Files
ik_llama.cpp/common/ngram-map.cpp
T
NexesenexandGitHub c3b075f069 Chores : tidy up more typos project wide (ggml directory excluded), new -ptcall alias (#2237)
* common: fix coding mistakes (typos in identifiers, flags and log strings)

Fix misspelled identifiers and user-facing strings across common, server
and model loading:

- allow_ruless -> allow_rules (misspelled identifier used in the allowlist
  CLI parsing and the server slot/context code)
- get_formated_timings/get_formated_generation -> get_formatted_*
- 'termionated' -> 'terminated' in the fit-margin assert message
- 'defaulr' -> 'default' in the YAML dump
- 'overriden' -> 'overridden' in tensor buffer type override logs
- 'becausee' -> 'because' in the output-tensor split log
- 'etected NaNs' -> 'detected NaNs' in the imatrix error message

* common: fix comment typos across src, common, include and examples

Fix misspelled words in code comments:

- llama.h: 'typy' -> 'type', 'transfrom' -> 'transform', 'ecoder' ->
  'encoder', 'indicies' -> 'indices', 'Intializes' -> 'Initializes'
- common.h: 'embendings' -> 'embeddings', 'pr' -> 'or' in the
  fused-indexer-topk comment
- chat.cpp: 'overridde' -> 'override'
- ngram-map: 'occurences' -> 'occurrences', 'stastistics' -> 'statistics'
- speculative.cpp: 'dont'/'inehit' -> 'don't'/'inherit'
- llama-mmap.cpp: 'dont't' -> 'don't'
- llama-model.h: 'hcurrently andle' -> 'currently handle'
- build_gemma3/4.cpp: 'emdeddings' -> 'embeddings'
- examples: 'quantizuation', 'logprobe', 'throught', 'retrun', 'swich',
  'convinient', 'temporally' (-> 'temporary'), 'temproal', 'preceed'

* common: remove duplicate definitions and duplicate help entries

- clip-impl.h: drop the second, identical #define TN_FFN_GATE
- common.cpp: remove the duplicate '-t, --threads N' help entry that was
  misplaced in the export-lora section (already listed in the general
  section)
- common.cpp: merge the two 'embedding' help groups into a single group
  so the embedding options are listed together
- llama.cpp: remove the redundant LLAMA_MAX_LAYERS define (llama-hparams.h
  already defines the same value and is included by llama.cpp)

* common: fix remaining typos (accomodate, recommanded, occurences, occassionally)

- accomodate -> accommodate in src/llama.cpp comment
- recommanded -> recommended in quantize.cpp user-facing output
- occurences -> occurrences in test-chat.cpp JSON string
- occassionally -> occasionally in vendor/stb/stb_image_resize2.h comment

Note: tokenizer.ggml.seperator_token_id kept as-is to match GGUF spec

* common: remove duplicate help entries

- remove the duplicate '--reasoning-budget N' help entry that was repeated
  in the main section (introduced in e0596bf614 'Autoparser - complete
  refactoring of parser architecture (PR 1376)')
- remove the second '--parallel-tool-calls' help entry that advertised the
  '-ptc' short flag, which belongs to '--print-token-count' (introduced in
  e0596bf614 'Autoparser - complete refactoring of parser architecture
  (PR 1376)'); the '-ptc' alias was non-functional for '--parallel-tool-calls'
  because the parser only binds it to '--print-token-count'

The canonical help entries are kept:
- '--reasoning-budget N' is listed once
- '--parallel-tool-calls' is listed once (without the conflicting '-ptc' alias)

* common: remove duplicate LOG_ENABLE define

- the '#undef LOG_ENABLE / #define LOG_ENABLE() // dummy stub' pair was
  repeated verbatim inside the LOG_DISABLE_LOGS section
- remove the second occurrence (introduced in a2588b53e1 'main : log
  file (PR 2748)')

* llama-bench: align MLA and attention-max-batch flags with common tools

llama-bench used '--mla-attn' and '--attn-max-batch' while the common
CLI parsing (common/common.cpp) uses '--mla-use' and
'--attention-max-batch' for the same features. This made the flags
inconsistent across tools.

- update the help text to advertise the canonical names
  '--mla-use' and '--attention-max-batch'
- keep the old '--mla-attn' and '--attn-max-batch' spellings working
  as aliases so existing scripts are not broken

The divergent names were introduced in 3e536b95b0 'Add optional MLA
(PR 188)'.

