Safetensors
GGUF
Turkish
llama
Llama-3
instruct
finetune
chatml
gpt4
synthetic data
distillation
function calling
json mode
axolotl
roleplaying
chat
Instructions to use tda45/TdAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use tda45/TdAI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tda45/TdAI", filename="llama.cpp/models/ggml-vocab-aquila.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tda45/TdAI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tda45/TdAI # Run inference directly in the terminal: llama cli -hf tda45/TdAI
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./llama-cli -hf tda45/TdAI
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf tda45/TdAI # Run inference directly in the terminal: ./build/bin/llama-cli -hf tda45/TdAI
Use Docker
docker model run hf.co/tda45/TdAI
- LM Studio
- Jan
- Ollama
How to use tda45/TdAI with Ollama:
ollama run hf.co/tda45/TdAI
- Unsloth Studio
How to use tda45/TdAI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tda45/TdAI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for tda45/TdAI to start chatting
- Atomic Chat new
- Docker Model Runner
How to use tda45/TdAI with Docker Model Runner:
docker model run hf.co/tda45/TdAI
- Lemonade
How to use tda45/TdAI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tda45/TdAI
Run and chat with the model
lemonade run user.TdAI-{{QUANT_TAG}}List all available models
lemonade list
| common_time_meas::common_time_meas(int64_t & t_acc, bool disable) : t_start_us(disable ? -1 : ggml_time_us()), t_acc(t_acc) {} | |
| common_time_meas::~common_time_meas() { | |
| if (t_start_us >= 0) { | |
| t_acc += ggml_time_us() - t_start_us; | |
| } | |
| } | |
| // | |
| // CPU utils | |
| // | |
| int32_t common_cpu_get_num_physical_cores() { | |
| // enumerate the set of thread siblings, num entries is num cores | |
| std::unordered_set<std::string> siblings; | |
| for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) { | |
| std::ifstream thread_siblings("/sys/devices/system/cpu/cpu" | |
| + std::to_string(cpu) + "/topology/thread_siblings"); | |
| if (!thread_siblings.is_open()) { | |
| break; // no more cpus | |
| } | |
| std::string line; | |
| if (std::getline(thread_siblings, line)) { | |
| siblings.insert(line); | |
| } | |
| } | |
| if (!siblings.empty()) { | |
| return static_cast<int32_t>(siblings.size()); | |
| } | |
| int32_t num_physical_cores; | |
| size_t len = sizeof(num_physical_cores); | |
| int result = sysctlbyname("hw.perflevel0.physicalcpu", &num_physical_cores, &len, NULL, 0); | |
| if (result == 0) { | |
| return num_physical_cores; | |
| } | |
| result = sysctlbyname("hw.physicalcpu", &num_physical_cores, &len, NULL, 0); | |
| if (result == 0) { | |
| return num_physical_cores; | |
| } | |
| // TODO: windows + arm64 + mingw64 | |
| unsigned int n_threads_win = std::thread::hardware_concurrency(); | |
| unsigned int default_threads = n_threads_win > 0 ? (n_threads_win <= 4 ? n_threads_win : n_threads_win / 2) : 4; | |
| DWORD buffer_size = 0; | |
| if (!GetLogicalProcessorInformationEx(RelationProcessorCore, nullptr, &buffer_size)) { | |
| if (GetLastError() != ERROR_INSUFFICIENT_BUFFER) { | |
| return default_threads; | |
| } | |
| } | |
| std::vector<char> buffer(buffer_size); | |
| if (!GetLogicalProcessorInformationEx(RelationProcessorCore, reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()), &buffer_size)) { | |
| return default_threads; | |
| } | |
| int32_t num_physical_cores = 0; | |
| PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(buffer.data()); | |
| while (buffer_size > 0) { | |
| if (info->Relationship == RelationProcessorCore) { | |
| num_physical_cores += info->Processor.GroupCount; | |
| } | |
| buffer_size -= info->Size; | |
| info = reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(reinterpret_cast<char*>(info) + info->Size); | |
| } | |
| return num_physical_cores > 0 ? num_physical_cores : default_threads; | |
| unsigned int n_threads = std::thread::hardware_concurrency(); | |
| return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4; | |
| } | |
| static void cpuid(unsigned leaf, unsigned subleaf, | |
| unsigned *eax, unsigned *ebx, unsigned *ecx, unsigned *edx) { | |
| __asm__("movq\t%%rbx,%%rsi\n\t" | |
| "cpuid\n\t" | |
| "xchgq\t%%rbx,%%rsi" | |
| : "=a"(*eax), "=S"(*ebx), "=c"(*ecx), "=d"(*edx) | |
| : "0"(leaf), "2"(subleaf)); | |
| } | |
| static int pin_cpu(int cpu) { | |
| cpu_set_t mask; | |
| CPU_ZERO(&mask); | |
| CPU_SET(cpu, &mask); | |
| return pthread_setaffinity_np(pthread_self(), sizeof(mask), &mask); | |
| } | |
| static bool is_hybrid_cpu(void) { | |
| unsigned eax, ebx, ecx, edx; | |
| cpuid(7, 0, &eax, &ebx, &ecx, &edx); | |
| return !!(edx & (1u << 15)); | |
| } | |
| static bool is_running_on_efficiency_core(void) { | |
| unsigned eax, ebx, ecx, edx; | |
| cpuid(0x1a, 0, &eax, &ebx, &ecx, &edx); | |
| int intel_atom = 0x20; | |
| int core_type = (eax & 0xff000000u) >> 24; | |
| return core_type == intel_atom; | |
| } | |
| static int cpu_count_math_cpus(int n_cpu) { | |
| int result = 0; | |
| for (int cpu = 0; cpu < n_cpu; ++cpu) { | |
| if (pin_cpu(cpu)) { | |
| return -1; | |
| } | |
| if (is_running_on_efficiency_core()) { | |
| continue; // efficiency cores harm lockstep threading | |
| } | |
| ++cpu; // hyperthreading isn't useful for linear algebra | |
| ++result; | |
| } | |
| return result; | |
| } | |
| /** | |
| * Returns number of CPUs on system that are useful for math. | |
| */ | |
| int32_t common_cpu_get_num_math() { | |
| int n_cpu = sysconf(_SC_NPROCESSORS_ONLN); | |
| if (n_cpu < 1) { | |
| return common_cpu_get_num_physical_cores(); | |
| } | |
| if (is_hybrid_cpu()) { | |
| cpu_set_t affinity; | |
| if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { | |
| int result = cpu_count_math_cpus(n_cpu); | |
| pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); | |
| if (result > 0) { | |
| return result; | |
| } | |
| } | |
| } | |
| return common_cpu_get_num_physical_cores(); | |
| } | |
| // Helper for setting process priority | |
| bool set_process_priority(enum ggml_sched_priority prio) { | |
| if (prio == GGML_SCHED_PRIO_NORMAL) { | |
| return true; | |
| } | |
| DWORD p = NORMAL_PRIORITY_CLASS; | |
| switch (prio) { | |
| case GGML_SCHED_PRIO_LOW: p = BELOW_NORMAL_PRIORITY_CLASS; break; | |
| case GGML_SCHED_PRIO_NORMAL: p = NORMAL_PRIORITY_CLASS; break; | |
| case GGML_SCHED_PRIO_MEDIUM: p = ABOVE_NORMAL_PRIORITY_CLASS; break; | |
| case GGML_SCHED_PRIO_HIGH: p = HIGH_PRIORITY_CLASS; break; | |
| case GGML_SCHED_PRIO_REALTIME: p = REALTIME_PRIORITY_CLASS; break; | |
| } | |
| if (!SetPriorityClass(GetCurrentProcess(), p)) { | |
| COM_WRN("failed to set process priority class %d : (%d)\n", prio, (int) GetLastError()); | |
| return false; | |
| } | |
| return true; | |
| } | |
| bool set_process_priority(enum ggml_sched_priority prio) { | |
| if (prio == GGML_SCHED_PRIO_NORMAL) { | |
| return true; | |
| } | |
| int p = 0; | |
| switch (prio) { | |
| case GGML_SCHED_PRIO_LOW: p = 5; break; | |
| case GGML_SCHED_PRIO_NORMAL: p = 0; break; | |
| case GGML_SCHED_PRIO_MEDIUM: p = -5; break; | |
| case GGML_SCHED_PRIO_HIGH: p = -10; break; | |
| case GGML_SCHED_PRIO_REALTIME: p = -20; break; | |
| } | |
| if (setpriority(PRIO_PROCESS, 0, p) != 0) { | |
| COM_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno); | |
| return false; | |
| } | |
| return true; | |
| } | |
| // | |
| // CLI argument parsing | |
| // | |
| void postprocess_cpu_params(common_cpu_params & cpuparams, const common_cpu_params * role_model) { | |
| int32_t n_set = 0; | |
| if (cpuparams.n_threads < 0) { | |
| // Assuming everything about cpuparams is invalid | |
| if (role_model != nullptr) { | |
| cpuparams = *role_model; | |
| } else { | |
| cpuparams.n_threads = common_cpu_get_num_math(); | |
| } | |
| } | |
| for (int32_t i = 0; i < GGML_MAX_N_THREADS; i++) { | |
| if (cpuparams.cpumask[i]) { | |
