fix: Windows build warnings

vowpalwabbit/allreduce/src/allreduce_sockets.cc

@@ -114,7 +114,7 @@ void VW::all_reduce_sockets::all_reduce_init(VW::io::logger& logger)
   if (lastError != 0) THROWERRNO("WSAStartup() returned error:" << lastError);
 #endif
 
-  class hostent* master = gethostbyname(_span_server.c_str());
+  struct hostent* master = gethostbyname(_span_server.c_str());
 
   if (master == nullptr) THROWERRNO("gethostbyname(" << _span_server << ")");
 

vowpalwabbit/core/include/vw/core/action_score.h

@@ -9,6 +9,7 @@
 #include "vw/core/v_array.h"
 #include "vw/core/vw_fwd.h"
 
+#include <cstdint>
 #include <iterator>
 #include <string>
 
@@ -46,7 +47,7 @@ class action_scores_score_iterator
 public:
   using iterator_category = std::random_access_iterator_tag;
   using value_type = float;
-  using difference_type = long;
+  using difference_type = intptr_t;
   using pointer = float*;
   using reference = float;
 
@@ -104,4 +105,4 @@ inline VW::action_scores_score_iterator begin_scores(VW::action_scores& a_s) { r
 
 VW_DEPRECATED("ACTION_SCORE::end_scores renamed to VW::end_scores. ACTION_SCORE::end_scores will be removed in VW 10.")
 inline VW::action_scores_score_iterator end_scores(VW::action_scores& a_s) { return VW::end_scores(a_s); }
-}  // namespace ACTION_SCORE
+}  // namespace ACTION_SCORE

vowpalwabbit/core/include/vw/core/automl_impl.h

@@ -257,7 +257,7 @@ class interaction_config_manager
 bool count_namespaces(const multi_ex& ecs, std::map<namespace_index, uint64_t>& ns_counter);
 void apply_config(example* ec, interaction_vec_t* live_interactions);
 bool is_allowed_to_remove(const namespace_index ns);
-void clear_non_champ_weights(dense_parameters& weights, uint32_t total, uint32_t& feature_width);
+//void clear_non_champ_weights(dense_parameters& weights, uint32_t total, uint32_t& feature_width);
 bool worse();
 
 // all possible states of automl

vowpalwabbit/core/include/vw/core/estimators/confidence_sequence_robust.h

@@ -48,15 +48,15 @@ class countable_discrete_base
   countable_discrete_base(double tol_x = 1e-6, bool is_brentq = false, double eta = 0.95f, double k = 1.5,
       double lambda_max = 0.5, double xi = 1.6);
   double get_ci(double alpha) const;
-  double get_lam_sqrt_tp1(double j) const;
+  double get_lam_sqrt_tp1(uint64_t j) const;
   double get_v_impl(std::map<uint64_t, double>& memo, uint64_t j) const;
   double log_wealth_mix(double mu, double s, double thres, std::map<uint64_t, double>& memo) const;
   double root_bisect(double s, double thres, std::map<uint64_t, double>& memo, double min_mu, double max_mu) const;
   double root_brentq(double s, double thres, std::map<uint64_t, double>& memo, double min_mu, double max_mu) const;
   double log_sum_exp(const std::vector<double>& combined) const;
   double lb_log_wealth(double alpha) const;
-  double get_log_weight(double j) const;
-  double get_log_remaining_weight(double j) const;
+  double get_log_weight(uint64_t j) const;
+  double get_log_remaining_weight(uint64_t j) const;
   double get_s() const;
   double get_v(double lam_sqrt_tp1) const;
   void add_obs(double x);

vowpalwabbit/core/include/vw/core/hashstring.h

@@ -37,7 +37,7 @@ VW_STD14_CONSTEXPR inline uint32_t hashstring(const char* s, size_t len, uint32_
     else { return VW::uniform_hash(front, len, h); }
   }
 
-  return ret + h;
+  return static_cast<uint32_t>(ret + h);
 }
 }  // namespace details
 
@@ -51,4 +51,4 @@ hash_func_t get_hasher(const std::string& s);
 using hash_func_t VW_DEPRECATED("Moved into VW namespace") = VW::hash_func_t;
 
