Update brotli to v1.0.0-snapshot.

third_party/brotli/enc/cluster_inc.h

+/* NOLINT(build/header_guard) */
 /* Copyright 2013 Google Inc. All Rights Reserved.
 
    Distributed under MIT license.
    See file LICENSE for detail or copy at https://opensource.org/licenses/MIT
 */
 
-// Functions for clustering similar histograms together.
+/* template parameters: FN, CODE */
 
-#ifndef BROTLI_ENC_CLUSTER_H_
-#define BROTLI_ENC_CLUSTER_H_
+#define HistogramType FN(Histogram)
 
-#include <math.h>
-#include <algorithm>
-#include <utility>
-#include <vector>
-
-#include "./bit_cost.h"
-#include "./entropy_encode.h"
-#include "./fast_log.h"
-#include "./histogram.h"
-#include "./port.h"
-#include "./types.h"
-
-namespace brotli {
-
-struct HistogramPair {
-  uint32_t idx1;
-  uint32_t idx2;
-  double cost_combo;
-  double cost_diff;
-};
-
-inline bool operator<(const HistogramPair& p1, const HistogramPair& p2) {
-  if (p1.cost_diff != p2.cost_diff) {
-    return p1.cost_diff > p2.cost_diff;
-  }
-  return (p1.idx2 - p1.idx1) > (p2.idx2 - p2.idx1);
-}
-
-// Returns entropy reduction of the context map when we combine two clusters.
-inline double ClusterCostDiff(size_t size_a, size_t size_b) {
-  size_t size_c = size_a + size_b;
-  return static_cast<double>(size_a) * FastLog2(size_a) +
-      static_cast<double>(size_b) * FastLog2(size_b) -
-      static_cast<double>(size_c) * FastLog2(size_c);
-}
-
-// Computes the bit cost reduction by combining out[idx1] and out[idx2] and if
-// it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue.
-template<typename HistogramType>
-void CompareAndPushToQueue(const HistogramType* out,
-                           const uint32_t* cluster_size,
-                           uint32_t idx1, uint32_t idx2,
-                           size_t max_num_pairs,
-                           HistogramPair* pairs,
-                           size_t* num_pairs) {
+/* Computes the bit cost reduction by combining out[idx1] and out[idx2] and if
+   it is below a threshold, stores the pair (idx1, idx2) in the *pairs queue. */
+BROTLI_INTERNAL void FN(BrotliCompareAndPushToQueue)(
+    const HistogramType* out, const uint32_t* cluster_size, uint32_t idx1,
+    uint32_t idx2, size_t max_num_pairs, HistogramPair* pairs,
+    size_t* num_pairs) CODE({
+  BROTLI_BOOL is_good_pair = BROTLI_FALSE;
+  HistogramPair p = {0};
   if (idx1 == idx2) {
     return;
   }
   if (idx2 < idx1) {
     uint32_t t = idx2;
     idx2 = idx1;
     idx1 = t;
   }
-  bool store_pair = false;
-  HistogramPair p = {};
   p.idx1 = idx1;
   p.idx2 = idx2;
   p.cost_diff = 0.5 * ClusterCostDiff(cluster_size[idx1], cluster_size[idx2]);
   p.cost_diff -= out[idx1].bit_cost_;
   p.cost_diff -= out[idx2].bit_cost_;
 