| n_set++; | |
| } | |
| } | |
| if (n_set && n_set < cpuparams.n_threads) { | |
| // Not enough set bits, may experience performance issues. | |
| 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) { | |
| COM_ERR("%s", "Format of CPU range is invalid! Expected [<start>]-[<end>].\n"); | |
| return false; | |
| } | |
| size_t start_i; | |
| size_t end_i; | |
| if (dash_loc == 0) { | |
| start_i = 0; | |
| } else { | |
| start_i = std::stoull(range.substr(0, dash_loc)); | |
| if (start_i >= GGML_MAX_N_THREADS) { | |
| COM_ERR("%s", "Start index out of bounds!\n"); | |
| return false; | |
| } | |
| } | |
| if (dash_loc == range.length() - 1) { | |
| end_i = GGML_MAX_N_THREADS - 1; | |
| } else { | |
| end_i = std::stoull(range.substr(dash_loc + 1)); | |
| if (end_i >= GGML_MAX_N_THREADS) { | |
| COM_ERR("%s", "End index out of bounds!\n"); | |
| return false; | |
| } | |
| } | |
| for (size_t i = start_i; i <= end_i; i++) { | |
| boolmask[i] = true; | |
| } | |
| return true; | |
| } | |
| bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREADS]) { | |
| // Discard potential 0x prefix | |
| size_t start_i = 0; | |
| if (mask.length() >= 2 && mask.substr(0, 2) == "0x") { | |
| start_i = 2; | |
| } | |
| size_t num_digits = mask.length() - start_i; | |
| num_digits = std::min<size_t>(num_digits, 128); | |
| size_t end_i = num_digits + start_i; | |
| for (size_t i = start_i, n = (num_digits*4 - 1); i < end_i; i++, n-=4) { | |
| char c = mask.at(i); | |
| int8_t id = c; | |
| if ((c >= '0' && c <= '9')) { | |
| id -= '0'; | |
| } else if (c >= 'a' && c <= 'f') { | |
| id -= 'a' - 10; | |
| } else if (c >= 'A' && c <= 'F') { | |
| id -= 'A' - 10; | |
| } else { | |
| COM_ERR("Invalid hex character '%c' at position %d\n", c, int32_t(i)); | |
| return false; | |
| } | |
| boolmask[ n ] = boolmask[ n ] || ((id & 8) != 0); | |
| boolmask[n - 1] = boolmask[n - 1] || ((id & 4) != 0); | |
| boolmask[n - 2] = boolmask[n - 2] || ((id & 2) != 0); | |
| boolmask[n - 3] = boolmask[n - 3] || ((id & 1) != 0); | |
| } | |
| return true; | |
| } | |
| void common_init() { | |
| SetConsoleOutputCP(CP_UTF8); | |
| SetConsoleCP(CP_UTF8); | |
| common_log_set_prefix(common_log_main(), true); | |
| common_log_set_timestamps(common_log_main(), true); | |
| llama_log_set(common_log_default_callback, NULL); | |
| } | |
| void common_params_print_info(const common_params & params, bool print_devices) { | |
| const char * build_type = ""; | |
| const char * build_type = " (debug)"; | |
| 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); | |
| 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) { | |
| 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); | |
| 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); | |
| } | |
| } | |
| COM_TRC("%s\n", common_params_get_system_info(params).c_str()); | |
| } | |
| std::string common_params_get_system_info(const common_params & params) { | |
| std::ostringstream os; | |
| os << "system_info: n_threads = " << params.cpuparams.n_threads; | |
| if (params.cpuparams_batch.n_threads != -1) { | |
| os << " (n_threads_batch = " << params.cpuparams_batch.n_threads << ")"; | |
| } | |
| // TODO: windows + arm64 + mingw64 | |
| DWORD logicalProcessorCount = GetActiveProcessorCount(ALL_PROCESSOR_GROUPS); | |
| os << " / " << logicalProcessorCount << " | " << llama_print_system_info(); | |
| os << " / " << std::thread::hardware_concurrency() << " | " << llama_print_system_info(); | |
| return os.str(); | |
| } | |
| // | |
| // String utils | |
| // | |
| std::string string_format(const char * fmt, ...) { | |
| va_list ap; | |
| va_list ap2; | |
| va_start(ap, fmt); | |
| va_copy(ap2, ap); | |
| int size = vsnprintf(NULL, 0, fmt, ap); | |
| GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT | |
| std::vector<char> buf(size + 1); | |
| int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2); | |
| GGML_ASSERT(size2 == size); | |
| va_end(ap2); | |
| va_end(ap); | |
| return std::string(buf.data(), size); | |
| } | |
| std::string string_strip(const std::string & str) { | |
| size_t start = 0; | |
| size_t end = str.size(); | |
| while (start < end && std::isspace(str[start])) { | |
| start++; | |
| } | |
| while (end > start && std::isspace(str[end - 1])) { | |
| end--; | |
| } | |
| return str.substr(start, end - start); | |
| } | |
| std::string string_lcs(std::string_view a, std::string_view b) { | |
| if (a.empty() || b.empty()) return {}; | |
| std::vector<std::vector<size_t>> dp(a.size() + 1, std::vector<size_t>(b.size() + 1, 0)); | |
| size_t best_len = 0; | |
| size_t best_end_a = 0; | |
| for (size_t i = 1; i <= a.size(); ++i) { | |
| for (size_t j = 1; j <= b.size(); ++j) { | |
| if (a[i - 1] == b[j - 1]) { | |
| dp[i][j] = dp[i - 1][j - 1] + 1; | |
| if (dp[i][j] > best_len) { | |
| best_len = dp[i][j]; | |
| best_end_a = i; | |
| } | |
| } | |
| } | |
| } | |
| return std::string(a.substr(best_end_a - best_len, best_len)); | |
| } | |
| std::string string_get_sortable_timestamp() { | |
| using clock = std::chrono::system_clock; | |
| const clock::time_point current_time = clock::now(); | |
| const time_t as_time_t = clock::to_time_t(current_time); | |
| char timestamp_no_ns[100]; | |
| std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); | |
| const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>( | |
| current_time.time_since_epoch() % 1000000000).count(); | |
| char timestamp_ns[11]; | |
| snprintf(timestamp_ns, 11, "%09" PRId64, ns); | |
| return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); | |
| } | |
| void string_replace_all(std::string & s, const std::string & search, const std::string & replace) { | |
| if (search.empty()) { | |
| return; | |
| } | |
| std::string builder; | |
| builder.reserve(s.length()); | |
| size_t pos = 0; | |
| size_t last_pos = 0; | |
| while ((pos = s.find(search, last_pos)) != std::string::npos) { | |
| builder.append(s, last_pos, pos - last_pos); | |
| builder.append(replace); | |
| last_pos = pos + search.length(); | |
| } | |
| builder.append(s, last_pos, std::string::npos); | |
| s = std::move(builder); | |
| } | |
| std::string regex_escape(const std::string & s) { | |
| static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]"); | |
| return std::regex_replace(s, special_chars, "\\$&"); | |
| } | |
| std::string string_join(const std::vector<std::string> & values, const std::string & separator) { | |
| std::ostringstream result; | |
| for (size_t i = 0; i < values.size(); ++i) { | |
| if (i > 0) { | |
| result << separator; | |
| } | |
| result << values[i]; | |
| } | |
| return result.str(); | |
| } | |
| std::vector<std::string> string_split(const std::string & str, const std::string & delimiter) { | |
| std::vector<std::string> parts; | |
| size_t start = 0; | |
| size_t end = str.find(delimiter); | |
| while (end != std::string::npos) { | |
| parts.push_back(str.substr(start, end - start)); | |
| start = end + delimiter.length(); | |
| end = str.find(delimiter, start); | |
| } | |
| parts.push_back(str.substr(start)); | |
| return parts; | |
| } | |
| std::string string_repeat(const std::string & str, size_t n) { | |
| if (n == 0) { | |
| return ""; | |
| } | |
| std::string result; | |
| result.reserve(str.length() * n); | |
| for (size_t i = 0; i < n; ++i) { | |
| result += str; | |
| } | |
| return result; | |
| } | |
| std::string string_from(bool value) { | |
| return value ? "true" : "false"; | |
| } | |
| std::string string_from(const std::vector<int> & values) { | |
| std::stringstream buf; | |
| buf << "[ "; | |
| bool first = true; | |
| for (auto e : values) { | |
| if (first) { | |
| first = false; | |
| } else { | |
| buf << ", "; | |
| } | |
| buf << std::to_string(e); | |
| } | |
| buf << " ]"; | |
| return buf.str(); | |
| } | |
| std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens) { | |
| std::stringstream buf; | |
| buf << "[ "; | |
| bool first = true; | |
