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auc.cu
558 lines (502 loc) · 19.2 KB
/
auc.cu
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/*!
* Copyright 2021 by XGBoost Contributors
*/
#include <thrust/scan.h>
#include <cub/cub.cuh>
#include <cassert>
#include <limits>
#include <memory>
#include <utility>
#include <tuple>
#include "rabit/rabit.h"
#include "xgboost/span.h"
#include "xgboost/data.h"
#include "auc.h"
#include "../common/device_helpers.cuh"
#include "../common/ranking_utils.cuh"
namespace xgboost {
namespace metric {
namespace {
template <typename T>
using Discard = thrust::discard_iterator<T>;
struct GetWeightOp {
common::Span<float const> weights;
common::Span<size_t const> sorted_idx;
__device__ float operator()(size_t i) const {
return weights.empty() ? 1.0f : weights[sorted_idx[i]];
}
};
} // namespace
/**
* A cache to GPU data to avoid reallocating memory.
*/
struct DeviceAUCCache {
// Pair of FP/TP
using Pair = thrust::pair<float, float>;
// index sorted by prediction value
dh::device_vector<size_t> sorted_idx;
// track FP/TP for computation on trapesoid area
dh::device_vector<Pair> fptp;
// track FP_PREV/TP_PREV for computation on trapesoid area
dh::device_vector<Pair> neg_pos;
// index of unique prediction values.
dh::device_vector<size_t> unique_idx;
// p^T: transposed prediction matrix, used by MultiClassAUC
dh::device_vector<float> predts_t;
std::unique_ptr<dh::AllReducer> reducer;
void Init(common::Span<float const> predts, bool is_multi, int32_t device) {
if (sorted_idx.size() != predts.size()) {
sorted_idx.resize(predts.size());
fptp.resize(sorted_idx.size());
unique_idx.resize(sorted_idx.size());
neg_pos.resize(sorted_idx.size());
if (is_multi) {
predts_t.resize(sorted_idx.size());
}
}
if (is_multi && !reducer) {
reducer.reset(new dh::AllReducer);
reducer->Init(device);
}
}
};
/**
* The GPU implementation uses same calculation as CPU with a few more steps to distribute
* work across threads:
*
* - Run scan to obtain TP/FP values, which are right coordinates of trapesoid.
* - Find distinct prediction values and get the corresponding FP_PREV/TP_PREV value,
* which are left coordinates of trapesoids.
* - Reduce the scan array into 1 AUC value.
*/
std::tuple<float, float, float>
GPUBinaryAUC(common::Span<float const> predts, MetaInfo const &info,
int32_t device, std::shared_ptr<DeviceAUCCache> *p_cache) {
auto& cache = *p_cache;
if (!cache) {
cache.reset(new DeviceAUCCache);
}
cache->Init(predts, false, device);
auto labels = info.labels_.ConstDeviceSpan();
auto weights = info.weights_.ConstDeviceSpan();
dh::safe_cuda(cudaSetDevice(device));
CHECK(!labels.empty());
CHECK_EQ(labels.size(), predts.size());
/**
* Create sorted index for each class
*/
auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
dh::ArgSort<false>(predts, d_sorted_idx);
/**
* Linear scan
*/
auto get_weight = GetWeightOp{weights, d_sorted_idx};
using Pair = thrust::pair<float, float>;
auto get_fp_tp = [=]__device__(size_t i) {
size_t idx = d_sorted_idx[i];
float label = labels[idx];
float w = get_weight(i);
float fp = (1.0 - label) * w;
float tp = label * w;
return thrust::make_pair(fp, tp);
}; // NOLINT
auto d_fptp = dh::ToSpan(cache->fptp);
dh::LaunchN(d_sorted_idx.size(),
[=] __device__(size_t i) { d_fptp[i] = get_fp_tp(i); });
dh::XGBDeviceAllocator<char> alloc;
auto d_unique_idx = dh::ToSpan(cache->unique_idx);
dh::Iota(d_unique_idx);
auto uni_key = dh::MakeTransformIterator<float>(
thrust::make_counting_iterator(0),
[=] __device__(size_t i) { return predts[d_sorted_idx[i]]; });
auto end_unique = thrust::unique_by_key_copy(
thrust::cuda::par(alloc), uni_key, uni_key + d_sorted_idx.size(),
dh::tbegin(d_unique_idx), thrust::make_discard_iterator(),
dh::tbegin(d_unique_idx));
d_unique_idx = d_unique_idx.subspan(0, end_unique.second - dh::tbegin(d_unique_idx));
dh::InclusiveScan(
dh::tbegin(d_fptp), dh::tbegin(d_fptp),
[=] __device__(Pair const &l, Pair const &r) {
return thrust::make_pair(l.first + r.first, l.second + r.second);
},
d_fptp.size());
auto d_neg_pos = dh::ToSpan(cache->neg_pos);
// scatter unique negaive/positive values
// shift to right by 1 with initial value being 0
dh::LaunchN(d_unique_idx.size(), [=] __device__(size_t i) {
if (d_unique_idx[i] == 0) { // first unique index is 0
assert(i == 0);
d_neg_pos[0] = {0, 0};
return;
}
d_neg_pos[d_unique_idx[i]] = d_fptp[d_unique_idx[i] - 1];
if (i == d_unique_idx.size() - 1) {
// last one needs to be included, may override above assignment if the last
// prediction value is distinct from previous one.