 VW_DEPRECATED("Moved into VW namespace")
-VW::hash_func_t get_hasher(const std::string& s);
+VW::hash_func_t get_hasher(const std::string& s);

vowpalwabbit/core/include/vw/core/multi_model_utils.h

@@ -112,7 +112,7 @@ inline void reduce_innermost_model_weights(dense_parameters& weights, const size
   }
   for (auto weights_it = weights.begin(); weights_it < weights.end(); weights_it += total_feature_width)
   {
-    uint32_t cb_ind = weights_it.index() / innermost_feature_width_size;
+    uint32_t cb_ind = static_cast<uint32_t>(weights_it.index() / innermost_feature_width_size);
     for (size_t outer_offset = 0; outer_offset < overall_without_innermost_feature_width_size; ++outer_offset)
     {
       for (size_t stride_offset = 0; stride_offset < weights.stride(); ++stride_offset)
@@ -133,4 +133,4 @@ inline void reduce_innermost_model_weights(dense_parameters& weights, const size
 }
 }  // namespace multi_model
 }  // namespace reductions
-}  // namespace VW
+}  // namespace VW

vowpalwabbit/core/include/vw/core/vw.h

@@ -295,32 +295,32 @@ inline uint64_t hash_space_cstr(VW::workspace& all, const char* fstr)
 // Then use it as the seed for hashing features.
 inline uint64_t hash_feature(VW::workspace& all, const std::string& s, uint64_t u)
 {
-  return all.parser_runtime.example_parser->hasher(s.data(), s.length(), u) & all.runtime_state.parse_mask;
+  return all.parser_runtime.example_parser->hasher(s.data(), s.length(), static_cast<uint32_t>(u)) & all.runtime_state.parse_mask;
 }
 inline uint64_t hash_feature_static(const std::string& s, uint64_t u, const std::string& h, uint32_t num_bits)
 {
   size_t parse_mark = (1 << num_bits) - 1;
-  return get_hasher(h)(s.data(), s.length(), u) & parse_mark;
+  return get_hasher(h)(s.data(), s.length(), static_cast<uint32_t>(u)) & parse_mark;
 }
 
 inline uint64_t hash_feature_cstr(VW::workspace& all, const char* fstr, uint64_t u)
 {
-  return all.parser_runtime.example_parser->hasher(fstr, strlen(fstr), u) & all.runtime_state.parse_mask;
+  return all.parser_runtime.example_parser->hasher(fstr, strlen(fstr), static_cast<uint32_t>(u)) & all.runtime_state.parse_mask;
 }
 
 inline uint64_t chain_hash(VW::workspace& all, const std::string& name, const std::string& value, uint64_t u)
 {
   // chain hash is hash(feature_value, hash(feature_name, namespace_hash)) & parse_mask
   return all.parser_runtime.example_parser->hasher(
-             value.data(), value.length(), all.parser_runtime.example_parser->hasher(name.data(), name.length(), u)) &
+             value.data(), value.length(), all.parser_runtime.example_parser->hasher(name.data(), name.length(), static_cast<uint32_t>(u))) &
       all.runtime_state.parse_mask;
 }
 
 inline uint64_t chain_hash_static(
     const std::string& name, const std::string& value, uint64_t u, hash_func_t hash_func, uint64_t parse_mask)
 {
   // chain hash is hash(feature_value, hash(feature_name, namespace_hash)) & parse_mask
-  return hash_func(value.data(), value.length(), hash_func(name.data(), name.length(), u)) & parse_mask;
+  return hash_func(value.data(), value.length(), hash_func(name.data(), name.length(), static_cast<uint32_t>(u))) & parse_mask;
 }
 
 inline float get_weight(VW::workspace& all, uint32_t index, uint32_t offset)
@@ -335,7 +335,7 @@ inline void set_weight(VW::workspace& all, uint32_t index, uint32_t offset, floa
 
 inline uint32_t num_weights(VW::workspace& all) { return static_cast<uint32_t>(all.length()); }
 
-inline uint32_t get_stride(VW::workspace& all) { return all.weights.stride(); }
+inline uint32_t get_stride(VW::workspace& all) { return static_cast<uint32_t>(all.weights.stride()); }
 
 inline void init_features(primitive_feature_space& fs, size_t features_count)
 {

vowpalwabbit/core/src/confidence_sequence.cc

@@ -99,7 +99,7 @@ void confidence_sequence::update(double w, double r, double p_drop, double n_dro
     double bhigh = t + 1.0;
     sumxhathighsq += std::pow(ahigh, 2) * (approxpolygammaone(bhigh) - approxpolygammaone(bhigh + n_drop));
 
