common : reimplement logging (#9418)
https://github.com/ggerganov/llama.cpp/pull/9418
This commit is contained in:
Georgi Gerganov
GitHub
parent
e6deac31f7
commit
6262d13e0b
54 changed files with 2092 additions and 2419 deletions
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@ -3,6 +3,7 @@
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#endif
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#include "common.h"
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#include "log.h"
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// Change JSON_ASSERT from assert() to GGML_ASSERT:
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#define JSON_ASSERT GGML_ASSERT
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#include "json.hpp"
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@ -25,6 +26,7 @@
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#include <unordered_map>
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#include <unordered_set>
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#include <vector>
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#include <thread>
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#if defined(__APPLE__) && defined(__MACH__)
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#include <sys/types.h>
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@ -48,7 +50,6 @@
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#if defined(LLAMA_USE_CURL)
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#include <curl/curl.h>
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#include <curl/easy.h>
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#include <thread>
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#include <future>
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#endif
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@ -226,7 +227,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
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}
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if (!SetPriorityClass(GetCurrentProcess(), p)) {
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fprintf(stderr, "warn: failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
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LOG_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError());
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return false;
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}
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@ -251,7 +252,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
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}
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if (!setpriority(PRIO_PROCESS, 0, p)) {
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fprintf(stderr, "warn: failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
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LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
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return false;
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}
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return true;
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@ -284,14 +285,14 @@ void postprocess_cpu_params(cpu_params& cpuparams, const cpu_params* role_model)
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if (n_set && n_set < cpuparams.n_threads) {
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// Not enough set bits, may experience performance issues.
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fprintf(stderr, "warn: Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
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LOG_WRN("Not enough set bits in CPU mask (%d) to satisfy requested thread count: %d\n", n_set, cpuparams.n_threads);
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}
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}
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bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THREADS]) {
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size_t dash_loc = range.find('-');
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if (dash_loc == std::string::npos) {
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fprintf(stderr, "Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
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LOG_ERR("Format of CPU range is invalid! Expected [<start>]-[<end>].\n");
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return false;
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}
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@ -303,7 +304,7 @@ bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THRE
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} else {
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start_i = std::stoull(range.substr(0, dash_loc));
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if (start_i >= GGML_MAX_N_THREADS) {
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fprintf(stderr, "Start index out of bounds!\n");
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LOG_ERR("Start index out of bounds!\n");
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return false;
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}
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}
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@ -313,7 +314,7 @@ bool parse_cpu_range(const std::string & range, bool (&boolmask)[GGML_MAX_N_THRE
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} else {
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end_i = std::stoull(range.substr(dash_loc + 1));
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if (end_i >= GGML_MAX_N_THREADS) {
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fprintf(stderr, "End index out of bounds!\n");
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LOG_ERR("End index out of bounds!\n");
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return false;
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}
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}
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@ -348,7 +349,7 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
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} else if (c >= 'A' && c <= 'F') {
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id -= 'A' - 10;
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} else {
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fprintf(stderr, "Invalid hex character '%c' at position %d\n", c, int32_t(i));
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LOG_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i));
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return false;
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}
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@ -361,6 +362,22 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
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return true;
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}
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void gpt_init() {
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llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
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if (LOG_DEFAULT_LLAMA <= gpt_log_verbosity_thold) {
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gpt_log_add(gpt_log_main(), level, "%s", text);
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}
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}, NULL);
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#ifdef NDEBUG
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const char * build_type = "";
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#else
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const char * build_type = " (debug)";
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#endif
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LOG_INF("build: %d (%s) with %s for %s%s\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT, LLAMA_COMPILER, LLAMA_BUILD_TARGET, build_type);
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}
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std::string gpt_params_get_system_info(const gpt_params & params) {
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std::ostringstream os;
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@ -441,6 +458,94 @@ void string_replace_all(std::string & s, const std::string & search, const std::
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s = std::move(builder);
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}
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std::string string_from(bool value) {
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return value ? "true" : "false";
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}
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std::string string_from(const std::vector<int> & values) {