* fix typos in comments and user-facing strings

- ngram-map.cpp: 'Do we haven a existing' -> 'Do we have an existing'
  (introduced in 1cb7e1bf39 'spec : add self speculative decoding,
  ngram and refactor (PR 1261)')
- build_mamba.cpp: 'weigth' -> 'weight' (introduced in 8befd92ea5
  'Refactor model compute graphs (PR 1651)')
- gguf-split.cpp: 'one of splits have 0 tensors' -> 'one of the splits
  has 0 tensors' (introduced in 75b580db0a 'split: allow
  --split-max-size option (PR 6343)')
- gguf-split.cpp: 'merged from %d split' -> 'merged from %d splits'
  (introduced in 1b5523dc79 'gguf-split: split and merge gguf per
  batch of tensors (PR 6135)')
- convert-llama2c-to-ggml.cpp: missing opening quote in the help line,
  '(default %s\\')' -> '(default '%s\\')' (introduced in bb9ebb4394
  'Adding support for llama2.c models (PR 2559)')

* harmonize British and American spelling to American English

The codebase uses American English (e.g. --embd-normalize, --color),
but a few strings/comments still used British spellings. Unify them:

- 'normalisation' -> 'normalization' in common.h, common.cpp help text
  and code comment, and llama-build-context.cpp comment
- 'colorise' -> 'colorize' in the --color help text (common.cpp)
- 'behaviour' -> 'behavior' in a chat.cpp warning and a llama.cpp comment
- also fix 'openai' -> 'OpenAI' capitalization in the embedding help
  text and common.h comment (embedding output format is OpenAI-style)

* common: fix help text formatting inconsistencies

- '-smf16'/'--split-mode-f16' and '-smf32'/'--split-mode-f32' help
  entries displayed hardcoded 'true'/'false' as the default value;
  show the actual state derived from params.reduce_type instead
- '-no-mmad' help entry had 'fused_mmad?' without a space before the
  ternary operator
- '--reasoning-tokens' help continuation lines used tab characters for
  indentation while the sibling '--reasoning-format' entry uses spaces;
  convert to consistent space indentation

* common: revert smf16/smf32 help text default display change

Revert the '-smf16'/'--split-mode-f16' and '-smf32'/'--split-mode-f32'
help entries back to their original hardcoded 'true'/'false' default
display. The change to derive the default from params.reduce_type was
not desired; the split-mode options are legacy and the hardcoded
defaults reflect their intended meaning.

The other formatting fixes in the same area (fused_mmad ternary
spacing and the reasoning-tokens tab-to-space indentation) are kept.

* llama-bench: fix help text column alignment

The --mla-use and --attention-max-batch help lines introduced by the
flag alignment landed one column off from the sibling entries
((default: at column 51 instead of 50). Adjust the padding so all
help lines align.

* common: fix help text defaults for graph-reduce-type and log-format

Mismatch 1: -grt, --graph-reduce-type help shows default "f32", but actual default (common.h:463) is "f16" and llama.cpp uses GGML_TYPE_F16.

Mismatch 2: --log-format help shows default "json", but actual default (common.h:536 log_json=false) is text.

