}
if (!SetPriorityClass(GetCurrentProcess(), p)) {
- LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
+ COM_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
return false;
}
}
if (setpriority(PRIO_PROCESS, 0, p) != 0) {
- LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
+ COM_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
return false;
}
return true;
if (n_set && n_set < cpuparams.n_threads) {
// Not enough set bits, may experience performance issues.
- LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
+ COM_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
}
}
bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
size_t dash_loc = range.find('-');
if (dash_loc == std::string::npos) {
- LOG_ERR("Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
+ COM_ERR("%s", "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
return false;
}
} else {
start_i = std::stoull(range.substr(0, dash_loc));
if (start_i >= GGML_MAX_N_THREADS) {
- LOG_ERR("Start index out of bounds!\n");
+ COM_ERR("%s", "Start index out of bounds!\n");
return false;
}
}
} else {
end_i = std::stoull(range.substr(dash_loc + 1));
if (end_i >= GGML_MAX_N_THREADS) {
- LOG_ERR("End index out of bounds!\n");
+ COM_ERR("%s", "End index out of bounds!\n");
return false;
}
}
}
size_t num_digits = mask.length() - start_i;
- if (num_digits > 128) num_digits = 128;
+ num_digits = std::min<size_t>(num_digits, 128);
size_t end_i = num_digits + start_i;
} else if (c >= 'A' && c <= 'F') {
id -= 'A' - 10;
} else {
- LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
+ COM_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
return false;
}
#else
const char * build_type = " (debug)";
#endif
- LOG_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
+ COM_TRC("%s: build %d (%s) with %s for %s%s\n", __func__, llama_build_number(), llama_commit(), llama_compiler(), llama_build_target(), build_type);
- LOG_INF("log_info: verbosity = %d (adjust with the `-lv N` CLI arg)\n", common_log_get_verbosity_thold());
+ COM_INF("%s: verbosity = %d (adjust with the `-lv N` CLI arg)\n", __func__, common_log_get_verbosity_thold());
// device enumeration creates a primary context on CUDA backends, skip it when the caller does not own any device
if (print_devices) {
- LOG_INF("device_info:\n");
+ COM_TRC("%s", "device_info:\n");
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
auto * dev = ggml_backend_dev_get(i);
size_t free, total;
ggml_backend_dev_memory(dev, &free, &total);
- LOG_INF(" - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
+ COM_TRC(" - %-8s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
}
}
- LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+ COM_TRC("%s\n", common_params_get_system_info(params).c_str());
}
std::string common_params_get_system_info(const common_params & params) {
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char * sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
- LOG_ERR("%s: malformed KV override '%s'\n", __func__, data);
+ COM_ERR("%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
} else if (std::strcmp(sep, "false") == 0) {
kvo.val_bool = false;
} else {
- LOG_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data);
+ COM_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else if (strncmp(sep, "str:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
if (strlen(sep) > 127) {
- LOG_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+ COM_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
return false;
}
strncpy(kvo.val_str, sep, 127);
kvo.val_str[127] = '\0';
} else {
- LOG_ERR("%s: invalid type for KV override '%s'\n", __func__, data);
+ COM_ERR("%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
auto cparams = common_context_params_to_llama(params);
if (params.fit_params) {
- LOG_INF("%s: fitting params to device memory ...\n", __func__);
- LOG_INF("%s: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n", __func__);
+ COM_TRC("%s", "fitting params to device memory ...\n");
+ COM_TRC("%s", "(for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)\n");
common_fit_params(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split,
params.tensor_buft_overrides.data(),
llama_adapter_lora_ptr lora;
lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
if (lora == nullptr) {
- LOG_ERR("%s: failed to load lora adapter '%s'\n", __func__, la.path.c_str());
+ COM_ERR("failed to load lora adapter '%s'\n", la.path.c_str());
pimpl->model.reset(model);
return;
}
common_init_sampler_from_model(model, params.sampling);
if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
- LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
+ COM_WRN("%s", "vocab does not have an EOS token, ignoring --ignore-eos\n");
params.sampling.ignore_eos = false;
}
// initialize once
for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
if (llama_vocab_is_eog(vocab, i)) {
- LOG_TRC("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+ COM_TRC("added %s logit bias = %f\n", common_token_to_piece(vocab, i).c_str(), -INFINITY);
params.sampling.logit_bias_eog.push_back({i, -INFINITY});
}
}
llama_context * lctx = llama_init_from_model(model, cparams);
if (lctx == NULL) {
- LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
+ COM_ERR("failed to create context with model '%s'\n", params.model.path.c_str());
return;
}
llama_model * model = res->model();
if (model == NULL) {
- LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
+ COM_ERR("failed to load model '%s'\n", params.model.path.c_str());
return res;
}
llama_context * lctx = res->context();
if (lctx == NULL) {
- LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
+ COM_ERR("failed to create context with model '%s'\n", params.model.path.c_str());
return res;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
- LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
+ COM_WRN("%s", "KV cache shifting is not supported for this context, disabling KV cache shifting\n");
params.ctx_shift = false;
}
bool ok = true;
if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) {
- LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__);
+ COM_WRN("%s", "vocab does not have a BOS token, reranking will not work\n");
ok = false;
}
bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL;
if (!has_eos && !has_sep && !has_rerank_prompt) {
- LOG_WRN("%s: warning: vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n", __func__);
+ COM_WRN("%s", "vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n");
