mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 13:28:50 +01:00
17738 lines
703 KiB
C++
17738 lines
703 KiB
C++
#define LLAMA_API_INTERNAL
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#include "llama.h"
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#include "unicode.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#ifdef GGML_USE_CUDA
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# include "ggml-cuda.h"
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#elif defined(GGML_USE_CLBLAST)
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# include "ggml-opencl.h"
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#elif defined(GGML_USE_VULKAN)
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# include "ggml-vulkan.h"
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#elif defined(GGML_USE_SYCL)
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# include "ggml-sycl.h"
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#elif defined(GGML_USE_KOMPUTE)
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# include "ggml-kompute.h"
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#endif
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#ifdef GGML_USE_METAL
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# include "ggml-metal.h"
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#endif
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#ifdef GGML_USE_MPI
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# include "ggml-mpi.h"
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#endif
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#ifndef QK_K
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# ifdef GGML_QKK_64
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# define QK_K 64
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# else
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# define QK_K 256
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# endif
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#endif
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#ifdef __has_include
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#if __has_include(<unistd.h>)
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#include <unistd.h>
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#if defined(_POSIX_MAPPED_FILES)
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#include <sys/mman.h>
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#include <fcntl.h>
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#endif
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#if defined(_POSIX_MEMLOCK_RANGE)
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#include <sys/resource.h>
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#endif
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#endif
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#endif
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#if defined(_WIN32)
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#define WIN32_LEAN_AND_MEAN
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#ifndef NOMINMAX
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#define NOMINMAX
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#endif
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#include <windows.h>
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#ifndef PATH_MAX
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#define PATH_MAX MAX_PATH
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#endif
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#include <io.h>
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#endif
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#include <algorithm>
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#include <array>
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#include <cassert>
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#include <cctype>
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#include <cfloat>
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#include <cinttypes>
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#include <climits>
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#include <cmath>
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#include <cstdarg>
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#include <cstddef>
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#include <cstdint>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <forward_list>
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#include <fstream>
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#include <functional>
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#include <initializer_list>
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#include <locale>
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#include <map>
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#include <memory>
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#include <mutex>
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#include <numeric>
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#include <queue>
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#include <random>
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#include <regex>
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#include <set>
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#include <sstream>
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#include <thread>
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#include <type_traits>
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#include <unordered_map>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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#ifdef __GNUC__
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#ifdef __MINGW32__
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#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
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#else
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#define LLAMA_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
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#endif
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#else
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#define LLAMA_ATTRIBUTE_FORMAT(...)
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#endif
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#define LLAMA_MAX_NODES 8192
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#define LLAMA_MAX_EXPERTS 60
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//
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// logging
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//
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LLAMA_ATTRIBUTE_FORMAT(2, 3)
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static void llama_log_internal (ggml_log_level level, const char* format, ...);
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static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data);
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#define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)
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#define LLAMA_LOG_WARN(...) llama_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
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#define LLAMA_LOG_ERROR(...) llama_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
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//
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// helpers
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//
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static size_t utf8_len(char src) {
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const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
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uint8_t highbits = static_cast<uint8_t>(src) >> 4;
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return lookup[highbits];
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}
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static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
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std::string result;
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for (size_t pos = 0; ; pos += search.length()) {
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auto new_pos = s.find(search, pos);
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if (new_pos == std::string::npos) {
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result += s.substr(pos, s.size() - pos);
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break;
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}
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result += s.substr(pos, new_pos - pos) + replace;
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pos = new_pos;
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}
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s = std::move(result);
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}
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static bool is_float_close(float a, float b, float abs_tol) {
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// Check for non-negative tolerance
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if (abs_tol < 0.0) {
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throw std::invalid_argument("Tolerance must be non-negative");
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}
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// Exact equality check
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if (a == b) {
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return true;
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}
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// Check for infinities
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if (std::isinf(a) || std::isinf(b)) {
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return false;
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}
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// Regular comparison using the provided absolute tolerance
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return std::fabs(b - a) <= abs_tol;
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}
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static void zeros(std::ofstream & file, size_t n) {
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char zero = 0;
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for (size_t i = 0; i < n; ++i) {
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file.write(&zero, 1);
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}
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}
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LLAMA_ATTRIBUTE_FORMAT(1, 2)
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static std::string format(const char * fmt, ...) {
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va_list ap;
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va_list ap2;
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va_start(ap, fmt);
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va_copy(ap2, ap);
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int size = vsnprintf(NULL, 0, fmt, ap);
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GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
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std::vector<char> buf(size + 1);
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int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
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GGML_ASSERT(size2 == size);
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va_end(ap2);
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va_end(ap);
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return std::string(buf.data(), size);
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}
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//
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// gguf constants (sync with gguf.py)
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//
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enum llm_arch {
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LLM_ARCH_LLAMA,
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LLM_ARCH_FALCON,
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LLM_ARCH_BAICHUAN,
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LLM_ARCH_GROK,
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LLM_ARCH_GPT2,
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LLM_ARCH_GPTJ,
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LLM_ARCH_GPTNEOX,
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LLM_ARCH_MPT,
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LLM_ARCH_STARCODER,
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LLM_ARCH_PERSIMMON,
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LLM_ARCH_REFACT,
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LLM_ARCH_BERT,
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LLM_ARCH_NOMIC_BERT,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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LLM_ARCH_QWEN2,
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LLM_ARCH_QWEN2MOE,
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LLM_ARCH_PHI2,
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LLM_ARCH_PHI3,
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LLM_ARCH_PLAMO,
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LLM_ARCH_CODESHELL,
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LLM_ARCH_ORION,
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LLM_ARCH_INTERNLM2,
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LLM_ARCH_MINICPM,
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LLM_ARCH_GEMMA,
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LLM_ARCH_STARCODER2,
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LLM_ARCH_MAMBA,
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LLM_ARCH_XVERSE,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_DBRX,
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LLM_ARCH_OLMO,
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LLM_ARCH_UNKNOWN,
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};
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static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GROK, "grok" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_PERSIMMON, "persimmon" },
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BERT, "bert" },
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{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_QWEN2, "qwen2" },
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{ LLM_ARCH_QWEN2MOE, "qwen2moe" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PHI3, "phi3" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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{ LLM_ARCH_MINICPM, "minicpm" },
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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enum llm_kv {
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LLM_KV_GENERAL_ARCHITECTURE,
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LLM_KV_GENERAL_QUANTIZATION_VERSION,
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LLM_KV_GENERAL_ALIGNMENT,
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LLM_KV_GENERAL_NAME,
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LLM_KV_GENERAL_AUTHOR,
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LLM_KV_GENERAL_VERSION,
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LLM_KV_GENERAL_URL,
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LLM_KV_GENERAL_DESCRIPTION,
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LLM_KV_GENERAL_LICENSE,
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LLM_KV_GENERAL_SOURCE_URL,
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LLM_KV_GENERAL_SOURCE_HF_REPO,
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LLM_KV_VOCAB_SIZE,
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LLM_KV_CONTEXT_LENGTH,
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LLM_KV_EMBEDDING_LENGTH,
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LLM_KV_BLOCK_COUNT,
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LLM_KV_FEED_FORWARD_LENGTH,
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LLM_KV_USE_PARALLEL_RESIDUAL,
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LLM_KV_TENSOR_DATA_LAYOUT,
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LLM_KV_EXPERT_COUNT,
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LLM_KV_EXPERT_USED_COUNT,
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LLM_KV_POOLING_TYPE,
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LLM_KV_LOGIT_SCALE,
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LLM_KV_ATTENTION_HEAD_COUNT,
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LLM_KV_ATTENTION_HEAD_COUNT_KV,
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LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
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LLM_KV_ATTENTION_CLAMP_KQV,
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LLM_KV_ATTENTION_KEY_LENGTH,
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LLM_KV_ATTENTION_VALUE_LENGTH,
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LLM_KV_ATTENTION_LAYERNORM_EPS,
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LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
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LLM_KV_ATTENTION_CAUSAL,
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LLM_KV_ROPE_DIMENSION_COUNT,
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LLM_KV_ROPE_FREQ_BASE,
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LLM_KV_ROPE_SCALE_LINEAR,
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LLM_KV_ROPE_SCALING_TYPE,
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LLM_KV_ROPE_SCALING_FACTOR,
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LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
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LLM_KV_ROPE_SCALING_FINETUNED,
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LLM_KV_SPLIT_NO,
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LLM_KV_SPLIT_COUNT,
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LLM_KV_SPLIT_TENSORS_COUNT,
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LLM_KV_SSM_INNER_SIZE,
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LLM_KV_SSM_CONV_KERNEL,
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LLM_KV_SSM_STATE_SIZE,
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LLM_KV_SSM_TIME_STEP_RANK,
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LLM_KV_TOKENIZER_MODEL,
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LLM_KV_TOKENIZER_LIST,
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LLM_KV_TOKENIZER_TOKEN_TYPE,
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LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
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LLM_KV_TOKENIZER_SCORES,
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LLM_KV_TOKENIZER_MERGES,
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LLM_KV_TOKENIZER_BOS_ID,
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LLM_KV_TOKENIZER_EOS_ID,
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LLM_KV_TOKENIZER_UNK_ID,
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LLM_KV_TOKENIZER_SEP_ID,
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LLM_KV_TOKENIZER_PAD_ID,
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LLM_KV_TOKENIZER_CLS_ID,
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LLM_KV_TOKENIZER_MASK_ID,
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LLM_KV_TOKENIZER_ADD_BOS,
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LLM_KV_TOKENIZER_ADD_EOS,
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LLM_KV_TOKENIZER_ADD_PREFIX,
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LLM_KV_TOKENIZER_HF_JSON,
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LLM_KV_TOKENIZER_RWKV,
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LLM_KV_TOKENIZER_PREFIX_ID,
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LLM_KV_TOKENIZER_SUFFIX_ID,
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LLM_KV_TOKENIZER_MIDDLE_ID,
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LLM_KV_TOKENIZER_EOT_ID,
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};
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static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
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{ LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" },
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{ LLM_KV_GENERAL_ALIGNMENT, "general.alignment" },
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{ LLM_KV_GENERAL_NAME, "general.name" },
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{ LLM_KV_GENERAL_AUTHOR, "general.author" },
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{ LLM_KV_GENERAL_VERSION, "general.version" },
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{ LLM_KV_GENERAL_URL, "general.url" },
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{ LLM_KV_GENERAL_DESCRIPTION, "general.description" },
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{ LLM_KV_GENERAL_LICENSE, "general.license" },
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{ LLM_KV_GENERAL_SOURCE_URL, "general.source.url" },
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{ LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" },
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{ LLM_KV_VOCAB_SIZE, "%s.vocab_size" },
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{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
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{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
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{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
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{ LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
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{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
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{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
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{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
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{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
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{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
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{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
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{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
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{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
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{ LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
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{ LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
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{ LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
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{ LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
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{ LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
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{ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
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{ LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
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{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
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{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
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{ LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
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{ LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
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{ LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
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{ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
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{ LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
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{ LLM_KV_SPLIT_NO, "split.no" },
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{ LLM_KV_SPLIT_COUNT, "split.count" },
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{ LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },
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{ LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" },
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{ LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" },
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{ LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
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{ LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
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{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
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{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
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{ LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
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{ LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
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{ LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
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{ LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
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{ LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
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{ LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
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{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
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{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
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{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
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{ LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
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{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
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{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
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{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
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{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
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{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
|
|
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
|
|
{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
|
|
{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
|
|
{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
|
|
{ LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
|
|
};
|
|
|
|
struct LLM_KV {
|
|
LLM_KV(llm_arch arch) : arch(arch) {}
|
|
|
|
llm_arch arch;
|
|
|
|
std::string operator()(llm_kv kv) const {
|
|
return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
|
|
}
|
|
};
|
|
|
|
enum llm_tensor {
|
|
LLM_TENSOR_TOKEN_EMBD,
|
|
LLM_TENSOR_TOKEN_EMBD_NORM,
|
|
LLM_TENSOR_TOKEN_TYPES,
|
|
LLM_TENSOR_POS_EMBD,
|
|
LLM_TENSOR_OUTPUT,
|
|
LLM_TENSOR_OUTPUT_NORM,
|
|
LLM_TENSOR_ROPE_FREQS,
|
|
LLM_TENSOR_ATTN_Q,
|
|
LLM_TENSOR_ATTN_K,
|
|
LLM_TENSOR_ATTN_V,
|
|
LLM_TENSOR_ATTN_QKV,
|
|
LLM_TENSOR_ATTN_OUT,
|
|
LLM_TENSOR_ATTN_NORM,
|
|
LLM_TENSOR_ATTN_NORM_2,
|
|
LLM_TENSOR_ATTN_OUT_NORM,
|
|
LLM_TENSOR_ATTN_ROT_EMBD,
|
|
LLM_TENSOR_FFN_GATE_INP,
|
|
LLM_TENSOR_FFN_GATE_INP_SHEXP,
|
|
LLM_TENSOR_FFN_NORM,
|
|
LLM_TENSOR_FFN_GATE,
|
|
LLM_TENSOR_FFN_DOWN,
|
|
LLM_TENSOR_FFN_UP,
|
|
LLM_TENSOR_FFN_ACT,
|
|
LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
|
|
LLM_TENSOR_FFN_GATE_EXP,
|
|
LLM_TENSOR_FFN_UP_EXP,
|
|
LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
|
|
LLM_TENSOR_FFN_GATE_EXPS,
|
|
LLM_TENSOR_FFN_UP_EXPS,
|
|
LLM_TENSOR_FFN_DOWN_SHEXP,
|
|
LLM_TENSOR_FFN_GATE_SHEXP,
|
|
LLM_TENSOR_FFN_UP_SHEXP,
|
|
LLM_TENSOR_ATTN_Q_NORM,
|
|
LLM_TENSOR_ATTN_K_NORM,
|
|
LLM_TENSOR_LAYER_OUT_NORM,
|
|
LLM_TENSOR_SSM_IN,
|
|
LLM_TENSOR_SSM_CONV1D,
|
|
LLM_TENSOR_SSM_X,
|
|
LLM_TENSOR_SSM_DT,
|
|
LLM_TENSOR_SSM_A,
|
|
LLM_TENSOR_SSM_D,
|
|
LLM_TENSOR_SSM_OUT,
|
|
};
|
|
|
|
static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
|
|
{
|
|
LLM_ARCH_LLAMA,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
|
|
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
|
|
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
|
|
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_BAICHUAN,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_FALCON,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_GROK,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
|
|
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
|
|
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
|
|
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
|
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
|
|
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_GPT2,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_GPTJ,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_GPTNEOX,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_PERSIMMON,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd"},
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm"},
|
|
{ LLM_TENSOR_OUTPUT, "output"},
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"},
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"},
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"},
|
|
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
|
|
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm"},
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down"},
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up"},
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd"},
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_MPT,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output"},
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
|
|
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
|
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
|
|
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_STARCODER,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_REFACT,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_BERT,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
|
|
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
|
|
{ LLM_TENSOR_POS_EMBD, "position_embd" },
|
|
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_NOMIC_BERT,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
|
|
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
|
|
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_BLOOM,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_STABLELM,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
|
|
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_QWEN,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_QWEN2,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_QWEN2MOE,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
|
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
|
|
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
|
{ LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
|
|
{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
|
|
{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
|
|
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_PHI2,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_PHI3,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_PLAMO,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_CODESHELL,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_ORION,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_INTERNLM2,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_MINICPM,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
|
|
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_GEMMA,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_STARCODER2,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_MAMBA,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" },
|
|
{ LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
|
|
{ LLM_TENSOR_SSM_X, "blk.%d.ssm_x" },
|
|
{ LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
|
|
{ LLM_TENSOR_SSM_A, "blk.%d.ssm_a" },
|
|
{ LLM_TENSOR_SSM_D, "blk.%d.ssm_d" },
|
|
{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_XVERSE,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
|
|
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_COMMAND_R,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
|
|
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_DBRX,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
|
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
|
|
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
|
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
|
|
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
|
|
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_OLMO,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
{ LLM_TENSOR_OUTPUT, "output" },
|
|
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
|
|
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
|
|
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
|
|
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
|
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
|
|
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
|
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
|
},
|
|
},
|
|
{
|
|
LLM_ARCH_UNKNOWN,
|
|
{
|
|
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
|
},
|
|
},
|
|
};
|
|
|
|
static llm_arch llm_arch_from_string(const std::string & name) {
|
|
for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
|
|
if (kv.second == name) {
|
|
return kv.first;
|
|
}
|
|
}
|
|
|
|
return LLM_ARCH_UNKNOWN;
|
|
}
|
|
|
|
// helper to handle gguf constants
|
|
// usage:
|
|
//
|
|
// const auto tn = LLM_TN(LLM_ARCH_LLAMA);
|
|
//
|
|
// std::string name = tn(LLM_TENSOR_OUTPUT); -> "output"
|
|
// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
|
|
// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
|
|
//
|
|
struct LLM_TN {
|
|
LLM_TN(llm_arch arch) : arch(arch) {}
|
|
|
|
llm_arch arch;
|
|
|
|
std::string operator()(llm_tensor tensor) const {
|
|
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
|
|
return "__missing__";
|
|
}
|
|
return LLM_TENSOR_NAMES.at(arch).at(tensor);
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, const std::string & suffix) const {
|
|
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
|
|
return "__missing__";
|
|
}
|
|
return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, int bid) const {
|
|
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
|
|
return "__missing__";
|
|
}
|
|
return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
|
|
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
|
|
return "__missing__";
|
|
}
|
|
return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
|
|
if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
|
|
return "__missing__";
|
|
}
|
|
return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
|
|
}
|
|
};
|
|
|
|
//
|
|
// gguf helpers
|
|
//
|
|
|
|
static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
|
|
{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
|
|
{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
|
|
{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
|
|
};
|
|
|
|
static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
|
|
for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
|
|
if (kv.second == name) {
|
|
return (llama_rope_scaling_type) kv.first;
|
|
}
|
|
}
|
|
|
|
return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
|
|
}
|
|
|
|
static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
|
|
switch (type) {
|
|
case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]);
|
|
case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]);
|
|
case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]);
|
|
case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]);
|
|
case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]);
|
|
case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]);
|
|
case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]);
|
|
case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]);
|
|
case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]);
|
|
case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]);
|
|
case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false";
|
|
default: return format("unknown type %d", type);
|
|
}
|
|
}
|
|
|
|
static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
|
|
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
|
|
|
|
switch (type) {
|
|
case GGUF_TYPE_STRING:
|
|
return gguf_get_val_str(ctx_gguf, i);
|
|
case GGUF_TYPE_ARRAY:
|
|
{
|
|
const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
|
|
int arr_n = gguf_get_arr_n(ctx_gguf, i);
|
|
const void * data = gguf_get_arr_data(ctx_gguf, i);
|
|
std::stringstream ss;
|
|
ss << "[";
|
|
for (int j = 0; j < arr_n; j++) {
|
|
if (arr_type == GGUF_TYPE_STRING) {
|
|
std::string val = gguf_get_arr_str(ctx_gguf, i, j);
|
|
// escape quotes
|
|
replace_all(val, "\\", "\\\\");
|
|
replace_all(val, "\"", "\\\"");
|
|
ss << '"' << val << '"';
|
|
} else if (arr_type == GGUF_TYPE_ARRAY) {
|
|
ss << "???";
|
|
} else {
|
|
ss << gguf_data_to_str(arr_type, data, j);
|
|
}
|
|
if (j < arr_n - 1) {
|
|
ss << ", ";
|
|
}
|
|
}
|
|
ss << "]";
|
|
return ss.str();
|
|
}
|
|
default:
|
|
return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
|
|
}
|
|
}
|
|
|
|
//
|
|
// llama helpers
|
|
//
|
|
|
|
#if defined(_WIN32)
|
|
static std::string llama_format_win_err(DWORD err) {
|
|
LPSTR buf;
|
|
size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
|
|
NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
|
|
if (!size) {
|
|
return "FormatMessageA failed";
|
|
}
|
|
std::string ret(buf, size);
|
|
LocalFree(buf);
|
|
return ret;
|
|
}
|
|
#endif
|
|
|
|
template <typename T>
|
|
struct no_init {
|
|
T value;
|
|
no_init() { /* do nothing */ }
|
|
};
|
|
|
|
struct llama_file {
|
|
// use FILE * so we don't have to re-open the file to mmap
|
|
FILE * fp;
|
|
size_t size;
|
|
|
|
llama_file(const char * fname, const char * mode) {
|
|
fp = ggml_fopen(fname, mode);
|
|
if (fp == NULL) {
|
|
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
|
|
}
|
|
seek(0, SEEK_END);
|
|
size = tell();
|
|
seek(0, SEEK_SET);
|
|
}
|
|
|
|
size_t tell() const {
|
|
#ifdef _WIN32
|
|
__int64 ret = _ftelli64(fp);
|
|
#else
|
|
long ret = std::ftell(fp);
|
|
#endif
|
|
GGML_ASSERT(ret != -1); // this really shouldn't fail
|
|
return (size_t) ret;
|
|
}
|
|
|
|
void seek(size_t offset, int whence) const {
|
|
#ifdef _WIN32
|
|
int ret = _fseeki64(fp, (__int64) offset, whence);
|
|
#else
|
|
int ret = std::fseek(fp, (long) offset, whence);
|
|
#endif
|
|
GGML_ASSERT(ret == 0); // same
|
|
}
|
|
|
|
void read_raw(void * ptr, size_t len) const {
|
|
if (len == 0) {
|
|
return;
|
|
}
|
|
errno = 0;
|
|
std::size_t ret = std::fread(ptr, len, 1, fp);
|
|
if (ferror(fp)) {
|
|
throw std::runtime_error(format("read error: %s", strerror(errno)));
|
|
}
|
|
if (ret != 1) {
|
|
throw std::runtime_error("unexpectedly reached end of file");
|
|
}
|
|
}
|
|
|
|
uint32_t read_u32() const {
|
|
uint32_t ret;
|
|
read_raw(&ret, sizeof(ret));
|
|
return ret;
|
|
}
|
|
|
|
void write_raw(const void * ptr, size_t len) const {
|
|
if (len == 0) {
|
|
return;
|
|
}
|
|
errno = 0;
|
|
size_t ret = std::fwrite(ptr, len, 1, fp);
|
|
if (ret != 1) {
|
|
throw std::runtime_error(format("write error: %s", strerror(errno)));
|
|
}
|
|
}
|
|
|
|
void write_u32(std::uint32_t val) const {
|
|
write_raw(&val, sizeof(val));
|
|
}
|
|
|
|
~llama_file() {
|
|
if (fp) {
|
|
std::fclose(fp);
|
|
}
|
|
}
|
|
};
|
|
using llama_files = std::vector<std::unique_ptr<llama_file>>;
|
|
|
|
struct llama_mmap {
|
|
void * addr;
|
|
size_t size;
|
|
|
|
llama_mmap(const llama_mmap &) = delete;
|
|
|
|
#ifdef _POSIX_MAPPED_FILES
|
|
static constexpr bool SUPPORTED = true;
|
|
|
|
// list of mapped fragments (first_offset, last_offset)
|
|
std::vector<std::pair<size_t, size_t>> mapped_fragments;
|
|
|
|
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
|
|
size = file->size;
|
|
int fd = fileno(file->fp);
|
|
int flags = MAP_SHARED;
|
|
// prefetch/readahead impairs performance on NUMA systems
|
|
if (numa) { prefetch = 0; }
|
|
#ifdef __linux__
|
|
// advise the kernel to read the file sequentially (increases readahead)
|
|
if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
|
|
LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n",
|
|
strerror(errno));
|
|
}
|
|
if (prefetch) { flags |= MAP_POPULATE; }
|
|
#endif
|
|
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
|
|
if (addr == MAP_FAILED) { // NOLINT
|
|
throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
|
|
}
|
|
|
|
if (prefetch > 0) {
|
|
// advise the kernel to preload the mapped memory
|
|
if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
|
|
LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
|
|
strerror(errno));
|
|
}
|
|
}
|
|
if (numa) {
|
|
// advise the kernel not to use readahead
|
|
// (because the next page might not belong on the same node)
|
|
if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
|
|
LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
|
|
strerror(errno));
|
|
}
|
|
}
|
|
|
|
// initialize list of mapped_fragments
|
|
mapped_fragments.emplace_back(0, file->size);
|
|
}
|
|
|
|
static void align_range(size_t * first, size_t * last, size_t page_size) {
|
|
// align first to the next page
|
|
size_t offset_in_page = *first & (page_size - 1);
|
|
size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page;
|
|
*first += offset_to_page;
|
|
|
|
// align last to the previous page
|
|
*last = *last & ~(page_size - 1);
|
|
|
|
if (*last <= *first) {
|
|
*last = *first;
|
|
}
|
|
}
|
|
|
|
// partially unmap the file in the range [first, last)
|
|
void unmap_fragment(size_t first, size_t last) {
|
|
// note: this function must not be called multiple times with overlapping ranges
|
|
// otherwise, there is a risk of invalidating addresses that have been repurposed for other mappings
|
|
int page_size = sysconf(_SC_PAGESIZE);
|
|
align_range(&first, &last, page_size);
|
|
size_t len = last - first;
|
|
|
|
if (len == 0) {
|
|
return;
|
|
}
|
|
|
|
GGML_ASSERT(first % page_size == 0);
|
|
GGML_ASSERT(last % page_size == 0);
|
|
GGML_ASSERT(last > first);
|
|
|
|
void * next_page_start = (uint8_t *) addr + first;
|
|
|
|
// unmap the range
|
|
if (munmap(next_page_start, len)) {
|
|
LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
|
|
}
|
|
|
|
// update the list of mapped fragments to avoid unmapping the same range again in the destructor
|
|
std::vector<std::pair<size_t, size_t>> new_mapped_fragments;
|
|
for (const auto & frag : mapped_fragments) {
|
|
if (frag.first < first && frag.second > last) {
|
|
// the range is in the middle of the fragment, split it
|
|
new_mapped_fragments.emplace_back(frag.first, first);
|
|
new_mapped_fragments.emplace_back(last, frag.second);
|
|
} else if (frag.first < first && frag.second > first) {
|
|
// the range starts in the middle of the fragment
|
|
new_mapped_fragments.emplace_back(frag.first, first);
|
|
} else if (frag.first < last && frag.second > last) {
|
|
// the range ends in the middle of the fragment
|
|
new_mapped_fragments.emplace_back(last, frag.second);
|
|
} else if (frag.first >= first && frag.second <= last) {
|
|
// the range covers the entire fragment
|
|
} else {
|
|
// the range is outside the fragment
|
|
new_mapped_fragments.push_back(frag);
|
|
}
|
|
}
|
|
mapped_fragments = std::move(new_mapped_fragments);
|
|
}
|
|
|
|
~llama_mmap() {
|
|
for (const auto & frag : mapped_fragments) {
|
|
if (munmap((char *) addr + frag.first, frag.second - frag.first)) {
|
|
LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
|
|
}
|
|
}
|
|
}
|
|
#elif defined(_WIN32)
|
|
static constexpr bool SUPPORTED = true;
|
|
|
|
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) {
|
|
GGML_UNUSED(numa);
|
|
|
|
size = file->size;
|
|
|
|
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
|
|
|
|
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
|
|
|
|
if (hMapping == NULL) {
|
|
DWORD error = GetLastError();
|
|
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
|
|
}
|
|
|
|
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
|
|
DWORD error = GetLastError();
|
|
CloseHandle(hMapping);
|
|
|
|
if (addr == NULL) {
|
|
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
|
|
}
|
|
|
|
if (prefetch > 0) {
|
|
#if _WIN32_WINNT >= 0x602
|
|
// PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
|
|
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
|
|
HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
|
|
|
|
// may fail on pre-Windows 8 systems
|
|
pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
|
|
|
|
if (pPrefetchVirtualMemory) {
|
|
// advise the kernel to preload the mapped memory
|
|
WIN32_MEMORY_RANGE_ENTRY range;
|
|
range.VirtualAddress = addr;
|
|
range.NumberOfBytes = (SIZE_T) std::min(size, prefetch);
|
|
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
|
|
LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
}
|
|
}
|
|
#else
|
|
throw std::runtime_error("PrefetchVirtualMemory unavailable");
|
|
#endif
|
|
}
|
|
}
|
|
|
|
void unmap_fragment(size_t first, size_t last) {
|
|
// not supported
|
|
GGML_UNUSED(first);
|
|
GGML_UNUSED(last);
|
|
}
|
|
|
|
~llama_mmap() {
|
|
if (!UnmapViewOfFile(addr)) {
|
|
LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
}
|
|
}
|
|
#else
|
|
static constexpr bool SUPPORTED = false;
|
|
|
|
llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) {
|
|
GGML_UNUSED(file);
|
|
GGML_UNUSED(prefetch);
|
|
GGML_UNUSED(numa);
|
|
|
|
throw std::runtime_error("mmap not supported");
|
|
}
|
|
|
|
void unmap_fragment(size_t first, size_t last) {
|
|
GGML_UNUSED(first);
|
|
GGML_UNUSED(last);
|
|
|
|
throw std::runtime_error("mmap not supported");
|
|
}
|
|
#endif
|
|
};
|
|
using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>;
|
|
|
|
// Represents some region of memory being locked using mlock or VirtualLock;
|
|
// will automatically unlock on destruction.
|
|
struct llama_mlock {
|
|
void * addr = NULL;
|
|
size_t size = 0;
|
|
|
|
bool failed_already = false;
|
|
|
|
llama_mlock() {}
|
|
llama_mlock(const llama_mlock &) = delete;
|
|
|
|
~llama_mlock() {
|
|
if (size) {
|
|
raw_unlock(addr, size);
|
|
}
|
|
}
|
|
|
|
void init(void * ptr) {
|
|
GGML_ASSERT(addr == NULL && size == 0); // NOLINT
|
|
addr = ptr;
|
|
}
|
|
|
|
void grow_to(size_t target_size) {
|
|
GGML_ASSERT(addr);
|
|
if (failed_already) {
|
|
return;
|
|
}
|
|
size_t granularity = lock_granularity();
|
|
target_size = (target_size + granularity - 1) & ~(granularity - 1);
|
|
if (target_size > size) {
|
|
if (raw_lock((uint8_t *) addr + size, target_size - size)) {
|
|
size = target_size;
|
|
} else {
|
|
failed_already = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
#ifdef _POSIX_MEMLOCK_RANGE
|
|
static constexpr bool SUPPORTED = true;
|
|
|
|
static size_t lock_granularity() {
|
|
return (size_t) sysconf(_SC_PAGESIZE);
|
|
}
|
|
|
|
#ifdef __APPLE__
|
|
#define MLOCK_SUGGESTION \
|
|
"Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
|
|
"decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n"
|
|
#else
|
|
#define MLOCK_SUGGESTION \
|
|
"Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n"
|
|
#endif
|
|
|
|
bool raw_lock(const void * addr, size_t size) const {
|
|
if (!mlock(addr, size)) {
|
|
return true;
|
|
}
|
|
|
|
char* errmsg = std::strerror(errno);
|
|
bool suggest = (errno == ENOMEM);
|
|
|
|
// Check if the resource limit is fine after all
|
|
struct rlimit lock_limit;
|
|
if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
|
|
suggest = false;
|
|
}
|
|
if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
|
|
suggest = false;
|
|
}
|
|
|
|
LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
|
|
size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
|
|
return false;
|
|
}
|
|
|
|
#undef MLOCK_SUGGESTION
|
|
|
|
static void raw_unlock(void * addr, size_t size) {
|
|
if (munlock(addr, size)) {
|
|
LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno));
|
|
}
|
|
}
|
|
#elif defined(_WIN32)
|
|
static constexpr bool SUPPORTED = true;
|
|
|
|
static size_t lock_granularity() {
|
|
SYSTEM_INFO si;
|
|
GetSystemInfo(&si);
|
|
return (size_t) si.dwPageSize;
|
|
}
|
|
|
|
bool raw_lock(void * ptr, size_t len) const {
|
|
for (int tries = 1; ; tries++) {
|
|
if (VirtualLock(ptr, len)) {
|
|
return true;
|
|
}
|
|
if (tries == 2) {
|
|
LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
|
|
len, size, llama_format_win_err(GetLastError()).c_str());
|
|
return false;
|
|
}
|
|
|
|
// It failed but this was only the first try; increase the working
|
|
// set size and try again.
|
|
SIZE_T min_ws_size, max_ws_size;
|
|
if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
|
|
LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
return false;
|
|
}
|
|
// Per MSDN: "The maximum number of pages that a process can lock
|
|
// is equal to the number of pages in its minimum working set minus
|
|
// a small overhead."
|
|
// Hopefully a megabyte is enough overhead:
|
|
size_t increment = len + 1048576;
|
|
// The minimum must be <= the maximum, so we need to increase both:
|
|
min_ws_size += increment;
|
|
max_ws_size += increment;
|
|
if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
|
|
LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
static void raw_unlock(void * ptr, size_t len) {
|
|
if (!VirtualUnlock(ptr, len)) {
|
|
LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
}
|
|
}
|
|
#else
|
|
static constexpr bool SUPPORTED = false;
|
|
|
|
static size_t lock_granularity() {
|
|
return (size_t) 65536;
|
|
}
|
|
|
|
bool raw_lock(const void * addr, size_t len) const {
|
|
LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
|
|
return false;
|
|
}
|
|
|
|
static void raw_unlock(const void * addr, size_t len) {}
|
|
#endif
|
|
};
|
|
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
|
|
|
|
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
|
|
std::vector<char> result(8, 0);
|
|
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
|
GGML_ASSERT(check == -n_tokens);
|
|
}
|
|
else {
|
|
result.resize(n_tokens);
|
|
}
|
|
|
|
return std::string(result.data(), result.size());
|
|
}
|
|
|
|
static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
|
|
ggml_backend_buffer_type_t buft = nullptr;
|
|
|
|
#if defined(GGML_USE_CUDA)
|
|
// host buffers should only be used when data is expected to be copied to/from the GPU
|
|
if (host_buffer) {
|
|
buft = ggml_backend_cuda_host_buffer_type();
|
|
}
|
|
#elif defined(GGML_USE_SYCL)
|
|
if (host_buffer) {
|
|
buft = ggml_backend_sycl_host_buffer_type();
|
|
}
|
|
#elif defined(GGML_USE_CPU_HBM)
|
|
buft = ggml_backend_cpu_hbm_buffer_type();
|
|
#elif defined(GGML_USE_VULKAN)
|
|
if (host_buffer) {
|
|
buft = ggml_backend_vk_host_buffer_type();
|
|
}
|
|
#endif
|
|
|
|
if (buft == nullptr) {
|
|
buft = ggml_backend_cpu_buffer_type();
|
|
}
|
|
return buft;
|
|
|
|
GGML_UNUSED(host_buffer);
|
|
}
|
|
|
|
static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) {
|
|
ggml_backend_buffer_type_t buft = nullptr;
|
|
|
|
#ifdef GGML_USE_METAL
|
|
buft = ggml_backend_metal_buffer_type();
|
|
#elif defined(GGML_USE_CUDA)
|
|
buft = ggml_backend_cuda_buffer_type(gpu);
|
|
#elif defined(GGML_USE_VULKAN)
|
|
buft = ggml_backend_vk_buffer_type(gpu);
|
|
#elif defined(GGML_USE_SYCL)
|
|
buft = ggml_backend_sycl_buffer_type(gpu);
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
buft = ggml_backend_opencl_buffer_type();
|
|
#elif defined(GGML_USE_KOMPUTE)
|
|
buft = ggml_backend_kompute_buffer_type(gpu);
|
|
if (buft == nullptr) {
|
|
LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
|
|
}
|
|
#endif
|
|
|
|
if (buft == nullptr) {
|
|
buft = llama_default_buffer_type_cpu(true);
|
|
}
|
|
return buft;
|
|
|
|
GGML_UNUSED(gpu);
|
|
}
|
|
|
|
static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_gpu, const float * tensor_split) {
|
|
ggml_backend_buffer_type_t buft = nullptr;
|
|
|
|
#ifdef GGML_USE_CUDA
|
|
if (ggml_backend_cuda_get_device_count() > 1) {
|
|
buft = ggml_backend_cuda_split_buffer_type(tensor_split);
|
|
}
|
|
#endif
|
|
|
|
#ifdef GGML_USE_SYCL
|
|
if (ggml_backend_sycl_get_device_count() > 1) {
|
|
buft = ggml_backend_sycl_split_buffer_type(tensor_split);
|
|
}
|
|
#endif
|
|
|
|
if (buft == nullptr) {
|
|
buft = llama_default_buffer_type_offload(fallback_gpu);
|
|
}
|
|
return buft;
|
|
|
|
GGML_UNUSED(tensor_split);
|
|
}
|
|
|
|
static size_t llama_get_device_count() {
|
|
#if defined(GGML_USE_CUDA)
|
|
return ggml_backend_cuda_get_device_count();
|
|
#elif defined(GGML_USE_SYCL)
|
|
return ggml_backend_sycl_get_device_count();
|
|
#elif defined(GGML_USE_VULKAN)
|
|
return ggml_backend_vk_get_device_count();
|
|
#else
|
|
return 1;
|
|
#endif
|
|
}
|
|
|
|
static size_t llama_get_device_memory(int device) {
|
|
#if defined(GGML_USE_CUDA)
|
|
size_t total;
|
|
size_t free;
|
|
ggml_backend_cuda_get_device_memory(device, &free, &total);
|
|
return free;
|
|
#elif defined(GGML_USE_SYCL)
|
|
size_t total;
|
|
size_t free;
|
|
ggml_backend_sycl_get_device_memory(device, &free, &total);
|
|
return free;
|
|
#elif defined(GGML_USE_VULKAN)
|
|
size_t total;
|
|
size_t free;
|
|
ggml_backend_vk_get_device_memory(device, &free, &total);
|
|
return free;
|
|
#else
|
|
return 1;
|
|
GGML_UNUSED(device);
|
|
#endif
|
|
}
|
|
|
|
//
|
|
// globals
|
|
//
|
|
|
|
struct llama_state {
|
|
llama_state() {
|
|
#ifdef GGML_USE_METAL
|
|
ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
|
|
#endif
|
|
}
|
|
|
|
// We save the log callback globally
|
|
ggml_log_callback log_callback = llama_log_callback_default;
|
|
void * log_callback_user_data = nullptr;
|
|
};
|
|
|
|
static llama_state g_state;
|
|
|
|
// available llama models
|
|
enum e_model {
|
|
MODEL_UNKNOWN,
|
|
MODEL_17M,
|
|
MODEL_22M,
|
|
MODEL_33M,
|
|
MODEL_109M,
|
|
MODEL_137M,
|
|
MODEL_335M,
|
|
MODEL_0_5B,
|
|
MODEL_1B,
|
|
MODEL_2B,
|
|
MODEL_3B,
|
|
MODEL_4B,
|
|
MODEL_7B,
|
|
MODEL_8B,
|
|
MODEL_12B,
|
|
MODEL_13B,
|
|
MODEL_14B,
|
|
MODEL_15B,
|
|
MODEL_20B,
|
|
MODEL_30B,
|
|
MODEL_34B,
|
|
MODEL_35B,
|
|
MODEL_40B,
|
|
MODEL_65B,
|
|
MODEL_70B,
|
|
MODEL_314B,
|
|
MODEL_SMALL,
|
|
MODEL_MEDIUM,
|
|
MODEL_LARGE,
|
|
MODEL_XL,
|
|
MODEL_A2_7B,
|
|
MODEL_8x7B,
|
|
MODEL_8x22B,
|
|
MODEL_16x12B,
|
|
};
|
|
|
|
static const size_t kiB = 1024;
|
|
static const size_t MiB = 1024*kiB;
|
|
static const size_t GiB = 1024*MiB;
|
|
|
|
struct llama_hparams {
|
|
bool vocab_only;
|
|
bool rope_finetuned;
|
|
|
|
uint32_t n_vocab;
|
|
uint32_t n_ctx_train; // context size the model was trained on
|
|
uint32_t n_embd;
|
|
uint32_t n_head;
|
|
uint32_t n_head_kv;
|
|
uint32_t n_layer;
|
|
uint32_t n_rot;
|
|
uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
|
|
uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
|
|
uint32_t n_ff;
|
|
uint32_t n_expert = 0;
|
|
uint32_t n_expert_used = 0;
|
|
uint32_t n_vocab_type = 0; // for BERT-style token types
|
|
|
|
float f_norm_eps;
|
|
float f_norm_rms_eps;
|
|
|
|
float rope_freq_base_train;
|
|
float rope_freq_scale_train;
|
|
uint32_t n_yarn_orig_ctx;
|
|
|
|
// for State Space Models
|
|
uint32_t ssm_d_conv = 0;
|
|
uint32_t ssm_d_inner = 0;
|
|
uint32_t ssm_d_state = 0;
|
|
uint32_t ssm_dt_rank = 0;
|
|
|
|
float f_clamp_kqv = 0.0f;
|
|
float f_max_alibi_bias = 0.0f;
|
|
float f_logit_scale = 0.0f;
|
|
|
|
bool causal_attn = true;
|
|
bool need_kq_pos = false;
|
|
|
|
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
|
|
enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
|
|
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
|
|
|
|
bool operator!=(const llama_hparams & other) const {
|
|
if (this->vocab_only != other.vocab_only) return true;
|
|
if (this->n_vocab != other.n_vocab) return true;
|
|
if (this->n_ctx_train != other.n_ctx_train) return true;
|
|
if (this->n_embd != other.n_embd) return true;
|
|
if (this->n_head != other.n_head) return true;
|
|
if (this->n_head_kv != other.n_head_kv) return true;
|
|
if (this->n_layer != other.n_layer) return true;
|
|
if (this->n_rot != other.n_rot) return true;
|
|
if (this->n_embd_head_k != other.n_embd_head_k) return true;
|
|
if (this->n_embd_head_v != other.n_embd_head_v) return true;
|
|
if (this->n_ff != other.n_ff) return true;
|
|
if (this->n_expert != other.n_expert) return true;
|
|
if (this->n_expert_used != other.n_expert_used) return true;
|
|
|
|
if (this->rope_finetuned != other.rope_finetuned) return true;
|
|
if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true;
|
|
|
|
if (this->ssm_d_conv != other.ssm_d_conv) return true;
|
|
if (this->ssm_d_inner != other.ssm_d_inner) return true;
|
|
if (this->ssm_d_state != other.ssm_d_state) return true;
|
|
if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
|
|
|
|
const float EPSILON = 1e-9f;
|
|
|
|
if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
|
|
if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
|
|
if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true;
|
|
if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
|
|
|
|
return false;
|
|
}
|
|
|
|
uint32_t n_gqa() const {
|
|
if (n_head_kv == 0) {
|
|
return 0;
|
|
}
|
|
return n_head/n_head_kv;
|
|
}
|
|
|
|
uint32_t n_embd_k_gqa() const { // dimension of key embeddings across all k-v heads
|
|
return n_embd_head_k * n_head_kv;
|
|
}
|
|
|
|
uint32_t n_embd_v_gqa() const { // dimension of value embeddings across all k-v heads
|
|
return n_embd_head_v * n_head_kv;
|
|
}
|
|
|
|
uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
|
|
// corresponds to Mamba's conv_states size
|
|
// TODO: maybe support other convolution strides than 1
|
|
// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
|
|
return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
|
|
}
|
|
|
|
uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
|
|
// corresponds to Mamba's ssm_states size
|
|
return ssm_d_state * ssm_d_inner;
|
|
}
|
|
};
|
|
|
|
struct llama_cparams {
|
|
uint32_t n_ctx; // context size used during inference
|
|
uint32_t n_batch;
|
|
uint32_t n_ubatch;
|
|
uint32_t n_seq_max;
|
|
uint32_t n_threads; // number of threads to use for generation
|
|
uint32_t n_threads_batch; // number of threads to use for batch processing
|
|
|
|
float rope_freq_base;
|
|
float rope_freq_scale;
|
|
|
|
uint32_t n_yarn_orig_ctx;
|
|
// These hyperparameters are not exposed in GGUF, because all
|
|
// existing YaRN models use the same values for them.
|
|
float yarn_ext_factor;
|
|
float yarn_attn_factor;
|
|
float yarn_beta_fast;
|
|
float yarn_beta_slow;
|
|
float defrag_thold;
|
|
|
|
bool embeddings;
|
|
bool causal_attn;
|
|
bool offload_kqv;
|
|
|
|
enum llama_pooling_type pooling_type;
|
|
|
|
ggml_backend_sched_eval_callback cb_eval;
|
|
void * cb_eval_user_data;
|
|
};
|
|
|
|
struct llama_layer {
|
|
// normalization
|
|
struct ggml_tensor * attn_norm;
|
|
struct ggml_tensor * attn_norm_b;
|
|
struct ggml_tensor * attn_norm_2;
|
|
struct ggml_tensor * attn_norm_2_b;
|
|
struct ggml_tensor * attn_q_norm;
|
|
struct ggml_tensor * attn_q_norm_b;
|
|
struct ggml_tensor * attn_k_norm;
|
|
struct ggml_tensor * attn_k_norm_b;
|
|
struct ggml_tensor * attn_out_norm;
|
|
struct ggml_tensor * attn_out_norm_b;
|
|
|
|
// attention
|
|
struct ggml_tensor * wq;
|
|
struct ggml_tensor * wk;
|
|
struct ggml_tensor * wv;
|
|
struct ggml_tensor * wo;
|
|
struct ggml_tensor * wqkv;
|
|
|
|
// attention bias
|
|
struct ggml_tensor * bq;
|
|
struct ggml_tensor * bk;
|
|
struct ggml_tensor * bv;
|
|
struct ggml_tensor * bo;
|
|
struct ggml_tensor * bqkv;
|
|
|
|
// normalization
|
|
struct ggml_tensor * ffn_norm;
|
|
struct ggml_tensor * ffn_norm_b;
|
|
struct ggml_tensor * layer_out_norm;
|
|
struct ggml_tensor * layer_out_norm_b;
|
|
|
|
// ff
|
|
struct ggml_tensor * ffn_gate; // w1
|
|
struct ggml_tensor * ffn_down; // w2
|
|
struct ggml_tensor * ffn_up; // w3
|
|
|
|
// ff MoE
|
|
struct ggml_tensor * ffn_gate_inp;
|
|
struct ggml_tensor * ffn_gate_exps;
|
|
struct ggml_tensor * ffn_down_exps;
|
|
struct ggml_tensor * ffn_up_exps ;
|
|
|
|
// ff shared expert (shexp)
|
|
struct ggml_tensor * ffn_gate_inp_shexp;
|
|
struct ggml_tensor * ffn_gate_shexp;
|
|
struct ggml_tensor * ffn_down_shexp;
|
|
struct ggml_tensor * ffn_up_shexp;
|
|
|
|
// ff bias
|
|
struct ggml_tensor * ffn_down_b; // b2
|
|
struct ggml_tensor * ffn_up_b; // b3
|
|
struct ggml_tensor * ffn_act;
|
|
|
|
// mamba proj
|
|
struct ggml_tensor * ssm_in;
|
|
struct ggml_tensor * ssm_x;
|
|
struct ggml_tensor * ssm_dt;
|
|
struct ggml_tensor * ssm_out;
|
|
|
|
// mamba
|
|
struct ggml_tensor * ssm_conv1d;
|
|
struct ggml_tensor * ssm_a;
|
|
struct ggml_tensor * ssm_d;
|
|
|
|
// mamba bias
|
|
struct ggml_tensor * ssm_conv1d_b;
|
|
struct ggml_tensor * ssm_dt_b;
|
|
};
|
|
|
|
struct llama_kv_cell {
|
|
llama_pos pos = -1;
|
|
llama_pos delta = 0;
|
|
int32_t src = 0; // used by recurrent state models to copy states
|
|
|
|
std::set<llama_seq_id> seq_id;
|
|
|
|
bool has_seq_id(const llama_seq_id & id) const {
|
|
return seq_id.find(id) != seq_id.end();
|
|
}
|
|
|
|
bool is_empty() const {
|
|
return seq_id.empty();
|
|
}
|
|
|
|
bool is_same_seq(const llama_kv_cell & other) const {
|
|
return seq_id == other.seq_id;
|
|
}
|
|
};
|
|
|
|
// ring-buffer of cached KV data
|
|
struct llama_kv_cache {
|
|
bool has_shift = false;
|
|
bool do_defrag = false;
|
|
bool do_copy = false;
|
|
// with recurrent state models, a cell can hold the state for more than one past token
|
|
bool recurrent = false;
|
|
|
|
// Note: The value of head isn't only used to optimize searching
|
|
// for a free KV slot. llama_decode_internal also uses it, so it
|
|
// cannot be freely changed after a slot has been allocated.