-    t += n_drop;
+    t += static_cast<int>(n_drop);
   }
 
   sumXlow = (sumwr - sumw * rmin) / (rmax - rmin);
@@ -129,8 +129,8 @@ void confidence_sequence::persist(metric_sink& metrics, const std::string& suffi
   metrics.set_uint("upcnt" + suffix, update_count);
   metrics.set_float("lb" + suffix, lower_bound());
   metrics.set_float("ub" + suffix, upper_bound());
-  metrics.set_float("last_w" + suffix, last_w);
-  metrics.set_float("last_r" + suffix, last_r);
+  metrics.set_float("last_w" + suffix, static_cast<float>(last_w));
+  metrics.set_float("last_r" + suffix, static_cast<float>(last_r));
 }
 
 void confidence_sequence::reset_stats()
@@ -295,4 +295,4 @@ size_t write_model_field(
   return bytes;
 }
 }  // namespace model_utils
-}  // namespace VW
+}  // namespace VW

vowpalwabbit/core/src/confidence_sequence_robust.cc

@@ -40,7 +40,7 @@
 
 void g_tilde::histo_insert(double x)
 {
-  uint64_t n = std::floor(std::log(x) / log_k);
+  uint64_t n = static_cast<uint64_t>(std::floor(std::log(x) / log_k));
   double x1 = std::pow(k, n);
   double alpha = (k * x1 - x) / ((k - 1.0) * x1);
   // TODO: Kahan summation
@@ -107,9 +107,9 @@
 
 double countable_discrete_base::get_ci(double alpha) const { return lb_log_wealth(alpha); }
 
-double countable_discrete_base::get_lam_sqrt_tp1(double j) const
+double countable_discrete_base::get_lam_sqrt_tp1(uint64_t j) const
 {
-  double log_den = (j + 0.5) * log_xi - 0.5 * std::log(t + 1);
+  double log_den = (static_cast<double>(j) + 0.5) * log_xi - 0.5 * std::log(t + 1);
   return lambda_max * std::exp(-log_den);
 }
 
@@ -282,9 +282,9 @@
   else { return root_bisect(s, thres, memo, min_mu, max_mu); }
 }
 
-double countable_discrete_base::get_log_weight(double j) const { return log_scale_fac + log_xi_m1 - (1 + j) * log_xi; }
+double countable_discrete_base::get_log_weight(size_t j) const { return log_scale_fac + log_xi_m1 - (1 + static_cast<double>(j)) * log_xi; }
 
-double countable_discrete_base::get_log_remaining_weight(double j) const { return log_scale_fac - j * log_xi; }
+double countable_discrete_base::get_log_remaining_weight(size_t j) const { return log_scale_fac - static_cast<double>(j) * log_xi; }
 
 double countable_discrete_base::get_s() const { return gt.get_s(); }
 
@@ -324,10 +324,10 @@
 void confidence_sequence_robust::persist(metric_sink& metrics, const std::string& suffix)
 {
   metrics.set_uint("upcnt" + suffix, update_count);
-  metrics.set_float("lb" + suffix, lower_bound());
-  metrics.set_float("ub" + suffix, upper_bound());
-  metrics.set_float("last_w" + suffix, last_w);
-  metrics.set_float("last_r" + suffix, last_r);
+  metrics.set_float("lb" + suffix, static_cast<float>(lower_bound()));
+  metrics.set_float("ub" + suffix, static_cast<float>(upper_bound()));
+  metrics.set_float("last_w" + suffix, static_cast<float>(last_w));
+  metrics.set_float("last_r" + suffix, static_cast<float>(last_r));
 }
 
 void confidence_sequence_robust::reset_stats()
@@ -408,4 +408,4 @@
   return bytes;
 }
 }  // namespace model_utils
-}  // namespace VW
+}  // namespace VW

vowpalwabbit/core/src/cressieread.cc

@@ -40,9 +40,9 @@ void cressieread::reset_stats(double alpha, double tau)
   update_count = 0;
 }
 