ok = false;
} else if (!has_eos) {
- LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
+ COM_WRN("%s", "vocab does not have an EOS token, using SEP token as fallback\n");
}
if (!ok) {
}
if (params.warmup) {
- LOG_INF("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
+ COM_TRC("%s", "warming up the model with an empty run - please wait ... (--no-warmup to disable)\n");
std::vector<llama_token> tmp;
llama_token bos = llama_vocab_bos(vocab);
int ret = llama_decode(ctx, llama_batch_get_one(tmp.data(), tmp.size()));
if (ret != 0) {
- LOG_ERR("%s: llama_decode() failed: %d\n", __func__, ret);
+ COM_ERR("llama_decode() failed: %d\n", ret);
res = COMMON_CONTEXT_SEQ_RM_TYPE_NO;
goto done;
}
if (llama_n_rs_seq(ctx) > 0) {
- LOG_INF("%s: the context supports bounded partial sequence removal\n", __func__);
+ COM_TRC("%s", "the context supports bounded partial sequence removal\n");
res = COMMON_CONTEXT_SEQ_RM_TYPE_RS;
goto done;
}
// try to remove the last tokens
if (!llama_memory_seq_rm(mem, 0, 1, -1)) {
- LOG_TRC("%s: the context does not support partial sequence removal\n", __func__);
+ COM_TRC("%s", "the context does not support partial sequence removal\n");
res = COMMON_CONTEXT_SEQ_RM_TYPE_FULL;
goto done;
}
};
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
if (!ctx_gguf) {
- LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
+ COM_ERR("failed to load control vector file from %s\n", load_info.fname.c_str());
return result;
}
int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
if (n_tensors == 0) {
- LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
+ COM_WRN("no direction tensors found in %s\n", load_info.fname.c_str());
}
for (int i = 0; i < n_tensors; i++) {
}
}
if (layer_idx < 0) {
- LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+ COM_ERR("invalid/unparsable direction tensor layer index in %s\n", load_info.fname.c_str());
result.n_embd = -1;
break;
} else if (layer_idx == 0) {
- LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
+ COM_ERR("invalid (zero) direction tensor layer index in %s\n", load_info.fname.c_str());
result.n_embd = -1;
break;
}
struct ggml_tensor * tensor = ggml_get_tensor(ctx, name.c_str());
if (tensor->type != GGML_TYPE_F32) {
- LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
+ COM_ERR("invalid (non-F32) direction tensor type in %s\n", load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (ggml_n_dims(tensor) != 1) {
- LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
+ COM_ERR("invalid (non-1D) direction tensor shape in %s\n", load_info.fname.c_str());
result.n_embd = -1;
break;
}
if (result.n_embd == -1) {
result.n_embd = ggml_nelements(tensor);
} else if (ggml_nelements(tensor) != result.n_embd) {
- LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
+ COM_ERR("direction tensor in %s does not match previous dimensions\n", load_info.fname.c_str());
result.n_embd = -1;
break;
}
}
if (result.n_embd == -1) {
- LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
+ COM_WRN("skipping %s due to invalid direction tensors\n", load_info.fname.c_str());
result.data.clear();
}
break;
}
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
- LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
+ COM_ERR("control vectors in %s does not match previous dimensions\n", info.fname.c_str());
result.n_embd = -1;
break;
}
}
if (result.n_embd == -1) {
- LOG_ERR("%s: no valid control vector files passed\n", __func__);
+ COM_ERR("%s", "no valid control vector files passed\n");
result.data.clear();
}
// memory, so we can't just remove the last token from the memory and replay the last token which
// is the reason for this logic.
if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(all_tokens.data() + offset), n_tokens_before_last))) {
- LOG_ERR("%s : failed to eval\n", __func__);
+ COM_ERR("%s", "failed to eval\n");
return false;
}
n_past += n_tokens_before_last;
llama_state_save_file(ctx, state_path.data(), all_tokens.data(), all_tokens.size());
- LOG_INF("saved session before last token to %s, n_new = %zu\n", state_path.data(), all_tokens.size());
+ COM_INF("saved session before last token to %s, n_new = %zu\n", state_path.data(), all_tokens.size());
llama_token last_token = all_tokens.back();
llama_batch batch = llama_batch_get_one(&last_token, 1);
batch.pos = &pos;
if (llama_decode(ctx, batch)) {
- LOG_ERR("%s : failed to eval last token\n", __func__);
+ COM_ERR("%s", "failed to eval last token\n");
return false;
}
n_past++;
} else {
if (llama_decode(ctx, llama_batch_get_one(const_cast<llama_token*>(all_tokens.data() + offset), n_new))) {
- LOG_ERR("%s : failed to eval\n", __func__);
+ COM_ERR("%s", "failed to eval\n");
return false;
}
n_past += n_new;
#define DIRECTORY_SEPARATOR '/'
#endif // _WIN32
+#define COM_DBG(fmt, ...) LOG_DBG("cmn %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_TRC(fmt, ...) LOG_TRC("cmn %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_INF(fmt, ...) LOG_INF("cmn %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_WRN(fmt, ...) LOG_WRN("cmn %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_ERR(fmt, ...) LOG_ERR("cmn %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define COM_CNT(fmt, ...) LOG_CNT("" fmt, __VA_ARGS__)
+
#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0)
#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
sum_projected_used = dmds_full.back().mb.total();
sum_free = dmds_full.back().total;
sum_projected_free = sum_free - sum_projected_used;
- LOG_INF("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
+ LOG_TRC("%s: projected to use %" PRId64 " MiB of host memory vs. %" PRId64 " MiB of total host memory\n",
__func__, sum_projected_used/MiB, sum_free/MiB);
if (sum_projected_free >= margins[0]) {
LOG_TRC("%s: will leave %" PRId64 " >= %" PRId64 " MiB of system memory, no changes needed\n",
if (ctx->start_matcher.advance(token)) {
ctx->state = REASONING_BUDGET_COUNTING;
ctx->remaining = ctx->budget;
- LOG_INF("reasoning-budget: activated, budget=%d tokens\n", ctx->budget);
+ COM_TRC("activated, budget=%d tokens\n", ctx->budget);
if (ctx->remaining <= 0) {
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
- LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
+ COM_TRC("%s", "budget=0, forcing immediately\n");
}
}
break;
{
if (ctx->end_matcher.advance(token)) {
ctx->state = REASONING_BUDGET_DONE;
- LOG_INF("reasoning-budget: deactivated (natural end)\n");
+ COM_TRC("%s", "deactivated (natural end)\n");
break;
}