* common: add -ptcall short flag for --parallel-tool-calls

* typo
2026-08-03 08:01:18 +03:00

531 lines
19 KiB
C++

#include "common.h"
#include "log.h"
#include "ngram-map.h"
#include <cinttypes>
#include <cstdint>
#include <cstdio>
#include <sstream>
// prime number used for LCG hash function (32 bit), it is near (sqrt(5) - 1)/2 * 2^32.
#define LCG_FACTOR 2654435761UL
// Compute the LCG hash of a n-gram of size len at offset start.
static uint32_t common_ngram_map_hash(const llama_tokens & tokens, size_t start, size_t len) {
uint32_t hash = 0;
for (size_t i = 0; i < len; ++i) {
hash = hash * LCG_FACTOR + tokens[start + i];
}
return hash;
}
// Print the values of a sublist of `llama_tokens & inp` to a string in the form [v0, v1, v2, ...].
static std::string common_tokens_to_str(const llama_tokens & inp, size_t start, size_t length) {
std::ostringstream oss;
oss << '[';
for (size_t i = 0; i < length; ++i) {
if (i > 0) {
oss << ", ";
}
oss << inp[start + i];
}
oss << ']';
return oss.str();
}
// n-gram simple
//
/**
* Perform speculative generation using the model's own token history.
* Searches for a matching pattern in the token history and returns draft tokens.
*
* @param state Current state of this implementation
* @param tokens Token history to search in
* @param sampled Last sampled token
* @return Vector of draft tokens, empty if no matching pattern is found
*/
llama_tokens common_ngram_simple_draft(
const common_ngram_simple_config & config,
const llama_tokens & tokens, llama_token sampled) {
// Simple implementation of self-speculative decoding without a draft model.
//
const size_t cur_len = tokens.size();
const size_t n_draft_min = config.size_ngram; // size of n-gram to lookup in token history
const size_t n_draft_max = config.size_mgram; // the m-gram following the found n-gram is used for draft
// vector for tokens we want to verify.
// return empty vector if there is no match.
llama_tokens draft_tokens;
// We need at least n_draft_min + n_draft_max + 1 tokens.
if (cur_len <= static_cast<size_t>(n_draft_min + n_draft_max + 1)) {
return draft_tokens;
}
// pattern search
llama_tokens pattern;
pattern.reserve(n_draft_min);
for (size_t j = cur_len - n_draft_min + 1; j < cur_len; ++j) {
pattern.push_back(tokens[j]);
}
pattern.push_back(sampled); // add the last token to the pattern
size_t match_pos = 0; // we ignore position 0, position 0 == no match
// search backwards, but skip the current match (we are currently there)
for (size_t j = cur_len - n_draft_min - 1; j > 0; --j) {
bool match = true;
for (size_t k = 0; k < pattern.size(); ++k) {
if (tokens[j + k] != pattern[k]) {
match = false;
break;
}
}
if (match) {
match_pos = j;
break;
}
}
if (match_pos == 0) {
return draft_tokens;
}
const size_t copy_max = std::min(
n_draft_max,
cur_len - (match_pos + n_draft_min)
);
if (copy_max < n_draft_min) {
return draft_tokens;
}
LOG_DBG("%s: #tokens = %zu: found matching pattern at pos %zu, length %zu, draft length %zu\n",
__func__, cur_len,
match_pos, pattern.size(), copy_max);
draft_tokens.reserve(copy_max);
for (size_t j = 0; j < copy_max; ++j) {
draft_tokens.push_back(tokens[match_pos + n_draft_min + j]);
}
return draft_tokens;
}
// n-gram map
//
// maximum number of counted values of a ngram map value.
#define COMMON_NGRAM_MAX_VALUE_COUNT 16380
void common_ngram_map_begin(
common_ngram_map & map, const llama_tokens & tokens) {
size_t size_begin = tokens.size();
LOG_DBG("%s: begin, idx_last_draft=%zu, new begin=%zu, #keys=%zu\n", __func__,
map.idx_last_check, size_begin, map.keys.size());
size_t count_map_entries_upd = 0;
if (!map.key_map.empty() && size_begin < map.idx_last_check) {
if (map.show_key_map_stats) {
// Print statistics of hash map map_key.
size_t count_nonzero = 0;
uint32_t min_idx = UINT32_MAX;
uint32_t max_idx = 0;
for (size_t i = 0; i < map.key_map.size(); ++i) {
uint32_t key_idx = map.key_map[i];
if (key_idx != 0) {
++count_nonzero;
if (key_idx < min_idx) min_idx = key_idx;
