chore: run clang-format over codebase (#3610)

vowpalwabbit/allreduce_threads.cc

@@ -13,10 +13,7 @@ AllReduceSync::AllReduceSync(const size_t total) : m_total(total), m_count(0), m
   buffers = new void*[total];
 }
 
-AllReduceSync::~AllReduceSync()
-{
-  delete[] buffers;
-}
+AllReduceSync::~AllReduceSync() { delete[] buffers; }
 
 void AllReduceSync::waitForSynchronization()
 {

vowpalwabbit/beam.h

@@ -21,14 +21,14 @@ struct beam_element
 {
   uint32_t hash;  // a cached hash value -- if a ~= b then h(a) must== h(b)
   float cost;     // cost of this element
-  T *data;        // pointer to element data -- rarely accessed!
+  T* data;        // pointer to element data -- rarely accessed!
   bool active;    // is this currently active
   //  bool     recombined;                 // if we're not the BEST then we've been recombined
   //  v_array<T*> * recomb_friends;   // if we're the BEST (among ~= elements), then recomb_friends is everything that's
   //  equivalent to us but worse... NOT USED if we're not doing k-best predictions
 };
 
-inline int compare_on_cost(const void *void_a, const void *void_b)
+inline int compare_on_cost(const void* void_a, const void* void_b)
 {
   if (void_a == void_b) return 0;
   const beam_element<void>* a = static_cast<const beam_element<void>*>(void_a);
@@ -47,7 +47,7 @@ inline int compare_on_cost(const void *void_a, const void *void_b)
     return 0;
 }
 
-inline int compare_on_hash_then_cost(const void *void_a, const void *void_b)
+inline int compare_on_hash_then_cost(const void* void_a, const void* void_b)
 {
   if (void_a == void_b) return 0;
   const beam_element<void>* a = static_cast<const beam_element<void>*>(void_a);
@@ -81,17 +81,17 @@ class beam
   float worst_cost;           // what is the cost of the worst (highest cost) item in the beam
   float best_cost;            // what is the cost of the best (lowest cost) item in the beam
   float prune_if_gt;          // prune any element with cost greater than this
-  T *best_cost_data;          // easy access to best-cost item
+  T* best_cost_data;          // easy access to best-cost item
   bool do_kbest;
   v_array<beam_element<T>> A;  // the actual data
   //  v_array<v_array<beam_element<T>*>> recomb_buckets;
 
   //  static size_t NUM_RECOMB_BUCKETS = 10231;
 
-  bool (*is_equivalent)(T *, T *);  // test if two items are equivalent; nullptr means don't do hypothesis recombination
+  bool (*is_equivalent)(T*, T*);  // test if two items are equivalent; nullptr means don't do hypothesis recombination
 
 public:
-  beam(size_t beam_size, float prune_coeff = FLT_MAX, bool (*test_equiv)(T *, T *) = nullptr, bool kbest = false)
+  beam(size_t beam_size, float prune_coeff = FLT_MAX, bool (*test_equiv)(T*, T*) = nullptr, bool kbest = false)
       : beam_size(beam_size), pruning_coefficient(prune_coeff), do_kbest(kbest), is_equivalent(test_equiv)
   {
     count = 0;
@@ -108,7 +108,7 @@ class beam
 
   inline bool might_insert(float cost) { return (cost <= prune_if_gt) && ((count < beam_size) || (cost < worst_cost)); }
 
-  bool insert(T *data, float cost, uint32_t hash)  // returns TRUE iff element was actually added
+  bool insert(T* data, float cost, uint32_t hash)  // returns TRUE iff element was actually added
   {
     if (!might_insert(cost)) return false;
 
@@ -175,21 +175,21 @@ class beam
     return true;
   }
 
-  beam_element<T> *get_best_item()
+  beam_element<T>* get_best_item()
   {
     if (count == 0) return nullptr;
-    beam_element<T> *ret = A.begin;
+    beam_element<T>* ret = A.begin;
     while ((ret != A.end) && (!ret->active)) ++ret;
     return (ret == A.end) ? nullptr : ret;
   }
 
-  beam_element<T> *pop_best_item()
+  beam_element<T>* pop_best_item()
   {
     if (count == 0) return nullptr;
 
-    beam_element<T> *ret = nullptr;
+    beam_element<T>* ret = nullptr;
     float next_best_cost = FLT_MAX;
-    for (beam_element<T> *el = A.begin; el != A.end; el++)
+    for (beam_element<T>* el = A.begin; el != A.end; el++)
       if ((ret == nullptr) && el->active && (el->cost <= best_cost))
         ret = el;
       else if (el->active && (el->cost < next_best_cost))
@@ -250,7 +250,7 @@ class beam
     }
   }
 