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
ctx->end_matcher.reset();
- LOG_INF("reasoning-budget: UTF-8 complete, now forcing end sequence\n");
+ COM_TRC("%s", "UTF-8 complete, now forcing end sequence\n");
}
} else if (ctx->state == REASONING_BUDGET_COUNTING) {
ctx->remaining--;
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
ctx->end_matcher.reset();
- LOG_INF("reasoning-budget: budget exhausted, forcing end sequence\n");
+ COM_TRC("%s", "budget exhausted, forcing end sequence\n");
} else {
ctx->state = REASONING_BUDGET_WAITING_UTF8;
ctx->end_matcher.reset();
- LOG_INF("reasoning-budget: budget exhausted, waiting for UTF-8 completion\n");
+ COM_TRC("%s", "budget exhausted, waiting for UTF-8 completion\n");
}
}
}
ctx->force_pos++;
if (ctx->force_pos >= ctx->forced_tokens.size()) {
ctx->state = REASONING_BUDGET_DONE;
- LOG_INF("reasoning-budget: forced sequence complete, done\n");
+ COM_TRC("%s", "forced sequence complete, done\n");
}
break;
case REASONING_BUDGET_DONE:
ctx->state = REASONING_BUDGET_COUNTING;
ctx->remaining = ctx->budget;
ctx->end_matcher.reset();
- LOG_INF("reasoning-budget: re-activated on new start tag, budget=%d tokens\n", ctx->budget);
+ COM_TRC("re-activated on new start tag, budget=%d tokens\n", ctx->budget);
if (ctx->remaining <= 0) {
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
- LOG_INF("reasoning-budget: budget=0, forcing immediately\n");
+ COM_TRC("%s", "budget=0, forcing immediately\n");
}
}
break;
ctx->state = REASONING_BUDGET_FORCING;
ctx->force_pos = 0;
ctx->end_matcher.reset();
- LOG_INF("reasoning-budget: forced into forcing state (manual transition)\n");
+ COM_TRC("%s", "forced into forcing state (manual transition)\n");
return true;
}
#include <map>
#include <cinttypes>
+#define SPC_DBG(fmt, ...) LOG_DBG("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_TRC(fmt, ...) LOG_TRC("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_INF(fmt, ...) LOG_INF("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_WRN(fmt, ...) LOG_WRN("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_ERR(fmt, ...) LOG_ERR("spec %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SPC_CNT(fmt, ...) LOG_CNT("" fmt, __VA_ARGS__)
+
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
const auto vocab_type_tgt = llama_vocab_type(vocab_tgt);
- LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
+ SPC_DBG("vocab_type tgt: %d\n", vocab_type_tgt);
const auto vocab_type_dft = llama_vocab_type(vocab_dft);
- LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
+ SPC_DBG("vocab_type dft: %d\n", vocab_type_dft);
if (vocab_type_tgt != vocab_type_dft) {
- LOG_WRN("%s: draft model vocab type must match target model to use speculation but "
- "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
+ SPC_WRN("draft model vocab type must match target model to use speculation but "
+ "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
return false;
}
if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
(llama_vocab_get_add_bos(vocab_tgt) && llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft))) {
- LOG_WRN("%s: draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
- __func__,
+ SPC_WRN("draft model bos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
llama_vocab_get_add_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_dft),
llama_vocab_bos(vocab_tgt), llama_vocab_bos(vocab_dft));
return false;
if (llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
(llama_vocab_get_add_eos(vocab_tgt) && llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft))) {
- LOG_WRN("%s: draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
- __func__,
+ SPC_WRN("draft model eos tokens must match target model to use speculation. add: %d - %d, id: %d - %d)\n",
llama_vocab_get_add_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_dft),
llama_vocab_eos(vocab_tgt), llama_vocab_eos(vocab_dft));
return false;
: n_vocab_dft - n_vocab_tgt;
if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
- LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__);
- LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
+ SPC_DBG("draft model vocab must closely match target model to use speculation but "
+ "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
return false;
}
const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
- LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__);
- LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i,
+ SPC_DBG("draft model vocab must match target model to use speculation but "
+ "token %d content differs - target '%s', draft '%s'\n", i,
common_token_to_piece(vocab_tgt, i).c_str(),
common_token_to_piece(vocab_dft, i).c_str());
return false;
auto * ctx_dft = this->params.ctx_dft;
auto * ctx_tgt = this->params.ctx_tgt;
- LOG_INF("%s: adding speculative implementation 'draft-simple'\n", __func__);
- LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min);
- LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+ SPC_TRC("%s", "adding speculative implementation 'draft-simple'\n");
+ SPC_TRC("- n_max=%d, n_min=%d, p_min=%f\n", this->params.n_max, this->params.n_min, this->params.p_min);
+ SPC_TRC("- gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n",
this->params.n_gpu_layers,
ggml_type_name(this->params.cache_type_k),
ggml_type_name(this->params.cache_type_v),
}
const bool vocab_cmpt = common_speculative_are_compatible(llama_get_model(ctx_tgt), llama_get_model(ctx_dft));
- LOG_DBG("%s: vocab_cmpt = %d\n", __func__, vocab_cmpt);
+ SPC_DBG("vocab_cmpt = %d\n", vocab_cmpt);
if (!vocab_cmpt) {
- LOG_ERR("%s: the target and draft vocabs are not compatible\n", __func__);
+ SPC_ERR("%s", "the target and draft vocabs are not compatible\n");
throw std::runtime_error("draft model vocab type must match target model to use speculation");
}
if (n_seq != llama_n_seq_max(ctx_dft)) {
- LOG_ERR("%s: n_seq mismatch: %d != %d\n", __func__, n_seq, llama_n_seq_max(ctx_dft));
+ SPC_ERR("n_seq mismatch: %d != %d\n", n_seq, llama_n_seq_max(ctx_dft));
throw std::runtime_error("the draft model number of sequences is incompatible with the speculative n_seq");
}
const int ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
- LOG_ERR("%s: failed to decode draft batch, ret = %d\n", __func__, ret);
+ SPC_ERR("failed to decode draft batch, ret = %d\n", ret);
return false;
}
int ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
- LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+ SPC_ERR("llama_decode returned %d\n", ret);
return;
}