| for (const auto & token : tokens) { | |
| if (!first) { | |
| buf << ", "; | |
| } else { | |
| first = false; | |
| } | |
| auto detokenized = common_token_to_piece(ctx, token); | |
| buf << "'" << detokenized << "'" | |
| << ":" << std::to_string(token); | |
| } | |
| buf << " ]"; | |
| return buf.str(); | |
| } | |
| std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch) { | |
| std::stringstream buf; | |
| buf << "[ "; | |
| bool first = true; | |
| for (int i = 0; i < batch.n_tokens; ++i) { | |
| if (!first) { | |
| buf << ", "; | |
| } else { | |
| first = false; | |
| } | |
| auto detokenized = common_token_to_piece(ctx, batch.token[i]); | |
| buf << "\n" << std::to_string(i) | |
| << ", token '" << detokenized << "'" | |
| << ", pos " << std::to_string(batch.pos[i]) | |
| << ", n_seq_id " << std::to_string(batch.n_seq_id[i]) | |
| << ", seq_id " << std::to_string(batch.seq_id[i][0]) | |
| << ", logits " << std::to_string(batch.logits[i]); | |
| } | |
| buf << " ]"; | |
| return buf.str(); | |
| } | |
| void string_process_escapes(std::string & input) { | |
| std::size_t input_len = input.length(); | |
| std::size_t output_idx = 0; | |
| for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { | |
| if (input[input_idx] == '\\' && input_idx + 1 < input_len) { | |
| switch (input[++input_idx]) { | |
| case 'n': input[output_idx++] = '\n'; break; | |
| case 'r': input[output_idx++] = '\r'; break; | |
| case 't': input[output_idx++] = '\t'; break; | |
| case '\'': input[output_idx++] = '\''; break; | |
| case '\"': input[output_idx++] = '\"'; break; | |
| case '\\': input[output_idx++] = '\\'; break; | |
| case 'x': | |
| // Handle \x12, etc | |
| if (input_idx + 2 < input_len) { | |
| const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; | |
| char *err_p = nullptr; | |
| const long val = std::strtol(x, &err_p, 16); | |
| if (err_p == x + 2) { | |
| input_idx += 2; | |
| input[output_idx++] = char(val); | |
| break; | |
| } | |
| } | |
| // fall through | |
| default: input[output_idx++] = '\\'; | |
| input[output_idx++] = input[input_idx]; break; | |
| } | |
| } else { | |
| input[output_idx++] = input[input_idx]; | |
| } | |
| } | |
| input.resize(output_idx); | |
| } | |
| 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) { | |
| COM_ERR("%s: malformed KV override '%s'\n", __func__, data); | |
| return false; | |
| } | |
| llama_model_kv_override kvo; | |
| std::strncpy(kvo.key, data, sep - data); | |
| kvo.key[sep - data] = 0; | |
| sep++; | |
| if (strncmp(sep, "int:", 4) == 0) { | |
| sep += 4; | |
| kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; | |
| kvo.val_i64 = std::atol(sep); | |
| } else if (strncmp(sep, "float:", 6) == 0) { | |
| sep += 6; | |
| kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; | |
| kvo.val_f64 = std::atof(sep); | |
| } else if (strncmp(sep, "bool:", 5) == 0) { | |
| sep += 5; | |
| kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; | |
| if (std::strcmp(sep, "true") == 0) { | |
| kvo.val_bool = true; | |
| } else if (std::strcmp(sep, "false") == 0) { | |
| kvo.val_bool = false; | |
| } else { | |
| 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) { | |
| 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 { | |
| COM_ERR("%s: invalid type for KV override '%s'\n", __func__, data); | |
| return false; | |
| } | |
| overrides.emplace_back(std::move(kvo)); | |
| return true; | |
| } | |
| static inline bool glob_class_match(const char c, const char * pattern, const char * class_end) { | |
| const char * class_start = pattern; | |
| bool negated = false; | |
| if (*class_start == '!') { | |
| negated = true; | |
| class_start++; | |
| } | |
| // If first character after negation is ']' or '-', treat it as literal | |
| if (*class_start == ']' || *class_start == '-') { | |
| if (class_start < class_end && *class_start == c) { | |
| return !negated; | |
| } | |
| class_start++; | |
| } | |
| bool matched = false; | |
| while (class_start < class_end) { | |
| if (class_start + 2 < class_end && class_start[1] == '-' && class_start[2] != ']') { | |
| char start_char = *class_start; | |
| char end_char = class_start[2]; | |
| if (c >= start_char && c <= end_char) { | |
| matched = true; | |
| break; | |
| } | |
| class_start += 3; | |
| } else { | |
| if (*class_start == c) { | |
| matched = true; | |
| break; | |
| } | |
| class_start++; | |
| } | |
| } | |
| return negated ? !matched : matched; | |
| } | |
| // simple glob: * matches non-/ chars, ** matches anything including /, [] matches character class | |
| static inline bool glob_match(const char * pattern, const char * str) { | |
| if (*pattern == '\0') { | |
| return *str == '\0'; | |
| } | |
| if (pattern[0] == '*' && pattern[1] == '*') { | |
| const char * p = pattern + 2; | |
| if (glob_match(p, str)) return true; | |
| if (*str != '\0') return glob_match(pattern, str + 1); | |
| return false; | |
| } | |
| if (*pattern == '*') { | |
| const char * p = pattern + 1; | |
| for (; *str != '\0' && *str != '/'; str++) { | |
| if (glob_match(p, str)) return true; | |
| } | |
| return glob_match(p, str); | |
| } | |
| if (*pattern == '?' && *str != '\0' && *str != '/') { | |
| return glob_match(pattern + 1, str + 1); | |
| } | |
| if (*pattern == '[') { | |
| const char * class_end = pattern + 1; | |
| // If first character after '[' is ']' or '-', treat it as literal | |
| if (*class_end == ']' || *class_end == '-') { | |
| class_end++; | |
| } | |
| while (*class_end != '\0' && *class_end != ']') { | |
| class_end++; | |
| } | |
| if (*class_end == ']') { | |
| if (*str == '\0') return false; | |
| bool matched = glob_class_match(*str, pattern + 1, class_end); | |
| return matched && glob_match(class_end + 1, str + 1); | |
| } else { | |
| if (*str == '[') { | |
| return glob_match(pattern + 1, str + 1); | |
| } | |
| return false; | |
| } | |
| } | |
| if (*pattern == *str) { | |
| return glob_match(pattern + 1, str + 1); | |
| } | |
| return false; | |
| } | |
| bool glob_match(const std::string & pattern, const std::string & str) { | |
| return glob_match(pattern.c_str(), str.c_str()); | |
| } | |
| // | |
| // Filesystem utils | |
| // | |
| // Validate if a filename is safe to use | |
| // To validate a full path, split the path by the OS-specific path separator, and validate each part with this function | |
| bool fs_validate_filename(const std::string & filename, bool allow_subdirs) { | |
| if (!filename.length()) { | |
| // Empty filename invalid | |
| return false; | |
| } | |
| if (filename.length() > 255) { | |
| // Limit at common largest possible filename on Linux filesystems | |
| // to avoid unnecessary further validation | |
| // (On systems with smaller limits it will be caught by the OS) | |
| return false; | |
| } | |
| size_t offset = 0; | |
| while (offset < filename.size()) { | |
| utf8_parse_result result = common_parse_utf8_codepoint(filename, offset); | |
| if (result.status != utf8_parse_result::SUCCESS) { | |
| return false; | |
| } | |
| uint32_t c = result.codepoint; | |
| if ((result.bytes_consumed == 2 && c < 0x80) || | |
| (result.bytes_consumed == 3 && c < 0x800) || | |
| (result.bytes_consumed == 4 && c < 0x10000)) { | |
| return false; | |
| } | |
| // Check for forbidden codepoints: | |
| // - Control characters | |
| // - Unicode equivalents of illegal characters | |
| // - UTF-16 surrogate pairs | |
| // - UTF-8 replacement character | |
| // - Byte order mark (BOM) | |
| // - Illegal characters: / \ : * ? " < > | | |
| if (c <= 0x1F // Control characters (C0) | |
| || c == 0x7F // Control characters (DEL) | |
| || (c >= 0x80 && c <= 0x9F) // Control characters (C1) | |
| || c == 0xFF0E // Fullwidth Full Stop (period equivalent) | |
| || c == 0x2215 // Division Slash (forward slash equivalent) | |
| || c == 0x2216 // Set Minus (backslash equivalent) | |
| || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs | |
| || c > 0x10FFFF // Max Unicode limit | |
