mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 03:44:35 +00:00
8854 lines
323 KiB
C++
8854 lines
323 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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#ifdef GGML_USE_CUBLAS
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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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#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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#ifdef GGML_USE_K_QUANTS
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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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#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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#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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#include <io.h>
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#include <stdio.h> // for _fseeki64
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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 <cinttypes>
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#include <climits>
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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 <fstream>
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#include <initializer_list>
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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 <sstream>
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#include <thread>
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#include <unordered_map>
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#include <set>
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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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//
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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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#ifdef GGML_USE_CPU_HBM
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#include <hbwmalloc.h>
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#endif
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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_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_UNKNOWN,
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};
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static std::map<llm_arch, std::string> 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_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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};
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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_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_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_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_LAYERNORM_EPS,
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LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
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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_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_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_HF_JSON,
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LLM_KV_TOKENIZER_RWKV,
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};
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static std::map<llm_kv, std::string> 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_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_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_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_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_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_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_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_HF_JSON, "tokenizer.huggingface.json" },
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{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
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};
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struct LLM_KV {
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LLM_KV(llm_arch arch) : arch(arch) {}
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llm_arch arch;
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std::string operator()(llm_kv kv) const {
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return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
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}
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};
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enum llm_tensor {
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LLM_TENSOR_TOKEN_EMBD,
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LLM_TENSOR_POS_EMBD,
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LLM_TENSOR_OUTPUT,
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LLM_TENSOR_OUTPUT_NORM,
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LLM_TENSOR_ROPE_FREQS,
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LLM_TENSOR_ATTN_Q,
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LLM_TENSOR_ATTN_K,
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LLM_TENSOR_ATTN_V,
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LLM_TENSOR_ATTN_QKV,
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LLM_TENSOR_ATTN_OUT,
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LLM_TENSOR_ATTN_NORM,
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LLM_TENSOR_ATTN_NORM_2,
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LLM_TENSOR_ATTN_ROT_EMBD,
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LLM_TENSOR_FFN_GATE,
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LLM_TENSOR_FFN_DOWN,
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LLM_TENSOR_FFN_UP,
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LLM_TENSOR_FFN_NORM,
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LLM_TENSOR_ATTN_Q_NORM,
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LLM_TENSOR_ATTN_K_NORM,
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};
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static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
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{
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LLM_ARCH_LLAMA,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_BAICHUAN,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_FALCON,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_GPT2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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},
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},
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{
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LLM_ARCH_GPTJ,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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},
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},
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{
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LLM_ARCH_GPTNEOX,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_PERSIMMON,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd"},
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm"},
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{ LLM_TENSOR_OUTPUT, "output"},
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"},
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{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"},
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"},
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
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{ 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_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_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 {
|
|
return LLM_TENSOR_NAMES[arch].at(tensor);
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, const std::string & suffix) const {
|
|
return LLM_TENSOR_NAMES[arch].at(tensor) + "." + suffix;
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, int bid) const {
|
|
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid);
|
|
}
|
|
|
|
std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
|
|
return ::format(LLM_TENSOR_NAMES[arch].at(tensor).c_str(), bid) + "." + suffix;
|
|
}
|
|
};
|
|
|
|
//
|
|
// gguf helpers
|
|
//
|
|
|
|
#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \
|
|
do { \
|
|
const std::string skey(key); \
|
|
const int kid = gguf_find_key(ctx, skey.c_str()); \
|
|
if (kid >= 0) { \
|
|
enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \
|
|
if (ktype != (type)) { \
|
|
throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \
|
|
} \
|
|
(dst) = func(ctx, kid); \
|
|
} else if (req) { \
|
|
throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \
|
|
} \
|
|
} while (0)
|
|
|
|
//
|
|
// ggml helpers
|
|
//
|
|
|
|
static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
|
|
struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
|
|
|
|
if (plan.work_size > 0) {
|
|
buf.resize(plan.work_size);
|
|
plan.work_data = buf.data();
|
|
}
|
|
|
|
ggml_graph_compute(graph, &plan);
|
|
}
|
|
|
|
//
|
|
// llama helpers
|
|
//
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
# define llama_host_malloc(n) ggml_cuda_host_malloc(n)
|
|
# define llama_host_free(data) ggml_cuda_host_free(data)
|
|
#elif GGML_USE_METAL
|
|
# define llama_host_malloc(n) ggml_metal_host_malloc(n)
|
|
# define llama_host_free(data) ggml_metal_host_free(data)
|
|
#elif GGML_USE_CPU_HBM
|
|
# define llama_host_malloc(n) hbw_malloc(n)
|
|
# define llama_host_free(data) if (data != NULL) hbw_free(data)
|
|
#else
|
|
# define llama_host_malloc(n) malloc(n)
|
|
# define llama_host_free(data) free(data)
|
|
#endif
|
|
|
|
#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
|
|
|
|
struct llama_buffer {
|
|
void * data = NULL;
|
|
size_t size = 0;
|
|
|
|
// fallback to malloc / free
|
|
// useful in cases where CUDA can try to allocate PINNED memory
|
|
bool fallback = false;
|
|
|
|
void resize(size_t n) {
|
|
llama_host_free(data);
|
|
|
|
data = llama_host_malloc(n);
|
|
if (!data) {
|
|
fallback = true;
|
|
data = malloc(n);
|
|
} else {
|
|
fallback = false;
|
|
}
|
|
|
|
GGML_ASSERT(data);
|
|
size = n;
|
|
}
|
|
|
|
~llama_buffer() {
|
|
if (data) {
|
|
if (fallback) { // NOLINT
|
|
free(data);
|
|
} else {
|
|
llama_host_free(data);
|
|
}
|
|
}
|
|
|
|
data = NULL;
|
|
}
|
|
};
|
|
|
|
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 = std::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(std::string("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);
|
|
}
|
|
}
|
|
};
|
|
|
|
struct llama_mmap {
|
|
void * addr;
|
|
size_t size;
|
|
|
|
llama_mmap(const llama_mmap &) = delete;
|
|
|
|
#ifdef _POSIX_MAPPED_FILES
|
|
static constexpr bool SUPPORTED = true;
|
|
|
|
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__
|
|
if (prefetch) { flags |= MAP_POPULATE; }
|
|
#endif
|
|
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
|
|
if (addr == MAP_FAILED) {
|
|
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)) {
|
|
fprintf(stderr, "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)) {
|
|
fprintf(stderr, "warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
|
|
strerror(errno));
|
|
}
|
|
}
|
|
}
|
|
|
|
~llama_mmap() {
|
|
munmap(addr, size);
|
|
}
|
|
#elif defined(_WIN32)
|
|
static constexpr bool SUPPORTED = true;
|
|
|
|
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
|
|
(void) numa;
|
|
|
|
size = file->size;
|
|
|
|
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
|
|
|
|
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
|
|
DWORD error = GetLastError();
|
|
|
|
if (hMapping == NULL) {
|
|
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
|
|
}
|
|
|
|
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
|
|
error = GetLastError();
|
|
CloseHandle(hMapping);
|
|
|
|
if (addr == NULL) {
|
|
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
|
|
}
|
|
|
|
if (prefetch) {
|
|
// 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)size;
|
|
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
|
|
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
~llama_mmap() {
|
|
if (!UnmapViewOfFile(addr)) {
|
|
fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
|
|
llama_format_win_err(GetLastError()).c_str());
|
|
}
|
|
}
|
|
#else
|
|
static constexpr bool SUPPORTED = false;
|
|
|
|
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
|
|
(void) file;
|
|
(void) prefetch;
|
|
(void) numa;
|
|
|
|
throw std::runtime_error(std::string("mmap not supported"));
|
|
}
|
|
#endif
|
|
};
|
|
|
|
// 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_MLOCK (ulimit -l).\n"
|
|
#else
|
|
#define MLOCK_SUGGESTION \
|
|
"Try increasing RLIMIT_MLOCK ('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;
|
|
}
|
|
|
|
fprintf(stderr, "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)) {
|
|
fprintf(stderr, "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) {
|
|
fprintf(stderr, "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)) {
|
|
fprintf(stderr, "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)) {
|
|
fprintf(stderr, "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)) {
|
|
fprintf(stderr, "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 {
|
|
fprintf(stderr, "warning: mlock not supported on this system\n");
|
|
return false;
|
|
}
|
|
|
|
static void raw_unlock(const void * addr, size_t len) {}
|
|
#endif
|
|
};
|
|
|
|
typedef void (*offload_func_t)(struct ggml_tensor * tensor);
|
|
|
|
static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
|
|
(void) tensor;
|
|
}
|
|
|
|
static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
|
|
std::vector<char> result(8, 0);
|
|
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
|
if (n_tokens < 0) {
|
|
result.resize(-n_tokens);
|
|
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
|
GGML_ASSERT(check == -n_tokens);
|
|
} else {
|
|
result.resize(n_tokens);
|
|
}
|
|
|
|
return std::string(result.data(), result.size());
|
|
}
|
|
|
|
//
|
|
// globals
|
|
//
|
|
|
|
struct llama_state {
|
|
// 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_1B,
|
|
MODEL_3B,
|
|
MODEL_7B,
|
|
MODEL_8B,
|
|
MODEL_13B,
|
|
MODEL_15B,
|
|
MODEL_30B,
|
|
MODEL_34B,
|
|
MODEL_40B,
|
|
MODEL_65B,
|
|
MODEL_70B,
|
|
};
|
|
|
|
static const size_t kB = 1024;
|
|
static const size_t MB = kB*kB;
|
|
static const size_t GB = kB*kB*kB;
|
|
|
|
struct llama_hparams {
|
|
bool vocab_only;
|
|
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_ff;
|
|