const auto * cur_p = common_sampler_get_candidates(smpl, true);
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
- LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+ SPC_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
}
// evaluate the drafted tokens on the draft model
ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
- LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+ SPC_ERR("llama_decode[%d] returned %d\n", i, ret);
break;
}
: common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
, params(params.draft)
{
- LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
- LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f, backend_sampling=%d\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min, (int) params.draft.backend_sampling);
+ SPC_TRC("%s", "adding speculative implementation 'draft-eagle3'\n");
+ SPC_TRC("- n_max=%d, n_min=%d, p_min=%f, backend_sampling=%d\n", params.draft.n_max, params.draft.n_min, params.draft.p_min, (int) params.draft.backend_sampling);
auto * ctx_tgt = this->params.ctx_tgt;
auto * ctx_dft = this->params.ctx_dft;
llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
- LOG_WRN("%s: backend offload failed for seq_id=%d; using CPU sampler\n", __func__, (int) seq_id);
+ SPC_WRN("backend offload failed for seq_id=%d; using CPU sampler\n", (int) seq_id);
llama_sampler_free(chain);
chain = nullptr;
}
auto * ctx_dft = this->params.ctx_dft;
const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
if (pos_max < N - 2) {
- LOG_WRN("%s: ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
+ SPC_WRN("ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
"Drafts may degrade.\n",
- __func__, (int) pos_max, N - 2);
+ (int) pos_max, N - 2);
}
}
};
const int32_t rc = llama_encode(ctx_dft, enc_batch);
if (rc != 0) {
- LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
- __func__, rc, (int) n_chunk, (int) i);
+ SPC_ERR("llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+ rc, (int) n_chunk, (int) i);
return false;
}
if (batch.n_tokens > 0) {
const int32_t rc = llama_decode(ctx_dft, batch);
if (rc != 0) {
- LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
- __func__, rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
+ SPC_ERR("llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
+ rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
return false;
}
}
int ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
- LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+ SPC_ERR("llama_decode returned %d\n", ret);
return;
}
const auto * cur_p = common_sampler_get_candidates(smpl, true);
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
- LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+ SPC_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
}
ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
- LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+ SPC_ERR("llama_decode[%d] returned %d\n", i, ret);
break;
}
"MTP input row width must match the target h_nextn width");
n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));
- LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
- LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
- LOG_INF("%s: - gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n", __func__,
+ SPC_TRC("%s", "adding speculative implementation 'draft-mtp'\n");
+ SPC_TRC("- n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
+ SPC_TRC("- gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n",
this->params.n_gpu_layers,
ggml_type_name(this->params.cache_type_k),
ggml_type_name(this->params.cache_type_v),
llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
- LOG_WRN("%s: backend offload failed for seq_id=%d; using CPU sampler\n", __func__, (int) seq_id);
+ SPC_WRN("backend offload failed for seq_id=%d; using CPU sampler\n", (int) seq_id);
llama_sampler_free(chain);
chain = nullptr;
}
const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
if (pos_max < N - 1 && !is_mem_shared) {
- LOG_WRN("%s: ctx_dft pos_max=%d < N-1=%d - "
+ SPC_WRN("ctx_dft pos_max=%d < N-1=%d - "
"process() hook may not have run on every prefill ubatch "
"(need_embd / logits=1 on every prompt position?). "
"Drafts may degrade.\n",
- __func__, (int) pos_max, N - 1);
+ (int) pos_max, N - 1);
}
}
const int32_t rc = llama_decode(ctx_dft, batch);
if (rc != 0) {
- LOG_ERR("%s: llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
- __func__, head, (int) rc, (int) batch_in.pos[0]);
+ SPC_ERR("llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
+ head, (int) rc, (int) batch_in.pos[0]);
ok = false;
break;
}
int ret = llama_decode(ctx_dft, batch);
if (ret != 0) {
- LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+ SPC_ERR("llama_decode[%d] returned %d\n", i, ret);
break;
}
const auto * cur_p = common_sampler_get_candidates(smpl, true);
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
- LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+ SPC_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
}
, params(params.ngram_simple)
, config(config)
{
- LOG_INF("%s: adding speculative implementation 'ngram-simple'\n", __func__);
- LOG_INF("%s: - size_n=%d, size_m=%d, min_hits=%d\n", __func__,
+ SPC_TRC("%s", "adding speculative implementation 'ngram-simple'\n");
+ SPC_TRC("- size_n=%d, size_m=%d, min_hits=%d\n",
this->params.size_n, this->params.size_m, this->params.min_hits);
}
this->config.push_back(config);
}
- LOG_INF("%s: adding speculative implementation '%s'\n", __func__, common_speculative_type_to_str(this->type).c_str());
- LOG_INF("%s: - size_key=%d, size_value=%d, key_only=%d, min_hits=%d\n", __func__,
+ SPC_TRC("adding speculative implementation '%s'\n", common_speculative_type_to_str(this->type).c_str());
+ SPC_TRC("- size_key=%d, size_value=%d, key_only=%d, min_hits=%d\n",
config.size_key, config.size_value, config.key_only, config.min_hits);
}
, verbose(std::getenv("LLAMA_TRACE") != nullptr) {
static_assert(sizeof(llama_token) == sizeof(common_ngram_mod::entry_t));
- LOG_INF("%s: adding speculative implementation 'ngram-mod'\n", __func__);
- LOG_INF("%s: - n_match=%d, n_max=%d, n_min=%d\n", __func__,
+ SPC_TRC("%s", "adding speculative implementation 'ngram-mod'\n");
+ SPC_TRC("- n_match=%d, n_max=%d, n_min=%d\n",
this->params.n_match, this->params.n_max, this->params.n_min);
- LOG_INF("%s: - mod size=%zu (%.3f MB)\n", __func__,
+ SPC_TRC("- mod size=%zu (%.3f MB)\n",
mod.size(), (float)(mod.size_bytes())/1024/1024);
if (this->params.n_match < 16) {