-float cressieread::lower_bound() { return chisq.cressieread_lower_bound(); }
+float cressieread::lower_bound() { return static_cast<float>(chisq.cressieread_lower_bound()); }
 
-float cressieread::upper_bound() { return chisq.cressieread_upper_bound(); }
+float cressieread::upper_bound() { return static_cast<float>(chisq.cressieread_upper_bound()); }
 }  // namespace estimators
 
 namespace model_utils
@@ -69,4 +69,4 @@ size_t write_model_field(io_buf& io, const VW::estimators::cressieread& sc, cons
   return bytes;
 }
 }  // namespace model_utils
-}  // namespace VW
+}  // namespace VW

vowpalwabbit/core/src/merge.cc

@@ -180,7 +180,7 @@ VW::model_delta merge_deltas(const std::vector<const VW::model_delta*>& deltas_t
   // Get example counts and compute weighting of models
   std::vector<float> example_counts;
   example_counts.reserve(workspaces_to_merge.size());
-  for (const auto* delta : workspaces_to_merge) { example_counts.push_back(delta->sd->weighted_labeled_examples); }
+  for (const auto* delta : workspaces_to_merge) { example_counts.push_back(static_cast<float>(delta->sd->weighted_labeled_examples)); }
   const auto per_model_weighting = calc_per_model_weighting(example_counts);
 
   // Iterate through learners and merge each one

vowpalwabbit/core/src/parse_args.cc

@@ -83,7 +83,7 @@ uint64_t hash_file_contents(VW::io::reader* file_reader)
 
 bool directory_exists(const std::string& path)
 {
-  class stat info;
+  struct stat info;
   if (stat(path.c_str(), &info) != 0) { return false; }
   else { return (info.st_mode & S_IFDIR) > 0; }
   //  boost::filesystem::path p(path);

vowpalwabbit/core/src/reductions/automl.cc

@@ -17,13 +17,41 @@
 #include "vw/core/reductions/gd.h"
 #include "vw/core/setup_base.h"
 
+#include <time.h>
 #include <cfloat>
 #include <iomanip>
 
 using namespace VW::config;
 using namespace VW::LEARNER;
 using namespace VW::reductions::automl;
 
+// Under the Windows VC++ runtime, localtime_s is not available in the C++ standard library; instead
+// a pre-standard implementation (which reverses the order of the arguments from the standard) exists.
+// Note that if _CRT_USE_CONFORMING_ANNEX_K_TIME is defined and set to 1, localtime_s is available in
+// the C++ standard library.
+#ifdef _WIN32
+
+// If we do not have a standard-compliant localtime_s, we need to define it here.
+#if !defined(_CRT_USE_CONFORMING_ANNEX_K_TIME) || _CRT_USE_CONFORMING_ANNEX_K_TIME == 0
+namespace std
+{
+  errno_t localtime_s(const time_t* _Time, struct tm* _Tm)
+  {
+    return ::localtime_s(_Tm, _Time);
+  }
+}  // namespace std
+#else
+// If we do have a standard-compliant localtime_s, we need to forward to it.
+namespace std
+{
+  errno_t localtime_s(const time_t* _Time, struct tm* _Tm)
+  {
+    return ::localtime_s(_Time, _Tm);
+  }
+}  // namespace std
+#endif
+#endif
+
 namespace
 {
 template <typename CMType, bool is_explore>
@@ -159,11 +187,16 @@
 
   if (trace_logging)
   {
+    struct tm lc;
     auto t = std::time(nullptr);
-    auto lc = *std::localtime(&t);
-    std::ostringstream oss;
-    oss << std::put_time(&lc, "%d%m.%H%M%S");
-    trace_file_name_prefix = oss.str();
+
+    if (std::localtime_s(&t, &lc) == 0)
+    {
+      std::ostringstream oss;
+      oss << std::put_time(&lc, "%d%m.%H%M%S");
+      trace_file_name_prefix = oss.str();
+    }
+    else { trace_file_name_prefix = "trace"; }
   }
 
   auto cm = VW::make_unique<config_manager_type>(default_lease, max_live_configs, all.get_random_state(),

vowpalwabbit/core/src/reductions/bs.cc

@@ -163,7 +163,7 @@ void output_example_prediction_bs(
     const auto min_max = std::minmax_element(data.pred_vec.begin(), data.pred_vec.end());
     for (auto& sink : all.output_runtime.final_prediction_sink)
     {
-      print_result(sink.get(), ec.pred.scalar, ec.tag, *min_max.first, *min_max.second, logger);
+      print_result(sink.get(), ec.pred.scalar, ec.tag, float(*min_max.first), float(*min_max.second), logger);
     }
   }
 }

vowpalwabbit/core/src/reductions/cb/cb_explore_adf_large_action_space.cc

@@ -43,7 +43,7 @@ class A_triplet_constructor
   {
     if (feature_value != 0.f)
     {
-      _triplets.emplace_back(Eigen::Triplet<float>(_row_index, index & _weights_mask, feature_value));
+      _triplets.emplace_back(Eigen::Triplet<float>(static_cast<int>(_row_index), static_cast<int>(index & _weights_mask), feature_value));
       if ((index & _weights_mask) > _max_col) { _max_col = (index & _weights_mask); }
     }
   }
@@ -240,6 +240,7 @@ cb_explore_adf_large_action_space<T, S>::cb_explore_adf_large_action_space(uint6
     , _all(all)
     , _seed(seed)
     , _impl_type(impl_type)
+    , _non_degenerate_singular_values(0)
     , spanner_state(c, d)
     , shrink_fact_config(apply_shrink_factor)
     , impl(all, d, _seed, total_size, thread_pool_size, block_size, action_cache_slack, use_explicit_simd)
@@ -294,7 +295,7 @@ std::shared_ptr<VW::LEARNER::learner> make_las_with_impl(VW::setup_base_i& stack
     bool apply_shrink_factor, size_t thread_pool_size, size_t block_size, size_t action_cache_slack,
     bool use_explicit_simd)
 {
-  float seed = (all.get_random_state()->get_random() + 1) * 10.f;
+  uint64_t seed = static_cast<uint64_t>((all.get_random_state()->get_random() + 1) * 10.f);
 
   auto data = VW::make_unique<cb_explore_adf_large_action_space<T, S>>(d, c, apply_shrink_factor, &all, seed,
       1 << all.initial_weights_config.num_bits, thread_pool_size, block_size, action_cache_slack, use_explicit_simd,

vowpalwabbit/core/src/reductions/cb/details/large_action/kernel_impl.h

@@ -22,9 +22,10 @@ inline void kernel_impl(float feature_value, uint64_t index, uint64_t weights_ma
   constexpr static std::array<float, 4> VALUE_MAP = {0.f, 0.f, 1.f, -1.f};
 
 #ifdef _MSC_VER
-  int select_sparsity = __popcnt((index & weights_mask) + column_index) & 1;
+  // TODO: should we be using __popcnt64 here?
+  int select_sparsity = __popcnt(static_cast<unsigned int>((index & weights_mask) + column_index)) & 1;
   int sparsity_index = INDEX_MAP[select_sparsity];
-  int select_sign = __popcnt((index & weights_mask) + column_index + seed) & 1;
+  int select_sign = __popcnt(static_cast<unsigned int>((index & weights_mask) + column_index + seed)) & 1;
   int value_index = sparsity_index + select_sign;
   float val = VALUE_MAP[value_index];
 #else
@@ -35,6 +36,8 @@ inline void kernel_impl(float feature_value, uint64_t index, uint64_t weights_ma
   float val = VALUE_MAP[value_index];
 #endif
 
+
+
   final_dot_product += feature_value * val;
 }
 

vowpalwabbit/core/src/reductions/cb/details/large_action/one_pass_svd_impl.cc

@@ -80,7 +80,7 @@ void one_pass_svd_impl::generate_AOmega(const multi_ex& examples, const std::vec
     }
   }
 
-  const float scaling_factor = 1.f / std::sqrt(p);
+  const float scaling_factor = static_cast<float>(1.f / std::sqrt(p));
   // resize is a no-op if size does not change
   AOmega.resize(num_actions, p);
 
@@ -194,4 +194,4 @@ one_pass_svd_impl::one_pass_svd_impl(VW::workspace* all, uint64_t d, uint64_t se
 }
 
 }  // namespace cb_explore_adf
-}  // namespace VW
+}  // namespace VW