d_neg_pos.back() = d_fptp[d_unique_idx[i] - 1];
return;
}
});
auto in = dh::MakeTransformIterator<float>(
thrust::make_counting_iterator(0), [=] __device__(size_t i) {
float fp, tp;
float fp_prev, tp_prev;
if (i == 0) {
// handle the last element
thrust::tie(fp, tp) = d_fptp.back();
thrust::tie(fp_prev, tp_prev) = d_neg_pos[d_unique_idx.back()];
} else {
thrust::tie(fp, tp) = d_fptp[d_unique_idx[i] - 1];
thrust::tie(fp_prev, tp_prev) = d_neg_pos[d_unique_idx[i - 1]];
}
return TrapesoidArea(fp_prev, fp, tp_prev, tp);
});
Pair last = cache->fptp.back();
float auc = thrust::reduce(thrust::cuda::par(alloc), in, in + d_unique_idx.size());
return std::make_tuple(last.first, last.second, auc);
}
void Transpose(common::Span<float const> in, common::Span<float> out, size_t m,
size_t n, int32_t device) {
CHECK_EQ(in.size(), out.size());
CHECK_EQ(in.size(), m * n);
dh::LaunchN(in.size(), [=] __device__(size_t i) {
size_t col = i / m;
size_t row = i % m;
size_t idx = row * n + col;
out[i] = in[idx];
});
}
/**
* Last index of a group in a CSR style of index pointer.
*/
template <typename Idx>
XGBOOST_DEVICE size_t LastOf(size_t group, common::Span<Idx> indptr) {
return indptr[group + 1] - 1;
}
float ScaleClasses(common::Span<float> results, common::Span<float> local_area,
common::Span<float> fp, common::Span<float> tp,
common::Span<float> auc, std::shared_ptr<DeviceAUCCache> cache,
size_t n_classes) {
dh::XGBDeviceAllocator<char> alloc;
if (rabit::IsDistributed()) {
CHECK_EQ(dh::CudaGetPointerDevice(results.data()), dh::CurrentDevice());
cache->reducer->AllReduceSum(results.data(), results.data(), results.size());
}
auto reduce_in = dh::MakeTransformIterator<thrust::pair<float, float>>(
thrust::make_counting_iterator(0), [=] __device__(size_t i) {
if (local_area[i] > 0) {
return thrust::make_pair(auc[i] / local_area[i] * tp[i], tp[i]);
}
return thrust::make_pair(std::numeric_limits<float>::quiet_NaN(), 0.0f);
});
float tp_sum;
float auc_sum;
thrust::tie(auc_sum, tp_sum) = thrust::reduce(
thrust::cuda::par(alloc), reduce_in, reduce_in + n_classes,
thrust::make_pair(0.0f, 0.0f),
[=] __device__(auto const &l, auto const &r) {
return thrust::make_pair(l.first + r.first, l.second + r.second);
});
if (tp_sum != 0 && !std::isnan(auc_sum)) {
auc_sum /= tp_sum;
} else {
return std::numeric_limits<float>::quiet_NaN();
}
return auc_sum;
}
/**
* MultiClass implementation is similar to binary classification, except we need to split
* up each class in all kernels.