- LOG_WRN("%s: ngram_mod n_match=%d is too small - poor quality is possible, "
- "see: https://github.com/ggml-org/llama.cpp/pull/19164\n", __func__, this->params.n_match);
+ SPC_WRN("ngram_mod n_match=%d is too small - poor quality is possible, "
+ "see: https://github.com/ggml-org/llama.cpp/pull/19164\n", this->params.n_match);
}
sinfos.resize(n_seq);
sinfo.i_last = prompt.size() - n;
const double f = (double)mod.get_used() / (double)mod.size();
- LOG_INF("%s: ngram_mod occupancy = %zu/%zu (%.2f)\n", __func__, mod.get_used(), mod.size(), f);
+ SPC_TRC("ngram_mod occupancy = %zu/%zu (%.2f)\n", mod.get_used(), mod.size(), f);
constexpr double f_thold = 0.25;
if (f > f_thold) {
- LOG_WRN("%s: ngram_mod occupancy %.2f exceeds threshold (%.2f) - resetting\n", __func__, f, f_thold);
+ SPC_WRN("ngram_mod occupancy %.2f exceeds threshold (%.2f) - resetting\n", f, f_thold);
mod.reset();
}
sinfo.n_low++;
if (sinfo.n_low >= 5) {
if (verbose) {
- LOG_WRN("%s: low acceptance streak (%d) - resetting ngram_mod\n", __func__, sinfo.n_low);
+ SPC_TRC("low acceptance streak (%d) - resetting ngram_mod\n", sinfo.n_low);
}
mod.reset();
, save_dynamic(save_dynamic)
, save_static(save_static)
{
- LOG_INF("%s: adding speculative implementation 'ngram-cache'\n", __func__);
- LOG_INF("%s: - n_draft=%d, cache_static=%s, cache_dynamic=%s\n", __func__,
+ SPC_TRC("%s", "adding speculative implementation 'ngram-cache'\n");
+ SPC_TRC("- n_draft=%d, cache_static=%s, cache_dynamic=%s\n",
n_draft,
path_static.empty() ? "none" : path_static.c_str(),
path_dynamic.empty() ? "none" : path_dynamic.c_str());
sinfo.ngram_cache_static = ngram_cache_static;
}
} catch (...) {
- LOG_ERR("failed to open static lookup cache: %s", path_static.c_str());
+ SPC_ERR("failed to open static lookup cache: %s", path_static.c_str());
GGML_ABORT("Couldn't read static lookup cache");
}
}
sinfo.ngram_cache_dynamic = ngram_cache_dynamic;
}
} catch (...) {
- LOG_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str());
+ SPC_ERR("failed to open dynamic lookup cache: %s", path_dynamic.c_str());
GGML_ABORT("Couldn't read dynamic lookup cache");
}
}
}
if (impls.empty()) {
- LOG_WRN("%s: no implementations specified for speculative decoding\n", __func__);
+ SPC_TRC("%s", "no implementations specified for speculative decoding\n");
return nullptr;
}
if (dp.n_max > 0) {
if (!result.empty() && (int) result.size() > dp.n_max) {
- LOG_DBG("%s: truncating draft to %d tokens\n", __func__, dp.n_max);
+ SPC_DBG("truncating draft to %d tokens\n", dp.n_max);
result.resize(dp.n_max);
}
}
if (!result.empty()) {
- LOG_DBG("%s: called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n", __func__,
+ SPC_DBG("called impl %s, hist size = %zu, call_count = %zu, gen = %zu\n",
common_speculative_type_to_str(impl.get()->type).c_str(), dp.prompt->size(),
impl.get()->n_call_draft, result.size());
str_stats = ", #mean acc len = " + oss.str() + ", #acc rate/pos = (" + tmp.str() + ")";
}
- LOG_INF("statistics %16s: #calls(b,g,a) = %4zu %6zu %6zu, #gen drafts = %6zu, #acc drafts = %5zu, #gen tokens = %6zu, #acc tokens = %5zu%s%s\n",
+ SPC_TRC("statistics %16s: #calls(b,g,a) = %4zu %6zu %6zu, #gen drafts = %6zu, #acc drafts = %5zu, #gen tokens = %6zu, #acc tokens = %5zu%s%s\n",
common_speculative_type_to_str(impl->type).c_str(),
impl->n_call_begin, impl->n_call_draft, impl->n_call_accept,
impl->n_gen_drafts,
LLAMA_LOG_INFO("%s: n_outputs_max = %u\n", __func__, cparams.n_outputs_max);
if (cparams.n_ctx_seq < hparams.n_ctx_train) {
- LLAMA_LOG_WARN("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
+ LLAMA_LOG_INFO("%s: n_ctx_seq (%u) < n_ctx_train (%u) -- the full capacity of the model will not be utilized\n",
__func__, cparams.n_ctx_seq, hparams.n_ctx_train);
}
if ((int32_t)tokens.size() >= n_tokens_alloc) {
return false;
}
- // LOG_INF("adding token to batch: slot=%d, token=%d, pos=%d, output=%d\n", id_slot, token, pos, output);
tokens.push_back({ id_slot, token, pos, output });
return true;
}
const size_t cur_size = cur_size_tgt + cur_size_dft;
- SRV_WRN(" - saving prompt with length %d, total state size = %.3f MiB (draft: %.3f MiB)\n",
+ SRV_TRC(" - saving prompt with length %d, total state size = %.3f MiB (draft: %.3f MiB)\n",
(int) prompt.tokens.size(), cur_size / (1024.0 * 1024.0), cur_size_dft / (1024.0 * 1024.0));
auto * cur = prompt_cache.alloc(prompt, cur_size_tgt, cur_size_dft);
GGML_ASSERT(!is_processing());
}
- SLT_INF(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
+ SLT_TRC(*this, "clearing prompt with %zu tokens\n", prompt.tokens.size());
common_context_seq_rm(ctx_tgt, id, -1, -1);
if (ctx_dft) {
}
SLT_INF(*this,
- "draft acceptance = %0.5f (%5d accepted / %5d generated), mean acceptance length = %5.2f, acceptance rate per position = (%s)\n",
- draft_ratio, n_draft_accepted, n_draft_total, mean_acc_len, acceptance_rates_per_pos.c_str());
+ "draft acceptance = %0.5f (%5d accepted / %5d generated), mean len = %5.2f\n",
+ draft_ratio, n_draft_accepted, n_draft_total, mean_acc_len);
+ SLT_TRC(*this,
+ " acc per pos = (%s)\n", acceptance_rates_per_pos.c_str());
}
common_speculative_print_stats(spec);
}
// TODO @ngxson : move this log line to debug when it become more stable
- SLT_INF(*this, "encoding mtmd batch from idx = %zu, n_chunks = %d\n", idx, n_added);
+ SLT_TRC(*this, "encoding mtmd batch from idx = %zu, n_chunks = %d\n", idx, n_added);
res = mtmd_batch_encode(mbatch.get());
if (res != 0) {
}
- SRV_INF("loading model '%s'\n", params.model.path.c_str());
+ SRV_INF("loading model '%s'\n", params.model.get_name().c_str());
+ SRV_TRC("local path '%s'\n", params.model.path.c_str());
std::string & mmproj_path = params_base.mmproj.path;
mtmd_context_params mparams = mtmd_context_params_default();
for (auto & [dev, size] : mmproj_mem) {
total += size;
}
- SRV_INF("[mtmd] estimated worst-case memory usage of mmproj is %.2f MiB (took %.2f ms)\n", total / (1024.0 * 1024.0), t_elapsed / 1000.0);
+ SRV_TRC("[mtmd] estimated worst-case memory usage of mmproj is %.2f MiB (took %.2f ms)\n", total / (1024.0 * 1024.0), t_elapsed / 1000.0);
GGML_ASSERT(!params_base.fit_params_target.empty());
for (auto & [dev, size] : mmproj_mem) {
for (size_t i = 0; i < ggml_backend_dev_count(); i++) {
}
}
}
- SRV_INF("[spec] estimated memory usage of %s is %.2f MiB\n",
+ SRV_TRC("[spec] estimated memory usage of %s is %.2f MiB\n",
has_draft ? "draft model" : "MTP context",
total / (1024.0 * 1024.0));
} catch (const std::exception & e) {
// TODO speculative: move to common/speculative.cpp?