if (key_idx > max_idx) max_idx = key_idx;
}
}
if (count_nonzero == 0) {
min_idx = 0;
}
LOG_INF("%s: key_map stats: entries=%zu, min_idx=%u, max_idx=%u, key_map_last_idx=%u\n",
__func__, count_nonzero, min_idx, max_idx, map.key_map_last_idx);
}
// Update the map from hash to key index (clear outdated entries).
for (size_t i = 0; i < map.key_map.size(); ++i) {
uint32_t key_idx = map.key_map[i];
if (key_idx >= map.size_last_begin) {
map.key_map[i] = 0;
count_map_entries_upd++;
}
}
map.key_map_last_idx = (map.size_last_begin > 0) ? map.size_last_begin - 1 : 0;
}
if (size_begin < map.idx_last_check && !map.keys.empty()) {
// The next token generation will start at index size_begin.
// The tokens between map.size_last_begin and size_begin are no longer valid.
//
// Refresh map: Remove all entries with index >= map.size_last_begin.
size_t count_keys = map.keys.size();
size_t count_keys_del = 0;
size_t count_values_del = 0;
for (int32_t i = map.keys.size() - 1; i >= 0; --i) {
common_ngram_map_key & key = map.keys[i];
if (key.key_idx >= map.size_last_begin) {
// Delete the key.
LOG_DBG("%s: delete key %d at index %zu (>= size_last_begin=%zu)\n", __func__, i, key.key_idx, map.size_last_begin);
map.keys.erase(map.keys.begin() + i);
count_keys_del++;
continue;
}
if (map.key_only) {
continue;
}
// Check the indices of the values.
for (int16_t j = COMMON_NGRAM_MAX_VALUES - 1; j >= 0; --j) {
common_ngram_map_value & value = key.values[j];
if (value.value_idx >= map.size_last_begin) {
// Delete the value.
count_values_del++;
// Move all values after this value to the left.
for (uint16_t k = j; k < COMMON_NGRAM_MAX_VALUES - 1; ++k) {
key.values[k] = key.values[k + 1];
}
// Clear the last value.
key.values[COMMON_NGRAM_MAX_VALUES - 1].value_idx = 0;
key.values[COMMON_NGRAM_MAX_VALUES - 1].value_num = 0;
}
}
if (key.values[0].value_idx == 0) {
// No values left, delete the key.
LOG_DBG("%s: delete key %d at index %zu (no values left)\n", __func__, i, key.key_idx);
map.keys.erase(map.keys.begin() + i);
count_keys_del++;
}
}
LOG_INF("%s: refresh map: idx_last_draft=%zu, new begin=%zu, #keys_checked=%zu, #keys_del=%zu, #values_del=%zu, #hashes_upd=%zu\n", __func__,
map.idx_last_check, size_begin,
count_keys, count_keys_del, count_values_del, count_map_entries_upd);
}
map.idx_last_check = size_begin;
map.size_last_begin = size_begin;
}
void common_ngram_map_draft(common_ngram_map & map,
const llama_tokens & inp, llama_token sampled,
llama_tokens & draft) {
// reset last key and value.
map.last_draft_created = false;
map.last_draft_key_idx = 0;
map.last_draft_value_idx = 0;
const size_t cur_len = inp.size();
const uint16_t n = map.size_key;
const uint16_t m = map.size_value;
if (cur_len < static_cast<size_t>(2 * n + m)) {
return;
}
if (cur_len >= static_cast<size_t>(UINT32_MAX)) {
// key_map uses uint32_t instead of size_t.
GGML_ABORT("%s: cur_len exceeds UINT32_MAX: %zu", __func__, cur_len);
}
if (map.idx_last_check > cur_len) {
// Should not happen because of common_ngram_map_begin().
LLAMA_LOG_WARN("%s: map.idx_last_check > cur_len: %zu > %zu", __func__, map.idx_last_check, cur_len);
}
map.idx_last_check = cur_len;
// search pattern, the key n-gram
std::vector<llama_token> key_tokens;
key_tokens.reserve(n);
for (size_t j = cur_len - n + 1; j < cur_len; ++j) {
key_tokens.push_back(inp[j]);
}
key_tokens.push_back(sampled);
// search for the key in the map
size_t match_pos = 0;
if (map.size_last_begin > cur_len) {
LLAMA_LOG_WARN("%s: map.size_last_begin > cur_len: %zu > %zu", __func__, map.size_last_begin, cur_len);
}
if (!map.key_map.empty()) {
// Search for the key in the map key_map from hash of ngrams to index of ngram.
uint32_t idx_hash = (common_ngram_map_hash(key_tokens, 0, n) % map.key_map.size());
uint32_t idx_key = map.key_map[idx_hash];
if (idx_key != 0 && idx_key < cur_len - n - m - 1) {
// Check if the key matches the key at idx_key (because of possible collisions).
bool match = true;
for (size_t k = 0; k < n; ++k) {