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std::stringstream buf;
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buf << "[ ";
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bool first = true;
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for (auto e : values) {
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if (first) {
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first = false;
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} else {
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buf << ", ";
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}
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buf << std::to_string(e);
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}
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buf << " ]";
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return buf.str();
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}
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std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens) {
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std::stringstream buf;
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buf << "[ ";
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bool first = true;
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for (const auto & token : tokens) {
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if (!first) {
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buf << ", ";
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} else {
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first = false;
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}
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auto detokenized = llama_token_to_piece(ctx, token);
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detokenized.erase(
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std::remove_if(
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detokenized.begin(),
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detokenized.end(),
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[](const unsigned char c) { return !std::isprint(c); }),
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detokenized.end());
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buf << "'" << detokenized << "'"
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<< ":" << std::to_string(token);
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}
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buf << " ]";
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return buf.str();
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}
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std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch) {
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std::stringstream buf;
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buf << "[ ";
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bool first = true;
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for (int i = 0; i < batch.n_tokens; ++i) {
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if (!first) {
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buf << ", ";
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} else {
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first = false;
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}
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auto detokenized = llama_token_to_piece(ctx, batch.token[i]);
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detokenized.erase(
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std::remove_if(
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detokenized.begin(),
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detokenized.end(),
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[](const unsigned char c) { return !std::isprint(c); }),
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detokenized.end());
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buf << "\n" << std::to_string(i)
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<< ":token '" << detokenized << "'"
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<< ":pos " << std::to_string(batch.pos[i])
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<< ":n_seq_id " << std::to_string(batch.n_seq_id[i])
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<< ":seq_id " << std::to_string(batch.seq_id[i][0])
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<< ":logits " << std::to_string(batch.logits[i]);
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}
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buf << " ]";
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return buf.str();
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}
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void string_process_escapes(std::string & input) {
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std::size_t input_len = input.length();
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std::size_t output_idx = 0;
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@ -481,7 +586,7 @@ void string_process_escapes(std::string & input) {
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bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
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const char * sep = strchr(data, '=');
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if (sep == nullptr || sep - data >= 128) {
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fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
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LOG_ERR("%s: malformed KV override '%s'\n", __func__, data);
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return false;
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}
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llama_model_kv_override kvo;
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@ -504,20 +609,20 @@ bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_over
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} else if (std::strcmp(sep, "false") == 0) {
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kvo.val_bool = false;
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} else {
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fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
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LOG_ERR("%s: invalid boolean value for KV override '%s'\n", __func__, data);
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return false;
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}
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} else if (strncmp(sep, "str:", 4) == 0) {
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sep += 4;
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kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
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if (strlen(sep) > 127) {
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fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
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LOG_ERR("%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
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return false;
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}
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strncpy(kvo.val_str, sep, 127);
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kvo.val_str[127] = '\0';
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} else {
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fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
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LOG_ERR("%s: invalid type for KV override '%s'\n", __func__, data);
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return false;
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}
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overrides.emplace_back(std::move(kvo));
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@ -729,7 +834,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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}
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if (model == NULL) {
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fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
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LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.c_str());
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return iparams;
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}
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@ -737,7 +842,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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llama_context * lctx = llama_new_context_with_model(model, cparams);
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if (lctx == NULL) {
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fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