   if (out[idx1].total_count_ == 0) {
     p.cost_combo = out[idx2].bit_cost_;
-    store_pair = true;
+    is_good_pair = BROTLI_TRUE;
   } else if (out[idx2].total_count_ == 0) {
     p.cost_combo = out[idx1].bit_cost_;
-    store_pair = true;
+    is_good_pair = BROTLI_TRUE;
   } else {
     double threshold = *num_pairs == 0 ? 1e99 :
-        std::max(0.0, pairs[0].cost_diff);
+        BROTLI_MAX(double, 0.0, pairs[0].cost_diff);
     HistogramType combo = out[idx1];
-    combo.AddHistogram(out[idx2]);
-    double cost_combo = PopulationCost(combo);
+    double cost_combo;
+    FN(HistogramAddHistogram)(&combo, &out[idx2]);
+    cost_combo = FN(BrotliPopulationCost)(&combo);
     if (cost_combo < threshold - p.cost_diff) {
       p.cost_combo = cost_combo;
-      store_pair = true;
+      is_good_pair = BROTLI_TRUE;
     }
   }
-  if (store_pair) {
+  if (is_good_pair) {
     p.cost_diff += p.cost_combo;
-    if (*num_pairs > 0 && pairs[0] < p) {
-      // Replace the top of the queue if needed.
+    if (*num_pairs > 0 && HistogramPairIsLess(&pairs[0], &p)) {
+      /* Replace the top of the queue if needed. */
       if (*num_pairs < max_num_pairs) {
         pairs[*num_pairs] = pairs[0];
         ++(*num_pairs);
       }
       pairs[0] = p;
     } else if (*num_pairs < max_num_pairs) {
       pairs[*num_pairs] = p;
       ++(*num_pairs);
     }
   }
-}
+})
 
-template<typename HistogramType>
-size_t HistogramCombine(HistogramType* out,
-                        uint32_t* cluster_size,
-                        uint32_t* symbols,
-                        uint32_t* clusters,
-                        HistogramPair* pairs,
-                        size_t num_clusters,
-                        size_t symbols_size,
-                        size_t max_clusters,
-                        size_t max_num_pairs) {
+BROTLI_INTERNAL size_t FN(BrotliHistogramCombine)(HistogramType* out,
+                                                  uint32_t* cluster_size,
+                                                  uint32_t* symbols,
+                                                  uint32_t* clusters,
+                                                  HistogramPair* pairs,
+                                                  size_t num_clusters,
+                                                  size_t symbols_size,
+                                                  size_t max_clusters,
+                                                  size_t max_num_pairs) CODE({
   double cost_diff_threshold = 0.0;
   size_t min_cluster_size = 1;
+  size_t num_pairs = 0;
 
-  // We maintain a vector of histogram pairs, with the property that the pair
-  // with the maximum bit cost reduction is the first.
-  size_t num_pairs = 0;
-  for (size_t idx1 = 0; idx1 < num_clusters; ++idx1) {
-    for (size_t idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) {
-      CompareAndPushToQueue(out, cluster_size, clusters[idx1], clusters[idx2],
-                            max_num_pairs, &pairs[0], &num_pairs);
+  {
+    /* We maintain a vector of histogram pairs, with the property that the pair
+       with the maximum bit cost reduction is the first. */
+    size_t idx1;
+    for (idx1 = 0; idx1 < num_clusters; ++idx1) {
+      size_t idx2;
+      for (idx2 = idx1 + 1; idx2 < num_clusters; ++idx2) {
+        FN(BrotliCompareAndPushToQueue)(out, cluster_size, clusters[idx1],
+            clusters[idx2], max_num_pairs, &pairs[0], &num_pairs);
+      }
     }
   }
 