|
|
uint32_t head = 0;
|
|
uint32_t size = 0;
|
|
uint32_t used = 0; // used cells (i.e. at least one seq_id)
|
|
|
|
// computed before each graph build
|
|
uint32_t n = 0;
|
|
|
|
ggml_type type_k = GGML_TYPE_F16;
|
|
ggml_type type_v = GGML_TYPE_F16;
|
|
|
|
std::vector<llama_kv_cell> cells;
|
|
|
|
std::vector<struct ggml_tensor *> k_l; // per layer
|
|
std::vector<struct ggml_tensor *> v_l;
|
|
|
|
std::vector<struct ggml_context *> ctxs;
|
|
std::vector<ggml_backend_buffer_t> bufs;
|
|
|
|
size_t total_size() const {
|
|
size_t size = 0;
|
|
for (ggml_backend_buffer_t buf : bufs) {
|
|
size += ggml_backend_buffer_get_size(buf);
|
|
}
|
|
return size;
|
|
}
|
|
|
|
~llama_kv_cache() {
|
|
for (struct ggml_context * ctx : ctxs) {
|
|
ggml_free(ctx);
|
|
}
|
|
for (ggml_backend_buffer_t buf : bufs) {
|
|
ggml_backend_buffer_free(buf);
|
|
}
|
|
}
|
|
};
|
|
|
|
struct llama_control_vector {
|
|
std::vector<struct ggml_tensor *> tensors; // per layer
|
|
std::vector<struct ggml_context *> ctxs;
|
|
std::vector<ggml_backend_buffer_t> bufs;
|
|
|
|
int32_t layer_start = -1;
|
|
int32_t layer_end = -1;
|
|
|
|
ggml_tensor * tensor_for(int il) const {
|
|
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
|
|
return nullptr;
|
|
}
|
|
return tensors[il];
|
|
}
|
|
|
|
~llama_control_vector() {
|
|
for (struct ggml_context * ctx : ctxs) {
|
|
ggml_free(ctx);
|
|
}
|
|
for (ggml_backend_buffer_t buf : bufs) {
|
|
ggml_backend_buffer_free(buf);
|
|
}
|
|
}
|
|
};
|
|
|
|
struct llama_vocab {
|
|
using id = int32_t;
|
|
using token = std::string;
|
|
using ttype = llama_token_type;
|
|
|
|
struct token_data {
|
|
token text;
|
|
float score;
|
|
ttype type;
|
|
};
|
|
|
|
enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM;
|
|
|
|
std::unordered_map<token, id> token_to_id;
|
|
std::vector<token_data> id_to_token;
|
|
|
|
std::unordered_map<token, id> special_tokens_cache;
|
|
|
|
std::map<std::pair<std::string, std::string>, int> bpe_ranks;
|
|
|
|
// default LLaMA special tokens
|
|
id special_bos_id = 1;
|
|
id special_eos_id = 2;
|
|
id special_unk_id = 0;
|
|
id special_sep_id = -1;
|
|
id special_pad_id = -1;
|
|
id special_cls_id = -1;
|
|
id special_mask_id = -1;
|
|
|
|
int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
|
|
int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
|
|
|
|
id linefeed_id = 13;
|
|
id special_prefix_id = -1;
|
|
id special_suffix_id = -1;
|
|
id special_middle_id = -1;
|
|
id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token
|
|
|
|
bool add_space_prefix = true;
|
|
|
|
int find_bpe_rank(const std::string & token_left, const std::string & token_right) const {
|
|
GGML_ASSERT(token_left.find(' ') == std::string::npos);
|
|
GGML_ASSERT(token_left.find('\n') == std::string::npos);
|
|
GGML_ASSERT(token_right.find(' ') == std::string::npos);
|
|
GGML_ASSERT(token_right.find('\n') == std::string::npos);
|
|
|
|
auto it = bpe_ranks.find(std::make_pair(token_left, token_right));
|
|
if (it == bpe_ranks.end()) {
|
|
return -1;
|
|
}
|
|
|
|
return it->second;
|
|
}
|
|
};
|
|
|
|
struct llama_model {
|
|
e_model type = MODEL_UNKNOWN;
|
|
llm_arch arch = LLM_ARCH_UNKNOWN;
|
|
llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
|
|
|
|
std::string name = "n/a";
|
|
|
|
llama_hparams hparams = {};
|
|
llama_vocab vocab;
|
|
|
|
struct ggml_tensor * tok_embd;
|
|
struct ggml_tensor * type_embd;
|
|
struct ggml_tensor * pos_embd;
|
|
struct ggml_tensor * tok_norm;
|
|
struct ggml_tensor * tok_norm_b;
|
|
|
|
struct ggml_tensor * output_norm;
|
|
struct ggml_tensor * output_norm_b;
|
|
struct ggml_tensor * output;
|
|
struct ggml_tensor * output_b;
|
|
|
|
std::vector<llama_layer> layers;
|
|
|
|
llama_split_mode split_mode;
|
|
int main_gpu;
|
|
int n_gpu_layers;
|
|
|
|
// gguf metadata
|
|
std::unordered_map<std::string, std::string> gguf_kv;
|
|
|
|
// layer -> buffer type mapping
|
|
struct layer_buft {
|
|
layer_buft() : buft_matrix(nullptr), buft(nullptr) {}
|
|
layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {}
|
|
layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {}
|
|
|
|
ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication
|
|
ggml_backend_buffer_type_t buft; // everything else
|
|
};
|
|
|
|
layer_buft buft_input;
|
|
layer_buft buft_output;
|
|
std::vector<layer_buft> buft_layer;
|
|
|
|
// contexts where the model tensors metadata is stored
|
|
std::vector<struct ggml_context *> ctxs;
|
|
|
|
// the model memory buffers for the tensor data
|
|
std::vector<ggml_backend_buffer_t> bufs;
|
|
|
|
// model memory mapped files
|
|
llama_mmaps mappings;
|
|
|
|
// objects representing data potentially being locked in memory
|
|
llama_mlocks mlock_bufs;
|
|
llama_mlocks mlock_mmaps;
|
|
|
|
// for quantize-stats only
|
|
std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
|
|
|
|
int64_t t_load_us = 0;
|
|
int64_t t_start_us = 0;
|
|
|
|
~llama_model() {
|
|
for (struct ggml_context * ctx : ctxs) {
|
|
ggml_free(ctx);
|
|
}
|
|
for (ggml_backend_buffer_t buf : bufs) {
|
|
#ifdef GGML_USE_CUDA
|
|
if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
|
|
ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
|
|
}
|
|
#endif
|
|
ggml_backend_buffer_free(buf);
|
|
}
|
|
}
|
|
};
|
|
|
|
struct llama_context {
|
|
llama_context(const llama_model & model) : model(model), t_start_us(model.t_start_us), t_load_us(model.t_load_us) {}
|
|
~llama_context() {
|
|
ggml_backend_sched_free(sched);
|
|
|
|
for (ggml_backend_t backend : backends) {
|
|
ggml_backend_free(backend);
|
|
}
|
|
|
|
ggml_backend_buffer_free(buf_output);
|
|
}
|
|
|
|
llama_cparams cparams;
|
|
|
|
std::vector<ggml_backend_t> backends;
|
|
#ifdef GGML_USE_METAL
|
|
ggml_backend_t backend_metal = nullptr;
|
|
#endif
|
|
ggml_backend_t backend_cpu = nullptr;
|
|
|
|
const llama_model & model;
|
|
|
|
// key + value cache for the self attention
|
|
struct llama_kv_cache kv_self;
|
|
|
|
std::mt19937 rng;
|
|
|
|
bool has_evaluated_once = false;
|
|
|
|
int64_t t_start_us;
|
|
int64_t t_load_us;
|
|
int64_t t_sample_us = 0;
|
|
int64_t t_p_eval_us = 0;
|
|
int64_t t_eval_us = 0;
|
|
|
|
int64_t t_compute_start_us = 0;
|
|
int64_t n_queued_tokens = 0;
|
|
|
|
int32_t n_sample = 0; // number of tokens sampled
|
|
int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
|
|
int32_t n_eval = 0; // number of eval calls
|
|
|
|
// host buffer for the model output (logits and embeddings)
|
|
ggml_backend_buffer_t buf_output = nullptr;
|
|
|
|
// decode output (2-dimensional array: [n_outputs][n_vocab])
|
|
size_t logits_size = 0; // capacity (of floats) for logits
|
|
float * logits = nullptr;
|
|
|
|
std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
|
|
size_t output_size = 0; // capacity (of tokens positions) for the output buffers
|
|
int32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch
|
|
|
|
bool logits_all = false;
|
|
|
|
// embeddings output (2-dimensional array: [n_outputs][n_embd])
|
|
// populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
|
|
size_t embd_size = 0; // capacity (of floats) for embeddings
|
|
float * embd = nullptr;
|
|
|
|
// sequence embeddings output (map of [n_embd] vectors)
|
|
// populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
|
|
std::map<llama_seq_id, std::vector<float>> embd_seq;
|
|
|
|
// memory buffers used to evaluate the model
|
|
std::vector<uint8_t> buf_compute_meta;
|
|
ggml_backend_sched_t sched = nullptr;
|
|
|
|
ggml_abort_callback abort_callback = nullptr;
|
|
void * abort_callback_data = nullptr;
|
|
|
|
// input tensors
|
|
struct ggml_tensor * inp_tokens; // I32 [n_batch]
|
|
struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch]
|
|
struct ggml_tensor * inp_pos; // I32 [n_batch]
|
|
struct ggml_tensor * inp_out_ids; // I32 [n_outputs]
|
|
struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch]
|
|
struct ggml_tensor * inp_KQ_pos; // F32 [n_kv]
|
|
struct ggml_tensor * inp_K_shift; // I32 [kv_size]
|
|
struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch]
|
|
struct ggml_tensor * inp_cls; // I32 [n_batch]
|
|
struct ggml_tensor * inp_s_copy; // I32 [kv_size]
|
|
struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
|
|
struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch]
|
|
|
|
// control vectors
|
|
struct llama_control_vector cvec;
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_context * ctx_mpi = NULL;
|
|
#endif
|
|
};
|
|
|
|
//
|
|
// kv cache helpers
|
|
//
|
|
|
|
static bool llama_kv_cache_init(
|
|
struct llama_kv_cache & cache,
|
|
const llama_model & model,
|
|
ggml_type type_k,
|
|
ggml_type type_v,
|
|
uint32_t kv_size,
|
|
bool offload) {
|
|
const struct llama_hparams & hparams = model.hparams;
|
|
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
|
|
const int64_t n_layer = hparams.n_layer;
|
|
|
|
cache.has_shift = false;
|
|
|
|
// TODO: find a nicer way to add other recurrent model architectures
|
|
cache.recurrent = model.arch == LLM_ARCH_MAMBA;
|
|
|
|
// TODO: support mixed reccurent Transformer architectues
|
|
// NOTE: (!a || b) is a logical implication (a -> b)
|
|
GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s());
|
|
GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s());
|
|
GGML_ASSERT( cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_gqa());
|
|
GGML_ASSERT( cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_gqa());
|
|
|
|
cache.head = 0;
|
|
cache.size = kv_size;
|
|
cache.used = 0;
|
|
|
|
cache.type_k = type_k;
|
|
cache.type_v = type_v;
|
|
|
|
cache.cells.clear();
|
|
cache.cells.resize(kv_size);
|
|
|
|
if (cache.recurrent) {
|
|
// init state copy sources
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
cache.cells[i].src = i;
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_USE_CLBLAST
|
|
offload = false;
|
|
#endif
|
|
|
|
// count used buffer types
|
|
std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
|
|
if (offload) {
|
|
for (int64_t i = 0; i < n_layer; ++i) {
|
|
buft_layer_count[model.buft_layer[i].buft]++;
|
|
}
|
|
} else {
|
|
buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer;
|
|
}
|
|
|
|
// create a context for each buffer type
|
|
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
|
for (auto & it : buft_layer_count) {
|
|
int n_layers = it.second;
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ 2u*n_layers*ggml_tensor_overhead(),
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
ggml_context * ctx = ggml_init(params);
|
|
if (!ctx) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
ctx_map[it.first] = ctx;
|
|
cache.ctxs.push_back(ctx);
|
|
}
|
|
|
|
cache.k_l.reserve(n_layer);
|
|
cache.v_l.reserve(n_layer);
|
|
|
|
for (int i = 0; i < (int) n_layer; i++) {
|
|
struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
|
|
ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
|
|
ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
|
|
ggml_format_name(k, "cache_k_l%d", i);
|
|
ggml_format_name(v, "cache_v_l%d", i);
|
|
cache.k_l.push_back(k);
|
|
cache.v_l.push_back(v);
|
|
}
|
|
|
|
// allocate tensors and initialize the buffers to avoid NaNs in the padding
|
|
for (auto it : ctx_map) {
|
|
ggml_backend_buffer_type_t buft = it.first;
|
|
ggml_context * ctx = it.second;
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
|
|
if (!buf) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
ggml_backend_buffer_clear(buf, 0);
|
|
LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
|
|
cache.bufs.push_back(buf);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// find an empty slot of size "n_tokens" in the cache
|
|
// updates the cache head
|
|
// Note: On success, it's important that cache.head points
|
|
// to the first cell of the slot.
|
|
static bool llama_kv_cache_find_slot(
|
|
struct llama_kv_cache & cache,
|
|
const struct llama_batch & batch) {
|
|
const uint32_t n_ctx = cache.size;
|
|
const uint32_t n_tokens = batch.n_tokens;
|
|
|
|
if (cache.recurrent) {
|
|
// For recurrent state architectures (like Mamba),
|
|
// each KV cache cell can store the state for a whole sequence.
|
|
|
|
llama_seq_id min = cache.size - 1;
|
|
llama_seq_id max = 0;
|
|
|
|
for (uint32_t i = 0; i < n_tokens; ++i) {
|
|
for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) {
|
|
llama_seq_id seq_id = batch.seq_id[i][j];
|
|
// make sure it's a valid seq_id
|
|
if ((uint32_t) seq_id < cache.size) {
|
|
if (seq_id > max) {
|
|
max = seq_id;
|
|
}
|
|
if (seq_id < min) {
|
|
min = seq_id;
|
|
}
|
|
// Assuming the tokens are in-order
|
|
if (batch.pos[i] != cache.cells[seq_id].pos + 1) {
|
|
// What should happen when the pos backtracks or skips a value?
|
|
// Clearing the state mid-batch would require special-casing which isn't done.
|
|
LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n",
|
|
__func__, batch.pos[i], cache.cells[seq_id].pos, seq_id);
|
|
}
|
|
if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) {
|
|
cache.used += 1;
|
|
}
|
|
cache.cells[seq_id].pos = batch.pos[i];
|
|
// NOTE: seq_ids are not inserted here; they are handled when the input tensors are set
|
|
} else {
|
|
// too big seq_id
|
|
// TODO: would it be possible to resize the KV cache size instead?
|
|
LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
// allow getting the range of used cells, from head to head + n
|
|
cache.head = min;
|
|
cache.n = max - min + 1;
|
|
|
|
// sanity check
|
|
return max >= min;
|
|
}
|
|
// otherwise, one cell per token.
|
|
|
|
if (n_tokens > n_ctx) {
|
|
LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
|
|
return false;
|
|
}
|
|
|
|
uint32_t n_tested = 0;
|
|
|
|
while (true) {
|
|
if (cache.head + n_tokens > n_ctx) {
|
|
n_tested += n_ctx - cache.head;
|
|
cache.head = 0;
|
|
continue;
|
|
}
|
|
|
|
bool found = true;
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
if (cache.cells[cache.head + i].pos >= 0) {
|
|
found = false;
|
|
cache.head += i + 1;
|
|
n_tested += i + 1;
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (found) {
|
|
break;
|
|
}
|
|
|
|
if (n_tested >= n_ctx) {
|
|
//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
cache.cells[cache.head + i].pos = batch.pos[i];
|
|
|
|
for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
|
|
cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
|
|
}
|
|
}
|
|
|
|
cache.used += n_tokens;
|
|
|
|
return true;
|
|
}
|
|
|
|
// find how many cells are currently in use
|
|
static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
|
|
for (uint32_t i = cache.size; i > 0; --i) {
|
|
const llama_kv_cell & cell = cache.cells[i - 1];
|
|
|
|
if (cell.pos >= 0 && !cell.is_empty()) {
|
|
return i;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
|
|
for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
}
|
|
cache.head = 0;
|
|
cache.used = 0;
|
|
}
|
|
|
|
static bool llama_kv_cache_seq_rm(
|
|
struct llama_kv_cache & cache,
|
|
llama_seq_id seq_id,
|
|
llama_pos p0,
|
|
llama_pos p1) {
|
|
uint32_t new_head = cache.size;
|
|
|
|
if (p0 < 0) p0 = 0;
|
|
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
|
|
|
|
// models like Mamba can't have a state partially erased
|
|
if (cache.recurrent) {
|
|
if (seq_id >= (int64_t) cache.size) {
|
|
// could be fatal
|
|
return false;
|
|
}
|
|
if (0 <= seq_id) {
|
|
// partial intersection is invalid
|
|
if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) {
|
|
return false;
|
|
}
|
|
} else {
|
|
// seq_id is negative, then the range should include everything or nothing
|
|
if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
if (seq_id < 0) {
|
|
cache.cells[i].seq_id.clear();
|
|
} else if (cache.cells[i].has_seq_id(seq_id)) {
|
|
cache.cells[i].seq_id.erase(seq_id);
|
|
} else {
|
|
continue;
|
|
}
|
|
if (cache.cells[i].is_empty()) {
|
|
// keep count of the number of used cells
|
|
if (cache.cells[i].pos >= 0) cache.used--;
|
|
|
|
cache.cells[i].pos = -1;
|
|
if (new_head == cache.size) new_head = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
// If we freed up a slot, set head to it so searching can start there.
|
|
if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
|
|
|
|
return true;
|
|
}
|
|
|
|
static void llama_kv_cache_seq_cp(
|
|
struct llama_kv_cache & cache,
|
|
llama_seq_id seq_id_src,
|
|
llama_seq_id seq_id_dst,
|
|
llama_pos p0,
|
|
llama_pos p1) {
|
|
if (p0 < 0) p0 = 0;
|
|
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
|
|
|
|
if (cache.recurrent) {
|
|
if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
|
|
seq_id_src = cache.cells[seq_id_src].src;
|
|
GGML_ASSERT((uint32_t) seq_id_src < cache.size);
|
|
// intent to "copy from"
|
|
// supports copy chains thanks to taking the source of the source
|
|
cache.cells[seq_id_dst].src = seq_id_src;
|
|
|
|
// preserve the "keep or clear" status of the copied sequence
|
|
if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) {
|
|
cache.cells[seq_id_dst].seq_id.insert(seq_id_dst);
|
|
} else {
|
|
cache.cells[seq_id_dst].seq_id.erase(seq_id_dst);
|
|
}
|
|
|
|
cache.do_copy = true;
|
|
|
|
cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos;
|
|
}
|
|
return;
|
|
}
|
|
// otherwise, this is the KV cache of a Transformer-like model
|
|
|
|
cache.head = 0;
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
cache.cells[i].seq_id.insert(seq_id_dst);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
|
|
uint32_t new_head = cache.size;
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (!cache.cells[i].has_seq_id(seq_id)) {
|
|
if (cache.cells[i].pos >= 0) cache.used--;
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
if (new_head == cache.size) new_head = i;
|
|
} else {
|
|
cache.cells[i].seq_id.clear();
|
|
cache.cells[i].seq_id.insert(seq_id);
|
|
}
|
|
}
|
|
|
|
// If we freed up a slot, set head to it so searching can start there.
|
|
if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
|
|
}
|
|
|
|
static void llama_kv_cache_seq_add(
|
|
struct llama_kv_cache & cache,
|
|
llama_seq_id seq_id,
|
|
llama_pos p0,
|
|
llama_pos p1,
|
|
llama_pos delta) {
|
|
uint32_t new_head = cache.size;
|
|
|
|
if (p0 < 0) p0 = 0;
|
|
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
|
|
|
|
if (cache.recurrent) {
|
|
// for Mamba-like models, only the pos needs to be shifted
|
|
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
|
|
llama_kv_cell & cell = cache.cells[seq_id];
|
|
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
|
|
cell.pos += delta;
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
cache.has_shift = true;
|
|
cache.cells[i].pos += delta;
|
|
cache.cells[i].delta += delta;
|
|
|
|
if (cache.cells[i].pos < 0) {
|
|
if (!cache.cells[i].is_empty()) {
|
|
cache.used--;
|
|
}
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
if (new_head == cache.size) {
|
|
new_head = i;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// If we freed up a slot, set head to it so searching can start there.
|
|
// Otherwise we just start the next search from the beginning.
|
|
cache.head = new_head != cache.size ? new_head : 0;
|
|
}
|
|
|
|
static void llama_kv_cache_seq_div(
|
|
struct llama_kv_cache & cache,
|
|
llama_seq_id seq_id,
|
|
llama_pos p0,
|
|
llama_pos p1,
|
|
int d) {
|
|
if (p0 < 0) p0 = 0;
|
|
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
|
|
|
|
if (cache.recurrent) {
|
|
// for Mamba-like models, only the pos needs to be changed
|
|
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
|
|
llama_kv_cell & cell = cache.cells[seq_id];
|
|
if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
|
|
cell.pos /= d;
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
|
|
cache.has_shift = true;
|
|
|
|
{
|
|
llama_pos p_old = cache.cells[i].pos;
|
|
cache.cells[i].pos /= d;
|
|
cache.cells[i].delta += cache.cells[i].pos - p_old;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
|
|
llama_pos result = 0;
|
|
|
|
for (uint32_t i = 0; i < cache.size; ++i) {
|
|
if (cache.cells[i].has_seq_id(seq_id)) {
|
|
result = std::max(result, cache.cells[i].pos);
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
|
|
cache.do_defrag = true;
|
|
}
|
|
|
|
//
|
|
// model loading and saving
|
|
//
|
|
|
|
enum llama_fver {
|
|
GGUF_FILE_VERSION_V1 = 1,
|
|
GGUF_FILE_VERSION_V2 = 2,
|
|
GGUF_FILE_VERSION_V3 = 3,
|
|
};
|
|
|
|
static const char * llama_file_version_name(llama_fver version) {
|
|
switch (version) {
|
|
case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
|
|
case GGUF_FILE_VERSION_V2: return "GGUF V2";
|
|
case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
|
|
}
|
|
|
|
return "unknown";
|
|
}
|
|
|
|
static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
|
|
char buf[256];
|
|
snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
|
|
for (size_t i = 1; i < ne.size(); i++) {
|
|
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
|
|
}
|
|
return buf;
|
|
}
|
|
|
|
static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
|
|
char buf[256];
|
|
snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
|
|
for (int i = 1; i < GGML_MAX_DIMS; i++) {
|
|
snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
|
|
}
|
|
return buf;
|
|
}
|
|
|
|
namespace GGUFMeta {
|
|
template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
|
|
struct GKV_Base_Type {
|
|
static constexpr gguf_type gt = gt_;
|
|
|
|
static T getter(const gguf_context * ctx, const int kid) {
|
|
return gfun(ctx, kid);
|
|
}
|
|
};
|
|
|
|
template<typename T> struct GKV_Base;
|
|
|
|
template<> struct GKV_Base<bool >: GKV_Base_Type<bool, GGUF_TYPE_BOOL, gguf_get_val_bool> {};
|
|
template<> struct GKV_Base<uint8_t >: GKV_Base_Type<uint8_t, GGUF_TYPE_UINT8, gguf_get_val_u8 > {};
|
|
template<> struct GKV_Base<uint16_t >: GKV_Base_Type<uint16_t, GGUF_TYPE_UINT16, gguf_get_val_u16 > {};
|
|
template<> struct GKV_Base<uint32_t >: GKV_Base_Type<uint32_t, GGUF_TYPE_UINT32, gguf_get_val_u32 > {};
|
|
template<> struct GKV_Base<uint64_t >: GKV_Base_Type<uint64_t, GGUF_TYPE_UINT64, gguf_get_val_u64 > {};
|
|
template<> struct GKV_Base<int8_t >: GKV_Base_Type<int8_t, GGUF_TYPE_INT8, gguf_get_val_i8 > {};
|
|
template<> struct GKV_Base<int16_t >: GKV_Base_Type<int16_t, GGUF_TYPE_INT16, gguf_get_val_i16 > {};
|
|
template<> struct GKV_Base<int32_t >: GKV_Base_Type<int32_t, GGUF_TYPE_INT32, gguf_get_val_i32 > {};
|
|
template<> struct GKV_Base<int64_t >: GKV_Base_Type<int64_t, GGUF_TYPE_INT64, gguf_get_val_i64 > {};
|
|
template<> struct GKV_Base<float >: GKV_Base_Type<float, GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
|
|
template<> struct GKV_Base<double >: GKV_Base_Type<double, GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
|
|
template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING, gguf_get_val_str > {};
|
|
|
|
template<> struct GKV_Base<std::string> {
|
|
static constexpr gguf_type gt = GGUF_TYPE_STRING;
|
|
|
|
static std::string getter(const gguf_context * ctx, const int kid) {
|
|
return gguf_get_val_str(ctx, kid);
|
|
}
|
|
};
|
|
|
|
struct ArrayInfo {
|
|
const gguf_type gt;
|
|
const size_t length;
|
|
const void * data;
|
|
};
|
|
|
|
template<> struct GKV_Base<ArrayInfo> {
|
|
public:
|
|
static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
|
|
static ArrayInfo getter(const gguf_context *ctx, const int k) {
|
|
return ArrayInfo {
|
|
gguf_get_arr_type(ctx, k),
|
|
size_t(gguf_get_arr_n(ctx, k)),
|
|
gguf_get_arr_data(ctx, k),
|
|
};
|
|
}
|
|
};
|
|
|
|
template<typename T>
|
|
class GKV : public GKV_Base<T> {
|
|
GKV() = delete;
|
|
|
|
public:
|
|
static T get_kv(const gguf_context * ctx, const int k) {
|
|
const enum gguf_type kt = gguf_get_kv_type(ctx, k);
|
|
|
|
if (kt != GKV::gt) {
|
|
throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
|
|
gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
|
|
}
|
|
return GKV::getter(ctx, k);
|
|
}
|
|
|
|
static const char * override_type_to_str(const llama_model_kv_override_type ty) {
|
|
switch (ty) {
|
|
case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool";
|
|
case LLAMA_KV_OVERRIDE_TYPE_INT: return "int";
|
|
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
|
|
}
|
|
return "unknown";
|
|
}
|
|
|
|
static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
|
|
if (!ovrd) { return false; }
|
|
if (ovrd->tag == expected_type) {
|
|
LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
|
|
__func__, override_type_to_str(ovrd->tag), ovrd->key);
|
|
switch (ovrd->tag) {
|
|
case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
|
|
LLAMA_LOG_INFO("%s\n", ovrd->bool_value ? "true" : "false");
|
|
} break;
|
|
case LLAMA_KV_OVERRIDE_TYPE_INT: {
|
|
LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->int_value);
|
|
} break;
|
|
case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
|
|
LLAMA_LOG_INFO("%.6f\n", ovrd->float_value);
|
|
} break;
|
|
default:
|
|
// Shouldn't be possible to end up here, but just in case...
|
|
throw std::runtime_error(
|
|
format("Unsupported attempt to override %s type for metadata key %s\n",
|
|
override_type_to_str(ovrd->tag), ovrd->key));
|
|
}
|
|
return true;
|
|
}
|
|
LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
|
|
__func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
|
|
return false;
|
|
}
|
|
|
|
template<typename OT>
|
|
static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
|
|
try_override(OT & target, const struct llama_model_kv_override * ovrd) {
|
|
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
|
|
target = ovrd->bool_value;
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
template<typename OT>
|
|
static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
|
|
try_override(OT & target, const struct llama_model_kv_override * ovrd) {
|
|
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
|
|
target = ovrd->int_value;
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
template<typename OT>
|
|
static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
|
|
try_override(T & target, const struct llama_model_kv_override * ovrd) {
|
|
if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
|
|
target = ovrd->float_value;
|
|
return true;
|
|
}
|
|
return false;
|
|
}
|
|
|
|
template<typename OT>
|
|
static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
|
|
try_override(T & target, const struct llama_model_kv_override * ovrd) {
|
|
(void)target;
|
|
(void)ovrd;
|
|
if (!ovrd) { return false; }
|
|
// Currently, we should never end up here so it would be a bug if we do.
|
|
throw std::runtime_error(format("Unsupported attempt to override string type for metadata key %s\n",
|
|
ovrd ? ovrd->key : "NULL"));
|
|
}
|
|
|
|
static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
|
|
if (try_override<T>(target, ovrd)) {
|
|
return true;
|
|
}
|
|
if (k < 0) { return false; }
|
|
target = get_kv(ctx, k);
|
|
return true;
|
|
}
|
|
|
|
static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
|
|
return set(ctx, gguf_find_key(ctx, key), target, ovrd);
|
|
}
|
|
|
|
static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
|
|
return set(ctx, key.c_str(), target, ovrd);
|
|
}
|
|
};
|
|
}
|
|
|
|
using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
|
|
|
|
struct llama_model_loader {
|
|
int n_kv = 0;
|
|
int n_tensors = 0;
|
|
int n_created = 0;
|
|
|
|
int64_t n_elements = 0;
|
|
size_t n_bytes = 0;
|
|
|
|
bool use_mmap = false;
|
|
|
|
llama_files files;
|
|
llama_ftype ftype;
|
|
llama_fver fver;
|
|
|
|
llama_mmaps mappings;
|
|
|
|
// Holds information on a model weight
|
|
struct llama_tensor_weight {
|
|
uint16_t idx; // source file index
|
|
size_t offs; // tensor data offset in the original file
|
|
|
|
ggml_tensor * tensor;
|
|
|
|
llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
|
|
const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
|
|
offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
|
|
|
|
if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) {
|
|
throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name));
|
|
}
|
|
}
|
|
};
|
|
std::vector<llama_tensor_weight> weights;
|
|
|
|
std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
|
|
|
|
struct gguf_context * meta = NULL;
|
|
std::vector<ggml_context *> contexts;
|
|
|
|
std::string arch_name;
|
|
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
|
|
|
|
llama_model_loader(const std::string & fname, bool use_mmap, const struct llama_model_kv_override * param_overrides_p) {
|
|
int trace = 0;
|
|
if (getenv("LLAMA_TRACE")) {
|
|
trace = atoi(getenv("LLAMA_TRACE"));
|
|
}
|
|
|
|
if (param_overrides_p != nullptr) {
|
|
for (const struct llama_model_kv_override *p = param_overrides_p; p->key[0] != 0; p++) {
|
|
kv_overrides.insert({std::string(p->key), *p});
|
|
}
|
|
}
|
|
|
|
struct ggml_context * ctx = NULL;
|
|
struct gguf_init_params params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ &ctx,
|
|
};
|
|
|
|
meta = gguf_init_from_file(fname.c_str(), params);
|
|
if (!meta) {
|
|
throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
|
|
}
|
|
|
|
get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
|
|
llm_kv = LLM_KV(llm_arch_from_string(arch_name));
|
|
|
|
files.emplace_back(new llama_file(fname.c_str(), "rb"));
|
|
contexts.emplace_back(ctx);
|
|
|
|
// Save tensors data offset of the main file.
|
|
// For subsidiary files, `meta` tensor data offset must not be used,
|
|
// so we build a unified tensors index for weights.
|
|
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
|
|
weights.emplace_back(files.back().get(), 0, cur->name, meta, cur);
|
|
}
|
|
uint16_t n_split = 0;
|
|
get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
|
|
|
|
// Load additional GGML contexts
|
|
if (n_split > 1) {
|
|
uint16_t idx = 0;
|
|
get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
|
|
if (idx != 0) {
|
|
throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
|
|
}
|
|
|
|
char split_prefix[PATH_MAX] = {0};
|
|
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
|
|
throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
|
|
}
|
|
|
|
if (trace > 0) {
|
|
LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
|
|
}
|
|
|
|
char split_path[PATH_MAX] = {0};
|
|
for (idx = 1; idx < n_split; idx++) {
|
|
llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
|
|
|
|
struct gguf_init_params split_params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ &ctx,
|
|
};
|
|
struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params);
|
|
if (!ctx_gguf) {
|
|
throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
|
|
}
|
|
|
|
files.emplace_back(new llama_file(split_path, "rb"));
|
|
contexts.emplace_back(ctx);
|
|
|
|
// Save tensors data offset info of the shard.
|
|
for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
|
|
weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur);
|
|
}
|
|
|
|
gguf_free(ctx_gguf);
|
|
}
|
|
|
|
get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
|
|
|
|
// sanity check
|
|
{
|
|
const int n_tensors_loaded = (int) weights.size();
|
|
if (n_tensors != n_tensors_loaded) {
|
|
throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
|
|
}
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1);
|
|
}
|
|
|
|
n_kv = gguf_get_n_kv(meta);
|
|
n_tensors = weights.size();
|
|
|
|
fver = (enum llama_fver) gguf_get_version(meta);
|
|
|
|
for (auto & w : weights) {
|
|
n_elements += ggml_nelements(w.tensor);
|
|
n_bytes += ggml_nbytes(w.tensor);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
|
|
__func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
|
|
|
|
// determine file type based on the number of tensors for each quantization and print meta data
|
|
// TODO: make optional
|
|
{
|
|
std::map<enum ggml_type, uint32_t> n_type;
|
|
|
|
uint32_t n_type_max = 0;
|
|
enum ggml_type type_max = GGML_TYPE_F32;
|
|
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
const ggml_tensor * tensor = weights.at(i).tensor;
|
|
enum ggml_type type = tensor->type;
|
|
|
|
n_type[type]++;
|
|
|
|
if (n_type_max < n_type[type]) {
|
|
n_type_max = n_type[type];
|
|
type_max = type;
|
|
}
|
|
|
|
if (trace > 0) {
|
|
const uint16_t sid = weights.at(i).idx;
|
|
LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
|
|
}
|
|
}
|
|
|
|
switch (type_max) {
|
|
case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break;
|
|
case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break;
|
|
case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break;
|
|
case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break;
|
|
case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break;
|
|
case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break;
|
|
case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break;
|
|
case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
|
|
case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
|
|
case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
|
|
case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
|
|
case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
|
|
case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
|
|
case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
|
|
case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
|
|
case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
|
|
case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
|
|
case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
|
|
case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
|
|
case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
|
|
case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
|
|
default:
|
|
{
|
|
LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
|
|
ftype = LLAMA_FTYPE_ALL_F32;
|
|
} break;
|
|
}
|
|
|
|
// this is a way to mark that we have "guessed" the file type
|
|
ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
|
|
|
|
{
|
|
const int kid = gguf_find_key(meta, "general.file_type");
|
|
if (kid >= 0) {
|
|
ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
|
|
}
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
|
|
|
|
for (int i = 0; i < n_kv; i++) {
|
|
const char * name = gguf_get_key(meta, i);
|
|
const enum gguf_type type = gguf_get_kv_type(meta, i);
|
|
const std::string type_name =
|
|
type == GGUF_TYPE_ARRAY
|
|
? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i))
|
|
: gguf_type_name(type);
|
|
|
|
std::string value = gguf_kv_to_str(meta, i);
|
|
const size_t MAX_VALUE_LEN = 40;
|
|
if (value.size() > MAX_VALUE_LEN) {
|
|
value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
|
|
}
|
|
replace_all(value, "\n", "\\n");
|
|
|
|
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
|
|
}
|
|
|
|
// print type counts
|
|
for (auto & kv : n_type) {
|
|
if (kv.second == 0) {
|
|
continue;
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
|
|
}
|
|
}
|
|
|
|
if (!llama_mmap::SUPPORTED) {
|
|
LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
|
|
use_mmap = false;
|
|
}
|
|
|
|
this->use_mmap = use_mmap;
|
|
}
|
|
|
|
~llama_model_loader() {
|
|
if (meta) {
|
|
gguf_free(meta);
|
|
}
|
|
for (auto * ctx : contexts) {
|
|
ggml_free(ctx);
|
|
}
|
|
}
|
|
|
|
template<typename T>
|
|
typename std::enable_if<std::is_integral<T>::value, bool>::type
|
|
get_arr_n(const std::string & key, T & result, const bool required = true) {
|
|
const int kid = gguf_find_key(meta, key.c_str());
|
|
|
|
if (kid < 0) {
|
|
if (required) {
|
|
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
|
|
}
|
|
return false;
|
|
}
|
|
|
|
struct GGUFMeta::ArrayInfo arr_info =
|
|
GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
|
|
|
|
|
|
result = arr_info.length;
|
|
return true;
|
|
}
|
|
|
|
template<typename T>
|
|
typename std::enable_if<std::is_integral<T>::value, bool>::type
|
|
get_arr_n(const enum llm_kv kid, T & result, const bool required = true) {
|
|
return get_arr_n(llm_kv(kid), result, required);
|
|
}
|
|
|
|
template<typename T>
|
|
bool get_key(const std::string & key, T & result, const bool required = true) {
|
|
auto it = kv_overrides.find(key);
|
|
|
|
const struct llama_model_kv_override * override =
|
|
it != kv_overrides.end() ? &it->second : nullptr;
|
|
|
|
const bool found = GGUFMeta::GKV<T>::set(meta, key, result, override);
|
|
|
|
if (required && !found) {
|
|
throw std::runtime_error(format("key not found in model: %s", key.c_str()));
|
|
}
|
|
|
|
return found;
|
|
}
|
|
|
|
template<typename T>
|
|
bool get_key(const enum llm_kv kid, T & result, const bool required = true) {
|
|
return get_key(llm_kv(kid), result, required);
|
|
}
|
|
|
|
std::string get_arch_name() const {
|
|
return arch_name;
|
|
}
|
|
|
|
enum llm_arch get_arch() const {
|
|
return llm_kv.arch;
|
|
}
|
|
|
|
const char * get_tensor_name(int i) const {
|
|
return weights.at(i).tensor->name;
|
|
}
|
|
|
|
const llama_tensor_weight * get_weight(const char * name) const {
|
|
for (const auto & weight : weights) {
|
|
if (strcmp(name, weight.tensor->name) == 0) {
|
|
return &weight;
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
const llama_tensor_weight * get_weight(int i) const {
|
|
return get_weight(get_tensor_name(i));
|
|
}
|
|
|
|
const llama_tensor_weight & require_weight(const char * name) const {
|
|
const llama_tensor_weight * weight = get_weight(name);
|
|
if (!weight) {
|
|
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
|
|
}
|
|
return *weight;
|
|
}
|
|
|
|
struct ggml_tensor * get_tensor_meta(const char * name) const {
|
|
const auto * weight = get_weight(name);
|
|
if (!weight) {
|
|
return nullptr;
|
|
}
|
|
return weight->tensor;
|
|
}
|
|
|
|
struct ggml_tensor * require_tensor_meta(const char * name) const {
|
|
struct ggml_tensor * tensor = get_tensor_meta(name);
|
|
if (!tensor) {
|
|
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
|
|
}
|
|
return tensor;
|
|
}
|
|
|
|
struct ggml_tensor * get_tensor_meta(int i) const {
|
|
return get_tensor_meta(get_tensor_name(i));
|
|
}
|
|
|
|
struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) {
|
|
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
|
|
ggml_set_name(tensor, ggml_get_name(cur));
|
|
|
|
n_created++;
|
|
|
|
return tensor;
|
|
}
|
|
|
|
const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
|
|
const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
|
|
|
|
if (cur == NULL) {
|
|
if (!required) {
|
|
return NULL;
|
|
}
|
|
throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
|
|
}
|
|
|
|
{
|
|
bool is_ok = true;
|
|
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
|
|
if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
|
|
is_ok = false;
|
|
break;
|
|
}
|
|
}
|
|
if (!is_ok) {
|
|
throw std::runtime_error(
|
|
format("%s: tensor '%s' has wrong shape; expected %s, got %s",
|
|
__func__, name.c_str(),
|
|
llama_format_tensor_shape(ne).c_str(),
|
|
llama_format_tensor_shape(cur).c_str()));
|
|
}
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, bool required = true) {
|
|
const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
|
|
|
|
if (cur == NULL) {
|
|
return NULL;
|
|
}
|
|
|
|
return create_tensor_for(ctx, cur);
|
|
}
|
|
|
|
struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
|
|
const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
|
|
|
|
if (cur == NULL) {
|
|
return NULL;
|
|
}
|
|
|
|
if (cur->type != base->type) {
|
|
throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
|
|
}
|
|
|
|
std::array<int64_t, GGML_MAX_DIMS> dims;
|
|
for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
|
|
dims[i] = i < ne.size() ? ne[i] : 1;
|
|
}
|
|
|
|
struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
|
|
dims[0], dims[1], dims[2], dims[3],
|
|
cur->nb[1], cur->nb[2], cur->nb[3],
|
|
offset);
|
|
|
|
ggml_set_name(tensor, name.c_str());
|
|
|
|
n_created++;
|
|
|
|
return tensor;
|
|
}
|
|
|
|
void done_getting_tensors() const {
|
|
if (n_created != n_tensors) {
|
|
throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
|
|
}
|
|
}
|
|
|
|
void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) {
|
|
if (use_mmap) {
|
|
mappings.reserve(files.size());
|
|
mmaps_used.reserve(files.size());
|
|
for (const auto & file : files) {
|
|
std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa()));
|
|
mmaps_used.emplace_back(mapping->size, 0);
|
|
if (mlock_mmaps) {
|
|
std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
|
|
mlock_mmap->init(mapping->addr);
|
|
mlock_mmaps->emplace_back(std::move(mlock_mmap));
|
|
}
|
|
mappings.emplace_back(std::move(mapping));
|
|
}
|
|
}
|
|
|
|
// compute the total size of all tensors for progress reporting
|
|
for (auto & w : weights) {
|
|
size_data += ggml_nbytes(w.tensor);
|
|
}
|
|
}
|
|
|
|
void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
|
|
GGML_ASSERT(!mappings.empty());
|
|
const auto & mapping = mappings.at(idx);
|
|
|
|
*first = mapping->size;
|
|
*last = 0;
|
|
*addr = mapping->addr;
|
|
for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
|
|
try {
|
|
const auto * weight = get_weight(ggml_get_name(tensor));
|
|
if (!weight) {
|
|
continue;
|
|
}
|
|
if (weight->idx != idx) {
|
|
continue;
|
|
}
|
|
*first = std::min(*first, weight->offs);
|
|
*last = std::max(*last, weight->offs + ggml_nbytes(tensor));
|
|
} catch(...) {
|
|
// the tensor is not in the model
|
|
}
|
|
}
|
|
}
|
|
|
|
// for backwards compatibility, does not support ggml-backend
|
|
void load_data_for(struct ggml_tensor * cur) const {
|
|
const auto & w = require_weight(ggml_get_name(cur));
|
|
|
|
if (use_mmap) {
|
|
const auto & mapping = mappings.at(w.idx);
|
|
if (cur->data == nullptr) {
|
|
cur->data = (uint8_t *)mapping->addr + w.offs;
|
|
} else {
|
|
memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur));
|
|
}
|
|
} else {
|
|
GGML_ASSERT(cur->data != nullptr);
|
|
GGML_ASSERT(w.idx < files.size());
|
|
const auto & file = files.at(w.idx);
|
|
file->seek(w.offs, SEEK_SET);
|
|
file->read_raw(cur->data, ggml_nbytes(cur));
|
|
}
|
|
}
|
|
|
|
size_t size_done = 0;
|
|
size_t size_data = 0;
|
|
std::vector<std::pair<size_t, size_t>> mmaps_used;
|
|
|
|
// Returns false if cancelled by progress_callback
|
|
bool load_all_data(
|
|
struct ggml_context * ctx,
|
|
llama_buf_map & bufs_mmap,
|
|
llama_mlocks * lmlocks,
|
|
llama_progress_callback progress_callback,
|
|
void * progress_callback_user_data) {
|
|
GGML_ASSERT(size_data != 0 && "call init_mappings() first");
|
|
|
|
std::vector<no_init<uint8_t>> read_buf;
|
|
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
|
|
const auto * weight = get_weight(ggml_get_name(cur));
|
|
if (weight == nullptr) {
|
|
// this can happen with split experts models
|
|
continue;
|
|
}
|
|
|
|
if (progress_callback) {
|
|
if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
size_t n_size = ggml_nbytes(cur);
|
|
|
|
if (use_mmap) {
|
|
const auto & mapping = mappings.at(weight->idx);
|
|
ggml_backend_buffer_t buf_mmap = nullptr;
|
|
if (bufs_mmap.count(weight->idx)) {
|
|
buf_mmap = bufs_mmap.at(weight->idx);
|
|
}
|
|
GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
|
|
if (buf_mmap && cur->data == nullptr) {
|
|
ggml_backend_tensor_alloc(buf_mmap, cur, (uint8_t *) mapping->addr + weight->offs);
|
|
if (lmlocks) {
|
|
const auto & lmlock = lmlocks->at(weight->idx);
|
|
lmlock->grow_to(weight->offs + ggml_nbytes(cur));
|
|
}
|
|
|
|
auto & mmap_used = mmaps_used[weight->idx];
|
|
mmap_used.first = std::min(mmap_used.first, weight->offs);
|
|
mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
|
|
} else {
|
|
ggml_backend_tensor_set(cur, (uint8_t *) mapping->addr + weight->offs, 0, n_size);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(weight->idx < files.size());
|
|
const auto & file = files.at(weight->idx);
|
|
if (ggml_backend_buffer_is_host(cur->buffer)) {
|
|
file->seek(weight->offs, SEEK_SET);
|
|
file->read_raw(cur->data, ggml_nbytes(cur));
|
|
} else {
|
|
read_buf.resize(ggml_nbytes(cur));
|
|
file->seek(weight->offs, SEEK_SET);
|
|
file->read_raw(read_buf.data(), ggml_nbytes(cur));
|
|
ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
|
|
}
|
|
}
|
|
|
|
size_done += n_size;
|
|
}
|
|
|
|
// check if this is the last call and do final cleanup
|
|
if (size_done >= size_data) {
|
|
// unmap offloaded tensors and metadata
|
|
if (use_mmap) {
|
|
for (uint32_t idx = 0; idx < mappings.size(); idx++) {
|
|
const auto & mmap_used = mmaps_used.at(idx);
|
|
auto & mapping = mappings.at(idx);
|
|
mapping->unmap_fragment(0, mmap_used.first);
|
|
if (mmap_used.second != 0) {
|
|
mapping->unmap_fragment(mmap_used.second, mapping->size);
|
|
}
|
|
}
|
|
}
|
|
if (progress_callback) {
|
|
// Even though the model is done loading, we still honor
|
|
// cancellation since we need to free allocations.