| || c == 0xFFFD // Replacement Character (UTF-8) | |
| || c == 0xFEFF // Byte Order Mark (BOM) | |
| || c == ':' || c == '*' // Illegal characters | |
| || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') { | |
| return false; | |
| } | |
| if (!allow_subdirs && (c == '/' || c == '\\')) { | |
| // Subdirectories not allowed, reject path separators | |
| return false; | |
| } | |
| offset += result.bytes_consumed; | |
| } | |
| // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename | |
| // Unicode and other whitespace is not affected, only 0x20 space | |
| if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') { | |
| return false; | |
| } | |
| // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead) | |
| if (filename.find("..") != std::string::npos) { | |
| return false; | |
| } | |
| // Reject "." | |
| if (filename == ".") { | |
| return false; | |
| } | |
| return true; | |
| } | |
| static std::wstring utf8_to_wstring(const std::string & str) { | |
| if (str.empty()) { | |
| return std::wstring(); | |
| } | |
| int size = MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), NULL, 0); | |
| if (size <= 0) { | |
| return std::wstring(); | |
| } | |
| std::wstring wstr(size, 0); | |
| MultiByteToWideChar(CP_UTF8, 0, str.c_str(), (int)str.size(), &wstr[0], size); | |
| return wstr; | |
| } | |
| // returns true if successful, false otherwise | |
| bool fs_create_directory_with_parents(const std::string & path) { | |
| std::wstring wpath = utf8_to_wstring(path); | |
| // if the path already exists, check whether it's a directory | |
| const DWORD attributes = GetFileAttributesW(wpath.c_str()); | |
| if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { | |
| return true; | |
| } | |
| size_t pos_slash = 0; | |
| // process path from front to back, procedurally creating directories | |
| while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { | |
| const std::wstring subpath = wpath.substr(0, pos_slash); | |
| pos_slash += 1; | |
| // skip the drive letter, in some systems it can return an access denied error | |
| if (subpath.length() == 2 && subpath[1] == ':') { | |
| continue; | |
| } | |
| const bool success = CreateDirectoryW(subpath.c_str(), NULL); | |
| if (!success) { | |
| const DWORD error = GetLastError(); | |
| // if the path already exists, ensure that it's a directory | |
| if (error == ERROR_ALREADY_EXISTS) { | |
| const DWORD attributes = GetFileAttributesW(subpath.c_str()); | |
| if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { | |
| return false; | |
| } | |
| } else { | |
| return false; | |
| } | |
| } | |
| } | |
| return true; | |
| // if the path already exists, check whether it's a directory | |
| struct stat info; | |
| if (stat(path.c_str(), &info) == 0) { | |
| return S_ISDIR(info.st_mode); | |
| } | |
| size_t pos_slash = 1; // skip leading slashes for directory creation | |
| // process path from front to back, procedurally creating directories | |
| while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { | |
| const std::string subpath = path.substr(0, pos_slash); | |
| struct stat info; | |
| // if the path already exists, ensure that it's a directory | |
| if (stat(subpath.c_str(), &info) == 0) { | |
| if (!S_ISDIR(info.st_mode)) { | |
| return false; | |
| } | |
| } else { | |
| // create parent directories | |
| const int ret = mkdir(subpath.c_str(), 0755); | |
| if (ret != 0) { | |
| return false; | |
| } | |
| } | |
| pos_slash += 1; | |
| } | |
| return true; | |
| } | |
| bool fs_is_directory(const std::string & path) { | |
| std::filesystem::path dir(path); | |
| return std::filesystem::exists(dir) && std::filesystem::is_directory(dir); | |
| } | |
| std::string fs_get_cache_directory() { | |
| std::string cache_directory = ""; | |
| auto ensure_trailing_slash = [](std::string p) { | |
| // Make sure to add trailing slash | |
| if (p.back() != DIRECTORY_SEPARATOR) { | |
| p += DIRECTORY_SEPARATOR; | |
| } | |
| return p; | |
| }; | |
| if (getenv("LLAMA_CACHE")) { | |
| cache_directory = std::getenv("LLAMA_CACHE"); | |
| } else { | |
| if (std::getenv("XDG_CACHE_HOME")) { | |
| cache_directory = std::getenv("XDG_CACHE_HOME"); | |
| } else if (std::getenv("HOME")) { | |
| cache_directory = std::getenv("HOME") + std::string("/.cache/"); | |
| } else { | |
| /* no $HOME is defined, fallback to getpwuid */ | |
| struct passwd *pw = getpwuid(getuid()); | |
| if ((!pw) || (!pw->pw_dir)) { | |
| throw std::runtime_error("Failed to find $HOME directory"); | |
| } | |
| cache_directory = std::string(pw->pw_dir) + std::string("/.cache/"); | |
| throw std::runtime_error("Failed to find $HOME directory"); | |
| } | |
| cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); | |
| cache_directory = std::getenv("LOCALAPPDATA"); | |
| GGML_ABORT("not implemented on this platform"); | |
| cache_directory = ensure_trailing_slash(cache_directory); | |
| cache_directory += "llama.cpp"; | |
| } | |
| return ensure_trailing_slash(cache_directory); | |
| } | |
| std::string fs_get_cache_file(const std::string & filename) { | |
| GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos); | |
| std::string cache_directory = fs_get_cache_directory(); | |
| const bool success = fs_create_directory_with_parents(cache_directory); | |
| if (!success) { | |
| throw std::runtime_error("failed to create cache directory: " + cache_directory); | |
| } | |
| return cache_directory + filename; | |
| } | |
| std::vector<common_file_info> fs_list(const std::string & path, bool include_directories) { | |
| std::vector<common_file_info> files; | |
| if (path.empty()) return files; | |
| std::filesystem::path dir(path); | |
| if (!std::filesystem::exists(dir) || !std::filesystem::is_directory(dir)) { | |
| return files; | |
| } | |
| for (const auto & entry : std::filesystem::directory_iterator(dir)) { | |
| try { | |
| // Only include regular files (skip directories) | |
| const auto & p = entry.path(); | |
| if (std::filesystem::is_regular_file(p)) { | |
| common_file_info info; | |
| info.path = p.string(); | |
| info.name = p.filename().string(); | |
| info.is_dir = false; | |
| try { | |
| info.size = static_cast<size_t>(std::filesystem::file_size(p)); | |
| } catch (const std::filesystem::filesystem_error &) { | |
| info.size = 0; | |
| } | |
| files.push_back(std::move(info)); | |
| } else if (include_directories && std::filesystem::is_directory(p)) { | |
| common_file_info info; | |
| info.path = p.string(); | |
| info.name = p.filename().string(); | |
| info.size = 0; // Directories have no size | |
| info.is_dir = true; | |
| files.push_back(std::move(info)); | |
| } | |
| } catch (const std::filesystem::filesystem_error &) { | |
| // skip entries we cannot inspect | |
| continue; | |
| } | |
| } | |
| return files; | |
| } | |
| std::ifstream fs_open_ifstream(const std::string & fname, std::ios_base::openmode mode) { | |
| int wlen = MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, NULL, 0); | |
| if (!wlen) { return std::ifstream(); } | |
| std::vector<wchar_t> wfname(wlen); | |
| (void)MultiByteToWideChar(CP_UTF8, 0, fname.c_str(), -1, wfname.data(), wlen); | |
| return std::ifstream(wfname.data(), mode); | |
| return std::ifstream(fname, mode); | |
| } | |
| // | |
| // TTY utils | |
| // | |
| bool tty_can_use_colors() { | |
| // Check NO_COLOR environment variable (https://no-color.org/) | |
| if (const char * no_color = std::getenv("NO_COLOR")) { | |
| if (no_color[0] != '\0') { | |
| return false; | |
| } | |
| } | |
| // Check TERM environment variable | |
| if (const char * term = std::getenv("TERM")) { | |
| if (std::strcmp(term, "dumb") == 0) { | |
| return false; | |
| } | |
| } | |
| // Check if stdout and stderr are connected to a terminal | |
| // We check both because log messages can go to either | |
| bool stdout_is_tty = isatty(fileno(stdout)); | |
| bool stderr_is_tty = isatty(fileno(stderr)); | |