-  void compact(void (*free_data)(T *) = nullptr)
+  void compact(void (*free_data)(T*) = nullptr)
   {
     if (is_equivalent) do_recombination();
     qsort(A.begin, A.size(), sizeof(beam_element<T>), compare_on_cost);  // TODO: quick select
@@ -262,7 +262,7 @@ class beam
       while ((count > 1) && !A[count - 1].active) count--;
 
     if (free_data)
-      for (beam_element<T> *be = A.begin + count; be != A.end; ++be) free_data(be->data);
+      for (beam_element<T>* be = A.begin + count; be != A.end; ++be) free_data(be->data);
 
     A.end = A.begin + count;
 
@@ -272,15 +272,15 @@ class beam
     best_cost_data = A[0].data;
   }
 
-  void maybe_compact(void (*free_data)(T *) = nullptr)
+  void maybe_compact(void (*free_data)(T*) = nullptr)
   {
     if (count >= beam_size * 10) compact(free_data);
   }
 
-  void erase(void (*free_data)(T *) = nullptr)
+  void erase(void (*free_data)(T*) = nullptr)
   {
     if (free_data)
-      for (beam_element<T> *be = A.begin; be != A.end; ++be) free_data(be->data);
+      for (beam_element<T>* be = A.begin; be != A.end; ++be) free_data(be->data);
     A.erase();
     count = 0;
     worst_cost = -FLT_MAX;
@@ -289,13 +289,10 @@ class beam
     best_cost_data = nullptr;
   }
 
-  ~beam()
-  {
-    assert(A.size() == 0);
-  }
+  ~beam() { assert(A.size() == 0); }
 
-  beam_element<T> *begin() { return A.begin; }
-  beam_element<T> *end() { return A.end; }
+  beam_element<T>* begin() { return A.begin; }
+  beam_element<T>* end() { return A.end; }
   size_t size() { return count; }
   bool empty() { return A.empty(); }
   size_t get_beam_size() { return beam_size; }

vowpalwabbit/boosting.cc

@@ -297,20 +297,14 @@ void save_load_sampling(boosting& o, io_buf& model_file, bool read, bool text)
 
   // avoid making syscalls multiple times
   fmt::memory_buffer buffer;
-  if (read)
-  {
-    fmt::format_to(buffer, "Loading alpha and v: \n");
-  }
+  if (read) { fmt::format_to(buffer, "Loading alpha and v: \n"); }
   else
   {
     fmt::format_to(buffer, "Saving alpha and v, current weighted_examples = {}\n",
-		      o.all->sd->weighted_labeled_examples + o.all->sd->weighted_unlabeled_examples);
+        o.all->sd->weighted_labeled_examples + o.all->sd->weighted_unlabeled_examples);
   }
 
-  for (int i = 0; i < o.N; i++)
-  {
-    fmt::format_to(buffer, "{0} {1}\n", o.alpha[i], o.v[i]);
-  }
+  for (int i = 0; i < o.N; i++) { fmt::format_to(buffer, "{0} {1}\n", o.alpha[i], o.v[i]); }
   o.logger.err_info("{}", fmt::to_string(buffer));
 }
 
@@ -347,20 +341,13 @@ void save_load(boosting& o, io_buf& model_file, bool read, bool text)
   {
     // avoid making syscalls multiple times
     fmt::memory_buffer buffer;
-    if (read)
-    {
-      fmt::format_to(buffer, "Loading alpha: \n");
-    }
+    if (read) { fmt::format_to(buffer, "Loading alpha: \n"); }
     else
     {
-      fmt::format_to(buffer, "Saving alpha, current weighted_examples = {)\n",
-		       o.all->sd->weighted_examples());
+      fmt::format_to(buffer, "Saving alpha, current weighted_examples = {)\n", o.all->sd->weighted_examples());
     }
 
-    for (int i = 0; i < o.N; i++)
-    {
-      fmt::format_to(buffer, "{} \n", o.alpha[i]);
-    }
+    for (int i = 0; i < o.N; i++) { fmt::format_to(buffer, "{} \n", o.alpha[i]); }
     o.logger.err_info("{}", fmt::to_string(buffer));
   }
 }

vowpalwabbit/cb_dro.cc

@@ -20,7 +20,7 @@ struct cb_dro_data
   bool isValid() { return chisq.isValid(); }
 
   template <bool is_learn, bool is_explore>
-  inline void learn_or_predict(multi_learner &base, multi_ex &examples)
+  inline void learn_or_predict(multi_learner& base, multi_ex& examples)
   {
     // Some explanation required.
     //
@@ -39,7 +39,7 @@ struct cb_dro_data
     if (is_learn)
     {
       const auto it =
-          std::find_if(examples.begin(), examples.end(), [](example *item) { return !item->l.cb.costs.empty(); });
+          std::find_if(examples.begin(), examples.end(), [](example* item) { return !item->l.cb.costs.empty(); });
 