|
|
return progress_callback(1.0f, progress_callback_user_data);
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
};
|
|
|
|
template<>
|
|
bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
|
|
uint32_t tmp;
|
|
const bool found = get_key(kid, tmp, required);
|
|
if (found) {
|
|
result = (enum llama_pooling_type) tmp;
|
|
} else {
|
|
result = LLAMA_POOLING_TYPE_UNSPECIFIED;
|
|
}
|
|
return found;
|
|
}
|
|
|
|
|
|
//
|
|
// load LLaMA models
|
|
//
|
|
|
|
static const char * llama_model_arch_name(llm_arch arch) {
|
|
auto it = LLM_ARCH_NAMES.find(arch);
|
|
if (it == LLM_ARCH_NAMES.end()) {
|
|
return "unknown";
|
|
}
|
|
return it->second;
|
|
}
|
|
|
|
static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|
if (ftype & LLAMA_FTYPE_GUESSED) {
|
|
return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
|
|
}
|
|
|
|
switch (ftype) {
|
|
case LLAMA_FTYPE_ALL_F32: return "all F32";
|
|
case LLAMA_FTYPE_MOSTLY_F16: return "F16";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
|
|
return "Q4_1, some F16";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
|
|
case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
|
|
|
|
// K-quants
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K";
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ1_S :return "IQ1_S - 1.5625 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ1_M :return "IQ1_M - 1.75 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
|
|
|
|
default: return "unknown, may not work";
|
|
}
|
|
}
|
|
|
|
static const char * llama_model_type_name(e_model type) {
|
|
switch (type) {
|
|
case MODEL_22M: return "22M";
|
|
case MODEL_33M: return "33M";
|
|
case MODEL_109M: return "109M";
|
|
case MODEL_137M: return "137M";
|
|
case MODEL_0_5B: return "0.5B";
|
|
case MODEL_1B: return "1B";
|
|
case MODEL_2B: return "2B";
|
|
case MODEL_3B: return "3B";
|
|
case MODEL_7B: return "7B";
|
|
case MODEL_8B: return "8B";
|
|
case MODEL_12B: return "12B";
|
|
case MODEL_13B: return "13B";
|
|
case MODEL_14B: return "14B";
|
|
case MODEL_15B: return "15B";
|
|
case MODEL_20B: return "20B";
|
|
case MODEL_30B: return "30B";
|
|
case MODEL_34B: return "34B";
|
|
case MODEL_35B: return "35B";
|
|
case MODEL_40B: return "40B";
|
|
case MODEL_65B: return "65B";
|
|
case MODEL_70B: return "70B";
|
|
case MODEL_314B: return "314B";
|
|
case MODEL_SMALL: return "0.1B";
|
|
case MODEL_MEDIUM: return "0.4B";
|
|
case MODEL_LARGE: return "0.8B";
|
|
case MODEL_XL: return "1.5B";
|
|
case MODEL_A2_7B: return "A2.7B";
|
|
case MODEL_8x7B: return "8x7B";
|
|
case MODEL_8x22B: return "8x22B";
|
|
case MODEL_16x12B: return "16x12B";
|
|
default: return "?B";
|
|
}
|
|
}
|
|
|
|
static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
|
|
switch (type) {
|
|
case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
|
|
case LLAMA_VOCAB_TYPE_SPM: return "SPM";
|
|
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
|
|
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
|
|
default: return "unknown";
|
|
}
|
|
}
|
|
|
|
static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
|
|
model.arch = ml.get_arch();
|
|
if (model.arch == LLM_ARCH_UNKNOWN) {
|
|
throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
|
|
}
|
|
}
|
|
|
|
static void llm_load_hparams(
|
|
llama_model_loader & ml,
|
|
llama_model & model) {
|
|
auto & hparams = model.hparams;
|
|
const gguf_context * ctx = ml.meta;
|
|
|
|
// get metadata as string
|
|
for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
|
|
enum gguf_type type = gguf_get_kv_type(ctx, i);
|
|
if (type == GGUF_TYPE_ARRAY) {
|
|
continue;
|
|
}
|
|
const char * name = gguf_get_key(ctx, i);
|
|
const std::string value = gguf_kv_to_str(ctx, i);
|
|
model.gguf_kv.emplace(name, value);
|
|
}
|
|
|
|
// get general kv
|
|
ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
|
|
|
|
// get hparams kv
|
|
ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
|
|
ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
|
|
ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
|
|
ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
|
|
ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
|
|
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
|
|
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
|
|
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
|
|
|
|
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
|
|
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
|
|
if (hparams.n_expert > 0) {
|
|
GGML_ASSERT(hparams.n_expert_used > 0);
|
|
} else {
|
|
GGML_ASSERT(hparams.n_expert_used == 0);
|
|
}
|
|
|
|
// n_head_kv is optional, default to n_head
|
|
hparams.n_head_kv = hparams.n_head;
|
|
ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv, false);
|
|
|
|
bool rope_finetuned = false;
|
|
ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
|
|
hparams.rope_finetuned = rope_finetuned;
|
|
|
|
hparams.n_yarn_orig_ctx = hparams.n_ctx_train;
|
|
ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_yarn_orig_ctx, false);
|
|
|
|
// rope_freq_base (optional)
|
|
hparams.rope_freq_base_train = 10000.0f;
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
|
|
|
|
std::string rope_scaling("linear");
|
|
ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
|
|
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
|
|
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
|
|
|
|
// rope_freq_scale (inverse of the kv) is optional
|
|
float ropescale = 0.0f;
|
|
if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
|
|
// try the old key name
|
|
ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
|
|
}
|
|
hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
|
|
|
|
// sanity check for n_rot (optional)
|
|
{
|
|
hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
|
|
|
|
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
|
|
|
|
if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
|
|
if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
|
|
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
|
|
}
|
|
}
|
|
// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
|
|
// gpt-j n_rot = rotary_dim
|
|
}
|
|
|
|
hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
|
|
|
|
hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
|
|
|
|
// arch-specific KVs
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
if (hparams.n_expert == 8) {
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_8x7B; break;
|
|
case 56: model.type = e_model::MODEL_8x22B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} else {
|
|
switch (hparams.n_layer) {
|
|
case 22: model.type = e_model::MODEL_1B; break;
|
|
case 26: model.type = e_model::MODEL_3B; break;
|
|
case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
case 48: model.type = e_model::MODEL_34B; break;
|
|
case 60: model.type = e_model::MODEL_30B; break;
|
|
case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MINICPM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_2B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GROK:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 64: model.type = e_model::MODEL_314B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 60: model.type = e_model::MODEL_40B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
|
|
if (model.type == e_model::MODEL_13B) {
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 36: model.type = e_model::MODEL_3B; break;
|
|
case 42: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_15B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PERSIMMON:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 36: model.type = e_model::MODEL_8B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_REFACT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_1B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
} break;
|
|
case LLM_ARCH_BERT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 3:
|
|
model.type = e_model::MODEL_17M; break; // bge-micro
|
|
case 6:
|
|
model.type = e_model::MODEL_22M; break; // MiniLM-L6
|
|
case 12:
|
|
switch (hparams.n_embd) {
|
|
case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
|
|
case 768: model.type = e_model::MODEL_109M; break; // bge-base
|
|
} break;
|
|
case 24:
|
|
model.type = e_model::MODEL_335M; break; // bge-large
|
|
}
|
|
} break;
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
|
|
|
|
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
|
|
model.type = e_model::MODEL_137M;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BLOOM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 30:
|
|
switch (hparams.n_embd) {
|
|
case 2560: model.type = e_model::MODEL_3B; break;
|
|
case 4096: model.type = e_model::MODEL_7B; break;
|
|
} break;
|
|
}
|
|
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
} break;
|
|
case LLM_ARCH_MPT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
|
ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 48: model.type = e_model::MODEL_30B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STABLELM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
case 40: model.type = e_model::MODEL_12B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
|
|
case 80: model.type = e_model::MODEL_70B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_A2_7B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PLAMO:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPT2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 12: model.type = e_model::MODEL_SMALL; break;
|
|
case 24: model.type = e_model::MODEL_MEDIUM; break;
|
|
case 36: model.type = e_model::MODEL_LARGE; break;
|
|
case 48: model.type = e_model::MODEL_XL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CODESHELL:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 42: model.type = e_model::MODEL_SMALL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ORION:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_14B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 48: model.type = e_model::MODEL_20B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 18: model.type = e_model::MODEL_2B; break;
|
|
case 28: model.type = e_model::MODEL_7B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 30: model.type = e_model::MODEL_3B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_15B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MAMBA:
|
|
{
|
|
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
|
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
|
|
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
|
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24:
|
|
switch (hparams.n_embd) {
|
|
case 768: model.type = e_model::MODEL_SMALL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 48:
|
|
switch (hparams.n_embd) {
|
|
case 1024: model.type = e_model::MODEL_MEDIUM; break;
|
|
case 1536: model.type = e_model::MODEL_LARGE; break;
|
|
case 2048: model.type = e_model::MODEL_XL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 64:
|
|
switch (hparams.n_embd) {
|
|
case 2560: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_XVERSE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
case 80: model.type = e_model::MODEL_65B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
{
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_35B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DBRX:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_16x12B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMO:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 22: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 80: model.type = e_model::MODEL_70B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
default: (void)0;
|
|
}
|
|
|
|
model.ftype = ml.ftype;
|
|
|
|
if (hparams.f_max_alibi_bias > 0.0f) {
|
|
hparams.need_kq_pos = true;
|
|
}
|
|
|
|
hparams.rope_type = llama_rope_type(&model);
|
|
}
|
|
|
|
// TODO: This should probably be in llama.h
|
|
static std::vector<llama_vocab::id> llama_tokenize_internal(
|
|
const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special = false
|
|
);
|
|
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
|
|
|
|
static void llm_load_vocab(
|
|
llama_model_loader & ml,
|
|
llama_model & model) {
|
|
auto & vocab = model.vocab;
|
|
|
|
struct gguf_context * ctx = ml.meta;
|
|
|
|
const auto kv = LLM_KV(model.arch);
|
|
|
|
// determine vocab type
|
|
{
|
|
std::string tokenizer_name;
|
|
|
|
ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_name);
|
|
|
|
if (tokenizer_name == "no_vocab") {
|
|
vocab.type = LLAMA_VOCAB_TYPE_NONE;
|
|
|
|
// default special tokens
|
|
vocab.special_bos_id = -1;
|
|
vocab.special_eos_id = -1;
|
|
vocab.special_unk_id = -1;
|
|
vocab.special_sep_id = -1;
|
|
vocab.special_pad_id = -1;
|
|
vocab.special_cls_id = -1;
|
|
vocab.special_mask_id = -1;
|
|
vocab.linefeed_id = -1;
|
|
|
|
return;
|
|
} else if (tokenizer_name == "llama") {
|
|
vocab.type = LLAMA_VOCAB_TYPE_SPM;
|
|
|
|
// default special tokens
|
|
vocab.special_bos_id = 1;
|
|
vocab.special_eos_id = 2;
|
|
vocab.special_unk_id = 0;
|
|
vocab.special_sep_id = -1;
|
|
vocab.special_pad_id = -1;
|
|
vocab.special_cls_id = -1;
|
|
vocab.special_mask_id = -1;
|
|
|
|
// For Fill-In-the-Middle (FIM)/infill models which where converted
|
|
// prior to support of FIM special tokens in GGUF, the following
|
|
// will allow those models to continue to work. The general names
|
|
// of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
|
|
// CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
|
|
// new versions of these models have been published.
|
|
std::string gen_name;
|
|
ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
|
|
|
|
std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
|
|
[](unsigned char c){ return std::tolower(c); });
|
|
|
|
if (gen_name.find("code") != std::string::npos) {
|
|
if (model.arch == LLM_ARCH_LLAMA) {
|
|
vocab.special_prefix_id = 32007;
|
|
vocab.special_suffix_id = 32008;
|
|
vocab.special_middle_id = 32009;
|
|
vocab.special_eot_id = 32010;
|
|
} else if (model.arch == LLM_ARCH_GEMMA) {
|
|
vocab.special_prefix_id = 67;
|
|
vocab.special_suffix_id = 69;
|
|
vocab.special_middle_id = 68;
|
|
// TODO: this is not EOT, it is "file separator" token, needs fix
|
|
// https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
|
|
//vocab.special_eot_id = 70;
|
|
vocab.special_eot_id = 107;
|
|
}
|
|
}
|
|
|
|
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
|
|
if (add_space_prefix_keyidx != -1) {
|
|
vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx);
|
|
} // The default value of add_space_prefix is true.
|
|
} else if (tokenizer_name == "gpt2") {
|
|
vocab.type = LLAMA_VOCAB_TYPE_BPE;
|
|
|
|
// read bpe merges and populate bpe ranks
|
|
const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
|
|
if (merges_keyidx == -1) {
|
|
throw std::runtime_error("cannot find tokenizer merges in model file\n");
|
|
}
|
|
|
|
const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
|
|
|
|
for (int i = 0; i < n_merges; i++) {
|
|
const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
|
|
GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
|
|
|
|
std::string first;
|
|
std::string second;
|
|
|
|
const size_t pos = word.find(' ', 1);
|
|
|
|
if (pos != std::string::npos) {
|
|
first = word.substr(0, pos);
|
|
second = word.substr(pos + 1);
|
|
}
|
|
|
|
vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
|
|
}
|
|
|
|
// default special tokens
|
|
vocab.special_bos_id = 11;
|
|
vocab.special_eos_id = 11;
|
|
vocab.special_unk_id = -1;
|
|
vocab.special_sep_id = -1;
|
|
vocab.special_pad_id = -1;
|
|
vocab.special_cls_id = -1;
|
|
vocab.special_mask_id = -1;
|
|
} else if (tokenizer_name == "bert") {
|
|
vocab.type = LLAMA_VOCAB_TYPE_WPM;
|
|
|
|
// default special tokens
|
|
vocab.special_bos_id = -1;
|
|
vocab.special_eos_id = -1;
|
|
vocab.special_unk_id = 100;
|
|
vocab.special_sep_id = 102;
|
|
vocab.special_pad_id = 0;
|
|
vocab.special_cls_id = 101;
|
|
vocab.special_mask_id = 103;
|
|
vocab.add_space_prefix = false;
|
|
} else {
|
|
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
|
|
LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__);
|
|
|
|
vocab.type = LLAMA_VOCAB_TYPE_SPM;
|
|
}
|
|
}
|
|
|
|
const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
|
|
if (token_idx == -1) {
|
|
throw std::runtime_error("cannot find tokenizer vocab in model file\n");
|
|
}
|
|
|
|
const float * scores = nullptr;
|
|
const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
|
|
if (score_idx != -1) {
|
|
scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
|
|
}
|
|
|
|
const int * toktypes = nullptr;
|
|
const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
|
|
if (toktype_idx != -1) {
|
|
toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
|
|
}
|
|
|
|
const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
|
|
|
|
vocab.id_to_token.resize(n_vocab);
|
|
|
|
for (uint32_t i = 0; i < n_vocab; i++) {
|
|
std::string word = gguf_get_arr_str(ctx, token_idx, i);
|
|
GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
|
|
|
|
vocab.token_to_id[word] = i;
|
|
|
|
auto & token_data = vocab.id_to_token[i];
|
|
token_data.text = std::move(word);
|
|
token_data.score = scores ? scores[i] : 0.0f;
|
|
token_data.type = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL;
|
|
}
|
|
GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
|
|
|
|
// determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
|
|
if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
|
|
try {
|
|
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
|
|
} catch (const std::exception & e) {
|
|
LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
|
|
vocab.linefeed_id = vocab.special_pad_id;
|
|
}
|
|
} else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
|
|
vocab.linefeed_id = vocab.special_pad_id;
|
|
} else {
|
|
const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
|
|
GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
|
|
vocab.linefeed_id = ids[0];
|
|
}
|
|
|
|
// special tokens
|
|
{
|
|
const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
|
|
{ LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
|
|
{ LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
|
|
{ LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
|
|
{ LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
|
|
{ LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
|
|
{ LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id },
|
|
{ LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id },
|
|
{ LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id },
|
|
{ LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id },
|
|
{ LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
|
|
{ LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id },
|
|
};
|
|
|
|
for (const auto & it : special_token_types) {
|
|
const std::string & key = kv(std::get<0>(it));
|
|
int32_t & id = std::get<1>(it);
|
|
|
|
uint32_t new_id;
|
|
if (!ml.get_key(std::get<0>(it), new_id, false)) {
|
|
continue;
|
|
}
|
|
if (new_id >= vocab.id_to_token.size()) {
|
|
LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
|
|
__func__, key.c_str(), new_id, id);
|
|
} else {
|
|
id = new_id;
|
|
}
|
|
}
|
|
|
|
// Handle add_bos_token and add_eos_token
|
|
{
|
|
bool temp = true;
|
|
|
|
if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
|
|
vocab.special_add_bos = int(temp);
|
|
}
|
|
if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
|
|
vocab.special_add_eos = int(temp);
|
|
}
|
|
}
|
|
|
|
// find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
|
|
//
|
|
// TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
|
|
// for now, we apply this workaround to find the EOT token based on its text
|
|
if (vocab.special_eot_id == -1) {
|
|
for (const auto & t : vocab.token_to_id) {
|
|
if (
|
|
// TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
|
|
// need to fix convert script
|
|
//vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
|
|
(t.first == "<|eot_id|>" ||
|
|
t.first == "<|im_end|>" ||
|
|
t.first == "<|end|>" ||
|
|
t.first == "<end_of_turn>"
|
|
)
|
|
) {
|
|
vocab.special_eot_id = t.second;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// build special tokens cache
|
|
{
|
|
// TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
|
|
// and will always be correctly labeled in 'added_tokens.json' etc.
|
|
// The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
|
|
// to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
|
|
// are special tokens.
|
|
// From testing, this appears to correlate 1:1 with special tokens.
|
|
//
|
|
|
|
// Counting special tokens and verifying in only one direction
|
|
// is sufficient to detect difference in those two sets.
|
|
//
|
|
uint32_t special_tokens_count_by_type = 0;
|
|
uint32_t special_tokens_count_from_verification = 0;
|
|
|
|
bool special_tokens_definition_mismatch = false;
|
|
|
|
for (const auto & t : vocab.token_to_id) {
|
|
const auto & token = t.first;
|
|
const auto & id = t.second;
|
|
|
|
// Count all non-normal tokens in the vocab while iterating
|
|
if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
|
|
special_tokens_count_by_type++;
|
|
}
|
|
|
|
// Skip single character tokens
|
|
if (token.length() > 1) {
|
|
bool is_tokenizable = false;
|
|
|
|
// Split token string representation in two, in all possible ways
|
|
// and check if both halves can be matched to a valid token
|
|
for (unsigned i = 1; i < token.length();) {
|
|
const auto left = token.substr(0, i);
|
|
const auto right = token.substr(i);
|
|
|
|
// check if we didnt partition in the middle of a utf sequence
|
|
auto utf = utf8_len(left.at(left.length() - 1));
|
|
|
|
if (utf == 1) {
|
|
if (vocab.token_to_id.find(left) != vocab.token_to_id.end() &&
|
|
vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
|
|
is_tokenizable = true;
|
|
break;
|
|
}
|
|
i++;
|
|
} else {
|
|
// skip over the rest of multibyte utf sequence
|
|
i += utf - 1;
|
|
}
|
|
}
|
|
|
|
if (!is_tokenizable) {
|
|
// Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
|
|
// it's faster to re-filter them here, since there are way less candidates now
|
|
|
|
// Calculate a total "utf" length of a token string representation
|
|
size_t utf8_str_len = 0;
|
|
for (unsigned i = 0; i < token.length();) {
|
|
utf8_str_len++;
|
|
i += utf8_len(token.at(i));
|
|
}
|
|
|
|
// And skip the ones which are one character
|
|
if (utf8_str_len > 1) {
|
|
// At this point what we have left are special tokens only
|
|
vocab.special_tokens_cache[token] = id;
|
|
|
|
// Count manually found special tokens
|
|
special_tokens_count_from_verification++;
|
|
|
|
// If this manually found special token is not marked as such, flag a mismatch
|
|
if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
|
|
special_tokens_definition_mismatch = true;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
|
|
LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
|
|
__func__,
|
|
special_tokens_count_from_verification, vocab.id_to_token.size(),
|
|
special_tokens_count_by_type, vocab.id_to_token.size()
|
|
);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
|
|
__func__,
|
|
special_tokens_count_from_verification, vocab.id_to_token.size()
|
|
);
|
|
}
|
|
}
|
|
}
|
|
|
|
static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|
const auto & hparams = model.hparams;
|
|
const auto & vocab = model.vocab;
|
|
|
|
const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
|
|
|
|
// hparams
|
|
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver));
|
|
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch));
|
|
LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type));
|
|
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
|
|
LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size());
|
|
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
|
|
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
|
|
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
|
|
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
|
|
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
|
|
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v);
|
|
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
|
|
LLAMA_LOG_INFO("%s: n_embd_k_gqa = %u\n", __func__, hparams.n_embd_k_gqa());
|
|
LLAMA_LOG_INFO("%s: n_embd_v_gqa = %u\n", __func__, hparams.n_embd_v_gqa());
|
|
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
|
|
LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
|
|
LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
|
|
LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
|
|
LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale);
|
|
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
|
|
LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
|
|
LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
|
|
LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn);
|
|
LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type);
|
|
LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type);
|
|
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type);
|
|
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
|
|
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
|
|
LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx);
|
|
LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");
|
|
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
|
|
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
|
|
LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
|
|
LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank);
|
|
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
|
|
LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str());
|
|
if (ml.n_elements >= 1e12) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f T\n", __func__, ml.n_elements*1e-12);
|
|
} else if (ml.n_elements >= 1e9) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9);
|
|
} else if (ml.n_elements >= 1e6) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f M\n", __func__, ml.n_elements*1e-6);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f K\n", __func__, ml.n_elements*1e-3);
|
|
}
|
|
if (ml.n_bytes < GiB) {
|
|
LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: model size = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
|
|
}
|
|
|
|
// general kv
|
|
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
|
|
|
|
// special tokens
|
|
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
|
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
|
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
|
|
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
|
|
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
|
|
if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
|
|
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
|
|
|
|
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
|
|
if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
|
|
if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
|
|
if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
|
|
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
|
|
}
|
|
|
|
// Returns false if cancelled by progress_callback
|
|
static bool llm_load_tensors(
|
|
llama_model_loader & ml,
|
|
llama_model & model,
|
|
int n_gpu_layers,
|
|
enum llama_split_mode split_mode,
|
|
int main_gpu,
|
|
const float * tensor_split,
|
|
bool use_mlock,
|
|
llama_progress_callback progress_callback,
|
|
void * progress_callback_user_data) {
|
|
model.t_start_us = ggml_time_us();
|
|
|
|
auto & hparams = model.hparams;
|
|
|
|
#ifdef GGML_USE_SYCL
|
|
// disable MoE with SYCL until mul_mat_id is updated
|
|
if (hparams.n_expert > 0) {
|
|
n_gpu_layers = 0;
|
|
}
|
|
#endif
|
|
|
|
model.split_mode = split_mode;
|
|
model.main_gpu = main_gpu;
|
|
model.n_gpu_layers = n_gpu_layers;
|
|
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t i_gpu_start = std::max((int64_t) hparams.n_layer - n_gpu_layers, (int64_t) 0);
|
|
bool use_mmap_buffer = true;
|
|
|
|
// there is very little benefit to offloading the input layer, so always keep it on the CPU
|
|
model.buft_input = llama_default_buffer_type_cpu(true);
|
|
//model.buft_input = llama_default_buffer_type_offload(main_gpu);
|
|
|
|
model.buft_layer.resize(n_layer);
|
|
|
|
// assign cpu layers
|
|
for (int64_t i = 0; i < i_gpu_start; ++i) {
|
|
model.buft_layer[i] = llama_default_buffer_type_cpu(true);
|
|
}
|
|
|
|
if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
|
|
// calculate the split points
|
|
int device_count = llama_get_device_count();
|
|
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
|
|
std::vector<float> splits(device_count);
|
|
if (all_zero) {
|
|
// default split, by free memory
|
|
for (int i = 0; i < device_count; ++i) {
|
|
splits[i] = llama_get_device_memory(i);
|
|
}
|
|
} else {
|
|
std::copy(tensor_split, tensor_split + device_count, splits.begin());
|
|
}
|
|
|
|
// sum and normalize the splits to get the split points
|
|
float split_sum = 0.0f;
|
|
for (int i = 0; i < device_count; ++i) {
|
|
split_sum += splits[i];
|
|
splits[i] = split_sum;
|
|
}
|
|
for (int i = 0; i < device_count; ++i) {
|
|
splits[i] /= split_sum;
|
|
}
|
|
|
|
// assign the repeating layers to the devices according to the splits
|
|
int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
|
|
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
|
|
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
|
|
model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu);
|
|
}
|
|
// assign the output layer
|
|
if (n_gpu_layers > n_layer) {
|
|
int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
|
|
model.buft_output = llama_default_buffer_type_offload(layer_gpu);
|
|
} else {
|
|
model.buft_output = llama_default_buffer_type_cpu(true);
|
|
}
|
|
} else {
|
|
ggml_backend_buffer_type_t split_buft;
|
|
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
|
|
split_buft = llama_default_buffer_type_split(main_gpu, tensor_split);
|
|
} else {
|
|
// LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
|
|
split_buft = llama_default_buffer_type_offload(main_gpu);
|
|
}
|
|
// assign the repeating layers
|
|
for (int64_t i = i_gpu_start; i < n_layer; ++i) {
|
|
model.buft_layer[i] = {
|
|
split_buft,
|
|
llama_default_buffer_type_offload(main_gpu)
|
|
};
|
|
}
|
|
// assign the output layer
|
|
if (n_gpu_layers > n_layer) {
|
|
model.buft_output = {
|
|
split_buft,
|
|
llama_default_buffer_type_offload(main_gpu)
|
|
};
|
|
} else {
|
|
model.buft_output = llama_default_buffer_type_cpu(true);
|
|
}
|
|
}
|
|
|
|
// count used buffer types
|
|
std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
|
|
buft_layer_count[model.buft_input.buft]++;
|
|
buft_layer_count[model.buft_input.buft_matrix]++;
|
|
buft_layer_count[model.buft_output.buft]++;
|
|
buft_layer_count[model.buft_output.buft_matrix]++;
|
|
for (int64_t i = 0; i < n_layer; ++i) {
|
|
buft_layer_count[model.buft_layer[i].buft]++;
|
|
buft_layer_count[model.buft_layer[i].buft_matrix]++;
|
|
}
|
|
|
|
// create one context per buffer type
|
|
size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
|
|
|
|
// for moe merged tensors
|
|
ctx_size += ggml_tensor_overhead()*n_layer*3;
|
|
|
|
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
|
for (auto & it : buft_layer_count) {
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ ctx_size,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
ggml_context * ctx = ggml_init(params);
|
|
if (!ctx) {
|
|
throw std::runtime_error(format("failed to create context"));
|
|
}
|
|
ctx_map[it.first] = ctx;
|
|
model.ctxs.push_back(ctx);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0);
|
|
|
|
// create tensors for the weights
|
|
{
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
|
|
const int64_t n_embd_gqa = n_embd_v_gqa;
|
|
const int64_t n_vocab = hparams.n_vocab;
|
|
const int64_t n_vocab_type = hparams.n_vocab_type;
|
|
const int64_t n_ff = hparams.n_ff;
|
|
const int64_t n_expert = hparams.n_expert;
|
|
|
|
if (n_expert > 0 && hparams.n_expert_used == 0) {
|
|
throw std::runtime_error("model has expert layers but no expert layers are used");
|
|
}
|
|
|
|
GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
|
|
|
|
ggml_context * ctx_input = ctx_map.at(model.buft_input.buft);
|
|
ggml_context * ctx_output = ctx_map.at(model.buft_output.buft);
|
|
ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
|
|
auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
|
|
auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
const auto tn = LLM_TN(model.arch);
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_REFACT:
|
|
case LLM_ARCH_MINICPM:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
if (model.arch != LLM_ARCH_MINICPM){
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false);
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false);
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false);
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false);
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
if (n_expert == 0) {
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
} else {
|
|
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
|
|
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
|
|
if (layer.ffn_gate_exps) {
|
|
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
|
|
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
} else {
|
|
// merge split expert into a single tensor for compatibility with older models
|
|
// requires disabling mmap
|
|
use_mmap_buffer = false;
|
|
|
|
ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
|
|
|
|
layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
|
|
layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
|
|
layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
|
|
|
|
ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
|
|
|
|
for (uint32_t x = 0; x < n_expert; ++x) {
|
|
// the individual experts are loaded into a view of the merged tensor
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GROK:
|
|
{
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("Grok model cannot have zero experts");
|
|
}
|
|
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
|
|
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
|
|
if (layer.ffn_gate_exps) {
|
|
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
|
|
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
} else {
|
|
// merge split expert into a single tensor for compatibility with older models
|
|
// requires disabling mmap
|
|
use_mmap_buffer = false;
|
|
|
|
ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
|
|
|
|
layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
|
|
layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
|
|
layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
|
|
|
|
ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
|
|
|
|
for (uint32_t x = 0; x < n_expert; ++x) {
|
|
// the individual experts are loaded into a view of the merged tensor
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
|
|
}
|
|
}
|
|
|
|
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DBRX:
|
|
{
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("DBRX model cannot have zero experts");
|
|
}
|
|
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
|
|
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
if (!model.output) {
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false);
|
|
layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, false);
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PERSIMMON:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64});
|
|
layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64});
|
|
|
|
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64});
|
|
layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
|
|
}
|
|
|
|
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
|
|
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
} else {
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
}
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
|
layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
} else {
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
}
|
|
|
|
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
|
|
layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BLOOM:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
|
|
model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MPT:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false);
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false);
|
|
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
if (!model.output) {
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, false);
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false);
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false);
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false);
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, false);
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false);
|
|
|
|
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
|
|
layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
|
|
|
|
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
|
|
layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
|
|
|
|
// AWQ ScaleActivation layer
|
|
layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STABLELM:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors, present in Stable LM 2 1.6B
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false);
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false);
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false);
|
|
|
|
// optional q and k layernorms, present in StableLM 2 12B
|
|
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false);
|
|
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false);
|
|
|
|
// optional FFN norm, not present in StableLM 2 12B which uses parallel residual
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false);
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false);
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
|
|
GGML_ASSERT(hparams.n_expert > 0);
|
|
GGML_ASSERT(hparams.n_expert_used > 0);
|
|
|
|
// MoE branch
|
|
auto n_ff_exp = n_ff / hparams.n_expert_used;
|
|
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
|
|
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
|
|
// Shared expert branch
|
|
layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
|
|
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI2:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false);
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false);
|
|
|
|
if (layer.wqkv == nullptr) {
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
}
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI3:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd });
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab });
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context* ctx_layer = ctx_for_layer(i);
|
|
ggml_context* ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto& layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false);
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd });
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff });
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PLAMO:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPT2:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CODESHELL:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ORION:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
// layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
|
|
const int64_t n_ff = hparams.n_ff;
|
|
const int64_t n_embd_head_k = hparams.n_embd_head_k;
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER2:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
|
|
// optional bias tensors
|
|
layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MAMBA:
|
|
{
|
|
const int64_t d_conv = hparams.ssm_d_conv;
|
|
const int64_t d_inner = hparams.ssm_d_inner;
|
|
const int64_t d_state = hparams.ssm_d_state;
|
|
const int64_t dt_rank = hparams.ssm_dt_rank;
|
|
// only an expansion factor of 2 is supported for now
|
|
GGML_ASSERT(2 * n_embd == d_inner);
|
|
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
// if output is NULL, init from the input tok embed, duplicated to allow offloading
|
|
if (model.output == NULL) {
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
// norm
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});
|
|
|
|
layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
|
|
layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});
|
|
|
|
layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});
|
|
|
|
layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
|
|
layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});
|
|
|
|
// no "weight" suffix for these
|
|
layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
|
|
layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});
|
|
|
|
// out_proj
|
|
layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_XVERSE:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
auto & layer = model.layers[i];
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
// init output from the input tok embed
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
if (n_layer >= 64){
|
|
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head});
|
|
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv});
|
|
}
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
|
|
{
|
|
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
ml.n_created--; // artificial tensor
|
|
ml.size_data += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
|
|
layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
} break;
|
|
default:
|
|
throw std::runtime_error("unknown architecture");
|
|
}
|
|
}
|
|
|
|
ml.done_getting_tensors();
|
|
|
|
ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
|
|
model.mappings.reserve(ml.mappings.size());
|
|
|
|
// create the backend buffers
|
|
std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
|
|
ctx_bufs.reserve(ctx_map.size());
|
|
|
|
// Ensure we have enough capacity for the maximum backend buffer we will potentially create
|
|
size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
|
|
model.bufs.reserve(n_max_backend_buffer);
|
|
|
|
for (auto & it : ctx_map) {
|
|
ggml_backend_buffer_type_t buft = it.first;
|
|
ggml_context * ctx = it.second;
|
|
|
|
llama_buf_map bufs;
|
|
bufs.reserve(n_max_backend_buffer);
|
|
|
|
// only the mmap region containing the tensors in the model is mapped to the backend buffer
|
|
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
|
|
// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
|
|
if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
void * addr = nullptr;
|
|
size_t first, last;
|
|
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
|
|
if (first >= last) {
|
|
continue;
|
|
}
|
|
ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error("unable to allocate backend CPU buffer");
|
|
}
|
|
model.bufs.push_back(buf);
|
|
bufs.emplace(idx, buf);
|
|
#ifdef GGML_USE_CUDA
|
|
if (n_layer >= n_gpu_layers) {
|
|
ggml_backend_cuda_register_host_buffer(
|
|
ggml_backend_buffer_get_base(buf),
|
|
ggml_backend_buffer_get_size(buf));
|
|
}
|
|
#endif
|
|
}
|
|
}
|
|
#ifdef GGML_USE_METAL
|
|
else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
const size_t max_size = ggml_get_max_tensor_size(ctx);
|
|
void * addr = nullptr;
|
|
size_t first, last;
|
|
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
|
|
if (first >= last) {
|
|
continue;
|
|
}
|
|
ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error("unable to allocate backend metal buffer");
|
|
}
|
|
model.bufs.push_back(buf);
|
|
bufs.emplace(idx, buf);
|
|
}
|
|
}
|
|
#endif
|
|
else {
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error("unable to allocate backend buffer");
|
|
}
|
|
model.bufs.push_back(buf);
|
|
if (use_mlock && ggml_backend_buffer_is_host(buf)) {
|
|
model.mlock_bufs.emplace_back(new llama_mlock);
|
|
auto & mlock_buf = model.mlock_bufs.back();
|
|
mlock_buf->init (ggml_backend_buffer_get_base(buf));
|
|
mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
|
|
}
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
bufs.emplace(idx, buf);
|
|
}
|
|
}
|
|
|
|
if (bufs.empty()) {
|
|
throw std::runtime_error("failed to allocate buffer");
|
|
}
|
|
|
|
for (auto & buf : bufs) {
|
|
// indicate that this buffer contains weights
|
|
// this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
|
|
ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
|
|
}
|
|
|
|
ctx_bufs.emplace_back(ctx, bufs);
|
|
}
|
|
|
|
if (llama_supports_gpu_offload()) {
|
|
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
|
|
|
LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
|
|
if (n_gpu_layers > (int) hparams.n_layer) {
|
|
LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
|
|
}
|
|
|
|
const int max_backend_supported_layers = hparams.n_layer + 1;
|
|
const int max_offloadable_layers = hparams.n_layer + 1;
|
|
|
|
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
|
|
}
|
|
|
|
// print memory requirements
|
|
for (ggml_backend_buffer_t buf : model.bufs) {
|
|
LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// populate tensors_by_name
|
|
for (ggml_context * ctx : model.ctxs) {
|
|
for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
|
|
model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
|
|
}
|
|
}
|
|
|
|
// load tensor data
|
|
for (auto & it : ctx_bufs) {
|
|
ggml_context * ctx = it.first;
|
|
auto & bufs = it.second;
|
|
if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (use_mmap_buffer) {
|
|
for (auto & mapping : ml.mappings) {
|
|
model.mappings.emplace_back(std::move(mapping));
|
|
}
|
|
}
|
|
|
|
// loading time will be recalculate after the first eval, so
|
|
// we take page faults deferred by mmap() into consideration
|
|
model.t_load_us = ggml_time_us() - model.t_start_us;
|
|
return true;
|
|
}
|
|
|
|
// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
|
|
static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
|
|
try {
|
|
llama_model_loader ml(fname, params.use_mmap, params.kv_overrides);
|
|
|
|
model.hparams.vocab_only = params.vocab_only;
|
|
|
|
try {
|
|
llm_load_arch(ml, model);
|
|
} catch(const std::exception & e) {
|
|
throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
|
|
}
|
|
try {
|
|
llm_load_hparams(ml, model);
|
|
} catch(const std::exception & e) {
|
|
throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
|
|
}
|
|
try {
|
|
llm_load_vocab(ml, model);
|
|
} catch(const std::exception & e) {
|
|
throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
|
|
}
|
|
|
|
llm_load_print_meta(ml, model);
|
|
|
|
if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
|
|
model.hparams.n_vocab != model.vocab.id_to_token.size()) {
|
|
throw std::runtime_error("vocab size mismatch");
|
|
}
|
|
|
|
if (params.vocab_only) {
|
|
LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
|
|
return 0;
|
|
}
|
|
|
|
#ifdef GGML_USE_KOMPUTE
|
|
if (params.n_gpu_layers > 0 && (
|
|
!(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
|
|
|| !(
|
|
model.ftype == LLAMA_FTYPE_ALL_F32 ||
|
|
model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
|
|
model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
|
|
model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
|
|
)
|
|
)) {
|
|
// TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
|
|
LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
|
|
params.n_gpu_layers = 0;
|
|
}
|
|
#endif
|
|
|
|
#ifdef GGML_USE_SYCL
|
|
if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
|
|
ggml_backend_sycl_set_single_device_mode(params.main_gpu);
|
|
//SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
|
|
params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
|
|
} else {
|
|
ggml_backend_sycl_set_mul_device_mode();
|
|
}
|
|
#endif
|
|
|
|
if (!llm_load_tensors(
|
|
ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock,
|
|
params.progress_callback, params.progress_callback_user_data
|
|
)) {
|
|
return -2;
|
|
}
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
|
|
return -1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
//
|
|
// llm_build
|
|
//
|
|
|
|
using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
|
|
|
|
enum llm_ffn_op_type {
|
|
LLM_FFN_SILU,
|
|
LLM_FFN_GELU,
|
|
LLM_FFN_RELU,
|
|
LLM_FFN_RELU_SQR,
|
|
};
|
|
|
|
enum llm_ffn_gate_type {
|
|
LLM_FFN_SEQ,
|
|
LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
|
|
};
|
|
|
|
enum llm_norm_type {
|
|
LLM_NORM,
|
|
LLM_NORM_RMS,
|
|
};
|
|
|
|
static struct ggml_tensor * llm_build_inp_embd(
|
|
struct ggml_context * ctx,
|
|
struct llama_context & lctx,
|
|
const llama_hparams & hparams,
|
|
const llama_batch & batch,
|
|
struct ggml_tensor * tok_embd,
|
|
const llm_build_cb & cb) {
|
|
const int64_t n_embd = hparams.n_embd;
|
|
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
|
|
cb(lctx.inp_tokens, "inp_tokens", -1);
|
|
ggml_set_input(lctx.inp_tokens);
|
|
|
|
inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
|
|
inpL = lctx.inp_embd;
|
|
ggml_set_input(lctx.inp_embd);
|
|
}
|
|
|
|
cb(inpL, "inp_embd", -1);
|
|
|
|
return inpL;
|
|
}
|
|
|
|
static void llm_build_kv_store(
|
|
struct ggml_context * ctx,
|
|
const llama_hparams & hparams,
|
|
const llama_kv_cache & kv,
|
|
struct ggml_cgraph * graph,
|
|
struct ggml_tensor * k_cur,
|
|
struct ggml_tensor * v_cur,
|
|
int64_t n_ctx,
|
|
int32_t n_tokens,
|
|
int32_t kv_head,
|
|
const llm_build_cb & cb,
|
|
int64_t il) {
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
|
|
|
|
GGML_ASSERT(kv.size == n_ctx);
|
|
|
|
// compute the transposed [n_tokens, n_embd] V matrix
|
|
assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
|
|
struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur);
|
|
cb(v_cur_t, "v_cur_t", il);
|
|
|
|
struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
|
|
(ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
|
|
cb(k_cache_view, "k_cache_view", il);
|
|
|
|
struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
|
|
( n_ctx)*ggml_element_size(kv.v_l[il]),
|
|
(kv_head)*ggml_element_size(kv.v_l[il]));
|
|
cb(v_cache_view, "v_cache_view", il);
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
|
|
ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view));
|
|
}
|
|
|
|
static struct ggml_tensor * llm_build_norm(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * cur,
|
|
const llama_hparams & hparams,
|
|
struct ggml_tensor * mw,
|
|
struct ggml_tensor * mb,
|
|
llm_norm_type type,
|
|
const llm_build_cb & cb,
|
|
int il) {
|
|
switch (type) {
|
|
case LLM_NORM: cur = ggml_norm (ctx, cur, hparams.f_norm_eps); break;
|
|
case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hparams.f_norm_rms_eps); break;
|
|
}
|
|
|
|
if (mw || mb) {
|
|
cb(cur, "norm", il);
|
|
}
|
|
|
|
if (mw) {
|
|
cur = ggml_mul(ctx, cur, mw);
|
|
if (mb) {
|
|
cb(cur, "norm_w", il);
|
|
}
|
|
}
|
|
|
|
if (mb) {
|
|
cur = ggml_add(ctx, cur, mb);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
static struct ggml_tensor * llm_build_ffn(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * cur,
|
|
struct ggml_tensor * up,
|
|
struct ggml_tensor * up_b,
|
|
struct ggml_tensor * gate,
|
|
struct ggml_tensor * gate_b,
|
|
struct ggml_tensor * down,
|
|
struct ggml_tensor * down_b,
|
|
struct ggml_tensor * act_scales,
|
|
llm_ffn_op_type type_op,
|
|
llm_ffn_gate_type type_gate,
|
|
const llm_build_cb & cb,
|
|
int il) {
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
|
|
cb(tmp, "ffn_up", il);
|
|
|
|
if (up_b) {
|
|
tmp = ggml_add(ctx, tmp, up_b);
|
|
cb(tmp, "ffn_up_b", il);
|
|
}
|
|
|
|
if (gate) {
|
|
switch (type_gate) {
|
|
case LLM_FFN_SEQ:
|
|
{
|
|
cur = ggml_mul_mat(ctx, gate, tmp);
|
|
cb(cur, "ffn_gate", il);
|
|
} break;
|
|
case LLM_FFN_PAR:
|
|
{
|
|
cur = ggml_mul_mat(ctx, gate, cur);
|
|
cb(cur, "ffn_gate", il);
|
|
} break;
|
|
}
|
|
|
|
if (gate_b) {
|
|
cur = ggml_add(ctx, cur, gate_b);
|
|
cb(cur, "ffn_gate_b", il);
|
|
}
|
|
} else {
|
|
cur = tmp;
|
|
}
|
|
|
|
switch (type_op) {
|
|
case LLM_FFN_SILU:
|
|
{
|
|
cur = ggml_silu(ctx, cur);
|
|
cb(cur, "ffn_silu", il);
|
|
} break;
|
|
case LLM_FFN_GELU:
|
|
{
|
|
cur = ggml_gelu(ctx, cur);
|
|
cb(cur, "ffn_gelu", il);
|
|
if (act_scales != NULL) {
|
|
cur = ggml_div(ctx, cur, act_scales);
|
|
cb(cur, "ffn_act", il);
|
|
}
|
|
} break;
|
|
case LLM_FFN_RELU:
|
|
{
|
|
cur = ggml_relu(ctx, cur);
|
|
cb(cur, "ffn_relu", il);
|
|
} break;
|
|
case LLM_FFN_RELU_SQR:
|
|
{
|
|
cur = ggml_relu(ctx, cur);
|
|
cb(cur, "ffn_relu", il);
|
|
|
|
cur = ggml_sqr(ctx, cur);
|
|
cb(cur, "ffn_sqr(relu)", il);
|
|
} break;
|
|
}
|
|
|
|
if (type_gate == LLM_FFN_PAR) {
|
|
cur = ggml_mul(ctx, cur, tmp);
|
|
cb(cur, "ffn_gate_par", il);
|
|
}
|
|
|
|
cur = ggml_mul_mat(ctx, down, cur);
|
|
if (down_b) {
|
|
cb(cur, "ffn_down", il);
|
|
}
|
|
|
|
if (down_b) {
|
|
cur = ggml_add(ctx, cur, down_b);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
static struct ggml_tensor * llm_build_moe_ffn(
|
|
struct ggml_context * ctx,
|
|
struct ggml_tensor * cur,
|
|
struct ggml_tensor * gate_inp,
|
|
struct ggml_tensor * up_exps,
|
|
struct ggml_tensor * gate_exps,
|
|
struct ggml_tensor * down_exps,
|
|
int64_t n_expert,
|
|
int64_t n_expert_used,
|
|
llm_ffn_op_type type_op,
|
|
bool norm_w,
|
|
const llm_build_cb & cb,
|
|
int il) {
|
|
int64_t n_embd = cur->ne[0];
|
|
int64_t n_tokens = cur->ne[1];
|
|
|
|
ggml_tensor * logits = ggml_mul_mat(ctx, gate_inp, cur); // [n_expert, n_tokens]
|
|
cb(logits, "ffn_moe_logits", il);
|
|
|
|
ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
|
|
cb(probs, "ffn_moe_probs", il);
|
|
|
|
// select experts
|
|
ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
|
|
cb(selected_experts->src[0], "ffn_moe_argsort", il);
|
|
cb(selected_experts, "ffn_moe_topk", il);
|
|
|
|
ggml_tensor * weights = ggml_get_rows(ctx,
|
|
ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
|
|
cb(weights, "ffn_moe_weights", il);
|
|
|
|
if (norm_w) {
|
|
weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
|
|
|
|
ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
|
|
cb(weights_sum, "ffn_moe_weights_sum", il);
|
|
|
|
weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
|
|
cb(weights, "ffn_moe_weights_norm", il);
|
|
|
|
weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
|
|
}
|
|
|
|
cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
|
|
ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
|
|
cb(up, "ffn_moe_up", il);
|
|
|
|
ggml_tensor * gate = ggml_mul_mat_id(ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
|
|
cb(gate, "ffn_moe_gate", il);
|
|
|
|
switch (type_op) {
|
|
case LLM_FFN_SILU:
|
|
{
|
|
gate = ggml_silu(ctx, gate);
|
|
cb(gate, "ffn_moe_silu", il);
|
|
} break;
|
|
case LLM_FFN_GELU:
|
|
{
|
|
gate = ggml_gelu(ctx, gate);
|
|
cb(gate, "ffn_moe_gelu", il);
|
|
} break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
|
|
cb(par, "ffn_moe_gate_par", il);
|
|
|
|
ggml_tensor * experts = ggml_mul_mat_id(ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
|
|
cb(experts, "ffn_moe_down", il);
|
|
|
|
experts = ggml_mul(ctx, experts, weights);
|
|
|
|
// aggregate experts
|
|
ggml_tensor * moe_out = nullptr;
|
|
for (int i = 0; i < n_expert_used; ++i) {
|
|
ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
|
|
experts->nb[2], i*experts->nb[1]);
|
|
|
|
if (i == 0) {
|
|
moe_out = cur_expert;
|
|
} else {
|
|
moe_out = ggml_add(ctx, moe_out, cur_expert);
|
|
}
|
|
}
|
|
|
|
if (n_expert_used == 1) {
|
|
// avoid returning a non-contiguous tensor
|
|
moe_out = ggml_cont(ctx, moe_out);
|
|
}
|
|
|
|
return moe_out;
|
|
}
|
|
|
|
// if max_alibi_bias > 0 then apply ALiBi
|
|
static struct ggml_tensor * llm_build_kqv(
|
|
struct ggml_context * ctx,
|
|
const llama_model & model,
|
|
const llama_hparams & hparams,
|
|
const llama_kv_cache & kv,
|
|
struct ggml_cgraph * graph,
|
|
struct ggml_tensor * wo,
|
|
struct ggml_tensor * wo_b,
|
|
struct ggml_tensor * q_cur,
|
|
struct ggml_tensor * kq_mask,
|
|
struct ggml_tensor * kq_pos,
|
|
int64_t n_ctx,
|
|
int32_t n_tokens,
|
|
int32_t n_kv,
|
|
float kq_scale,
|
|
const llm_build_cb & cb,
|
|
int il) {
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head_k = hparams.n_embd_head_k;
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
|
|
const int64_t n_embd_head_v = hparams.n_embd_head_v;
|
|
|
|
struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
|
|
cb(q, "q", il);
|
|
|
|
struct ggml_tensor * k =
|
|
ggml_view_3d(ctx, kv.k_l[il],
|
|
n_embd_head_k, n_kv, n_head_kv,
|
|
ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
|
|
ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
|
|
0);
|
|
cb(k, "k", il);
|
|
|
|
struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
|
|
cb(kq, "kq", il);
|
|
|
|
if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) {
|
|
// for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
|
|
// ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
|
|
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
|
|
}
|
|
|
|
if (model.arch == LLM_ARCH_GROK) {
|
|
// need to do the following:
|
|
// multiply by attn_output_multiplyer of 0.08838834764831845
|
|
// and then :
|
|
// kq = 30 * tanh(kq / 30)
|
|
// before the softmax below
|
|
|
|
//try from phi2
|
|
//ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
|
|
|
|
kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
|
|
kq = ggml_scale(ctx, kq, 30);
|
|
}
|
|
|
|
#if defined(GGML_USE_KOMPUTE)
|
|
#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute")
|
|
#pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024")
|
|
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488")
|
|
if (hparams.f_max_alibi_bias > 0.0f) {
|
|
kq = ggml_scale(ctx, kq, kq_scale);
|
|
cb(kq, "kq_scaled", il);
|
|
|
|
kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias);
|
|
cb(kq, "kq_scaled_alibi", il);
|
|
|
|
kq = ggml_add(ctx, kq, kq_mask);
|
|
cb(kq, "kq_masked", il);
|
|
|
|
kq = ggml_soft_max(ctx, kq);
|
|
cb(kq, "kq_soft_max", il);
|
|
} else
|
|
#endif
|
|
{
|
|
kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias);
|
|
cb(kq, "kq_soft_max_ext", il);
|
|
}
|
|
|
|
GGML_ASSERT(kv.size == n_ctx);
|
|
|
|
// split cached v into n_head heads
|
|
struct ggml_tensor * v =
|
|
ggml_view_3d(ctx, kv.v_l[il],
|
|
n_kv, n_embd_head_v, n_head_kv,
|
|
ggml_element_size(kv.v_l[il])*n_ctx,
|
|
ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
|
|
0);
|
|
cb(v, "v", il);
|
|
|
|
struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
|
|
cb(kqv, "kqv", il);
|
|
|
|
struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
|
|
cb(kqv_merged, "kqv_merged", il);
|
|
|
|
struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens);
|
|
cb(cur, "kqv_merged_cont", il);
|
|
|
|
ggml_build_forward_expand(graph, cur);
|
|
|
|
cur = ggml_mul_mat(ctx, wo, cur);
|
|
if (wo_b) {
|
|
cb(cur, "kqv_wo", il);
|
|
}
|
|
|
|
if (wo_b) {
|
|
cur = ggml_add(ctx, cur, wo_b);
|
|
}
|
|
|
|
return cur;
|
|
}
|
|
|
|
static struct ggml_tensor * llm_build_kv(
|
|
struct ggml_context * ctx,
|
|
const llama_model & model,
|
|
const llama_hparams & hparams,
|
|
const llama_kv_cache & kv,
|
|
struct ggml_cgraph * graph,
|
|
struct ggml_tensor * wo,
|
|
struct ggml_tensor * wo_b,
|
|
struct ggml_tensor * k_cur,
|
|
struct ggml_tensor * v_cur,
|
|
struct ggml_tensor * q_cur,
|
|
struct ggml_tensor * kq_mask,
|
|
struct ggml_tensor * kq_pos,
|
|
int64_t n_ctx,
|
|
int32_t n_tokens,
|
|
int32_t kv_head,
|
|
int32_t n_kv,
|
|
float kq_scale,
|
|
const llm_build_cb & cb,
|
|
int il) {
|
|
|
|
// these nodes are added to the graph together so that they are not reordered
|
|
// by doing so, the number of splits in the graph is reduced
|
|
ggml_build_forward_expand(graph, q_cur);
|
|
ggml_build_forward_expand(graph, k_cur);
|
|
ggml_build_forward_expand(graph, v_cur);
|
|
|
|
llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il);
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b,
|
|
q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il);
|
|
cb(cur, "kqv_out", il);
|
|
|
|
return cur;
|
|
}
|
|
|
|
struct llm_build_context {
|
|
const llama_model & model;
|
|
llama_context & lctx;
|
|
const llama_hparams & hparams;
|
|
const llama_cparams & cparams;
|
|
const llama_batch & batch;
|
|
const llama_kv_cache & kv_self;
|
|
|
|
const int64_t n_embd;
|
|
const int64_t n_layer;
|
|
const int64_t n_rot;
|
|
const int64_t n_ctx; // user-specified context size (can be different from n_ctx_train)
|
|
const int64_t n_head;
|
|
const int64_t n_head_kv;
|
|
const int64_t n_embd_head_k;
|
|
const int64_t n_embd_k_gqa;
|
|
const int64_t n_embd_head_v;
|
|
const int64_t n_embd_v_gqa;
|
|
const int64_t n_expert;
|
|
const int64_t n_expert_used;
|
|
|
|
const float freq_base;
|
|
const float freq_scale;
|
|
const float ext_factor;
|
|
const float attn_factor;
|
|
const float beta_fast;
|
|
const float beta_slow;
|
|
const float norm_eps;
|
|
const float norm_rms_eps;
|
|
|
|
const int32_t n_tokens;
|
|
const int32_t n_kv; // size of KV cache to consider (n_kv <= kv_self.size)
|
|
const int32_t n_outputs;
|
|
const int32_t kv_head; // index of where we store new KV data in the cache
|
|
const int32_t n_orig_ctx;
|
|
|
|
const enum llama_pooling_type pooling_type;
|
|
const enum llama_rope_type rope_type;
|
|
|
|
const llm_build_cb & cb;
|
|
|
|
std::vector<uint8_t> & buf_compute_meta;
|
|
|
|
struct ggml_context * ctx0 = nullptr;
|
|
|
|
// TODO: consider making the entire interface noexcept
|
|
llm_build_context(
|
|
llama_context & lctx,
|
|
const llama_batch & batch,
|
|
const llm_build_cb & cb,
|
|
bool worst_case) :
|
|
model (lctx.model),
|
|
lctx (lctx),
|
|
hparams (model.hparams),
|
|
cparams (lctx.cparams),
|
|
batch (batch),
|
|
kv_self (lctx.kv_self),
|
|
n_embd (hparams.n_embd),
|
|
n_layer (hparams.n_layer),
|
|
n_rot (hparams.n_rot),
|
|
n_ctx (cparams.n_ctx),
|
|
n_head (hparams.n_head),
|
|
n_head_kv (hparams.n_head_kv),
|
|
n_embd_head_k (hparams.n_embd_head_k),
|
|
n_embd_k_gqa (hparams.n_embd_k_gqa()),
|
|
n_embd_head_v (hparams.n_embd_head_v),
|
|
n_embd_v_gqa (hparams.n_embd_v_gqa()),
|
|
n_expert (hparams.n_expert),
|
|
n_expert_used (hparams.n_expert_used),
|
|
freq_base (cparams.rope_freq_base),
|
|
freq_scale (cparams.rope_freq_scale),
|
|
ext_factor (cparams.yarn_ext_factor),
|
|
attn_factor (cparams.yarn_attn_factor),
|
|
beta_fast (cparams.yarn_beta_fast),
|
|
beta_slow (cparams.yarn_beta_slow),
|
|
norm_eps (hparams.f_norm_eps),
|
|
norm_rms_eps (hparams.f_norm_rms_eps),
|
|
n_tokens (batch.n_tokens),
|
|
n_kv (worst_case ? kv_self.size : kv_self.n),
|
|
n_outputs (worst_case ? n_tokens : lctx.n_outputs),
|
|
kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
|
|
n_orig_ctx (cparams.n_yarn_orig_ctx),
|
|
pooling_type (cparams.pooling_type),
|
|
rope_type (hparams.rope_type),
|
|
cb (cb),
|
|
buf_compute_meta (lctx.buf_compute_meta) {
|
|
// all initializations should be done in init()
|
|
}
|
|
|
|
void init() {
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute_meta.size(),
|
|
/*.mem_buffer =*/ buf_compute_meta.data(),
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ctx0 = ggml_init(params);
|
|
|
|
lctx.inp_tokens = nullptr;
|
|
lctx.inp_embd = nullptr;
|
|
lctx.inp_pos = nullptr;
|
|
lctx.inp_out_ids = nullptr;
|
|
lctx.inp_KQ_mask = nullptr;
|
|
lctx.inp_KQ_pos = nullptr;
|
|
lctx.inp_K_shift = nullptr;
|
|
lctx.inp_mean = nullptr;
|
|
lctx.inp_cls = nullptr;
|
|
lctx.inp_s_copy = nullptr;
|
|
lctx.inp_s_mask = nullptr;
|
|
lctx.inp_s_seq = nullptr;
|
|
}
|
|
|
|
void free() {
|
|
if (ctx0) {
|
|
ggml_free(ctx0);
|
|
ctx0 = nullptr;
|
|
}
|
|
}
|
|
|
|
struct ggml_cgraph * build_k_shift() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
GGML_ASSERT(kv_self.size == n_ctx);
|
|
|
|
lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
|
|
cb(lctx.inp_K_shift, "K_shift", -1);
|
|
ggml_set_input(lctx.inp_K_shift);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * tmp =
|
|
// we rotate only the first n_rot dimensions
|
|
ggml_rope_custom_inplace(ctx0,
|
|
ggml_view_3d(ctx0, kv_self.k_l[il],
|
|
n_embd_head_k, n_head_kv, n_ctx,
|
|
ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
|
|
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
|
|
0),
|
|
lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(tmp, "K_shifted", il);
|
|
ggml_build_forward_expand(gf, tmp);
|
|
}
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_s_copy() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
GGML_ASSERT(kv_self.recurrent);
|
|
|
|
struct ggml_tensor * state_copy = build_inp_s_copy();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
|
|
struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
|
|
|
|
conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
|
|
ssm_states = ggml_get_rows(ctx0, ssm_states, state_copy);
|
|
|
|
// TODO: name the intermediate tensors with cb()
|
|
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, ssm_states, kv_self.v_l[il]));
|
|
}
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
for (uint32_t i = 0; i < ids.size(); ++i) {
|
|
const uint32_t id = ids[i];
|
|
|
|
if (i == id || id == ids.size()) {
|
|
continue;
|
|
}
|
|
|
|
uint32_t nm = 1;
|
|
|
|
while (i + nm < ids.size() && ids[i + nm] == id + nm) {
|
|
nm++;
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
|
|
n_embd_k_gqa, nm,
|
|
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
|
|
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
|
|
|
|
ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
|
|
n_embd_k_gqa, nm,
|
|
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
|
|
ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
|
|
|
|
ggml_tensor * view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
|
|
nm, n_embd_v_gqa,
|
|
ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
|
|
ggml_row_size(kv_self.v_l[il]->type, i));
|
|
|
|
ggml_tensor * view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
|
|
nm, n_embd_v_gqa,
|
|
ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
|
|
ggml_row_size(kv_self.v_l[il]->type, id));
|
|
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
|
|
}
|
|
|
|
i += nm - 1;
|
|
}
|
|
|
|
//LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_pos() {
|
|
lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
cb(lctx.inp_pos, "inp_pos", -1);
|
|
ggml_set_input(lctx.inp_pos);
|
|
return lctx.inp_pos;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_out_ids() {
|
|
lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
|
|
cb(lctx.inp_out_ids, "inp_out_ids", -1);
|
|
ggml_set_input(lctx.inp_out_ids);
|
|
return lctx.inp_out_ids;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
|
|
if (causal) {
|
|
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens);
|
|
} else {
|
|
lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
|
|
}
|
|
cb(lctx.inp_KQ_mask, "KQ_mask", -1);
|
|
ggml_set_input(lctx.inp_KQ_mask);
|
|
return lctx.inp_KQ_mask;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_KQ_pos() {
|
|
lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv);
|
|
cb(lctx.inp_KQ_pos, "KQ_pos", -1);
|
|
ggml_set_input(lctx.inp_KQ_pos);
|
|
return lctx.inp_KQ_pos;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_mean() {
|
|
lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
|
|
cb(lctx.inp_mean, "inp_mean", -1);
|
|
ggml_set_input(lctx.inp_mean);
|
|
return lctx.inp_mean;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_cls() {
|
|
lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
cb(lctx.inp_cls, "inp_cls", -1);
|
|
ggml_set_input(lctx.inp_cls);
|
|
return lctx.inp_cls;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_s_copy() {
|
|
lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
|
|
cb(lctx.inp_s_copy, "inp_s_copy", -1);
|
|
ggml_set_input(lctx.inp_s_copy);
|
|
return lctx.inp_s_copy;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_s_mask() {
|
|
lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
|
|
cb(lctx.inp_s_mask, "inp_s_mask", -1);
|
|
ggml_set_input(lctx.inp_s_mask);
|
|
return lctx.inp_s_mask;
|
|
}
|
|
|
|
struct ggml_tensor * build_inp_s_seq() {
|
|
lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
|
|
cb(lctx.inp_s_seq, "inp_s_seq", -1);
|
|
ggml_set_input(lctx.inp_s_seq);
|
|
return lctx.inp_s_seq;
|
|
}
|
|
|
|
struct ggml_cgraph * build_llama() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
|
int32_t n_tokens = this->n_tokens;
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
n_tokens = n_outputs;
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
if (model.layers[il].ffn_gate_inp == nullptr) {
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
} else {
|
|
// MoE branch
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_moe_ffn(ctx0, cur,
|
|
model.layers[il].ffn_gate_inp,
|
|
model.layers[il].ffn_up_exps,
|
|
model.layers[il].ffn_gate_exps,
|
|
model.layers[il].ffn_down_exps,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU, true,
|
|
cb, il);
|
|
cb(cur, "ffn_moe_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
|
if (layer_dir != nullptr) {
|
|
cur = ggml_add(ctx0, cur, layer_dir);
|
|
}
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_baichuan() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
// positions of the tokens in the KV cache
|
|
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
switch (model.type) {
|
|
case MODEL_7B:
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
break;
|
|
case MODEL_13B:
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_xverse() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
// positions of the tokens in the KV cache
|
|
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_falcon() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * attn_norm;
|
|
|
|
attn_norm = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(attn_norm, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
if (model.layers[il].attn_norm_2) {
|
|
// Falcon-40B
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm_2,
|
|
model.layers[il].attn_norm_2_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm_2", il);
|
|
} else {
|
|
cur = attn_norm;
|
|
}
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
// using mode = 2 for neox mode
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = cur;
|
|
|
|
// feed forward
|
|
{
|
|
cur = llm_build_ffn(ctx0, attn_norm, // !! use the attn norm, not the result
|
|
model.layers[il].ffn_up, NULL,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_grok() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
|
int32_t n_tokens = this->n_tokens;
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// multiply by embedding_multiplier_scale of 78.38367176906169
|
|
inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
n_tokens = n_outputs;
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
// Grok
|
|
// if attn_out_norm is present then apply it before adding the input
|
|
if (model.layers[il].attn_out_norm) {
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.layers[il].attn_out_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_out_norm", il);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
// MoE branch
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_moe_ffn(ctx0, cur,
|
|
model.layers[il].ffn_gate_inp,
|
|
model.layers[il].ffn_up_exps,
|
|
model.layers[il].ffn_gate_exps,
|
|
model.layers[il].ffn_down_exps,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_GELU, true,
|
|
cb, il);
|
|
cb(cur, "ffn_moe_out", il);
|
|
|
|
// Grok
|
|
// if layer_out_norm is present then apply it before adding the input
|
|
// Idea: maybe ffn_out_norm is a better name
|
|
if (model.layers[il].layer_out_norm) {
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.layers[il].layer_out_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "layer_out_norm", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
|
if (layer_dir != nullptr) {
|
|
cur = ggml_add(ctx0, cur, layer_dir);
|
|
}
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
|
|
// Grok
|
|
// multiply logits by output_multiplier_scale of 0.5773502691896257
|
|
|
|
cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
|
|
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_dbrx() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
|
int32_t n_tokens = this->n_tokens;
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = nullptr;
|
|
struct ggml_tensor * Kcur = nullptr;
|
|
struct ggml_tensor * Vcur = nullptr;
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
|
cb(cur, "wqkv_clamped", il);
|
|
|
|
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
n_tokens = n_outputs;
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
// MoE branch
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].attn_out_norm, NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_out_norm", il);
|
|
|
|
cur = llm_build_moe_ffn(ctx0, cur,
|
|
model.layers[il].ffn_gate_inp,
|
|
model.layers[il].ffn_up_exps,
|
|
model.layers[il].ffn_gate_exps,
|
|
model.layers[il].ffn_down_exps,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU, true,
|
|
cb, il);
|
|
cb(cur, "ffn_moe_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
|
if (layer_dir != nullptr) {
|
|
cur = ggml_add(ctx0, cur, layer_dir);
|
|
}
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_starcoder() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
|
|
cb(pos, "pos_embd", -1);
|
|
|
|
inpL = ggml_add(ctx0, inpL, pos);
|
|
cb(inpL, "inpL", -1);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// add the input
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// FF
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(inpL, "l_out", il);
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_persimmon() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head/2 == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * residual = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
// split qkv
|
|
GGML_ASSERT(n_head_kv == n_head);
|
|
|
|
struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
|
|
cb(tmpqkv, "tmpqkv", il);
|
|
|
|
struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
|
|
cb(tmpqkv_perm, "tmpqkv", il);
|
|
|
|
struct ggml_tensor * tmpq = ggml_view_3d(
|
|
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
|
0
|
|
);
|
|
cb(tmpq, "tmpq", il);
|
|
|
|
struct ggml_tensor * tmpk = ggml_view_3d(
|
|
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
|
|
);
|
|
cb(tmpk, "tmpk", il);
|
|
|
|
// Q/K Layernorm
|
|
tmpq = llm_build_norm(ctx0, tmpq, hparams,
|
|
model.layers[il].attn_q_norm,
|
|
model.layers[il].attn_q_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(tmpq, "tmpq", il);
|
|
|
|
tmpk = llm_build_norm(ctx0, tmpk, hparams,
|
|
model.layers[il].attn_k_norm,
|
|
model.layers[il].attn_k_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(tmpk, "tmpk", il);
|
|
|
|
// RoPE the first n_rot of q/k, pass the other half, and concat.
