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Diffstat (limited to '')
| -rw-r--r-- | modules/brotli/enc/cluster_inc.h | 320 |
1 files changed, 320 insertions, 0 deletions
diff --git a/modules/brotli/enc/cluster_inc.h b/modules/brotli/enc/cluster_inc.h new file mode 100644 index 0000000000..3d4f40e601 --- /dev/null +++ b/modules/brotli/enc/cluster_inc.h @@ -0,0 +1,320 @@ +/* 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 +*/ + +/* template parameters: FN, CODE */ + +#define HistogramType FN(Histogram) + +/* 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; + p.idx1 = p.idx2 = 0; + p.cost_diff = p.cost_combo = 0; + if (idx1 == idx2) { + return; + } + if (idx2 < idx1) { + uint32_t t = idx2; + idx2 = idx1; + idx1 = t; + } + 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_; + is_good_pair = BROTLI_TRUE; + } else if (out[idx2].total_count_ == 0) { + p.cost_combo = out[idx1].bit_cost_; + is_good_pair = BROTLI_TRUE; + } else { + double threshold = *num_pairs == 0 ? 1e99 : + BROTLI_MAX(double, 0.0, pairs[0].cost_diff); + HistogramType combo = out[idx1]; + 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; + is_good_pair = BROTLI_TRUE; + } + } + if (is_good_pair) { + p.cost_diff += p.cost_combo; + 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); + } + } +}) + +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 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. */ + 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 (i = 0; i < symbols_size; ++i) { + if (symbols[i] == best_idx2) { + symbols[i] = best_idx1; + } + } + 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 (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; + } + num_pairs = copy_to_idx; + } + + /* 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; +}) + +/* 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_; + } +}) + +/* 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 = + 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 (i = 0; i < num_clusters; ++i) { + FN(HistogramClear)(&out[clusters[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[]. */ +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) || BROTLI_IS_NULL(new_index)) 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; + } + } + /* 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) || BROTLI_IS_NULL(tmp)) return 0; + next_index = 0; + 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]]; + } + BROTLI_FREE(m, new_index); + for (i = 0; i < next_index; ++i) { + out[i] = tmp[i]; + } + BROTLI_FREE(m, tmp); + return next_index; +}) + +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; + 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) || BROTLI_IS_NULL(cluster_size) || + BROTLI_IS_NULL(clusters) || BROTLI_IS_NULL(pairs)) { + return; + } + + for (i = 0; i < in_size; ++i) { + cluster_size[i] = 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 (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); + } + 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. */ + 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 = 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; +}) + +#undef HistogramType |