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LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
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llama_free_model(model);
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return iparams;
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}
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@ -773,7 +878,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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loaded_la.scale = la.scale;
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loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str());
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if (loaded_la.adapter == nullptr) {
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fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
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LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
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llama_free(lctx);
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llama_free_model(model);
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return iparams;
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@ -785,12 +890,12 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
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}
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if (params.sparams.ignore_eos && llama_token_eos(model) == -1) {
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fprintf(stderr, "%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
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LOG_WRN("%s: warning: model does not have an EOS token, ignoring --ignore-eos\n", __func__);
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params.sparams.ignore_eos = false;
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}
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if (params.warmup) {
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LOG("warming up the model with an empty run\n");
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LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
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std::vector<llama_token> tmp;
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llama_token bos = llama_token_bos(model);
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@ -955,7 +1060,7 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
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int remaining_attempts = max_attempts;
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while (remaining_attempts > 0) {
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fprintf(stderr, "%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
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LOG_INF("%s: Trying to download from %s (attempt %d of %d)...\n", __func__ , url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
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CURLcode res = curl_easy_perform(curl);
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if (res == CURLE_OK) {
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@ -963,13 +1068,14 @@ static bool curl_perform_with_retry(const std::string& url, CURL* curl, int max_
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}
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int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
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fprintf(stderr, "%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
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LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
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remaining_attempts--;
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std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
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}
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fprintf(stderr, "%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
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LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
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return false;
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}
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@ -978,7 +1084,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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// Initialize libcurl
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std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
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if (!curl) {
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fprintf(stderr, "%s: error initializing libcurl\n", __func__);
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LOG_ERR("%s: error initializing libcurl\n", __func__);
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return false;
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}
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@ -1019,11 +1125,11 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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if (metadata_in.good()) {
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try {
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metadata_in >> metadata;
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fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
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LOG_INF("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
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if (metadata.contains("url") && metadata.at("url").is_string()) {
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auto previous_url = metadata.at("url").get<std::string>();
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if (previous_url != url) {
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fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
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LOG_ERR("%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
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return false;
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}
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}
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@ -1034,12 +1140,12 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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last_modified = metadata.at("lastModified");
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}
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} catch (const nlohmann::json::exception & e) {
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fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
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LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
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return false;
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}
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}
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} else {
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fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
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LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
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}
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// Send a HEAD request to retrieve the etag and last-modified headers
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@ -1087,26 +1193,26 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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// HEAD not supported, we don't know if the file has changed
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// force trigger downloading
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force_download = true;
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fprintf(stderr, "%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
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LOG_ERR("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
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}
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}
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bool should_download = !file_exists || force_download;
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if (!should_download) {
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if (!etag.empty() && etag != headers.etag) {
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fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
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LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
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should_download = true;
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} else if (!last_modified.empty() && last_modified != headers.last_modified) {
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fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