if (inp[idx_key + k] != key_tokens[k]) {
match = false;
break;
}
}
LOG_DBG("%s: key hash %x -> idx_key %d: match %d\n", __func__, idx_hash, idx_key, match ? 1 : 0);
if (match) {
match_pos = idx_key;
}
}
}
if (match_pos == 0 && map.size_last_begin > (size_t) (n + m + 1)) {
// Search for the key in [1, map.size_last_begin - n - m -1], descending.
for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) {
// Check if the key matches the key.
bool match = true;
for (size_t k = 0; k < n; ++k) {
if (inp[j + k] != key_tokens[k]) {
match = false;
break;
}
}
if (match) {
match_pos = j;
break;
}
}
}
if (match_pos == 0) {
// In case of a reasoning chat, the part after size_last_begin may be deleted/reordered later.
//
// Search in [size_last_begin, cur_len - n - m - 1], descending.
for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) {
bool match = true;
for (size_t k = 0; k < n; ++k) {
if (inp[j + k] != key_tokens[k]) {
match = false;
break;
}
}
if (match) {
match_pos = j;
break;
}
}
}
if (match_pos > 0) {
LOG_DBG("%s: cur_len = %zu, n = %d, m = %d, sz_tkns = %zu, sampled = %d, match_pos = %zu\n", __func__,
cur_len, n, m, key_tokens.size(), sampled, match_pos);
}
if (!map.key_map.empty()) {
// Add hashes of new ngrams in key_map.
//
// Use the same order as above.
if (map.size_last_begin > (size_t) (n + m + 1)) {
for (size_t j = map.size_last_begin - n - m - 1; j > map.key_map_last_idx; --j) {
// compute hash and store index of ngram at idx j in the map.
uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size());
if (map.key_map[idx_hash] == 0) {
map.key_map[idx_hash] = j; // collisions may occur
}
}
}
for (size_t j = cur_len - n - m - 1; j > map.size_last_begin && j > map.key_map_last_idx; --j) {
// compute hash and store index of ngram at idx j in the map.
uint32_t idx_hash = (common_ngram_map_hash(inp, j, n) % map.key_map.size());
if (map.key_map[idx_hash] == 0) {
map.key_map[idx_hash] = j;
}
}
map.key_map_last_idx = std::max(static_cast<uint32_t>(cur_len - n - m - 1), map.key_map_last_idx);
}
if (match_pos == 0) {
return;
}
// We have a match, now we look for the statistics of the key.
size_t key_offset = map.keys.size(); // offset in the map
// We iterate through the std::vector<common_ngram_map_key> map->keys.
for (size_t i = 0; i < map.keys.size(); ++i) {
bool match = true;
for (size_t j = 0; j < n; ++j) {
if (inp[map.keys[i].key_idx + j] != key_tokens[j]) {
match = false;
break;
}
}
if (match) {
key_offset = i;
break;
}
}
if (key_offset == map.keys.size()) {
// We create a new key-entry, it will get offset key_offset.
common_ngram_map_key new_key;
new_key.key_idx = match_pos;
new_key.stat_idx = 0;
new_key.key_num = 0;
for (int i = 0; i < COMMON_NGRAM_MAX_VALUES; ++i) {
new_key.values[i].value_num = 0;
new_key.values[i].n_accepted = m;
}
map.keys.push_back(new_key);
}
// our key n-gram:
common_ngram_map_key & curr_key = map.keys[key_offset];
// update number of key hits
curr_key.key_num = (uint16_t) std::min((int) map.keys[key_offset].key_num + 1,
(int) COMMON_NGRAM_MAX_VALUE_COUNT);
if (map.key_only) {
// simple mode:
// Fill in the draft with the m tokens following the key.
// We work with value values[0] only.
int n_draft_tokens = std::min((int) m, (int) curr_key.values[0].n_accepted);
for (int i = 0; i < n_draft_tokens; ++i) {
draft.push_back(inp[match_pos + n + i]);
}
LOG_DBG("%s: key_idx = %zu, key_offset = %zu, key_num = %d, draft.size = %zu\n", __func__,
curr_key.key_idx, key_offset, curr_key.key_num, draft.size());
map.last_draft_created = true;
map.last_draft_key_idx = key_offset;
map.last_draft_value_idx = 0; // value 0 is used for simple mode
return;
}
if (curr_key.key_num < map.min_hits) {
// not enough hits to consider this a good draft
LOG_DBG("%s: key_offset = %zu, key_num = %d, min_hits = %d, no draft\n", __func__,
key_offset, curr_key.key_num, map.min_hits);
return;
}
// complex mode: examine the different m-grams after this key n-gram.
//
