Remove use of device_idx in dh::LaunchN. (#7063)
It's an unused parameter, removing it can make the CI log more readable.
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@@ -96,7 +96,7 @@ void RowPartitioner::SortPosition(common::Span<bst_node_t> position,
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void Reset(int device_idx, common::Span<RowPartitioner::RowIndexT> ridx,
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common::Span<bst_node_t> position) {
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CHECK_EQ(ridx.size(), position.size());
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dh::LaunchN(device_idx, ridx.size(), [=] __device__(size_t idx) {
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dh::LaunchN(ridx.size(), [=] __device__(size_t idx) {
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ridx[idx] = idx;
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position[idx] = 0;
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});
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@@ -131,7 +131,7 @@ common::Span<const RowPartitioner::RowIndexT> RowPartitioner::GetRows(
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// Return empty span here as a valid result
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// Will error if we try to construct a span from a pointer with size 0
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if (segment.Size() == 0) {
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return common::Span<const RowPartitioner::RowIndexT>();
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return {};
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}
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return ridx_.CurrentSpan().subspan(segment.begin, segment.Size());
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}
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@@ -180,7 +180,7 @@ void RowPartitioner::SortPositionAndCopy(const Segment& segment,
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const auto d_position_other = position_.Other() + segment.begin;
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const auto d_ridx_current = ridx_.Current() + segment.begin;
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const auto d_ridx_other = ridx_.Other() + segment.begin;
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dh::LaunchN(device_idx_, segment.Size(), stream, [=] __device__(size_t idx) {
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dh::LaunchN(segment.Size(), stream, [=] __device__(size_t idx) {
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d_position_current[idx] = d_position_other[idx];
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d_ridx_current[idx] = d_ridx_other[idx];
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});
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@@ -120,7 +120,7 @@ class RowPartitioner {
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int64_t* d_left_count = left_counts_.data().get() + nidx;
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// Launch 1 thread for each row
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dh::LaunchN<1, 128>(device_idx_, segment.Size(), [=] __device__(size_t idx) {
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dh::LaunchN<1, 128>(segment.Size(), [=] __device__(size_t idx) {
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// LaunchN starts from zero, so we restore the row index by adding segment.begin
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idx += segment.begin;
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RowIndexT ridx = d_ridx[idx];
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@@ -160,7 +160,7 @@ class RowPartitioner {
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void FinalisePosition(FinalisePositionOpT op) {
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auto d_position = position_.Current();
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const auto d_ridx = ridx_.Current();
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dh::LaunchN(device_idx_, position_.Size(), [=] __device__(size_t idx) {
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dh::LaunchN(position_.Size(), [=] __device__(size_t idx) {
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auto position = d_position[idx];
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RowIndexT ridx = d_ridx[idx];
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bst_node_t new_position = op(ridx, position);
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@@ -94,8 +94,8 @@ class DeviceHistogram {
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void Reset() {
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auto d_data = data_.data().get();
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dh::LaunchN(device_id_, data_.size(),
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[=] __device__(size_t idx) { d_data[idx] = 0.0f; });
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dh::LaunchN(data_.size(),
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[=] __device__(size_t idx) { d_data[idx] = 0.0f; });
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nidx_map_.clear();
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}
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bool HistogramExists(int nidx) const {
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@@ -130,7 +130,7 @@ class DeviceHistogram {
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}
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// Zero recycled memory
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auto d_data = data_.data().get() + nidx_map_[nidx];
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dh::LaunchN(device_id_, n_bins_ * 2,
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dh::LaunchN(n_bins_ * 2,
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[=] __device__(size_t idx) { d_data[idx] = 0.0f; });
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} else {
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// Append new node histogram
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@@ -367,7 +367,7 @@ struct GPUHistMakerDevice {
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dh::TemporaryArray<GPUExpandEntry> entries(2);
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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auto d_entries = entries.data().get();
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dh::LaunchN(device_id, 2, [=] __device__(size_t idx) {
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dh::LaunchN(2, [=] __device__(size_t idx) {
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auto split = d_splits_out[idx];
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auto nidx = idx == 0 ? left_nidx : right_nidx;
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@@ -402,7 +402,7 @@ struct GPUHistMakerDevice {
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auto d_node_hist_histogram = hist.GetNodeHistogram(nidx_histogram);
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auto d_node_hist_subtraction = hist.GetNodeHistogram(nidx_subtraction);
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dh::LaunchN(device_id, page->Cuts().TotalBins(), [=] __device__(size_t idx) {
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dh::LaunchN(page->Cuts().TotalBins(), [=] __device__(size_t idx) {
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d_node_hist_subtraction[idx] =
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d_node_hist_parent[idx] - d_node_hist_histogram[idx];
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});
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@@ -545,7 +545,7 @@ struct GPUHistMakerDevice {
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auto d_node_sum_gradients = device_node_sum_gradients.data().get();
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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dh::LaunchN(device_id, d_ridx.size(), [=] __device__(int local_idx) {
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dh::LaunchN(d_ridx.size(), [=] __device__(int local_idx) {
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int pos = d_position[local_idx];
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bst_float weight = evaluator.CalcWeight(
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pos, param_d, GradStats{d_node_sum_gradients[pos]});
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@@ -676,7 +676,7 @@ struct GPUHistMakerDevice {
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auto evaluator = tree_evaluator.GetEvaluator<GPUTrainingParam>();
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GPUTrainingParam gpu_param(param);
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auto depth = p_tree->GetDepth(kRootNIdx);
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dh::LaunchN(device_id, 1, [=] __device__(size_t idx) {
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dh::LaunchN(1, [=] __device__(size_t idx) {
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float left_weight = evaluator.CalcWeight(kRootNIdx, gpu_param,
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GradStats{split.left_sum});
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float right_weight = evaluator.CalcWeight(
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