const auto & params_spec = params_base.speculative.draft;
- SRV_INF("loading draft model '%s'\n", params_spec.mparams.path.c_str());
+ SRV_TRC("loading draft model '%s'\n", params_spec.mparams.path.c_str());
auto params_dft = params_base;
// no new model load, so we simply report 0.0 and 1.0 progress
load_progress_callback(0.0f, &load_progress_spec);
- SRV_INF("creating MTP draft context against the target model '%s'\n",
+ SRV_TRC("creating MTP draft context against the target model '%s'\n",
params_base.model.path.c_str());
auto cparams_mtp = common_context_params_to_llama(params_base);
// Necessary similarity of prompt for slot selection
slot_prompt_similarity = params_base.slot_prompt_similarity;
- // setup slots
- SRV_INF("initializing slots, n_slots = %d\n", params_base.n_parallel);
-
const int n_ctx_train = llama_model_n_ctx_train(model_tgt);
int n_ctx_slot = llama_n_ctx_seq(ctx_tgt);
}
if (ctx_tgt_seq_rm_type == COMMON_CONTEXT_SEQ_RM_TYPE_FULL) {
- SRV_WRN("%s", "speculative decoding will use checkpoints\n");
+ SRV_TRC("%s", "speculative decoding will use checkpoints\n");
}
+ // setup slots
+ SRV_INF("initializing, n_slots = %d, n_ctx_slot = %d, kv_unified = '%s'\n",
+ params_base.n_parallel, n_ctx_slot, params_base.kv_unified ? "true" : "false");
+
// initialize slots
for (int i = 0; i < params_base.n_parallel; i++) {
slots.emplace_back();
}
if (spec) {
- SRV_INF("%s", "speculative decoding context initialized\n");
+ SRV_TRC("%s", "speculative decoding context initialized\n");
} else {
ctx_dft.reset();
}
slot.mctx = mctx;
slot.prompt.tokens.has_mtmd = mctx != nullptr;
- SLT_INF(slot, "new slot, n_ctx = %d\n", slot.n_ctx);
+ SLT_TRC(slot, "new slot, n_ctx = %d\n", slot.n_ctx);
slot.callback_on_release = [this](int id_slot) {
queue_tasks.pop_deferred_task(id_slot);
if (params_base.cache_ram_mib != 0) {
if (params_base.cache_ram_mib < 0) {
- SRV_INF("prompt cache is enabled, size limit: %s\n", "no limit");
+ SRV_TRC("prompt cache is enabled, size limit: %s\n", "no limit");
} else {
- SRV_INF("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
+ SRV_TRC("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
}
- SRV_INF("%s", "use `--cache-ram 0` to disable the prompt cache\n");
+ SRV_TRC("%s", "use `--cache-ram 0` to disable the prompt cache\n");
prompt_cache = std::make_unique<server_prompt_cache>(params_base.cache_ram_mib, n_ctx);
} else {
- SRV_INF("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
+ SRV_TRC("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
}
- SRV_INF("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");
+ SRV_TRC("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");
if (params_base.n_ctx_checkpoints > 0) {
- SRV_INF("context checkpoints enabled, max = %d, min spacing = %d\n",
+ SRV_TRC("context checkpoints enabled, max = %d, min spacing = %d\n",
params_base.n_ctx_checkpoints, params_base.checkpoint_min_step);
} else {
- SRV_INF("%s", "context checkpoints disabled\n");
+ SRV_TRC("%s", "context checkpoints disabled\n");
}
if (!params_base.model_alias.empty()) {
params_base.cache_idle_slots = false;
} else {
if (params_base.kv_unified) {
- SRV_INF("%s", "idle slots will be saved to prompt cache and cleared upon starting a new task\n");
+ SRV_TRC("%s", "idle slots will be saved to prompt cache and cleared upon starting a new task\n");
} else {
// without a unified KV cache, clearing a slot frees no reusable room, so we only
// publish a RAM-cache copy of idle slots (their KV stays in VRAM) [TAG_IDLE_SLOT_CLEAR]
- SRV_INF("%s", "idle slots will be saved to prompt cache upon starting a new task\n");
+ SRV_TRC("%s", "idle slots will be saved to prompt cache upon starting a new task\n");
}
SRV_DBG("%s", "__TEST_TAG_CACHE_IDLE_SLOTS_ENABLED__\n");
}
try {
chat_templates = common_chat_templates_init(model_tgt, params_base.chat_template);
- LOG_INF("%s: chat template, example_format: '%s'\n", __func__,
+ SRV_TRC("%s: chat template, example_format: '%s'\n", __func__,
common_chat_format_example(chat_templates.get(), params_base.use_jinja, params_base.default_template_kwargs).c_str());
} catch (const std::exception & e) {
// 2. The chat template supports it
const bool template_supports_thinking = params_base.use_jinja && common_chat_templates_support_enable_thinking(chat_templates.get());
const bool enable_thinking = params_base.enable_reasoning != 0 && template_supports_thinking;
- SRV_INF("%s: chat template, thinking = %d\n", __func__, enable_thinking);
+ SRV_TRC("%s: chat template, thinking = %d\n", __func__, enable_thinking);
// IMPORTANT: chat_params is reused across sleeping / resuming states,
// never store llama_context/llama_model pointers in chat_params,
update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION;
if (update_cache) {
- SRV_INF("%s", "updating prompt cache\n");
+ SRV_TRC("%s", "updating prompt cache\n");
const int64_t t_start = ggml_time_us();
prompt_cache->update();
- SRV_INF("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
+ SRV_TRC("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
}
}
int id_parent = parent_task.id;
- SRV_INF("launching slots for parent task id_task = %d with %zu child tasks\n", id_parent, parent_task.child_tasks.size());
+ SRV_TRC("launching slots for parent task id_task = %d with %zu child tasks\n", id_parent, parent_task.child_tasks.size());
// to be called in case of failure to release all launched slots
auto release_slots = [this, id_parent]() {
// stash the draft's speculative state with the checkpoint
common_speculative_get_state(spec.get(), slot.id, cur.data_spec);
- SLT_INF(slot,
+ SLT_TRC(slot,