|
|
struct ggml_tensor * qrot = ggml_view_3d(
|
|
ctx0, tmpq, n_rot, n_head, n_tokens,
|
|
ggml_element_size(tmpq) * n_embd_head,
|
|
ggml_element_size(tmpq) * n_embd_head * n_head,
|
|
0
|
|
);
|
|
cb(qrot, "qrot", il);
|
|
|
|
struct ggml_tensor * krot = ggml_view_3d(
|
|
ctx0, tmpk, n_rot, n_head, n_tokens,
|
|
ggml_element_size(tmpk) * n_embd_head,
|
|
ggml_element_size(tmpk) * n_embd_head * n_head,
|
|
0
|
|
);
|
|
cb(krot, "krot", il);
|
|
|
|
// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
|
|
struct ggml_tensor * qpass = ggml_view_3d(
|
|
ctx0, tmpq, n_rot, n_head, n_tokens,
|
|
ggml_element_size(tmpq) * n_embd_head,
|
|
ggml_element_size(tmpq) * n_embd_head * n_head,
|
|
ggml_element_size(tmpq) * n_rot
|
|
);
|
|
cb(qpass, "qpass", il);
|
|
|
|
struct ggml_tensor * kpass = ggml_view_3d(
|
|
ctx0, tmpk, n_rot, n_head, n_tokens,
|
|
ggml_element_size(tmpk) * n_embd_head,
|
|
ggml_element_size(tmpk) * n_embd_head * n_head,
|
|
ggml_element_size(tmpk) * n_rot
|
|
);
|
|
cb(kpass, "kpass", il);
|
|
|
|
struct ggml_tensor * qrotated = ggml_rope_custom(
|
|
ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(qrotated, "qrotated", il);
|
|
|
|
struct ggml_tensor * krotated = ggml_rope_custom(
|
|
ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(krotated, "krotated", il);
|
|
|
|
// ggml currently only supports concatenation on dim=2
|
|
// so we need to permute qrot, qpass, concat, then permute back.
|
|
qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
|
|
cb(qrotated, "qrotated", il);
|
|
|
|
krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
|
|
cb(krotated, "krotated", il);
|
|
|
|
qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
|
|
cb(qpass, "qpass", il);
|
|
|
|
kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
|
|
cb(kpass, "kpass", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
|
|
cb(Q, "Q", il);
|
|
|
|
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_view_3d(
|
|
ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
|
|
ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
|
|
);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_refact() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
// positions of the tokens in the KV cache
|
|
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_bert() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
struct ggml_tensor * inp_mean = build_inp_mean();
|
|
struct ggml_tensor * inp_cls = build_inp_cls();
|
|
|
|
// construct input embeddings (token, type, position)
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// token types are hardcoded to zero ("Sentence A")
|
|
struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
|
|
inpL = ggml_add(ctx0, inpL, type_row0);
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
|
|
}
|
|
cb(inpL, "inp_embd", -1);
|
|
|
|
// embed layer norm
|
|
inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
|
|
cb(inpL, "inp_norm", -1);
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);
|
|
|
|
// iterate layers
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * cur = inpL;
|
|
|
|
struct ggml_tensor * Qcur;
|
|
struct ggml_tensor * Kcur;
|
|
struct ggml_tensor * Vcur;
|
|
|
|
// self-attention
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
} else {
|
|
// compute Q and K and RoPE them
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
|
|
|
|
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
|
|
cb(kq, "kq", il);
|
|
|
|
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
|
|
cb(kq, "kq_soft_max_ext", il);
|
|
|
|
struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
|
|
cb(v, "v", il);
|
|
|
|
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
|
|
cb(kqv, "kqv", il);
|
|
|
|
struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
|
|
cb(kqv_merged, "kqv_merged", il);
|
|
|
|
cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
|
|
cb(cur, "kqv_merged_cont", il);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
|
|
if (model.layers[il].bo) {
|
|
cb(cur, "kqv_wo", il);
|
|
}
|
|
|
|
if (model.layers[il].bo) {
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bo);
|
|
}
|
|
cb(cur, "kqv_out", il);
|
|
|
|
if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// re-add the layer input
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
|
|
// attention layer norm
|
|
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
|
|
|
|
struct ggml_tensor * ffn_inp = cur;
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
} else {
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
}
|
|
cb(cur, "ffn_out", il);
|
|
|
|
// attentions bypass the intermediate layer
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
|
|
// output layer norm
|
|
cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
// final output
|
|
cur = inpL;
|
|
cb(cur, "result_embd", -1);
|
|
|
|
// pooling layer
|
|
switch (pooling_type) {
|
|
case LLAMA_POOLING_TYPE_NONE:
|
|
{
|
|
// nop
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_MEAN:
|
|
{
|
|
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
|
|
cb(cur, "result_embd_pooled", -1);
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_CLS:
|
|
{
|
|
cur = ggml_get_rows(ctx0, cur, inp_cls);
|
|
cb(cur, "result_embd_pooled", -1);
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_UNSPECIFIED:
|
|
{
|
|
GGML_ASSERT(false && "Invalid pooling type");
|
|
} break;
|
|
}
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_bloom() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
// positions of the tokens in the KV cache
|
|
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
|
|
|
|
inpL = llm_build_norm(ctx0, inpL, hparams,
|
|
model.tok_norm,
|
|
model.tok_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(inpL, "inp_norm", -1);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// Add the input
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// FF
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(inpL, "l_out", il);
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_mpt() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * pos;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
// positions of the tokens in the KV cache
|
|
struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
|
|
|
|
if (model.pos_embd) {
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
|
|
cb(pos, "pos_embd", -1);
|
|
|
|
inpL = ggml_add(ctx0, inpL, pos);
|
|
cb(inpL, "inpL", -1);
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * attn_norm;
|
|
|
|
attn_norm = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(attn_norm, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
cur = attn_norm;
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
if (model.layers[il].bqkv){
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
}
|
|
|
|
if (hparams.f_clamp_kqv > 0.0f) {
|
|
cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
|
cb(cur, "wqkv_clamped", il);
|
|
}
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
// Q/K Layernorm
|
|
if (model.layers[il].attn_q_norm) {
|
|
Qcur = llm_build_norm(ctx0, Qcur, hparams,
|
|
model.layers[il].attn_q_norm,
|
|
model.layers[il].attn_q_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = llm_build_norm(ctx0, Kcur, hparams,
|
|
model.layers[il].attn_k_norm,
|
|
model.layers[il].attn_k_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
} else {
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// Add the input
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed forward
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
model.layers[il].ffn_act,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_stablelm() {
|
|
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
struct ggml_tensor * inpSA = cur;
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
cb(Qcur, "Qcur", il);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
if (model.layers[il].attn_q_norm) {
|
|
Qcur = llm_build_norm(ctx0, Qcur, hparams,
|
|
model.layers[il].attn_q_norm,
|
|
NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
if (model.layers[il].attn_k_norm) {
|
|
Kcur = llm_build_norm(ctx0, Kcur, hparams,
|
|
model.layers[il].attn_k_norm,
|
|
NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, Qcur, inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, Kcur, inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
if (model.layers[il].ffn_norm) {
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
} else {
|
|
// parallel residual
|
|
cur = inpSA;
|
|
}
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_qwen() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
// using mode = 2 for neox mode
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward forward
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_qwen2() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_qwen2moe() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
|
int32_t n_tokens = this->n_tokens;
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self_attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
n_tokens = n_outputs;
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// MoE branch
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
ggml_tensor * moe_out =
|
|
llm_build_moe_ffn(ctx0, cur,
|
|
model.layers[il].ffn_gate_inp,
|
|
model.layers[il].ffn_up_exps,
|
|
model.layers[il].ffn_gate_exps,
|
|
model.layers[il].ffn_down_exps,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU, false,
|
|
cb, il);
|
|
cb(cur, "ffn_moe_out", il);
|
|
|
|
// FFN shared expert
|
|
{
|
|
ggml_tensor * cur_gate_inp = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
|
|
cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
|
|
|
|
// sigmoid
|
|
ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
|
|
cb(cur_gate, "ffn_shexp_gate", il);
|
|
|
|
ggml_tensor * cur_ffn = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up_shexp, NULL,
|
|
model.layers[il].ffn_gate_shexp, NULL,
|
|
model.layers[il].ffn_down_shexp, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur_ffn, "ffn_shexp", il);
|
|
|
|
ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
|
|
cb(ffn_shexp_out, "ffn_shexp_out", il);
|
|
|
|
moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
|
|
cb(moe_out, "ffn_out", il);
|
|
|
|
cur = moe_out;
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_phi2() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * attn_norm_output;
|
|
struct ggml_tensor * ffn_output;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(attn_norm_output, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor * Qcur = nullptr;
|
|
struct ggml_tensor * Kcur = nullptr;
|
|
struct ggml_tensor * Vcur = nullptr;
|
|
|
|
if (model.layers[il].wqkv) {
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
} else {
|
|
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
|
|
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
|
|
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
|
|
}
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
// with phi2, we scale the Q to avoid precision issues
|
|
// ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
|
|
Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
|
|
}
|
|
|
|
// FF
|
|
{
|
|
ffn_output = llm_build_ffn(ctx0, attn_norm_output,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(ffn_output, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_output);
|
|
cb(cur, "l_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
cb(cur, "l_out", il);
|
|
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output_no_bias", -1);
|
|
|
|
cur = ggml_add(ctx0, cur, model.output_b);
|
|
cb(cur, "result_output", -1);
|
|
ggml_build_forward_expand(gf, cur);
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_phi3() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
auto residual = inpL;
|
|
|
|
// self-attention
|
|
{
|
|
struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(attn_norm_output, "attn_norm", il);
|
|
|
|
struct ggml_tensor * Qcur = nullptr;
|
|
struct ggml_tensor * Kcur = nullptr;
|
|
struct ggml_tensor * Vcur = nullptr;
|
|
|
|
if (model.layers[il].wqkv) {
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, attn_norm_output);
|
|
cb(cur, "wqkv", il);
|
|
|
|
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
|
|
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
|
|
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
|
|
}
|
|
else {
|
|
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
|
|
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
|
|
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
|
|
}
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx,
|
|
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor* inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
residual = ggml_get_rows(ctx0, residual, inp_out_ids);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, residual);
|
|
residual = cur;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
// FF
|
|
// special-case: the up and gate tensors are merged into a single tensor
|
|
// TOOD: support into llm_build_ffn
|
|
{
|
|
struct ggml_tensor* up = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
|
|
cb(up, "ffn_up", il);
|
|
|
|
auto g = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), 0));
|
|
auto y = ggml_cont(ctx0, ggml_view_2d(ctx0, up, up->ne[0] / 2, up->ne[1], ggml_row_size(up->type, up->ne[0]), up->nb[1] / 2));
|
|
|
|
y = ggml_mul(ctx0, y, ggml_silu(ctx0, g));
|
|
cb(y, "ffn_gate", il);
|
|
|
|
auto down = ggml_mul_mat(ctx0, model.layers[il].ffn_down, y);
|
|
cb(down, "ffn_down", il);
|
|
|
|
cur = down;
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, residual, cur);
|
|
cb(cur, "l_out", il);
|
|
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm,
|
|
NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
|
|
struct ggml_cgraph * build_plamo() {
|
|
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
struct ggml_tensor * attention_norm = cur;
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos,
|
|
n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos,
|
|
n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
struct ggml_tensor * sa_out = cur;
|
|
|
|
cur = attention_norm;
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, sa_out);
|
|
cb(cur, "l_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_gpt2() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * pos;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
|
|
cb(pos, "pos_embd", -1);
|
|
|
|
inpL = ggml_add(ctx0, inpL, pos);
|
|
cb(inpL, "inpL", -1);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(Qcur, "Qcur", il);
|
|
cb(Kcur, "Kcur", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// add the input
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// FF
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(inpL, "l_out", il);
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_codeshell() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm,
|
|
model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
cb(cur, "wqkv", il);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
cb(cur, "bqkv", il);
|
|
|
|
struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
|
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
|
|
|
cb(tmpq, "tmpq", il);
|
|
cb(tmpk, "tmpk", il);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
struct ggml_tensor * Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// add the input
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// FF
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm,
|
|
model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(inpL, "l_out", il);
|
|
}
|
|
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm,
|
|
model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_orion() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
// if (model.layers[il].bq) {
|
|
// Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
// cb(Qcur, "Qcur", il);
|
|
// }
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
// if (model.layers[il].bk) {
|
|
// Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
// cb(Kcur, "Kcur", il);
|
|
// }
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
// if (model.layers[il].bv) {
|
|
// Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
// cb(Vcur, "Vcur", il);
|
|
// }
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_internlm2() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
// ref: https://arxiv.org/abs/2203.03466
|
|
// https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
|
|
// based on the original build_llama() function
|
|
struct ggml_cgraph * build_minicpm() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
//TODO: if the model varies, these parameters need to be read from the model
|
|
const int64_t n_embd_base = 256;
|
|
const float scale_embd = 12.0f;
|
|
const float scale_depth = 1.4f;
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// scale the input embeddings
|
|
inpL = ggml_scale(ctx0, inpL, scale_embd);
|
|
cb(inpL, "inp_scaled", -1);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
// scale_res - scale the hidden states for residual connection
|
|
const float scale_res = scale_depth/sqrtf(float(n_layer));
|
|
cur = ggml_scale(ctx0, cur, scale_res);
|
|
cb(cur, "hidden_scaled", -1);
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
// scale the hidden states for residual connection
|
|
cur = ggml_scale(ctx0, cur, scale_res);
|
|
cb(cur, "hidden_scaled_ffn", -1);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head scaling
|
|
const float scale_lmhead = float(n_embd_base)/float(n_embd);
|
|
cur = ggml_scale(ctx0, cur, scale_lmhead);
|
|
cb(cur, "lmhead_scaling", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_gemma() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head_k = hparams.n_embd_head_k;
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
|
|
cb(inpL, "inp_scaled", -1);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos,
|
|
n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
|
|
cb(Qcur, "Qcur_scaled", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos,
|
|
n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, NULL,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
|
|
cb(sa_out, "sa_out", il);
|
|
|
|
cur = llm_build_norm(ctx0, sa_out, hparams,
|
|
model.layers[il].ffn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, sa_out);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_starcoder2() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, model.layers[il].attn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, model.layers[il].ffn_up_b,
|
|
NULL, NULL,
|
|
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
|
NULL,
|
|
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, model.output_norm_b,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_mamba() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t d_model = n_embd;
|
|
const int64_t d_conv = hparams.ssm_d_conv;
|
|
const int64_t d_inner = hparams.ssm_d_inner;
|
|
GGML_ASSERT(2 * d_model == d_inner);
|
|
const int64_t d_state = hparams.ssm_d_state;
|
|
const int64_t dt_rank = hparams.ssm_dt_rank;
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
// {n_embd, n_tokens}
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
struct ggml_tensor * state_mask = build_inp_s_mask();
|
|
struct ggml_tensor * state_seq = build_inp_s_seq();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
// (ab)using the KV cache to store the states
|
|
struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
|
|
struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
|
|
|
|
// clear states of sequences which are starting at the beginning of this batch
|
|
{
|
|
conv_states = ggml_mul(ctx0,
|
|
ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]),
|
|
state_mask);
|
|
ssm_states = ggml_mul(ctx0,
|
|
ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]),
|
|
state_mask);
|
|
}
|
|
|
|
conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv);
|
|
ssm_states = ggml_reshape_3d(ctx0, ssm_states, d_state, d_inner, n_kv);
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM_RMS, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
|
|
struct ggml_tensor * xz = ggml_mul_mat(ctx0, model.layers[il].ssm_in, cur);
|
|
// split the above in two
|
|
// => {d_inner, n_tokens}
|
|
struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
|
|
struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner);
|
|
|
|
// conv
|
|
{
|
|
// Custom operator which is needed only to ease simultaneous sequence processing.
|
|
// For a single sequence, the equivalent is to concatenate the columns of conv_states and x,
|
|
// then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension,
|
|
// then element-wise multiply that with the conv1d weigth,
|
|
// then sum the elements of each row,
|
|
// (the last two steps are a dot product over rows (also doable with mul_mat))
|
|
// then permute away the ne[0] dimension,
|
|
// and then you're left with the resulting x tensor.
|
|
// The new conv_states is the last (d_conv - 1) columns
|
|
// of the last 3rd dimensional "layer" of the self-overlapping view.
|
|
// For simultaneous sequences, it's more complicated.
|
|
struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq);
|
|
|
|
// store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache
|
|
ggml_build_forward_expand(gf,
|
|
ggml_cpy(ctx0,
|
|
ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)),
|
|
ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv))));
|
|
|
|
// extract x from x_conv
|
|
x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0);
|
|
|
|
// bias
|
|
x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b);
|
|
|
|
x = ggml_silu(ctx0, x);
|
|
}
|
|
|
|
// ssm
|
|
{
|
|
// {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
|
|
struct ggml_tensor * x_db = ggml_mul_mat(ctx0, model.layers[il].ssm_x, x);
|
|
// split
|
|
struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
|
|
struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
|
|
struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
|
|
|
|
// {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
|
|
dt = ggml_mul_mat(ctx0, model.layers[il].ssm_dt, dt);
|
|
dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
|
|
|
|
// Custom operator to optimize the parallel associative scan
|
|
// as described in the Annex D of the Mamba paper.
|
|
// => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined,
|
|
// because only a single tensor can be returned.
|
|
struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq);
|
|
|
|
// store last states (the second part of y_ssm_states)
|
|
ggml_build_forward_expand(gf,
|
|
ggml_cpy(ctx0,
|
|
ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)),
|
|
ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states))));
|
|
|
|
struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0);
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
x = ggml_get_rows(ctx0, x, inp_out_ids);
|
|
y = ggml_get_rows(ctx0, y, inp_out_ids);
|
|
z = ggml_get_rows(ctx0, z, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
}
|
|
|
|
// {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens}
|
|
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
|
|
y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
|
|
|
|
// {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].ssm_out, y);
|
|
}
|
|
|
|
// residual
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
// final rmsnorm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM_RMS, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
|
|
struct ggml_cgraph * build_command_r() {
|
|
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
const float f_logit_scale = hparams.f_logit_scale;
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
model.layers[il].attn_norm, NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
struct ggml_tensor * ffn_inp = cur;
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (model.layers[il].bq) {
|
|
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (model.layers[il].bk) {
|
|
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (model.layers[il].bv) {
|
|
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
if (model.layers[il].attn_q_norm) {
|
|
Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
|
|
ggml_element_size(Qcur) * n_embd_head,
|
|
ggml_element_size(Qcur) * n_embd_head * n_head,
|
|
0);
|
|
cb(Qcur, "Qcur", il);
|
|
Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
|
|
ggml_element_size(Kcur) * n_embd_head,
|
|
ggml_element_size(Kcur) * n_embd_head * n_head_kv,
|
|
0);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
Qcur = llm_build_norm(ctx0, Qcur, hparams,
|
|
model.layers[il].attn_q_norm,
|
|
NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = llm_build_norm(ctx0, Kcur, hparams,
|
|
model.layers[il].attn_k_norm,
|
|
NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, model.layers[il].bo,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
|
|
ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * attn_out = cur;
|
|
|
|
// feed-forward network
|
|
{
|
|
cur = llm_build_ffn(ctx0, ffn_inp,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
// add together residual + FFN + self-attention
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
cur = ggml_add(ctx0, cur, attn_out);
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
model.output_norm, NULL,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
|
|
if (f_logit_scale) {
|
|
cur = ggml_scale(ctx0, cur, f_logit_scale);
|
|
}
|
|
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
|
|
}
|
|
|
|
// ref: https://allenai.org/olmo
|
|
// based on the original build_llama() function, changes:
|
|
// * non-parametric layer norm
|
|
// * clamp qkv
|
|
// * removed bias
|
|
// * removed MoE
|
|
struct ggml_cgraph * build_olmo() {
|
|
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
|
|
|
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
|
int32_t n_tokens = this->n_tokens;
|
|
|
|
const int64_t n_embd_head = hparams.n_embd_head_v;
|
|
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
|
|
|
// inp_pos - contains the positions
|
|
struct ggml_tensor * inp_pos = build_inp_pos();
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
cur = llm_build_norm(ctx0, inpL, hparams,
|
|
NULL, NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "attn_norm", il);
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
cb(Qcur, "Qcur", il);
|
|
if (hparams.f_clamp_kqv > 0.0f) {
|
|
Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
|
cb(Qcur, "Qcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
cb(Kcur, "Kcur", il);
|
|
if (hparams.f_clamp_kqv > 0.0f) {
|
|
Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
|
cb(Kcur, "Kcur", il);
|
|
}
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
cb(Vcur, "Vcur", il);
|
|
if (hparams.f_clamp_kqv > 0.0f) {
|
|
Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
|
cb(Vcur, "Vcur", il);
|
|
}
|
|
|
|
Qcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
Kcur = ggml_rope_custom(
|
|
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
|
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow
|
|
);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
|
model.layers[il].wo, nullptr,
|
|
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
|
}
|
|
|
|
if (il == n_layer - 1) {
|
|
// skip computing output for unused tokens
|
|
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
|
n_tokens = n_outputs;
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
// feed-forward network
|
|
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
|
NULL, NULL,
|
|
LLM_NORM, cb, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
cur = llm_build_ffn(ctx0, cur,
|
|
model.layers[il].ffn_up, NULL,
|
|
model.layers[il].ffn_gate, NULL,
|
|
model.layers[il].ffn_down, NULL,
|
|
NULL,
|
|
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
cb(cur, "ffn_out", il);
|
|
|
|
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
|
if (layer_dir != nullptr) {
|
|
cur = ggml_add(ctx0, cur, layer_dir);
|
|
}
|
|
cb(cur, "l_out", il);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = llm_build_norm(ctx0, cur, hparams,
|
|
NULL, NULL,
|
|
LLM_NORM, cb, -1);
|
|
cb(cur, "result_norm", -1);
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
cb(cur, "result_output", -1);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
return gf;
|
|
}
|
|
};
|
|
|
|
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
|
llama_batch dummy;
|
|
dummy.n_tokens = 0;
|
|
|
|
llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
|
|
|
|
struct llm_build_context llm(lctx, dummy, cb, false);
|
|
|
|
llm.init();
|
|
|
|
struct ggml_cgraph * result = llm.build_defrag(ids);
|
|
|
|
llm.free();
|
|
|
|
return result;
|
|
}
|
|
|
|
static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
|
|
llama_batch dummy;
|
|
dummy.n_tokens = 0;
|
|
|
|
llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
|
|
|
|
struct llm_build_context llm(lctx, dummy, cb, false);
|
|
|
|
llm.init();
|
|
|
|
struct ggml_cgraph * result = llm.build_k_shift();
|
|
|
|
llm.free();
|
|
|
|
return result;
|
|
}
|
|
|
|
static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
|
|
llama_batch dummy;
|
|
dummy.n_tokens = 0;
|
|
|
|
llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
|
|
|
|
struct llm_build_context llm(lctx, dummy, cb, false);
|
|
|
|
llm.init();
|
|
|
|
struct ggml_cgraph * result = llm.build_s_copy();
|
|
|
|
llm.free();
|
|
|
|
return result;
|
|
}
|
|
|
|
static struct ggml_cgraph * llama_build_graph(
|
|
llama_context & lctx,
|
|
const llama_batch & batch,
|
|
bool worst_case) {
|
|
const auto & model = lctx.model;
|
|
|
|
// this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
|
|
llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
|
|
if (il >= 0) {
|
|
ggml_format_name(cur, "%s-%d", name, il);
|
|
} else {
|
|
ggml_set_name(cur, name);
|
|
}
|
|
|
|
if (!lctx.cparams.offload_kqv) {
|
|
if (strcmp(name, "kqv_merged_cont") == 0) {
|
|
// all nodes between the KV store and the attention output are run on the CPU
|
|
ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
|
|
}
|
|
}
|
|
|
|
// norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
|
|
// FIXME: fix in ggml_backend_sched
|
|
const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
|
|
if (batch.n_tokens < 32 || full_offload) {
|
|
if (il != -1 && strcmp(name, "norm") == 0) {
|
|
for (auto * backend : lctx.backends) {
|
|
if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
|
|
ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
struct ggml_cgraph * result = NULL;
|
|
|
|
struct llm_build_context llm(lctx, batch, cb, worst_case);
|
|
|
|
llm.init();
|
|
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
{
|
|
result = llm.build_llama();
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
result = llm.build_baichuan();
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
result = llm.build_falcon();
|
|
} break;
|
|
case LLM_ARCH_GROK:
|
|
{
|
|
result = llm.build_grok();
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
result = llm.build_starcoder();
|
|
} break;
|
|
case LLM_ARCH_PERSIMMON:
|
|
{
|
|
result = llm.build_persimmon();
|
|
} break;
|
|
case LLM_ARCH_REFACT:
|
|
{
|
|
result = llm.build_refact();
|
|
} break;
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
{
|
|
result = llm.build_bert();
|
|
} break;
|
|
case LLM_ARCH_BLOOM:
|
|
{
|
|
result = llm.build_bloom();
|
|
} break;
|
|
case LLM_ARCH_MPT:
|
|
{
|
|
result = llm.build_mpt();
|
|
} break;
|
|
case LLM_ARCH_STABLELM:
|
|
{
|
|
result = llm.build_stablelm();
|
|
} break;
|
|
case LLM_ARCH_QWEN:
|
|
{
|
|
result = llm.build_qwen();
|
|
} break;
|
|
case LLM_ARCH_QWEN2:
|
|
{
|
|
result = llm.build_qwen2();
|
|
} break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
{
|
|
result = llm.build_qwen2moe();
|
|
} break;
|
|
case LLM_ARCH_PHI2:
|
|
{
|
|
result = llm.build_phi2();
|
|
} break;
|
|
case LLM_ARCH_PHI3:
|
|
{
|
|
result = llm.build_phi3();
|
|
} break;
|
|
case LLM_ARCH_PLAMO:
|
|
{
|
|
result = llm.build_plamo();
|
|
} break;
|
|
case LLM_ARCH_GPT2:
|
|
{
|
|
result = llm.build_gpt2();
|
|
} break;
|
|
case LLM_ARCH_CODESHELL:
|
|
{
|
|
result = llm.build_codeshell();
|
|
} break;
|
|
case LLM_ARCH_ORION:
|
|
{
|
|
result = llm.build_orion();
|
|
} break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
{
|
|
result = llm.build_internlm2();
|
|
} break;
|
|
case LLM_ARCH_MINICPM:
|
|
{
|
|
result = llm.build_minicpm();
|
|
} break;
|
|
case LLM_ARCH_GEMMA:
|
|
{
|
|
result = llm.build_gemma();
|
|
} break;
|
|
case LLM_ARCH_STARCODER2:
|
|
{
|
|
result = llm.build_starcoder2();
|
|
} break;
|
|
case LLM_ARCH_MAMBA:
|
|
{
|
|
result = llm.build_mamba();
|
|
} break;
|
|
case LLM_ARCH_XVERSE:
|
|
{
|
|
result = llm.build_xverse();
|
|
} break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
{
|
|
result = llm.build_command_r();
|
|
} break;
|
|
case LLM_ARCH_DBRX:
|
|
{
|
|
result = llm.build_dbrx();
|
|
} break;
|
|
case LLM_ARCH_OLMO:
|
|
{
|
|
result = llm.build_olmo();
|
|
} break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
llm.free();
|
|
|
|
return result;
|
|
}
|
|
|
|
static void llama_set_k_shift(llama_context & lctx) {
|
|
const int64_t kv_size = lctx.kv_self.size;
|
|
|
|
assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
|
|
|
|
int32_t * data = (int32_t *) lctx.inp_K_shift->data;
|
|
|
|
for (int i = 0; i < kv_size; ++i) {
|
|
data[i] = lctx.kv_self.cells[i].delta;
|
|
}
|
|
}
|
|
|
|
static void llama_set_s_copy(llama_context & lctx) {
|
|
const int64_t kv_size = lctx.kv_self.size;
|
|
|
|
assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
|
|
|
|
int32_t * data = (int32_t *) lctx.inp_s_copy->data;
|
|
|
|
for (int i = 0; i < kv_size; ++i) {
|
|
data[i] = lctx.kv_self.cells[i].src;
|
|
}
|
|
}
|
|
|
|
static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
|
|
//
|
|
// set input data
|
|
//
|
|
|
|
const auto & hparams = lctx.model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
if (batch.token) {
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
|
|
}
|
|
|
|
if (batch.embd) {
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
|
|
}
|
|
|
|
if (batch.pos && lctx.inp_pos) {
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
|
|
}
|
|
|
|
if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
|
|
GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
|
|
int32_t * data = (int32_t *) lctx.inp_out_ids->data;
|
|
|
|
if (lctx.n_outputs == n_tokens) {
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
data[i] = i;
|
|
}
|
|
} else if (batch.logits) {
|
|
int32_t n_outputs = 0;
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
if (batch.logits[i]) {
|
|
data[n_outputs++] = i;
|
|
}
|
|
}
|
|
// the graph needs to have been passed the correct number of outputs
|
|
GGML_ASSERT(lctx.n_outputs == n_outputs);
|
|
} else if (lctx.n_outputs == 1) {
|
|
// only keep last output
|
|
data[0] = n_tokens - 1;
|
|
} else {
|
|
GGML_ASSERT(lctx.n_outputs == 0);
|
|
}
|
|
}
|
|
|
|
GGML_ASSERT(
|
|
// (!a || b) is a logical implication (a -> b)
|
|
// !hparams.causal_attn -> !cparams.causal_attn
|
|
(hparams.causal_attn || !cparams.causal_attn) &&
|
|
"causal attention with embedding models is not supported"
|
|
);
|
|
|
|
if (lctx.inp_KQ_mask) {
|
|
// NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
|
|
if (cparams.causal_attn) {
|
|
const int64_t n_kv = kv_self.n;
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
|
|
|
|
float * data = (float *) lctx.inp_KQ_mask->data;
|
|
|
|
// For causal attention, use only the previous KV cells
|
|
// of the correct sequence for each token of the batch.
|
|
// It's assumed that if a token in the batch has multiple sequences, they are equivalent.
|
|
for (int h = 0; h < 1; ++h) {
|
|
for (int j = 0; j < n_tokens; ++j) {
|
|
const llama_pos pos = batch.pos[j];
|
|
const llama_seq_id seq_id = batch.seq_id[j][0];
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
float f;
|
|
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
|
|
f = -INFINITY;
|
|
} else {
|
|
f = 0.0f;
|
|
}
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
// when using kv cache, the mask needs to match the kv cache size
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
|
|
|
|
float * data = (float *) lctx.inp_KQ_mask->data;
|
|
|
|
for (int h = 0; h < 1; ++h) {
|
|
for (int j = 0; j < n_tokens; ++j) {
|
|
const llama_seq_id seq_id = batch.seq_id[j][0];
|
|
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
float f = -INFINITY;
|
|
for (int s = 0; s < batch.n_seq_id[i]; ++s) {
|
|
if (batch.seq_id[i][s] == seq_id) {
|
|
f = 0.0f;
|
|
break;
|
|
}
|
|
}
|
|
|
|
data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
|
|
}
|
|
|
|
for (int i = n_tokens; i < n_stride; ++i) {
|
|
data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if (hparams.need_kq_pos) {
|
|
const int64_t n_kv = kv_self.n;
|
|
|
|
GGML_ASSERT(lctx.inp_KQ_pos);
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer));
|
|
|
|
float * data = (float *) lctx.inp_KQ_pos->data;
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
data[i] = float(lctx.kv_self.cells[i].pos);
|
|
}
|
|
}
|
|
|
|
if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
GGML_ASSERT(lctx.inp_mean);
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
|
|
|
|
float * data = (float *) lctx.inp_mean->data;
|
|
memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
|
|
|
|
std::vector<uint64_t> sum(n_tokens, 0);
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
const llama_seq_id seq_id = batch.seq_id[i][0];
|
|
|
|
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
|
|
|
|
sum[seq_id] += 1;
|
|
}
|
|
|
|
std::vector<float> div(n_tokens, 0.0f);
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
const uint64_t s = sum[i];
|
|
if (s > 0) {
|
|
div[i] = 1.0f/float(s);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
const llama_seq_id seq_id = batch.seq_id[i][0];
|
|
data[seq_id*n_tokens + i] = div[seq_id];
|
|
}
|
|
}
|
|
|
|
if (cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
GGML_ASSERT(lctx.inp_cls);
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
|
|
|
|
uint32_t * data = (uint32_t *) lctx.inp_cls->data;
|
|
memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
|
|
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
const llama_seq_id seq_id = batch.seq_id[i][0];
|
|
const llama_pos pos = batch.pos[i];
|
|
|
|
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
|
|
|
|
if (pos == 0) {
|
|
data[seq_id] = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (kv_self.recurrent) {
|
|
const int64_t n_kv = kv_self.n;
|
|
|
|
if (lctx.inp_s_mask) {
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
|
|
float * data = (float *) lctx.inp_s_mask->data;
|
|
|
|
// states which are not affected by the current batch are left untouched
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
llama_seq_id seq_id = i + lctx.kv_self.head;
|
|
llama_kv_cell & kv_cell = lctx.kv_self.cells[seq_id];
|
|
bool has_self_seq = kv_cell.has_seq_id(seq_id);
|
|
|
|
data[i] = (float) has_self_seq;
|
|
|
|
// ensure current sequences will be kept
|
|
if (!has_self_seq && kv_cell.pos >= 0) {
|
|
kv_cell.seq_id.insert(seq_id);
|
|
}
|
|
}
|
|
}
|
|
// For Mamba (and other recurrent architectures),
|
|
// update the correct state(s)/sequence(s) for each token of the batch.
|
|
// Like with the KQ_mask, if a token in the batch has multiple sequences,
|
|
// they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
|
|
if (lctx.inp_s_seq) {
|
|
const int64_t n_tokens = batch.n_tokens;
|
|
|
|
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
|
|
int32_t * data = (int32_t *) lctx.inp_s_seq->data;
|
|
|
|
for (int j = 0; j < n_tokens; ++j) {
|
|
const int32_t n_seq = batch.n_seq_id[j];
|
|
GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (i < n_seq) {
|
|
// for this type of model, the head is the minimum seq_id of the batch
|
|
data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
|
|
} else {
|
|
data[j*n_kv + i] = -1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Make sure enough space is available for outputs.
|
|
// Returns max number of outputs for which space was reserved.
