0; i < n_offsets; ++i) {
offsets[i] = cut.Ptrs()[i];
}
index.SetBinOffset(cut.Ptrs());
}
uint32_t const *offsets = index.Offset();
if (isDense_) {
// Inside the lambda functions, bin_idx is the index for cut value across all
// features. By subtracting it with starting pointer of each feature, we can reduce
// it to smaller value and compress it to smaller types.
common::BinTypeSize curent_bin_size = index.GetBinTypeSize();
if (curent_bin_size == common::kUint8BinsTypeSize) {
common::Span<uint8_t> index_data_span = {index.data<uint8_t>(), n_index};
SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
[offsets](auto idx, auto j) {
return static_cast<uint8_t>(idx - offsets[j]);
[offsets](auto bin_idx, auto fidx) {
return static_cast<uint8_t>(bin_idx - offsets[fidx]);
});
} else if (curent_bin_size == common::kUint16BinsTypeSize) {
common::Span<uint16_t> index_data_span = {index.data<uint16_t>(), n_index};
SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
[offsets](auto idx, auto j) {
return static_cast<uint16_t>(idx - offsets[j]);
[offsets](auto bin_idx, auto fidx) {
return static_cast<uint16_t>(bin_idx - offsets[fidx]);
});
} else {
CHECK_EQ(curent_bin_size, common::kUint32BinsTypeSize);
common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
[offsets](auto idx, auto j) {
return static_cast<uint32_t>(idx - offsets[j]);
[offsets](auto bin_idx, auto fidx) {
return static_cast<uint32_t>(bin_idx - offsets[fidx]);
});
}
} else {
/* For sparse DMatrix we have to store index of feature for each bin
in index field to chose right offset. So offset is nullptr and index is
not reduced */
} else {
common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
[](auto idx, auto) { return idx; });
@@ -194,11 +191,13 @@ void GHistIndexMatrix::Init(SparsePage const &batch, common::Span<FeatureType co
void GHistIndexMatrix::ResizeIndex(const size_t n_index, const bool isDense) {
if ((max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint8_t>::max())) && isDense) {
// compress dense index to uint8
index.SetBinTypeSize(common::kUint8BinsTypeSize);
index.Resize((sizeof(uint8_t)) * n_index);
} else if ((max_num_bins - 1 > static_cast<int>(std::numeric_limits<uint8_t>::max()) &&
max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint16_t>::max())) &&
isDense) {
// compress dense index to uint16
index.SetBinTypeSize(common::kUint16BinsTypeSize);
index.Resize((sizeof(uint16_t)) * n_index);
} else {