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Remove use of device_idx in dh::LaunchN. (#7063)

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It's an unused parameter, removing it can make the CI log more readable.
This commit is contained in:
Jiaming Yuan
2021-06-29 11:37:26 +08:00
committed by GitHub
parent dd4db347f3
commit 1c8fdf2218
25 changed files with 105 additions and 107 deletions
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26
src/metric/auc.cu
View File

@@ -118,12 +118,12 @@ GPUBinaryAUC(common::Span<float const> predts, MetaInfo const &info,
return thrust::make_pair(fp, tp);
}; // NOLINT
auto d_fptp = dh::ToSpan(cache->fptp);
dh::LaunchN(device, d_sorted_idx.size(),
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, device);
dh::Iota(d_unique_idx);
auto uni_key = dh::MakeTransformIterator<float>(
thrust::make_counting_iterator(0),
@@ -144,7 +144,7 @@ GPUBinaryAUC(common::Span<float const> predts, MetaInfo const &info,
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(device, d_unique_idx.size(), [=] __device__(size_t i) {
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};
@@ -183,7 +183,7 @@ 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(device, in.size(), [=] __device__(size_t i) {
dh::LaunchN(in.size(), [=] __device__(size_t i) {
size_t col = i / m;
size_t row = i % m;
size_t idx = row * n + col;
@@ -255,9 +255,8 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
if (n_samples == 0) {
dh::TemporaryArray<float> resutls(n_classes * 4, 0.0f);
auto d_results = dh::ToSpan(resutls);
dh::LaunchN(device, n_classes * 4, [=]__device__(size_t i) {
d_results[i] = 0.0f;
});
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);
@@ -273,9 +272,8 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
dh::TemporaryArray<uint32_t> class_ptr(n_classes + 1, 0);
auto d_class_ptr = dh::ToSpan(class_ptr);
dh::LaunchN(device, n_classes + 1, [=]__device__(size_t i) {
d_class_ptr[i] = i * n_samples;
});
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);
@@ -301,7 +299,7 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
float tp = label * w;
return thrust::make_pair(fp, tp);
}; // NOLINT
dh::LaunchN(device, d_sorted_idx.size(),
dh::LaunchN(d_sorted_idx.size(),
[=] __device__(size_t i) { d_fptp[i] = get_fp_tp(i); });
/**
@@ -309,7 +307,7 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
*/
dh::XGBDeviceAllocator<char> alloc;
auto d_unique_idx = dh::ToSpan(cache->unique_idx);
dh::Iota(d_unique_idx, device);
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;
@@ -363,7 +361,7 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
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(device, d_unique_idx.size(), [=]__device__(size_t i) {
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
@@ -419,7 +417,7 @@ float GPUMultiClassAUCOVR(common::Span<float const> predts, MetaInfo const &info
auto tp = d_results.subspan(2 * n_classes, n_classes);
auto auc = d_results.subspan(3 * n_classes, n_classes);
dh::LaunchN(device, n_classes, [=] __device__(size_t c) {
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;

12
src/metric/rank_metric.cu
View File

@@ -107,7 +107,7 @@ struct EvalPrecisionGpu {
int device_id = -1;
dh::safe_cuda(cudaGetDevice(&device_id));
// For each group item compute the aggregated precision
dh::LaunchN(device_id, nitems, nullptr, [=] __device__(uint32_t idx) {
dh::LaunchN(nitems, nullptr, [=] __device__(uint32_t idx) {
const auto group_idx = dgroup_idx[idx];
const auto group_begin = dgroups[group_idx];
const auto ridx = idx - group_begin;
@@ -151,7 +151,7 @@ struct EvalNDCGGpu {
dh::safe_cuda(cudaGetDevice(&device_id));
// For each group item compute the aggregated precision
dh::LaunchN(device_id, nitems, nullptr, [=] __device__(uint32_t idx) {
dh::LaunchN(nitems, nullptr, [=] __device__(uint32_t idx) {
const auto group_idx = dgroup_idx[idx];
const auto group_begin = dgroups[group_idx];
const auto ridx = idx - group_begin;
@@ -185,7 +185,7 @@ struct EvalNDCGGpu {
int device_id = -1;
dh::safe_cuda(cudaGetDevice(&device_id));
// Compute the group's DCG and reduce it across all groups
dh::LaunchN(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
dh::LaunchN(ngroups, nullptr, [=] __device__(uint32_t gidx) {
if (didcg[gidx] == 0.0f) {
ddcg[gidx] = (ecfg.minus) ? 0.0f : 1.0f;
} else {
@@ -244,7 +244,7 @@ struct EvalMAPGpu {
int device_id = -1;
dh::safe_cuda(cudaGetDevice(&device_id));
// For each group item compute the aggregated precision
dh::LaunchN(device_id, nitems, nullptr, [=] __device__(uint32_t idx) {
dh::LaunchN(nitems, nullptr, [=] __device__(uint32_t idx) {
if (DetermineNonTrivialLabelLambda(idx)) {
const auto group_idx = dgroup_idx[idx];
const auto group_begin = dgroups[group_idx];
@@ -257,7 +257,7 @@ struct EvalMAPGpu {
});
// Aggregate the group's item precisions
dh::LaunchN(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
dh::LaunchN(ngroups, nullptr, [=] __device__(uint32_t gidx) {
auto nhits = dgroups[gidx + 1] ? dhits[dgroups[gidx + 1] - 1] : 0;
if (nhits != 0) {
dsumap[gidx] /= nhits;
@@ -391,7 +391,7 @@ struct EvalAucPRGpu : public Metric {
int device_id = -1;
dh::safe_cuda(cudaGetDevice(&device_id));
// For each group item compute the aggregated precision
dh::LaunchN<1, 32>(device_id, ngroups, nullptr, [=] __device__(uint32_t gidx) {
dh::LaunchN<1, 32>(ngroups, nullptr, [=] __device__(uint32_t gidx) {
// We need pos > 0 && neg > 0
if (dtotal_pos[gidx] <= 0.0 || dtotal_neg[gidx] <= 0.0) {
atomicAdd(dauc_error, 1);