*/
float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info,
int32_t device, std::shared_ptr<DeviceAUCCache>* p_cache,
size_t n_classes) {
dh::safe_cuda(cudaSetDevice(device));
auto& cache = *p_cache;
if (!cache) {
cache.reset(new DeviceAUCCache);
}
cache->Init(predts, true, device);
auto labels = info.labels_.ConstDeviceSpan();
auto weights = info.weights_.ConstDeviceSpan();
size_t n_samples = labels.size();
if (n_samples == 0) {
dh::TemporaryArray<float> resutls(n_classes * 4, 0.0f);
auto d_results = dh::ToSpan(resutls);
dh::LaunchN(n_classes * 4,
[=] __device__(size_t i) { d_results[i] = 0.0f; });
auto local_area = d_results.subspan(0, n_classes);
auto fp = d_results.subspan(n_classes, n_classes);
auto tp = d_results.subspan(2 * n_classes, n_classes);
auto auc = d_results.subspan(3 * n_classes, n_classes);
return ScaleClasses(d_results, local_area, fp, tp, auc, cache, n_classes);
}
/**
* Create sorted index for each class
*/
auto d_predts_t = dh::ToSpan(cache->predts_t);
Transpose(predts, d_predts_t, n_samples, n_classes, device);
dh::TemporaryArray<uint32_t> class_ptr(n_classes + 1, 0);
auto d_class_ptr = dh::ToSpan(class_ptr);
dh::LaunchN(n_classes + 1,
[=] __device__(size_t i) { d_class_ptr[i] = i * n_samples; });
// no out-of-place sort for thrust, cub sort doesn't accept general iterator. So can't
// use transform iterator in sorting.
auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
dh::SegmentedArgSort<false>(d_predts_t, d_class_ptr, d_sorted_idx);
/**
* Linear scan
*/
dh::caching_device_vector<float> d_auc(n_classes, 0);
auto s_d_auc = dh::ToSpan(d_auc);
auto get_weight = GetWeightOp{weights, d_sorted_idx};
using Pair = thrust::pair<float, float>;
auto d_fptp = dh::ToSpan(cache->fptp);
auto get_fp_tp = [=]__device__(size_t i) {
size_t idx = d_sorted_idx[i];
size_t class_id = i / n_samples;
// labels is a vector of size n_samples.
float label = labels[idx % n_samples] == class_id;
float w = weights.empty() ? 1.0f : weights[d_sorted_idx[i] % n_samples];
float fp = (1.0 - label) * w;
float tp = label * w;
return thrust::make_pair(fp, tp);
}; // NOLINT
dh::LaunchN(d_sorted_idx.size(),
[=] __device__(size_t i) { d_fptp[i] = get_fp_tp(i); });
/**
* Handle duplicated predictions
*/
dh::XGBDeviceAllocator<char> alloc;
auto d_unique_idx = dh::ToSpan(cache->unique_idx);
dh::Iota(d_unique_idx);
auto uni_key = dh::MakeTransformIterator<thrust::pair<uint32_t, float>>(
thrust::make_counting_iterator(0), [=] __device__(size_t i) {
uint32_t class_id = i / n_samples;
float predt = d_predts_t[d_sorted_idx[i]];
return thrust::make_pair(class_id, predt);
});
// unique values are sparse, so we need a CSR style indptr
dh::TemporaryArray<uint32_t> unique_class_ptr(class_ptr.size());
auto d_unique_class_ptr = dh::ToSpan(unique_class_ptr);
auto n_uniques = dh::SegmentedUniqueByKey(
thrust::cuda::par(alloc),
dh::tbegin(d_class_ptr),
dh::tend(d_class_ptr),
uni_key,
uni_key + d_sorted_idx.size(),
dh::tbegin(d_unique_idx),
d_unique_class_ptr.data(),
dh::tbegin(d_unique_idx),
thrust::equal_to<thrust::pair<uint32_t, float>>{});
d_unique_idx = d_unique_idx.subspan(0, n_uniques);
using Triple = thrust::tuple<uint32_t, float, float>;
// expand to tuple to include class id
auto fptp_it_in = dh::MakeTransformIterator<Triple>(
thrust::make_counting_iterator(0), [=] __device__(size_t i) {
return thrust::make_tuple(i, d_fptp[i].first, d_fptp[i].second);
});
// shrink down to pair
auto fptp_it_out = thrust::make_transform_output_iterator(
dh::TypedDiscard<Triple>{}, [d_fptp] __device__(Triple const &t) {
d_fptp[thrust::get<0>(t)] =
thrust::make_pair(thrust::get<1>(t), thrust::get<2>(t));
return t;
});
dh::InclusiveScan(
fptp_it_in, fptp_it_out,
[=] __device__(Triple const &l, Triple const &r) {
uint32_t l_cid = thrust::get<0>(l) / n_samples;
uint32_t r_cid = thrust::get<0>(r) / n_samples;
if (l_cid != r_cid) {
return r;
}
return Triple(thrust::get<0>(r),
thrust::get<1>(l) + thrust::get<1>(r), // fp
thrust::get<2>(l) + thrust::get<2>(r)); // tp
},
d_fptp.size());
// scatter unique FP_PREV/TP_PREV values
auto d_neg_pos = dh::ToSpan(cache->neg_pos);
// When dataset is not empty, each class must have at least 1 (unique) sample
// prediction, so no need to handle special case.