| return stdout_is_tty || stderr_is_tty; | |
| } | |
| // | |
| // Model utils | |
| // | |
| // TODO: move to common/sampling | |
| static void common_init_sampler_from_model( | |
| const llama_model * model, | |
| common_params_sampling & sparams) { | |
| const uint64_t config = sparams.user_sampling_config; | |
| auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) { | |
| if (config & user_config) { | |
| return; | |
| } | |
| char buf[64] = {0}; | |
| if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { | |
| char * end = nullptr; | |
| int32_t v = strtol(buf, &end, 10); | |
| if (end && end != buf) { | |
| dst = v; | |
| } | |
| } | |
| }; | |
| auto get_float = [&](const char * key, float & dst, uint64_t user_config) { | |
| if (config & user_config) { | |
| return; | |
| } | |
| char buf[128] = {0}; | |
| if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { | |
| char * end = nullptr; | |
| float v = strtof(buf, &end); | |
| if (end && end != buf) { | |
| dst = v; | |
| } | |
| } | |
| }; | |
| // Sampling sequence | |
| if (!(config & common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS)) { | |
| char buf[512] = {0}; | |
| if (llama_model_meta_val_str(model, llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE), buf, sizeof(buf)) > 0) { | |
| const std::vector<std::string> sampler_names = string_split<std::string>(std::string(buf), ';'); | |
| if (!sampler_names.empty()) { | |
| sparams.samplers = common_sampler_types_from_names(sampler_names); | |
| } | |
| } | |
| } | |
| get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_K), sparams.top_k, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TOP_P), sparams.top_p, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_P); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIN_P), sparams.min_p, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIN_P); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY), sparams.xtc_probability, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD), sparams.xtc_threshold, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_TEMP), sparams.temp, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TEMP); | |
| get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N), sparams.penalty_last_n, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT), sparams.penalty_repeat, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT); | |
| get_int32(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT), sparams.mirostat, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU), sparams.mirostat_tau, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU); | |
| get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA), sparams.mirostat_eta, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA); | |
| } | |
| struct common_init_result::impl { | |
| impl() = default; | |
| ~impl() = default; | |
| // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top | |
| llama_model_ptr model; | |
| llama_context_ptr context; | |
| std::vector<llama_adapter_lora_ptr> lora; | |
| std::vector<common_sampler_ptr> samplers; | |
| std::vector<llama_sampler_seq_config> samplers_seq_config; | |
| }; | |
| common_init_result::common_init_result(common_params & params, bool model_only) : | |
| pimpl(new impl{}) { | |
| auto mparams = common_model_params_to_llama(params); | |
| auto cparams = common_context_params_to_llama(params); | |
| if (params.fit_params) { | |
| 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(), | |
| params.fit_params_target.data(), | |
| params.fit_params_min_ctx, | |
| params.verbosity >= LOG_LEVEL_DEBUG ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); | |
| } | |
| llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); | |
| if (model == NULL) { | |
| return; | |
| } | |
| pimpl->model.reset(model); | |
| if (model_only) { | |
| return; | |
| } | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| // load and optionally apply lora adapters | |
| for (auto & la : params.lora_adapters) { | |
| llama_adapter_lora_ptr lora; | |
| lora.reset(llama_adapter_lora_init(model, la.path.c_str())); | |
| if (lora == nullptr) { | |
| COM_ERR("failed to load lora adapter '%s'\n", la.path.c_str()); | |
| pimpl->model.reset(model); | |
| return; | |
| } | |
| char buf[1024]; | |
| la.ptr = lora.get(); | |
| llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf)); | |
| la.task_name = buf; | |
| llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf)); | |
| la.prompt_prefix = buf; | |
| pimpl->lora.emplace_back(std::move(lora)); // copy to list of loaded adapters | |
| } | |
| // updates params.sampling | |
| // TODO: fix naming | |
| common_init_sampler_from_model(model, params.sampling); | |
| if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { | |
| 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)) { | |
| 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}); | |
| } | |
| } | |
| if (params.sampling.ignore_eos) { | |
| // add EOG biases to the active set of logit biases | |
| params.sampling.logit_bias.insert( | |
| params.sampling.logit_bias.end(), | |
| params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end()); | |
| } | |
| //if (params.sampling.penalty_last_n == -1) { | |
| // LOG_TRC("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); | |
| // params.sampling.penalty_last_n = llama_n_ctx(lctx); | |
| //} | |
| //if (params.sampling.dry_penalty_last_n == -1) { | |
| // LOG_TRC("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); | |
| // params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); | |
| //} | |
| // init the backend samplers as part of the context creation | |
| pimpl->samplers.resize(cparams.n_seq_max); | |
| pimpl->samplers_seq_config.resize(cparams.n_seq_max); | |
| for (int i = 0; i < (int) cparams.n_seq_max; ++i) { | |
| pimpl->samplers[i].reset(common_sampler_init(model, params.sampling)); | |
| pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) }; | |
| } | |
| if (params.sampling.backend_sampling) { | |
| cparams.samplers = pimpl->samplers_seq_config.data(); | |
| cparams.n_samplers = pimpl->samplers_seq_config.size(); | |
| } | |
| llama_context * lctx = llama_init_from_model(model, cparams); | |
| if (lctx == NULL) { | |
| COM_ERR("failed to create context with model '%s'\n", params.model.path.c_str()); | |
| return; | |
| } | |
| pimpl->context.reset(lctx); | |
| } | |
| llama_model * common_init_result::model() { | |
| return pimpl->model.get(); | |
| } | |
| llama_context * common_init_result::context() { | |
| return pimpl->context.get(); | |
| } | |
| common_sampler * common_init_result::sampler(llama_seq_id seq_id) { | |
| if (seq_id < 0 || seq_id >= (int) pimpl->samplers.size()) { | |
| return nullptr; | |
| } | |
| return pimpl->samplers[seq_id].get(); | |
| } | |
| void common_init_result::reset_samplers() { | |
| for (int i = 0; i < (int) pimpl->samplers.size(); ++i) { | |
| llama_sampler_reset(common_sampler_get(pimpl->samplers[i].get())); | |
| } | |
| } | |
| std::vector<llama_adapter_lora_ptr> & common_init_result::lora() { | |
| return pimpl->lora; | |
| } | |
| common_init_result_ptr common_init_from_params(common_params & params, bool model_only) { | |
| common_init_result_ptr res(new common_init_result(params, model_only)); | |
| llama_model * model = res->model(); | |
| if (model == NULL) { | |
| COM_ERR("failed to load model '%s'\n", params.model.path.c_str()); | |
| return res; | |
| } | |
| if (model_only) { | |
| return res; | |
| } | |
| llama_context * lctx = res->context(); | |
| if (lctx == NULL) { | |
| 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))) { | |
| COM_WRN("%s", "KV cache shifting is not supported for this context, disabling KV cache shifting\n"); | |
| params.ctx_shift = false; | |
| } | |
| if (!params.control_vectors.empty()) { | |
| if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; | |
| if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_model_n_layer(model); | |