// determine all (max COMMON_NGRAM_MAX_VALUES) m-grams after the key n-gram.
for (size_t i = curr_key.stat_idx; i <= match_pos; ++i) {
// begins the key n-gram at index i?
bool match_key = true;
for (size_t k = 0; k < n; ++k) {
if (inp[i + k] != key_tokens[k]) {
match_key = false;
break;
}
}
if (!match_key) {
continue;
}
// Do we have an existing value m-gram or a new one after the key at index i?
size_t idx_begin_value_key = i + n;
int idx_value = -1;
for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
size_t idx_begin_value_v = curr_key.values[v].value_idx;
if (idx_begin_value_v == 0) {
// We found an empty value slot => we found a new value m-gram after the key n-gram.
curr_key.values[v].value_idx = idx_begin_value_key;
curr_key.values[v].value_num = 0;
curr_key.values[v].n_accepted = m;
idx_value = v;
break;
}
bool match = true;
for (size_t j = 0; j < m; ++j) {
if (inp[idx_begin_value_key + j] != inp[idx_begin_value_v + j]) {
match = false;
break;
}
}
if (match) {
// We found an existing value m-gram after the key n-gram.
idx_value = v;
break;
}
}
if (idx_value >= 0) {
// We found a value m-gram of the key n-gram.
curr_key.values[idx_value].value_num = (uint16_t) std::min((int) curr_key.values[idx_value].value_num + 1,
(int) COMMON_NGRAM_MAX_VALUE_COUNT);
}
}
// the statistics are updated up to match_pos.
curr_key.stat_idx = match_pos;
// Do we have a value we could use for the draft?
uint16_t max_occur = 0;
int slot_max = 0;
for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
uint16_t curr_occur = curr_key.values[v].value_num;
if (curr_occur > max_occur) {
max_occur = curr_occur;
slot_max = v;
}
}
// What is sum of the other occurrences?
uint32_t sum_occur = 0;
for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
if (v == slot_max) {
continue;
}
uint16_t curr_occur = curr_key.values[v].value_num;
sum_occur += curr_occur;
}
LOG_INF("%s: key_offset = %zu, max_occur = %d, sum_occur = %d, slot_max = %d [%zu/%d, %zu/%d, %zu/%d, %zu/%d]\n", __func__,
key_offset,
max_occur, sum_occur, slot_max,
curr_key.values[0].value_idx, curr_key.values[0].value_num,
curr_key.values[1].value_idx, curr_key.values[1].value_num,
curr_key.values[2].value_idx, curr_key.values[2].value_num,
curr_key.values[3].value_idx, curr_key.values[3].value_num
);
// Print the tokens of the four values (if idx != 0), use LOG_INF
for (int v = 0; v < COMMON_NGRAM_MAX_VALUES; ++v) {
if (curr_key.values[v].value_idx != 0) {
LOG_INF("%s: value[%d] = %s\n", __func__, v, common_tokens_to_str(inp, curr_key.values[v].value_idx, m).c_str());
}
}
if (sum_occur > 0 && max_occur < 2 * sum_occur) {
// The most frequent value is not much more frequent than the other values.
// We do not use the draft.
return;
}
// We use the most frequent value values[slot_max] for the draft.
// Fill in the draft with the m tokens following the key.
int n_draft_tokens = std::min((int) m, (int) curr_key.values[slot_max].n_accepted);
for (int i = 0; i < n_draft_tokens; ++i) {
draft.push_back(inp[match_pos + n + i]);
}
LOG_INF("%s: key_offset = %zu, slot_max = %d, key_num = %d, draft.size = %zu\n", __func__,
key_offset, slot_max,
curr_key.key_num, draft.size());
map.last_draft_created = true;
map.last_draft_key_idx = key_offset;
map.last_draft_value_idx = slot_max; // value used for draft generation.
}
void common_ngram_map_accept(common_ngram_map & map, uint16_t n_accepted) {
if (!map.last_draft_created) {
return;
}
// find the key and its chosen value.
const size_t key_idx = map.last_draft_key_idx;
const size_t val_idx = map.last_draft_value_idx;
// find key corresponding to key_idx.
common_ngram_map_key & curr_key = map.keys[key_idx];
// find value corresponding to val_idx.
struct common_ngram_map_value & curr_value = curr_key.values[val_idx]; // value used for draft generation.
// update the value statistics
LOG_INF("common_ngram_map_send_accepted: n_accepted = %d, prev value_num = %d\n",
n_accepted, curr_value.n_accepted);
curr_value.n_accepted = n_accepted;
}