"created context checkpoint %d of %d (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", size = %.3f MiB)\n",
(int) slot.prompt.checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min,
cur.pos_max, cur.n_tokens, (float) cur.size() / 1024 / 1024);
if (params_base.cache_idle_slots) {
for (auto & slot : slots) {
if (!slot.is_processing()) {
- SLT_INF(slot, "%s", "saving idle slot to prompt cache\n");
+ SLT_TRC(slot, "%s", "saving idle slot to prompt cache\n");
if (slot.prompt_save(*prompt_cache)) {
SLT_DBG(slot, "%s", "__TEST_TAG_CACHE_IDLE_SLOT__\n");
auto new_loras = construct_lora_list(task.set_lora);
// logging
for (size_t i = 0; i < new_loras.size(); ++i) {
- SRV_INF("set lora adapter idx=%zu scale=%f\n", i, new_loras[i].scale);
+ SRV_TRC("set lora adapter idx=%zu scale=%f\n", i, new_loras[i].scale);
}
// TODO @ngxson : make lora_adapters a dedicated member of server_context
params_base.lora_adapters = new_loras;
}
if (all_idle) {
- SRV_INF("%s", "all slots are idle\n");
+ SRV_TRC("%s", "all slots are idle\n");
return; // skip further processing
} else {
const auto it = std::find_if(
slot.prompt.checkpoints.rbegin(),
slot.prompt.checkpoints.rend(),
- [&, func_name = __func__](const auto & cur) {
+ [&](const auto & cur) {
// guarantee that a checkpoint will result in at least one token being processed [TAG_PROMPT_LOGITS]
- LOG_INF("slot %12.*s: id %2d | task %d | Checking checkpoint with [%d, %d] against %d...\n", 12,
- func_name, (slot).id, ((slot).task ? (slot).task->id : -1), cur.pos_min, cur.pos_max, pos_min_thold);
+ SLT_TRC(slot, "checking checkpoint with [%d, %d] against %d...\n", cur.pos_min, cur.pos_max, pos_min_thold);
// workaround for [TAG_CHECKPOINTS_FIX_POS_MIN]
if (cur.pos_max > pos_next) {
return false;
pos_next = std::min(pos_next, std::max(it->pos_min + 1, it->pos_max));
n_past = std::min(slot.prompt.tokens.size_up_to_pos(pos_next), (size_t) it->n_tokens);
- SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) it->size() / 1024 / 1024);
+ SLT_TRC(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_past = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, it->n_tokens, n_past, (float) it->size() / 1024 / 1024);
}
if (do_reset) {
- SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory, see %s)\n",
+ SLT_TRC(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory, see %s)\n",
"https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
pos_next = 0;
n_past = 0;
for (auto it = slot.prompt.checkpoints.begin(); it != slot.prompt.checkpoints.end();) {
const auto & cur = *it;
if (cur.pos_max > pos_next) {
- SLT_WRN(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_swa = %d, pos_next = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, cur.n_tokens, n_swa, pos_next, (float) cur.size() / 1024 / 1024);
+ SLT_TRC(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_tokens = %" PRId64 ", n_swa = %d, pos_next = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, cur.n_tokens, n_swa, pos_next, (float) cur.size() / 1024 / 1024);
it = slot.prompt.checkpoints.erase(it);
} else {
++it;
// all children slots should already launched by launch_slots_with_parent_task()
// copy state to the child slots
for (auto & child : children) {
- SLT_INF(slot, " - copying state to child %d\n", child->id);
+ SLT_TRC(slot, " - copying state to child %d\n", child->id);
GGML_ASSERT(child->state == SLOT_STATE_WAIT_OTHER);
hostname = params.hostname;
if (gcp.enabled) {
- SRV_INF("Google Cloud Platform compat: health route = %s, predict route = %s, port = %d\n", gcp.path_health.c_str(), gcp.path_predict.c_str(), gcp.port);
+ SRV_TRC("Google Cloud Platform compat: health route = %s, predict route = %s, port = %d\n", gcp.path_health.c_str(), gcp.path_predict.c_str(), gcp.port);
if (port != gcp.port) {
SRV_WRN("Google Cloud Platform compat: overriding server port %d with AIP_HTTP_PORT %d\n", port, gcp.port);
#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
if (!params.ssl_file_key.empty() && !params.ssl_file_cert.empty()) {
- SRV_INF("running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str());
+ SRV_TRC("running with SSL: key = %s, cert = %s\n", params.ssl_file_key.c_str(), params.ssl_file_cert.c_str());
srv = std::make_unique<httplib::SSLServer>(
params.ssl_file_cert.c_str(), params.ssl_file_key.c_str()
);
is_ssl = true;
} else {
- SRV_INF("%s", "running without SSL\n");
+ SRV_TRC("%s", "running without SSL\n");
srv = std::make_unique<httplib::Server>();
}
#else
if (params.api_keys.size() == 1) {
const auto key = params.api_keys[0];
const std::string substr = key.substr(std::max(static_cast<int>(key.length() - 4), 0));
- SRV_INF("api_keys: ****%s\n", substr.c_str());
+ SRV_TRC("api_keys: ****%s\n", substr.c_str());
} else if (params.api_keys.size() > 1) {
- SRV_INF("api_keys: %zu keys loaded\n", params.api_keys.size());
+ SRV_TRC("api_keys: %zu keys loaded\n", params.api_keys.size());
}
//
// +4 threads for monitoring, health and some threads reserved for MCP and other tasks in the future
n_threads_http = std::max(params.n_parallel + 4, static_cast<int32_t>(std::thread::hardware_concurrency() - 1));
}
- SRV_INF("using %d threads for HTTP server\n", n_threads_http);
+ SRV_TRC("using %d threads for HTTP server\n", n_threads_http);
srv->new_task_queue = [n_threads_http] {
// spawn n_threads_http fixed thread (always alive), while allow up to 1024 max possible additional threads
// when n_threads_http is used, server will create new "dynamic" threads that will be destroyed after processing each request
auto is_sock = false;
if (string_ends_with(std::string(hostname), ".sock")) {
is_sock = true;