       if (it != examples.end())
       {
@@ -53,7 +53,7 @@ struct cb_dro_data
 
         const auto maxit = is_explore
             ? std::max_element(action_scores.begin(), action_scores.end(),
-                  [](const ACTION_SCORE::action_score &a, const ACTION_SCORE::action_score &b) {
+                  [](const ACTION_SCORE::action_score& a, const ACTION_SCORE::action_score& b) {
                     return ACTION_SCORE::score_comp(&a, &b) < 0;
                   })
             : action_scores.begin();
@@ -73,16 +73,16 @@ struct cb_dro_data
         save_weight.clear();
         save_weight.reserve(examples.size());
         std::transform(examples.cbegin(), examples.cend(), std::back_inserter(save_weight),
-            [](example *item) { return item->weight; });
-        std::for_each(examples.begin(), examples.end(), [qlb](example *item) { item->weight *= qlb; });
+            [](example* item) { return item->weight; });
+        std::for_each(examples.begin(), examples.end(), [qlb](example* item) { item->weight *= qlb; });
 
         // TODO: make sure descendants "do the right thing" with example->weight
         multiline_learn_or_predict<true>(base, examples, examples[0]->ft_offset);
 
         // restore the original weights
         auto save_weight_it = save_weight.begin();
         std::for_each(
-            examples.begin(), examples.end(), [&save_weight_it](example *item) { item->weight = *save_weight_it++; });
+            examples.begin(), examples.end(), [&save_weight_it](example* item) { item->weight = *save_weight_it++; });
       }
     }
   }
@@ -94,7 +94,7 @@ struct cb_dro_data
 }  // namespace VW
 
 template <bool is_learn, bool is_explore>
-void learn_or_predict(cb_dro_data &data, multi_learner &base, multi_ex &examples)
+void learn_or_predict(cb_dro_data& data, multi_learner& base, multi_ex& examples)
 {
   data.learn_or_predict<is_learn, is_explore>(base, examples);
 }

vowpalwabbit/cb_explore_adf_bag.cc

@@ -47,10 +47,10 @@ struct cb_explore_adf_bag
       float epsilon, size_t bag_size, bool greedify, bool first_only, std::shared_ptr<VW::rand_state> random_state);
 
   // Should be called through cb_explore_adf_base for pre/post-processing
-  void predict(VW::LEARNER::multi_learner &base, multi_ex &examples);
-  void learn(VW::LEARNER::multi_learner &base, multi_ex &examples);
+  void predict(VW::LEARNER::multi_learner& base, multi_ex& examples);
+  void learn(VW::LEARNER::multi_learner& base, multi_ex& examples);
 
-  const PredictionT &get_cached_prediction() { return _action_probs; };
+  const PredictionT& get_cached_prediction() { return _action_probs; };
 
 private:
   uint32_t get_bag_learner_update_count(uint32_t learner_index);
@@ -76,7 +76,7 @@ uint32_t cb_explore_adf_bag::get_bag_learner_update_count(uint32_t learner_index
     return BS::weight_gen(_random_state);
 }
 
-void cb_explore_adf_bag::predict(VW::LEARNER::multi_learner &base, multi_ex &examples)
+void cb_explore_adf_bag::predict(VW::LEARNER::multi_learner& base, multi_ex& examples)
 {
   // Randomize over predictions from a base set of predictors
   v_array<ACTION_SCORE::action_score>& preds = examples[0]->pred.a_s;
@@ -118,7 +118,7 @@ void cb_explore_adf_bag::predict(VW::LEARNER::multi_learner &base, multi_ex &exa
   std::copy(std::begin(_action_probs), std::end(_action_probs), std::begin(preds));
 }
 
-void cb_explore_adf_bag::learn(VW::LEARNER::multi_learner &base, multi_ex &examples)
+void cb_explore_adf_bag::learn(VW::LEARNER::multi_learner& base, multi_ex& examples)
 {
   for (uint32_t i = 0; i < _bag_size; i++)
   {

vowpalwabbit/cb_sample.cc

@@ -27,7 +27,7 @@ struct cb_sample_data
   explicit cb_sample_data(std::shared_ptr<VW::rand_state>&& random_state) : _random_state(random_state) {}
 
   template <bool is_learn>
-  inline void learn_or_predict(multi_learner &base, multi_ex &examples)
+  inline void learn_or_predict(multi_learner& base, multi_ex& examples)
   {
     // If base.learn() does not return prediction then we need to predict first
     // so that there is something to sample from
@@ -39,13 +39,13 @@ struct cb_sample_data
 
     multiline_learn_or_predict<is_learn>(base, examples, examples[0]->ft_offset);
 
