Added brotli enc/ and tools/ directories.

third_party/brotli/enc/cluster.h

+/* 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.
+
+#ifndef BROTLI_ENC_CLUSTER_H_
+#define BROTLI_ENC_CLUSTER_H_
+
+#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) {
+  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;
+  } else if (out[idx2].total_count_ == 0) {
+    p.cost_combo = out[idx1].bit_cost_;
+    store_pair = true;
+  } else {
+    double threshold = *num_pairs == 0 ? 1e99 :
+        std::max(0.0, pairs[0].cost_diff);
+    HistogramType combo = out[idx1];
+    combo.AddHistogram(out[idx2]);
+    double cost_combo = PopulationCost(combo);
+    if (cost_combo < threshold - p.cost_diff) {
+      p.cost_combo = cost_combo;
+      store_pair = true;
+    }
+  }
+  if (store_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 < 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) {
+  double cost_diff_threshold = 0.0;
+  size_t min_cluster_size = 1;
+
+  // 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);
+    }
+  }
+
+  while (num_clusters > min_cluster_size) {
+    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]);
+    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) {
+      if (symbols[i] == best_idx2) {
+        symbols[i] = best_idx1;
+      }
+    }
+    for (size_t 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;
+      }
+      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;
+
+    // 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);
+    }
+  }
+  return num_clusters;
+}
+
+// -----------------------------------------------------------------------------
+// Histogram refinement
+
+// 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) {
+    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]]);
+      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();
+  }
+  for (size_t i = 0; i < in_size; ++i) {
+    out[symbols[i]].AddHistogram(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) {
+    if (new_index[symbols[i]] == kInvalidIndex) {
+      new_index[symbols[i]] = next_index;
+      ++next_index;
+    }
+  }
+  std::vector<HistogramType> tmp(next_index);
+  next_index = 0;
+  for (size_t 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) {
+    out[i] = tmp[i];
+  }
+  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);
+  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;
+  // 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 (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);
+    }
+    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_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);
+
+  // 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);
+
+  // 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_