llama : more consistent names of count variables

README.md

@@ -10,7 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ### Recent API changes
 
-- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_max_seq()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
+- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
 - [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
 - [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
 

common/common.cpp

@@ -1288,7 +1288,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
 
  cparams.n_ctx = params.n_ctx;
  cparams.n_batch = params.n_batch;
- cparams.n_parallel = params.n_parallel;
+ cparams.n_seq_max  = params.n_parallel;
  cparams.n_threads = params.n_threads;
  cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
  cparams.seed = params.seed;
@@ -1786,17 +1786,17 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) {
  static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
 
  printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
- view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+ view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
  llama_kv_cache_view_cell * c_curr = view.cells;
  llama_seq_id * cs_curr = view.cells_sequences;
 
- for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
+ for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
  if (i % row_size == 0) {
  printf("\n%5d: ", i);
  }
  int seq_count = 0;
- for (int j = 0; j < view.n_max_seq; j++) {
+ for (int j = 0; j < view.n_seq_max; j++) {
  if (cs_curr[j] >= 0) { seq_count++; }
  }
  putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
@@ -1809,14 +1809,14 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
  static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
 
  printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
- view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
+ view.n_cells, view.n_seq_max, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
 
  std::unordered_map<llama_seq_id, size_t> seqs;
  llama_kv_cache_view_cell * c_curr = view.cells;
  llama_seq_id * cs_curr = view.cells_sequences;
 
- for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
- for (int j = 0; j < view.n_max_seq; j++) {
+ for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
+ for (int j = 0; j < view.n_seq_max; j++) {
  if (cs_curr[j] < 0) { continue; }
  if (seqs.find(cs_curr[j]) == seqs.end()) {
  if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
@@ -1835,11 +1835,11 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
 
  c_curr = view.cells;
  cs_curr = view.cells_sequences;
- for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
+ for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_seq_max) {
  if (i % row_size == 0) {
  printf("\n%5d: ", i);
  }
- for (int j = 0; j < view.n_max_seq; j++) {
+ for (int j = 0; j < view.n_seq_max; j++) {
  if (cs_curr[j] >= 0) {
  const auto & it = seqs.find(cs_curr[j]);
  putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');

examples/batched-bench/batched-bench.cpp

@@ -106,7 +106,7 @@ int main(int argc, char ** argv) {
  ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
  // ensure enough sequences are available
- ctx_params.n_parallel = *std::max_element(n_pl.begin(), n_pl.end());
+ ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
 
  llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 

examples/batched/batched.cpp

@@ -80,7 +80,7 @@ int main(int argc, char ** argv) {
  ctx_params.seed = 1234;
  ctx_params.n_ctx = n_kv_req;
  ctx_params.n_batch = std::max(n_len, n_parallel);
- ctx_params.n_parallel = n_parallel;
+ ctx_params.n_seq_max  = n_parallel;
  ctx_params.n_threads = params.n_threads;
  ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 

examples/main/main.cpp

@@ -878,6 +878,7 @@ int main(int argc, char ** argv) {
  const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
  const auto line_inp = ::llama_tokenize(ctx, buffer, false, false);
  const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
+
  LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
  embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());

examples/perplexity/perplexity.cpp

@@ -841,7 +841,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
  const int n_batch = params.n_batch;
 
  const int max_tasks_per_batch = 32;
- const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx));
+ const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
  llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1118,7 +1118,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
  const int n_batch = params.n_batch;
 
  const int max_tasks_per_batch = 128;
- const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_max_seq(ctx));
+ const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
  llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1470,7 +1470,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
  const int n_batch = params.n_batch;
 
  const int max_tasks_per_batch = 32;
- const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_max_seq(ctx));
+ const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
 
  llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 

llama.cpp

@@ -12551,7 +12551,7 @@ struct llama_context_params llama_context_default_params() {
  /*.seed =*/ LLAMA_DEFAULT_SEED,
  /*.n_ctx =*/ 512,
  /*.n_batch =*/ 512,
- /*.n_parallel =*/ 1,
+ /*.n_seq_max  =*/ 1,
  /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
  /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
  /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
@@ -12713,7 +12713,7 @@ struct llama_context * llama_new_context_with_model(
  auto & cparams = ctx->cparams;
 
  cparams.n_batch = params.n_batch;
- // TODO: maybe add n_parallel here too
+ // TODO: maybe add n_seq_max here too
  cparams.n_threads = params.n_threads;
  cparams.n_threads_batch = params.n_threads_batch;
  cparams.yarn_ext_factor = params.yarn_ext_factor;
@@ -12780,7 +12780,7 @@ struct llama_context * llama_new_context_with_model(
  // Mamba only needs a constant number of KV cache cells per sequence
  if (model->arch == LLM_ARCH_MAMBA) {
  // Mamba needs at least as many KV cells as there are sequences kept at any time
- kv_size = std::max((uint32_t) 1, params.n_parallel);
+ kv_size = std::max((uint32_t) 1, params.n_seq_max);
  // it's probably best to keep as much precision as possible for the states
  type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
  type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
@@ -13037,7 +13037,7 @@ uint32_t llama_n_batch(const struct llama_context * ctx) {
  return ctx->cparams.n_batch;
 }
 