|
|
static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
|
|
const auto & cparams = lctx.cparams;
|
|
const auto & hparams = lctx.model.hparams;
|
|
|
|
const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
|
|
|
|
const auto n_batch = cparams.n_batch;
|
|
const auto n_vocab = hparams.n_vocab;
|
|
const auto n_embd = hparams.n_embd;
|
|
|
|
// TODO: use a per-batch flag for logits presence instead
|
|
const bool has_logits = cparams.causal_attn;
|
|
const bool has_embd = cparams.embeddings && (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
|
|
|
|
const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
|
|
const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0;
|
|
|
|
if (lctx.output_ids.empty()) {
|
|
// init, never resized afterwards
|
|
lctx.output_ids.resize(n_batch);
|
|
}
|
|
|
|
const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
|
|
const size_t new_size = (logits_size + embd_size) * sizeof(float);
|
|
|
|
// alloc only when more than the current capacity is required
|
|
// TODO: also consider shrinking the buffer
|
|
if (!lctx.buf_output || prev_size < new_size) {
|
|
if (lctx.buf_output) {
|
|
#ifndef NDEBUG
|
|
// This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
|
|
LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
|
|
#endif
|
|
ggml_backend_buffer_free(lctx.buf_output);
|
|
lctx.buf_output = nullptr;
|
|
lctx.logits = nullptr;
|
|
lctx.embd = nullptr;
|
|
}
|
|
|
|
lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
|
|
if (lctx.buf_output == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
|
|
|
|
lctx.logits = has_logits ? output_base : nullptr;
|
|
lctx.embd = has_embd ? output_base + logits_size : nullptr;
|
|
|
|
lctx.output_size = n_outputs_max;
|
|
lctx.logits_size = logits_size;
|
|
lctx.embd_size = embd_size;
|
|
|
|
// set all ids as invalid (negative)
|
|
std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
|
|
|
|
ggml_backend_buffer_clear(lctx.buf_output, 0);
|
|
|
|
lctx.n_outputs = 0;
|
|
|
|
return n_outputs_max;
|
|
}
|
|
|
|
|
|
static void llama_graph_compute(
|
|
llama_context & lctx,
|
|
ggml_cgraph * gf,
|
|
int n_threads) {
|
|
#ifdef GGML_USE_MPI
|
|
const int64_t n_layer = lctx.model.hparams.n_layer;
|
|
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
|
|
#endif
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (ggml_backend_is_metal(lctx.backend_metal)) {
|
|
ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
|
|
}
|
|
#endif
|
|
|
|
if (lctx.backend_cpu != nullptr) {
|
|
ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
|
|
ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
|
|
}
|
|
|
|
ggml_backend_sched_graph_compute_async(lctx.sched, gf);
|
|
|
|
// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
|
|
#endif
|
|
}
|
|
|
|
// decode a batch of tokens by evaluating the transformer
|
|
//
|
|
// - lctx: llama context
|
|
// - batch: batch to evaluate
|
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//
|
|
// return 0 on success
|
|
// return positive int on warning
|
|
// return negative int on error
|
|
//
|
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static int llama_decode_internal(
|
|
llama_context & lctx,
|
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llama_batch batch_all) { // TODO: rename back to batch
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|
|
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const uint32_t n_tokens_all = batch_all.n_tokens;
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|
|
if (n_tokens_all == 0) {
|
|
LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
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|
return -1;
|
|
}
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|
|
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const auto & model = lctx.model;
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|
const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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|
|
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GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
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|
|
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GGML_ASSERT(n_tokens_all <= cparams.n_batch);
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|
|
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GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
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|
|
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if (lctx.t_compute_start_us == 0) {
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|
lctx.t_compute_start_us = ggml_time_us();
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|
}
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lctx.n_queued_tokens += n_tokens_all;
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|
|
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#ifdef GGML_USE_MPI
|
|
// TODO: needs fix after #3228
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GGML_ASSERT(false && "not implemented");
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//ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
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#endif
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|
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auto & kv_self = lctx.kv_self;
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|
|
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_vocab = hparams.n_vocab;
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|
|
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uint32_t n_outputs = 0;
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uint32_t n_outputs_prev = 0;
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|
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const auto n_ubatch = cparams.n_ubatch;
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|
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std::vector<llama_pos> pos;
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std::vector<int32_t> n_seq_id;
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std::vector<llama_seq_id *> seq_id_arr;
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std::vector<std::vector<llama_seq_id>> seq_id;
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|
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// count outputs
|
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if (batch_all.logits) {
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for (uint32_t i = 0; i < n_tokens_all; ++i) {
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n_outputs += batch_all.logits[i] != 0;
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|
}
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} else if (lctx.logits_all || (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
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n_outputs = n_tokens_all;
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} else {
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// keep last output only
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n_outputs = 1;
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}
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|
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// reserve output buffer
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if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
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LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
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return -2;
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|
};
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|
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// set output mappings
|
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if (batch_all.logits) {
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int32_t i_logits = 0;
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for (uint32_t i = 0; i < n_tokens_all; ++i) {
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if (batch_all.logits[i]) {
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lctx.output_ids[i] = i_logits++;
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}
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}
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|
} else {
|
|
for (uint32_t i = 0; i < n_outputs; ++i) {
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lctx.output_ids[i] = i;
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}
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}
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|
|
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for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
|
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const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
|
|
llama_batch u_batch = {
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/* .n_tokens = */ (int32_t) n_tokens,
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/* .token = */ batch_all.token ? batch_all.token + cur_token : nullptr,
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/* .embd = */ batch_all.embd ? batch_all.embd + cur_token*n_embd : nullptr,
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/* .pos = */ batch_all.pos ? batch_all.pos + cur_token : nullptr,
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|
/* .n_seq_id = */ batch_all.n_seq_id ? batch_all.n_seq_id + cur_token : nullptr,
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/* .seq_id = */ batch_all.seq_id ? batch_all.seq_id + cur_token : nullptr,
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/* .logits = */ batch_all.logits ? batch_all.logits + cur_token : nullptr,
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/* .all_pos_0 = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
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/* .all_pos_1 = */ batch_all.all_pos_1,
|
|
/* .all_seq_id = */ batch_all.all_seq_id,
|
|
};
|
|
|
|
// count the outputs in this u_batch
|
|
{
|
|
int32_t n_outputs_new = 0;
|
|
|
|
if (u_batch.logits) {
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
n_outputs_new += u_batch.logits[i] != 0;
|
|
}
|
|
} else if (n_outputs == n_tokens_all) {
|
|
n_outputs_new = n_tokens;
|
|
} else {
|
|
// keep last output only
|
|
if (cur_token + n_tokens >= n_tokens_all) {
|
|
n_outputs_new = 1;
|
|
}
|
|
}
|
|
|
|
// needs to happen before the graph is built
|
|
lctx.n_outputs = n_outputs_new;
|
|
}
|
|
|
|
int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
|
|
GGML_ASSERT(n_threads > 0);
|
|
|
|
// helpers for smoother batch API transition
|
|
// after deprecating the llama_eval calls, these will be removed
|
|
if (u_batch.pos == nullptr) {
|
|
pos.resize(n_tokens);
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
|
|
}
|
|
|
|
u_batch.pos = pos.data();
|
|
}
|
|
|
|
if (u_batch.seq_id == nullptr) {
|
|
n_seq_id.resize(n_tokens);
|
|
seq_id.resize(n_tokens);
|
|
seq_id_arr.resize(n_tokens);
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
n_seq_id[i] = 1;
|
|
seq_id[i].resize(1);
|
|
seq_id[i][0] = u_batch.all_seq_id;
|
|
seq_id_arr[i] = seq_id[i].data();
|
|
}
|
|
|
|
u_batch.n_seq_id = n_seq_id.data();
|
|
u_batch.seq_id = seq_id_arr.data();
|
|
}
|
|
|
|
// non-causal masks do not use the KV cache
|
|
if (hparams.causal_attn) {
|
|
llama_kv_cache_update(&lctx);
|
|
|
|
// if we have enough unused cells before the current head ->
|
|
// better to start searching from the beginning of the cache, hoping to fill it
|
|
if (kv_self.head > kv_self.used + 2*n_tokens) {
|
|
kv_self.head = 0;
|
|
}
|
|
|
|
if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
|
|
return 1;
|
|
}
|
|
|
|
if (!kv_self.recurrent) {
|
|
// a heuristic, to avoid attending the full cache if it is not yet utilized
|
|
// after enough generations, the benefit from this heuristic disappears
|
|
// if we start defragmenting the cache, the benefit from this will be more important
|
|
kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
|
|
//kv_self.n = llama_kv_cache_cell_max(kv_self);
|
|
}
|
|
}
|
|
|
|
//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
|
|
|
|
ggml_backend_sched_reset(lctx.sched);
|
|
ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
|
|
|
|
ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
|
|
|
|
// the output is always the last tensor in the graph
|
|
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
|
|
struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
|
|
|
|
if (lctx.n_outputs == 0) {
|
|
// no output
|
|
res = nullptr;
|
|
embd = nullptr;
|
|
} else if (!hparams.causal_attn) {
|
|
res = nullptr; // do not extract logits for embedding models such as BERT
|
|
|
|
// token or sequence embeddings
|
|
embd = gf->nodes[gf->n_nodes - 1];
|
|
|
|
GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
|
|
} else if (cparams.embeddings) {
|
|
// the embeddings could be in the second to last tensor, or any of the previous tensors
|
|
int i_embd = gf->n_nodes - 2;
|
|
for (int i = 3; strcmp(embd->name, "result_norm") != 0; ++i) {
|
|
i_embd = gf->n_nodes - i;
|
|
if (i_embd < 0) { break; }
|
|
embd = gf->nodes[i_embd];
|
|
}
|
|
GGML_ASSERT(i_embd >= 0 && "missing result_norm tensor");
|
|
|
|
// TODO: use a per-batch flag to know when to skip logits while keeping embeddings
|
|
if (!cparams.causal_attn) {
|
|
res = nullptr; // do not extract logits when not needed
|
|
// skip computing logits
|
|
// TODO: is this safe?
|
|
gf->n_nodes = i_embd + 1;
|
|
}
|
|
} else {
|
|
embd = nullptr; // do not extract embeddings when not needed
|
|
GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
|
|
}
|
|
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
|
|
|
|
// for big prompts, if BLAS is enabled, it is better to use only one thread
|
|
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
|
|
// TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
|
|
// we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
|
|
// with the BLAS calls. need a better solution
|
|
// MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
|
|
// being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
|
|
if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
|
|
n_threads = std::min(4, n_threads);
|
|
}
|
|
|
|
ggml_backend_sched_alloc_graph(lctx.sched, gf);
|
|
|
|
llama_set_inputs(lctx, u_batch);
|
|
|
|
llama_graph_compute(lctx, gf, n_threads);
|
|
|
|
// update the kv ring buffer
|
|
{
|
|
kv_self.head += n_tokens;
|
|
|
|
// Ensure kv cache head points to a valid index.
|
|
if (kv_self.head >= kv_self.size) {
|
|
kv_self.head = 0;
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_PERF
|
|
// print timing information per ggml operation (for debugging purposes)
|
|
// requires GGML_PERF to be defined
|
|
ggml_graph_print(gf);
|
|
#endif
|
|
|
|
// plot the computation graph in dot format (for debugging purposes)
|
|
//if (n_past%100 == 0) {
|
|
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
|
|
//}
|
|
|
|
// extract logits
|
|
if (res) {
|
|
ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
|
|
GGML_ASSERT(backend_res != nullptr);
|
|
GGML_ASSERT(lctx.logits != nullptr);
|
|
|
|
float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
|
|
const int32_t n_outputs_new = lctx.n_outputs;
|
|
|
|
if (n_outputs_new) {
|
|
GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
|
|
GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
|
|
ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
|
|
}
|
|
}
|
|
|
|
// extract embeddings
|
|
if (embd) {
|
|
ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
|
|
GGML_ASSERT(backend_embd != nullptr);
|
|
|
|
switch (cparams.pooling_type) {
|
|
case LLAMA_POOLING_TYPE_NONE:
|
|
{
|
|
// extract token embeddings
|
|
GGML_ASSERT(lctx.embd != nullptr);
|
|
float * embd_out = lctx.embd + n_outputs_prev*n_embd;
|
|
const int32_t n_outputs_new = lctx.n_outputs;
|
|
|
|
if (n_outputs_new) {
|
|
GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
|
|
GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
|
|
ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
|
|
}
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_CLS:
|
|
case LLAMA_POOLING_TYPE_MEAN:
|
|
{
|
|
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
|
|
|
|
// extract sequence embeddings
|
|
auto & embd_seq_out = lctx.embd_seq;
|
|
embd_seq_out.clear();
|
|
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
const llama_seq_id seq_id = u_batch.seq_id[i][0];
|
|
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
|
|
continue;
|
|
}
|
|
embd_seq_out[seq_id].resize(n_embd);
|
|
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
|
|
}
|
|
} break;
|
|
case LLAMA_POOLING_TYPE_UNSPECIFIED:
|
|
{
|
|
GGML_ASSERT(false && "unknown pooling type");
|
|
} break;
|
|
}
|
|
}
|
|
n_outputs_prev += lctx.n_outputs;
|
|
}
|
|
|
|
// set to total number of outputs in the batch, for use in llama_get_logits_ith
|
|
lctx.n_outputs = n_outputs;
|
|
|
|
// wait for the computation to finish (automatically done when obtaining the model output)
|
|
//llama_synchronize(&lctx);
|
|
|
|
// decide if we need to defrag the kv cache
|
|
if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
|
|
const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
|
|
|
|
// queue defragmentation for next llama_kv_cache_update
|
|
if (fragmentation > cparams.defrag_thold) {
|
|
//LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
|
|
|
|
llama_kv_cache_defrag(kv_self);
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
|
|
// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
|
|
static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
|
|
auto & kv_self = lctx.kv_self;
|
|
|
|
const auto & hparams = lctx.model.hparams;
|
|
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
|
|
const uint32_t n_kv = llama_kv_cache_cell_max(kv_self);
|
|
const uint32_t n_used = kv_self.used;
|
|
|
|
assert(n_used <= n_kv);
|
|
|
|
//const int64_t t_start = ggml_time_us();
|
|
|
|
// number of cells moved
|
|
uint32_t n_moves = 0;
|
|
|
|
// each move requires 6*n_layer tensors (see build_defrag)
|
|
// - source view, destination view, copy operation
|
|
// - x2 for keys and values
|
|
const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer);
|
|
|
|
// determine which KV cells to move where
|
|
//
|
|
// cell i moves to ids[i]
|
|
//
|
|
// if ids[i] == i || ids[i] == n_kv, then cell i is not moved
|
|
//
|
|
std::vector<uint32_t> ids(n_kv, n_kv);
|
|
|
|
for (uint32_t i0 = 0; i0 < n_used; ++i0) {
|
|
const auto & cell0 = kv_self.cells[i0];
|
|
|
|
if (!cell0.is_empty()) {
|
|
ids[i0] = i0;
|
|
|
|
continue;
|
|
}
|
|
|
|
// found a hole - fill it with data from the end of the cache
|
|
|
|
uint32_t nh = 1;
|
|
|
|
// determine the size of the hole
|
|
while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
|
|
nh++;
|
|
}
|
|
|
|
uint32_t nf = 0;
|
|
uint32_t is = n_kv - 1;
|
|
|
|
// starting from the end, find nh non-empty cells
|
|
for (; is > i0; --is) {
|
|
const auto & cell1 = kv_self.cells[is];
|
|
|
|
if (cell1.is_empty() || ids[is] != n_kv) {
|
|
continue;
|
|
}
|
|
|
|
// non-empty cell which is not yet moved
|
|
nf++;
|
|
|
|
if (nf == nh) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
// this can only happen if `n_used` is not accurate, which would be a bug
|
|
GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
|
|
|
|
nf = 0;
|
|
|
|
uint32_t i1 = is;
|
|
|
|
// are we moving a continuous block of memory?
|
|
bool cont = false;
|
|
|
|
// should we stop searching for the next move?
|
|
bool stop = false;
|
|
|
|
// go back and move the nf cells to the hole
|
|
for (; i1 < n_kv; ++i1) {
|
|
auto & cell1 = kv_self.cells[i1];
|
|
|
|
if (cell1.is_empty() || ids[i1] != n_kv) {
|
|
if (n_moves == max_moves) {
|
|
stop = true;
|
|
break;
|
|
}
|
|
|
|
cont = false;
|
|
continue;
|
|
}
|
|
|
|
// this cell goes to (i0 + nf)
|
|
ids[i1] = i0 + nf;
|
|
|
|
// move the cell meta data
|
|
kv_self.cells[i0 + nf] = cell1;
|
|
|
|
// clear the old cell and move the head there
|
|
cell1 = llama_kv_cell();
|
|
kv_self.head = n_used;
|
|
|
|
if (!cont) {
|
|
n_moves++;
|
|
cont = true;
|
|
}
|
|
|
|
nf++;
|
|
|
|
if (nf == nh) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
if (stop || n_moves == max_moves) {
|
|
break;
|
|
}
|
|
|
|
//LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
|
|
|
|
i0 += nh - 1;
|
|
}
|
|
|
|
if (n_moves == 0) {
|
|
return;
|
|
}
|
|
|
|
//LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
|
|
|
|
//LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
|
|
|
|
#if 0
|
|
// CPU defrag
|
|
//
|
|
// TODO: optimizations are possible:
|
|
// - multiple threads
|
|
// - avoid copying to the host memory when already there
|
|
//
|
|
// likely not worth the effort, as we have ggml_graph based defrag
|
|
//
|
|
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
|
|
|
|
const uint32_t kv_size = kv_self.size;
|
|
|
|
std::vector<uint8_t> buf_k;
|
|
std::vector<uint8_t> buf_v;
|
|
|
|
for (uint32_t il = 0; il < n_layer; ++il) {
|
|
const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
|
|
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
|
|
|
|
const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
|
|
const size_t v_size = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
|
|
|
|
buf_k.resize(k_size);
|
|
buf_v.resize(v_size);
|
|
|
|
ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
|
|
ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
|
|
|
|
// batch move [i, i+nm) to [id, id+nm)
|
|
// note: cells can move only to a lower index
|
|
for (uint32_t i = 0; i < n_kv; ++i) {
|
|
const uint32_t id = ids[i];
|
|
|
|
if (i == id || id == n_kv) {
|
|
continue;
|
|
}
|
|
|
|
uint32_t nm = 1;
|
|
|
|
while (i + nm < n_kv && ids[i + nm] == id + nm) {
|
|
nm++;
|
|
}
|
|
|
|
// move keys
|
|
{
|
|
const int64_t os = i*k_size_row;
|
|
const int64_t od = id*k_size_row;
|
|
|
|
memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
|
|
}
|
|
|
|
// move values (note: they are transposed)
|
|
{
|
|
const int64_t os = i;
|
|
const int64_t od = id;
|
|
|
|
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
|
|
memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
|
|
}
|
|
}
|
|
|
|
i += nm - 1;
|
|
}
|
|
|
|
ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
|
|
ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
|
|
}
|
|
#else
|
|
// ggml_graph defrag
|
|
|
|
ggml_backend_sched_reset(lctx.sched);
|
|
|
|
ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
|
|
|
|
llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
|
|
#endif
|
|
|
|
//const int64_t t_end = ggml_time_us();
|
|
|
|
//LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
|
|
}
|
|
|
|
static void llama_kv_cache_update_internal(struct llama_context & lctx) {
|
|
bool need_reserve = false;
|
|
|
|
// apply K-shift if needed
|
|
if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
|
|
{
|
|
ggml_backend_sched_reset(lctx.sched);
|
|
|
|
ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
|
|
|
|
ggml_backend_sched_alloc_graph(lctx.sched, gf);
|
|
|
|
llama_set_k_shift(lctx);
|
|
|
|
llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
|
|
|
|
need_reserve = true;
|
|
}
|
|
|
|
{
|
|
auto & kv_self = lctx.kv_self;
|
|
|
|
kv_self.has_shift = false;
|
|
|
|
for (uint32_t i = 0; i < kv_self.size; ++i) {
|
|
kv_self.cells[i].delta = 0;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
|
|
{
|
|
ggml_backend_sched_reset(lctx.sched);
|
|
|
|
ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
|
|
|
|
ggml_backend_sched_alloc_graph(lctx.sched, gf);
|
|
|
|
llama_set_s_copy(lctx);
|
|
|
|
llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
|
|
|
|
need_reserve = true;
|
|
}
|
|
|
|
{
|
|
auto & kv_self = lctx.kv_self;
|
|
|
|
kv_self.do_copy = false;
|
|
|
|
for (uint32_t i = 0; i < kv_self.size; ++i) {
|
|
kv_self.cells[i].src = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
// defragment the KV cache if needed
|
|
if (lctx.kv_self.do_defrag) {
|
|
llama_kv_cache_defrag_internal(lctx);
|
|
|
|
need_reserve = true;
|
|
|
|
lctx.kv_self.do_defrag = false;
|
|
}
|
|
|
|
// reserve a worst case graph again
|
|
if (need_reserve) {
|
|
// TODO: extract to a function
|
|
// build worst-case graph
|
|
int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
|
|
int n_past = lctx.cparams.n_ctx - n_tokens;
|
|
llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
|
|
ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
|
|
|
|
// initialize scheduler with the worst-case graph
|
|
ggml_backend_sched_reset(lctx.sched);
|
|
if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
|
|
}
|
|
}
|
|
}
|
|
|
|
//
|
|
// tokenizer
|
|
//
|
|
|
|
static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
|
|
return vocab.type;
|
|
}
|
|
|
|
static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
|
|
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
|
|
}
|
|
|
|
static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
|
|
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
|
|
}
|
|
|
|
static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
|
|
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
|
|
}
|
|
|
|
static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
|
|
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
|
|
}
|
|
|
|
static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
|
|
GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
|
|
}
|
|
|
|
static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
|
|
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
|
|
GGML_ASSERT(llama_is_byte_token(vocab, id));
|
|
const auto& token_data = vocab.id_to_token.at(id);
|
|
switch (llama_vocab_get_type(vocab)) {
|
|
case LLAMA_VOCAB_TYPE_SPM: {
|
|
auto buf = token_data.text.substr(3, 2);
|
|
return strtol(buf.c_str(), NULL, 16);
|
|
}
|
|
case LLAMA_VOCAB_TYPE_BPE: {
|
|
GGML_ASSERT(false);
|
|
return unicode_utf8_to_byte(token_data.text);
|
|
}
|
|
case LLAMA_VOCAB_TYPE_WPM: {
|
|
GGML_ASSERT(false);
|
|
}
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
|
|
GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE);
|
|
static const char * hex = "0123456789ABCDEF";
|
|
switch (llama_vocab_get_type(vocab)) {
|
|
case LLAMA_VOCAB_TYPE_SPM: {
|
|
const char buf[7] = { '<', '0', 'x', hex[ch >> 4], hex[ch & 15], '>', 0 };
|
|
auto token = vocab.token_to_id.find(buf);
|
|
if (token != vocab.token_to_id.end()) {
|
|
return (*token).second;
|
|
}
|
|
// Try to fall back to just the byte as a string
|
|
const char buf2[2] = { (char)ch, 0 };
|
|
return vocab.token_to_id.at(buf2);
|
|
}
|
|
case LLAMA_VOCAB_TYPE_WPM:
|
|
case LLAMA_VOCAB_TYPE_BPE: {
|
|
return vocab.token_to_id.at(unicode_byte_to_utf8(ch));
|
|
}
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
static void llama_escape_whitespace(std::string & text) {
|
|
replace_all(text, " ", "\xe2\x96\x81");
|
|
}
|
|
|
|
static void llama_unescape_whitespace(std::string & word) {
|
|
replace_all(word, "\xe2\x96\x81", " ");
|
|
}
|
|
|
|
struct llm_symbol {
|
|
using index = int;
|
|
index prev;
|
|
index next;
|
|
const char * text;
|
|
size_t n;
|
|
};
|
|
|
|
static_assert(std::is_trivially_copyable<llm_symbol>::value, "llm_symbol is not trivially copyable");
|
|
|
|
// SPM tokenizer
|
|
// original implementation:
|
|
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
|
|
|
|
struct llm_bigram_spm {
|
|
struct comparator {
|
|
bool operator()(llm_bigram_spm & l, llm_bigram_spm & r) {
|
|
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
|
|
}
|
|
};
|
|
using queue_storage = std::vector<llm_bigram_spm>;
|
|
using queue = std::priority_queue<llm_bigram_spm, queue_storage, comparator>;
|
|
llm_symbol::index left;
|
|
llm_symbol::index right;
|
|
float score;
|
|
size_t size;
|
|
};
|
|
|
|
struct llm_tokenizer_spm {
|
|
llm_tokenizer_spm(const llama_vocab & vocab) : vocab(vocab) {}
|
|
|
|
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
|
|
// split string into utf8 chars
|
|
int index = 0;
|
|
size_t offs = 0;
|
|
while (offs < text.size()) {
|
|
llm_symbol sym;
|
|
size_t len = utf8_len(text[offs]);
|
|
sym.text = text.c_str() + offs;
|
|
sym.n = std::min(len, text.size() - offs);
|
|
offs += sym.n;
|
|
sym.prev = index - 1;
|
|
sym.next = offs == text.size() ? -1 : index + 1;
|
|
index++;
|
|
symbols.emplace_back(sym);
|
|
}
|
|
|
|
// seed the work queue with all possible 2-character tokens.
|
|
for (size_t i = 1; i < symbols.size(); ++i) {
|
|
try_add_bigram(i - 1, i);
|
|
}
|
|
|
|
// keep substituting the highest frequency pairs for as long as we can.
|
|
while (!work_queue.empty()) {
|
|
auto bigram = work_queue.top();
|
|
work_queue.pop();
|
|
|
|
auto & left_sym = symbols[bigram.left];
|
|
auto & right_sym = symbols[bigram.right];
|
|
|
|
// if one of the symbols already got merged, skip it.
|
|
if (left_sym.n == 0 || right_sym.n == 0 ||
|
|
left_sym.n + right_sym.n != bigram.size) {
|
|
continue;
|
|
}
|
|
|
|
// merge the right sym into the left one
|
|
left_sym.n += right_sym.n;
|
|
right_sym.n = 0;
|
|
|
|
//LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
|
|
|
|
// remove the right sym from the chain
|
|
left_sym.next = right_sym.next;
|
|
if (right_sym.next >= 0) {
|
|
symbols[right_sym.next].prev = bigram.left;
|
|
}
|
|
|
|
// find more substitutions
|
|
try_add_bigram(left_sym.prev, bigram.left);
|
|
try_add_bigram(bigram.left, left_sym.next);
|
|
}
|
|
|
|
for (int i = 0; i != -1; i = symbols[i].next) {
|
|
auto & symbol = symbols[i];
|
|
resegment(symbol, output);
|
|
}
|
|
}
|
|
|
|
private:
|
|
void resegment(llm_symbol & symbol, std::vector<llama_vocab::id> & output) {
|
|
auto text = std::string(symbol.text, symbol.n);
|
|
auto token = vocab.token_to_id.find(text);
|
|
|
|
// Do we need to support is_unused?
|
|
if (token != vocab.token_to_id.end()) {
|
|
output.push_back((*token).second);
|
|
return;
|
|
}
|
|
|
|
const auto p = rev_merge.find(text);
|
|
|
|
if (p == rev_merge.end()) {
|
|
// output any symbols that did not form tokens as bytes.
|
|
output.reserve(output.size() + symbol.n);
|
|
for (int j = 0; j < (int)symbol.n; ++j) {
|
|
llama_vocab::id token_id = llama_byte_to_token(vocab, symbol.text[j]);
|
|
output.push_back(token_id);
|
|
}
|
|
return;
|
|
}
|
|
|
|
resegment(symbols[p->second.first], output);
|
|
resegment(symbols[p->second.second], output);
|
|
}
|
|
|
|
void try_add_bigram(int left, int right) {
|
|
if (left == -1 || right == -1) {
|
|
return;
|
|
}
|
|
|
|
const std::string text = std::string(symbols[left].text, symbols[left].n + symbols[right].n);
|
|
auto token = vocab.token_to_id.find(text);
|
|
|
|
if (token == vocab.token_to_id.end()) {
|
|
return;
|
|
}
|
|
|
|
if (static_cast<size_t>((*token).second) >= vocab.id_to_token.size()) {
|
|
return;
|
|
}
|
|
|
|
const auto & tok_data = vocab.id_to_token[(*token).second];
|
|
|
|
llm_bigram_spm bigram;
|
|
bigram.left = left;
|
|
bigram.right = right;
|
|
bigram.score = tok_data.score;
|
|
bigram.size = text.size();
|
|
|
|
work_queue.push(bigram);
|
|
|
|
// Do we need to support is_unused?
|
|
rev_merge[text] = std::make_pair(left, right);
|
|
}
|
|
|
|
const llama_vocab & vocab;
|
|
|
|
std::vector<llm_symbol> symbols;
|
|
llm_bigram_spm::queue work_queue;
|
|
|
|
std::map<std::string, std::pair<int, int>> rev_merge;
|
|
};
|
|
|
|
// BPE tokenizer
|
|
// adapted from https://github.com/cmp-nct/ggllm.cpp [MIT License]
|
|
// tried to simplify unicode stuff, so most likely does not work 100% correctly!
|
|
|
|
// TODO: there are a lot of common parts between spm and bpe tokenizers, should be refactored and reused
|
|
|
|
struct llm_bigram_bpe {
|
|
struct comparator {
|
|
bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const {
|
|
return l.rank > r.rank || (l.rank == r.rank && l.left > r.left);
|
|
}
|
|
};
|
|
|
|
using queue_storage = std::vector<llm_bigram_bpe>;
|
|
using queue = std::priority_queue<llm_bigram_bpe, queue_storage, comparator>;
|
|
llm_symbol::index left;
|
|
llm_symbol::index right;
|
|
std::string text;
|
|
int rank;
|
|
size_t size;
|
|
};
|
|
|
|
struct llm_tokenizer_bpe {
|
|
llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {}
|
|
|
|
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
|
|
int final_prev_index = -1;
|
|
auto word_collection = bpe_gpt2_preprocess(text);
|
|
|
|
symbols_final.clear();
|
|
|
|
for (auto & word : word_collection) {
|
|
work_queue = llm_bigram_bpe::queue();
|
|
symbols.clear();
|
|
|
|
int index = 0;
|
|
size_t offset = 0;
|
|
|
|
while (offset < word.size()) {
|
|
llm_symbol sym;
|
|
size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset]));
|
|
sym.text = word.c_str() + offset;
|
|
sym.n = char_len;
|
|
offset += sym.n;
|
|
sym.prev = index - 1;
|
|
sym.next = offset == word.size() ? -1 : index + 1;
|
|
index++;
|
|
symbols.emplace_back(sym);
|
|
}
|
|
for (size_t i = 1; i < symbols.size(); ++i) {
|
|
add_new_bigram(i - 1, i);
|
|
}
|
|
|
|
// build token(s)
|
|
while (!work_queue.empty()) {
|
|
auto bigram = work_queue.top();
|
|
work_queue.pop();
|
|
|
|
auto & left_symbol = symbols[bigram.left];
|
|
auto & right_symbol = symbols[bigram.right];
|
|
|
|
if (left_symbol.n == 0 || right_symbol.n == 0) {
|
|
continue;
|
|
}
|
|
std::string left_token = std::string(left_symbol.text, left_symbol.n);
|
|
std::string right_token = std::string(right_symbol.text, right_symbol.n);
|
|
if (left_token + right_token != bigram.text) {
|
|
continue; // Skip this bigram if it's outdated
|
|
}
|
|
|
|
// merge the right sym into the left one
|
|
left_symbol.n += right_symbol.n;
|
|
right_symbol.n = 0;
|
|
|
|
// remove the right sym from the chain
|
|
left_symbol.next = right_symbol.next;
|
|
if (right_symbol.next >= 0) {
|
|
symbols[right_symbol.next].prev = bigram.left;
|
|
}
|
|
|
|
add_new_bigram(left_symbol.prev, bigram.left); // left side of current symbol
|
|
add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol
|
|
}
|
|
|
|
// add the finished tokens to the final list keeping correct order for next and prev
|
|
for (auto & sym : symbols) {
|
|
if (sym.n > 0) {
|
|
sym.prev = final_prev_index;
|
|
sym.next = -1;
|
|
if (final_prev_index != -1) {
|
|
symbols_final[final_prev_index].next = symbols_final.size();
|
|
}
|
|
symbols_final.emplace_back(sym);
|
|
final_prev_index = symbols_final.size() - 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
symbols = symbols_final;
|
|
|
|
if (!symbols.empty()) {
|
|
for (int i = 0; i != -1; i = symbols[i].next) {
|
|
auto & symbol = symbols[i];
|
|
if (symbol.n == 0) {
|
|
continue;
|
|
}
|
|
|
|
const std::string str = std::string(symbol.text, symbol.n);
|
|
const auto token = vocab.token_to_id.find(str);
|
|
|
|
if (token == vocab.token_to_id.end()) {
|
|
for (auto j = str.begin(); j != str.end(); ++j) {
|
|
std::string byte_str(1, *j);
|
|
auto token_multibyte = vocab.token_to_id.find(byte_str);
|
|
if (token_multibyte == vocab.token_to_id.end()) {
|
|
throw std::runtime_error("ERROR: byte not found in vocab");
|
|
}
|
|
output.push_back((*token_multibyte).second);
|
|
}
|
|
} else {
|
|
output.push_back((*token).second);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
void add_new_bigram(int left, int right) {
|
|
if (left == -1 || right == -1) {
|
|
return;
|
|
}
|
|
|
|
std::string left_token = std::string(symbols[left].text, symbols[left].n);
|
|
std::string right_token = std::string(symbols[right].text, symbols[right].n);
|
|
|
|
int rank_found = -1;
|
|
|
|
rank_found = vocab.find_bpe_rank(left_token, right_token);
|
|
|
|
if (rank_found < 0) {
|
|
return;
|
|
}
|
|
|
|
llm_bigram_bpe bigram;
|
|
|
|
bigram.left = left;
|
|
bigram.right = right;
|
|
bigram.text = left_token + right_token;
|
|
bigram.size = left_token.size() + right_token.size();
|
|
bigram.rank = rank_found;
|
|
|
|
work_queue.push(bigram);
|
|
}
|
|
|
|
std::vector<std::string> bpe_gpt2_preprocess(const std::string & text) {
|
|
std::vector<std::string> bpe_words;
|
|
std::vector<std::string> bpe_encoded_words;
|
|
|
|
std::string token = "";
|
|
// GPT2 system regex: 's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
|
|
bool collecting_numeric = false;
|
|
bool collecting_letter = false;
|
|
bool collecting_special = false;
|
|
bool collecting_whitespace_lookahead = false;
|
|
bool collecting = false;
|
|
|
|
std::vector<std::string> text_utf;
|
|
text_utf.reserve(text.size());
|
|
bpe_words.reserve(text.size());
|
|
bpe_encoded_words.reserve(text.size());
|
|
|
|
const auto cpts = unicode_cpts_from_utf8(text);
|
|
for (size_t i = 0; i < cpts.size(); ++i)
|
|
text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i]));
|
|
|
|
for (int i = 0; i < (int)text_utf.size(); i++) {
|
|
const std::string & utf_char = text_utf[i];
|
|
bool split_condition = false;
|
|
int bytes_remain = text_utf.size() - i;
|
|
// forward backward lookups
|
|
const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : "";
|
|
const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : "";
|
|
|
|
// handling contractions
|
|
if (!split_condition && bytes_remain >= 2) {
|
|
// 's|'t|'m|'d
|
|
if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) {
|
|
split_condition = true;
|
|
}
|
|
if (split_condition) {
|
|
if (token.size()) {
|
|
bpe_words.emplace_back(token); // push previous content as token
|
|
}
|
|
token = utf_char + utf_char_next;
|
|
bpe_words.emplace_back(token);
|
|
token = "";
|
|
i++;
|
|
continue;
|
|
}
|
|
}
|
|
if (!split_condition && bytes_remain >= 3) {
|
|
// 're|'ve|'ll
|
|
if (utf_char == "\'" && (
|
|
(utf_char_next == "r" && utf_char_next_next == "e") ||
|
|
(utf_char_next == "v" && utf_char_next_next == "e") ||
|
|
(utf_char_next == "l" && utf_char_next_next == "l"))
|
|
) {
|
|
split_condition = true;
|
|
}
|
|
if (split_condition) {
|
|
// current token + next token can be defined
|
|
if (token.size()) {
|
|
bpe_words.emplace_back(token); // push previous content as token
|
|
}
|
|
token = utf_char + utf_char_next + utf_char_next_next;
|
|
bpe_words.emplace_back(token); // the contraction
|
|
token = "";
|
|
i += 2;
|
|
continue;
|
|
}
|
|
}
|
|
|
|
if (!split_condition && !collecting) {
|
|
if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
|
|
collecting_letter = true;
|
|
collecting = true;
|
|
}
|
|
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
|
|
collecting_numeric = true;
|
|
collecting = true;
|
|
}
|
|
else if (
|
|
((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
|
|
(!token.size() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
|
|
) {
|
|
collecting_special = true;
|
|
collecting = true;
|
|
}
|
|
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
|
|
collecting_whitespace_lookahead = true;
|
|
collecting = true;
|
|
}
|
|
else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
|
|
split_condition = true;
|
|
}
|
|
}
|
|
else if (!split_condition && collecting) {
|
|
if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) {
|
|
split_condition = true;
|
|
}
|
|
else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
|
|
split_condition = true;
|
|
}
|
|
else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
|
|
split_condition = true;
|
|
}
|
|
else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
|
|
split_condition = true;
|
|
}
|
|
}
|
|
|
|
if (utf_char_next == "") {
|
|
split_condition = true; // final
|
|
token += utf_char;
|
|
}
|
|
|
|
if (split_condition) {
|
|
if (token.size()) {
|
|
bpe_words.emplace_back(token);
|
|
}
|
|
token = utf_char;
|
|
collecting = false;
|
|
collecting_letter = false;
|
|
collecting_numeric = false;
|
|
collecting_special = false;
|
|
collecting_whitespace_lookahead = false;
|
|
}
|
|
else {
|
|
token += utf_char;
|
|
}
|
|
}
|
|
|
|
for (std::string & word : bpe_words) {
|
|
std::string encoded_token = "";
|
|
for (char & c : word) {
|
|
encoded_token += unicode_byte_to_utf8(c);
|
|
}
|
|
bpe_encoded_words.emplace_back(encoded_token);
|
|
}
|
|
|
|
return bpe_encoded_words;
|
|
}
|
|
|
|
const llama_vocab & vocab;
|
|
|
|
std::vector<llm_symbol> symbols;
|
|
std::vector<llm_symbol> symbols_final;
|
|
|
|
llm_bigram_bpe::queue work_queue;
|
|
};
|
|
|
|
struct llm_tokenizer_wpm {
|
|
llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
|
|
|
|
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
|
|
auto * token_map = &vocab.token_to_id;
|
|
|
|
// normalize and split by whitespace
|
|
std::vector<std::string> words = preprocess(text);
|
|
|
|
// bos token prepended already
|
|
|
|
// find the longest tokens that form the words
|
|
for (const std::string &word : words) {
|
|
// skip empty words
|
|
if (word.size() == 0) {
|
|
continue;
|
|
}
|
|
|
|
// prepend phantom space
|
|
std::string word1 = "\xe2\x96\x81" + word;
|
|
int n = word1.size();
|
|
|
|
// we're at the start of a new word
|
|
int i = 0;
|
|
bool match_any = false;
|
|
|
|
// move through character position in word
|
|
while (i < n) {
|
|
// loop through possible match length
|
|
bool match = false;
|
|
for (int j = n; j > i; j--) {
|
|
auto it = token_map->find(word1.substr(i, j - i));
|
|
if (it != token_map->end()) {
|
|
output.push_back(it->second);
|
|
match = true;
|
|
match_any = true;
|
|
i = j;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// must be an unknown character
|
|
if (!match) {
|
|
i++;
|
|
}
|
|
}
|
|
|
|
// we didn't find any matches for this word
|
|
if (!match_any) {
|
|
output.push_back(vocab.special_unk_id);
|
|
}
|
|
}
|
|
}
|
|
|
|
std::vector<std::string> preprocess(const std::string & text) {
|
|
std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
|
|
|
|
// strip accents, strip control, uniformize whitespace,
|
|
// to lowercase, pad chinese characters, pad punctuation
|
|
std::string new_str = "";
|
|
for (uint32_t code : cpts_nfd) {
|
|
int type = unicode_cpt_type(code);
|
|
if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) {
|
|
continue;
|
|
}
|
|
code = unicode_tolower(code);
|
|
if (type == CODEPOINT_TYPE_WHITESPACE) {
|
|
code = ' ';
|
|
}
|
|
std::string s = unicode_cpt_to_utf8(code);
|
|
if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) {
|
|
new_str += " ";
|
|
new_str += s;
|
|
new_str += " ";
|
|
} else {
|
|
new_str += s;
|
|
}
|
|
}
|
|
|
|
// split by whitespace
|
|
uint64_t l = 0;
|
|
uint64_t r = 0;
|
|
std::vector<std::string> words;
|
|
while (r < new_str.size()) {
|
|
// if is whitespace
|
|
if (isspace(new_str[r], std::locale::classic())) {
|
|
if (r > l) words.push_back(new_str.substr(l, (r - l)));
|
|
l = r + 1;
|
|
r = l;
|
|
} else {
|
|
r += 1;
|
|
}
|
|
}
|
|
if (r > l) {
|
|
words.push_back(new_str.substr(l, (r - l)));
|
|
}
|
|
return words;
|
|
}
|
|
|
|
bool is_ascii_punct(uint32_t code) {
|
|
if (code > 0xFF) {
|
|
return false;
|
|
}
|
|
auto c = char(static_cast<unsigned char>(code));
|
|
return ispunct(c, std::locale::classic());
|
|
}
|
|
|
|
bool is_chinese_char(uint32_t cpt) {
|
|
if ((cpt >= 0x4E00 && cpt <= 0x9FFF) ||
|
|
(cpt >= 0x3400 && cpt <= 0x4DBF) ||
|
|
(cpt >= 0x20000 && cpt <= 0x2A6DF) ||
|
|
(cpt >= 0x2A700 && cpt <= 0x2B73F) ||
|
|
(cpt >= 0x2B740 && cpt <= 0x2B81F) ||
|
|
(cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
|
|
(cpt >= 0xF900 && cpt <= 0xFAFF) ||
|
|
(cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
|
|
(cpt >= 0x3000 && cpt <= 0x303F) ||
|
|
(cpt >= 0xFF00 && cpt <= 0xFFEF)) {
|
|
return true; // NOLINT
|
|
}
|
|
return false;
|
|
}
|
|
|
|
const llama_vocab & vocab;
|
|
};
|
|
|
|
typedef enum FRAGMENT_BUFFER_VARIANT_TYPE {
|
|
FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN,
|
|
FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT
|
|
} FRAGMENT_BUFFER_VARIANT_TYPE;
|
|
|
|
struct fragment_buffer_variant {
|
|
fragment_buffer_variant(llama_vocab::id _token)
|
|
:
|
|
type(FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN),
|
|
token(_token),
|
|
raw_text(_dummy),
|
|
offset(0),
|
|
length(0) {}
|
|
|
|
fragment_buffer_variant(const std::string & _raw_text, int64_t _offset, int64_t _length)
|
|
:
|
|
type(FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT),
|
|
token((llama_vocab::id) - 1),
|
|
raw_text(_raw_text),
|
|
offset(_offset),
|
|
length(_length){
|
|
GGML_ASSERT(_offset >= 0);
|
|
GGML_ASSERT(_length >= 1);
|
|
GGML_ASSERT(offset + length <= raw_text.length());
|
|
}
|
|
|
|
const FRAGMENT_BUFFER_VARIANT_TYPE type;
|
|
const llama_vocab::id token;
|
|
const std::string _dummy;
|
|
const std::string & raw_text;
|
|
const uint64_t offset;
|
|
const uint64_t length;
|
|
};
|
|
|
|
// #define PRETOKENIZERDEBUG
|
|
|
|
static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<fragment_buffer_variant> & buffer) {
|
|
// for each special token
|
|
for (const auto & st: vocab.special_tokens_cache) {
|
|
const auto & special_token = st.first;
|
|
const auto & special_id = st.second;
|
|
|
|
// for each text fragment
|
|
std::forward_list<fragment_buffer_variant>::iterator it = buffer.begin();
|
|
while (it != buffer.end()) {
|
|
auto & fragment = (*it);
|
|
|
|
// if a fragment is text ( not yet processed )
|
|
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
|
auto * raw_text = &(fragment.raw_text);
|
|
|
|
auto raw_text_base_offset = fragment.offset;
|
|
auto raw_text_base_length = fragment.length;
|
|
|
|
// loop over the text
|
|
while (true) {
|
|
// find the first occurrence of a given special token in this fragment
|
|
// passing offset argument only limit the "search area" but match coordinates
|
|
// are still relative to the source full raw_text
|
|
auto match = raw_text->find(special_token, raw_text_base_offset);
|
|
|
|
// no occurrences found, stop processing this fragment for a given special token
|
|
if (match == std::string::npos) break;
|
|
|
|
// check if match is within bounds of offset <-> length
|
|
if (match + special_token.length() > raw_text_base_offset + raw_text_base_length) break;
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("FF: (%ld %ld %ld) '%s'\n", raw_text->length(), raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
|
|
#endif
|
|
auto source = std::distance(buffer.begin(), it);
|
|
|
|
// if match is further than base offset
|
|
// then we have some text to the left of it
|
|
if (match > raw_text_base_offset) {
|
|
// left
|
|
const int64_t left_reminder_offset = raw_text_base_offset + 0;
|
|
const int64_t left_reminder_length = match - raw_text_base_offset;
|
|
buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
|
|
#endif
|
|
it++;
|
|
}
|
|
|
|
// special token
|
|
buffer.emplace_after(it, special_id);
|
|
it++;
|
|
|
|
// right
|
|
if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
|
|
const int64_t right_reminder_offset = match + special_token.length();
|
|
const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
|
|
buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
|
|
#endif
|
|
|
|
it++;
|
|
|
|
if (source == 0) {
|
|
buffer.erase_after(buffer.before_begin());
|
|
} else {
|
|
buffer.erase_after(std::next(buffer.begin(), (source-1)));
|
|
}
|
|
|
|
// repeat for the right side
|
|
raw_text_base_offset = right_reminder_offset;
|
|
raw_text_base_length = right_reminder_length;
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("RR: (%ld %ld) '%s'\n", raw_text_base_offset, raw_text_base_length, raw_text->substr(raw_text_base_offset, raw_text_base_length).c_str());
|
|
#endif
|
|
} else {
|
|
if (source == 0) {
|
|
buffer.erase_after(buffer.before_begin());
|
|
} else {
|
|
buffer.erase_after(std::next(buffer.begin(), (source-1)));
|
|
}
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
it++;
|
|
}
|
|
}
|
|
}
|
|
|
|
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
|
|
std::vector<llama_vocab::id> output;
|
|
std::forward_list<fragment_buffer_variant> fragment_buffer;
|
|
|
|
if (!raw_text.empty()) {
|
|
fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
|
|
if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
|
|
}
|
|
|
|
switch (vocab.type) {
|
|
case LLAMA_VOCAB_TYPE_SPM:
|
|
{
|
|
// OG tokenizer behavior:
|
|
//
|
|
// tokenizer.encode('', add_special_tokens=True) returns [1]
|
|
// tokenizer.encode('', add_special_tokens=False) returns []
|
|
|
|
if (add_special && vocab.special_add_bos != 0) {
|
|
GGML_ASSERT(vocab.special_bos_id != -1);
|
|
output.push_back(vocab.special_bos_id);
|
|
}
|
|
|
|
for (const auto & fragment : fragment_buffer) {
|
|
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
|
// without adding this leading whitespace, we do not get the same results as the original tokenizer
|
|
|
|
// TODO: It's likely possible to get rid of this string copy entirely
|
|
// by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
|
|
// and passing 'add space prefix' as bool argument
|
|
//
|
|
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
|
if (&fragment == &fragment_buffer.front()) {
|
|
if (vocab.add_space_prefix) {
|
|
raw_text = " " + raw_text; // prefix with space if the first token is not special
|
|
}
|
|
}
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
|
#endif
|
|
llm_tokenizer_spm tokenizer(vocab);
|
|
llama_escape_whitespace(raw_text);
|
|
tokenizer.tokenize(raw_text, output);
|
|
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
|
output.push_back(fragment.token);
|
|
}
|
|
}
|
|
|
|
if (add_special && vocab.special_add_eos == 1) {
|
|
GGML_ASSERT(vocab.special_eos_id != -1);
|
|
output.push_back(vocab.special_eos_id);
|
|
}
|
|
} break;
|
|
case LLAMA_VOCAB_TYPE_BPE:
|
|
{
|
|
if (add_special && vocab.special_add_bos == 1) {
|
|
GGML_ASSERT(vocab.special_bos_id != -1);
|
|
output.push_back(vocab.special_bos_id);
|
|
}
|
|
|
|
for (const auto & fragment : fragment_buffer) {
|
|
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
|
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
|
#endif
|
|
llm_tokenizer_bpe tokenizer(vocab);
|
|
tokenizer.tokenize(raw_text, output);
|
|
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
|
output.push_back(fragment.token);
|
|
}
|
|
}
|
|
|
|
GGML_ASSERT(vocab.special_add_eos != 1);
|
|
} break;
|
|
case LLAMA_VOCAB_TYPE_WPM:
|
|
{
|
|
if (add_special) {
|
|
GGML_ASSERT(vocab.special_cls_id != -1);
|
|
output.push_back(vocab.special_cls_id);
|
|
}
|
|
|
|
for (const auto & fragment : fragment_buffer) {
|
|
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
|
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
|
|
|
#ifdef PRETOKENIZERDEBUG
|
|
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
|
|
#endif
|
|
llm_tokenizer_wpm tokenizer(vocab);
|
|
tokenizer.tokenize(raw_text, output);
|
|
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
|
|
output.push_back(fragment.token);
|
|
}
|
|
}
|
|
|
|
if (add_special) {
|
|
GGML_ASSERT(vocab.special_sep_id != -1);
|
|
output.push_back(vocab.special_sep_id);
|
|
}
|
|
} break;
|
|
case LLAMA_VOCAB_TYPE_NONE:
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
return output;
|
|
}
|
|
|
|
//
|
|
// grammar - internal
|
|
//
|
|
|
|
|
|
// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
|
|
// pointer. If an invalid sequence is encountered, returns `llama_partial_utf8.n_remain == -1`.
|
|
std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
|
|
const std::string & src,
|
|
llama_partial_utf8 partial_start) {
|
|
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
|
|
const char * pos = src.c_str();
|
|
std::vector<uint32_t> code_points;
|
|
// common english strings have the same number of codepoints and bytes. `+ 1` for the terminating 0.