-    auto &action_scores = examples[0]->pred.a_s;
+    auto& action_scores = examples[0]->pred.a_s;
 
     uint32_t chosen_action = 0;
     int64_t maybe_labelled_action = -1;
 
     // Find that chosen action in the learning case, skip the shared example.
-    auto it = std::find_if(examples.begin(), examples.end(), [](example *item) { return !item->l.cb.costs.empty(); });
+    auto it = std::find_if(examples.begin(), examples.end(), [](example* item) { return !item->l.cb.costs.empty(); });
     if (it != examples.end()) { maybe_labelled_action = static_cast<int64_t>(std::distance(examples.begin(), it)); }
 
     // If we are learning and have a label, then take that action as the chosen action. Otherwise sample the
@@ -57,7 +57,7 @@ struct cb_sample_data
       // This only matters if the prediction decided to explore, but the same output should happen for the learn case.
       for (size_t i = 0; i < action_scores.size(); i++)
       {
-        auto &a_s = action_scores[i];
+        auto& a_s = action_scores[i];
         if (a_s.action == static_cast<uint32_t>(maybe_labelled_action))
         {
           chosen_action = static_cast<uint32_t>(i);
@@ -92,7 +92,7 @@ struct cb_sample_data
     _UNUSED(result);
   }
 
-  std::string cb_decision_to_string(const ACTION_SCORE::action_scores &action_scores)
+  std::string cb_decision_to_string(const ACTION_SCORE::action_scores& action_scores)
   {
     std::ostringstream ostrm;
     if (action_scores.empty()) return "";
@@ -106,7 +106,7 @@ struct cb_sample_data
 }  // namespace VW
 
 template <bool is_learn>
-void learn_or_predict(cb_sample_data &data, multi_learner &base, multi_ex &examples)
+void learn_or_predict(cb_sample_data& data, multi_learner& base, multi_ex& examples)
 {
   data.learn_or_predict<is_learn>(base, examples);
 }

vowpalwabbit/ccb_reduction_features.h

@@ -8,7 +8,8 @@
 
 #include <cstdint>
 
-namespace CCB {
+namespace CCB
+{
 enum class example_type : uint8_t
 {
   unset = 0,
@@ -23,7 +24,7 @@ struct reduction_features
   v_array<uint32_t> explicit_included_actions;
   void clear() { explicit_included_actions.clear(); }
 };
-}
+}  // namespace CCB
 
 namespace VW
 {

vowpalwabbit/constant.h

@@ -26,11 +26,11 @@ constexpr unsigned char nn_output_namespace = 129;
 constexpr unsigned char autolink_namespace = 130;
 constexpr unsigned char neighbor_namespace =
     131;  // this is \x83 -- to do quadratic, say "-q a`printf "\x83"` on the command line
-constexpr unsigned char affix_namespace = 132;         // this is \x84
-constexpr unsigned char spelling_namespace = 133;      // this is \x85
-constexpr unsigned char conditioning_namespace = 134;  // this is \x86
-constexpr unsigned char dictionary_namespace = 135;    // this is \x87
-constexpr unsigned char node_id_namespace = 136;       // this is \x88
+constexpr unsigned char affix_namespace = 132;                     // this is \x84
+constexpr unsigned char spelling_namespace = 133;                  // this is \x85
+constexpr unsigned char conditioning_namespace = 134;              // this is \x86
+constexpr unsigned char dictionary_namespace = 135;                // this is \x87
+constexpr unsigned char node_id_namespace = 136;                   // this is \x88
 constexpr unsigned char baseline_enabled_message_namespace = 137;  // this is \x89
 constexpr unsigned char ccb_slot_namespace = 139;
 constexpr unsigned char ccb_id_namespace = 140;

vowpalwabbit/continuous_actions_reduction_features.h

@@ -21,10 +21,7 @@ struct reduction_features
   bool is_chosen_action_set() const { return !std::isnan(chosen_action); }
   bool is_pdf_set() const { return pdf.size() > 0; }
 
-  reduction_features()
-  {
-    chosen_action = std::numeric_limits<float>::quiet_NaN();
-  }
+  reduction_features() { chosen_action = std::numeric_limits<float>::quiet_NaN(); }
 
   void clear()
   {

vowpalwabbit/cost_sensitive.cc

@@ -15,7 +15,6 @@
 
 #include "io/logger.h"
 
-
 namespace COST_SENSITIVE
 {
 void name_value(VW::string_view s, std::vector<VW::string_view>& name, float& v, VW::io::logger& logger)