   while (num_clusters > min_cluster_size) {
+    uint32_t best_idx1;
+    uint32_t best_idx2;
+    size_t i;
     if (pairs[0].cost_diff >= cost_diff_threshold) {
       cost_diff_threshold = 1e99;
       min_cluster_size = max_clusters;
       continue;
     }
-    // Take the best pair from the top of heap.
-    uint32_t best_idx1 = pairs[0].idx1;
-    uint32_t best_idx2 = pairs[0].idx2;
-    out[best_idx1].AddHistogram(out[best_idx2]);
+    /* Take the best pair from the top of heap. */
+    best_idx1 = pairs[0].idx1;
+    best_idx2 = pairs[0].idx2;
+    FN(HistogramAddHistogram)(&out[best_idx1], &out[best_idx2]);
     out[best_idx1].bit_cost_ = pairs[0].cost_combo;
     cluster_size[best_idx1] += cluster_size[best_idx2];
-    for (size_t i = 0; i < symbols_size; ++i) {
+    for (i = 0; i < symbols_size; ++i) {
       if (symbols[i] == best_idx2) {
         symbols[i] = best_idx1;
       }
     }
-    for (size_t i = 0; i < num_clusters; ++i) {
+    for (i = 0; i < num_clusters; ++i) {
       if (clusters[i] == best_idx2) {
         memmove(&clusters[i], &clusters[i + 1],
                 (num_clusters - i - 1) * sizeof(clusters[0]));
         break;
       }
     }
     --num_clusters;
-    // Remove pairs intersecting the just combined best pair.
-    size_t copy_to_idx = 0;
-    for (size_t i = 0; i < num_pairs; ++i) {
-      HistogramPair& p = pairs[i];
-      if (p.idx1 == best_idx1 || p.idx2 == best_idx1 ||
-          p.idx1 == best_idx2 || p.idx2 == best_idx2) {
-        // Remove invalid pair from the queue.
-        continue;
+    {
+      /* Remove pairs intersecting the just combined best pair. */
+      size_t copy_to_idx = 0;
+      for (i = 0; i < num_pairs; ++i) {
+        HistogramPair* p = &pairs[i];
+        if (p->idx1 == best_idx1 || p->idx2 == best_idx1 ||
+            p->idx1 == best_idx2 || p->idx2 == best_idx2) {
+          /* Remove invalid pair from the queue. */
+          continue;
+        }
+        if (HistogramPairIsLess(&pairs[0], p)) {
+          /* Replace the top of the queue if needed. */
+          HistogramPair front = pairs[0];
+          pairs[0] = *p;
+          pairs[copy_to_idx] = front;
+        } else {
+          pairs[copy_to_idx] = *p;
+        }
+        ++copy_to_idx;
       }
-      if (pairs[0] < p) {
-        // Replace the top of the queue if needed.
-        HistogramPair front = pairs[0];
-        pairs[0] = p;
-        pairs[copy_to_idx] = front;
-      } else {
-        pairs[copy_to_idx] = p;
-      }
-      ++copy_to_idx;
+      num_pairs = copy_to_idx;
     }
-    num_pairs = copy_to_idx;
 
-    // Push new pairs formed with the combined histogram to the heap.
-    for (size_t i = 0; i < num_clusters; ++i) {
-      CompareAndPushToQueue(out, cluster_size, best_idx1, clusters[i],
-                            max_num_pairs, &pairs[0], &num_pairs);
+    /* Push new pairs formed with the combined histogram to the heap. */
+    for (i = 0; i < num_clusters; ++i) {
+      FN(BrotliCompareAndPushToQueue)(out, cluster_size, best_idx1, clusters[i],
+                                      max_num_pairs, &pairs[0], &num_pairs);
     }
   }
   return num_clusters;
-}
+})
 
-// -----------------------------------------------------------------------------
-// Histogram refinement
+/* What is the bit cost of moving histogram from cur_symbol to candidate. */
+BROTLI_INTERNAL double FN(BrotliHistogramBitCostDistance)(
+    const HistogramType* histogram, const HistogramType* candidate) CODE({
+  if (histogram->total_count_ == 0) {
+    return 0.0;
+  } else {
+    HistogramType tmp = *histogram;
+    FN(HistogramAddHistogram)(&tmp, candidate);
+    return FN(BrotliPopulationCost)(&tmp) - candidate->bit_cost_;
+  }
+})
 
