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Retire DVec class in favour of c++20 style span for device memory. (#4293)

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This commit is contained in:
Rory Mitchell
2019-03-28 13:59:58 +13:00
committed by GitHub
parent c85181dd8a
commit 3f312e30db
7 changed files with 288 additions and 369 deletions
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85
src/tree/updater_gpu_hist.cu
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@@ -601,7 +601,7 @@ struct DeviceShard {
int n_bins;
int device_id;
dh::BulkAllocator<dh::MemoryType::kDevice> ba;
dh::BulkAllocator ba;
ELLPackMatrix ellpack_matrix;
@@ -610,27 +610,26 @@ struct DeviceShard {
DeviceHistogram<GradientSumT> hist;
/*! \brief row_ptr form HistCutMatrix. */
dh::DVec<uint32_t> feature_segments;
common::Span<uint32_t> feature_segments;
/*! \brief minimum value for each feature. */
dh::DVec<bst_float> min_fvalue;
common::Span<bst_float> min_fvalue;
/*! \brief Cut. */
dh::DVec<bst_float> gidx_fvalue_map;
common::Span<bst_float> gidx_fvalue_map;
/*! \brief global index of histogram, which is stored in ELLPack format. */
dh::DVec<common::CompressedByteT> gidx_buffer;
common::Span<common::CompressedByteT> gidx_buffer;
/*! \brief Row indices relative to this shard, necessary for sorting rows. */
dh::DVec2<bst_uint> ridx;
dh::DoubleBuffer<bst_uint> ridx;
dh::DoubleBuffer<int> position;
/*! \brief Gradient pair for each row. */
dh::DVec<GradientPair> gpair;
common::Span<GradientPair> gpair;
dh::DVec2<int> position;
dh::DVec<int> monotone_constraints;
dh::DVec<bst_float> prediction_cache;
common::Span<int> monotone_constraints;
common::Span<bst_float> prediction_cache;
/*! \brief Sum gradient for each node. */
std::vector<GradientPair> node_sum_gradients;
dh::DVec<GradientPair> node_sum_gradients_d;
common::Span<GradientPair> node_sum_gradients_d;
/*! \brief row offset in SparsePage (the input data). */
thrust::device_vector<size_t> row_ptrs;
/*! \brief On-device feature set, only actually used on one of the devices */
@@ -718,7 +717,9 @@ struct DeviceShard {
// Reset values for each update iteration
void Reset(HostDeviceVector<GradientPair>* dh_gpair) {
dh::safe_cuda(cudaSetDevice(device_id));
position.CurrentDVec().Fill(0);
thrust::fill(
thrust::device_pointer_cast(position.Current()),
thrust::device_pointer_cast(position.Current() + position.Size()), 0);
std::fill(node_sum_gradients.begin(), node_sum_gradients.end(),
GradientPair());
if (left_counts.size() < 256) {
@@ -727,13 +728,16 @@ struct DeviceShard {
dh::safe_cuda(cudaMemsetAsync(left_counts.data().get(), 0,
sizeof(int64_t) * left_counts.size()));
}
thrust::sequence(ridx.CurrentDVec().tbegin(), ridx.CurrentDVec().tend());
thrust::sequence(
thrust::device_pointer_cast(ridx.CurrentSpan().data()),
thrust::device_pointer_cast(ridx.CurrentSpan().data() + ridx.Size()));
std::fill(ridx_segments.begin(), ridx_segments.end(), Segment(0, 0));
ridx_segments.front() = Segment(0, ridx.Size());
this->gpair.copy(dh_gpair->tcbegin(device_id),
dh_gpair->tcend(device_id));
SubsampleGradientPair(&gpair, param.subsample, row_begin_idx);
dh::safe_cuda(cudaMemcpyAsync(
gpair.data(), dh_gpair->ConstDevicePointer(device_id),
gpair.size() * sizeof(GradientPair), cudaMemcpyHostToHost));
SubsampleGradientPair(device_id, gpair, param.subsample, row_begin_idx);
hist.Reset();
}
@@ -788,7 +792,7 @@ struct DeviceShard {
<<<uint32_t(d_feature_set.size()), kBlockThreads, 0, streams[i]>>>(
hist.GetNodeHistogram(nidx), d_feature_set, node, ellpack_matrix,
gpu_param, d_split_candidates, value_constraints[nidx],
monotone_constraints.GetSpan());
monotone_constraints);
// Reduce over features to find best feature
auto d_result = d_result_all.subspan(i, 1);
@@ -943,8 +947,8 @@ struct DeviceShard {
void UpdatePredictionCache(bst_float* out_preds_d) {
dh::safe_cuda(cudaSetDevice(device_id));
if (!prediction_cache_initialised) {
dh::safe_cuda(cudaMemcpyAsync(prediction_cache.Data(), out_preds_d,
prediction_cache.Size() * sizeof(bst_float),
dh::safe_cuda(cudaMemcpyAsync(prediction_cache.data(), out_preds_d,
prediction_cache.size() * sizeof(bst_float),
cudaMemcpyDefault));
}
prediction_cache_initialised = true;
@@ -952,16 +956,16 @@ struct DeviceShard {
CalcWeightTrainParam param_d(param);
dh::safe_cuda(
cudaMemcpyAsync(node_sum_gradients_d.Data(), node_sum_gradients.data(),
cudaMemcpyAsync(node_sum_gradients_d.data(), node_sum_gradients.data(),
sizeof(GradientPair) * node_sum_gradients.size(),
cudaMemcpyHostToDevice));
auto d_position = position.Current();
auto d_ridx = ridx.Current();
auto d_node_sum_gradients = node_sum_gradients_d.Data();
auto d_prediction_cache = prediction_cache.Data();
auto d_node_sum_gradients = node_sum_gradients_d.data();
auto d_prediction_cache = prediction_cache.data();
dh::LaunchN(
device_id, prediction_cache.Size(), [=] __device__(int local_idx) {
device_id, prediction_cache.size(), [=] __device__(int local_idx) {
int pos = d_position[local_idx];
bst_float weight = CalcWeight(param_d, d_node_sum_gradients[pos]);
d_prediction_cache[d_ridx[local_idx]] +=
@@ -969,8 +973,8 @@ struct DeviceShard {
});
dh::safe_cuda(cudaMemcpy(
out_preds_d, prediction_cache.Data(),
prediction_cache.Size() * sizeof(bst_float), cudaMemcpyDefault));
out_preds_d, prediction_cache.data(),
prediction_cache.size() * sizeof(bst_float), cudaMemcpyDefault));
}
};
@@ -981,7 +985,7 @@ struct SharedMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
auto segment_begin = segment.begin;
auto d_node_hist = shard->hist.GetNodeHistogram(nidx);
auto d_ridx = shard->ridx.Current();
auto d_gpair = shard->gpair.Data();
auto d_gpair = shard->gpair.data();
auto n_elements = segment.Size() * shard->ellpack_matrix.row_stride;
@@ -1006,7 +1010,7 @@ struct GlobalMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
Segment segment = shard->ridx_segments[nidx];
auto d_node_hist = shard->hist.GetNodeHistogram(nidx).data();
bst_uint* d_ridx = shard->ridx.Current();
GradientPair* d_gpair = shard->gpair.Data();
GradientPair* d_gpair = shard->gpair.data();
size_t const n_elements = segment.Size() * shard->ellpack_matrix.row_stride;
auto d_matrix = shard->ellpack_matrix;
@@ -1043,10 +1047,11 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
&gidx_fvalue_map, hmat.cut.size(),
&min_fvalue, hmat.min_val.size(),
&monotone_constraints, param.monotone_constraints.size());
gidx_fvalue_map = hmat.cut;
min_fvalue = hmat.min_val;
feature_segments = hmat.row_ptr;
monotone_constraints = param.monotone_constraints;
dh::CopyVectorToDeviceSpan(gidx_fvalue_map, hmat.cut);
dh::CopyVectorToDeviceSpan(min_fvalue, hmat.min_val);
dh::CopyVectorToDeviceSpan(feature_segments, hmat.row_ptr);
dh::CopyVectorToDeviceSpan(monotone_constraints, param.monotone_constraints);
node_sum_gradients.resize(max_nodes);
ridx_segments.resize(max_nodes);
@@ -1063,14 +1068,16 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
<< "Max leaves and max depth cannot both be unconstrained for "
"gpu_hist.";
ba.Allocate(device_id, &gidx_buffer, compressed_size_bytes);
gidx_buffer.Fill(0);
thrust::fill(
thrust::device_pointer_cast(gidx_buffer.data()),
thrust::device_pointer_cast(gidx_buffer.data() + gidx_buffer.size()), 0);
this->CreateHistIndices(row_batch, row_stride, null_gidx_value);
ellpack_matrix.Init(
feature_segments.GetSpan(), min_fvalue.GetSpan(),
gidx_fvalue_map.GetSpan(), row_stride,
common::CompressedIterator<uint32_t>(gidx_buffer.Data(), num_symbols),
feature_segments, min_fvalue,
gidx_fvalue_map, row_stride,
common::CompressedIterator<uint32_t>(gidx_buffer.data(), num_symbols),
is_dense, null_gidx_value);
// check if we can use shared memory for building histograms
@@ -1121,10 +1128,10 @@ inline void DeviceShard<GradientSumT>::CreateHistIndices(
dh::DivRoundUp(row_stride, block3.y), 1);
CompressBinEllpackKernel<<<grid3, block3>>>
(common::CompressedBufferWriter(num_symbols),
gidx_buffer.Data(),
gidx_buffer.data(),
row_ptrs.data().get() + batch_row_begin,
entries_d.data().get(),
gidx_fvalue_map.Data(), feature_segments.Data(),
gidx_fvalue_map.data(), feature_segments.data(),
batch_row_begin, batch_nrows,
row_ptrs[batch_row_begin],
row_stride, null_gidx_value);
@@ -1355,7 +1362,7 @@ class GPUHistMakerSpecialised{
[&](int i, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
dh::safe_cuda(cudaSetDevice(shard->device_id));
tmp_sums[i] = dh::SumReduction(
shard->temp_memory, shard->gpair.Data(), shard->gpair.Size());
shard->temp_memory, shard->gpair.data(), shard->gpair.size());
});
GradientPair sum_gradient =