|
|
code_points.reserve(src.size() + 1);
|
|
uint32_t value = partial_start.value;
|
|
int n_remain = partial_start.n_remain;
|
|
|
|
// continue previous decode, if applicable
|
|
while (*pos != 0 && n_remain > 0) {
|
|
uint8_t next_byte = static_cast<uint8_t>(*pos);
|
|
if ((next_byte >> 6) != 2) {
|
|
// invalid sequence, abort
|
|
code_points.push_back(0);
|
|
return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, -1 });
|
|
}
|
|
value = (value << 6) + (next_byte & 0x3F);
|
|
++pos;
|
|
--n_remain;
|
|
}
|
|
|
|
if (partial_start.n_remain > 0 && n_remain == 0) {
|
|
code_points.push_back(value);
|
|
}
|
|
|
|
// decode any subsequent utf-8 sequences, which may end in an incomplete one
|
|
while (*pos != 0) {
|
|
uint8_t first_byte = static_cast<uint8_t>(*pos);
|
|
uint8_t highbits = first_byte >> 4;
|
|
n_remain = lookup[highbits] - 1;
|
|
|
|
if (n_remain < 0) {
|
|
// invalid sequence, abort
|
|
code_points.clear();
|
|
code_points.push_back(0);
|
|
return std::make_pair(std::move(code_points), llama_partial_utf8{ 0, n_remain });
|
|
}
|
|
|
|
uint8_t mask = (1 << (7 - n_remain)) - 1;
|
|
value = first_byte & mask;
|
|
++pos;
|
|
while (*pos != 0 && n_remain > 0) {
|
|
value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
|
|
++pos;
|
|
--n_remain;
|
|
}
|
|
if (n_remain == 0) {
|
|
code_points.push_back(value);
|
|
}
|
|
}
|
|
code_points.push_back(0);
|
|
|
|
return std::make_pair(std::move(code_points), llama_partial_utf8{ value, n_remain });
|
|
}
|
|
|
|
// returns true iff pos points to the end of one of the definitions of a rule
|
|
static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
|
|
switch (pos->type) {
|
|
case LLAMA_GRETYPE_END: return true; // NOLINT
|
|
case LLAMA_GRETYPE_ALT: return true; // NOLINT
|
|
default: return false;
|
|
}
|
|
}
|
|
|
|
// returns true iff chr satisfies the char range at pos (regular or inverse range)
|
|
// asserts that pos is pointing to a char range element
|
|
static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
|
|
const llama_grammar_element * pos,
|
|
const uint32_t chr) {
|
|
|
|
bool found = false;
|
|
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
|
|
|
|
GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
|
|
|
|
do {
|
|
if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
|
|
// inclusive range, e.g. [a-z]
|
|
found = found || (pos->value <= chr && chr <= pos[1].value);
|
|
pos += 2;
|
|
} else {
|
|
// exact char match, e.g. [a] or "a"
|
|
found = found || pos->value == chr;
|
|
pos += 1;
|
|
}
|
|
} while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
|
|
|
|
return std::make_pair(found == is_positive_char, pos);
|
|
}
|
|
|
|
// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
|
|
// range at pos (regular or inverse range)
|
|
// asserts that pos is pointing to a char range element
|
|
static bool llama_grammar_match_partial_char(
|
|
const llama_grammar_element * pos,
|
|
const llama_partial_utf8 partial_utf8) {
|
|
|
|
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
|
|
GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
|
|
|
|
uint32_t partial_value = partial_utf8.value;
|
|
int n_remain = partial_utf8.n_remain;
|
|
|
|
// invalid sequence or 7-bit char split across 2 bytes (overlong)
|
|
if (n_remain < 0 || (n_remain == 1 && partial_value < 2)) {
|
|
return false;
|
|
}
|
|
|
|
// range of possible code points this partial UTF-8 sequence could complete to
|
|
uint32_t low = partial_value << (n_remain * 6);
|
|
uint32_t high = low | ((1 << (n_remain * 6)) - 1);
|
|
|
|
if (low == 0) {
|
|
if (n_remain == 2) {
|
|
low = 1 << 11;
|
|
} else if (n_remain == 3) {
|
|
low = 1 << 16;
|
|
}
|
|
}
|
|
|
|
do {
|
|
if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
|
|
// inclusive range, e.g. [a-z]
|
|
if (pos->value <= high && low <= pos[1].value) {
|
|
return is_positive_char;
|
|
}
|
|
pos += 2;
|
|
} else {
|
|
// exact char match, e.g. [a] or "a"
|
|
if (low <= pos->value && pos->value <= high) {
|
|
return is_positive_char;
|
|
}
|
|
pos += 1;
|
|
}
|
|
} while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
|
|
|
|
return !is_positive_char;
|
|
}
|
|
|
|
|
|
// transforms a grammar pushdown stack into N possible stacks, all ending
|
|
// at a character range (terminal element)
|
|
static void llama_grammar_advance_stack(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<const llama_grammar_element *> & stack,
|
|
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
|
|
|
|
if (stack.empty()) {
|
|
if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
|
|
new_stacks.emplace_back(stack);
|
|
}
|
|
return;
|
|
}
|
|
|
|
const llama_grammar_element * pos = stack.back();
|
|
|
|
switch (pos->type) {
|
|
case LLAMA_GRETYPE_RULE_REF: {
|
|
const size_t rule_id = static_cast<size_t>(pos->value);
|
|
const llama_grammar_element * subpos = rules[rule_id].data();
|
|
do {
|
|
// init new stack without the top (pos)
|
|
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
|
|
if (!llama_grammar_is_end_of_sequence(pos + 1)) {
|
|
// if this rule ref is followed by another element, add that to stack
|
|
new_stack.push_back(pos + 1);
|
|
}
|
|
if (!llama_grammar_is_end_of_sequence(subpos)) {
|
|
// if alternate is nonempty, add to stack
|
|
new_stack.push_back(subpos);
|
|
}
|
|
llama_grammar_advance_stack(rules, new_stack, new_stacks);
|
|
while (!llama_grammar_is_end_of_sequence(subpos)) {
|
|
// scan to end of alternate def
|
|
subpos++;
|
|
}
|
|
if (subpos->type == LLAMA_GRETYPE_ALT) {
|
|
// there's another alternate def of this rule to process
|
|
subpos++;
|
|
} else {
|
|
break;
|
|
}
|
|
} while (true);
|
|
break;
|
|
}
|
|
case LLAMA_GRETYPE_CHAR:
|
|
case LLAMA_GRETYPE_CHAR_NOT:
|
|
if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
|
|
// only add the stack if it's not a duplicate of one we already have
|
|
new_stacks.emplace_back(stack);
|
|
}
|
|
break;
|
|
default:
|
|
// end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
|
|
// (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
|
|
// those
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
// takes a set of possible pushdown stacks on a grammar, which are required to
|
|
// be positioned at a character range (see `llama_grammar_advance_stack`), and
|
|
// produces the N possible stacks if the given char is accepted at those
|
|
// positions
|
|
void llama_grammar_accept(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
|
const uint32_t chr,
|
|
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
|
|
|
|
new_stacks.clear();
|
|
|
|
for (const auto & stack : stacks) {
|
|
if (stack.empty()) {
|
|
continue;
|
|
}
|
|
|
|
auto match = llama_grammar_match_char(stack.back(), chr);
|
|
if (match.first) {
|
|
const llama_grammar_element * pos = match.second;
|
|
|
|
// update top of stack to next element, if any
|
|
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
|
|
if (!llama_grammar_is_end_of_sequence(pos)) {
|
|
new_stack.push_back(pos);
|
|
}
|
|
llama_grammar_advance_stack(rules, new_stack, new_stacks);
|
|
}
|
|
}
|
|
}
|
|
|
|
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
|
const std::vector<llama_grammar_candidate> & candidates);
|
|
|
|
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<const llama_grammar_element *> & stack,
|
|
const std::vector<llama_grammar_candidate> & candidates) {
|
|
|
|
std::vector<llama_grammar_candidate> rejects;
|
|
rejects.reserve(candidates.size());
|
|
|
|
if (stack.empty()) {
|
|
for (const auto & tok : candidates) {
|
|
if (*tok.code_points != 0 || tok.partial_utf8.n_remain != 0) {
|
|
rejects.push_back(tok);
|
|
}
|
|
}
|
|
return rejects;
|
|
}
|
|
|
|
const llama_grammar_element * stack_pos = stack.back();
|
|
|
|
std::vector<llama_grammar_candidate> next_candidates;
|
|
next_candidates.reserve(candidates.size());
|
|
|
|
for (const auto & tok : candidates) {
|
|
if (*tok.code_points == 0) {
|
|
// reached end of full codepoints in token, reject iff it ended in a partial sequence
|
|
// that cannot satisfy this position in grammar
|
|
if (tok.partial_utf8.n_remain != 0 &&
|
|
!llama_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
|
|
rejects.push_back(tok);
|
|
}
|
|
} else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
|
|
next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
|
|
} else {
|
|
rejects.push_back(tok);
|
|
}
|
|
}
|
|
|
|
const auto * stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
|
|
|
|
// update top of stack to next element, if any
|
|
std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
|
|
if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
|
|
stack_after.push_back(stack_pos_after);
|
|
}
|
|
std::vector<std::vector<const llama_grammar_element *>> next_stacks;
|
|
llama_grammar_advance_stack(rules, stack_after, next_stacks);
|
|
|
|
auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
|
|
for (const auto & tok : next_rejects) {
|
|
rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
|
|
}
|
|
|
|
return rejects;
|
|
}
|
|
|
|
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
|
const std::vector<llama_grammar_candidate> & candidates) {
|
|
GGML_ASSERT(!stacks.empty()); // REVIEW
|
|
|
|
if (candidates.empty()) {
|
|
return std::vector<llama_grammar_candidate>();
|
|
}
|
|
|
|
auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
|
|
|
|
for (size_t i = 1, size = stacks.size(); i < size; ++i) {
|
|
rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
|
|
}
|
|
return rejects;
|
|
}
|
|
|
|
//
|
|
// grammar - external
|
|
//
|
|
|
|
struct llama_grammar * llama_grammar_init(
|
|
const llama_grammar_element ** rules,
|
|
size_t n_rules,
|
|
size_t start_rule_index) {
|
|
const llama_grammar_element * pos;
|
|
|
|
// copy rule definitions into vectors
|
|
std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
|
|
for (size_t i = 0; i < n_rules; i++) {
|
|
for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
|
|
vec_rules[i].push_back(*pos);
|
|
}
|
|
vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
|
|
}
|
|
|
|
// loop over alternates of start rule to build initial stacks
|
|
std::vector<std::vector<const llama_grammar_element *>> stacks;
|
|
pos = vec_rules[start_rule_index].data();
|
|
do {
|
|
std::vector<const llama_grammar_element *> stack;
|
|
if (!llama_grammar_is_end_of_sequence(pos)) {
|
|
// if alternate is nonempty, add to stack
|
|
stack.push_back(pos);
|
|
}
|
|
llama_grammar_advance_stack(vec_rules, stack, stacks);
|
|
while (!llama_grammar_is_end_of_sequence(pos)) {
|
|
// scan to end of alternate def
|
|
pos++;
|
|
}
|
|
if (pos->type == LLAMA_GRETYPE_ALT) {
|
|
// there's another alternate def of this rule to process
|
|
pos++;
|
|
} else {
|
|
break;
|
|
}
|
|
} while (true);
|
|
|
|
return new llama_grammar{ std::move(vec_rules), std::move(stacks), {} };
|
|
}
|
|
|
|
void llama_grammar_free(struct llama_grammar * grammar) {
|
|
delete grammar;
|
|
}
|
|
|
|
struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
|
|
llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
|
|
|
|
// redirect elements in stacks to point to new rules
|
|
for (size_t is = 0; is < result->stacks.size(); is++) {
|
|
for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
|
|
for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
|
|
for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
|
|
if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
|
|
result->stacks[is][ie] = &result->rules[ir0][ir1];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
//
|
|
// sampling
|
|
//
|
|
|
|
void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
|
|
if (seed == LLAMA_DEFAULT_SEED) {
|
|
seed = time(NULL);
|
|
}
|
|
ctx->rng.seed(seed);
|
|
}
|
|
|
|
void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
GGML_ASSERT(candidates->size > 0);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Sort the logits in descending order
|
|
if (!candidates->sorted) {
|
|
std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit > b.logit;
|
|
});
|
|
candidates->sorted = true;
|
|
}
|
|
|
|
float max_l = candidates->data[0].logit;
|
|
float cum_sum = 0.0f;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
float p = expf(candidates->data[i].logit - max_l);
|
|
candidates->data[i].p = p;
|
|
cum_sum += p;
|
|
}
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
candidates->data[i].p /= cum_sum;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
|
|
// TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
|
|
// if (k >= (int32_t)candidates->size) {
|
|
// return;
|
|
// }
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
if (k <= 0) {
|
|
k = candidates->size;
|
|
}
|
|
|
|
k = std::max(k, (int) min_keep);
|
|
k = std::min(k, (int) candidates->size);
|
|
|
|
// Sort scores in descending order
|
|
if (!candidates->sorted) {
|
|
auto comp = [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit > b.logit;
|
|
};
|
|
if (k <= 128) {
|
|
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
|
|
} else {
|
|
constexpr int nbuckets = 128;
|
|
constexpr float bucket_low = -10.0f;
|
|
constexpr float bucket_high = 10.0f;
|
|
constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low);
|
|
constexpr float bucker_inter = -bucket_low * bucket_scale;
|
|
|
|
std::vector<int> bucket_idx(candidates->size);
|
|
std::vector<int> histo(nbuckets, 0);
|
|
|
|
for (int i = 0; i < (int)candidates->size; ++i) {
|
|
const float val = candidates->data[i].logit;
|
|
int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
|
|
ib = std::max(0, std::min(nbuckets-1, ib));
|
|
bucket_idx[i] = ib;
|
|
++histo[ib];
|
|
}
|
|
int nhave = 0;
|
|
int ib = nbuckets - 1;
|
|
for ( ; ib >= 0; --ib) {
|
|
nhave += histo[ib];
|
|
if (nhave >= k) break;
|
|
}
|
|
std::vector<llama_token_data> tmp_tokens(nhave);
|
|
auto ptr = tmp_tokens.data();
|
|
std::vector<llama_token_data*> bucket_ptrs;
|
|
bucket_ptrs.reserve(nbuckets - ib);
|
|
for (int j = nbuckets - 1; j >= ib; --j) {
|
|
bucket_ptrs.push_back(ptr);
|
|
ptr += histo[j];
|
|
}
|
|
for (int i = 0; i < (int)candidates->size; ++i) {
|
|
int j = bucket_idx[i];
|
|
if (j >= ib) {
|
|
*bucket_ptrs[nbuckets-1-j]++ = candidates->data[i];
|
|
}
|
|
}
|
|
|
|
ptr = tmp_tokens.data();
|
|
int ndone = 0;
|
|
for (int j = nbuckets-1; j > ib; --j) {
|
|
std::sort(ptr, ptr + histo[j], comp);
|
|
ptr += histo[j];
|
|
ndone += histo[j];
|
|
}
|
|
std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp);
|
|
|
|
std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data));
|
|
|
|
}
|
|
candidates->sorted = true;
|
|
}
|
|
candidates->size = k;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
|
if (p >= 1.0f) {
|
|
return;
|
|
}
|
|
|
|
llama_sample_softmax(ctx, candidates);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute the cumulative probabilities
|
|
float cum_sum = 0.0f;
|
|
size_t last_idx = candidates->size;
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
cum_sum += candidates->data[i].p;
|
|
|
|
// Check if the running sum is at least p or if we have kept at least min_keep tokens
|
|
// we set the last index to i+1 to indicate that the current iterate should be included in the set
|
|
if (cum_sum >= p && i + 1 >= min_keep) {
|
|
last_idx = i + 1;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the top-p tokens
|
|
candidates->size = last_idx;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
|
if (p <= 0.0f || !candidates->size) {
|
|
return;
|
|
}
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
bool min_p_applied = false;
|
|
|
|
// if the candidates aren't sorted, try the unsorted implementation first
|
|
if (!candidates->sorted) {
|
|
std::vector<llama_token_data> filtered_tokens;
|
|
|
|
float max_logit = -FLT_MAX;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
max_logit = std::max(max_logit, candidates->data[i].logit);
|
|
}
|
|
const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
if (candidates->data[i].logit >= min_logit) {
|
|
filtered_tokens.push_back(candidates->data[i]);
|
|
}
|
|
}
|
|
|
|
// if we have enough values the operation was a success
|
|
if (filtered_tokens.size() >= min_keep) {
|
|
memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
|
|
candidates->size = filtered_tokens.size();
|
|
min_p_applied = true;
|
|
}
|
|
}
|
|
|
|
// if the candidates are sorted or the unsorted implementation failed, use this implementation
|
|
if (!min_p_applied) {
|
|
// Sort the logits in descending order
|
|
if (!candidates->sorted) {
|
|
std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit > b.logit;
|
|
});
|
|
candidates->sorted = true;
|
|
}
|
|
|
|
const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
|
|
size_t i = 1; // first token always matches
|
|
|
|
for (; i < candidates->size; ++i) {
|
|
if (candidates->data[i].logit < min_logit && i >= min_keep) {
|
|
break; // prob too small
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the matching tokens
|
|
candidates->size = i;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
|
|
if (z >= 1.0f || candidates->size <= 2) {
|
|
return;
|
|
}
|
|
|
|
llama_sample_softmax(nullptr, candidates);
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute the first and second derivatives
|
|
std::vector<float> first_derivatives(candidates->size - 1);
|
|
std::vector<float> second_derivatives(candidates->size - 2);
|
|
|
|
for (size_t i = 0; i < first_derivatives.size(); ++i) {
|
|
first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
|
|
}
|
|
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
|
second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
|
|
}
|
|
|
|
// Calculate absolute value of second derivatives
|
|
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
|
second_derivatives[i] = std::abs(second_derivatives[i]);
|
|
}
|
|
|
|
// Normalize the second derivatives
|
|
{
|
|
const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
|
|
|
|
if (second_derivatives_sum > 1e-6f) {
|
|
for (float & value : second_derivatives) {
|
|
value /= second_derivatives_sum;
|
|
}
|
|
} else {
|
|
for (float & value : second_derivatives) {
|
|
value = 1.0f / second_derivatives.size();
|
|
}
|
|
}
|
|
}
|
|
|
|
float cum_sum = 0.0f;
|
|
size_t last_idx = candidates->size;
|
|
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
|
cum_sum += second_derivatives[i];
|
|
|
|
// Check if the running sum is greater than z or if we have kept at least min_keep tokens
|
|
if (cum_sum > z && i >= min_keep) {
|
|
last_idx = i;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the tokens above the tail location
|
|
candidates->size = last_idx;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
|
// Reference implementation:
|
|
// https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
|
|
if (p >= 1.0f) {
|
|
return;
|
|
}
|
|
|
|
// Compute the softmax of logits and calculate entropy
|
|
llama_sample_softmax(nullptr, candidates);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
float entropy = 0.0f;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
entropy += -candidates->data[i].p * logf(candidates->data[i].p);
|
|
}
|
|
|
|
// Compute the absolute difference between negative log probability and entropy for each candidate
|
|
std::vector<float> shifted_scores;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
|
|
shifted_scores.push_back(shifted_score);
|
|
}
|
|
|
|
// Sort tokens based on the shifted_scores and their corresponding indices
|
|
std::vector<size_t> indices(candidates->size);
|
|
std::iota(indices.begin(), indices.end(), 0);
|
|
|
|
std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
|
|
return shifted_scores[a] < shifted_scores[b];
|
|
});
|
|
|
|
// Compute the cumulative probabilities
|
|
float cum_sum = 0.0f;
|
|
size_t last_idx = indices.size();
|
|
|
|
for (size_t i = 0; i < indices.size(); ++i) {
|
|
size_t idx = indices[i];
|
|
cum_sum += candidates->data[idx].p;
|
|
|
|
// Check if the running sum is greater than typical or if we have kept at least min_keep tokens
|
|
if (cum_sum > p && i >= min_keep - 1) {
|
|
last_idx = i + 1;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the locally typical tokens
|
|
std::vector<llama_token_data> new_candidates;
|
|
for (size_t i = 0; i < last_idx; ++i) {
|
|
size_t idx = indices[i];
|
|
new_candidates.push_back(candidates->data[idx]);
|
|
}
|
|
|
|
// Replace the data in candidates with the new_candidates data
|
|
std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
|
|
candidates->size = new_candidates.size();
|
|
candidates->sorted = false;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// no need to do anything if there is only one (or zero) candidates
|
|
if(candidates_p->size <= 1) {
|
|
return;
|
|
}
|
|
|
|
// Calculate maximum possible entropy
|
|
float max_entropy = -logf(1.0f / candidates_p->size);
|
|
|
|
llama_sample_softmax(nullptr, candidates_p);
|
|
|
|
// Calculate entropy of the softmax probabilities
|
|
float entropy = 0.0f;
|
|
for (size_t i = 0; i < candidates_p->size; ++i) {
|
|
float prob = candidates_p->data[i].p;
|
|
if (prob > 0.0f) { // Ensure no log(0)
|
|
entropy -= prob * logf(prob);
|
|
}
|
|
}
|
|
|
|
// Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above)
|
|
float normalized_entropy = entropy / max_entropy;
|
|
|
|
// Map the normalized entropy to the desired temperature range using the power function
|
|
float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val);
|
|
|
|
#ifdef DEBUG
|
|
LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp);
|
|
LLAMA_LOG_INFO("Entropy: %f\n", entropy);
|
|
LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy);
|
|
LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy);
|
|
LLAMA_LOG_INFO("Exponent: %f\n", exponent_val);
|
|
LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp);
|
|
#endif
|
|
|
|
// Apply the dynamically calculated temperature scaling
|
|
for (size_t i = 0; i < candidates_p->size; ++i) {
|
|
candidates_p->data[i].logit /= dyn_temp;
|
|
}
|
|
|
|
// Re-compute softmax probabilities after scaling logits with dynamic temperature
|
|
double max_l_double = candidates_p->data[0].logit;
|
|
double cum_sum_double = 0.0;
|
|
for (size_t i = 0; i < candidates_p->size; ++i) {
|
|
double p = exp(candidates_p->data[i].logit - max_l_double);
|
|
candidates_p->data[i].p = p; // Store the scaled probability
|
|
cum_sum_double += p;
|
|
}
|
|
for (size_t i = 0; i < candidates_p->size; ++i) {
|
|
candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities
|
|
}
|
|
|
|
#ifdef DEBUG
|
|
// Print the updated top 25 probabilities after temperature scaling
|
|
LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n");
|
|
for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) {
|
|
LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f);
|
|
}
|
|
#endif
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
for (size_t i = 0; i < candidates_p->size; ++i) {
|
|
candidates_p->data[i].logit /= temp;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_repetition_penalties(
|
|
struct llama_context * ctx,
|
|
llama_token_data_array * candidates,
|
|
const llama_token * last_tokens,
|
|
size_t penalty_last_n,
|
|
float penalty_repeat,
|
|
float penalty_freq,
|
|
float penalty_present) {
|
|
if (penalty_last_n == 0 || (penalty_repeat == 1.0f && penalty_freq == 0.0f && penalty_present == 0.0f)) {
|
|
return;
|
|
}
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Create a frequency map to count occurrences of each token in last_tokens
|
|
std::unordered_map<llama_token, int> token_count;
|
|
for (size_t i = 0; i < penalty_last_n; ++i) {
|
|
token_count[last_tokens[i]]++;
|
|
}
|
|
|
|
// Apply frequency and presence penalties to the candidates
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
const auto token_iter = token_count.find(candidates->data[i].id);
|
|
if (token_iter == token_count.end()) {
|
|
continue;
|
|
}
|
|
|
|
const int count = token_iter->second;
|
|
|
|
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
|
|
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
|
|
if (candidates->data[i].logit <= 0) {
|
|
candidates->data[i].logit *= penalty_repeat;
|
|
} else {
|
|
candidates->data[i].logit /= penalty_repeat;
|
|
}
|
|
|
|
candidates->data[i].logit -= float(count) * penalty_freq + float(count > 0) * penalty_present;
|
|
}
|
|
|
|
candidates->sorted = false;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
|
|
GGML_ASSERT(ctx);
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
bool allow_eog = false;
|
|
for (const auto & stack : grammar->stacks) {
|
|
if (stack.empty()) {
|
|
allow_eog = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
|
|
candidates_decoded.reserve(candidates->size);
|
|
std::vector<llama_grammar_candidate> candidates_grammar;
|
|
candidates_grammar.reserve(candidates->size);
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
const llama_token id = candidates->data[i].id;
|
|
const std::string piece = llama_token_to_piece(ctx, id, false);
|
|
|
|
if (llama_token_is_eog(&ctx->model, id)) {
|
|
if (!allow_eog) {
|
|
candidates->data[i].logit = -INFINITY;
|
|
}
|
|
} else if (piece.empty() || piece[0] == 0) {
|
|
candidates->data[i].logit = -INFINITY;
|
|
} else {
|
|
candidates_decoded.push_back(decode_utf8(piece, grammar->partial_utf8));
|
|
candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
|
|
}
|
|
}
|
|
|
|
const auto rejects = llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
|
|
for (const auto & reject : rejects) {
|
|
candidates->data[reject.index].logit = -INFINITY;
|
|
}
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
static void llama_log_softmax(float * array, size_t size) {
|
|
float max_l = *std::max_element(array, array + size);
|
|
float sum = 0.f;
|
|
for (size_t i = 0; i < size; ++i) {
|
|
float p = expf(array[i] - max_l);
|
|
sum += p;
|
|
array[i] = p;
|
|
}
|
|
|
|
for (size_t i = 0; i < size; ++i) {
|
|
array[i] = logf(array[i] / sum);
|
|
}
|
|
}
|
|
|
|
void llama_sample_apply_guidance(
|
|
struct llama_context * ctx,
|
|
float * logits,
|
|
float * logits_guidance,
|
|
float scale) {
|
|
GGML_ASSERT(ctx);
|
|
|
|
const auto t_start_sample_us = ggml_time_us();
|
|
const auto n_vocab = llama_n_vocab(llama_get_model(ctx));
|
|
|
|
llama_log_softmax(logits, n_vocab);
|
|
llama_log_softmax(logits_guidance, n_vocab);
|
|
|
|
for (int i = 0; i < n_vocab; ++i) {
|
|
auto & l = logits[i];
|
|
const auto & g = logits_guidance[i];
|
|
|
|
l = scale * (l - g) + g;
|
|
}
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
|
|
GGML_ASSERT(ctx);
|
|
|
|
auto N = float(llama_n_vocab(llama_get_model(ctx)));
|
|
int64_t t_start_sample_us;
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
llama_sample_softmax(nullptr, candidates);
|
|
|
|
// Estimate s_hat using the most probable m tokens
|
|
float s_hat = 0.0;
|
|
float sum_ti_bi = 0.0;
|
|
float sum_ti_sq = 0.0;
|
|
for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
|
|
float t_i = logf(float(i + 2) / float(i + 1));
|
|
float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
|
|
sum_ti_bi += t_i * b_i;
|
|
sum_ti_sq += t_i * t_i;
|
|
}
|
|
s_hat = sum_ti_bi / sum_ti_sq;
|
|
|
|
// Compute k from the estimated s_hat and target surprise value
|
|
float epsilon_hat = s_hat - 1;
|
|
float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
|
|
|
|
// Sample the next word X using top-k sampling
|
|
llama_sample_top_k(nullptr, candidates, int(k), 1);
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
llama_token X = llama_sample_token(ctx, candidates);
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute error as the difference between observed surprise and target surprise value
|
|
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
|
return candidate.id == X;
|
|
}));
|
|
float observed_surprise = -log2f(candidates->data[X_idx].p);
|
|
float e = observed_surprise - tau;
|
|
|
|
// Update mu using the learning rate and error
|
|
*mu = *mu - eta * e;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
return X;
|
|
}
|
|
|
|
llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
|
|
int64_t t_start_sample_us;
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
llama_sample_softmax(ctx, candidates);
|
|
|
|
// Truncate the words with surprise values greater than mu
|
|
candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
|
return -log2f(candidate.p) > *mu;
|
|
}));
|
|
|
|
if (candidates->size == 0) {
|
|
candidates->size = 1;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
// Normalize the probabilities of the remaining words
|
|
llama_sample_softmax(ctx, candidates);
|
|
|
|
// Sample the next word X from the remaining words
|
|
llama_token X = llama_sample_token(ctx, candidates);
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute error as the difference between observed surprise and target surprise value
|
|
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
|
return candidate.id == X;
|
|
}));
|
|
float observed_surprise = -log2f(candidates->data[X_idx].p);
|
|
float e = observed_surprise - tau;
|
|
|
|
// Update mu using the learning rate and error
|
|
*mu = *mu - eta * e;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
return X;
|
|
}
|
|
|
|
llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Find max element
|
|
auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit < b.logit;
|
|
});
|
|
|
|
llama_token result = max_iter->id;
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
}
|
|
return result;
|
|
}
|
|
|
|
llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
|
|
GGML_ASSERT(ctx);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
llama_sample_softmax(nullptr, candidates);
|
|
|
|
std::vector<float> probs;
|
|
probs.reserve(candidates->size);
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
probs.push_back(candidates->data[i].p);
|
|
}
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
int idx = dist(rng);
|
|
|
|
llama_token result = candidates->data[idx].id;
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
return result;
|
|
}
|
|
|
|
llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
return llama_sample_token_with_rng(ctx, candidates, ctx->rng);
|
|
}
|
|
|
|
void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
if (llama_token_is_eog(&ctx->model, token)) {
|
|
for (const auto & stack : grammar->stacks) {
|
|
if (stack.empty()) {
|
|
return;
|
|
}
|
|
}
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const std::string piece = llama_token_to_piece(ctx, token, false);
|
|
|
|
// Note terminating 0 in decoded string
|
|
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
|
|
const auto & code_points = decoded.first;
|
|
std::vector<std::vector<const llama_grammar_element *>> tmp_new_stacks;
|
|
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
|
|
llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
|
|
grammar->stacks = tmp_new_stacks;
|
|
}
|
|
grammar->partial_utf8 = decoded.second;
|
|
GGML_ASSERT(!grammar->stacks.empty());
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
//
|
|
// Beam search
|
|
//
|
|
|
|
struct llama_beam {
|
|
std::vector<llama_token> tokens;
|
|
float p; // Cumulative beam probability (renormalized relative to all beams)
|
|
bool eob; // Initialize end-of-beam to false. Callback sets this to true.
|
|
// Sort beams by probability. In case of ties, prefer beams at eob.
|
|
bool operator<(const llama_beam & rhs) const {
|
|
return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
|
|
}
|
|
// Shift off first n tokens and discard them.
|
|
void shift_tokens(const size_t n) {
|
|
if (n) {
|
|
std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
|
|
tokens.resize(tokens.size() - n);
|
|
}
|
|
}
|
|
llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
|
|
};
|
|
|
|
// A struct for calculating logit-related info.
|
|
struct llama_logit_info {
|
|
const float * const logits;
|
|
const int n_vocab;
|
|
const float max_l;
|
|
const float normalizer;
|
|
struct sum_exp {
|
|
float max_l;
|
|
float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
|
|
};
|
|
llama_logit_info(llama_context * ctx)
|
|
: logits(llama_get_logits(ctx))
|
|
, n_vocab(llama_n_vocab(llama_get_model(ctx)))
|
|
, max_l(*std::max_element(logits, logits + n_vocab))
|
|
, normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
|
|
{ }
|
|
llama_token_data get_token_data(const llama_token token_id) const {
|
|
constexpr auto p = std::numeric_limits<float>::quiet_NaN(); // never used
|
|
return {token_id, logits[token_id], p};
|
|
}
|
|
// Return top k token_data by logit.
|
|
std::vector<llama_token_data> top_k(size_t k) {
|
|
std::vector<llama_token_data> min_heap; // min-heap by logit
|
|
const llama_token k_min = std::min(static_cast<llama_token>(k), n_vocab);
|
|
min_heap.reserve(k_min);
|
|
for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
|
|
min_heap.push_back(get_token_data(token_id));
|
|
}
|
|
auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
|
|
std::make_heap(min_heap.begin(), min_heap.end(), comp);
|
|
for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
|
|
if (min_heap.front().logit < logits[token_id]) {
|
|
std::pop_heap(min_heap.begin(), min_heap.end(), comp);
|
|
min_heap.back().id = token_id;
|
|
min_heap.back().logit = logits[token_id];
|
|
std::push_heap(min_heap.begin(), min_heap.end(), comp);
|
|
}
|
|
}
|
|
return min_heap;
|
|
}
|
|
float probability_from_logit(float logit) const {
|
|
return normalizer * std::exp(logit - max_l);
|
|
}
|
|
};
|
|
|
|
struct llama_beam_search_data {
|
|
llama_context * ctx;
|
|
size_t n_beams;
|
|
int n_past;
|
|
int n_predict;
|
|
std::vector<llama_beam> beams;
|
|
std::vector<llama_beam> next_beams;
|
|
|
|
// Re-calculated on each loop iteration
|
|
size_t common_prefix_length;
|
|
|
|
// Used to communicate to/from callback on beams state.
|
|
std::vector<llama_beam_view> beam_views;
|
|
|
|
llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
|
|
: ctx(ctx)
|
|
, n_beams(n_beams)
|
|
, n_past(n_past)
|
|
, n_predict(n_predict)
|
|
, beam_views(n_beams) {
|
|
beams.reserve(n_beams);
|
|
next_beams.reserve(n_beams);
|
|
}
|
|
|
|
// Collapse beams to a single beam given by index.
|
|
void collapse_beams(const size_t beam_idx) {
|
|
if (0u < beam_idx) {
|
|
std::swap(beams[0], beams[beam_idx]);
|
|
}
|
|
beams.resize(1);
|
|
}
|
|
|
|
// Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
|
|
// The repetitive patterns below reflect the 2 stages of heaps:
|
|
// * Gather elements until the vector is full, then call std::make_heap() on it.
|
|
// * If the heap is full and a new element is found that should be included, pop the
|
|
// least element to the back(), replace it with the new, then push it into the heap.
|
|
void fill_next_beams_by_top_probabilities(llama_beam & beam) {
|
|
// Min-heaps use a greater-than comparator.
|
|
const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
|
|
if (beam.eob) {
|
|
// beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
|
|
if (next_beams.size() < n_beams) {
|
|
next_beams.push_back(std::move(beam));
|
|
if (next_beams.size() == n_beams) {
|
|
std::make_heap(next_beams.begin(), next_beams.end(), comp);
|
|
}
|
|
} else if (next_beams.front().p < beam.p) {
|
|
std::pop_heap(next_beams.begin(), next_beams.end(), comp);
|
|
next_beams.back() = std::move(beam);
|
|
std::push_heap(next_beams.begin(), next_beams.end(), comp);
|
|
}
|
|
} else {
|
|
// beam is not at end-of-sentence, so branch with next top_k tokens.
|
|
if (!beam.tokens.empty()) {
|
|
llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
|
|
}
|
|
llama_logit_info logit_info(ctx);
|
|
std::vector<llama_token_data> next_tokens = logit_info.top_k(n_beams);
|
|
|
|
// Clear the kv slot so that other beams may try different tokens at this position. The llama_decode()
|
|
// call in loop() will conclusively fill in the kv slot once the beams converge at this position.
|
|
llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
|
|
|
|
size_t i=0;
|
|
if (next_beams.size() < n_beams) {
|
|
for (; next_beams.size() < n_beams ; ++i) {
|
|
llama_beam next_beam = beam;
|
|
next_beam.tokens.push_back(next_tokens[i].id);
|
|
next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
|
|
next_beams.push_back(std::move(next_beam));
|
|
}
|
|
std::make_heap(next_beams.begin(), next_beams.end(), comp);
|
|
} else {
|
|
for (; next_beams.front().p == 0.0f ; ++i) {
|
|
std::pop_heap(next_beams.begin(), next_beams.end(), comp);
|
|
next_beams.back() = beam;
|
|
next_beams.back().tokens.push_back(next_tokens[i].id);
|
|
next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
|
|
std::push_heap(next_beams.begin(), next_beams.end(), comp);
|
|
}
|
|
}
|
|
for (; i < n_beams ; ++i) {
|
|
const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
|
|
if (next_beams.front().p < next_p) {
|
|
std::pop_heap(next_beams.begin(), next_beams.end(), comp);
|
|
next_beams.back() = beam;
|
|
next_beams.back().tokens.push_back(next_tokens[i].id);
|
|
next_beams.back().p = next_p;
|
|
std::push_heap(next_beams.begin(), next_beams.end(), comp);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// Find common_prefix_length based on beams.
|
|
// Requires beams is not empty.
|
|
size_t find_common_prefix_length() {
|
|
size_t common_prefix_length = beams[0].tokens.size();
|
|
for (size_t i = 1 ; i < beams.size() ; ++i) {
|
|
common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
|
|
for (size_t j = 0 ; j < common_prefix_length ; ++j) {
|
|
if (beams[0].tokens[j] != beams[i].tokens[j]) {
|
|
common_prefix_length = j;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
return common_prefix_length;
|
|
}
|
|
|
|
// Construct beams_state to send back to caller via the callback function.
|
|
// Side effect: set common_prefix_length = find_common_prefix_length();
|
|
llama_beams_state get_beams_state(const bool last_call) {
|
|
for (size_t i = 0 ; i < beams.size() ; ++i) {
|
|
beam_views[i] = beams[i].view();
|
|
}
|
|
common_prefix_length = find_common_prefix_length();
|
|
return {beam_views.data(), beams.size(), common_prefix_length, last_call};
|
|
}
|
|
|
|
// Loop:
|
|
// * while i < n_predict, AND
|
|
// * any of the beams have not yet reached end-of-beam (eob), AND
|
|
// * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
|
|
// (since all other beam probabilities can only decrease)
|
|
void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
|
|
beams.push_back({{}, 1.0f, false}); // Start with one empty beam w/ probability = 1.0 and !eob.
|
|
const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
|
|
for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
|
|
!beams[top_beam_index()].eob ; ++i) {
|
|
callback(callback_data, get_beams_state(false)); // Sets common_prefix_length
|
|
update_beams_from_beam_views(); // Update values (p,eob) that callback may have changed.
|
|
if (common_prefix_length) {
|
|
llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
|
|
n_past += common_prefix_length;
|
|
}
|
|
// Zero-out next_beam probabilities to place them last in following min-heap.
|
|
std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
|
|
for (llama_beam & beam : beams) {
|
|
beam.shift_tokens(common_prefix_length);
|
|
fill_next_beams_by_top_probabilities(beam);
|
|
}
|
|
// next_beams become the beams of next/final iteration. Swap them to re-use memory.
|
|
beams.swap(next_beams);
|
|
renormalize_beam_probabilities(beams);
|
|
}
|
|
collapse_beams(top_beam_index());
|
|
callback(callback_data, get_beams_state(true));
|
|
}
|
|
|
|
// As beams grow, the cumulative probabilities decrease.
|
|
// Renormalize them to avoid floating point underflow.
|
|
static void renormalize_beam_probabilities(std::vector<llama_beam> & beams) {
|
|
const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
|
|
const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
|
|
std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
|
|
}
|
|
|
|
// Assumes beams is non-empty. Uses llama_beam::operator<() for ordering.
|
|
size_t top_beam_index() {
|
|
return std::max_element(beams.begin(), beams.end()) - beams.begin();
|
|
}
|
|
|
|
// Copy (p,eob) for each beam which may have been changed by the callback.
|
|
void update_beams_from_beam_views() {
|
|
for (size_t i = 0 ; i < beams.size() ; ++i) {
|
|
beams[i].p = beam_views[i].p;
|
|
beams[i].eob = beam_views[i].eob;
|
|
}
|
|
}
|
|
};
|
|
|
|
void llama_beam_search(llama_context * ctx,
|
|
llama_beam_search_callback_fn_t callback, void * callback_data,
|
|
size_t n_beams, int n_past, int n_predict) {
|
|
assert(ctx);
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);
|
|
|
|
beam_search_data.loop(callback, callback_data);
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
}
|
|
|
|
//
|
|
// quantization
|
|
//
|
|
|
|
struct quantize_state_internal {
|
|
const llama_model & model;
|
|
const llama_model_quantize_params * params;
|
|
|
|
int n_attention_wv = 0;
|
|
int n_ffn_down = 0;
|
|
int n_ffn_gate = 0;
|
|
int n_ffn_up = 0;
|
|
int i_attention_wv = 0;
|
|
int i_ffn_down = 0;
|
|
int i_ffn_gate = 0;
|
|
int i_ffn_up = 0;
|
|
|
|
int n_k_quantized = 0;
|
|
int n_fallback = 0;
|
|
|
|
bool has_imatrix = false;
|
|
|
|
// used to figure out if a model shares tok_embd with the output weight
|
|
bool has_output = false;
|
|
|
|
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
|
|
: model(model)
|
|
, params(params)
|
|
{}
|
|
};
|
|
|
|
static void llama_tensor_dequantize_internal(
|
|
struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
|
|
const size_t nelements, const int nthread
|
|
) {
|
|
if (output.size() < nelements) {
|
|
output.resize(nelements);
|
|
}
|
|
float * f32_output = (float *) output.data();
|
|
|
|
ggml_type_traits_t qtype;
|
|
if (ggml_is_quantized(tensor->type)) {
|
|
qtype = ggml_internal_get_type_traits(tensor->type);
|
|
if (qtype.to_float == NULL) {
|
|
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
|
|
}
|
|
} else if (tensor->type != GGML_TYPE_F16) {
|
|
throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
|
|
}
|
|
|
|
if (nthread < 2) {
|
|
if (tensor->type == GGML_TYPE_F16) {
|
|
ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
|
|
} else if (ggml_is_quantized(tensor->type)) {
|
|
qtype.to_float(tensor->data, f32_output, nelements);
|
|
} else {
|
|
GGML_ASSERT(false); // unreachable
|
|
}
|
|
return;
|
|
}
|
|
|
|
size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
|
|
size_t block_size_bytes = ggml_type_size(tensor->type);
|
|
|
|
GGML_ASSERT(nelements % block_size == 0);
|
|
size_t nblocks = nelements / block_size;
|
|
size_t blocks_per_thread = nblocks / nthread;
|
|
size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
|
|
|
size_t in_buff_offs = 0;
|
|
size_t out_buff_offs = 0;
|
|
|
|
for (int tnum = 0; tnum < nthread; tnum++) {
|
|
size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
|
size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
|
|
size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
|
|
|
|
auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
|
|
if (typ == GGML_TYPE_F16) {
|
|
ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
|
|
} else {
|
|
qtype.to_float(inbuf, outbuf, nels);
|
|
}
|
|
};
|
|
workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
|
|
in_buff_offs += thr_block_bytes;
|
|
out_buff_offs += thr_elems;
|
|
}
|
|
for (auto & w : workers) { w.join(); }
|
|
workers.clear();
|
|
}
|
|
|
|
static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
|
|
const std::string name = ggml_get_name(tensor);
|
|
|
|
// TODO: avoid hardcoded tensor names - use the TN_* constants
|
|
const llm_arch arch = qs.model.arch;
|
|
const auto tn = LLM_TN(arch);
|
|
|
|
auto use_more_bits = [](int i_layer, int num_layers) -> bool {
|
|
return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
|
|
};
|
|
const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
|
|
auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
|
|
if (n_expert > 1) {
|
|
// Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
|
|
// sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
|
|
// for getting the current layer as I initially thought, and we need to resort to parsing the
|
|
// tensor name.
|
|
if (sscanf(name, "blk.%d.", &i_layer) != 1) {
|
|
throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
|
|
}
|
|
if (i_layer < 0 || i_layer >= n_layer) {
|
|
throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
|
|
}
|
|
}
|
|
return std::make_pair(i_layer, n_layer);
|
|
};
|
|
|
|
// for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
|
|
// with the quantization of the output tensor
|
|
if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
|
|
if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
|
|
new_type = qs.params->output_tensor_type;
|
|
} else {
|
|
int nx = tensor->ne[0];
|
|
if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
|
|
new_type = GGML_TYPE_Q8_0;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
else if (new_type != GGML_TYPE_Q8_0) {
|
|
new_type = GGML_TYPE_Q6_K;
|
|
}
|
|
}
|
|
} else if (name == "token_embd.weight") {
|
|
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
|
|
new_type = qs.params->token_embedding_type;
|
|
} else {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
|
|
new_type = GGML_TYPE_Q2_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
|
|
new_type = GGML_TYPE_IQ3_S;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
|
new_type = GGML_TYPE_IQ3_S;
|
|
}
|
|
}
|
|
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
|
|
if (name.find("attn_v.weight") != std::string::npos) {
|
|
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
|
|
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
|
|
++qs.i_attention_wv;
|
|
}
|
|
else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
|
|
new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (name.find("ffn_down") != std::string::npos) {
|
|
if (qs.i_ffn_down < qs.n_ffn_down/8) {
|
|
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
|
|
}
|
|
++qs.i_ffn_down;
|
|
}
|
|
else if (name.find("attn_output.weight") != std::string::npos) {
|
|
if (qs.model.hparams.n_expert == 8) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
} else {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
|
|
}
|
|
}
|
|
} else if (name.find("attn_v.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
|
|
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
|
|
new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
|
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
|
|
}
|
|
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
|
|
new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
|
|
new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
|
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
|
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
|
use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
|
|
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
|
|
(qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
|
|
if (qs.model.type == MODEL_70B) {
|
|
// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
|
|
// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
|
|
// nearly negligible increase in model size by quantizing this tensor with more bits:
|
|
if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
if (qs.model.hparams.n_expert == 8) {
|
|
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
|
|
// TODO: explore better strategies
|
|
new_type = GGML_TYPE_Q8_0;
|
|
}
|
|
++qs.i_attention_wv;
|
|
} else if (name.find("attn_k.weight") != std::string::npos) {
|
|
if (qs.model.hparams.n_expert == 8) {
|
|
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
|
|
// TODO: explore better strategies
|
|
new_type = GGML_TYPE_Q8_0;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
|
|
new_type = GGML_TYPE_IQ3_XXS;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
|
new_type = GGML_TYPE_IQ2_S;
|
|
}
|
|
} else if (name.find("attn_q.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
|
|
new_type = GGML_TYPE_IQ3_XXS;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
|
|
new_type = GGML_TYPE_IQ2_S;
|
|
}
|
|
} else if (name.find("ffn_down") != std::string::npos) {
|
|
auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
|
|
int i_layer = info.first, n_layer = info.second;
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
|
|
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
|
|
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
|
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
|
|
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
|
|
: GGML_TYPE_Q3_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
|
|
(qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
|
|
new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
|
|
new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
|
|
if (arch == LLM_ARCH_FALCON) {
|
|
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
|
|
use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
|
} else {
|
|
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
|
}
|
|
}
|
|
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
|
|
&& qs.has_imatrix && i_layer < n_layer/8) {
|
|
// Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
|
|
// We only do it when an imatrix is provided because a) we want to make sure that one can always get the
|
|
// same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
|
|
new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
|
|
}
|
|
++qs.i_ffn_down;
|
|
} else if (name.find("attn_output.weight") != std::string::npos) {
|
|
if (arch != LLM_ARCH_FALCON) {
|
|
if (qs.model.hparams.n_expert == 8) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
|
|
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
} else {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
} else {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
}
|
|
else if (name.find("attn_qkv.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
|
|
new_type = GGML_TYPE_Q4_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
|
|
}
|
|
else if (name.find("ffn_gate") != std::string::npos) {
|
|
auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
|
|
int i_layer = info.first, n_layer = info.second;
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
|
|
new_type = GGML_TYPE_IQ3_XXS;
|
|
}
|
|
++qs.i_ffn_gate;
|
|
}
|
|
else if (name.find("ffn_up") != std::string::npos) {
|
|
auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
|
|
int i_layer = info.first, n_layer = info.second;
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
|
|
new_type = GGML_TYPE_IQ3_XXS;
|
|
}
|
|
++qs.i_ffn_up;
|
|
}
|
|
|
|
// if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
|
|
//}
|
|
// IK: let's remove this, else Q2_K is almost the same as Q3_K_S
|
|
//else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
|
|
// if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
|
|
//}
|
|
// This can be used to reduce the size of the Q5_K_S model.
|
|
// The associated PPL increase is fully in line with the size reduction
|
|
//else {
|
|
// if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
|
|
//}
|
|
bool convert_incompatible_tensor = false;
|
|
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
|
|
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
|
|
new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
|
|
new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
|
|
new_type == GGML_TYPE_IQ1_M) {
|
|
int nx = tensor->ne[0];
|
|
int ny = tensor->ne[1];
|
|
if (nx % QK_K != 0) {
|
|
LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
|
|
convert_incompatible_tensor = true;
|
|
} else {
|
|
++qs.n_k_quantized;
|
|
}
|
|
}
|
|
if (convert_incompatible_tensor) {
|
|
switch (new_type) {
|
|
case GGML_TYPE_IQ2_XXS:
|
|
case GGML_TYPE_IQ2_XS:
|
|
case GGML_TYPE_IQ2_S:
|
|
case GGML_TYPE_IQ3_XXS:
|
|
case GGML_TYPE_IQ3_S:
|
|
case GGML_TYPE_IQ1_S:
|
|
case GGML_TYPE_IQ1_M:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
|
|
case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
|
|
case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
|
|
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
|
|
default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
|
|
}
|
|
LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
|
|
++qs.n_fallback;
|
|
}
|
|
|
|
return new_type;
|
|
}
|
|
|
|
static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
|
|
std::mutex mutex;
|
|
int64_t counter = 0;
|
|
size_t new_size = 0;
|
|
if (nthread < 2) {
|
|
// single-thread
|
|
return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
|
|
}
|
|
auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
|
|
nrows, n_per_row, imatrix]() {
|
|
const int64_t nrows_per_chunk = chunk_size / n_per_row;
|
|
size_t local_size = 0;
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex);
|
|
int64_t first_row = counter; counter += nrows_per_chunk;
|
|
if (first_row >= nrows) {
|
|
if (local_size > 0) {
|
|
new_size += local_size;
|
|
}
|
|
break;
|
|
}
|
|
lock.unlock();
|
|
const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
|
|
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
|
|
}
|
|
};
|
|
for (int it = 0; it < nthread - 1; ++it) {
|
|
workers.emplace_back(compute);
|
|
}
|
|
compute();
|
|
for (auto & w : workers) { w.join(); }
|
|
workers.clear();
|
|
return new_size;
|
|
}
|
|
|
|
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
|
|
ggml_type default_type;
|
|
llama_ftype ftype = params->ftype;
|
|
|
|
switch (params->ftype) {
|
|
case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_F16: default_type = GGML_TYPE_F16; break;
|
|
case LLAMA_FTYPE_ALL_F32: default_type = GGML_TYPE_F32; break;
|
|
|
|
// K-quants
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K: default_type = GGML_TYPE_Q2_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_XS: default_type = GGML_TYPE_IQ3_S; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_L: default_type = GGML_TYPE_Q3_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_M: default_type = GGML_TYPE_Q4_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_M: default_type = GGML_TYPE_Q5_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q6_K: default_type = GGML_TYPE_Q6_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_XS: default_type = GGML_TYPE_IQ2_XS; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
|
|
case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; break;
|
|
|
|
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
|
|
}
|
|
|
|
int nthread = params->nthread;
|
|
|
|
if (nthread <= 0) {
|
|
nthread = std::thread::hardware_concurrency();
|
|
}
|
|
|
|
// mmap consistently increases speed Linux, and also increases speed on Windows with
|
|
// hot cache. It may cause a slowdown on macOS, possibly related to free memory.
|
|
#if defined(__linux__) || defined(_WIN32)
|
|
constexpr bool use_mmap = true;
|
|
#else
|
|
constexpr bool use_mmap = false;
|
|
#endif
|
|
|
|
llama_model_kv_override * kv_overrides = nullptr;
|
|
if (params->kv_overrides) {
|
|
auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
|
|
kv_overrides = v->data();
|
|
}
|
|
llama_model_loader ml(fname_inp, use_mmap, kv_overrides);
|
|
ml.init_mappings(false); // no prefetching
|
|
|
|
llama_model model;
|
|
llm_load_arch(ml, model);
|
|
llm_load_hparams(ml, model);
|
|
|
|
struct quantize_state_internal qs(model, params);
|
|
|
|
if (params->only_copy) {
|
|
ftype = model.ftype;
|
|
}
|
|
const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
|
|
if (params->imatrix) {
|
|
imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
|
|
if (imatrix_data) {
|
|
LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
|
|
qs.has_imatrix = true;
|
|
}
|
|
}
|
|
|
|
const size_t align = GGUF_DEFAULT_ALIGNMENT;
|
|
struct gguf_context * ctx_out = gguf_init_empty();
|
|
|
|
// copy the KV pairs from the input file
|
|
gguf_set_kv (ctx_out, ml.meta);
|
|
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
|
|
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
|
|
// Remove split metadata
|
|
gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
|
|
gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
|
|
gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
|
|
|
|
if (params->kv_overrides) {
|
|
const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
|
|
for (auto & o : overrides) {
|
|
if (o.key[0] == 0) break;
|
|
if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
|
|
gguf_set_val_f32(ctx_out, o.key, o.float_value);
|
|
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
|
|
gguf_set_val_i32(ctx_out, o.key, o.int_value);
|
|
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
|
|
gguf_set_val_bool(ctx_out, o.key, o.bool_value);
|
|
} else {
|
|
LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
const struct ggml_tensor * meta = ml.get_tensor_meta(i);
|
|
|
|
const std::string name = ggml_get_name(meta);
|
|
|
|
// TODO: avoid hardcoded tensor names - use the TN_* constants
|
|
if (name.find("attn_v.weight") != std::string::npos ||
|
|
name.find("attn_qkv.weight") != std::string::npos) {
|
|
++qs.n_attention_wv;
|
|
} else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
|
|
qs.has_output = true;
|
|
}
|
|
}
|
|
|
|
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
|
|
|
|
// sanity checks
|
|
//
|
|
// - qs.n_attention_wv == 0 for Mamba models
|
|
// - qs.n_attention_wv == model.hparams.n_layer for Transformer models
|
|
//
|
|
GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer) && "n_attention_wv is unexpected");
|
|
|
|
size_t total_size_org = 0;
|
|
size_t total_size_new = 0;
|
|
|
|
std::vector<std::thread> workers;
|
|
workers.reserve(nthread);
|
|
|
|
int idx = 0;
|
|
|
|
std::vector<no_init<uint8_t>> read_data;
|
|
std::vector<no_init<uint8_t>> work;
|
|
std::vector<no_init<float>> f32_conv_buf;
|
|
|
|
uint16_t n_split = 1;
|
|
// Assume split index is continuous
|
|
if (params->keep_split) {
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split);
|
|
}
|
|
}
|
|
std::vector<gguf_context*> ctx_outs(n_split, NULL);
|
|
ctx_outs[0] = ctx_out;
|
|
|
|
// populate the original tensors so we get an initial meta data
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
auto weight = ml.get_weight(i);
|
|
uint16_t i_split = params->keep_split ? weight->idx : 0;
|
|
struct ggml_tensor * tensor = weight->tensor;
|
|
if (ctx_outs[i_split] == NULL) {
|
|
ctx_outs[i_split] = gguf_init_empty();
|
|
}
|
|
gguf_add_tensor(ctx_outs[i_split], tensor);
|
|
}
|
|
|
|
// Set split info if needed
|
|
if (n_split > 1) {
|
|
for (size_t i = 0; i < ctx_outs.size(); ++i) {
|
|
gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
|
|
gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
|
|
gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
|
|
}
|
|
}
|
|
|
|
int cur_split = -1;
|
|
std::ofstream fout;
|
|
auto close_ofstream = [&]() {
|
|
// Write metadata and close file handler
|
|
if (fout.is_open()) {
|
|
fout.seekp(0);
|
|
std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split]));
|
|
gguf_get_meta_data(ctx_outs[cur_split], data.data());
|
|
fout.write((const char *) data.data(), data.size());
|
|
fout.close();
|
|
}
|
|
};
|
|
auto new_ofstream = [&](int index) {
|
|
cur_split = index;
|
|
GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
|
|
std::string fname = fname_out;
|
|
if (params->keep_split) {
|
|
char split_path[PATH_MAX] = {0};
|
|
llama_split_path(split_path, sizeof(split_path), fname_out.c_str(), cur_split, n_split);
|
|
fname = std::string(split_path);
|
|
}
|
|
|
|
fout = std::ofstream(fname, std::ios::binary);
|
|
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
|
|
const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]);
|
|
// placeholder for the meta data
|
|
::zeros(fout, meta_size);
|
|
};
|
|
|
|
const auto tn = LLM_TN(model.arch);
|
|
new_ofstream(0);
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
auto weight = ml.get_weight(i);
|
|
struct ggml_tensor * tensor = weight->tensor;
|
|
if (weight->idx != cur_split && params->keep_split) {
|
|
close_ofstream();
|
|
new_ofstream(weight->idx);
|
|
}
|
|
|
|
const std::string name = ggml_get_name(tensor);
|
|
|
|
if (!ml.use_mmap) {
|
|
if (read_data.size() < ggml_nbytes(tensor)) {
|
|
read_data.resize(ggml_nbytes(tensor));
|
|
}
|
|
tensor->data = read_data.data();
|
|
}
|
|
ml.load_data_for(tensor);
|
|
|
|
LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
|
|
++idx, ml.n_tensors,
|
|
ggml_get_name(tensor),
|
|
llama_format_tensor_shape(tensor).c_str(),
|
|
ggml_type_name(tensor->type));
|
|
|
|
// This used to be a regex, but <regex> has an extreme cost to compile times.
|
|
bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
|
|
|
|
// quantize only 2D and 3D tensors (experts)
|
|
quantize &= (ggml_n_dims(tensor) >= 2);
|
|
|
|
// do not quantize norm tensors
|
|
quantize &= name.find("_norm.weight") == std::string::npos;
|
|
|
|
quantize &= params->quantize_output_tensor || name != "output.weight";
|
|
quantize &= !params->only_copy;
|
|
|
|
// do not quantize expert gating tensors
|
|
// NOTE: can't use LLM_TN here because the layer number is not known
|
|
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
|
|
|
|
// do not quantize positional embeddings and token types (BERT)
|
|
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight");
|
|
quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
|
|
|
|
// do not quantize Mamba's small yet 2D weights
|
|
// NOTE: can't use LLM_TN here because the layer number is not known
|
|
quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
|
|
quantize &= name.find("ssm_x.weight") == std::string::npos;
|
|
quantize &= name.find("ssm_dt.weight") == std::string::npos;
|
|
|
|
enum ggml_type new_type;
|
|
void * new_data;
|
|
size_t new_size;
|
|
|
|
if (quantize) {
|
|
new_type = default_type;
|
|
|
|
// get more optimal quantization type based on the tensor shape, layer, etc.
|
|
if (!params->pure && ggml_is_quantized(default_type)) {
|
|
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
|
|
}
|
|
if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
|
|
new_type = params->token_embedding_type;
|
|
}
|
|
if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
|
|
new_type = params->output_tensor_type;
|
|
}
|
|
|
|
// If we've decided to quantize to the same type the tensor is already
|
|
// in then there's nothing to do.
|
|
quantize = tensor->type != new_type;
|
|
}
|
|
|
|
if (!quantize) {
|
|
new_type = tensor->type;
|
|
new_data = tensor->data;
|
|
new_size = ggml_nbytes(tensor);
|
|
LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
|
|
} else {
|
|
const int64_t nelements = ggml_nelements(tensor);
|
|
|
|
const float * imatrix = nullptr;
|
|
if (imatrix_data) {
|
|
auto it = imatrix_data->find(tensor->name);
|
|
if (it == imatrix_data->end()) {
|
|
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
|
|
} else {
|
|
if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
|
|
imatrix = it->second.data();
|
|
} else {
|
|
LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
|
|
int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
|
|
|
|
// this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
|
|
// this is a significant error and it may be good idea to abort the process if this happens,
|
|
// since many people will miss the error and not realize that most of the model is being quantized without an imatrix
|
|
// tok_embd should be ignored in this case, since it always causes this warning
|
|
if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
|
|
throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
|
|
int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if ((new_type == GGML_TYPE_IQ2_XXS ||
|
|
new_type == GGML_TYPE_IQ2_XS ||
|
|
new_type == GGML_TYPE_IQ2_S ||
|
|
new_type == GGML_TYPE_IQ1_S ||
|
|
(new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
|
|
(new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
|
|
LLAMA_LOG_ERROR("\n\n============================================================\n");
|
|
LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
|
|
LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
|
|
LLAMA_LOG_ERROR("============================================================\n\n");
|
|
throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
|
|
}
|
|
|
|
float * f32_data;
|
|
|
|
if (tensor->type == GGML_TYPE_F32) {
|
|
f32_data = (float *) tensor->data;
|
|
} else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
|
|
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
|
|
} else {
|
|
llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
|
|
f32_data = (float *) f32_conv_buf.data();
|
|
}
|
|
|
|
LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
|
|
fflush(stdout);
|
|
|
|
if (work.size() < (size_t)nelements * 4) {
|
|
work.resize(nelements * 4); // upper bound on size
|
|
}
|
|
new_data = work.data();
|
|
|
|
const int64_t n_per_row = tensor->ne[0];
|
|
const int64_t nrows = tensor->ne[1];
|
|
|
|
static const int64_t min_chunk_size = 32 * 512;
|
|
const int64_t chunk_size = n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row);
|
|
|
|
const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
|
|
const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
|
|
const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;
|
|
|
|
// quantize each expert separately since they have different importance matrices
|
|
new_size = 0;
|
|
for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
|
|
const float * f32_data_03 = f32_data + i03 * nelements_matrix;
|
|
void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
|
|
const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
|
|
|
|
new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
|
|
}
|
|
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
|
|
}
|
|
total_size_org += ggml_nbytes(tensor);
|
|
total_size_new += new_size;
|
|
|
|
// update the gguf meta data as we go
|
|
gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type);
|
|
gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size);
|
|
|
|
// write tensor data + padding
|
|
fout.write((const char *) new_data, new_size);
|
|
zeros(fout, GGML_PAD(new_size, align) - new_size);
|
|
}
|
|
close_ofstream();
|
|
for (auto & c:ctx_outs) {
|
|
gguf_free(c);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
|
|
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
|
|
|
|
if (qs.n_fallback > 0) {
|
|
LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
|
|
__func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
|
|
}
|
|
}
|
|
|
|
static int llama_apply_lora_from_file_internal(
|
|
const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads
|
|
) {
|
|
LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
|
|
|
|
const int64_t t_start_lora_us = ggml_time_us();
|
|
|
|
llama_file fin(path_lora, "rb");
|
|
|
|
// verify magic and version
|
|
{
|
|
uint32_t magic = fin.read_u32();
|
|
if (magic != LLAMA_FILE_MAGIC_GGLA) {
|
|
LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
uint32_t format_version = fin.read_u32();
|
|
if (format_version != 1) {
|
|
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int32_t lora_r = fin.read_u32();
|
|
int32_t lora_alpha = fin.read_u32();
|
|
float scaling = scale * (float)lora_alpha / (float)lora_r;
|
|
|
|
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
|
|
|
|
// load base model
|
|
std::unique_ptr<llama_model_loader> ml;
|
|
if (path_base_model) {
|
|
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
|
|
ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*kv_overrides*/ nullptr));
|
|
ml->init_mappings(/*prefetch*/ false); // no prefetching
|
|
}
|
|
|
|
struct tensor_meta {
|
|
std::string name;
|
|
ggml_type type;
|
|
int32_t ne[2];
|
|
size_t offset;
|
|
};
|
|
std::map<std::string, tensor_meta> tensor_meta_map;
|
|
|
|
// load all tensor meta
|
|
while (true) {
|
|
if (fin.tell() == fin.size) {
|
|
// eof
|
|
break;
|
|
}
|
|
|
|
int32_t n_dims;
|
|
int32_t name_len;
|
|
int32_t ftype;
|
|
|
|
fin.read_raw(&n_dims, sizeof(n_dims));
|
|
fin.read_raw(&name_len, sizeof(name_len));
|
|
fin.read_raw(&ftype, sizeof(ftype));
|
|
|
|
if (n_dims != 1 && n_dims != 2) {
|
|
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
|
|
return 1;
|
|
}
|
|
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read_raw(&ne[i], sizeof(ne[i]));
|
|
}
|
|
|
|
std::string name;
|
|
{
|
|
GGML_ASSERT(name_len < GGML_MAX_NAME);
|
|
char buf[GGML_MAX_NAME];
|
|
fin.read_raw(buf, name_len);
|
|
name = std::string(buf, name_len);
|
|
}
|
|
|
|
// check for lora suffix
|
|
std::string lora_suffix;
|
|
if (name.length() > 6) {
|
|
lora_suffix = name.substr(name.length() - 6);
|
|
}
|
|
if (lora_suffix != ".loraA" && lora_suffix != ".loraB") {
|
|
LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
|
|
return 1;
|
|
}
|
|
|
|
// tensor type
|
|
ggml_type wtype;
|
|
switch (ftype) {
|
|
case 0: wtype = GGML_TYPE_F32; break;
|
|
case 1: wtype = GGML_TYPE_F16; break;
|
|
default:
|
|
{
|
|
LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
|
|
__func__, ftype);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
// data offset
|
|
size_t offset = fin.tell();
|
|
offset = (offset + 31) & -32;
|
|
|
|
// skip tensor data
|
|
fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET);
|
|
|
|
tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset });
|
|
}
|
|
|
|
bool warned = false;
|
|
int n_tensors = 0;
|
|
|
|
// apply
|
|
ggml_backend_t backend_cpu = ggml_backend_cpu_init();
|
|
if (backend_cpu == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__);
|
|
return 1;
|
|
}
|
|
ggml_backend_cpu_set_n_threads(backend_cpu, n_threads);
|
|
|
|
std::vector<no_init<uint8_t>> read_buf;
|
|
for (const auto & it : model.tensors_by_name) {
|
|
const std::string & base_name = it.first;
|
|
ggml_tensor * model_t = it.second;
|
|
|
|
if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() ||
|
|
tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) {
|
|
continue;
|
|
}
|
|
|
|
tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA");
|
|
tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB");
|
|
|
|
ggml_init_params lora_init_params = {
|
|
/* .mem_size */ ggml_tensor_overhead()*128 + ggml_graph_overhead(),
|
|
/* .mem_buffer */ nullptr,
|
|
/* .no_alloc */ true,
|
|
};
|
|
ggml_context * lora_ctx = ggml_init(lora_init_params);
|
|
if (lora_ctx == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__);
|
|
ggml_backend_free(backend_cpu);
|
|
return 1;
|
|
}
|
|
|
|
// create tensors
|
|
ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]);
|
|
ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]);
|
|
ggml_set_name(loraA, metaA.name.c_str());
|
|
ggml_set_name(loraB, metaB.name.c_str());
|
|
|
|
ggml_tensor * base_t;
|
|
if (ml) {
|
|
if (!ml->get_tensor_meta(base_name.c_str())) {
|
|
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
|
|
return 1;
|
|
}
|
|
base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str()));
|
|
} else {
|
|
base_t = ggml_dup_tensor(lora_ctx, model_t);
|
|
}
|
|
ggml_set_name(base_t, base_name.c_str());
|
|
|
|
// allocate in backend buffer
|
|
ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
|
|
if (lora_buf == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// load tensor data
|
|
auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) {
|
|
read_buf.resize(ggml_nbytes(tensor));
|
|
fin.seek(tensor_meta.offset, SEEK_SET);
|
|
fin.read_raw(read_buf.data(), ggml_nbytes(tensor));
|
|
ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size());
|
|
};
|
|
load_tensor(metaA, loraA);
|
|
load_tensor(metaB, loraB);
|
|
|
|
// load base model tensor data
|
|
if (ml) {
|
|
ml->load_data_for(base_t);
|
|
} else {
|
|
ggml_backend_tensor_copy(model_t, base_t);
|
|
}
|
|
|
|
if (ggml_is_quantized(base_t->type) && !warned) {
|
|
LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
|
|
"use a f16 or f32 base model with --lora-base\n", __func__);
|
|
warned = true;
|
|
}
|
|
|
|
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
|
|
LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
|
|
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
|
|
ggml_free(lora_ctx);
|
|
ggml_backend_buffer_free(lora_buf);
|
|
ggml_backend_free(backend_cpu);
|
|
return 1;
|
|
}
|
|
|
|
auto build_lora_graph = [&]() {
|
|
// w = w + BA*s
|
|
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
|
|
ggml_set_name(BA, "BA");
|
|
|
|
if (scaling != 1.0f) {
|
|
BA = ggml_scale(lora_ctx, BA, scaling);
|
|
ggml_set_name(BA, "BA_scaled");
|
|
}
|
|
|
|
ggml_tensor * r;
|
|
r = ggml_add_inplace(lora_ctx, base_t, BA);
|
|
ggml_set_name(r, "r_add");
|
|
|
|
if (base_t->type != model_t->type) {
|
|
// convert the result to the model type
|
|
r = ggml_cast(lora_ctx, r, model_t->type);
|
|
ggml_set_name(r, "r_cast");
|
|
}
|
|
|
|
return r;
|
|
};
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(lora_ctx);
|
|
ggml_tensor * r = build_lora_graph();
|
|
ggml_build_forward_expand(gf, r);
|
|
|
|
ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type());
|
|
if (graph_buf == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__);
|
|
ggml_free(lora_ctx);
|
|
ggml_backend_buffer_free(lora_buf);
|
|
ggml_backend_free(backend_cpu);
|
|
return 1;
|
|
}
|
|
|
|
ggml_backend_graph_compute(backend_cpu, gf);
|
|
|
|
ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r));
|
|
|
|
#if 0
|
|
// TODO: use scheduler with fallback to CPU for less copies between CPU and GPU
|
|
//ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE);
|
|
|
|
// sched compute
|
|
ggml_build_forward_expand(gf, build_graph());
|
|
ggml_backend_sched_init_measure(sched, gf);
|
|
|
|
// create the graph again, since the previous one was destroyed by the measure
|
|
ggml_graph_clear(gf);
|
|
ggml_build_forward_expand(gf, build_graph());
|
|
ggml_backend_sched_graph_compute(sched, gf);
|
|
ggml_backend_sched_free(sched);
|
|
#endif
|
|
|
|
ggml_backend_buffer_free(lora_buf);
|
|
ggml_backend_buffer_free(graph_buf);
|
|
ggml_free(lora_ctx);
|
|
|
|
n_tensors++;
|
|
if (n_tensors % 4 == 0) {
|
|
LLAMA_LOG_INFO(".");
|
|
}
|
|
}
|
|
|
|
ggml_backend_free(backend_cpu);
|
|
|
|
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
|
|
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
|
|
|
|
return 0;
|
|
}
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
struct llama_model_params llama_model_default_params() {
|
|
struct llama_model_params result = {
|
|
/*.n_gpu_layers =*/ 0,
|
|
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
|
|
/*.main_gpu =*/ 0,
|
|
/*.tensor_split =*/ nullptr,
|
|
/*.progress_callback =*/ nullptr,
|
|
/*.progress_callback_user_data =*/ nullptr,
|
|
/*.kv_overrides =*/ nullptr,
|
|
/*.vocab_only =*/ false,
|
|
/*.use_mmap =*/ true,
|
|
/*.use_mlock =*/ false,
|
|
};
|
|
|
|
#ifdef GGML_USE_METAL
|
|
// note: we usually have plenty of VRAM, so by default offload all layers to the GPU
|
|
result.n_gpu_layers = 999;
|
|
#endif
|
|
|
|
return result;
|
|
}
|
|
|
|
struct llama_context_params llama_context_default_params() {
|
|
struct llama_context_params result = {
|
|
/*.seed =*/ LLAMA_DEFAULT_SEED,
|
|
/*.n_ctx =*/ 512,
|
|
/*.n_batch =*/ 2048,
|
|
/*.n_ubatch =*/ 512,
|
|
/*.n_seq_max =*/ 1,
|
|
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
|
|
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
|
|
/*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
|
|
/*.pooling_type =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
|
|
/*.rope_freq_base =*/ 0.0f,
|
|
/*.rope_freq_scale =*/ 0.0f,
|
|
/*.yarn_ext_factor =*/ -1.0f,
|
|
/*.yarn_attn_factor =*/ 1.0f,
|
|
/*.yarn_beta_fast =*/ 32.0f,
|
|
/*.yarn_beta_slow =*/ 1.0f,
|
|
/*.yarn_orig_ctx =*/ 0,
|
|
/*.defrag_thold =*/ -1.0f,
|
|
/*.cb_eval =*/ nullptr,
|
|
/*.cb_eval_user_data =*/ nullptr,
|
|
/*.type_k =*/ GGML_TYPE_F16,
|
|
/*.type_v =*/ GGML_TYPE_F16,
|
|
/*.logits_all =*/ false,
|
|
/*.embeddings =*/ false,
|
|
/*.offload_kqv =*/ true,
|
|
/*.abort_callback =*/ nullptr,
|
|
/*.abort_callback_data =*/ nullptr,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
struct llama_model_quantize_params llama_model_quantize_default_params() {
|
|
struct llama_model_quantize_params result = {
|
|
/*.nthread =*/ 0,
|
|
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
|
|
/*.output_tensor_type =*/ GGML_TYPE_COUNT,
|
|
/*.token_embedding_type =*/ GGML_TYPE_COUNT,
|
|
/*.allow_requantize =*/ false,
|
|
/*.quantize_output_tensor =*/ true,
|
|
/*.only_copy =*/ false,
|
|
/*.pure =*/ false,
|
|
/*.keep_split =*/ false,
|
|
/*.imatrix =*/ nullptr,
|
|
/*.kv_overrides =*/ nullptr,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
size_t llama_max_devices(void) {
|
|
#if defined(GGML_USE_METAL)
|
|
return 1;
|
|
#elif defined(GGML_USE_CUDA)
|
|
return GGML_CUDA_MAX_DEVICES;
|
|
#elif defined(GGML_USE_SYCL)
|
|
return GGML_SYCL_MAX_DEVICES;
|
|
#elif defined(GGML_USE_VULKAN)
|
|
return GGML_VK_MAX_DEVICES;
|
|
#else
|
|
return 1;
|
|
#endif
|
|
}
|
|
|
|
bool llama_supports_mmap(void) {
|
|
return llama_mmap::SUPPORTED;
|
|
}
|
|
|
|
bool llama_supports_mlock(void) {
|
|
return llama_mlock::SUPPORTED;
|
|
}
|
|
|
|
bool llama_supports_gpu_offload(void) {
|
|
#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
|
|
defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE)
|
|
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
|
|
return true;
|
|
#else
|
|
return false;
|
|
#endif
|
|
}
|
|
|
|
void llama_backend_init(void) {
|
|
ggml_time_init();
|
|
|
|
// needed to initialize f16 tables
|
|
{
|
|
struct ggml_init_params params = { 0, NULL, false };
|
|
struct ggml_context * ctx = ggml_init(params);
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_backend_init();
|
|
#endif
|
|
}
|
|
|
|
void llama_numa_init(enum ggml_numa_strategy numa) {
|
|
if (numa != GGML_NUMA_STRATEGY_DISABLED) {
|
|
ggml_numa_init(numa);
|
|
}
|
|
}
|
|
|
|
void llama_backend_free(void) {
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_backend_free();
|
|
#endif
|
|
ggml_quantize_free();
|
|
}
|
|
|
|
int64_t llama_time_us(void) {
|
|
return ggml_time_us();
|
|
}
|
|
|
|
struct llama_model * llama_load_model_from_file(
|
|
const char * path_model,
|
|
struct llama_model_params params) {
|
|
ggml_time_init();
|
|
|
|
llama_model * model = new llama_model;
|
|
|
|
unsigned cur_percentage = 0;
|
|
if (params.progress_callback == NULL) {
|
|
params.progress_callback_user_data = &cur_percentage;
|
|
params.progress_callback = [](float progress, void * ctx) {
|
|
unsigned * cur_percentage_p = (unsigned *) ctx;
|
|
unsigned percentage = (unsigned) (100 * progress);
|
|
while (percentage > *cur_percentage_p) {
|
|
*cur_percentage_p = percentage;
|
|
LLAMA_LOG_INFO(".");
|
|
if (percentage >= 100) {
|
|
LLAMA_LOG_INFO("\n");
|
|
}
|
|
}
|
|
return true;
|
|
};
|
|
}
|
|
|
|
int status = llama_model_load(path_model, *model, params);
|
|
GGML_ASSERT(status <= 0);
|
|
if (status < 0) {
|
|
if (status == -1) {
|
|
LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
|
|
} else if (status == -2) {
|
|
LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
|
|
}
|
|
delete model;
|
|
return nullptr;
|
|
}
|
|
|
|
return model;
|
|
}
|
|
|
|
void llama_free_model(struct llama_model * model) {
|
|
delete model;
|
|
}
|
|
|
|
struct llama_context * llama_new_context_with_model(
|
|
struct llama_model * model,
|
|
struct llama_context_params params) {
|
|
|
|
if (!model) {
|
|
LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
if (params.n_batch == 0 && params.n_ubatch == 0) {
|
|
LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
|
|
LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
llama_context * ctx = new llama_context(*model);
|
|
|
|
const auto & hparams = model->hparams;
|
|
auto & cparams = ctx->cparams;
|
|
|
|
cparams.n_seq_max = std::max(1u, params.n_seq_max);
|
|
cparams.n_threads = params.n_threads;
|
|
cparams.n_threads_batch = params.n_threads_batch;
|
|
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.defrag_thold = params.defrag_thold;
|
|
cparams.embeddings = params.embeddings;
|
|
cparams.offload_kqv = params.offload_kqv;
|
|
cparams.pooling_type = params.pooling_type;
|
|
|
|
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
|
|
cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
|
|
cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
|
|
|
|
// this is necessary due to kv_self.n being padded later during inference
|
|
cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32);
|
|
|
|
// with causal attention, the batch size is limited by the context size
|
|
cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
|
|
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
|
|
|
|
|
|
cparams.n_yarn_orig_ctx = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx :
|
|
hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
|
|
hparams.n_ctx_train;
|
|
|
|
cparams.cb_eval = params.cb_eval;
|
|
cparams.cb_eval_user_data = params.cb_eval_user_data;
|
|
|
|
auto rope_scaling_type = params.rope_scaling_type;
|
|
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
|
|
rope_scaling_type = hparams.rope_scaling_type_train;
|
|
}
|
|
|
|
if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
|
|
cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
|
|
}
|
|
|
|
if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
|
|
cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
|
|
}
|
|
|
|
cparams.causal_attn = hparams.causal_attn;
|
|
|
|
if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
|
|
if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
|
|
cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
|
|
} else {
|
|
cparams.pooling_type = hparams.pooling_type;
|
|
}
|
|
}
|
|
|
|
if (params.seed == LLAMA_DEFAULT_SEED) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
|
|
LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
|
|
LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
|
|
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
|
|
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
|
|
|
|
ctx->abort_callback = params.abort_callback;
|
|
ctx->abort_callback_data = params.abort_callback_data;
|
|
|
|
ctx->rng = std::mt19937(params.seed);
|
|
ctx->logits_all = params.logits_all;
|
|
|
|
uint32_t kv_size = cparams.n_ctx;
|
|
ggml_type type_k = params.type_k;
|
|
ggml_type type_v = params.type_v;
|
|
|
|
// Mamba only needs a constant number of KV cache cells per sequence
|
|
if (model->arch == LLM_ARCH_MAMBA) {
|
|
// Mamba needs at least as many KV cells as there are sequences kept at any time
|
|
kv_size = std::max((uint32_t) 1, params.n_seq_max);
|
|
// it's probably best to keep as much precision as possible for the states
|
|
type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
|
|
type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
|
|
}
|
|
|
|
GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
|
|
GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
|
|
|
|
if (!hparams.vocab_only) {
|
|
// initialize backends
|
|
#ifdef GGML_USE_METAL
|
|
if (model->n_gpu_layers > 0) {
|
|
ctx->backend_metal = ggml_backend_metal_init();
|
|
if (ctx->backend_metal == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(ctx->backend_metal);
|
|
}
|
|
#elif defined(GGML_USE_CUDA)
|
|
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
|
|
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
|
|
ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
|
|
if (backend == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
} else {
|
|
// LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
|
|
for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
|
|
ggml_backend_t backend = ggml_backend_cuda_init(device);
|
|
if (backend == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
}
|
|
}
|
|
#elif defined(GGML_USE_VULKAN)
|
|
if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
|
|
LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
|
|
ggml_backend_t backend = ggml_backend_vk_init(0);
|
|
if (backend == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
} else {
|
|
for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
|
|
ggml_backend_t backend = ggml_backend_vk_init(device);
|
|
if (backend == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
}
|
|
}
|
|
#elif defined(GGML_USE_SYCL)
|
|
// with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
|
|
if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
|
|
ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
|
|
if (backend == nullptr) {
|
|
int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
|
|
LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
} else {
|
|
// LLAMA_SPLIT_LAYER requires a backend for each GPU
|
|
for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
|
|
ggml_backend_t backend = ggml_backend_sycl_init(i);
|
|
if (backend == nullptr) {
|
|
int id_list[GGML_SYCL_MAX_DEVICES];
|
|
ggml_sycl_get_gpu_list(id_list, GGML_SYCL_MAX_DEVICES);
|
|
LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, id_list[i], i);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
}
|
|
}
|
|
#elif defined(GGML_USE_KOMPUTE)
|
|
if (model->n_gpu_layers > 0) {
|
|
auto * backend = ggml_backend_kompute_init(model->main_gpu);
|
|
if (backend == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(backend);
|
|
}
|
|
#endif
|
|
ctx->backend_cpu = ggml_backend_cpu_init();
|
|
if (ctx->backend_cpu == nullptr) {
|
|
LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
ctx->backends.push_back(ctx->backend_cpu);
|
|
|
|
if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, cparams.offload_kqv)) {
|
|
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
{
|
|
size_t memory_size_k = 0;
|
|
size_t memory_size_v = 0;
|
|
|
|
for (auto & k : ctx->kv_self.k_l) {
|
|
memory_size_k += ggml_nbytes(k);
|
|
}
|
|
|
|
for (auto & v : ctx->kv_self.v_l) {
|
|
memory_size_v += ggml_nbytes(v);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
|
|
(float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
|
|
ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
|
|
ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
|
|
}
|
|
|
|
// graph outputs buffer
|
|
{
|
|
// resized during inference when a batch uses more outputs
|
|
if (llama_output_reserve(*ctx, params.n_seq_max) < params.n_seq_max) {
|
|
LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__,
|
|
ggml_backend_buffer_name(ctx->buf_output),
|
|
ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// scheduler and compute buffers
|
|
{
|
|
// buffer types used for the compute buffer of each backend
|
|
std::vector<ggml_backend_buffer_type_t> backend_buft;
|
|
for (auto * backend : ctx->backends) {
|
|
if (ggml_backend_is_cpu(backend)) {
|
|
// use host buffers for the CPU backend compute buffer
|
|
backend_buft.push_back(llama_default_buffer_type_cpu(true));
|
|
} else {
|
|
backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
|
|
}
|
|
}
|
|
|
|
// buffer used to store the computation graph and the tensor meta data
|
|
ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
|
|
|
|
// enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
|
|
bool pipeline_parallel = llama_get_device_count() > 1 && model->n_gpu_layers > (int)model->hparams.n_layer && model->split_mode == LLAMA_SPLIT_MODE_LAYER;
|
|
#ifndef GGML_USE_CUDA
|
|
// pipeline parallelism requires support for async compute and events
|
|
// currently this is only implemented in the CUDA backend
|
|
pipeline_parallel = false;
|
|
#endif
|
|
ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);
|
|
|
|
if (pipeline_parallel) {
|
|
LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched));
|
|
}
|
|
|
|
// build worst-case graph
|
|
int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch);
|
|
int n_past = cparams.n_ctx - n_tokens;
|
|
llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
|
|
ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
|
|
|
|
// initialize scheduler with the worst-case graph
|
|
if (!ggml_backend_sched_reserve(ctx->sched, gf)) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
for (size_t i = 0; i < ctx->backends.size(); i++) {
|
|
ggml_backend_t backend = ctx->backends[i];
|
|
ggml_backend_buffer_type_t buft = backend_buft[i];
|
|
size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
|
|
if (size > 1) {
|
|
LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
|
|
ggml_backend_buft_name(buft),
|
|
size / 1024.0 / 1024.0);
|
|
}
|
|
}
|
|
|
|
// note: the number of splits during measure is higher than during inference due to the kv shift
|
|
int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
|
|
LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, gf->n_nodes);
|
|
LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
|
|
}
|
|
}
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ctx->ctx_mpi = ggml_mpi_init();
|
|
|
|
if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
|
|
// Enter a blocking eval loop with dummy input, letting rank=0 drive the process
|
|
// TODO: needs fix after #3228
|
|
GGML_ASSERT(false && "not implemented");
|
|
//const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx));
|
|
//while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
|
|
llama_backend_free();
|
|
exit(1);
|
|
}
|
|
#endif
|
|
|
|
return ctx;
|
|
}
|
|
|
|
void llama_free(struct llama_context * ctx) {
|
|
delete ctx;
|
|
}
|
|
|
|
const llama_model * llama_get_model(const struct llama_context * ctx) {
|
|
return &ctx->model;
|
|
}
|
|
|
|
uint32_t llama_n_ctx(const struct llama_context * ctx) {
|
|
return ctx->cparams.n_ctx;
|
|
}
|
|
|
|
uint32_t llama_n_batch(const struct llama_context * ctx) {
|
|
return ctx->cparams.n_batch;
|
|
}
|
|
|
|
uint32_t llama_n_ubatch(const struct llama_context * ctx) {
|
|
return ctx->cparams.n_ubatch;
|
|
}
|
|
|
|
uint32_t llama_n_seq_max(const struct llama_context * ctx) {
|
|
return ctx->kv_self.size;
|
|
}
|
|
|
|
enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
|
|
return model->vocab.type;
|
|
}
|
|
|
|
enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|
switch (model->arch) {
|
|
// these models do not use RoPE
|
|
case LLM_ARCH_GPT2:
|
|
case LLM_ARCH_GPTJ:
|
|
case LLM_ARCH_GPTNEOX:
|
|
case LLM_ARCH_MPT:
|
|
case LLM_ARCH_REFACT:
|
|
case LLM_ARCH_BLOOM:
|
|
case LLM_ARCH_MAMBA:
|
|
return LLAMA_ROPE_TYPE_NONE;
|
|
|
|
// use what we call a normal RoPE, operating on pairs of consecutive head values
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_BAICHUAN:
|
|
case LLM_ARCH_STARCODER:
|
|
case LLM_ARCH_PLAMO:
|
|
case LLM_ARCH_CODESHELL:
|
|
case LLM_ARCH_ORION:
|
|
case LLM_ARCH_INTERNLM2:
|
|
case LLM_ARCH_MINICPM:
|
|
case LLM_ARCH_XVERSE:
|
|
case LLM_ARCH_COMMAND_R:
|
|
case LLM_ARCH_OLMO:
|
|
return LLAMA_ROPE_TYPE_NORM;
|
|
|
|
// the pairs of head values are offset by n_rot/2
|
|
case LLM_ARCH_FALCON:
|
|
case LLM_ARCH_GROK:
|
|
case LLM_ARCH_DBRX:
|
|
case LLM_ARCH_PERSIMMON:
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
case LLM_ARCH_STABLELM:
|
|
case LLM_ARCH_QWEN:
|
|
case LLM_ARCH_QWEN2:
|
|
case LLM_ARCH_QWEN2MOE:
|
|
case LLM_ARCH_PHI2:
|
|
case LLM_ARCH_PHI3:
|
|
case LLM_ARCH_GEMMA:
|
|
case LLM_ARCH_STARCODER2:
|
|
return LLAMA_ROPE_TYPE_NEOX;
|
|
|
|
// all model arches should be listed explicitly here
|
|
case LLM_ARCH_UNKNOWN:
|
|
GGML_ASSERT(false && "unknown architecture");
|
|
break;
|
|
}
|
|
|
|
return LLAMA_ROPE_TYPE_NONE;
|
|
}
|
|
|
|
enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
|
|
return ctx->cparams.pooling_type;
|
|
}
|
|
|
|
int32_t llama_n_vocab(const struct llama_model * model) {
|
|
return model->hparams.n_vocab;
|
|
}
|
|
|
|
int32_t llama_n_ctx_train(const struct llama_model * model) {
|
|
return model->hparams.n_ctx_train;
|
|
}
|
|
|
|
int32_t llama_n_embd(const struct llama_model * model) {
|
|
return model->hparams.n_embd;
|
|
}
|
|
|
|
int32_t llama_n_layer(const struct llama_model * model) {
|
|
return model->hparams.n_layer;
|
|
}
|
|
|
|
float llama_rope_freq_scale_train(const struct llama_model * model) {
|
|
return model->hparams.rope_freq_scale_train;
|
|
}
|
|
|
|
int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
|
|
const auto & it = model->gguf_kv.find(key);
|
|
if (it == model->gguf_kv.end()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
|
}
|
|
|
|
int32_t llama_model_meta_count(const struct llama_model * model) {
|
|
return (int)model->gguf_kv.size();
|
|
}
|
|
|
|
int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
|
|
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
auto it = model->gguf_kv.begin();
|
|
std::advance(it, i);
|
|
return snprintf(buf, buf_size, "%s", it->first.c_str());
|
|
}
|
|
|
|
int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
|
|
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
auto it = model->gguf_kv.begin();
|
|
std::advance(it, i);
|
|
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
|
}
|
|
|
|
int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
|
|
return snprintf(buf, buf_size, "%s %s %s",
|
|
llama_model_arch_name(model->arch),
|
|
llama_model_type_name(model->type),
|
|
llama_model_ftype_name(model->ftype).c_str());
|
|
}
|
|
|
|
uint64_t llama_model_size(const struct llama_model * model) {
|
|
uint64_t size = 0;
|
|
for (const auto & it : model->tensors_by_name) {
|
|
size += ggml_nbytes(it.second);
|
|
}
|
|
return size;
|
|
}
|
|
|
|
uint64_t llama_model_n_params(const struct llama_model * model) {
|
|
uint64_t nparams = 0;
|
|
for (const auto & it : model->tensors_by_name) {
|
|
nparams += ggml_nelements(it.second);
|
|
}
|
|
return nparams;
|
|
}
|
|
|
|
struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
|
|
auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
|
|
[name](const std::pair<std::string, struct ggml_tensor *> & it) {
|
|
return it.first == name;
|
|
});
|
|
if (it == model->tensors_by_name.end()) {
|
|
return nullptr;
|
|
}
|
|
return it->second;
|
|
}
|
|
|
|
uint32_t llama_model_quantize(
|
|
const char * fname_inp,
|
|
const char * fname_out,
|
|
const llama_model_quantize_params * params) {
|
|
try {
|
|
llama_model_quantize_internal(fname_inp, fname_out, params);
|
|
return 0;
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) {
|
|
try {
|
|
return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
|
|
GGML_ASSERT(cvec.tensors.empty());
|
|
GGML_ASSERT(cvec.ctxs.empty());
|
|
GGML_ASSERT(cvec.bufs.empty());
|
|
|
|
// count layer buffer types
|
|
std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
|
|
for (int64_t i = 0; i < model.hparams.n_layer; i++) {
|
|
buft_layer_count[model.buft_layer[i].buft]++;
|
|
}
|
|
|
|
// allocate contexts
|
|
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
|
for (auto & it : buft_layer_count) {
|
|
int n_layers = it.second;
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ n_layers * ggml_tensor_overhead(),
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
ggml_context * ctx = ggml_init(params);
|
|
if (!ctx) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
|
|
return 1;
|
|
}
|
|
ctx_map[it.first] = ctx;
|
|
}
|
|
|
|
// make tensors
|
|
cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
|
|
for (size_t il = 1; il < model.hparams.n_layer; il++) {
|
|
struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
|
|
ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
|
|
cvec.tensors.push_back(tensor);
|
|
}
|
|
|
|
// allocate tensors / buffers and zero
|
|
for (auto it : ctx_map) {
|
|
ggml_backend_buffer_type_t buft = it.first;
|
|
ggml_context * ctx = it.second;
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
|
|
if (!buf) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
|
|
return false;
|
|
}
|
|
ggml_backend_buffer_clear(buf, 0);
|
|
cvec.ctxs.push_back(ctx);
|
|
cvec.bufs.push_back(buf);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
|
|
const llama_model & model = lctx->model;
|
|
llama_control_vector & cvec = lctx->cvec;
|
|
|
|
if (data == nullptr) {
|
|
// disable the current control vector (but leave allocated for later)
|
|
cvec.layer_start = -1;
|
|
cvec.layer_end = -1;
|
|
return 0;
|
|
}
|
|
|
|
if (n_embd != (int) model.hparams.n_embd) {
|
|
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
if (cvec.tensors.empty()) {
|
|
if (!llama_control_vector_init(cvec, model)) {
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
cvec.layer_start = il_start;
|
|
cvec.layer_end = il_end;
|
|
|
|
for (size_t il = 1; il < model.hparams.n_layer; il++) {
|
|
assert(cvec.tensors[il] != nullptr);
|
|
|
|
const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
|
|
if (off + n_embd <= len) {
|
|
ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
|
|
struct llama_kv_cache_view result = {
|
|
/*.n_cells = */ 0,
|
|
/*.n_seq_max = */ n_seq_max,
|
|
/*.token_count = */ 0,
|
|
/*.used_cells = */ llama_get_kv_cache_used_cells(ctx),
|
|
/*.max_contiguous = */ 0,
|
|
/*.max_contiguous_idx = */ -1,
|
|
/*.cells = */ nullptr,
|
|
/*.cells_sequences = */ nullptr,
|
|
};
|
|
return result;
|
|
}
|
|
|
|
void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
|
|
if (view->cells != nullptr) {
|
|
free(view->cells);
|
|
view->cells = nullptr;
|
|
}
|
|
if (view->cells_sequences != nullptr) {
|
|
free(view->cells_sequences);
|
|
view->cells_sequences = nullptr;
|
|
}
|
|
}
|
|
|
|
void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) {
|
|
if (uint32_t(view->n_cells) < ctx->kv_self.size || view->cells == nullptr) {
|
|
view->n_cells = int32_t(ctx->kv_self.size);
|
|
void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
|
|
GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
|
|
view->cells = (struct llama_kv_cache_view_cell *)p;
|
|
p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
|
|
GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
|
|
view->cells_sequences = (llama_seq_id *)p;
|
|
}
|
|
|
|
const std::vector<llama_kv_cell> & kv_cells = ctx->kv_self.cells;
|
|
llama_kv_cache_view_cell * c_curr = view->cells;
|
|
llama_seq_id * cs_curr = view->cells_sequences;
|
|
int32_t used_cells = 0;
|
|
int32_t token_count = 0;
|
|
int32_t curr_contig_idx = -1;
|
|
uint32_t max_contig = 0;
|
|
int32_t max_contig_idx = -1;
|
|
|
|
for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
|
|
const size_t curr_size = kv_cells[i].seq_id.size();
|
|
token_count += curr_size;
|
|
c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
|
|
|
|
if (curr_size > 0) {
|
|
if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
|
|
max_contig = i - curr_contig_idx;
|
|
max_contig_idx = curr_contig_idx;
|
|
}
|
|
curr_contig_idx = -1;
|
|
} else if (curr_contig_idx < 0) {
|
|
curr_contig_idx = i;
|
|
}
|
|
|
|
int seq_idx = 0;
|
|
for (const llama_seq_id it : kv_cells[i].seq_id) {
|
|
if (seq_idx >= view->n_seq_max) {
|
|
break;
|
|
}
|
|
cs_curr[seq_idx] = it;
|
|
seq_idx++;
|
|
}
|
|
if (seq_idx != 0) {
|
|
used_cells++;
|
|
}
|
|
for (; seq_idx < view->n_seq_max; seq_idx++) {
|
|
cs_curr[seq_idx] = -1;
|
|
}
|
|
}
|
|
if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
|
|
max_contig_idx = curr_contig_idx;
|
|
max_contig = kv_cells.size() - curr_contig_idx;
|
|
}
|
|
view->max_contiguous = max_contig;
|
|
view->max_contiguous_idx = max_contig_idx;
|
|
view->token_count = token_count;
|
|
view->used_cells = used_cells;
|
|
if (uint32_t(used_cells) != ctx->kv_self.used) {
|
|
LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
|
|
__func__, ctx->kv_self.used, used_cells);
|
|
}
|
|
}
|
|
|
|
int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
|
int result = 0;
|
|
|
|
for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
|
|
result += ctx->kv_self.cells[i].seq_id.size();
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
|
|
return ctx->kv_self.used;
|
|
}
|
|
|
|
void llama_kv_cache_clear(struct llama_context * ctx) {
|
|
llama_kv_cache_clear(ctx->kv_self);
|
|
}
|
|
|
|
bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
|
return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
|
|
}
|
|
|
|
void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
|
|
if (seq_id_src == seq_id_dst) {
|
|
return;
|
|
}
|
|
llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
|
|
}
|
|
|
|
void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
|
|
llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
|
|
}
|
|
|
|
void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
|
|
if (delta == 0) {
|
|
return;
|
|
}
|
|
|
|
llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta);
|
|
}
|
|
|
|
void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
|
|
if (d == 1) {
|
|
return;
|
|
}
|
|
|
|
llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
|
|
}
|
|
|
|
llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
|
|
return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
|
|
}
|
|
|
|
void llama_kv_cache_defrag(struct llama_context * ctx) {
|
|
llama_kv_cache_defrag(ctx->kv_self);
|
|
}
|
|
|
|
void llama_kv_cache_update(struct llama_context * ctx) {
|
|
llama_kv_cache_update_internal(*ctx);
|
|
}
|
|
|
|
// deprecated
|
|
size_t llama_get_state_size(const struct llama_context * ctx) {
|
|
return llama_state_get_size(ctx);
|
|
}
|
|
|
|
// deprecated
|
|
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
|
|
return llama_state_get_data(ctx, dst);
|
|
}
|
|
|
|
// deprecated
|
|
size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
|
|
return llama_state_set_data(ctx, src);
|
|
}
|
|
|
|
// deprecated
|
|
bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
|
|
}
|
|
|
|
// deprecated
|
|
bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
|
|
return llama_state_save_file(ctx, path_session, tokens, n_token_count);
|
|
}
|
|
|
|
// Returns the *maximum* size of the state
|
|
size_t llama_state_get_size(const struct llama_context * ctx) {
|
|
const auto & cparams = ctx->cparams;
|
|
const auto & hparams = ctx->model.hparams;
|
|
|
|
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
|
|
// for reference, std::mt19937(1337) serializes to 6701 bytes.