-// What is the bit cost of moving histogram from cur_symbol to candidate.
-template<typename HistogramType>
-double HistogramBitCostDistance(const HistogramType& histogram,
-                                const HistogramType& candidate) {
-  if (histogram.total_count_ == 0) {
-    return 0.0;
-  }
-  HistogramType tmp = histogram;
-  tmp.AddHistogram(candidate);
-  return PopulationCost(tmp) - candidate.bit_cost_;
-}
-
-// Find the best 'out' histogram for each of the 'in' histograms.
-// When called, clusters[0..num_clusters) contains the unique values from
-// symbols[0..in_size), but this property is not preserved in this function.
-// Note: we assume that out[]->bit_cost_ is already up-to-date.
-template<typename HistogramType>
-void HistogramRemap(const HistogramType* in, size_t in_size,
-                    const uint32_t* clusters, size_t num_clusters,
-                    HistogramType* out, uint32_t* symbols) {
-  for (size_t i = 0; i < in_size; ++i) {
+/* Find the best 'out' histogram for each of the 'in' histograms.
+   When called, clusters[0..num_clusters) contains the unique values from
+   symbols[0..in_size), but this property is not preserved in this function.
+   Note: we assume that out[]->bit_cost_ is already up-to-date. */
+BROTLI_INTERNAL void FN(BrotliHistogramRemap)(const HistogramType* in,
+    size_t in_size, const uint32_t* clusters, size_t num_clusters,
+    HistogramType* out, uint32_t* symbols) CODE({
+  size_t i;
+  for (i = 0; i < in_size; ++i) {
     uint32_t best_out = i == 0 ? symbols[0] : symbols[i - 1];
-    double best_bits = HistogramBitCostDistance(in[i], out[best_out]);
-    for (size_t j = 0; j < num_clusters; ++j) {
-      const double cur_bits = HistogramBitCostDistance(in[i], out[clusters[j]]);
+    double best_bits =
+        FN(BrotliHistogramBitCostDistance)(&in[i], &out[best_out]);
+    size_t j;
+    for (j = 0; j < num_clusters; ++j) {
+      const double cur_bits =
+          FN(BrotliHistogramBitCostDistance)(&in[i], &out[clusters[j]]);
       if (cur_bits < best_bits) {
         best_bits = cur_bits;
         best_out = clusters[j];
       }
     }
     symbols[i] = best_out;
   }
 
-  // Recompute each out based on raw and symbols.
-  for (size_t j = 0; j < num_clusters; ++j) {
-    out[clusters[j]].Clear();
+  /* Recompute each out based on raw and symbols. */
+  for (i = 0; i < num_clusters; ++i) {
+    FN(HistogramClear)(&out[clusters[i]]);
   }
-  for (size_t i = 0; i < in_size; ++i) {
-    out[symbols[i]].AddHistogram(in[i]);
+  for (i = 0; i < in_size; ++i) {
+    FN(HistogramAddHistogram)(&out[symbols[i]], &in[i]);
   }
-}
+})
 