- SRV_INF("%s", "setting address family to AF_UNIX\n");
+ SRV_TRC("%s", "setting address family to AF_UNIX\n");
srv->set_address_family(AF_UNIX);
// bind_to_port requires a second arg, any value other than 0 should
// simply get ignored
was_bound = srv->bind_to_port(hostname, 8080);
} else {
- SRV_INF("%s", "binding port with default address family\n");
+ SRV_TRC("%s", "binding port with default address family\n");
// bind HTTP listen port
if (port == 0) {
const auto bound_port = srv->bind_to_any_port(hostname);
->set_desc("Chat format used internally by the server")
->set_handler([&](field_eval_context & ctx, const json & data) {
ctx.params.chat_parser_params.format = static_cast<common_chat_format>(data.at("chat_format").get<int>());
- SRV_INF("Chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
+ SRV_TRC("chat format: %s\n", common_chat_format_name(ctx.params.chat_parser_params.format));
}));
add((new field_str("reasoning_format"))
// httplib bails its content provider the moment is_peer_alive() goes false, so pump the rest
// of the generation into the ring buffer here. a DELETE flips is_cancelled and cuts it short
if (done_ || session_->is_cancelled()) {
- SRV_INF("stream_pipe close: skip drain (done=%d cancelled=%d) conv=%s\n",
+ SRV_TRC("stream_pipe close: skip drain (done=%d cancelled=%d) conv=%s\n",
done_ ? 1 : 0, session_->is_cancelled() ? 1 : 0, session_->conversation_id.c_str());
return;
}
- SRV_INF("stream_pipe close: draining conv=%s\n", session_->conversation_id.c_str());
+ SRV_TRC("stream_pipe close: draining conv=%s\n", session_->conversation_id.c_str());
size_t drained = 0;
std::string chunk;
while (true) {
break;
}
}
- SRV_INF("stream_pipe close: drain ended conv=%s bytes=%zu\n", session_->conversation_id.c_str(), drained);
+ SRV_TRC("stream_pipe close: drain ended conv=%s bytes=%zu\n", session_->conversation_id.c_str(), drained);
}
std::shared_ptr<stream_pipe_producer> stream_pipe_producer::create(stream_session_ptr session,
if (conv_id.empty()) {
return make_error_response(400, "Missing conversation id in path", ERROR_TYPE_INVALID_REQUEST);
}
- SRV_INF("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str());
+ SRV_TRC("DELETE /v1/stream/%s -> evict_and_cancel\n", conv_id.c_str());
g_stream_sessions.evict_and_cancel(conv_id);
auto res = std::make_unique<server_http_res>();
res->status = 204;
void stream_session_attach_pipe(server_http_res & res, const std::map<std::string, std::string> & headers) {
std::string conversation_id = stream_conv_id_from_headers(headers);
- SRV_INF("stream_session_attach_pipe: conv_id=%s (empty=%d)\n",
- conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
+ SRV_TRC("conv_id=%s (empty=%d)\n", conversation_id.c_str(), conversation_id.empty() ? 1 : 0);
if (conversation_id.empty()) {
return;
}
const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);
if (cur_lcp_len == (int) prompt.tokens.size()) {
- SRV_INF("%s", " - prompt is already in the cache, skipping\n");
+ SRV_TRC("%s", " - prompt is already in the cache, skipping\n");
return nullptr;
}
}
const int len = it->tokens.get_common_prefix(prompt.tokens);
if (len == (int) it->tokens.size()) {
- SRV_WRN(" - removing obsolete cached prompt with length %d\n", len);
+ SRV_TRC(" - removing obsolete cached prompt with length %d\n", len);
it = states.erase(it);
} else {
float f_keep_best = prompt.tokens.size() > 0 ? float(lcp_best) / prompt.tokens.size() : -1.0f; // empty slot: any cache entry wins
float sim_best = float(lcp_best) / tokens_new.size();
- SRV_INF(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+ SRV_TRC(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
auto it_best = states.end();
}
if (it_best != states.end()) {
- SRV_INF(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
+ SRV_TRC(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);
{
auto & data = it_best->data.main;
}
}
- SRV_INF(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
+ SRV_TRC(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);
for (const auto & state : states) {
- SRV_INF(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
+ SRV_TRC(" - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
(const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0));
}
}
}
if (params.n_parallel < 0) {
- SRV_INF("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
+ SRV_TRC("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
params.n_parallel = 4;
params.kv_unified = true;
std::function<void()> clean_up;
if (is_router_server) {
- SRV_INF("%s", "starting router server, no model will be loaded in this process\n");
+ SRV_INF("%s", "starting server in router mode. models will be automatically loaded on-demand\n");
clean_up = [&models_routes]() {
SRV_INF("%s: cleaning up before exit...\n", __func__);
});
}
- // load the model
- SRV_INF("%s", "loading model\n");
-
if (!ctx_server.load_model(params)) {
clean_up();
if (ctx_http.thread.joinable()) {
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
+ SRV_INF("listening on %s\n", ctx_http.listening_address.c_str());
+
if (is_router_server) {
- SRV_INF("router server is listening on %s\n", ctx_http.listening_address.c_str());
SRV_WRN("%s", "NOTE: router mode is experimental\n");
SRV_WRN("%s", " it is not recommended to use this mode in untrusted environments\n");
// when the HTTP server stops, clean up and exit
clean_up();
} else {
- SRV_INF("server is listening on %s\n", ctx_http.listening_address.c_str());
-
// optionally, notify router server that this instance is ready
std::thread monitor_thread;
if (child.is_child()) {
overview / pkg / ggml / sources / llama.cpp / commitdiff