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LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
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should_download = true;
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}
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}
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if (should_download) {
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std::string path_temporary = path + ".downloadInProgress";
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if (file_exists) {
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fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
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LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
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if (remove(path.c_str()) != 0) {
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fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
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LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
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return false;
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}
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}
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@ -1121,7 +1227,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
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std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
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if (!outfile) {
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fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
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LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path.c_str());
|
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return false;
|
||||
}
|
||||
|
||||
|
|
@ -1152,7 +1258,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
|
|||
};
|
||||
|
||||
// start the download
|
||||
fprintf(stderr, "%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
|
||||
LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
|
||||
llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
|
||||
bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS);
|
||||
if (!was_perform_successful) {
|
||||
|
|
@ -1162,7 +1268,7 @@ static bool llama_download_file(const std::string & url, const std::string & pat
|
|||
long http_code = 0;
|
||||
curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
|
||||
if (http_code < 200 || http_code >= 400) {
|
||||
fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
|
||||
LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -1176,10 +1282,10 @@ static bool llama_download_file(const std::string & url, const std::string & pat
|
|||
{"lastModified", headers.last_modified}
|
||||
});
|
||||
std::ofstream(metadata_path) << metadata.dump(4);
|
||||
fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
||||
LOG_INF("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
|
||||
|
||||
if (rename(path_temporary.c_str(), path.c_str()) != 0) {
|
||||
fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
||||
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
|
@ -1194,7 +1300,7 @@ struct llama_model * llama_load_model_from_url(
|
|||
const struct llama_model_params & params) {
|
||||
// Basic validation of the model_url
|
||||
if (!model_url || strlen(model_url) == 0) {
|
||||
fprintf(stderr, "%s: invalid model_url\n", __func__);
|
||||
LOG_ERR("%s: invalid model_url\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -1211,7 +1317,7 @@ struct llama_model * llama_load_model_from_url(
|
|||
};
|
||||
auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
|
||||
if (!ctx_gguf) {
|
||||
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model);
|
||||
LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, path_model);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -1231,14 +1337,12 @@ struct llama_model * llama_load_model_from_url(
|
|||
// and extract split URL and PATH prefixes
|
||||
{
|
||||
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), path_model, 0, n_split)) {
|
||||
fprintf(stderr, "\n%s: unexpected model file name: %s"
|
||||
" n_split=%d\n", __func__, path_model, n_split);
|
||||
LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, path_model, n_split);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model_url, 0, n_split)) {
|
||||
fprintf(stderr, "\n%s: unexpected model url: %s"
|
||||
" n_split=%d\n", __func__, model_url, n_split);
|
||||
LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model_url, n_split);
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
|
|
@ -1298,7 +1402,7 @@ struct llama_model * llama_load_model_from_url(
|
|||
const char * /*path_model*/,
|
||||
const char * /*hf_token*/,
|
||||
const struct llama_model_params & /*params*/) {
|
||||
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
|
||||
LOG_WRN("%s: llama.cpp built without libcurl, downloading from an url not supported.\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
@ -1308,7 +1412,7 @@ struct llama_model * llama_load_model_from_hf(
|
|||
const char * /*path_model*/,
|
||||
const char * /*hf_token*/,
|
||||
const struct llama_model_params & /*params*/) {
|
||||
fprintf(stderr, "%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
|
||||
LOG_WRN("%s: llama.cpp built without libcurl, downloading from Hugging Face not supported.\n", __func__);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
@ -1636,13 +1740,13 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
|
|||
};
|
||||
struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params);
|
||||
if (!ctx_gguf) {
|
||||
fprintf(stderr, "%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
|
||||
LOG_ERR("%s: failed to load control vector file from %s\n", __func__, load_info.fname.c_str());
|
||||
return result;
|
||||
}
|
||||
|
||||
int32_t n_tensors = gguf_get_n_tensors(ctx_gguf);
|
||||
if (n_tensors == 0) {
|
||||
fprintf(stderr, "%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
|
||||
LOG_WRN("%s: no direction tensors found in %s\n", __func__, load_info.fname.c_str());
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_tensors; i++) {
|
||||
|
|
@ -1660,23 +1764,23 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
|
|||
}
|
||||
}
|
||||
if (layer_idx < 0) {
|
||||
fprintf(stderr, "%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
|
||||
LOG_ERR("%s: invalid/unparsable direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
|
||||
result.n_embd = -1;
|
||||
break;
|
||||
} else if (layer_idx == 0) {
|
||||
fprintf(stderr, "%s: invalid (zero) direction tensor layer index in %s\n", __func__, load_info.fname.c_str());
|
||||
LOG_ERR("%s: invalid (zero) direction tensor layer index in %s\n", __func__, 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) {
|
||||
fprintf(stderr, "%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
|
||||
LOG_ERR("%s: invalid (non-F32) direction tensor type in %s\n", __func__, load_info.fname.c_str());
|
||||
result.n_embd = -1;
|
||||
break;
|
||||
}
|
||||
if (ggml_n_dims(tensor) != 1) {
|
||||
fprintf(stderr, "%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
|
||||
LOG_ERR("%s: invalid (non-1D) direction tensor shape in %s\n", __func__, load_info.fname.c_str());
|
||||
result.n_embd = -1;
|
||||
break;
|
||||
}
|
||||
|
|
@ -1684,7 +1788,7 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
|
|||
if (result.n_embd == -1) {
|
||||
result.n_embd = ggml_nelements(tensor);
|
||||
} else if (ggml_nelements(tensor) != result.n_embd) {
|
||||
fprintf(stderr, "%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
|
||||
LOG_ERR("%s: direction tensor in %s does not match previous dimensions\n", __func__, load_info.fname.c_str());
|
||||
result.n_embd = -1;
|
||||
break;
|
||||
}
|
||||
|
|
@ -1701,7 +1805,7 @@ static llama_control_vector_data llama_control_vector_load_one(const llama_contr
|
|||
}
|
||||
|
||||
if (result.n_embd == -1) {
|
||||
fprintf(stderr, "%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
|
||||
LOG_WRN("%s: skipping %s due to invalid direction tensors\n", __func__, load_info.fname.c_str());
|
||||
result.data.clear();
|
||||
}
|
||||
|
||||
|
|
@ -1722,7 +1826,7 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
|
|||
break;
|
||||
}
|
||||
if (result.n_embd != -1 && result.n_embd != cur.n_embd) {
|
||||
fprintf(stderr, "%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
|
||||
LOG_ERR("%s: control vectors in %s does not match previous dimensions\n", __func__, info.fname.c_str());
|
||||
result.n_embd = -1;
|
||||
break;
|
||||
}
|
||||
|
|
@ -1738,7 +1842,7 @@ llama_control_vector_data llama_control_vector_load(const std::vector<llama_cont
|
|||
}
|
||||
|
||||
if (result.n_embd == -1) {
|
||||
fprintf(stderr, "%s: no valid control vector files passed\n", __func__);
|
||||
LOG_ERR("%s: no valid control vector files passed\n", __func__);
|
||||
result.data.clear();
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue