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Various bug fixes (#2825)

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* Fatal error if GPU algorithm selected without GPU support compiled

* Resolve type conversion warnings

* Fix gpu unit test failure

* Fix compressed iterator edge case

* Fix python unit test failures due to flake8 update on pip
This commit is contained in:
Rory Mitchell
2017-10-25 14:45:01 +13:00
committed by GitHub
parent c71b62d48d
commit 13e7a2cff0
21 changed files with 163 additions and 180 deletions
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42
src/predictor/gpu_predictor.cu
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@@ -216,11 +216,11 @@ __device__ float GetLeafWeight(bst_uint ridx, const DevicePredictionNode* tree,
template <int BLOCK_THREADS>
__global__ void PredictKernel(const DevicePredictionNode* d_nodes,
float* d_out_predictions, int* d_tree_segments,
float* d_out_predictions, size_t* d_tree_segments,
int* d_tree_group, size_t* d_row_ptr,
SparseBatch::Entry* d_data, int tree_begin,
int tree_end, int num_features, bst_uint num_rows,
bool use_shared, int num_group) {
SparseBatch::Entry* d_data, size_t tree_begin,
size_t tree_end, size_t num_features,
size_t num_rows, bool use_shared, int num_group) {
extern __shared__ float smem[];
bst_uint global_idx = blockDim.x * blockIdx.x + threadIdx.x;
ElementLoader loader(use_shared, d_row_ptr, d_data, num_features, smem,
@@ -249,8 +249,8 @@ __global__ void PredictKernel(const DevicePredictionNode* d_nodes,
class GPUPredictor : public xgboost::Predictor {
private:
void DevicePredictInternal(DMatrix* dmat, std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model, int tree_begin,
int tree_end) {
const gbm::GBTreeModel& model, size_t tree_begin,
size_t tree_end) {
if (tree_end - tree_begin == 0) {
return;
}
@@ -267,17 +267,17 @@ class GPUPredictor : public xgboost::Predictor {
dh::safe_cuda(cudaSetDevice(param.gpu_id));
CHECK_EQ(model.param.size_leaf_vector, 0);
// Copy decision trees to device
thrust::host_vector<int> h_tree_segments;
thrust::host_vector<size_t> h_tree_segments;
h_tree_segments.reserve((tree_end - tree_end) + 1);
int sum = 0;
size_t sum = 0;
h_tree_segments.push_back(sum);
for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
sum += model.trees[tree_idx]->GetNodes().size();
h_tree_segments.push_back(sum);
}
thrust::host_vector<DevicePredictionNode> h_nodes(h_tree_segments.back());
for (int tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
for (auto tree_idx = tree_begin; tree_idx < tree_end; tree_idx++) {
auto& src_nodes = model.trees[tree_idx]->GetNodes();
std::copy(src_nodes.begin(), src_nodes.end(),
h_nodes.begin() + h_tree_segments[tree_idx - tree_begin]);
@@ -299,11 +299,11 @@ class GPUPredictor : public xgboost::Predictor {
}
const int BLOCK_THREADS = 128;
const int GRID_SIZE =
dh::div_round_up(device_matrix->row_ptr.size() - 1, BLOCK_THREADS);
const int GRID_SIZE = static_cast<int>(
dh::div_round_up(device_matrix->row_ptr.size() - 1, BLOCK_THREADS));
int shared_memory_bytes =
sizeof(float) * device_matrix->p_mat->info().num_col * BLOCK_THREADS;
int shared_memory_bytes = static_cast<int>(
sizeof(float) * device_matrix->p_mat->info().num_col * BLOCK_THREADS);
bool use_shared = true;
if (shared_memory_bytes > dh::max_shared_memory(param.gpu_id)) {
shared_memory_bytes = 0;
@@ -347,8 +347,7 @@ class GPUPredictor : public xgboost::Predictor {
const gbm::GBTreeModel& model,
std::vector<std::unique_ptr<TreeUpdater>>* updaters,
int num_new_trees) override {
// dh::Timer t;
int old_ntree = model.trees.size() - num_new_trees;
auto old_ntree = model.trees.size() - num_new_trees;
// update cache entry
for (auto& kv : cache_) {
PredictionCacheEntry& e = kv.second;
@@ -356,7 +355,7 @@ class GPUPredictor : public xgboost::Predictor {
if (e.predictions.size() == 0) {
cpu_predictor->PredictBatch(dmat, &(e.predictions), model, 0,
model.trees.size());
static_cast<bst_uint>(model.trees.size()));
} else if (model.param.num_output_group == 1 && updaters->size() > 0 &&
num_new_trees == 1 &&
updaters->back()->UpdatePredictionCache(e.data.get(),
@@ -383,11 +382,10 @@ class GPUPredictor : public xgboost::Predictor {
void PredictContribution(DMatrix* p_fmat,
std::vector<bst_float>* out_contribs,
const gbm::GBTreeModel& model,
unsigned ntree_limit,
const gbm::GBTreeModel& model, unsigned ntree_limit,
bool approximate) override {
cpu_predictor->PredictContribution(p_fmat, out_contribs, model,
ntree_limit, approximate);
cpu_predictor->PredictContribution(p_fmat, out_contribs, model, ntree_limit,
approximate);
}
void Init(const std::vector<std::pair<std::string, std::string>>& cfg,
@@ -403,7 +401,7 @@ class GPUPredictor : public xgboost::Predictor {
std::unordered_map<DMatrix*, std::unique_ptr<DeviceMatrix>>
device_matrix_cache_;
thrust::device_vector<DevicePredictionNode> nodes;
thrust::device_vector<int> tree_segments;
thrust::device_vector<size_t> tree_segments;
thrust::device_vector<int> tree_group;
};
XGBOOST_REGISTER_PREDICTOR(GPUPredictor, "gpu_predictor")