-// Reorders elements of the out[0..length) array and changes values in
-// symbols[0..length) array in the following way:
-//   * when called, symbols[] contains indexes into out[], and has N unique
-//     values (possibly N < length)
-//   * on return, symbols'[i] = f(symbols[i]) and
-//                out'[symbols'[i]] = out[symbols[i]], for each 0 <= i < length,
-//     where f is a bijection between the range of symbols[] and [0..N), and
-//     the first occurrences of values in symbols'[i] come in consecutive
-//     increasing order.
-// Returns N, the number of unique values in symbols[].
-template<typename HistogramType>
-size_t HistogramReindex(HistogramType* out, uint32_t* symbols, size_t length) {
-  static const uint32_t kInvalidIndex = std::numeric_limits<uint32_t>::max();
-  std::vector<uint32_t> new_index(length, kInvalidIndex);
-  uint32_t next_index = 0;
-  for (size_t i = 0; i < length; ++i) {
+/* Reorders elements of the out[0..length) array and changes values in
+   symbols[0..length) array in the following way:
+     * when called, symbols[] contains indexes into out[], and has N unique
+       values (possibly N < length)
+     * on return, symbols'[i] = f(symbols[i]) and
+                  out'[symbols'[i]] = out[symbols[i]], for each 0 <= i < length,
+       where f is a bijection between the range of symbols[] and [0..N), and
+       the first occurrences of values in symbols'[i] come in consecutive
+       increasing order.
+   Returns N, the number of unique values in symbols[]. */
+BROTLI_INTERNAL size_t FN(BrotliHistogramReindex)(MemoryManager* m,
+    HistogramType* out, uint32_t* symbols, size_t length) CODE({
+  static const uint32_t kInvalidIndex = BROTLI_UINT32_MAX;
+  uint32_t* new_index = BROTLI_ALLOC(m, uint32_t, length);
+  uint32_t next_index;
+  HistogramType* tmp;
+  size_t i;
+  if (BROTLI_IS_OOM(m)) return 0;
+  for (i = 0; i < length; ++i) {
+      new_index[i] = kInvalidIndex;
+  }
+  next_index = 0;
+  for (i = 0; i < length; ++i) {
     if (new_index[symbols[i]] == kInvalidIndex) {
       new_index[symbols[i]] = next_index;
       ++next_index;
     }
   }
-  std::vector<HistogramType> tmp(next_index);
+  /* TODO: by using idea of "cycle-sort" we can avoid allocation of
+     tmp and reduce the number of copying by the factor of 2. */
+  tmp = BROTLI_ALLOC(m, HistogramType, next_index);
+  if (BROTLI_IS_OOM(m)) return 0;
   next_index = 0;
-  for (size_t i = 0; i < length; ++i) {
+  for (i = 0; i < length; ++i) {
     if (new_index[symbols[i]] == next_index) {
       tmp[next_index] = out[symbols[i]];
       ++next_index;
     }
     symbols[i] = new_index[symbols[i]];
   }
-  for (size_t i = 0; i < next_index; ++i) {
+  BROTLI_FREE(m, new_index);
+  for (i = 0; i < next_index; ++i) {
     out[i] = tmp[i];
   }
+  BROTLI_FREE(m, tmp);
   return next_index;
-}
+})
 
-// Clusters similar histograms in 'in' together, the selected histograms are
-// placed in 'out', and for each index in 'in', *histogram_symbols will
-// indicate which of the 'out' histograms is the best approximation.
-template<typename HistogramType>
-void ClusterHistograms(const std::vector<HistogramType>& in,
-                       size_t num_contexts, size_t num_blocks,
-                       size_t max_histograms,
-                       std::vector<HistogramType>* out,
-                       std::vector<uint32_t>* histogram_symbols) {
-  const size_t in_size = num_contexts * num_blocks;
-  assert(in_size == in.size());
-  std::vector<uint32_t> cluster_size(in_size, 1);
-  std::vector<uint32_t> clusters(in_size);
+BROTLI_INTERNAL void FN(BrotliClusterHistograms)(
+    MemoryManager* m, const HistogramType* in, const size_t in_size,
+    size_t max_histograms, HistogramType* out, size_t* out_size,
+    uint32_t* histogram_symbols) CODE({
+  uint32_t* cluster_size = BROTLI_ALLOC(m, uint32_t, in_size);
+  uint32_t* clusters = BROTLI_ALLOC(m, uint32_t, in_size);
   size_t num_clusters = 0;
-  out->resize(in_size);
-  histogram_symbols->resize(in_size);
-  for (size_t i = 0; i < in_size; ++i) {
-    (*out)[i] = in[i];
-    (*out)[i].bit_cost_ = PopulationCost(in[i]);
-    (*histogram_symbols)[i] = static_cast<uint32_t>(i);
+  const size_t max_input_histograms = 64;
+  size_t pairs_capacity = max_input_histograms * max_input_histograms / 2;
+  /* For the first pass of clustering, we allow all pairs. */
+  HistogramPair* pairs = BROTLI_ALLOC(m, HistogramPair, pairs_capacity + 1);
+  size_t i;
+
+  if (BROTLI_IS_OOM(m)) return;
+
+  for (i = 0; i < in_size; ++i) {
+    cluster_size[i] = 1;
   }
 