| const auto cvec = common_control_vector_load(params.control_vectors); | |
| if (cvec.n_embd == -1) { | |
| return res; | |
| } | |
| int err = llama_set_adapter_cvec( | |
| lctx, | |
| cvec.data.data(), | |
| cvec.data.size(), | |
| cvec.n_embd, | |
| params.control_vector_layer_start, | |
| params.control_vector_layer_end); | |
| if (err) { | |
| return res; | |
| } | |
| } | |
| if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) { | |
| bool ok = true; | |
| if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) { | |
| COM_WRN("%s", "vocab does not have a BOS token, reranking will not work\n"); | |
| ok = false; | |
| } | |
| bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL; | |
| bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL; | |
| bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL; | |
| if (!has_eos && !has_sep && !has_rerank_prompt) { | |
| 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) { | |
| COM_WRN("%s", "vocab does not have an EOS token, using SEP token as fallback\n"); | |
| } | |
| if (!ok) { | |
| return res; | |
| } | |
| } | |
| if (!params.lora_init_without_apply) { | |
| common_set_adapter_lora(lctx, params.lora_adapters); | |
| } | |
| if (params.warmup) { | |
| 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); | |
| llama_token eos = llama_vocab_eos(vocab); | |
| // some models (e.g. T5) don't have a BOS token | |
| if (bos != LLAMA_TOKEN_NULL) { | |
| tmp.push_back(bos); | |
| } | |
| if (eos != LLAMA_TOKEN_NULL) { | |
| tmp.push_back(eos); | |
| } | |
| if (tmp.empty()) { | |
| tmp.push_back(0); | |
| } | |
| if (llama_model_has_encoder(model)) { | |
| llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size())); | |
| llama_token decoder_start_token_id = llama_model_decoder_start_token(model); | |
| if (decoder_start_token_id == LLAMA_TOKEN_NULL) { | |
| decoder_start_token_id = bos; | |
| } | |
| tmp.clear(); | |
| tmp.push_back(decoder_start_token_id); | |
| } | |
| if (llama_model_has_decoder(model)) { | |
| llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch))); | |
| } | |
| llama_memory_clear(llama_get_memory(lctx), true); | |
| llama_synchronize(lctx); | |
| llama_perf_context_reset(lctx); | |
| // reset samplers to reset RNG state after warmup to the seeded state | |
| res->reset_samplers(); | |
| } | |
| return res; | |
| } | |
| common_init_result::~common_init_result() = default; | |
| std::string common_get_model_endpoint() { | |
| const char * model_endpoint_env = getenv("MODEL_ENDPOINT"); | |
| // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility. | |
| const char * hf_endpoint_env = getenv("HF_ENDPOINT"); | |
| const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env; | |
| std::string model_endpoint = "https://huggingface.co/"; | |
| if (endpoint_env) { | |
| model_endpoint = endpoint_env; | |
| if (model_endpoint.back() != '/') { | |
| model_endpoint += '/'; | |
| } | |
| } | |
| return model_endpoint; | |
| } | |
| common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) { | |
| auto * mem = llama_get_memory(ctx); | |
| if (mem == nullptr) { | |
| return COMMON_CONTEXT_SEQ_RM_TYPE_NO; | |
| } | |
| common_context_seq_rm_type res = COMMON_CONTEXT_SEQ_RM_TYPE_PART; | |
| llama_memory_clear(mem, true); | |
| // eval 2 tokens to check if the context is compatible | |
| std::vector<llama_token> tmp; | |
| tmp.push_back(0); | |
| tmp.push_back(0); | |
| int ret = llama_decode(ctx, llama_batch_get_one(tmp.data(), tmp.size())); | |
| if (ret != 0) { | |
| COM_ERR("llama_decode() failed: %d\n", ret); | |
| res = COMMON_CONTEXT_SEQ_RM_TYPE_NO; | |
| goto done; | |
| } | |
| if (llama_n_rs_seq(ctx) > 0) { | |
| 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)) { | |
| COM_TRC("%s", "the context does not support partial sequence removal\n"); | |
| res = COMMON_CONTEXT_SEQ_RM_TYPE_FULL; | |
| goto done; | |
| } | |
| done: | |
| llama_memory_clear(mem, true); | |
| llama_synchronize(ctx); | |
| return res; | |
| } | |
| void common_context_seq_rm(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) { | |
| auto * mem = llama_get_memory(ctx); | |
| if (!llama_memory_seq_rm(mem, seq_id, p0, p1)) { | |
| GGML_ABORT("%s", string_format("failed to remove sequence %d with p0=%d, p1=%d\n", seq_id, p0, p1).c_str()); | |
| } | |
| } | |
| void common_context_seq_cp(llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { | |
| auto * mem = llama_get_memory(ctx); | |
| llama_memory_seq_cp(mem, seq_id_src, seq_id_dst, p0, p1); | |
| } | |
| void common_context_seq_add(llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) { | |
| auto * mem = llama_get_memory(ctx); | |
| llama_memory_seq_add(mem, seq_id, p0, p1, delta); | |
| } | |
| void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora) { | |
| std::vector<llama_adapter_lora *> loras; | |
| std::vector<float> scales; | |
| for (auto & la: lora) { | |
| loras.push_back(la.ptr); | |
| scales.push_back(la.scale); | |
| } | |
| llama_set_adapters_lora(ctx, loras.data(), loras.size(), scales.data()); | |
| } | |
| struct llama_model_params common_model_params_to_llama(common_params & params) { | |
| auto mparams = llama_model_default_params(); | |
| if (!params.devices.empty()) { | |
| mparams.devices = params.devices.data(); | |
| } | |
| mparams.n_gpu_layers = params.n_gpu_layers; | |
| mparams.main_gpu = params.main_gpu; | |
| mparams.split_mode = params.split_mode; | |
| mparams.tensor_split = params.tensor_split; | |
| mparams.use_mmap = params.use_mmap; | |
| mparams.use_direct_io = params.use_direct_io; | |
| mparams.use_mlock = params.use_mlock; | |
| mparams.check_tensors = params.check_tensors; | |
| mparams.use_extra_bufts = !params.no_extra_bufts; | |
| mparams.no_host = params.no_host; | |
| if (params.kv_overrides.empty()) { | |
| mparams.kv_overrides = NULL; | |
| } else { | |
| GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key"); | |
| mparams.kv_overrides = params.kv_overrides.data(); | |
| } | |
| if (params.tensor_buft_overrides.empty()) { | |
| mparams.tensor_buft_overrides = NULL; | |
| } else { | |
| GGML_ASSERT(params.tensor_buft_overrides.back().pattern == nullptr && "Tensor buffer overrides not terminated with empty pattern"); | |
| mparams.tensor_buft_overrides = params.tensor_buft_overrides.data(); | |
| } | |
| mparams.progress_callback = params.load_progress_callback; | |
| mparams.progress_callback_user_data = params.load_progress_callback_user_data; | |
| mparams.no_alloc = params.no_alloc; | |
| return mparams; | |
| } | |
| struct llama_context_params common_context_params_to_llama(const common_params & params) { | |
| auto cparams = llama_context_default_params(); | |
| cparams.n_ctx = params.n_ctx; | |
| cparams.n_seq_max = params.n_parallel; | |
| cparams.n_rs_seq = params.speculative.need_n_rs_seq(); | |
| cparams.n_outputs_max = std::max(params.n_outputs_max, 0); | |
| cparams.n_batch = params.n_batch; | |
| cparams.n_ubatch = params.n_ubatch; | |
| cparams.n_threads = params.cpuparams.n_threads; | |
| cparams.n_threads_batch = params.cpuparams_batch.n_threads == -1 ? | |
| params.cpuparams.n_threads : params.cpuparams_batch.n_threads; | |
| cparams.embeddings = params.embedding; | |
| cparams.rope_scaling_type = params.rope_scaling_type; | |
| cparams.rope_freq_base = params.rope_freq_base; | |
| cparams.rope_freq_scale = params.rope_freq_scale; | |
| cparams.yarn_ext_factor = params.yarn_ext_factor; | |
| cparams.yarn_attn_factor = params.yarn_attn_factor; | |
| cparams.yarn_beta_fast = params.yarn_beta_fast; | |
| cparams.yarn_beta_slow = params.yarn_beta_slow; | |
| cparams.yarn_orig_ctx = params.yarn_orig_ctx; | |
| cparams.pooling_type = params.pooling_type; | |
| cparams.attention_type = params.attention_type; | |
| cparams.flash_attn_type = params.flash_attn_type; | |
| cparams.cb_eval = params.cb_eval; | |
| cparams.cb_eval_user_data = params.cb_eval_user_data; | |