dh::LaunchN(d_unique_idx.size(), [=] __device__(size_t i) {
if (d_unique_idx[i] % n_samples == 0) { // first unique index is 0
assert(d_unique_idx[i] % n_samples == 0);
d_neg_pos[d_unique_idx[i]] = {0, 0}; // class_id * n_samples = i
return;
}
uint32_t class_id = d_unique_idx[i] / n_samples;
d_neg_pos[d_unique_idx[i]] = d_fptp[d_unique_idx[i] - 1];
if (i == LastOf(class_id, d_unique_class_ptr)) {
// last one needs to be included.
size_t last = d_unique_idx[LastOf(class_id, d_unique_class_ptr)];
d_neg_pos[LastOf(class_id, d_class_ptr)] = d_fptp[last - 1];
return;
}
});
/**
* Reduce the result for each class
*/
auto key_in = dh::MakeTransformIterator<uint32_t>(
thrust::make_counting_iterator(0), [=] __device__(size_t i) {
size_t class_id = d_unique_idx[i] / n_samples;
return class_id;
});
auto val_in = dh::MakeTransformIterator<float>(
thrust::make_counting_iterator(0), [=] __device__(size_t i) {
size_t class_id = d_unique_idx[i] / n_samples;
float fp, tp;
float fp_prev, tp_prev;
if (i == d_unique_class_ptr[class_id]) {
// first item is ignored, we use this thread to calculate the last item
thrust::tie(fp, tp) = d_fptp[class_id * n_samples + (n_samples - 1)];
thrust::tie(fp_prev, tp_prev) =
d_neg_pos[d_unique_idx[LastOf(class_id, d_unique_class_ptr)]];
} else {
thrust::tie(fp, tp) = d_fptp[d_unique_idx[i] - 1];
thrust::tie(fp_prev, tp_prev) = d_neg_pos[d_unique_idx[i - 1]];
}
float auc = TrapesoidArea(fp_prev, fp, tp_prev, tp);
return auc;
});
thrust::reduce_by_key(thrust::cuda::par(alloc), key_in,
key_in + d_unique_idx.size(), val_in,
thrust::make_discard_iterator(), d_auc.begin());
/**
* Scale the classes with number of samples for each class.
*/
dh::TemporaryArray<float> resutls(n_classes * 4);
auto d_results = dh::ToSpan(resutls);
auto local_area = d_results.subspan(0, n_classes);
auto fp = d_results.subspan(n_classes, n_classes);
auto tp = d_results.subspan(2 * n_classes, n_classes);
auto auc = d_results.subspan(3 * n_classes, n_classes);
dh::LaunchN(n_classes, [=] __device__(size_t c) {
auc[c] = s_d_auc[c];
auto last = d_fptp[n_samples * c + (n_samples - 1)];
fp[c] = last.first;
tp[c] = last.second;
local_area[c] = last.first * last.second;
});
return ScaleClasses(d_results, local_area, fp, tp, auc, cache, n_classes);
}
namespace {
struct RankScanItem {
size_t idx;
float predt;
float w;
bst_group_t group_id;
};
} // anonymous namespace
std::pair<float, uint32_t>
GPURankingAUC(common::Span<float const> predts, MetaInfo const &info,
int32_t device, std::shared_ptr<DeviceAUCCache> *p_cache) {
auto& cache = *p_cache;
if (!cache) {
cache.reset(new DeviceAUCCache);
}
cache->Init(predts, false, device);
dh::caching_device_vector<bst_group_t> group_ptr(info.group_ptr_);
dh::XGBCachingDeviceAllocator<char> alloc;
auto d_group_ptr = dh::ToSpan(group_ptr);
/**
* Validate the dataset
*/
auto check_it = dh::MakeTransformIterator<size_t>(
thrust::make_counting_iterator(0),
[=] __device__(size_t i) { return d_group_ptr[i + 1] - d_group_ptr[i]; });
size_t n_valid = thrust::count_if(
thrust::cuda::par(alloc), check_it, check_it + group_ptr.size() - 1,
[=] __device__(size_t len) { return len >= 3; });
if (n_valid < info.group_ptr_.size() - 1) {
InvalidGroupAUC();
}
if (n_valid == 0) {
return std::make_pair(0.0f, 0);
}
/**
* Sort the labels
*/
auto d_labels = info.labels_.ConstDeviceSpan();
auto d_sorted_idx = dh::ToSpan(cache->sorted_idx);
dh::SegmentedArgSort<false>(d_labels, d_group_ptr, d_sorted_idx);
auto d_weights = info.weights_.ConstDeviceSpan();
dh::caching_device_vector<size_t> threads_group_ptr(group_ptr.size(), 0);
auto d_threads_group_ptr = dh::ToSpan(threads_group_ptr);
// Use max to represent triangle
auto n_threads = common::SegmentedTrapezoidThreads(
d_group_ptr, d_threads_group_ptr, std::numeric_limits<size_t>::max());
// get the coordinate in nested summation
auto get_i_j = [=]__device__(size_t idx, size_t query_group_idx) {
auto data_group_begin = d_group_ptr[query_group_idx];
size_t n_samples = d_group_ptr[query_group_idx + 1] - data_group_begin;
auto thread_group_begin = d_threads_group_ptr[query_group_idx];
auto idx_in_thread_group = idx - thread_group_begin;
size_t i, j;
common::UnravelTrapeziodIdx(idx_in_thread_group, n_samples, &i, &j);
// we use global index among all groups for sorted idx, so i, j should also be global
// index.
i += data_group_begin;
j += data_group_begin;
return thrust::make_pair(i, j);
}; // NOLINT
auto in = dh::MakeTransformIterator<RankScanItem>(
thrust::make_counting_iterator(0), [=] __device__(size_t idx) {
bst_group_t query_group_idx = dh::SegmentId(d_threads_group_ptr, idx);
auto data_group_begin = d_group_ptr[query_group_idx];
size_t n_samples = d_group_ptr[query_group_idx + 1] - data_group_begin;
if (n_samples < 3) {
// at least 3 documents are required.
return RankScanItem{idx, 0, 0, query_group_idx};
}
size_t i, j;
thrust::tie(i, j) = get_i_j(idx, query_group_idx);
float predt = predts[d_sorted_idx[i]] - predts[d_sorted_idx[j]];
float w = common::Sqr(d_weights.empty() ? 1.0f : d_weights[query_group_idx]);
if (predt > 0) {
predt = 1.0;
} else if (predt == 0) {
predt = 0.5;
} else {
predt = 0;
}
predt *= w;
return RankScanItem{idx, predt, w, query_group_idx};
});
dh::TemporaryArray<float> d_auc(group_ptr.size() - 1);
auto s_d_auc = dh::ToSpan(d_auc);
auto out = thrust::make_transform_output_iterator(
dh::TypedDiscard<RankScanItem>{}, [=] __device__(RankScanItem const &item) -> RankScanItem {
auto group_id = item.group_id;
assert(group_id < d_group_ptr.size());
auto data_group_begin = d_group_ptr[group_id];
size_t n_samples = d_group_ptr[group_id + 1] - data_group_begin;
// last item of current group
if (item.idx == LastOf(group_id, d_threads_group_ptr)) {
if (item.w > 0) {
s_d_auc[group_id] = item.predt / item.w;
} else {
s_d_auc[group_id] = 0;
}
}
return {}; // discard
});
dh::InclusiveScan(
in, out,
[] __device__(RankScanItem const &l, RankScanItem const &r) {
if (l.group_id != r.group_id) {
return r;
}
return RankScanItem{r.idx, l.predt + r.predt, l.w + r.w, l.group_id};
},
n_threads);
/**
* Scale the AUC with number of items in each group.
*/
float auc = thrust::reduce(thrust::cuda::par(alloc), dh::tbegin(s_d_auc),
dh::tend(s_d_auc), 0.0f);
return std::make_pair(auc, n_valid);
}
} // namespace metric
} // namespace xgboost