-  const size_t max_input_histograms = 64;
-  // For the first pass of clustering, we allow all pairs.
-  size_t max_num_pairs = max_input_histograms * max_input_histograms / 2;
-  std::vector<HistogramPair> pairs(max_num_pairs + 1);
+  for (i = 0; i < in_size; ++i) {
+    out[i] = in[i];
+    out[i].bit_cost_ = FN(BrotliPopulationCost)(&in[i]);
+    histogram_symbols[i] = (uint32_t)i;
+  }
 
-  for (size_t i = 0; i < in_size; i += max_input_histograms) {
-    size_t num_to_combine = std::min(in_size - i, max_input_histograms);
-    for (size_t j = 0; j < num_to_combine; ++j) {
-      clusters[num_clusters + j] = static_cast<uint32_t>(i + j);
+  for (i = 0; i < in_size; i += max_input_histograms) {
+    size_t num_to_combine =
+        BROTLI_MIN(size_t, in_size - i, max_input_histograms);
+    size_t num_new_clusters;
+    size_t j;
+    for (j = 0; j < num_to_combine; ++j) {
+      clusters[num_clusters + j] = (uint32_t)(i + j);
     }
-    size_t num_new_clusters =
-        HistogramCombine(&(*out)[0], &cluster_size[0],
-                         &(*histogram_symbols)[i],
-                         &clusters[num_clusters], &pairs[0],
-                         num_to_combine, num_to_combine,
-                         max_histograms, max_num_pairs);
+    num_new_clusters =
+        FN(BrotliHistogramCombine)(out, cluster_size,
+                                   &histogram_symbols[i],
+                                   &clusters[num_clusters], pairs,
+                                   num_to_combine, num_to_combine,
+                                   max_histograms, pairs_capacity);
     num_clusters += num_new_clusters;
   }
 
-  // For the second pass, we limit the total number of histogram pairs.
-  // After this limit is reached, we only keep searching for the best pair.
-  max_num_pairs =
-      std::min(64 * num_clusters, (num_clusters / 2) * num_clusters);
-  pairs.resize(max_num_pairs + 1);
+  {
+    /* For the second pass, we limit the total number of histogram pairs.
+       After this limit is reached, we only keep searching for the best pair. */
+    size_t max_num_pairs = BROTLI_MIN(size_t,
+        64 * num_clusters, (num_clusters / 2) * num_clusters);
+    BROTLI_ENSURE_CAPACITY(
+        m, HistogramPair, pairs, pairs_capacity, max_num_pairs + 1);
+    if (BROTLI_IS_OOM(m)) return;
 
-  // Collapse similar histograms.
-  num_clusters = HistogramCombine(&(*out)[0], &cluster_size[0],
-                                  &(*histogram_symbols)[0], &clusters[0],
-                                  &pairs[0], num_clusters, in_size,
-                                  max_histograms, max_num_pairs);
+    /* Collapse similar histograms. */
+    num_clusters = FN(BrotliHistogramCombine)(out, cluster_size,
+                                              histogram_symbols, clusters,
+                                              pairs, num_clusters, in_size,
+                                              max_histograms, max_num_pairs);
+  }
+  BROTLI_FREE(m, pairs);
+  BROTLI_FREE(m, cluster_size);
+  /* Find the optimal map from original histograms to the final ones. */
+  FN(BrotliHistogramRemap)(in, in_size, clusters, num_clusters,
+                           out, histogram_symbols);
+  BROTLI_FREE(m, clusters);
+  /* Convert the context map to a canonical form. */
+  *out_size = FN(BrotliHistogramReindex)(m, out, histogram_symbols, in_size);
+  if (BROTLI_IS_OOM(m)) return;
+})
 
-  // Find the optimal map from original histograms to the final ones.
-  HistogramRemap(&in[0], in_size, &clusters[0], num_clusters,
-                 &(*out)[0], &(*histogram_symbols)[0]);
-
-  // Convert the context map to a canonical form.
-  size_t num_histograms =
-      HistogramReindex(&(*out)[0], &(*histogram_symbols)[0], in_size);
-  out->resize(num_histograms);
-}
-
-}  // namespace brotli
-
-#endif  // BROTLI_ENC_CLUSTER_H_
+#undef HistogramType