| cparams.offload_kqv = !params.no_kv_offload; | |
| cparams.no_perf = params.no_perf; | |
| cparams.op_offload = !params.no_op_offload; | |
| cparams.swa_full = params.swa_full; | |
| cparams.kv_unified = params.kv_unified; | |
| cparams.type_k = params.cache_type_k; | |
| cparams.type_v = params.cache_type_v; | |
| return cparams; | |
| } | |
| struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params) { | |
| struct ggml_threadpool_params tpp; | |
| ggml_threadpool_params_init(&tpp, params.n_threads); // setup the defaults | |
| if (params.mask_valid) { | |
| std::memcpy(&tpp.cpumask, ¶ms.cpumask, GGML_MAX_N_THREADS); | |
| } | |
| tpp.prio = params.priority; | |
| tpp.poll = params.poll; | |
| tpp.strict_cpu = params.strict_cpu; | |
| return tpp; | |
| } | |
| // | |
| // Batch utils | |
| // | |
| void common_batch_clear(struct llama_batch & batch) { | |
| batch.n_tokens = 0; | |
| } | |
| void common_batch_add( | |
| struct llama_batch & batch, | |
| llama_token id, | |
| llama_pos pos, | |
| const std::vector<llama_seq_id> & seq_ids, | |
| bool logits) { | |
| GGML_ASSERT(batch.seq_id[batch.n_tokens] && "llama_batch size exceeded"); | |
| batch.token [batch.n_tokens] = id; | |
| batch.pos [batch.n_tokens] = pos; | |
| batch.n_seq_id[batch.n_tokens] = seq_ids.size(); | |
| for (size_t i = 0; i < seq_ids.size(); ++i) { | |
| batch.seq_id[batch.n_tokens][i] = seq_ids[i]; | |
| } | |
| batch.logits [batch.n_tokens] = logits; | |
| batch.n_tokens++; | |
| } | |
| // | |
| // Vocab utils | |
| // | |
| std::vector<llama_token> common_tokenize( | |
| const struct llama_context * ctx, | |
| const std::string & text, | |
| bool add_special, | |
| bool parse_special) { | |
| const llama_model * model = llama_get_model(ctx); | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| return common_tokenize(vocab, text, add_special, parse_special); | |
| } | |
| std::vector<llama_token> common_tokenize( | |
| const struct llama_vocab * vocab, | |
| const std::string & text, | |
| bool add_special, | |
| bool parse_special) { | |
| // upper limit for the number of tokens | |
| int n_tokens = text.length() + 2 * add_special; | |
| std::vector<llama_token> result(n_tokens); | |
| n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); | |
| if (n_tokens == std::numeric_limits<int32_t>::min()) { | |
| throw std::runtime_error("Tokenization failed: input text too large, tokenization result exceeds int32_t limit"); | |
| } | |
| if (n_tokens < 0) { | |
| result.resize(-n_tokens); | |
| int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special); | |
| GGML_ASSERT(check == -n_tokens); | |
| } else { | |
| result.resize(n_tokens); | |
| } | |
| return result; | |
| } | |
| std::string common_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { | |
| const llama_model * model = llama_get_model(ctx); | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| return common_token_to_piece(vocab, token, special); | |
| } | |
| std::string common_token_to_piece(const struct llama_vocab * vocab, llama_token token, bool special) { | |
| std::string piece; | |
| piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n' | |
| const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); | |
| if (n_chars < 0) { | |
| piece.resize(-n_chars); | |
| int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special); | |
| GGML_ASSERT(check == -n_chars); | |
| } | |
| else { | |
| piece.resize(n_chars); | |
| } | |
| return piece; | |
| } | |
| std::string common_detokenize(const struct llama_context * ctx, const std::vector<llama_token> & tokens, bool special) { | |
| const llama_model * model = llama_get_model(ctx); | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| return common_detokenize(vocab, tokens, special); | |
| } | |
| std::string common_detokenize(const struct llama_vocab * vocab, const std::vector<llama_token> & tokens, bool special) { | |
| std::string text; | |
| text.resize(std::max(text.capacity(), tokens.size())); | |
| int32_t n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); | |
| if (n_chars < 0) { | |
| text.resize(-n_chars); | |
| n_chars = llama_detokenize(vocab, tokens.data(), (int32_t)tokens.size(), &text[0], (int32_t)text.size(), false, special); | |
| GGML_ASSERT(n_chars <= (int32_t)text.size()); // whitespace trimming is performed after per-token detokenization | |
| } | |
| text.resize(n_chars); | |
| // NOTE: the original tokenizer decodes bytes after collecting the pieces. | |
| return text; | |
| } | |
| // | |
| // Embedding utils | |
| // | |
| void common_embd_normalize(const float * inp, float * out, int n, int embd_norm) { | |
| double sum = 0.0; | |
| switch (embd_norm) { | |
| case -1: // no normalisation | |
| sum = 1.0; | |
| break; | |
| case 0: // max absolute | |
| for (int i = 0; i < n; i++) { | |
| if (sum < std::abs(inp[i])) { | |
| sum = std::abs(inp[i]); | |
| } | |
| } | |
| sum /= 32760.0; // make an int16 range | |
| break; | |
| case 2: // euclidean | |
| for (int i = 0; i < n; i++) { | |
| sum += inp[i] * inp[i]; | |
| } | |
| sum = std::sqrt(sum); | |
| break; | |
| default: // p-norm (euclidean is p-norm p=2) | |
| for (int i = 0; i < n; i++) { | |
| sum += std::pow(std::abs(inp[i]), embd_norm); | |
| } | |
| sum = std::pow(sum, 1.0 / embd_norm); | |
| break; | |
| } | |
| const float norm = sum > 0.0 ? 1.0 / sum : 0.0f; | |
| for (int i = 0; i < n; i++) { | |
| out[i] = inp[i] * norm; | |
| } | |
| } | |
| float common_embd_similarity_cos(const float * embd1, const float * embd2, int n){ | |
| double sum = 0.0; | |
| double sum1 = 0.0; | |
| double sum2 = 0.0; | |
| for (int i = 0; i < n; i++) { | |
| sum += embd1[i] * embd2[i]; | |
| sum1 += embd1[i] * embd1[i]; | |
| sum2 += embd2[i] * embd2[i]; | |
| } | |
| // Handle the case where one or both vectors are zero vectors | |
| if (sum1 == 0.0 || sum2 == 0.0) { | |
| if (sum1 == 0.0 && sum2 == 0.0) { | |
| return 1.0f; // two zero vectors are similar | |
| } | |
| return 0.0f; | |
| } | |
| return sum / (sqrt(sum1) * sqrt(sum2)); | |
| } | |
| // | |
| // Control vector utils | |
| // | |
| static common_control_vector_data common_control_vector_load_one(const common_control_vector_load_info & load_info) { | |
| common_control_vector_data result = { -1, {} }; | |
| ggml_context * ctx = nullptr; | |
| struct gguf_init_params meta_gguf_params = { | |
| /* .no_alloc = */ false, | |
| /* .ctx = */ &ctx, | |
| }; | |
| struct gguf_context * ctx_gguf = gguf_init_from_file(load_info.fname.c_str(), meta_gguf_params); | |
| if (!ctx_gguf) { | |
| 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) { | |
| COM_WRN("no direction tensors found in %s\n", load_info.fname.c_str()); | |
| } | |
| for (int i = 0; i < n_tensors; i++) { | |
| std::string name = gguf_get_tensor_name(ctx_gguf, i); | |
| int layer_idx = -1; | |
| // split on '.' | |
| size_t dotpos = name.find('.'); | |
| if (dotpos != std::string::npos && name.substr(0, dotpos) == "direction") { | |
| try { | |
| layer_idx = std::stoi(name.substr(dotpos + 1)); | |
| } catch (...) { | |
| layer_idx = -1; | |
| } | |
| } | |
| if (layer_idx < 0) { | |
| 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) { | |
| 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) { | |
| 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) { | |
| 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) { | |
| COM_ERR("direction tensor in %s does not match previous dimensions\n", load_info.fname.c_str()); | |
| result.n_embd = -1; | |
| break; | |
| } | |
| // extend if necessary - do not store data for layer 0 (it's not used) | |
| result.data.resize(std::max(result.data.size(), static_cast<size_t>(result.n_embd * layer_idx)), 0.0f); | |
| const float * src = (const float *) tensor->data; | |
| float * dst = result.data.data() + result.n_embd * (layer_idx - 1); // layer 1 at [0] | |
| for (int j = 0; j < result.n_embd; j++) { | |
| dst[j] += src[j] * load_info.strength; // allows multiple directions for same layer in same file | |
| } | |
| } | |
| if (result.n_embd == -1) { | |
| COM_WRN("skipping %s due to invalid direction tensors\n", load_info.fname.c_str()); | |
| result.data.clear(); | |
| } | |
| gguf_free(ctx_gguf); | |
| ggml_free(ctx); | |
| return result; | |
| } | |
| common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos) { | |
| common_control_vector_data result = { -1, {} }; | |
| for (const auto & info : load_infos) { | |
| auto cur = common_control_vector_load_one(info); | |
| if (cur.n_embd == -1) { | |
| result.n_embd = -1; | |
| break; | |
| } | |
| if (result.n_embd != -1 && result.n_embd != cur.n_embd) { | |
| 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) { | |
| result = std::move(cur); | |
| } else { | |
| result.data.resize(std::max(result.data.size(), cur.data.size()), 0.0f); // extend if necessary | |
| for (size_t i = 0; i < cur.data.size(); i++) { | |
| result.data[i] += cur.data[i]; | |
| } | |
| } | |
| } | |
| if (result.n_embd == -1) { | |
| COM_ERR("%s", "no valid control vector files passed\n"); | |
| result.data.clear(); | |
| } | |
| return result; | |
| } | |
| ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride) { | |
| const int64_t ne_datapoint = llama_n_ctx(ctx); | |
| const int64_t ndata = (tokens.size() - ne_datapoint - 1) / stride; | |
| ggml_opt_dataset_t result = ggml_opt_dataset_init( | |
| GGML_TYPE_I32, GGML_TYPE_I32, ne_datapoint, ne_datapoint, ndata, /*ndata_shard =*/ 1); | |
| llama_token * data = (llama_token *) ggml_opt_dataset_data(result)->data; | |
| llama_token * labels = (llama_token *) ggml_opt_dataset_labels(result)->data; | |
| for (int64_t idata = 0; idata < ndata; ++idata) { | |
| memcpy(data + idata*ne_datapoint, tokens.data() + idata*stride + 0, ne_datapoint*sizeof(llama_token)); | |
| memcpy(labels + idata*ne_datapoint, tokens.data() + idata*stride + 1, ne_datapoint*sizeof(llama_token)); | |
| } | |
| return result; | |
| } | |
| ggml_opt_optimizer_params common_opt_lr_pars(void * userdata) { | |
| ggml_opt_optimizer_params result = ggml_opt_get_default_optimizer_params(nullptr); | |
| const lr_opt & d = *(lr_opt *) userdata; | |
| result.adamw.alpha = result.sgd.alpha = d.get_lr(d.epoch); | |
| result.sgd.wd = result.adamw.wd = d.wd; | |
| return result; | |
| } | |
| // TODO make all command line args case-insensitive | |
| static inline bool eq_case_insensitive(char const* a, char const* b) { | |
| return ! | |
| _stricmp | |
| strcasecmp | |
| (a, b); | |
| } | |
| enum ggml_opt_optimizer_type common_opt_get_optimizer(const char * n) { | |
| if (eq_case_insensitive("adamw", n)) { | |
| return GGML_OPT_OPTIMIZER_TYPE_ADAMW; | |
| } | |
| if (eq_case_insensitive("sgd", n)) { | |
| return GGML_OPT_OPTIMIZER_TYPE_SGD; | |
| } | |
| return GGML_OPT_OPTIMIZER_TYPE_COUNT; | |
| } | |
| // TODO simplify to use just log and exp | |
| static float const k_log_2 = std::log(2.f); | |
| void lr_opt::init() { | |
| if (lr_min > 0 && lr_min < lr0) { | |
| float nhalf = std::log(lr0 / lr_min) / k_log_2; | |
| float e = epochs; | |
| if (decay_epochs > 0 && decay_epochs < e) { | |
| e = decay_epochs; | |
| } else { | |
| decay_epochs = e; | |
| } | |
| scale_epoch = nhalf / e; | |
| } | |
| } | |
| float lr_opt::get_lr(float epoch) const { | |
| float r = lr_min <= 0 ? lr0 : | |
| epoch >= decay_epochs ? lr_min : | |
| lr0 * std::pow(0.5f, epoch * scale_epoch); | |
| LOG_INF("epoch %.2g lr=%.2g\n", epoch, r); | |
| return r; | |
| } | |
| bool common_replay_last_token(struct llama_context * ctx, llama_token last_token, int32_t pos) { | |
| llama_batch batch = llama_batch_get_one(&last_token, 1); | |
| batch.pos = &pos; | |
| if (llama_decode(ctx, batch)) { | |
| LOG_ERR("%s: failed to replay last token\n", __func__); | |
| return false; | |
| } | |
| return true; | |
| } | |
| bool common_prompt_batch_decode( | |
| struct llama_context * ctx, | |
| const std::vector<llama_token> & all_tokens, | |
| int n_new, | |
| int & n_past, | |
| int n_batch, | |
| std::string_view state_path, | |
| bool save_state) { | |
| if (n_new == 0) { | |
| return true; | |
| } | |
| const int offset = all_tokens.size() - n_new; | |
| if (save_state && n_new > 1) { | |
| const int n_tokens_before_last = n_new - 1; | |
| GGML_ASSERT(n_new <= n_batch); | |
| // Decode all but the last token so we can save the memory state before decoding the last token. | |
| // This is done so we can restore the session state later and replay the last token. | |
| // Memory implementations in recurrent/hybrid models don't support removing tokens from their | |
| // 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))) { | |
| 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()); | |
| 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); | |
| int32_t pos = n_past; | |
| batch.pos = &pos; | |
| if (llama_decode(ctx, batch)) { | |
| 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))) { | |
| COM_ERR("%s", "failed to eval\n"); | |
| return false; | |
| } | |
| n_past += n_new; | |
| } | |
| return true; | |
| } | |
| size_t common_prompt_checkpoint::size() const { | |
| return data_tgt.size() + data_dft.size() + data_spec.size(); | |
| } | |
| bool common_prompt_checkpoint::empty() const { | |
| return data_tgt.empty(); | |
| } | |
| void common_prompt_checkpoint::clear() { | |
| n_tokens = 0; | |
| pos_min = 0; | |
| pos_max = 0; | |
| data_tgt.clear(); | |
| data_dft.clear(); | |
| data_spec.clear(); | |
| } | |
| void common_prompt_checkpoint::update_pos( | |
| int64_t n_tokens, | |
| llama_pos pos_min, | |
| llama_pos pos_max) { | |
| this->n_tokens = n_tokens; | |
| this->pos_min = pos_min; | |
| this->pos_max = pos_max; | |
| } | |
| void common_prompt_checkpoint::update_tgt( | |
| llama_context * ctx, | |
| llama_seq_id seq_id, | |
| llama_state_seq_flags flags) { | |
| if (ctx == nullptr) { | |
| return; | |
| } | |
| const size_t ckpt_size = llama_state_seq_get_size_ext(ctx, seq_id, flags); | |
| data_tgt.resize(ckpt_size); | |
| const size_t n = llama_state_seq_get_data_ext(ctx, data_tgt.data(), ckpt_size, seq_id, flags); | |
| if (n != ckpt_size) { | |
| GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", ckpt_size, n); | |
| } | |
| } | |
| void common_prompt_checkpoint::update_dft( | |
| llama_context * ctx, | |
| llama_seq_id seq_id, | |
| llama_state_seq_flags flags) { | |
| if (ctx == nullptr) { | |
| return; | |
| } | |
| const size_t ckpt_size = llama_state_seq_get_size_ext(ctx, seq_id, flags); | |
| data_dft.resize(ckpt_size); | |
| const size_t n = llama_state_seq_get_data_ext(ctx, data_dft.data(), ckpt_size, seq_id, flags); | |
| if (n != ckpt_size) { | |
| GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", ckpt_size, n); | |
| } | |
| } | |
| void common_prompt_checkpoint::load_tgt( | |
| llama_context * ctx, | |
| llama_seq_id seq_id, | |
| llama_state_seq_flags flags) const { | |
| if (ctx == nullptr) { | |
| return; | |
| } | |
| if (data_tgt.empty()) { | |
| return; | |
| } | |
| const size_t n = llama_state_seq_set_data_ext(ctx, data_tgt.data(), data_tgt.size(), seq_id, flags); | |
| if (n != data_tgt.size()) { | |
| GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_tgt.size(), n); | |
| } | |
| } | |
| void common_prompt_checkpoint::load_dft( | |
| llama_context * ctx, | |
| llama_seq_id seq_id, | |
| llama_state_seq_flags flags) const { | |
| if (ctx == nullptr) { | |
| return; | |
| } | |
| if (data_dft.empty()) { | |
| return; | |
| } | |
| const size_t n = llama_state_seq_set_data_ext(ctx, data_dft.data(), data_dft.size(), seq_id, flags); | |
| if (n != data_dft.size()) { | |
| GGML_ABORT("checkpoint size mismatch: expected %zu, got %zu\n", data_dft.size(), n); | |
| } | |
| } | |
| void common_prompt_checkpoint::clear_tgt() { | |
| data_tgt.clear(); | |
| } | |
| void common_prompt_checkpoint::clear_dft() { | |
| data_dft.clear(); | |
| data_spec.clear(); | |
| } | |