|
|
const size_t s_rng_size = sizeof(size_t);
|
|
const size_t s_rng = LLAMA_MAX_RNG_STATE;
|
|
const size_t s_n_outputs = sizeof(size_t);
|
|
// assume worst case for outputs although only currently set ones are serialized
|
|
const size_t s_output_pos = ctx->cparams.n_batch * sizeof(int32_t);
|
|
const size_t s_logits_size = sizeof(size_t);
|
|
const size_t s_logits = ctx->logits_size ? cparams.n_batch * hparams.n_vocab * sizeof(float) : 0;
|
|
const size_t s_embedding_size = sizeof(size_t);
|
|
const size_t s_embedding = ctx->embd_size ? cparams.n_batch * hparams.n_embd * sizeof(float) : 0;
|
|
const size_t s_kv_buf_size = sizeof(size_t);
|
|
const size_t s_kv_head = sizeof(uint32_t);
|
|
const size_t s_kv_size = sizeof(uint32_t);
|
|
const size_t s_kv_used = sizeof(uint32_t);
|
|
const size_t s_kv = ctx->kv_self.total_size();
|
|
const size_t s_kv_cell = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id);
|
|
const size_t s_kv_cells = ctx->kv_self.size * s_kv_cell;
|
|
|
|
const size_t s_total = (
|
|
+ s_rng_size
|
|
+ s_rng
|
|
+ s_n_outputs
|
|
+ s_output_pos
|
|
+ s_logits_size
|
|
+ s_logits
|
|
+ s_embedding_size
|
|
+ s_embedding
|
|
+ s_kv_buf_size
|
|
+ s_kv_head
|
|
+ s_kv_size
|
|
+ s_kv_used
|
|
+ s_kv
|
|
+ s_kv_cells
|
|
);
|
|
|
|
return s_total;
|
|
}
|
|
|
|
// llama_context_data
|
|
struct llama_data_context {
|
|
virtual void write(const void * src, size_t size) = 0;
|
|
virtual size_t get_size_written() = 0;
|
|
virtual ~llama_data_context() = default;
|
|
};
|
|
|
|
struct llama_data_buffer_context : llama_data_context {
|
|
uint8_t * ptr;
|
|
size_t size_written = 0;
|
|
|
|
llama_data_buffer_context(uint8_t * p) : ptr(p) {}
|
|
|
|
void write(const void * src, size_t size) override {
|
|
memcpy(ptr, src, size);
|
|
ptr += size;
|
|
size_written += size;
|
|
}
|
|
|
|
size_t get_size_written() override {
|
|
return size_written;
|
|
}
|
|
};
|
|
|
|
struct llama_data_file_context : llama_data_context {
|
|
llama_file * file;
|
|
size_t size_written = 0;
|
|
|
|
llama_data_file_context(llama_file * f) : file(f) {}
|
|
|
|
void write(const void * src, size_t size) override {
|
|
file->write_raw(src, size);
|
|
size_written += size;
|
|
}
|
|
|
|
size_t get_size_written() override {
|
|
return size_written;
|
|
}
|
|
};
|
|
|
|
/** copy state data into either a buffer or file depending on the passed in context
|
|
*
|
|
* file context:
|
|
* llama_file file("/path", "wb");
|
|
* llama_data_file_context data_ctx(&file);
|
|
* llama_state_get_data(ctx, &data_ctx);
|
|
*
|
|
* buffer context:
|
|
* std::vector<uint8_t> buf(max_size, 0);
|
|
* llama_data_buffer_context data_ctx(&buf.data());
|
|
* llama_state_get_data(ctx, &data_ctx);
|
|
*
|
|
*/
|
|
static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
|
|
llama_synchronize(ctx);
|
|
|
|
// copy rng
|
|
{
|
|
std::ostringstream rng_ss;
|
|
rng_ss << ctx->rng;
|
|
|
|
const std::string & rng_str = rng_ss.str();
|
|
const size_t rng_size = rng_str.size();
|
|
|
|
GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
|
|
|
|
data_ctx->write(&rng_size, sizeof(rng_size));
|
|
data_ctx->write(rng_str.data(), rng_size);
|
|
}
|
|
|
|
// copy outputs
|
|
{
|
|
// Can't use ctx->n_outputs because it's not for the
|
|
// entire last batch when n_ubatch is smaller than n_batch
|
|
size_t n_outputs = 0;
|
|
|
|
// copy output ids
|
|
{
|
|
std::vector<int32_t> output_pos;
|
|
|
|
const size_t n_batch = ctx->cparams.n_batch;
|
|
const auto & output_ids = ctx->output_ids;
|
|
|
|
output_pos.resize(ctx->output_size);
|
|
|
|
// build a more compact representation of the output ids
|
|
for (size_t i = 0; i < n_batch; ++i) {
|
|
// map an output id to a position in the batch
|
|
int32_t pos = output_ids[i];
|
|
if (pos >= 0) {
|
|
if ((size_t) pos >= n_outputs) {
|
|
n_outputs = pos + 1;
|
|
}
|
|
GGML_ASSERT((size_t) pos < ctx->output_size);
|
|
output_pos[pos] = i;
|
|
}
|
|
}
|
|
|
|
data_ctx->write(&n_outputs, sizeof(n_outputs));
|
|
|
|
if (n_outputs) {
|
|
data_ctx->write(output_pos.data(), n_outputs * sizeof(int32_t));
|
|
}
|
|
}
|
|
|
|
// copy logits
|
|
{
|
|
const size_t logits_size = std::min(ctx->logits_size, n_outputs * ctx->model.hparams.n_vocab);
|
|
|
|
data_ctx->write(&logits_size, sizeof(logits_size));
|
|
|
|
if (logits_size) {
|
|
data_ctx->write(ctx->logits, logits_size * sizeof(float));
|
|
}
|
|
}
|
|
|
|
// copy embeddings
|
|
{
|
|
const size_t embeddings_size = std::min(ctx->embd_size, n_outputs * ctx->model.hparams.n_embd);
|
|
|
|
data_ctx->write(&embeddings_size, sizeof(embeddings_size));
|
|
|
|
if (embeddings_size) {
|
|
data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
|
|
}
|
|
}
|
|
}
|
|
|
|
// copy kv cache
|
|
{
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
|
|
|
|
// NOTE: kv_size and kv_buf_size are mostly used for sanity checks
|
|
const uint32_t kv_head = llama_kv_cache_cell_max(kv_self);
|
|
const uint32_t kv_size = kv_self.size;
|
|
const size_t kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head;
|
|
const uint32_t kv_used = kv_self.used;
|
|
|
|
data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
|
|
data_ctx->write(&kv_head, sizeof(kv_head));
|
|
data_ctx->write(&kv_size, sizeof(kv_size));
|
|
data_ctx->write(&kv_used, sizeof(kv_used));
|
|
|
|
if (kv_buf_size) {
|
|
const size_t pre_kv_buf_size = data_ctx->get_size_written();
|
|
|
|
std::vector<uint8_t> tmp_buf;
|
|
for (int il = 0; il < (int) n_layer; ++il) {
|
|
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
|
|
|
|
tmp_buf.resize(k_size);
|
|
ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
|
|
data_ctx->write(tmp_buf.data(), tmp_buf.size());
|
|
|
|
if (kv_self.recurrent) {
|
|
// v is contiguous for recurrent models
|
|
// TODO: use other tensors for state models than k and v
|
|
const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
|
|
|
|
tmp_buf.resize(v_size);
|
|
ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
|
|
data_ctx->write(tmp_buf.data(), tmp_buf.size());
|
|
continue;
|
|
}
|
|
|
|
// v is not contiguous, copy row by row
|
|
const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
|
|
const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
|
|
|
|
tmp_buf.resize(v_row_size);
|
|
for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
|
|
ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
|
|
data_ctx->write(tmp_buf.data(), tmp_buf.size());
|
|
}
|
|
}
|
|
GGML_ASSERT(kv_buf_size == data_ctx->get_size_written() - pre_kv_buf_size);
|
|
}
|
|
|
|
for (uint32_t i = 0; i < kv_head; ++i) {
|
|
const auto & cell = kv_self.cells[i];
|
|
|
|
const llama_pos pos = cell.pos;
|
|
const size_t seq_id_size = cell.seq_id.size();
|
|
|
|
data_ctx->write(&pos, sizeof(pos));
|
|
data_ctx->write(&seq_id_size, sizeof(seq_id_size));
|
|
|
|
for (auto seq_id : cell.seq_id) {
|
|
data_ctx->write(&seq_id, sizeof(seq_id));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) {
|
|
llama_data_buffer_context data_ctx(dst);
|
|
llama_state_get_data_internal(ctx, &data_ctx);
|
|
|
|
return data_ctx.get_size_written();
|
|
}
|
|
|
|
// Sets the state reading from the specified source address
|
|
size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
|
|
llama_synchronize(ctx);
|
|
|
|
const uint8_t * inp = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
|
|
|
|
GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
|
|
|
|
std::string rng_str((const char *)inp, rng_size); inp += rng_size;
|
|
|
|
std::istringstream rng_ss(rng_str);
|
|
rng_ss >> ctx->rng;
|
|
|
|
GGML_ASSERT(!rng_ss.fail());
|
|
}
|
|
|
|
// set output ids
|
|
{
|
|
size_t n_outputs;
|
|
std::vector<int32_t> output_pos;
|
|
|
|
memcpy(&n_outputs, inp, sizeof(n_outputs)); inp += sizeof(n_outputs);
|
|
|
|
GGML_ASSERT(n_outputs <= llama_output_reserve(*ctx, n_outputs));
|
|
|
|
if (n_outputs) {
|
|
output_pos.resize(n_outputs);
|
|
memcpy(output_pos.data(), inp, n_outputs * sizeof(int32_t));
|
|
inp += n_outputs * sizeof(int32_t);
|
|
|
|
for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
|
|
int32_t id = output_pos[i];
|
|
GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch);
|
|
ctx->output_ids[id] = i;
|
|
}
|
|
|
|
ctx->n_outputs = n_outputs;
|
|
}
|
|
}
|
|
|
|
// set logits
|
|
{
|
|
size_t logits_size;
|
|
|
|
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
|
|
|
|
GGML_ASSERT(ctx->logits_size >= logits_size);
|
|
|
|
if (logits_size) {
|
|
memcpy(ctx->logits, inp, logits_size * sizeof(float));
|
|
inp += logits_size * sizeof(float);
|
|
}
|
|
}
|
|
|
|
// set embeddings
|
|
{
|
|
size_t embeddings_size;
|
|
|
|
memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
|
|
|
|
GGML_ASSERT(ctx->embd_size >= embeddings_size);
|
|
|
|
if (embeddings_size) {
|
|
memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
|
|
inp += embeddings_size * sizeof(float);
|
|
}
|
|
}
|
|
|
|
// set kv cache
|
|
{
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
|
|
|
|
size_t kv_buf_size;
|
|
uint32_t kv_head;
|
|
uint32_t kv_size;
|
|
uint32_t kv_used;
|
|
|
|
memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
|
|
memcpy(&kv_head, inp, sizeof(kv_head)); inp += sizeof(kv_head);
|
|
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
|
|
memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used);
|
|
|
|
if (kv_self.size != kv_size) {
|
|
// the KV cache needs to be big enough to load all the KV cells from the saved state
|
|
GGML_ASSERT(kv_self.size >= kv_head);
|
|
|
|
LLAMA_LOG_INFO("%s: state contains %d KV cells, was saved with kv_size=%d, but is loaded with kv_size=%d (fine, but different)\n",
|
|
__func__, kv_head, kv_size, kv_self.size);
|
|
}
|
|
|
|
if (kv_buf_size) {
|
|
const size_t pre_kv_buf_size = inp - src;
|
|
|
|
GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
|
|
|
|
for (int il = 0; il < (int) n_layer; ++il) {
|
|
const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
|
|
|
|
ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
|
|
inp += k_size;
|
|
|
|
if (kv_self.recurrent) {
|
|
// v is contiguous for recurrent models
|
|
// TODO: use other tensors for state models than k and v
|
|
const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
|
|
|
|
ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
|
|
inp += v_size;
|
|
continue;
|
|
}
|
|
|
|
// v is not contiguous, copy row by row
|
|
const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
|
|
const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_self.size);
|
|
|
|
for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
|
|
ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
|
|
inp += v_row_size;
|
|
}
|
|
}
|
|
GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size);
|
|
}
|
|
|
|
llama_kv_cache_clear(ctx);
|
|
|
|
ctx->kv_self.head = kv_head;
|
|
ctx->kv_self.used = kv_used;
|
|
|
|
for (uint32_t i = 0; i < kv_head; ++i) {
|
|
llama_pos pos;
|
|
size_t seq_id_size;
|
|
|
|
memcpy(&pos, inp, sizeof(pos)); inp += sizeof(pos);
|
|
memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);
|
|
|
|
ctx->kv_self.cells[i].pos = pos;
|
|
|
|
llama_seq_id seq_id;
|
|
|
|
for (size_t j = 0; j < seq_id_size; ++j) {
|
|
memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
|
|
ctx->kv_self.cells[i].seq_id.insert(seq_id);
|
|
}
|
|
}
|
|
}
|
|
|
|
const size_t nread = inp - src;
|
|
const size_t max_size = llama_state_get_size(ctx);
|
|
|
|
GGML_ASSERT(nread <= max_size);
|
|
|
|
return nread;
|
|
}
|
|
|
|
static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
llama_file file(path_session, "rb");
|
|
|
|
// sanity checks
|
|
{
|
|
const uint32_t magic = file.read_u32();
|
|
const uint32_t version = file.read_u32();
|
|
|
|
if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
|
|
LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
|
|
return false;
|
|
}
|
|
|
|
llama_hparams session_hparams;
|
|
file.read_raw(&session_hparams, sizeof(llama_hparams));
|
|
|
|
if (session_hparams != ctx->model.hparams) {
|
|
LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// load the prompt
|
|
{
|
|
const uint32_t n_token_count = file.read_u32();
|
|
|
|
if (n_token_count > n_token_capacity) {
|
|
LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
|
|
return false;
|
|
}
|
|
|
|
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
|
|
*n_token_count_out = n_token_count;
|
|
}
|
|
|
|
// restore the context state
|
|
{
|
|
const size_t n_state_size_cur = file.size - file.tell();
|
|
const size_t n_state_size_max = llama_state_get_size(ctx);
|
|
|
|
if (n_state_size_cur > n_state_size_max) {
|
|
LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
|
|
return false;
|
|
}
|
|
|
|
std::vector<uint8_t> state_data(n_state_size_max);
|
|
file.read_raw(state_data.data(), n_state_size_cur);
|
|
|
|
llama_state_set_data(ctx, state_data.data());
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
try {
|
|
return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
|
|
llama_file file(path_session, "wb");
|
|
|
|
file.write_u32(LLAMA_SESSION_MAGIC);
|
|
file.write_u32(LLAMA_SESSION_VERSION);
|
|
|
|
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
|
|
|
|
// save the prompt
|
|
file.write_u32((uint32_t) n_token_count);
|
|
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
|
|
|
|
// save the context state using stream saving
|
|
llama_data_file_context data_ctx(&file);
|
|
llama_state_get_data_internal(ctx, &data_ctx);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
|
|
try {
|
|
return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error saving session file: %s\n", err.what());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) {
|
|
// save the size of size_t as a uint32_t for safety check
|
|
const size_t size_t_size_size = sizeof(uint32_t);
|
|
|
|
// other values
|
|
const size_t s_cell_count_size = sizeof(uint32_t);
|
|
const size_t s_layer_count_size = sizeof(uint32_t);
|
|
const size_t n_embd_v_gqa_size = sizeof(uint32_t);
|
|
|
|
size_t s_cell_count = 0;
|
|
size_t s_cell_data_size = 0;
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
|
|
|
|
for (uint32_t i = 0; i < kv_self.size; ++i) {
|
|
const auto & cell = kv_self.cells[i];
|
|
if (cell.seq_id.count(seq_id) > 0) {
|
|
++s_cell_count;
|
|
s_cell_data_size += sizeof(llama_pos);
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < (int)n_layer; ++il) {
|
|
// types of keys and values
|
|
s_cell_data_size += sizeof(int32_t) * 2;
|
|
// k_size_row and v_size_el values of layer
|
|
s_cell_data_size += sizeof(size_t) * 2;
|
|
|
|
// keys
|
|
const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
|
|
s_cell_data_size += k_size_row * s_cell_count;
|
|
|
|
// values (transposed)
|
|
const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
|
|
s_cell_data_size += v_size_el * s_cell_count * n_embd_v_gqa;
|
|
}
|
|
|
|
const size_t s_total = (
|
|
size_t_size_size +
|
|
s_cell_count_size +
|
|
s_layer_count_size +
|
|
n_embd_v_gqa_size +
|
|
s_cell_data_size
|
|
);
|
|
|
|
return s_total;
|
|
}
|
|
|
|
static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) {
|
|
llama_synchronize(ctx);
|
|
|
|
const auto & kv_self = ctx->kv_self;
|
|
GGML_ASSERT(!kv_self.recurrent); // not implemented
|
|
|
|
// Save the size of size_t as a uint32_t for safety check
|
|
const uint32_t size_t_size = sizeof(size_t);
|
|
data_ctx.write(&size_t_size, sizeof(size_t_size));
|
|
|
|
std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
|
|
uint32_t cell_count = 0;
|
|
|
|
// Count the number of cells with the specified seq_id
|
|
// Find all the ranges of cells with this seq id
|
|
{
|
|
uint32_t cell_range_begin = kv_self.size;
|
|
for (uint32_t i = 0; i < kv_self.size; ++i) {
|
|
const auto & cell = kv_self.cells[i];
|
|
if (cell.has_seq_id(seq_id)) {
|
|
++cell_count;
|
|
if (cell_range_begin == kv_self.size) {
|
|
cell_range_begin = i;
|
|
}
|
|
}
|
|
else {
|
|
if (cell_range_begin != kv_self.size) {
|
|
cell_ranges.push_back({ cell_range_begin, i });
|
|
cell_range_begin = kv_self.size;
|
|
}
|
|
}
|
|
}
|
|
if (cell_range_begin != kv_self.size) {
|
|
cell_ranges.push_back({ cell_range_begin, kv_self.size });
|
|
}
|
|
|
|
// DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
|
|
uint32_t cell_count_check = 0;
|
|
for (const auto & range : cell_ranges) {
|
|
cell_count_check += range.second - range.first;
|
|
}
|
|
GGML_ASSERT(cell_count == cell_count_check);
|
|
}
|
|
|
|
// Write the cell count
|
|
data_ctx.write(&cell_count, sizeof(cell_count));
|
|
|
|
const auto & hparams = ctx->model.hparams;
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
|
|
|
|
// Write the layer count
|
|
data_ctx.write(&n_layer, sizeof(n_layer));
|
|
|
|
// Write n_embd_v_gqa
|
|
data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
|
|
|
|
// Iterate the ranges and write all the pos (this is the token position in the prompt)
|
|
for (const auto & range : cell_ranges) {
|
|
for (uint32_t i = range.first; i < range.second; ++i) {
|
|
const auto & cell = kv_self.cells[i];
|
|
data_ctx.write(&cell.pos, sizeof(cell.pos));
|
|
}
|
|
}
|
|
|
|
// Iterate and write all the keys first, each row is a cell
|
|
// Get whole range at a time
|
|
std::vector<uint8_t> tmp_buf;
|
|
for (int il = 0; il < (int)n_layer; ++il) {
|
|
// Write key type
|
|
const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
|
|
data_ctx.write(&k_type_i, sizeof(k_type_i));
|
|
|
|
// Write row size of key
|
|
const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
|
|
data_ctx.write(&k_size_row, sizeof(k_size_row));
|
|
|
|
// Read each range of cells of k_size length each into tmp_buf and write out
|
|
for (const auto & range : cell_ranges) {
|
|
const size_t range_size = range.second - range.first;
|
|
tmp_buf.resize(range_size * k_size_row);
|
|
ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), range.first * k_size_row, range_size * k_size_row);
|
|
data_ctx.write(tmp_buf.data(), tmp_buf.size());
|
|
}
|
|
}
|
|
|
|
// For the values, they are transposed, so we also need the element size and get the element ranges from each row
|
|
const uint32_t kv_size = kv_self.size;
|
|
for (int il = 0; il < (int)n_layer; ++il) {
|
|
// Write value type
|
|
const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
|
|
data_ctx.write(&v_type_i, sizeof(v_type_i));
|
|
|
|
// Write element size
|
|
const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
|
|
data_ctx.write(&v_size_el, sizeof(v_size_el));
|
|
|
|
// For each row, we get the element values of each cell
|
|
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
|
|
// Read each range of cells of v_size_el length each into tmp_buf and write out
|
|
for (const auto & range : cell_ranges) {
|
|
const size_t range_size = range.second - range.first;
|
|
const size_t src_offset = (range.first + j * kv_size) * v_size_el;
|
|
tmp_buf.resize(range_size * v_size_el);
|
|
ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size());
|
|
data_ctx.write(tmp_buf.data(), tmp_buf.size());
|
|
}
|
|
}
|
|
}
|
|
|
|
return data_ctx.get_size_written();
|
|
}
|
|
|
|
size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_seq_id seq_id) {
|
|
llama_data_buffer_context data_ctx(dst);
|
|
return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
|
|
}
|
|
|
|
size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) {
|
|
llama_synchronize(ctx);
|
|
|
|
auto & kv_self = ctx->kv_self;
|
|
GGML_ASSERT(!kv_self.recurrent); // not implemented
|
|
|
|
// Wipe the slot
|
|
llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
|
|
|
|
const uint8_t * inp = src;
|
|
|
|
// Read size of size_t
|
|
uint32_t size_t_size;
|
|
memcpy(&size_t_size, inp, sizeof(size_t_size));
|
|
inp += sizeof(size_t_size);
|
|
if (size_t_size != sizeof(size_t)) {
|
|
LLAMA_LOG_ERROR("%s: size_t size mismatch\n", __func__);
|
|
return 0;
|
|
}
|
|
|
|
// Read the cell count
|
|
uint32_t cell_count;
|
|
memcpy(&cell_count, inp, sizeof(cell_count));
|
|
inp += sizeof(cell_count);
|
|
|
|
// Read the layer count
|
|
uint32_t n_layer_ref;
|
|
memcpy(&n_layer_ref, inp, sizeof(n_layer_ref));
|
|
inp += sizeof(n_layer_ref);
|
|
|
|
// Read n_embd_v_gqa
|
|
uint32_t n_embd_v_gqa_ref;
|
|
memcpy(&n_embd_v_gqa_ref, inp, sizeof(n_embd_v_gqa_ref));
|
|
inp += sizeof(n_embd_v_gqa_ref);
|
|
|
|
// Sanity check model compatibility
|
|
const auto & hparams = ctx->model.hparams;
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
|
|
const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
|
|
if (n_layer != n_layer_ref) {
|
|
LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref);
|
|
return 0;
|
|
}
|
|
if (n_embd_v_gqa != n_embd_v_gqa_ref) {
|
|
LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref);
|
|
return 0;
|
|
}
|
|
|
|
// Allocate the new cells for the slot
|
|
if (cell_count) {
|
|
llama_batch batch = llama_batch_init(cell_count, 0, 1);
|
|
batch.n_tokens = cell_count;
|
|
for (uint32_t i = 0; i < cell_count; ++i) {
|
|
llama_pos pos;
|
|
memcpy(&pos, inp, sizeof(pos));
|
|
inp += sizeof(pos);
|
|
|
|
batch.pos[i] = pos;
|
|
batch.n_seq_id[i] = 1;
|
|
batch.seq_id[i][0] = dest_seq_id;
|
|
}
|
|
if (!llama_kv_cache_find_slot(kv_self, batch)) {
|
|
llama_batch_free(batch);
|
|
LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
|
|
return 0;
|
|
}
|
|
|
|
// DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
|
|
// Assume that this is one contiguous block of cells
|
|
GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
|
|
GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
|
|
GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
|
|
GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
|
|
GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
|
|
|
|
// Cleanup
|
|
llama_batch_free(batch);
|
|
}
|
|
|
|
const uint32_t kv_size = kv_self.size;
|
|
const uint32_t kv_head = kv_self.head;
|
|
|
|
// For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo
|
|
for (int il = 0; il < (int)n_layer; ++il) {
|
|
// Read type of key
|
|
int32_t k_type_i_ref;
|
|
memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref));
|
|
inp += sizeof(k_type_i_ref);
|
|
const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
|
|
if (k_type_i != k_type_i_ref) {
|
|
llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
|
|
LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
|
|
return 0;
|
|
}
|
|
|
|
// Read row size of key
|
|
size_t k_size_row_ref;
|
|
memcpy(&k_size_row_ref, inp, sizeof(k_size_row_ref));
|
|
inp += sizeof(k_size_row_ref);
|
|
const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
|
|
if (k_size_row != k_size_row_ref) {
|
|
llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
|
|
LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, k_size_row_ref, il);
|
|
return 0;
|
|
}
|
|
|
|
if (cell_count) {
|
|
// Read and set the keys for the whole cell range
|
|
ggml_backend_tensor_set(kv_self.k_l[il], inp, kv_head * k_size_row, cell_count * k_size_row);
|
|
inp += cell_count * k_size_row;
|
|
}
|
|
}
|
|
|
|
// For each layer, read the values for each cell (transposed)
|
|
for (int il = 0; il < (int)n_layer; ++il) {
|
|
// Read type of value
|
|
int32_t v_type_i_ref;
|
|
memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
|
|
inp += sizeof(v_type_i_ref);
|
|
const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
|
|
if (v_type_i != v_type_i_ref) {
|
|
llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
|
|
LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
|
|
return 0;
|
|
}
|
|
|
|
// Read element size of value
|
|
size_t v_size_el_ref;
|
|
memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref));
|
|
inp += sizeof(v_size_el_ref);
|
|
const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
|
|
if (v_size_el != v_size_el_ref) {
|
|
llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
|
|
LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il);
|
|
return 0;
|
|
}
|
|
|
|
if (cell_count) {
|
|
// For each row in the transposed matrix, read the values for the whole cell range
|
|
for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
|
|
const size_t dst_offset = (kv_head + j * kv_size) * v_size_el;
|
|
ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el);
|
|
inp += cell_count * v_size_el;
|
|
}
|
|
}
|
|
}
|
|
|
|
const size_t nread = inp - src;
|
|
return nread;
|
|
}
|
|
|
|
static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
|
|
llama_file file(filepath, "wb");
|
|
|
|
file.write_u32(LLAMA_STATE_SEQ_MAGIC);
|
|
file.write_u32(LLAMA_STATE_SEQ_VERSION);
|
|
|
|
// save the prompt
|
|
file.write_u32((uint32_t)n_token_count);
|
|
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
|
|
|
|
// save the context state using stream saving
|
|
llama_data_file_context data_ctx(&file);
|
|
llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
|
|
|
|
const size_t res = file.tell();
|
|
GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written());
|
|
return res;
|
|
}
|
|
|
|
static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
llama_file file(filepath, "rb");
|
|
|
|
// version checks
|
|
{
|
|
const uint32_t magic = file.read_u32();
|
|
const uint32_t version = file.read_u32();
|
|
|
|
if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
|
|
LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
// load the prompt
|
|
{
|
|
const uint32_t n_token_count = file.read_u32();
|
|
|
|
if (n_token_count > n_token_capacity) {
|
|
LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
|
|
return 0;
|
|
}
|
|
|
|
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
|
|
*n_token_count_out = n_token_count;
|
|
}
|
|
|
|
// restore the context state
|
|
{
|
|
const size_t state_size = file.size - file.tell();
|
|
std::vector<uint8_t> state_data(state_size);
|
|
file.read_raw(state_data.data(), state_size);
|
|
const size_t nread = llama_state_seq_set_data(ctx, state_data.data(), dest_seq_id);
|
|
if (!nread) {
|
|
LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
|
|
return 0;
|
|
}
|
|
GGML_ASSERT(nread <= state_size);
|
|
GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
|
|
}
|
|
|
|
return file.tell();
|
|
}
|
|
|
|
size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
|
|
try {
|
|
return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error saving sequence state file: %s\n", err.what());
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
try {
|
|
return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error loading sequence state file: %s\n", err.what());
|
|
return 0;
|
|
}
|
|
}
|
|
|
|
void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
|
|
ctx->cparams.n_threads = n_threads;
|
|
ctx->cparams.n_threads_batch = n_threads_batch;
|
|
}
|
|
|
|
void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
|
|
ctx->abort_callback = abort_callback;
|
|
ctx->abort_callback_data = abort_callback_data;
|
|
}
|
|
|
|
void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
|
|
ctx->cparams.causal_attn = causal_attn;
|
|
}
|
|
|
|
struct llama_batch llama_batch_get_one(
|
|
llama_token * tokens,
|
|
int32_t n_tokens,
|
|
llama_pos pos_0,
|
|
llama_seq_id seq_id) {
|
|
return {
|
|
/*n_tokens =*/ n_tokens,
|
|
/*tokens =*/ tokens,
|
|
/*embd =*/ nullptr,
|
|
/*pos =*/ nullptr,
|
|
/*n_seq_id =*/ nullptr,
|
|
/*seq_id =*/ nullptr,
|
|
/*logits =*/ nullptr,
|
|
/*all_pos_0 =*/ pos_0,
|
|
/*all_pos_1 =*/ 1,
|
|
/*all_seq_id =*/ seq_id,
|
|
};
|
|
}
|
|
|
|
struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
|
|
llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };
|
|
|
|
if (embd) {
|
|
batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
|
|
} else {
|
|
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
|
|
}
|
|
|
|
batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc);
|
|
batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc);
|
|
batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
|
|
for (int i = 0; i < n_tokens_alloc; ++i) {
|
|
batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
|
|
}
|
|
batch.seq_id[n_tokens_alloc] = nullptr;
|
|
|
|
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc);
|
|
|
|
return batch;
|
|
}
|
|
|
|
void llama_batch_free(struct llama_batch batch) {
|
|
if (batch.token) free(batch.token);
|
|
if (batch.embd) free(batch.embd);
|
|
if (batch.pos) free(batch.pos);
|
|
if (batch.n_seq_id) free(batch.n_seq_id);
|
|
if (batch.seq_id) {
|
|
for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
|
|
free(batch.seq_id[i]);
|
|
}
|
|
free(batch.seq_id);
|
|
}
|
|
if (batch.logits) free(batch.logits);
|
|
}
|
|
|
|
int32_t llama_decode(
|
|
struct llama_context * ctx,
|
|
struct llama_batch batch) {
|
|
const int ret = llama_decode_internal(*ctx, batch);
|
|
if (ret < 0) {
|
|
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
void llama_synchronize(struct llama_context * ctx) {
|
|
ggml_backend_sched_synchronize(ctx->sched);
|
|
|
|
// FIXME: if multiple single tokens are evaluated without a synchronization,
|
|
// the stats will be added to the prompt evaluation stats
|
|
// this should only happen when using batch size 1 to evaluate a batch
|
|
|
|
// add the evaluation to the stats
|
|
if (ctx->n_queued_tokens == 1) {
|
|
ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
|
|
ctx->n_eval++;
|
|
} else if (ctx->n_queued_tokens > 1) {
|
|
ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
|
|
ctx->n_p_eval += ctx->n_queued_tokens;
|
|
}
|
|
|
|
// get a more accurate load time, upon first eval
|
|
if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
|
|
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
|
|
ctx->has_evaluated_once = true;
|
|
}
|
|
|
|
ctx->n_queued_tokens = 0;
|
|
ctx->t_compute_start_us = 0;
|
|
}
|
|
|
|
float * llama_get_logits(struct llama_context * ctx) {
|
|
llama_synchronize(ctx);
|
|
|
|
return ctx->logits;
|
|
}
|
|
|
|
float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
|
|
int32_t j = -1;
|
|
llama_synchronize(ctx);
|
|
|
|
try {
|
|
if (ctx->logits == nullptr) {
|
|
throw std::runtime_error("no logits");
|
|
}
|
|
|
|
if (i < 0) {
|
|
j = ctx->n_outputs + i;
|
|
if (j < 0) {
|
|
throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
|
|
}
|
|
} else if ((size_t) i >= ctx->output_ids.size()) {
|
|
throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
|
|
} else {
|
|
j = ctx->output_ids[i];
|
|
}
|
|
|
|
if (j < 0) {
|
|
throw std::runtime_error(format("batch.logits[%d] != true", i));
|
|
}
|
|
if (j >= ctx->n_outputs) {
|
|
// This should not happen
|
|
throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
|
|
}
|
|
|
|
return ctx->logits + j*ctx->model.hparams.n_vocab;
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
|
|
#ifndef NDEBUG
|
|
GGML_ASSERT(false);
|
|
#endif
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
float * llama_get_embeddings(struct llama_context * ctx) {
|
|
llama_synchronize(ctx);
|
|
|
|
return ctx->embd;
|
|
}
|
|
|
|
float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
|
|
int32_t j = -1;
|
|
|
|
llama_synchronize(ctx);
|
|
|
|
try {
|
|
if (ctx->embd == nullptr) {
|
|
throw std::runtime_error("no embeddings");
|
|
}
|
|
|
|
if (i < 0) {
|
|
j = ctx->n_outputs + i;
|
|
if (j < 0) {
|
|
throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
|
|
}
|
|
} else if ((size_t) i >= ctx->output_ids.size()) {
|
|
throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
|
|
} else {
|
|
j = ctx->output_ids[i];
|
|
}
|
|
|
|
if (j < 0) {
|
|
throw std::runtime_error(format("batch.logits[%d] != true", i));
|
|
}
|
|
if (j >= ctx->n_outputs) {
|
|
// This should not happen
|
|
throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
|
|
}
|
|
|
|
return ctx->embd + j*ctx->model.hparams.n_embd;
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
|
|
#ifndef NDEBUG
|
|
GGML_ASSERT(false);
|
|
#endif
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
|
|
llama_synchronize(ctx);
|
|
|
|
auto it = ctx->embd_seq.find(seq_id);
|
|
if (it == ctx->embd_seq.end()) {
|
|
return nullptr;
|
|
}
|
|
|
|
return it->second.data();
|
|
}
|
|
|
|
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
|
|
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return model->vocab.id_to_token[token].text.c_str();
|
|
}
|
|
|
|
float llama_token_get_score(const struct llama_model * model, llama_token token) {
|
|
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return model->vocab.id_to_token[token].score;
|
|
}
|
|
|
|
llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
|
|
GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
|
|
return model->vocab.id_to_token[token].type;
|
|
}
|
|
|
|
bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
|
|
return token != -1 && (
|
|
token == llama_token_eos(model) ||
|
|
token == llama_token_eot(model)
|
|
);
|
|
}
|
|
|
|
llama_token llama_token_bos(const struct llama_model * model) {
|
|
return model->vocab.special_bos_id;
|
|
}
|
|
|
|
llama_token llama_token_eos(const struct llama_model * model) {
|
|
return model->vocab.special_eos_id;
|
|
}
|
|
|
|
llama_token llama_token_cls(const struct llama_model * model) {
|
|
return model->vocab.special_cls_id;
|
|
}
|
|
|
|
llama_token llama_token_sep(const struct llama_model * model) {
|
|
return model->vocab.special_sep_id;
|
|
}
|
|
|
|
llama_token llama_token_nl(const struct llama_model * model) {
|
|
return model->vocab.linefeed_id;
|
|
}
|
|
|
|
int32_t llama_add_bos_token(const struct llama_model * model) {
|
|
return model->vocab.special_add_bos;
|
|
}
|
|
|
|
int32_t llama_add_eos_token(const struct llama_model * model) {
|
|
return model->vocab.special_add_eos;
|
|
}
|
|
|
|
llama_token llama_token_prefix(const struct llama_model * model) {
|
|
return model->vocab.special_prefix_id;
|
|
}
|
|
|
|
llama_token llama_token_middle(const struct llama_model * model) {
|
|
return model->vocab.special_middle_id;
|
|
}
|
|
|
|
llama_token llama_token_suffix(const struct llama_model * model) {
|
|
return model->vocab.special_suffix_id;
|
|
}
|
|
|
|
llama_token llama_token_eot(const struct llama_model * model) {
|
|
return model->vocab.special_eot_id;
|
|
}
|
|
|
|
int32_t llama_tokenize(
|
|
const struct llama_model * model,
|
|
const char * text,
|
|
int32_t text_len,
|
|
llama_token * tokens,
|
|
int32_t n_tokens_max,
|
|
bool add_special,
|
|
bool parse_special) {
|
|
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special);
|
|
|
|
if (n_tokens_max < (int) res.size()) {
|
|
// LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
|
|
return -((int) res.size());
|
|
}
|
|
|
|
for (size_t i = 0; i < res.size(); i++) {
|
|
tokens[i] = res[i];
|
|
}
|
|
|
|
return res.size();
|
|
}
|
|
|
|
static std::string llama_decode_text(const std::string & text) {
|
|
std::string decoded_text;
|
|
auto unicode_sequences = unicode_cpts_from_utf8(text);
|
|
for (auto & unicode_sequence : unicode_sequences) {
|
|
decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(unicode_sequence));
|
|
}
|
|
|
|
return decoded_text;
|
|
}
|
|
|
|
// does not write null-terminator to buf
|
|
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
|
|
if (0 <= token && token < llama_n_vocab(model)) {
|
|
switch (llama_vocab_get_type(model->vocab)) {
|
|
case LLAMA_VOCAB_TYPE_WPM:
|
|
case LLAMA_VOCAB_TYPE_SPM: {
|
|
// NOTE: we accept all unsupported token types,
|
|
// suppressing them like CONTROL tokens.
|
|
if (llama_is_normal_token(model->vocab, token)) {
|
|
std::string result = model->vocab.id_to_token[token].text;
|
|
llama_unescape_whitespace(result);
|
|
if (length < (int) result.length()) {
|
|
return -(int) result.length();
|
|
}
|
|
memcpy(buf, result.c_str(), result.length());
|
|
return result.length();
|
|
} else if (
|
|
(llama_is_user_defined_token(model->vocab, token)) ||
|
|
(llama_is_control_token (model->vocab, token) && special)) {
|
|
std::string result = model->vocab.id_to_token[token].text;
|
|
if (length < (int) result.length()) {
|
|
return -(int) result.length();
|
|
}
|
|
memcpy(buf, result.c_str(), result.length());
|
|
return result.length();
|
|
} else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
|
|
if (length < 3) {
|
|
return -3;
|
|
}
|
|
memcpy(buf, "\xe2\x96\x85", 3);
|
|
return 3;
|
|
} else if (llama_is_byte_token(model->vocab, token)) {
|
|
if (length < 1) {
|
|
return -1;
|
|
}
|
|
buf[0] = llama_token_to_byte(model->vocab, token);
|
|
return 1;
|
|
}
|
|
break;
|
|
}
|
|
case LLAMA_VOCAB_TYPE_BPE: {
|
|
// NOTE: we accept all unsupported token types,
|
|
// suppressing them like CONTROL tokens.
|
|
if (llama_is_normal_token(model->vocab, token)) {
|
|
std::string result = model->vocab.id_to_token[token].text;
|
|
result = llama_decode_text(result);
|
|
if (length < (int) result.length()) {
|
|
return -(int) result.length();
|
|
}
|
|
memcpy(buf, result.c_str(), result.length());
|
|
return result.length();
|
|
} else if (
|
|
(llama_is_user_defined_token(model->vocab, token)) ||
|
|
(llama_is_control_token (model->vocab, token) && special)) {
|
|
std::string result = model->vocab.id_to_token[token].text;
|
|
if (length < (int) result.length()) {
|
|
return -(int) result.length();
|
|
}
|
|
memcpy(buf, result.c_str(), result.length());
|
|
return result.length();
|
|
}
|
|
break;
|
|
}
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
// trim whitespace from the beginning and end of a string
|
|
static std::string trim(const std::string & str) {
|
|
size_t start = 0;
|
|
size_t end = str.size();
|
|
while (start < end && isspace(str[start])) {
|
|
start += 1;
|
|
}
|
|
while (end > start && isspace(str[end - 1])) {
|
|
end -= 1;
|
|
}
|
|
return str.substr(start, end - start);
|
|
}
|
|
|
|
// Simple version of "llama_apply_chat_template" that only works with strings
|
|
// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
|
|
static int32_t llama_chat_apply_template_internal(
|
|
const std::string & tmpl,
|
|
const std::vector<const llama_chat_message *> & chat,
|
|
std::string & dest, bool add_ass) {
|
|
// Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
|
|
std::stringstream ss;
|
|
if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
|
|
// chatml template
|
|
for (auto message : chat) {
|
|
ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
|
|
}
|
|
if (add_ass) {
|
|
ss << "<|im_start|>assistant\n";
|
|
}
|
|
} else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
|
|
// llama2 template and its variants
|
|
// [variant] support system message
|
|
bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
|
|
// [variant] space before + after response
|
|
bool space_around_response = tmpl.find("' ' + eos_token") != std::string::npos;
|
|
// [variant] add BOS inside history
|
|
bool add_bos_inside_history = tmpl.find("bos_token + '[INST]") != std::string::npos;
|
|
// [variant] trim spaces from the input message
|
|
bool strip_message = tmpl.find("content.strip()") != std::string::npos;
|
|
// construct the prompt
|
|
bool is_inside_turn = true; // skip BOS at the beginning
|
|
ss << "[INST] ";
|
|
for (auto message : chat) {
|
|
std::string content = strip_message ? trim(message->content) : message->content;
|
|
std::string role(message->role);
|
|
if (!is_inside_turn) {
|
|
is_inside_turn = true;
|
|
ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
|
|
}
|
|
if (role == "system") {
|
|
if (support_system_message) {
|
|
ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
|
|
} else {
|
|
// if the model does not support system message, we still include it in the first message, but without <<SYS>>
|
|
ss << content << "\n";
|
|
}
|
|
} else if (role == "user") {
|
|
ss << content << " [/INST]";
|
|
} else {
|
|
ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << "</s>";
|
|
is_inside_turn = false;
|
|
}
|
|
}
|
|
// llama2 templates seem to not care about "add_generation_prompt"
|
|
} else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
|
|
// zephyr template
|
|
for (auto message : chat) {
|
|
ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
|
|
}
|
|
if (add_ass) {
|
|
ss << "<|assistant|>\n";
|
|
}
|
|
} else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
|
|
// mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
|
|
for (auto message : chat) {
|
|
std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
|
|
ss << bos << message->role << "\n" << message->content << "</s>\n";
|
|
}
|
|
if (add_ass) {
|
|
ss << "<s>assistant\n";
|
|
}
|
|
} else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
|
|
// google/gemma-7b-it
|
|
std::string system_prompt = "";
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
if (role == "system") {
|
|
// there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
|
|
system_prompt = trim(message->content);
|
|
continue;
|
|
}
|
|
// in gemma, "assistant" is "model"
|
|
role = role == "assistant" ? "model" : message->role;
|
|
ss << "<start_of_turn>" << role << "\n";
|
|
if (!system_prompt.empty() && role != "model") {
|
|
ss << system_prompt << "\n\n";
|
|
system_prompt = "";
|
|
}
|
|
ss << trim(message->content) << "<end_of_turn>\n";
|
|
}
|
|
if (add_ass) {
|
|
ss << "<start_of_turn>model\n";
|
|
}
|
|
} else if (tmpl == "orion" || tmpl.find("'\\n\\nAssistant: ' + eos_token") != std::string::npos) {
|
|
// OrionStarAI/Orion-14B-Chat
|
|
std::string system_prompt = "";
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
if (role == "system") {
|
|
// there is no system message support, we will merge it with user prompt
|
|
system_prompt = message->content;
|
|
continue;
|
|
} else if (role == "user") {
|
|
ss << "Human: ";
|
|
if (!system_prompt.empty()) {
|
|
ss << system_prompt << "\n\n";
|
|
system_prompt = "";
|
|
}
|
|
ss << message->content << "\n\nAssistant: </s>";
|
|
} else {
|
|
ss << message->content << "</s>";
|
|
}
|
|
}
|
|
} else if (tmpl == "openchat" || tmpl.find("GPT4 Correct ") != std::string::npos) {
|
|
// openchat/openchat-3.5-0106,
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
if (role == "system") {
|
|
ss << message->content << "<|end_of_turn|>";
|
|
} else {
|
|
role[0] = toupper(role[0]);
|
|
ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
|
|
}
|
|
}
|
|
if (add_ass) {
|
|
ss << "GPT4 Correct Assistant:";
|
|
}
|
|
} else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl.find("USER: ") != std::string::npos && tmpl.find("ASSISTANT: ") != std::string::npos)) {
|
|
// eachadea/vicuna-13b-1.1 (and Orca variant)
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
if (role == "system") {
|
|
// Orca-Vicuna variant uses a system prefix
|
|
if (tmpl == "vicuna-orca" || tmpl.find("SYSTEM: ") != std::string::npos) {
|
|
ss << "SYSTEM: " << message->content << "\n";
|
|
} else {
|
|
ss << message->content << "\n\n";
|
|
}
|
|
} else if (role == "user") {
|
|
ss << "USER: " << message->content << "\n";
|
|
} else if (role == "assistant") {
|
|
ss << "ASSISTANT: " << message->content << "</s>\n";
|
|
}
|
|
}
|
|
if (add_ass) {
|
|
ss << "ASSISTANT:";
|
|
}
|
|
} else if (tmpl == "deepseek" || (tmpl.find("### Instruction:") != std::string::npos && tmpl.find("<|EOT|>") != std::string::npos)) {
|
|
// deepseek-ai/deepseek-coder-33b-instruct
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
if (role == "system") {
|
|
ss << message->content;
|
|
} else if (role == "user") {
|
|
ss << "### Instruction:\n" << message->content << "\n";
|
|
} else if (role == "assistant") {
|
|
ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
|
|
}
|
|
}
|
|
if (add_ass) {
|
|
ss << "### Response:\n";
|
|
}
|
|
} else if (tmpl == "command-r" || (tmpl.find("<|START_OF_TURN_TOKEN|>") != std::string::npos && tmpl.find("<|USER_TOKEN|>") != std::string::npos)) {
|
|
// CohereForAI/c4ai-command-r-plus
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
if (role == "system") {
|
|
ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
|
|
} else if (role == "user") {
|
|
ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
|
|
} else if (role == "assistant") {
|
|
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
|
|
}
|
|
}
|
|
if (add_ass) {
|
|
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
|
|
}
|
|
} else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
|
|
// Llama 3
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
|
|
}
|
|
if (add_ass) {
|
|
ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
|
|
}
|
|
} else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) {
|
|
// Phi 3
|
|
for (auto message : chat) {
|
|
std::string role(message->role);
|
|
ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n";
|
|
}
|
|
if (add_ass) {
|
|
ss << "<|assistant|>\n";
|
|
}
|
|
} else {
|
|
// template not supported
|
|
return -1;
|
|
}
|
|
dest = ss.str();
|
|
return dest.size();
|
|
}
|
|
|
|
LLAMA_API int32_t llama_chat_apply_template(
|
|
const struct llama_model * model,
|
|
const char * tmpl,
|
|
const struct llama_chat_message * chat,
|
|
size_t n_msg,
|
|
bool add_ass,
|
|
char * buf,
|
|
int32_t length) {
|
|
std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
|
|
if (tmpl == nullptr) {
|
|
GGML_ASSERT(model != nullptr);
|
|
// load template from model
|
|
std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
|
|
std::string template_key = "tokenizer.chat_template";
|
|
int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
|
|
if (res < 0) {
|
|
// worst case: there is no information about template, we will use chatml by default
|
|
curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
|
|
} else {
|
|
curr_tmpl = std::string(model_template.data(), model_template.size());
|
|
}
|
|
}
|
|
|
|
// format the chat to string
|
|
std::vector<const llama_chat_message *> chat_vec;
|
|
chat_vec.resize(n_msg);
|
|
for (size_t i = 0; i < n_msg; i++) {
|
|
chat_vec[i] = &chat[i];
|
|
}
|
|
|
|
std::string formatted_chat;
|
|
int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
|
|
if (res < 0) {
|
|
return res;
|
|
}
|
|
if (buf && length > 0) {
|
|
strncpy(buf, formatted_chat.c_str(), length);
|
|
}
|
|
return res;
|
|
}
|
|
|
|
LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
|
|
static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
|
|
if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
|
|
return strlen(split_path);
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
|
|
std::string str_split_path(split_path);
|
|
char postfix[32];
|
|
snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
|
|
std::string str_postfix(postfix);
|
|
|
|
// check if dest ends with postfix
|
|
int size_prefix = str_split_path.size() - str_postfix.size();
|
|
if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
|
|
snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
|
|
return size_prefix;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
struct llama_timings llama_get_timings(struct llama_context * ctx) {
|
|
struct llama_timings result = {
|
|
/*.t_start_ms =*/ 1e-3 * ctx->t_start_us,
|
|
/*.t_end_ms =*/ 1.00 * ggml_time_ms(),
|
|
/*.t_load_ms =*/ 1e-3 * ctx->t_load_us,
|
|
/*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
|
|
/*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
|
|
/*.t_eval_ms =*/ 1e-3 * ctx->t_eval_us,
|
|
|
|
/*.n_sample =*/ std::max(1, ctx->n_sample),
|
|
/*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
|
|
/*.n_eval =*/ std::max(1, ctx->n_eval),
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
void llama_print_timings(struct llama_context * ctx) {
|
|
const llama_timings timings = llama_get_timings(ctx);
|
|
|
|
LLAMA_LOG_INFO("\n");
|
|
LLAMA_LOG_INFO("%s: load time = %10.2f ms\n", __func__, timings.t_load_ms);
|
|
LLAMA_LOG_INFO("%s: sample time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
|
|
__func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
|
|
LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
|
|
__func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
|
|
LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
|
|
__func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
|
|
LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (timings.t_end_ms - timings.t_start_ms), (timings.n_p_eval + timings.n_eval));
|
|
}
|
|
|
|
void llama_reset_timings(struct llama_context * ctx) {
|
|
ctx->t_start_us = ggml_time_us();
|
|
ctx->t_sample_us = ctx->n_sample = 0;
|
|
ctx->t_eval_us = ctx->n_eval = 0;
|
|
ctx->t_p_eval_us = ctx->n_p_eval = 0;
|
|
}
|
|
|
|
const char * llama_print_system_info(void) {
|
|
static std::string s;
|
|
|
|
s = "";
|
|
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
|
|
s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | ";
|
|
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
|
|
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
|
|
s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
|
|
s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
|
|
s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
|
|
s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
|
|
s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
|
|
s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
|
|
s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
|
|
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
|
|
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
|
|
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
|
|
s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | ";
|
|
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
|
|
s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
|
|
#ifdef GGML_USE_LLAMAFILE
|
|
s += "LAMMAFILE = 1 | ";
|
|
#else
|
|
s += "LAMMAFILE = 0 | ";
|
|
#endif
|
|
|
|
return s.c_str();
|
|
}
|
|
|
|
void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
|
|
fprintf(stream, "\n");
|
|
fprintf(stream, "###########\n");
|
|
fprintf(stream, "# Timings #\n");
|
|
fprintf(stream, "###########\n");
|
|
fprintf(stream, "\n");
|
|
|
|
fprintf(stream, "mst_eval: %.2f # ms / token during generation\n",
|
|
1.0e-3 * ctx->t_eval_us / ctx->n_eval);
|
|
fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n",
|
|
1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
|
|
fprintf(stream, "mst_sample: %.2f # ms / token during sampling\n",
|
|
1.0e-3 * ctx->t_sample_us / ctx->n_sample);
|
|
fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval);
|
|
fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
|
|
fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->n_sample);
|
|
fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us);
|
|
fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us);
|
|
fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
|
|
fprintf(stream, "t_sample_us: %" PRId64 " # total microseconds spent sampling\n", ctx->t_sample_us);
|
|
fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n",
|
|
1.0e6 * ctx->n_eval / ctx->t_eval_us);
|
|
fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n",
|
|
1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
|
|
fprintf(stream, "ts_sample: %.2f # tokens / second during sampling\n",
|
|
1.0e6 * ctx->n_sample / ctx->t_sample_us);
|
|
}
|
|
|
|
// For internal test use
|
|
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
|
|
struct llama_context * ctx
|
|
) {
|
|
return ctx->model.tensors_by_name;
|
|
}
|
|
|
|
void llama_log_set(ggml_log_callback log_callback, void * user_data) {
|
|
g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
|
|
g_state.log_callback_user_data = user_data;
|
|
#ifdef GGML_USE_METAL
|
|
ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
|
|
#endif
|
|
}
|
|
|
|
static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
|
|
va_list args_copy;
|
|
va_copy(args_copy, args);
|
|
char buffer[128];
|
|
int len = vsnprintf(buffer, 128, format, args);
|
|
if (len < 128) {
|
|
g_state.log_callback(level, buffer, g_state.log_callback_user_data);
|
|
} else {
|
|
char* buffer2 = new char[len+1];
|
|
vsnprintf(buffer2, len+1, format, args_copy);
|
|
buffer2[len] = 0;
|
|
g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
|
|
delete[] buffer2;
|
|
}
|
|
va_end(args_copy);
|
|
}
|
|
|
|
static void llama_log_internal(ggml_log_level level, const char * format, ...) {
|
|
va_list args;
|
|
va_start(args, format);
|
|
llama_log_internal_v(level, format, args);
|
|
va_end(args);
|
|
}
|
|
|
|
static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
|
|
(void) level;
|
|
(void) user_data;
|
|
fputs(text, stderr);
|
|
fflush(stderr);
|
|
}
|