-uint32_t llama_n_max_seq(const struct llama_context * ctx) {
+uint32_t llama_n_seq_max(const struct llama_context * ctx) {
  return ctx->kv_self.size;
 }
 
@@ -13201,10 +13201,10 @@ int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const
  }
 }
 
-struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq) {
+struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
  struct llama_kv_cache_view result = {
  /*.n_cells = */ 0,
- /*.n_max_seq = */ n_max_seq,
+ /*.n_seq_max = */ n_seq_max,
  /*.token_count = */ 0,
  /*.used_cells = */ llama_get_kv_cache_used_cells(ctx),
  /*.max_contiguous = */ 0,
@@ -13232,7 +13232,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
  void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
  GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
  view->cells = (struct llama_kv_cache_view_cell *)p;
- p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_max_seq * view->n_cells);
+ p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
  GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
  view->cells_sequences = (llama_seq_id *)p;
  }
@@ -13246,7 +13246,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
  uint32_t max_contig = 0;
  int32_t max_contig_idx = -1;
 
- for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_max_seq) {
+ for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
  const size_t curr_size = kv_cells[i].seq_id.size();
  token_count += curr_size;
  c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
@@ -13263,7 +13263,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
 
  int seq_idx = 0;
  for (const llama_seq_id it : kv_cells[i].seq_id) {
- if (seq_idx >= view->n_max_seq) {
+ if (seq_idx >= view->n_seq_max) {
  break;
  }
  cs_curr[seq_idx] = it;
@@ -13272,7 +13272,7 @@ void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_k
  if (seq_idx != 0) {
  used_cells++;
  }
- for (; seq_idx < view->n_max_seq; seq_idx++) {
+ for (; seq_idx < view->n_seq_max; seq_idx++) {
  cs_curr[seq_idx] = -1;
  }
  }
@@ -13934,12 +13934,12 @@ int32_t llama_tokenize(
  const char * text,
  int32_t text_len,
  llama_token * tokens,
- int32_t n_max_tokens,
+ int32_t n_tokens_max,
  bool add_bos,
  bool special) {
  auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
 
- if (n_max_tokens < (int) res.size()) {
+ if (n_tokens_max < (int) res.size()) {
  // LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
  return -((int) res.size());
  }

llama.h

@@ -235,7 +235,7 @@ extern "C" {
  uint32_t seed; // RNG seed, -1 for random
  uint32_t n_ctx; // text context, 0 = from model
  uint32_t n_batch; // prompt processing maximum batch size
- uint32_t n_parallel; // number of parallel sequences (i.e. distinct states for recurrent models)
+ uint32_t n_seq_max;  // max number of sequences (i.e. distinct states for recurrent models)
  uint32_t n_threads; // number of threads to use for generation
  uint32_t n_threads_batch; // number of threads to use for batch processing
 
@@ -377,7 +377,7 @@ extern "C" {
 
  LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
  LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
- LLAMA_API uint32_t llama_n_max_seq (const struct llama_context * ctx);
+ LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
 
  LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model);
  LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
@@ -456,7 +456,7 @@ extern "C" {
  // Maximum number of sequences that can exist in a cell. It's not an error
  // if there are more sequences in a cell than this value, however they will
  // not be visible in the view cells_sequences.
- int32_t n_max_seq;
+ int32_t n_seq_max;
 
  // Number of tokens in the cache. For example, if there are two populated
  // cells, the first with 1 sequence id in it and the second with 2 sequence
@@ -476,12 +476,12 @@ extern "C" {
  // Information for an individual cell.
  struct llama_kv_cache_view_cell * cells;
 
- // The sequences for each cell. There will be n_max_seq items per cell.
+ // The sequences for each cell. There will be n_seq_max items per cell.
  llama_seq_id * cells_sequences;
  };
 
  // Create an empty KV cache view. (use only for debugging purposes)
- LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
+ LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
 
  // Free a KV cache view. (use only for debugging purposes)
  LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
@@ -708,7 +708,7 @@ extern "C" {
 
  /// @details Convert the provided text into tokens.
  /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
- /// @return Returns the number of tokens on success, no more than n_max_tokens
+ /// @return Returns the number of tokens on success, no more than n_tokens_max
  /// @return Returns a negative number on failure - the number of tokens that would have been returned
  /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
  /// Does not insert a leading space.
@@ -717,7 +717,7 @@ extern "C" {
  const char * text,
  int32_t text_len,
  llama_token * tokens,
- int32_t n_max_tokens,
+ int32_t n_tokens_max,
  bool add_bos,
  bool special);