|
|
float f_norm_eps;
|
|
float f_norm_rms_eps;
|
|
|
|
float rope_freq_base_train;
|
|
float rope_freq_scale_train;
|
|
|
|
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_ff != other.n_ff) return true;
|
|
|
|
const float EPSILON = 1e-9;
|
|
|
|
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 {
|
|
return n_head/n_head_kv;
|
|
}
|
|
|
|
uint32_t n_embd_head() const {
|
|
return n_embd/n_head;
|
|
}
|
|
|
|
uint32_t n_embd_gqa() const {
|
|
return n_embd/n_gqa();
|
|
}
|
|
};
|
|
|
|
struct llama_cparams {
|
|
uint32_t n_ctx; // context size used during inference
|
|
uint32_t n_batch;
|
|
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;
|
|
|
|
bool mul_mat_q;
|
|
};
|
|
|
|
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;
|
|
|
|
// 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 * bo;
|
|
struct ggml_tensor * bqkv;
|
|
|
|
// normalization
|
|
struct ggml_tensor * ffn_norm;
|
|
struct ggml_tensor * ffn_norm_b;
|
|
|
|
// ff
|
|
struct ggml_tensor * w1; // ffn_gate
|
|
struct ggml_tensor * w2; // ffn_down
|
|
struct ggml_tensor * w3; // ffn_up
|
|
|
|
// ff bias
|
|
struct ggml_tensor * b2; // ffn_down
|
|
struct ggml_tensor * b3; // ffn_up
|
|
};
|
|
|
|
struct llama_kv_cell {
|
|
llama_pos pos = -1;
|
|
llama_pos delta = 0;
|
|
|
|
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();
|
|
}
|
|
};
|
|
|
|
// ring-buffer of cached KV data
|
|
struct llama_kv_cache {
|
|
bool has_shift = 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;
|
|
|
|
// computed before each graph build
|
|
uint32_t n = 0;
|
|
|
|
std::vector<llama_kv_cell> cells;
|
|
|
|
struct ggml_tensor * k = NULL;
|
|
struct ggml_tensor * v = NULL;
|
|
|
|
struct ggml_context * ctx = NULL;
|
|
|
|
llama_buffer buf;
|
|
|
|
~llama_kv_cache() {
|
|
if (ctx) {
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
ggml_cuda_free_data(k);
|
|
ggml_cuda_free_data(v);
|
|
#endif // GGML_USE_CUBLAS
|
|
}
|
|
};
|
|
|
|
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::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 linefeed_id = 13;
|
|
id special_prefix_id = 32007;
|
|
id special_middle_id = 32009;
|
|
id special_suffix_id = 32008;
|
|
id special_eot_id = 32010;
|
|
|
|
int find_bpe_rank(std::string token_left, std::string token_right) const {
|
|
replace_all(token_left, " ", "\u0120");
|
|
replace_all(token_left, "\n", "\u010A");
|
|
replace_all(token_right, " ", "\u0120");
|
|
replace_all(token_right, "\n", "\u010A");
|
|
|
|
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_embeddings;
|
|
struct ggml_tensor * pos_embeddings;
|
|
|
|
struct ggml_tensor * output_norm;
|
|
struct ggml_tensor * output_norm_b;
|
|
struct ggml_tensor * output;
|
|
|
|
std::vector<llama_layer> layers;
|
|
|
|
int n_gpu_layers;
|
|
|
|
// context
|
|
struct ggml_context * ctx = NULL;
|
|
|
|
// the model memory buffer
|
|
llama_buffer buf;
|
|
|
|
// model memory mapped file
|
|
std::unique_ptr<llama_mmap> mapping;
|
|
|
|
// objects representing data potentially being locked in memory
|
|
llama_mlock mlock_buf;
|
|
llama_mlock mlock_mmap;
|
|
|
|
// 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() {
|
|
if (ctx) {
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
|
|
ggml_cuda_free_data(tensors_by_name[i].second);
|
|
}
|
|
ggml_cuda_free_scratch();
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
for (size_t i = 0; i < tensors_by_name.size(); ++i) {
|
|
ggml_cl_free_data(tensors_by_name[i].second);
|
|
}
|
|
#endif
|
|
}
|
|
};
|
|
|
|
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() {
|
|
#ifdef GGML_USE_METAL
|
|
if (ctx_metal) {
|
|
ggml_metal_free(ctx_metal);
|
|
}
|
|
#endif
|
|
if (alloc) {
|
|
ggml_allocr_free(alloc);
|
|
}
|
|
}
|
|
|
|
llama_cparams cparams;
|
|
|
|
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;
|
|
|
|
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
|
|
|
|
// decode output (2-dimensional array: [n_tokens][n_vocab])
|
|
std::vector<float> logits;
|
|
bool logits_all = false;
|
|
|
|
// input embedding (1-dimensional array: [n_embd])
|
|
std::vector<float> embedding;
|
|
|
|
// reusable buffer for `struct ggml_graph_plan.work_data`
|
|
std::vector<uint8_t> work_buffer;
|
|
|
|
// memory buffers used to evaluate the model
|
|
llama_buffer buf_compute;
|
|
|
|
llama_buffer buf_alloc;
|
|
ggml_allocr * alloc = NULL;
|
|
|
|
#ifdef GGML_USE_METAL
|
|
ggml_metal_context * ctx_metal = NULL;
|
|
#endif
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_context * ctx_mpi = NULL;
|
|
#endif
|
|
};
|
|
|
|
//
|
|
// kv cache helpers
|
|
//
|
|
|
|
static bool llama_kv_cache_init(
|
|
const struct llama_hparams & hparams,
|
|
struct llama_kv_cache & cache,
|
|
ggml_type wtype,
|
|
uint32_t n_ctx,
|
|
int n_gpu_layers) {
|
|
const uint32_t n_embd = hparams.n_embd_gqa();
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
|
|
const int64_t n_mem = n_layer*n_ctx;
|
|
const int64_t n_elements = n_embd*n_mem;
|
|
|
|
cache.has_shift = false;
|
|
|
|
cache.head = 0;
|
|
cache.size = n_ctx;
|
|
|
|
cache.cells.clear();
|
|
cache.cells.resize(n_ctx);
|
|
|
|
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
|
|
|
|
struct ggml_init_params params;
|
|
params.mem_size = cache.buf.size;
|
|
params.mem_buffer = cache.buf.data;
|
|
params.no_alloc = false;
|
|
|
|
cache.ctx = ggml_init(params);
|
|
|
|
if (!cache.ctx) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
|
|
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
|
|
ggml_set_name(cache.k, "cache_k");
|
|
ggml_set_name(cache.v, "cache_v");
|
|
|
|
(void) n_gpu_layers;
|
|
#ifdef GGML_USE_CUBLAS
|
|
size_t vram_kv_cache = 0;
|
|
|
|
if (n_gpu_layers > (int)n_layer + 1) {
|
|
ggml_cuda_assign_buffers_no_scratch(cache.v);
|
|
LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
|
|
vram_kv_cache += ggml_nbytes(cache.v);
|
|
}
|
|
if (n_gpu_layers > (int)n_layer + 2) {
|
|
ggml_cuda_assign_buffers_no_scratch(cache.k);
|
|
LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
|
|
vram_kv_cache += ggml_nbytes(cache.k);
|
|
}
|
|
if (vram_kv_cache > 0) {
|
|
LLAMA_LOG_INFO("%s: VRAM kv self = %.2f MB\n", __func__, vram_kv_cache / 1024.0 / 1024.0);
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
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 (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];
|
|
cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i]);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// find how many cells are currently in use
|
|
static int32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
|
|
for (uint32_t i = cache.size - 1; i > 0; --i) {
|
|
if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
|
|
return i + 1;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
static void llama_kv_cache_tokens_rm(struct llama_kv_cache & cache, int32_t c0, int32_t c1) {
|
|
if (c0 < 0) c0 = 0;
|
|
if (c1 < 0) c1 = cache.size;
|
|
|
|
for (int32_t i = c0; i < c1; ++i) {
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
}
|
|
|
|
// Searching for a free slot can start here since we know it will be empty.
|
|
cache.head = uint32_t(c0);
|
|
}
|
|
|
|
static void 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();
|
|
|
|
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.cells[i].seq_id.erase(seq_id);
|
|
if (cache.cells[i].seq_id.empty()) {
|
|
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) cache.head = new_head;
|
|
}
|
|
|
|
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();
|
|
|
|
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)) {
|
|
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.
|
|
if (new_head != cache.size) cache.head = new_head;
|
|
}
|
|
|
|
static void llama_kv_cache_seq_shift(
|
|
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();
|
|
|
|
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.cells[i].pos += delta;
|
|
if (cache.cells[i].pos < 0) {
|
|
cache.cells[i].pos = -1;
|
|
cache.cells[i].seq_id.clear();
|
|
if (new_head == cache.size) new_head = i;
|
|
} else {
|
|
cache.has_shift = true;
|
|
cache.cells[i].delta = delta;
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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;
|
|
}
|
|
|
|
//
|
|
// model loading and saving
|
|
//
|
|
|
|
enum llama_fver {
|
|
GGUF_FILE_VERSION_V1 = 1,
|
|
GGUF_FILE_VERSION_V2 = 2,
|
|
};
|
|
|
|
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 (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;
|
|
}
|
|
|
|
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_file file;
|
|
llama_ftype ftype;
|
|
llama_fver fver;
|
|
|
|
std::unique_ptr<llama_mmap> mapping;
|
|
|
|
struct gguf_context * ctx_gguf = NULL;
|
|
struct ggml_context * ctx_meta = NULL;
|
|
|
|
llama_model_loader(const std::string & fname, bool use_mmap) : file(fname.c_str(), "rb") {
|
|
struct gguf_init_params params = {
|
|
/*.no_alloc = */ true,
|
|
/*.ctx = */ &ctx_meta,
|
|
};
|
|
|
|
ctx_gguf = gguf_init_from_file(fname.c_str(), params);
|
|
if (!ctx_gguf) {
|
|
throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
|
|
}
|
|
|
|
n_kv = gguf_get_n_kv(ctx_gguf);
|
|
n_tensors = gguf_get_n_tensors(ctx_gguf);
|
|
|
|
fver = (enum llama_fver ) gguf_get_version(ctx_gguf);
|
|
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
const char * name = gguf_get_tensor_name(ctx_gguf, i);
|
|
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, name);
|
|
n_elements += ggml_nelements(t);
|
|
n_bytes += ggml_nbytes(t);
|
|
}
|
|
|
|
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 char * name = gguf_get_tensor_name(ctx_gguf, i);
|
|
struct ggml_tensor * meta = ggml_get_tensor(ctx_meta, name);
|
|
|
|
n_type[meta->type]++;
|
|
|
|
if (n_type_max < n_type[meta->type]) {
|
|
n_type_max = n_type[meta->type];
|
|
type_max = meta->type;
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: - tensor %4d: %32s %-8s [ %s ]\n", __func__, i, name, ggml_type_name(meta->type), llama_format_tensor_shape(meta).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;
|
|
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(ctx_gguf, "general.file_type");
|
|
if (kid >= 0) {
|
|
ftype = (llama_ftype) gguf_get_val_u32(ctx_gguf, kid);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_kv; i++) {
|
|
const char * name = gguf_get_key(ctx_gguf, i);
|
|
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
|
|
|
|
LLAMA_LOG_INFO("%s: - kv %3d: %42s %-8s\n", __func__, i, name, gguf_type_name(type));
|
|
}
|
|
|
|
// 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 (ctx_gguf) {
|
|
gguf_free(ctx_gguf);
|
|
}
|
|
if (ctx_meta) {
|
|
ggml_free(ctx_meta);
|
|
}
|
|
}
|
|
|
|
std::string get_arch_name() const {
|
|
const auto kv = LLM_KV(LLM_ARCH_UNKNOWN);
|
|
|
|
std::string arch_name;
|
|
GGUF_GET_KEY(ctx_gguf, arch_name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_ARCHITECTURE));
|
|
|
|
return arch_name;
|
|
}
|
|
|
|
enum llm_arch get_arch() const {
|
|
const std::string arch_name = get_arch_name();
|
|
|
|
return llm_arch_from_string(arch_name);
|
|
}
|
|
|
|
const char * get_tensor_name(int i) const {
|
|
return gguf_get_tensor_name(ctx_gguf, i);
|
|
}
|
|
|
|
struct ggml_tensor * get_tensor_meta(int i) const {
|
|
return ggml_get_tensor(ctx_meta, get_tensor_name(i));
|
|
}
|
|
|
|
void calc_sizes(size_t & ctx_size_p, size_t & mmapped_size_p) const {
|
|
ctx_size_p = 0;
|
|
mmapped_size_p = 0;
|
|
|
|
for (int i = 0; i < n_tensors; i++) {
|
|
struct ggml_tensor * meta = get_tensor_meta(i);
|
|
ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
|
|
(use_mmap ? mmapped_size_p : ctx_size_p) += ggml_nbytes_pad(meta);
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, struct ggml_tensor * meta, ggml_backend backend) {
|
|
if (backend != GGML_BACKEND_CPU) {
|
|
ggml_set_no_alloc(ctx, true);
|
|
}
|
|
|
|
struct ggml_tensor * tensor = ggml_dup_tensor(ctx, meta);
|
|
tensor->backend = backend; // TODO: ggml_set_backend
|
|
ggml_set_name(tensor, ggml_get_name(meta));
|
|
|
|
if (backend != GGML_BACKEND_CPU) {
|
|
ggml_set_no_alloc(ctx, use_mmap);
|
|
}
|
|
|
|
n_created++;
|
|
|
|
return tensor;
|
|
}
|
|
|
|
struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, ggml_backend backend) {
|
|
struct ggml_tensor * cur = ggml_get_tensor(ctx_meta, name.c_str());
|
|
|
|
if (cur == 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 < ne.size(); ++i) {
|
|
if (ne[i] != cur->ne[i]) {
|
|
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 create_tensor_for(ctx, cur, backend);
|
|
}
|
|
|
|
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));
|
|
}
|
|
}
|
|
|
|
size_t file_offset(const char * name) const {
|
|
const int idx = gguf_find_tensor(ctx_gguf, name);
|
|
|
|
if (idx < 0) {
|
|
throw std::runtime_error(format("%s: tensor '%s' not found in the file", __func__, name));
|
|
}
|
|
|
|
return gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, idx);
|
|
}
|
|
|
|
void load_data_for(struct ggml_tensor * cur) const {
|
|
const size_t offs = file_offset(ggml_get_name(cur));
|
|
|
|
if (use_mmap) {
|
|
cur->data = (uint8_t *) mapping->addr + offs;
|
|
} else {
|
|
file.seek(offs, SEEK_SET);
|
|
file.read_raw(cur->data, ggml_nbytes(cur));
|
|
}
|
|
}
|
|
|
|
void load_all_data(struct ggml_context * ctx, llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
|
|
size_t size_data = 0;
|
|
size_t size_lock = 0;
|
|
size_t size_pref = 0; // prefetch
|
|
|
|
for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
|
|
struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
|
|
size_data += ggml_nbytes(cur);
|
|
if (cur->backend == GGML_BACKEND_CPU) {
|
|
size_pref += ggml_nbytes(cur);
|
|
}
|
|
}
|
|
|
|
if (use_mmap) {
|
|
mapping.reset(new llama_mmap(&file, size_pref, ggml_is_numa()));
|
|
if (lmlock) {
|
|
lmlock->init(mapping->addr);
|
|
}
|
|
}
|
|
|
|
size_t done_size = 0;
|
|
for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
|
|
struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
|
|
GGML_ASSERT(cur); // unused tensors should have been caught by load_data already
|
|
|
|
if (progress_callback) {
|
|
progress_callback((float) done_size / size_data, progress_callback_user_data);
|
|
}
|
|
|
|
// allocate temp buffer if not using mmap
|
|
if (!use_mmap && cur->data == NULL) {
|
|
GGML_ASSERT(cur->backend != GGML_BACKEND_CPU);
|
|
#ifdef GGML_USE_CPU_HBM
|
|
cur->data = (uint8_t*)hbw_malloc(ggml_nbytes(cur));
|
|
#else
|
|
cur->data = (uint8_t*)malloc(ggml_nbytes(cur));
|
|
#endif
|
|
}
|
|
|
|
load_data_for(cur);
|
|
|
|
switch (cur->backend) {
|
|
case GGML_BACKEND_CPU:
|
|
if (use_mmap && lmlock) {
|
|
size_lock += ggml_nbytes(cur);
|
|
lmlock->grow_to(size_lock);
|
|
}
|
|
break;
|
|
#ifdef GGML_USE_CUBLAS
|
|
case GGML_BACKEND_GPU:
|
|
case GGML_BACKEND_GPU_SPLIT:
|
|
// old code:
|
|
//ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);
|
|
|
|
// TODO: test if this works !!
|
|
ggml_cuda_transform_tensor(cur->data, cur);
|
|
if (!use_mmap) {
|
|
free(cur->data);
|
|
}
|
|
break;
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
case GGML_BACKEND_GPU:
|
|
ggml_cl_transform_tensor(cur->data, cur);
|
|
if (!use_mmap) {
|
|
free(cur->data);
|
|
}
|
|
break;
|
|
#endif
|
|
default:
|
|
continue;
|
|
}
|
|
|
|
done_size += ggml_nbytes(cur);
|
|
}
|
|
}
|
|
};
|
|
|
|
//
|
|
// load LLaMA models
|
|
//
|
|
|
|
static std::string 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 "mostly F16";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
|
|
return "mostly Q4_1, some F16";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
|
|
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
|
|
|
|
// K-quants
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
|
|
|
|
default: return "unknown, may not work";
|
|
}
|
|
}
|
|
|
|
static const char * llama_model_type_name(e_model type) {
|
|
switch (type) {
|
|
case MODEL_1B: return "1B";
|
|
case MODEL_3B: return "3B";
|
|
case MODEL_7B: return "7B";
|
|
case MODEL_8B: return "8B";
|
|
case MODEL_13B: return "13B";
|
|
case MODEL_15B: return "15B";
|
|
case MODEL_30B: return "30B";
|
|
case MODEL_34B: return "34B";
|
|
case MODEL_40B: return "40B";
|
|
case MODEL_65B: return "65B";
|
|
case MODEL_70B: return "70B";
|
|
default: return "?B";
|
|
}
|
|
}
|
|
|
|
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) {
|
|
struct gguf_context * ctx = ml.ctx_gguf;
|
|
|
|
const auto kv = LLM_KV(model.arch);
|
|
|
|
auto & hparams = model.hparams;
|
|
|
|
// get general kv
|
|
GGUF_GET_KEY(ctx, model.name, gguf_get_val_str, GGUF_TYPE_STRING, false, kv(LLM_KV_GENERAL_NAME));
|
|
|
|
// get hparams kv
|
|
GGUF_GET_KEY(ctx, hparams.n_vocab, gguf_get_arr_n, GGUF_TYPE_ARRAY, true, kv(LLM_KV_TOKENIZER_LIST));
|
|
GGUF_GET_KEY(ctx, hparams.n_ctx_train, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_CONTEXT_LENGTH));
|
|
GGUF_GET_KEY(ctx, hparams.n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH));
|
|
GGUF_GET_KEY(ctx, hparams.n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH));
|
|
GGUF_GET_KEY(ctx, hparams.n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT));
|
|
GGUF_GET_KEY(ctx, hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT));
|
|
|
|
// n_head_kv is optional, default to n_head
|
|
hparams.n_head_kv = hparams.n_head;
|
|
GGUF_GET_KEY(ctx, hparams.n_head_kv, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ATTENTION_HEAD_COUNT_KV));
|
|
|
|
// rope_freq_base (optional)
|
|
hparams.rope_freq_base_train = 10000.0f;
|
|
GGUF_GET_KEY(ctx, hparams.rope_freq_base_train, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE));
|
|
|
|
// rope_freq_scale (inverse of the kv) is optional
|
|
float ropescale = 1.0f;
|
|
GGUF_GET_KEY(ctx, ropescale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR));
|
|
hparams.rope_freq_scale_train = 1.0f/ropescale;
|
|
|
|
// sanity check for n_rot (optional)
|
|
{
|
|
hparams.n_rot = hparams.n_embd / hparams.n_head;
|
|
|
|
GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT));
|
|
|
|
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
|
|
}
|
|
|
|
// arch-specific KVs
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
{
|
|
GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: model.type = e_model::MODEL_3B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
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_FALCON:
|
|
{
|
|
GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_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:
|
|
{
|
|
GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_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_STARCODER:
|
|
{
|
|
GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_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:
|
|
{
|
|
GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_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:
|
|
{
|
|
GGUF_GET_KEY(ctx, hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS));
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_1B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
default: (void)0;
|
|
}
|
|
|
|
model.ftype = ml.ftype;
|
|
}
|
|
|
|
// 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 bos);
|
|
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.ctx_gguf;
|
|
|
|
const auto kv = LLM_KV(model.arch);
|
|
|
|
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);
|
|
}
|
|
|
|
// determine vocab type
|
|
{
|
|
std::string tokenizer_name;
|
|
|
|
GGUF_GET_KEY(ctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL));
|
|
|
|
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;
|
|
} 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(codepoints_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;
|
|
} 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 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(codepoints_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) {
|
|
vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
|
|
} else {
|
|
vocab.linefeed_id = llama_tokenize_internal(vocab, "\u010A", false)[0];
|
|
}
|
|
|
|
// special tokens
|
|
GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID));
|
|
GGUF_GET_KEY(ctx, vocab.special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID));
|
|
GGUF_GET_KEY(ctx, vocab.special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID));
|
|
GGUF_GET_KEY(ctx, vocab.special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID));
|
|
GGUF_GET_KEY(ctx, vocab.special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID));
|
|
}
|
|
|
|
static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|
const auto & hparams = model.hparams;
|
|
const auto & vocab = model.vocab;
|
|
|
|
// 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).c_str());
|
|
LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix
|
|
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); // a.k.a. n_embd_head, n_head_dim
|
|
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_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: n_ff = %u\n", __func__, hparams.n_ff);
|
|
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: 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());
|
|
LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9);
|
|
if (ml.n_bytes < GB) {
|
|
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.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() ); }
|
|
}
|
|
|
|
static void llm_load_tensors(
|
|
llama_model_loader & ml,
|
|
llama_model & model,
|
|
int n_gpu_layers,
|
|
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 & ctx = model.ctx;
|
|
auto & hparams = model.hparams;
|
|
|
|
model.n_gpu_layers = n_gpu_layers;
|
|
|
|
size_t ctx_size;
|
|
size_t mmapped_size;
|
|
|
|
ml.calc_sizes(ctx_size, mmapped_size);
|
|
|
|
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
|
|
|
|
// create the ggml context
|
|
{
|
|
model.buf.resize(ctx_size);
|
|
if (use_mlock) {
|
|
model.mlock_buf.init (model.buf.data);
|
|
model.mlock_buf.grow_to(model.buf.size);
|
|
}
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ model.buf.size,
|
|
/*.mem_buffer =*/ model.buf.data,
|
|
/*.no_alloc =*/ ml.use_mmap,
|
|
};
|
|
|
|
model.ctx = ggml_init(params);
|
|
if (!model.ctx) {
|
|
throw std::runtime_error(format("ggml_init() failed"));
|
|
}
|
|
}
|
|
|
|
(void) main_gpu;
|
|
#ifdef GGML_USE_CUBLAS
|
|
LLAMA_LOG_INFO("%s: using " GGML_CUDA_NAME " for GPU acceleration\n", __func__);
|
|
ggml_cuda_set_main_device(main_gpu);
|
|
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
|
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
|
|
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
|
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
|
|
#else
|
|
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
|
|
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
|
|
#endif
|
|
|
|
// prepare memory for the weights
|
|
size_t vram_weights = 0;
|
|
{
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_vocab = hparams.n_vocab;
|
|
|
|
const auto tn = LLM_TN(model.arch);
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_REFACT:
|
|
{
|
|
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
|
|
|
// output
|
|
{
|
|
ggml_backend backend_norm;
|
|
ggml_backend backend_output;
|
|
|
|
if (n_gpu_layers > int(n_layer)) {
|
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
|
// on Windows however this is detrimental unless everything is on the GPU
|
|
#ifndef _WIN32
|
|
backend_norm = LLAMA_BACKEND_OFFLOAD;
|
|
#else
|
|
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#endif // _WIN32
|
|
|
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
} else {
|
|
backend_norm = GGML_BACKEND_CPU;
|
|
backend_output = GGML_BACKEND_CPU;
|
|
}
|
|
|
|
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
|
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
|
|
|
if (backend_norm == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(model.output_norm);
|
|
}
|
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
|
vram_weights += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
const uint32_t n_ff = hparams.n_ff;
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
|
|
|
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
|
|
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
|
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
|
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
|
|
|
layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
|
|
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
|
|
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
|
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
vram_weights +=
|
|
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
|
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
|
|
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
|
{
|
|
ggml_backend backend_norm;
|
|
ggml_backend backend_output;
|
|
|
|
if (n_gpu_layers > int(n_layer)) {
|
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
|
// on Windows however this is detrimental unless everything is on the GPU
|
|
#ifndef _WIN32
|
|
backend_norm = LLAMA_BACKEND_OFFLOAD;
|
|
#else
|
|
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#endif // _WIN32
|
|
|
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
} else {
|
|
backend_norm = GGML_BACKEND_CPU;
|
|
backend_output = GGML_BACKEND_CPU;
|
|
}
|
|
|
|
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
|
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
|
|
|
if (backend_norm == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(model.output_norm);
|
|
}
|
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
|
vram_weights += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
const uint32_t n_ff = hparams.n_ff;
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
|
|
|
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
|
|
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
|
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
|
|
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
|
|
|
layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
|
|
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
|
|
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
|
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
vram_weights +=
|
|
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
|
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
|
|
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
// TODO: CPU-only for now
|
|
|
|
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
|
|
|
// output
|
|
{
|
|
ggml_backend backend_norm;
|
|
ggml_backend backend_output;
|
|
|
|
if (n_gpu_layers > int(n_layer)) {
|
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
|
// on Windows however this is detrimental unless everything is on the GPU
|
|
#ifndef _WIN32
|
|
backend_norm = LLAMA_BACKEND_OFFLOAD;
|
|
#else
|
|
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#endif // _WIN32
|
|
|
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
} else {
|
|
backend_norm = GGML_BACKEND_CPU;
|
|
backend_output = GGML_BACKEND_CPU;
|
|
}
|
|
|
|
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
|
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
|
|
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
|
|
|
if (backend_norm == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(model.output_norm);
|
|
vram_weights += ggml_nbytes(model.output_norm_b);
|
|
}
|
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
|
vram_weights += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
const uint32_t n_ff = hparams.n_ff;
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
|
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
|
|
|
|
if (gguf_find_tensor(ml.ctx_gguf, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i).c_str()) >= 0) {
|
|
layer.attn_norm_2 = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, backend);
|
|
layer.attn_norm_2_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, backend);
|
|
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(layer.attn_norm_2);
|
|
vram_weights += ggml_nbytes(layer.attn_norm_2_b);
|
|
}
|
|
}
|
|
|
|
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
|
|
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
|
|
|
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
|
|
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
|
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
vram_weights +=
|
|
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
|
|
ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.wo) +
|
|
ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
|
model.pos_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, GGML_BACKEND_CPU);
|
|
|
|
// output
|
|
{
|
|
ggml_backend backend_norm;
|
|
ggml_backend backend_output;
|
|
|
|
if (n_gpu_layers > int(n_layer)) {
|
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
|
// on Windows however this is detrimental unless everything is on the GPU
|
|
#ifndef _WIN32
|
|
backend_norm = LLAMA_BACKEND_OFFLOAD;
|
|
#else
|
|
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#endif // _WIN32
|
|
|
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
} else {
|
|
backend_norm = GGML_BACKEND_CPU;
|
|
backend_output = GGML_BACKEND_CPU;
|
|
}
|
|
|
|
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
|
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
|
|
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
|
|
|
if (backend_norm == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(model.output_norm);
|
|
vram_weights += ggml_nbytes(model.output_norm_b);
|
|
}
|
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
|
vram_weights += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
const uint32_t n_ff = hparams.n_ff;
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
|
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
|
|
|
|
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
|
|
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split);
|
|
|
|
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
|
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split);
|
|
|
|
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
|
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
|
|
|
|
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
|
|
layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split);
|
|
|
|
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
|
layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split);
|
|
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
vram_weights +=
|
|
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.attn_norm_b) +
|
|
ggml_nbytes(layer.wqkv) + ggml_nbytes(layer.bqkv) +
|
|
ggml_nbytes(layer.wo) + ggml_nbytes(layer.bo) +
|
|
ggml_nbytes(layer.ffn_norm) + ggml_nbytes(layer.ffn_norm_b) +
|
|
ggml_nbytes(layer.w2) + ggml_nbytes(layer.b2) +
|
|
ggml_nbytes(layer.w3) + ggml_nbytes(layer.b3);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PERSIMMON:
|
|
{
|
|
model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
|
|
|
{
|
|
ggml_backend backend_norm;
|
|
ggml_backend backend_output;
|
|
|
|
if (n_gpu_layers > int(n_layer)) {
|
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
|
// on Windows however this is detrimental unless everything is on the GPU
|
|
#ifndef _WIN32
|
|
backend_norm = LLAMA_BACKEND_OFFLOAD;
|
|
#else
|
|
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#endif // _WIN32
|
|
|
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
} else {
|
|
backend_norm = GGML_BACKEND_CPU;
|
|
backend_output = GGML_BACKEND_CPU;
|
|
}
|
|
|
|
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
|
|
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
|
|
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
|
|
|
|
if (backend_norm == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(model.output_norm);
|
|
vram_weights += ggml_nbytes(model.output_norm_b);
|
|
}
|
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
|
vram_weights += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
const uint32_t n_ff = hparams.n_ff;
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
model.layers.resize(n_layer);
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
auto & layer = model.layers[i];
|
|
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
|
|
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
|
|
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
|
|
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split);
|
|
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
|
|
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split);
|
|
layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, backend_split);
|
|
layer.b2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, backend_split);
|
|
layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
|
|
layer.b3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, backend_split);
|
|
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
|
|
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
|
|
layer.attn_q_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}, backend);
|
|
layer.attn_q_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}, backend);
|
|
layer.attn_k_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}, backend);
|
|
layer.attn_k_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}, backend);
|
|
}
|
|
} break;
|
|
default:
|
|
throw std::runtime_error("unknown architecture");
|
|
}
|
|
}
|
|
|
|
ml.done_getting_tensors();
|
|
|
|
// print memory requirements
|
|
{
|
|
// this is the total memory required to run the inference
|
|
size_t mem_required =
|
|
ctx_size +
|
|
mmapped_size - vram_weights; // weights in VRAM not in memory
|
|
|
|
LLAMA_LOG_INFO("%s: mem required = %7.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
|
|
|
|
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
|
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__);
|
|
}
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
const int max_backend_supported_layers = hparams.n_layer + 3;
|
|
const int max_offloadable_layers = hparams.n_layer + 3;
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
const int max_backend_supported_layers = hparams.n_layer + 1;
|
|
const int max_offloadable_layers = hparams.n_layer + 1;
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
|
|
LLAMA_LOG_INFO("%s: VRAM used: %.2f MB\n", __func__, vram_weights / 1024.0 / 1024.0);
|
|
#else
|
|
(void) n_gpu_layers;
|
|
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
|
}
|
|
|
|
// populate `tensors_by_name`
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
struct ggml_tensor * cur = ggml_get_tensor(ctx, ml.get_tensor_name(i));
|
|
model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
|
|
}
|
|
|
|
(void) tensor_split;
|
|
#ifdef GGML_USE_CUBLAS
|
|
{
|
|
ggml_cuda_set_tensor_split(tensor_split);
|
|
}
|
|
#endif
|
|
|
|
ml.load_all_data(ctx, progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
|
|
|
|
if (progress_callback) {
|
|
progress_callback(1.0f, progress_callback_user_data);
|
|
}
|
|
|
|
model.mapping = std::move(ml.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;
|
|
}
|
|
|
|
static bool llama_model_load(
|
|
const std::string & fname,
|
|
llama_model & model,
|
|
int n_gpu_layers,
|
|
int main_gpu,
|
|
const float * tensor_split,
|
|
bool use_mmap,
|
|
bool use_mlock,
|
|
bool vocab_only,
|
|
llama_progress_callback progress_callback,
|
|
void *progress_callback_user_data) {
|
|
try {
|
|
llama_model_loader ml(fname, use_mmap);
|
|
|
|
model.hparams.vocab_only = vocab_only;
|
|
|
|
llm_load_arch (ml, model);
|
|
llm_load_hparams(ml, model);
|
|
llm_load_vocab (ml, model);
|
|
|
|
llm_load_print_meta(ml, model);
|
|
|
|
if (model.hparams.n_vocab != model.vocab.id_to_token.size()) {
|
|
throw std::runtime_error("vocab size mismatch");
|
|
}
|
|
|
|
if (vocab_only) {
|
|
LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
|
|
return true;
|
|
}
|
|
|
|
llm_load_tensors(
|
|
ml, model, n_gpu_layers,
|
|
main_gpu, tensor_split,
|
|
use_mlock, progress_callback, progress_callback_user_data);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
static struct ggml_cgraph * llm_build_llama(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = cparams.n_ctx;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
const float freq_base = cparams.rope_freq_base;
|
|
const float freq_scale = cparams.rope_freq_scale;
|
|
const float norm_rms_eps = hparams.f_norm_rms_eps;
|
|
|
|
const int n_gpu_layers = model.n_gpu_layers;
|
|
|
|
const int32_t n_tokens = batch.n_tokens;
|
|
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
|
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
|
|
|
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
|
|
|
|
//printf("n_kv = %d\n", n_kv);
|
|
|
|
auto & buf_compute = lctx.buf_compute;
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size,
|
|
/*.mem_buffer =*/ buf_compute.data,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
|
}
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
|
|
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inpL);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
|
|
}
|
|
}
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
(void) i_gpu_start;
|
|
|
|
// offload functions set the tensor output backend to GPU
|
|
// tensors are GPU-accelerated if any input or the output has been offloaded
|
|
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
|
offload_func_t offload_func_kq = llama_nop;
|
|
offload_func_t offload_func_v = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (n_gpu_layers > n_layer) {
|
|
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 1) {
|
|
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 2) {
|
|
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
// KQ_scale
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
|
|
}
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
|
offload_func_kq(KQ_mask);
|
|
ggml_set_name(KQ_mask, "KQ_mask");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
float * data = (float *) KQ_mask->data;
|
|
memset(data, 0, ggml_nbytes(KQ_mask));
|
|
|
|
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];
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// KQ_pos - contains the positions
|
|
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
offload_func_kq(KQ_pos);
|
|
ggml_set_name(KQ_pos, "KQ_pos");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_pos);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) KQ_pos->data;
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
data[i] = batch.pos[i];
|
|
}
|
|
}
|
|
|
|
// shift the entire K-cache if needed
|
|
if (do_rope_shift) {
|
|
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
|
|
offload_func_kq(K_shift);
|
|
ggml_set_name(K_shift, "K_shift");
|
|
ggml_allocr_alloc(lctx.alloc, K_shift);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) K_shift->data;
|
|
for (int i = 0; i < n_ctx; ++i) {
|
|
data[i] = kv_self.cells[i].delta;
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * tmp =
|
|
ggml_rope_custom_inplace(ctx0,
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_head_kv, n_ctx,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
|
|
K_shift, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
offload_func_kq(tmp);
|
|
ggml_build_forward_expand(gf, tmp);
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_format_name(inpL, "layer_inp_%d", il);
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (il >= i_gpu_start) {
|
|
offload_func = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_0");
|
|
|
|
// cur = cur*attn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "attention_norm_0");
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
offload_func_kq(tmpk);
|
|
ggml_set_name(tmpk, "tmpk");
|
|
|
|
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
offload_func_kq(tmpq);
|
|
ggml_set_name(tmpq, "tmpq");
|
|
|
|
struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
offload_func_kq(Kcur);
|
|
ggml_set_name(Kcur, "Kcur");
|
|
|
|
struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
offload_func_kq(Qcur);
|
|
ggml_set_name(Qcur, "Qcur");
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [n_tokens, n_embd] V matrix
|
|
|
|
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
offload_func_v(tmpv);
|
|
ggml_set_name(tmpv, "tmpv");
|
|
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
|
|
offload_func_v(Vcur);
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
|
offload_func_kq(k);
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
|
offload_func_v(v);
|
|
ggml_set_name(v, "v");
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
offload_func_kq(Q);
|
|
ggml_set_name(Q, "Q");
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_kv, n_head_kv,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
|
offload_func_kq(K);
|
|
ggml_set_name(K, "K");
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
offload_func_kq(KQ);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd_head)
|
|
// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
|
offload_func_kq(KQ_scaled);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
|
offload_func_kq(KQ_masked);
|
|
ggml_set_name(KQ_masked, "KQ_masked");
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
offload_func_v(KQ_soft_max);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
// split cached V into n_head heads
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_kv, n_embd_head, n_head_kv,
|
|
ggml_element_size(kv_self.v)*n_ctx,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
|
offload_func_v(V);
|
|
ggml_set_name(V, "V");
|
|
|
|
#if 1
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
offload_func_v(KQV);
|
|
ggml_set_name(KQV, "KQV");
|
|
#else
|
|
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
|
|
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
|
|
// is there a better way?
|
|
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
|
|
#endif
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
offload_func_v(KQV_merged);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
|
|
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
|
offload_func_v(cur);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_wo");
|
|
}
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
offload_func(inpFF);
|
|
ggml_set_name(inpFF, "inpFF");
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_1");
|
|
|
|
// cur = cur*ffn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "ffn_norm");
|
|
}
|
|
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model.layers[il].w3,
|
|
cur);
|
|
offload_func(tmp);
|
|
ggml_set_name(tmp, "result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w1,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w1");
|
|
|
|
// SILU activation
|
|
cur = ggml_silu(ctx0, cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu");
|
|
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu_x_result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w2,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w2");
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "inpFF_+_result_w2");
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
|
|
offload_func_nr(cur);
|
|
ggml_set_name(cur, "rms_norm_2");
|
|
|
|
// cur = cur*norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.output_norm);
|
|
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
|
ggml_set_name(cur, "result_norm");
|
|
}
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
static struct ggml_cgraph * llm_build_baichaun(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = cparams.n_ctx;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
const float freq_base = cparams.rope_freq_base;
|
|
const float freq_scale = cparams.rope_freq_scale;
|
|
const float norm_rms_eps = hparams.f_norm_rms_eps;
|
|
|
|
const int n_gpu_layers = model.n_gpu_layers;
|
|
|
|
const int32_t n_tokens = batch.n_tokens;
|
|
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
|
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
|
|
|
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
|
|
|
|
auto & buf_compute = lctx.buf_compute;
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size,
|
|
/*.mem_buffer =*/ buf_compute.data,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
|
}
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
|
|
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inpL);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
|
|
}
|
|
}
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
(void) i_gpu_start;
|
|
|
|
// offload functions set the tensor output backend to GPU
|
|
// tensors are GPU-accelerated if any input or the output has been offloaded
|
|
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
|
offload_func_t offload_func_kq = llama_nop;
|
|
offload_func_t offload_func_v = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (n_gpu_layers > n_layer) {
|
|
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 1) {
|
|
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 2) {
|
|
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
// KQ_scale
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
|
}
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
|
offload_func_kq(KQ_mask);
|
|
ggml_set_name(KQ_mask, "KQ_mask");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
float * data = (float *) KQ_mask->data;
|
|
memset(data, 0, ggml_nbytes(KQ_mask));
|
|
|
|
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];
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// KQ_pos - contains the positions
|
|
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
offload_func_kq(KQ_pos);
|
|
ggml_set_name(KQ_pos, "KQ_pos");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_pos);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) KQ_pos->data;
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
data[i] = batch.pos[i];
|
|
}
|
|
}
|
|
|
|
// shift the entire K-cache if needed
|
|
if (do_rope_shift) {
|
|
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
|
|
offload_func_kq(K_shift);
|
|
ggml_set_name(K_shift, "K_shift");
|
|
ggml_allocr_alloc(lctx.alloc, K_shift);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) K_shift->data;
|
|
for (int i = 0; i < n_ctx; ++i) {
|
|
data[i] = kv_self.cells[i].delta;
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * tmp =
|
|
ggml_rope_custom_inplace(ctx0,
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_head_kv, n_ctx,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
|
|
K_shift, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
offload_func_kq(tmp);
|
|
ggml_build_forward_expand(gf, tmp);
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_format_name(inpL, "layer_inp_%d", il);
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (il >= i_gpu_start) {
|
|
offload_func = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_0");
|
|
|
|
// cur = cur*attn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "attention_norm_0");
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
offload_func_kq(tmpk);
|
|
ggml_set_name(tmpk, "tmpk");
|
|
|
|
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
offload_func_kq(tmpq);
|
|
ggml_set_name(tmpq, "tmpq");
|
|
|
|
struct ggml_tensor * Kcur;
|
|
struct ggml_tensor * Qcur;
|
|
switch (model.type) {
|
|
case MODEL_7B:
|
|
Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
break;
|
|
case MODEL_13B:
|
|
Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, n_tokens);
|
|
Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, n_tokens);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
offload_func_kq(Kcur);
|
|
ggml_set_name(Kcur, "Kcur");
|
|
|
|
offload_func_kq(Qcur);
|
|
ggml_set_name(Qcur, "Qcur");
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [n_tokens, n_embd] V matrix
|
|
|
|
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
offload_func_v(tmpv);
|
|
ggml_set_name(tmpv, "tmpv");
|
|
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
|
|
offload_func_v(Vcur);
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
|
offload_func_kq(k);
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
|
offload_func_v(v);
|
|
ggml_set_name(v, "v");
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
offload_func_kq(Q);
|
|
ggml_set_name(Q, "Q");
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_kv, n_head_kv,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
|
offload_func_kq(K);
|
|
ggml_set_name(K, "K");
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
offload_func_kq(KQ);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd_head)
|
|
// KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
|
offload_func_kq(KQ_scaled);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
struct ggml_tensor * KQ_masked;
|
|
struct ggml_tensor * KQ_scaled_alibi;
|
|
|
|
switch (model.type) {
|
|
case MODEL_7B:
|
|
KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
|
break;
|
|
case MODEL_13B:
|
|
// TODO: replace with ggml_add()
|
|
KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
|
|
ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
|
|
KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
|
|
break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
offload_func_v(KQ_soft_max);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
// split cached V into n_head heads
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_kv, n_embd_head, n_head_kv,
|
|
ggml_element_size(kv_self.v)*n_ctx,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
|
offload_func_v(V);
|
|
ggml_set_name(V, "V");
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
offload_func_v(KQV);
|
|
ggml_set_name(KQV, "KQV");
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
offload_func_v(KQV_merged);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
|
|
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
|
offload_func_v(cur);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_wo");
|
|
}
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
offload_func(inpFF);
|
|
ggml_set_name(inpFF, "inpFF");
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_1");
|
|
|
|
// cur = cur*ffn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "ffn_norm");
|
|
}
|
|
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model.layers[il].w3,
|
|
cur);
|
|
offload_func(tmp);
|
|
ggml_set_name(tmp, "result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w1,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w1");
|
|
|
|
// SILU activation
|
|
cur = ggml_silu(ctx0, cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu");
|
|
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu_x_result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w2,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w2");
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "inpFF_+_result_w2");
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
|
|
offload_func_nr(cur);
|
|
ggml_set_name(cur, "rms_norm_2");
|
|
|
|
// cur = cur*norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.output_norm);
|
|
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
|
ggml_set_name(cur, "result_norm");
|
|
}
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
static struct ggml_cgraph * llm_build_refact(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = cparams.n_ctx;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
|
|
const float norm_rms_eps = hparams.f_norm_rms_eps;
|
|
|
|
const int n_gpu_layers = model.n_gpu_layers;
|
|
|
|
const int32_t n_tokens = batch.n_tokens;
|
|
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
|
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
|
|
|
// printf("n_kv = %d\n", n_kv);
|
|
|
|
auto & buf_compute = lctx.buf_compute;
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size,
|
|
/*.mem_buffer =*/ buf_compute.data,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
|
}
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
|
|
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inpL);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
|
|
}
|
|
}
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
(void) i_gpu_start;
|
|
|
|
// offload functions set the tensor output backend to GPU
|
|
// tensors are GPU-accelerated if any input or the output has been offloaded
|
|
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
|
offload_func_t offload_func_kq = llama_nop;
|
|
offload_func_t offload_func_v = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (n_gpu_layers > n_layer) {
|
|
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 1) {
|
|
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 2) {
|
|
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
// KQ_scale
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
|
|
}
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
|
offload_func_kq(KQ_mask);
|
|
ggml_set_name(KQ_mask, "KQ_mask");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
float * data = (float *) KQ_mask->data;
|
|
memset(data, 0, ggml_nbytes(KQ_mask));
|
|
|
|
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];
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_format_name(inpL, "layer_inp_%d", il);
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (il >= i_gpu_start) {
|
|
offload_func = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_0");
|
|
|
|
// cur = cur*attn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "attention_norm_0");
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K
|
|
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
offload_func_kq(tmpk);
|
|
ggml_set_name(tmpk, "tmpk");
|
|
|
|
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
offload_func_kq(tmpq);
|
|
ggml_set_name(tmpq, "tmpq");
|
|
|
|
struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
|
|
offload_func_kq(Kcur);
|
|
ggml_set_name(Kcur, "Kcur");
|
|
|
|
struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
|
|
offload_func_kq(Qcur);
|
|
ggml_set_name(Qcur, "Qcur");
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [n_tokens, n_embd] V matrix
|
|
|
|
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
offload_func_v(tmpv);
|
|
ggml_set_name(tmpv, "tmpv");
|
|
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
|
|
offload_func_v(Vcur);
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
|
offload_func_kq(k);
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
|
offload_func_v(v);
|
|
ggml_set_name(v, "v");
|
|
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
offload_func_kq(Q);
|
|
ggml_set_name(Q, "Q");
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_kv, n_head_kv,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
|
offload_func_kq(K);
|
|
ggml_set_name(K, "K");
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
offload_func_kq(KQ);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd_head)
|
|
// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
|
offload_func_kq(KQ_scaled);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
|
|
ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
|
|
|
|
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
|
|
offload_func_kq(KQ_masked);
|
|
ggml_set_name(KQ_masked, "KQ_masked");
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
offload_func_v(KQ_soft_max);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
// split cached V into n_head heads
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_kv, n_embd_head, n_head_kv,
|
|
ggml_element_size(kv_self.v)*n_ctx,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
|
offload_func_v(V);
|
|
ggml_set_name(V, "V");
|
|
|
|
#if 1
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
offload_func_v(KQV);
|
|
ggml_set_name(KQV, "KQV");
|
|
#else
|
|
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
|
|
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
|
|
// is there a better way?
|
|
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
|
|
#endif
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
offload_func_v(KQV_merged);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
|
|
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
|
offload_func_v(cur);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_wo");
|
|
}
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
offload_func(inpFF);
|
|
ggml_set_name(inpFF, "inpFF");
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_1");
|
|
|
|
// cur = cur*ffn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "ffn_norm");
|
|
}
|
|
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model.layers[il].w3,
|
|
cur);
|
|
offload_func(tmp);
|
|
ggml_set_name(tmp, "result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w1,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w1");
|
|
|
|
// SILU activation
|
|
cur = ggml_silu(ctx0, cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu");
|
|
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu_x_result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w2,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w2");
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "inpFF_+_result_w2");
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
|
|
offload_func_nr(cur);
|
|
ggml_set_name(cur, "rms_norm_2");
|
|
|
|
// cur = cur*norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.output_norm);
|
|
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
|
ggml_set_name(cur, "result_norm");
|
|
}
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
static struct ggml_cgraph * llm_build_falcon(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = cparams.n_ctx;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
const float freq_base = cparams.rope_freq_base;
|
|
const float freq_scale = cparams.rope_freq_scale;
|
|
const float norm_eps = hparams.f_norm_eps;
|
|
|
|
const int n_gpu_layers = model.n_gpu_layers;
|
|
|
|
const int32_t n_tokens = batch.n_tokens;
|
|
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
|
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
|
|
|
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
|
|
|
|
//printf("kv_head = %d, n_kv = %d, n_tokens = %d, n_ctx = %d, is_measure = %d, has_shift = %d\n",
|
|
// kv_head, n_kv, n_tokens, n_ctx, ggml_allocr_is_measure(lctx.alloc), kv_self.has_shift);
|
|
|
|
auto & buf_compute = lctx.buf_compute;
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size,
|
|
/*.mem_buffer =*/ buf_compute.data,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
|
}
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
|
|
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inpL);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
|
|
}
|
|
}
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
(void) i_gpu_start;
|
|
|
|
// offload functions set the tensor output backend to GPU
|
|
// tensors are GPU-accelerated if any input or the output has been offloaded
|
|
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
|
offload_func_t offload_func_kq = llama_nop;
|
|
offload_func_t offload_func_v = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (n_gpu_layers > n_layer) {
|
|
offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 1) {
|
|
offload_func_v = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
if (n_gpu_layers > n_layer + 2) {
|
|
offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
// KQ_scale
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
|
}
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
|
offload_func_kq(KQ_mask);
|
|
ggml_set_name(KQ_mask, "KQ_mask");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
float * data = (float *) KQ_mask->data;
|
|
memset(data, 0, ggml_nbytes(KQ_mask));
|
|
|
|
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];
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// KQ_pos - contains the positions
|
|
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
offload_func_kq(KQ_pos);
|
|
ggml_set_name(KQ_pos, "KQ_pos");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_pos);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) KQ_pos->data;
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
data[i] = batch.pos[i];
|
|
}
|
|
}
|
|
|
|
// shift the entire K-cache if needed
|
|
if (do_rope_shift) {
|
|
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
|
|
offload_func_kq(K_shift);
|
|
ggml_set_name(K_shift, "K_shift");
|
|
ggml_allocr_alloc(lctx.alloc, K_shift);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) K_shift->data;
|
|
for (int i = 0; i < n_ctx; ++i) {
|
|
data[i] = kv_self.cells[i].delta;
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * tmp =
|
|
ggml_rope_custom_inplace(ctx0,
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_head_kv, n_ctx,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
|
|
K_shift, n_embd_head, 2, 0, freq_base, freq_scale);
|
|
offload_func_kq(tmp);
|
|
ggml_build_forward_expand(gf, tmp);
|
|
}
|
|
}
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * attn_norm;
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (il >= i_gpu_start) {
|
|
offload_func = ggml_cuda_assign_buffers_no_alloc;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
// self-attention
|
|
// TODO: refactor into common function (shared with LLaMA)
|
|
{
|
|
attn_norm = ggml_norm(ctx0, inpL, norm_eps);
|
|
offload_func(attn_norm);
|
|
|
|
attn_norm = ggml_add(ctx0,
|
|
ggml_mul(ctx0, attn_norm, model.layers[il].attn_norm),
|
|
model.layers[il].attn_norm_b);
|
|
offload_func(attn_norm->src[0]);
|
|
offload_func(attn_norm);
|
|
|
|
if (model.layers[il].attn_norm_2) { // Falcon-40B
|
|
cur = ggml_norm(ctx0, inpL, norm_eps);
|
|
offload_func(cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0, cur, model.layers[il].attn_norm_2),
|
|
model.layers[il].attn_norm_2_b);
|
|
offload_func(cur->src[0]);
|
|
offload_func(cur);
|
|
} else { // Falcon 7B
|
|
cur = attn_norm;
|
|
}
|
|
|
|
// compute QKV
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
offload_func_kq(cur);
|
|
|
|
// Note that the strides for Kcur, Vcur are set up so that the
|
|
// resulting views are misaligned with the tensor's storage
|
|
// (by applying the K/V offset we shift the tensor's original
|
|
// view to stick out behind the viewed QKV tensor's allocated
|
|
// memory, so to say). This is ok because no actual accesses
|
|
// happen to that out-of-range memory, but it can require some
|
|
// trickery when trying to accurately dump these views for
|
|
// debugging.
|
|
|
|
const size_t wsize = ggml_type_size(cur->type);
|
|
|
|
// TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for
|
|
// non-contiguous views is added for the rope operator
|
|
struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
|
|
ctx0, cur, n_embd_head, n_head, n_tokens,
|
|
wsize * n_embd_head,
|
|
wsize * n_embd_head * (n_head + 2 * n_head_kv),
|
|
0));
|
|
offload_func_kq(tmpq);
|
|
|
|
struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d(
|
|
ctx0, cur, n_embd_head, n_head_kv, n_tokens,
|
|
wsize * n_embd_head,
|
|
wsize * n_embd_head * (n_head + 2 * n_head_kv),
|
|
wsize * n_embd_head * n_head));
|
|
offload_func_kq(tmpk);
|
|
|
|
struct ggml_tensor * tmpv = ggml_view_3d(
|
|
ctx0, cur, n_embd_head, n_head_kv, n_tokens,
|
|
wsize * n_embd_head,
|
|
wsize * n_embd_head * (n_head + 2 * n_head_kv),
|
|
wsize * n_embd_head * (n_head + n_head_kv));
|
|
offload_func_v(tmpv);
|
|
|
|
// using mode = 2 for neox mode
|
|
struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale);
|
|
offload_func_kq(Qcur);
|
|
struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale);
|
|
offload_func_kq(Kcur);
|
|
|
|
{
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
|
|
offload_func_v(Vcur);
|
|
offload_func_v(Vcur->src[0]->src[0]);
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
|
offload_func_kq(k);
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
|
offload_func_v(v);
|
|
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
|
offload_func_kq(Q);
|
|
ggml_set_name(Q, "Q");
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_kv, n_head_kv,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
|
offload_func_kq(K);
|
|
ggml_set_name(K, "K");
|
|
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
offload_func_kq(KQ);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
|
offload_func_kq(KQ_scaled);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
|
offload_func_kq(KQ_masked);
|
|
ggml_set_name(KQ_masked, "KQ_masked");
|
|
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
offload_func_v(KQ_soft_max);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_kv, n_embd_head, n_head_kv,
|
|
ggml_element_size(kv_self.v)*n_ctx,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
|
offload_func_v(V);
|
|
ggml_set_name(V, "V");
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
offload_func_v(KQV);
|
|
ggml_set_name(KQV, "KQV");
|
|
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
offload_func_v(KQV_merged);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
|
offload_func_v(cur);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_wo");
|
|
}
|
|
|
|
struct ggml_tensor * attn_out = cur;
|
|
|
|
// feed forward
|
|
{
|
|
struct ggml_tensor * inpFF = attn_norm;
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].w3, inpFF);
|
|
offload_func(cur);
|
|
|
|
cur = ggml_gelu(ctx0, cur);
|
|
offload_func(cur);
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
|
|
offload_func(cur);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, attn_out);
|
|
offload_func(cur);
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
offload_func(cur);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_norm(ctx0, cur, norm_eps);
|
|
offload_func_nr(cur);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0, cur, model.output_norm),
|
|
model.output_norm_b);
|
|
ggml_set_name(cur, "result_norm");
|
|
}
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
static struct ggml_cgraph * llm_build_starcoder(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = cparams.n_ctx;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
|
|
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
const float norm_eps = hparams.f_norm_eps;
|
|
|
|
const int32_t n_tokens = batch.n_tokens;
|
|
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
|
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
|
|
|
auto & buf_compute = lctx.buf_compute;
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size,
|
|
/*.mem_buffer =*/ buf_compute.data,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * token;
|
|
struct ggml_tensor * position;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
|
}
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
|
|
token = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
|
|
token = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, token);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(token->data, batch.embd, n_tokens * n_embd * ggml_element_size(token));
|
|
}
|
|
}
|
|
|
|
{
|
|
// Compute position embeddings.
|
|
struct ggml_tensor * inp_positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
ggml_allocr_alloc(lctx.alloc, inp_positions);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
((int32_t *) inp_positions->data)[i] = batch.pos[i];
|
|
}
|
|
}
|
|
ggml_set_name(inp_positions, "inp_positions");
|
|
|
|
position = ggml_get_rows(ctx0, model.pos_embeddings, inp_positions);
|
|
}
|
|
|
|
// KQ_scale
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
|
}
|
|
|
|
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
|
ggml_set_name(KQ_mask, "KQ_mask");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
float * data = (float *) KQ_mask->data;
|
|
memset(data, 0, ggml_nbytes(KQ_mask));
|
|
|
|
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];
|
|
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, token, position);
|
|
ggml_set_name(inpL, "inpL");
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
{
|
|
// Norm
|
|
cur = ggml_norm(ctx0, inpL, norm_eps);
|
|
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].attn_norm), model.layers[il].attn_norm_b);
|
|
}
|
|
|
|
{
|
|
// Self Attention
|
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);
|
|
|
|
struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*n_embd);
|
|
struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*n_embd);
|
|
struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));
|
|
|
|
struct ggml_tensor * Qcur = tmpq;
|
|
struct ggml_tensor * Kcur = tmpk;
|
|
|
|
{
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd_gqa, n_tokens));
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
|
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd_head, n_head, n_tokens)),
|
|
0, 2, 1, 3);
|
|
ggml_set_name(Q, "Q");
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_kv, n_head_kv,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
|
ggml_set_name(K, "K");
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd_head)
|
|
// KQ_scaled shape [n_past + n_tokens, n_tokens, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
|
ggml_set_name(KQ_masked, "KQ_masked");
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
// split cached V into n_head heads
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_kv, n_embd_head, n_head_kv,
|
|
ggml_element_size(kv_self.v)*n_ctx,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
|
ggml_set_name(V, "V");
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
ggml_set_name(KQV, "KQV");
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
|
|
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
}
|
|
|
|
// Projection
|
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wo, cur), model.layers[il].bo);
|
|
|
|
// Add the input
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
// FF
|
|
{
|
|
// Norm
|
|
{
|
|
cur = ggml_norm(ctx0, inpFF, norm_eps);
|
|
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ffn_norm), model.layers[il].ffn_norm_b);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w3, cur), model.layers[il].b3);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
// Projection
|
|
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].w2, cur), model.layers[il].b2);
|
|
}
|
|
|
|
inpL = ggml_add(ctx0, cur, inpFF);
|
|
}
|
|
|
|
// Output Norm
|
|
{
|
|
cur = ggml_norm(ctx0, inpL, norm_eps);
|
|
cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.output_norm), model.output_norm_b);
|
|
}
|
|
ggml_set_name(cur, "result_norm");
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
|
|
static struct ggml_cgraph * llm_build_persimmon(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const auto & cparams = lctx.cparams;
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = cparams.n_ctx;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
const size_t n_rot = n_embd_head / 2;
|
|
|
|
const float freq_base = cparams.rope_freq_base;
|
|
const float freq_scale = cparams.rope_freq_scale;
|
|
const float norm_eps = hparams.f_norm_eps;
|
|
|
|
const int n_gpu_layers = model.n_gpu_layers;
|
|
|
|
|
|
const int32_t n_tokens = batch.n_tokens;
|
|
const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
|
|
const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
|
|
|
|
const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
|
|
|
|
auto & buf_compute = lctx.buf_compute;
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size,
|
|
/*.mem_buffer =*/ buf_compute.data,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (batch.token) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
|
|
}
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
|
|
ggml_allocr_alloc(lctx.alloc, inpL);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
|
|
}
|
|
}
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
(void) i_gpu_start;
|
|
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
|
offload_func_t offload_func_kq = llama_nop;
|
|
offload_func_t offload_func_v = llama_nop;
|
|
// KQ_scale
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
|
|
}
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
|
|
offload_func_kq(KQ_mask);
|
|
ggml_set_name(KQ_mask, "KQ_mask");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_mask);
|
|
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
float * data = (float *) KQ_mask->data;
|
|
memset(data, 0, ggml_nbytes(KQ_mask));
|
|
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];
|
|
for (int i = 0; i < n_kv; ++i) {
|
|
if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
|
|
data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
|
|
offload_func_kq(KQ_pos);
|
|
ggml_set_name(KQ_pos, "KQ_pos");
|
|
ggml_allocr_alloc(lctx.alloc, KQ_pos);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) KQ_pos->data;
|
|
for (int i = 0; i < n_tokens; ++i) {
|
|
data[i] = batch.pos[i];
|
|
}
|
|
}
|
|
if (do_rope_shift) {
|
|
struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
|
|
offload_func_kq(K_shift);
|
|
ggml_set_name(K_shift, "K_shift");
|
|
ggml_allocr_alloc(lctx.alloc, K_shift);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
int * data = (int *) K_shift->data;
|
|
for (int i = 0; i < n_ctx; ++i) {
|
|
data[i] = kv_self.cells[i].delta;
|
|
}
|
|
}
|
|
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,
|
|
n_rot, n_head, n_ctx,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*(n_embd_head*n_ctx*il)
|
|
),
|
|
K_shift, n_rot, 2, 0, freq_base, freq_scale);
|
|
offload_func_kq(tmp);
|
|
ggml_build_forward_expand(gf, tmp);
|
|
}
|
|
}
|
|
for (int il=0; il < n_layer; ++il) {
|
|
struct ggml_tensor * residual = inpL;
|
|
offload_func_t offload_func = llama_nop;
|
|
{
|
|
cur = ggml_norm(ctx0, inpL, norm_eps);
|
|
offload_func(cur);
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
|
|
offload_func(cur);
|
|
cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
|
|
offload_func(cur);
|
|
ggml_format_name(cur, "input_layernorm_%d", il);
|
|
}
|
|
// self attention
|
|
{
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
|
offload_func_kq(cur);
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
|
|
offload_func_kq(cur);
|
|
|
|
// split qkv
|
|
GGML_ASSERT(n_head_kv == n_head);
|
|
ggml_set_name(cur, format("qkv_%d", il).c_str());
|
|
struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
|
|
offload_func_kq(tmpqkv);
|
|
struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
|
|
offload_func_kq(tmpqkv_perm);
|
|
ggml_format_name(tmpqkv_perm, "tmpqkv_perm_%d", 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
|
|
);
|
|
offload_func_kq(tmpq);
|
|
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
|
|
);
|
|
offload_func_kq(tmpk);
|
|
// Q/K Layernorm
|
|
tmpq = ggml_norm(ctx0, tmpq, norm_eps);
|
|
offload_func_kq(tmpq);
|
|
tmpq = ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm);
|
|
offload_func_kq(tmpq);
|
|
tmpq = ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b);
|
|
offload_func_kq(tmpq);
|
|
|
|
tmpk = ggml_norm(ctx0, tmpk, norm_eps);
|
|
offload_func_v(tmpk);
|
|
tmpk = ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
|
|
offload_func_v(tmpk);
|
|
tmpk = ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
|
|
offload_func_v(tmpk);
|
|
|
|
// 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
|
|
);
|
|
offload_func_kq(qrot);
|
|
ggml_format_name(qrot, "qrot_%d", 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
|
|
);
|
|
offload_func_kq(krot);
|
|
ggml_format_name(krot, "krot_%d", 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
|
|
);
|
|
offload_func_kq(qpass);
|
|
ggml_format_name(qpass, "qpass_%d", 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
|
|
);
|
|
offload_func_kq(kpass);
|
|
ggml_format_name(kpass, "kpass_%d", il);
|
|
|
|
struct ggml_tensor * qrotated = ggml_rope_custom(
|
|
ctx0, qrot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
|
|
);
|
|
offload_func_kq(qrotated);
|
|
struct ggml_tensor * krotated = ggml_rope_custom(
|
|
ctx0, krot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
|
|
);
|
|
offload_func_kq(krotated);
|
|
// 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));
|
|
offload_func_kq(qrotated);
|
|
krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
|
|
offload_func_kq(krotated);
|
|
|
|
qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
|
|
offload_func_kq(qpass);
|
|
kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
|
|
offload_func_kq(kpass);
|
|
|
|
struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
|
|
offload_func_kq(Qcur);
|
|
struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
|
|
offload_func_kq(Kcur);
|
|
|
|
struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
|
|
offload_func_kq(Q);
|
|
|
|
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
|
|
offload_func_kq(Kcur);
|
|
{
|
|
struct ggml_tensor * tmpv = 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
|
|
);
|
|
offload_func_v(tmpv);
|
|
// store K, V in cache
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
|
|
offload_func_v(Vcur);
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(
|
|
ctx0, kv_self.k, n_tokens*n_embd_gqa,
|
|
(ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)
|
|
);
|
|
offload_func_kq(k);
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
|
offload_func_v(v);
|
|
ggml_set_name(v, "v");
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
struct ggml_tensor * K = ggml_view_3d(ctx0, kv_self.k,
|
|
n_embd_head, n_kv, n_head_kv,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa,
|
|
ggml_element_size(kv_self.k)*n_embd_head,
|
|
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
|
|
|
offload_func_kq(K);
|
|
ggml_format_name(K, "K_%d", il);
|
|
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
offload_func_kq(KQ);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
|
offload_func_kq(KQ_scaled);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
|
offload_func_kq(KQ_masked);
|
|
ggml_set_name(KQ_masked, "KQ_masked");
|
|
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
|
offload_func_kq(KQ_soft_max);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_kv, n_embd_head, n_head_kv,
|
|
ggml_element_size(kv_self.v)*n_ctx,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
|
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
|
offload_func_v(V);
|
|
ggml_set_name(V, "V");
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
offload_func_v(KQV);
|
|
ggml_set_name(KQV, "KQV");
|
|
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
offload_func_v(KQV_merged);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
|
offload_func_v(cur);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
|
|
offload_func(cur);
|
|
cur = ggml_add(ctx0, cur, model.layers[il].bo);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_wo");
|
|
}
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, residual, cur);
|
|
offload_func(inpFF);
|
|
ggml_set_name(inpFF, "inpFF");
|
|
{
|
|
// MLP
|
|
{
|
|
// Norm
|
|
cur = ggml_norm(ctx0, inpFF, norm_eps);
|
|
offload_func(cur);
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0, cur, model.layers[il].ffn_norm),
|
|
model.layers[il].ffn_norm_b
|
|
);
|
|
ggml_set_name(cur, "ffn_norm");
|
|
offload_func(cur);
|
|
}
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
|
|
offload_func(cur);
|
|
|
|
cur = ggml_add(ctx0, cur, model.layers[il].b3);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_ffn_up");
|
|
|
|
cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur));
|
|
ggml_set_name(cur, "result_ffn_act");
|
|
offload_func(cur);
|
|
offload_func(cur->src[0]);
|
|
|
|
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
|
|
offload_func(cur);
|
|
cur = ggml_add(ctx0,
|
|
cur,
|
|
model.layers[il].b2);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "outFF");
|
|
}
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "inpFF_+_outFF");
|
|
inpL = cur;
|
|
}
|
|
cur = inpL;
|
|
{
|
|
cur = ggml_norm(ctx0, cur, norm_eps);
|
|
offload_func_nr(cur);
|
|
cur = ggml_mul(ctx0, cur, model.output_norm);
|
|
offload_func_nr(cur);
|
|
|
|
cur = ggml_add(ctx0, cur, model.output_norm_b);
|
|
// offload_func_nr(cur);
|
|
|
|
ggml_set_name(cur, "result_norm");
|
|
}
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
ggml_build_forward_expand(gf, cur);
|
|
ggml_free(ctx0);
|
|
return gf;
|
|
}
|
|
|
|
static struct ggml_cgraph * llama_build_graph(
|
|
llama_context & lctx,
|
|
const llama_batch & batch) {
|
|
const auto & model = lctx.model;
|
|
|
|
struct ggml_cgraph * result = NULL;
|
|
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
{
|
|
result = llm_build_llama(lctx, batch);
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
result = llm_build_baichaun(lctx, batch);
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
result = llm_build_falcon(lctx, batch);
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
result = llm_build_starcoder(lctx, batch);
|
|
} break;
|
|
case LLM_ARCH_PERSIMMON:
|
|
{
|
|
result = llm_build_persimmon(lctx, batch);
|
|
} break;
|
|
case LLM_ARCH_REFACT:
|
|
{
|
|
result = llm_build_refact(lctx, batch);
|
|
} break;
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
// decode a batch of tokens by evaluating the transformer
|
|
//
|
|
// - lctx: llama context
|
|
// - batch: batch to evaluate
|
|
// - n_threads: number of threads to use
|
|
//
|
|
// return 0 on success
|
|
// return positive int on warning
|
|
// return negative int on error
|
|
//
|
|
static int llama_decode_internal(
|
|
llama_context & lctx,
|
|
llama_batch batch) {
|
|
const uint32_t n_tokens = batch.n_tokens;
|
|
|
|
if (n_tokens == 0) {
|
|
LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
|
|
return -1;
|
|
}
|
|
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
const auto & cparams = lctx.cparams;
|
|
|
|
const auto n_batch = cparams.n_batch;
|
|
|
|
GGML_ASSERT(n_tokens <= n_batch);
|
|
|
|
int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
|
|
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
|
|
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
#ifdef GGML_USE_MPI
|
|
// TODO: needs fix after #3228
|
|
GGML_ASSERT(false && "not implemented");
|
|
//ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
|
|
#endif
|
|
|
|
GGML_ASSERT(n_threads > 0);
|
|
|
|
auto & kv_self = lctx.kv_self;
|
|
|
|
GGML_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_vocab = hparams.n_vocab;
|
|
|
|
// helpers for smoother batch API transistion
|
|
// after deprecating the llama_eval calls, these will be removed
|
|
std::vector<llama_pos> pos;
|
|
std::vector<llama_seq_id> seq_id;
|
|
|
|
if (batch.pos == nullptr) {
|
|
pos.resize(n_tokens);
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
|
|
}
|
|
|
|
batch.pos = pos.data();
|
|
}
|
|
|
|
if (batch.seq_id == nullptr) {
|
|
seq_id.resize(n_tokens);
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
seq_id[i] = batch.all_seq_id;
|
|
}
|
|
|
|
batch.seq_id = seq_id.data();
|
|
}
|
|
|
|
if (!llama_kv_cache_find_slot(kv_self, batch)) {
|
|
return 1;
|
|
}
|
|
|
|
// 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::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)); // TODO: this might be better for CUDA?
|
|
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self)));
|
|
|
|
//printf("kv_self.n = %d\n", kv_self.n);
|
|
|
|
ggml_allocr_reset(lctx.alloc);
|
|
|
|
ggml_cgraph * gf = llama_build_graph(lctx, batch);
|
|
|
|
ggml_allocr_alloc_graph(lctx.alloc, gf);
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
for (int i = 0; i < gf->n_leafs; i++) {
|
|
ggml_tensor * node = gf->leafs[i];
|
|
if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
|
|
ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
|
|
ggml_cuda_copy_to_device(node);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < gf->n_nodes; i++) {
|
|
ggml_tensor * node = gf->nodes[i];
|
|
if (node->backend == GGML_BACKEND_GPU && node->extra == NULL) {
|
|
ggml_cuda_assign_scratch_offset(node, (char*)node->data - (char *) lctx.buf_alloc.data);
|
|
}
|
|
}
|
|
|
|
ggml_cuda_set_mul_mat_q(cparams.mul_mat_q);
|
|
#endif
|
|
|
|
// 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
|
|
if (n_tokens >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
|
|
n_threads = std::min(4, n_threads);
|
|
}
|
|
|
|
// If all tensors can be run on the GPU then using more than 1 thread is detrimental.
|
|
const bool full_offload_supported = model.arch == LLM_ARCH_LLAMA ||
|
|
model.arch == LLM_ARCH_BAICHUAN ||
|
|
model.arch == LLM_ARCH_FALCON ||
|
|
model.arch == LLM_ARCH_REFACT;
|
|
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
|
|
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
|
|
n_threads = 1;
|
|
}
|
|
|
|
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
|
|
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
|
|
|
|
GGML_ASSERT(strcmp(res->name, "result_output") == 0);
|
|
GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
|
|
|
|
#if GGML_USE_MPI
|
|
const int64_t n_layer = hparams.n_layer;
|
|
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
|
|
#endif
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (lctx.ctx_metal) {
|
|
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
|
|
ggml_metal_graph_compute(lctx.ctx_metal, gf);
|
|
} else {
|
|
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
|
|
}
|
|
#else
|
|
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
|
|
#endif
|
|
|
|
#if GGML_USE_MPI
|
|
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
|
|
#endif
|
|
|
|
// update the kv ring buffer
|
|
lctx.kv_self.has_shift = false;
|
|
lctx.kv_self.head += n_tokens;
|
|
// Ensure kv cache head points to a valid index.
|
|
if (lctx.kv_self.head >= lctx.kv_self.size) {
|
|
lctx.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
|
|
{
|
|
auto & logits_out = lctx.logits;
|
|
|
|
if (batch.logits) {
|
|
logits_out.resize(n_vocab * n_tokens);
|
|
for (uint32_t i = 0; i < n_tokens; i++) {
|
|
if (batch.logits[i] == 0) {
|
|
continue;
|
|
}
|
|
memcpy(logits_out.data() + (n_vocab*i), (float *) ggml_get_data(res) + (n_vocab*i), sizeof(float)*n_vocab);
|
|
}
|
|
} else if (lctx.logits_all) {
|
|
logits_out.resize(n_vocab * n_tokens);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(res), sizeof(float)*n_vocab*n_tokens);
|
|
} else {
|
|
logits_out.resize(n_vocab);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(res) + (n_vocab*(n_tokens - 1)), sizeof(float)*n_vocab);
|
|
}
|
|
}
|
|
|
|
// extract embeddings
|
|
if (!lctx.embedding.empty()) {
|
|
auto & embedding_out = lctx.embedding;
|
|
|
|
embedding_out.resize(n_embd);
|
|
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(n_tokens - 1)), sizeof(float)*n_embd);
|
|
}
|
|
|
|
// measure the performance only for the single-token evals
|
|
if (n_tokens == 1) {
|
|
lctx.t_eval_us += ggml_time_us() - t_start_us;
|
|
lctx.n_eval++;
|
|
}
|
|
else if (n_tokens > 1) {
|
|
lctx.t_p_eval_us += ggml_time_us() - t_start_us;
|
|
lctx.n_p_eval += n_tokens;
|
|
}
|
|
|
|
// get a more accurate load time, upon first eval
|
|
// TODO: fix this
|
|
if (!lctx.has_evaluated_once) {
|
|
lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
|
|
lctx.has_evaluated_once = true;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
//
|
|
// 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) {
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
|
|
}
|
|
|
|
static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
|
|
}
|
|
|
|
static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
|
|
return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
|
|
}
|
|
|
|
static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
|
|
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) {
|
|
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_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_to_bytes_bpe(token_data.text);
|
|
}
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) {
|
|
switch (llama_vocab_get_type(vocab)) {
|
|
case LLAMA_VOCAB_TYPE_SPM: {
|
|
char buf[7];
|
|
int result = snprintf(buf, sizeof(buf), "<0x%02X>", ch);
|
|
GGML_ASSERT(0 <= result && result < 7);
|
|
return vocab.token_to_id.at(buf);
|
|
}
|
|
case LLAMA_VOCAB_TYPE_BPE: {
|
|
return vocab.token_to_id.at(bytes_to_unicode_bpe(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.
|
|
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 = 1;
|
|
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 fnished 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());
|
|
|
|
auto cps = codepoints_from_utf8(text);
|
|
for (size_t i = 0; i < cps.size(); ++i)
|
|
text_utf.emplace_back(codepoint_to_utf8(cps[i]));
|
|
|
|
for (int i = 0; i < (int)text_utf.size(); i++) {
|
|
const std::string & utf_char = text_utf[i];
|
|
bool split_condition = false;
|
|
// const char* text_pos = raw_text_p + utf_char.seq_offset_bytes;
|
|
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 (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) {
|
|
collecting_letter = true;
|
|
collecting = true;
|
|
}
|
|
else if (codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || (!token.size() && utf_char == " " && codepoint_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) {
|
|
collecting_numeric = true;
|
|
collecting = true;
|
|
}
|
|
else if (
|
|
((codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (codepoint_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) ||
|
|
(!token.size() && utf_char == " " && codepoint_type(utf_char_next) != CODEPOINT_TYPE_LETTER && codepoint_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE)
|
|
) {
|
|
collecting_special = true;
|
|
collecting = true;
|
|
}
|
|
else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && codepoint_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) {
|
|
collecting_whitespace_lookahead = true;
|
|
collecting = true;
|
|
}
|
|
else if (codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) {
|
|
split_condition = true;
|
|
}
|
|
}
|
|
else if (!split_condition && collecting) {
|
|
if (collecting_letter && codepoint_type(utf_char) != CODEPOINT_TYPE_LETTER) {
|
|
split_condition = true;
|
|
}
|
|
else if (collecting_numeric && codepoint_type(utf_char) != CODEPOINT_TYPE_DIGIT) {
|
|
split_condition = true;
|
|
}
|
|
else if (collecting_special && (codepoint_type(utf_char) == CODEPOINT_TYPE_LETTER || codepoint_type(utf_char) == CODEPOINT_TYPE_DIGIT || codepoint_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) {
|
|
split_condition = true;
|
|
}
|
|
else if (collecting_whitespace_lookahead && codepoint_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) {
|
|
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 += bytes_to_unicode_bpe(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;
|
|
};
|
|
|
|
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) {
|
|
std::vector<llama_vocab::id> output;
|
|
|
|
// OG tokenizer behavior:
|
|
//
|
|
// tokenizer.encode('', add_bos=True) returns [1]
|
|
// tokenizer.encode('', add_bos=False) returns []
|
|
|
|
if (bos && vocab.special_bos_id != -1) {
|
|
output.push_back(vocab.special_bos_id);
|
|
}
|
|
|
|
if (raw_text.empty()) {
|
|
return output;
|
|
}
|
|
|
|
switch (vocab.type) {
|
|
case LLAMA_VOCAB_TYPE_SPM:
|
|
{
|
|
// without adding this leading whitespace, we do not get the same results as the original tokenizer
|
|
raw_text = " " + raw_text;
|
|
|
|
llm_tokenizer_spm tokenizer(vocab);
|
|
llama_escape_whitespace(raw_text);
|
|
tokenizer.tokenize(raw_text, output);
|
|
} break;
|
|
case LLAMA_VOCAB_TYPE_BPE:
|
|
{
|
|
llm_tokenizer_bpe tokenizer(vocab);
|
|
tokenizer.tokenize(raw_text, output);
|
|
} break;
|
|
}
|
|
|
|
return output;
|
|
}
|
|
|
|
//
|
|
// grammar - internal
|
|
//
|
|
|
|
struct llama_partial_utf8 {
|
|
uint32_t value; // bit value so far (unshifted)
|
|
int n_remain; // num bytes remaining; -1 indicates invalid sequence
|
|
};
|
|
|
|
struct llama_grammar {
|
|
const std::vector<std::vector<llama_grammar_element>> rules;
|
|
std::vector<std::vector<const llama_grammar_element *>> stacks;
|
|
|
|
// buffer for partially generated UTF-8 sequence from accepted tokens
|
|
llama_partial_utf8 partial_utf8;
|
|
};
|
|
|
|
struct llama_grammar_candidate {
|
|
size_t index;
|
|
const uint32_t * code_points;
|
|
llama_partial_utf8 partial_utf8;
|
|
};
|
|
|
|
// 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`.
|
|
static std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
|
|
const char * 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;
|
|
std::vector<uint32_t> code_points;
|
|
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()) {
|
|
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:
|
|
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
|
|
static std::vector<std::vector<const llama_grammar_element *>> 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;
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
return 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;
|
|
|
|
if (stack.empty()) {
|
|
for (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;
|
|
for (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 (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 = rules[start_rule_index];
|
|
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, int k, size_t min_keep) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
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 == (int) candidates->size) {
|
|
std::sort(candidates->data, candidates->data + candidates->size, comp);
|
|
} else {
|
|
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
|
|
}
|
|
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_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();
|
|
|
|
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_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
|
|
llama_sample_temp(ctx, candidates_p, temp);
|
|
}
|
|
|
|
void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty) {
|
|
if (last_tokens_size == 0 || penalty == 1.0f) {
|
|
return;
|
|
}
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
|
|
if (token_iter == last_tokens + last_tokens_size) {
|
|
continue;
|
|
}
|
|
|
|
// 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;
|
|
} else {
|
|
candidates->data[i].logit /= penalty;
|
|
}
|
|
}
|
|
|
|
candidates->sorted = false;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens_p, size_t last_tokens_size, float alpha_frequency, float alpha_presence) {
|
|
if (last_tokens_size == 0 || (alpha_frequency == 0.0f && alpha_presence == 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 < last_tokens_size; ++i) {
|
|
token_count[last_tokens_p[i]]++;
|
|
}
|
|
|
|
// Apply frequency and presence penalties to the candidates
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
auto token_iter = token_count.find(candidates->data[i].id);
|
|
if (token_iter == token_count.end()) {
|
|
continue;
|
|
}
|
|
|
|
int count = token_iter->second;
|
|
candidates->data[i].logit -= float(count) * alpha_frequency + float(count > 0) * alpha_presence;
|
|
}
|
|
|
|
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_eos = false;
|
|
for (const auto & stack : grammar->stacks) {
|
|
if (stack.empty()) {
|
|
allow_eos = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
const llama_token eos = llama_token_eos(ctx);
|
|
|
|
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
|
|
std::vector<llama_grammar_candidate> candidates_grammar;
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
const llama_token id = candidates->data[i].id;
|
|
const std::string piece = llama_token_to_str(ctx, id);
|
|
if (id == eos) {
|
|
if (!allow_eos) {
|
|
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.c_str(), 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_classifier_free_guidance(
|
|
struct llama_context * ctx,
|
|
llama_token_data_array * candidates,
|
|
struct llama_context * guidance_ctx,
|
|
float scale) {
|
|
int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
GGML_ASSERT(ctx);
|
|
|
|
auto n_vocab = llama_n_vocab(llama_get_model(ctx));
|
|
|
|
GGML_ASSERT(n_vocab == (int)candidates->size);
|
|
GGML_ASSERT(!candidates->sorted);
|
|
|
|
std::vector<float> logits_base;
|
|
logits_base.reserve(candidates->size);
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
logits_base.push_back(candidates->data[i].logit);
|
|
}
|
|
llama_log_softmax(logits_base.data(), candidates->size);
|
|
|
|
float* logits_guidance = llama_get_logits(guidance_ctx);
|
|
llama_log_softmax(logits_guidance, n_vocab);
|
|
|
|
for (int i = 0; i < n_vocab; ++i) {
|
|
float logit_guidance = logits_guidance[i];
|
|
float logit_base = logits_base[i];
|
|
candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
|
|
}
|
|
|
|
if (ctx) {
|
|
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, int 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(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
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());
|
|
auto & rng = ctx->rng;
|
|
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;
|
|
}
|
|
|
|
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 (token == llama_token_eos(ctx)) {
|
|
for (const auto & stack : grammar->stacks) {
|
|
if (stack.empty()) {
|
|
return;
|
|
}
|
|
}
|
|
GGML_ASSERT(false);
|
|
}
|
|
|
|
const std::string piece = llama_token_to_str(ctx, token);
|
|
|
|
// Note terminating 0 in decoded string
|
|
const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
|
|
const auto & code_points = decoded.first;
|
|
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
|
|
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
|
|
}
|
|
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 repetative 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);
|
|
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
|
|
//
|
|
|
|
template <typename T>
|
|
struct no_init {
|
|
T value;
|
|
no_init() { /* do nothing */ }
|
|
};
|
|
|
|
static void llama_convert_tensor_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;
|
|
}
|
|
|
|
auto block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type);
|
|
auto block_size_bytes = ggml_type_size(tensor->type);
|
|
|
|
GGML_ASSERT(nelements % block_size == 0);
|
|
auto nblocks = nelements / block_size;
|
|
auto blocks_per_thread = nblocks / nthread;
|
|
auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
|
|
|
for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
|
|
auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
|
auto thr_elems = thr_blocks * block_size; // number of elements for this thread
|
|
auto 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();
|
|
}
|
|
|
|
#ifdef GGML_USE_K_QUANTS
|
|
static ggml_type get_k_quant_type(
|
|
ggml_type new_type, const ggml_tensor * tensor, const llama_model & model, llama_ftype ftype, int * i_attention_wv,
|
|
int n_attention_wv, int * i_feed_forward_w2, int n_feed_forward_w2
|
|
) {
|
|
const std::string name = ggml_get_name(tensor);
|
|
// TODO: avoid hardcoded tensor names - use the TN_* constants
|
|
const auto tn = LLM_TN(model.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;
|
|
};
|
|
|
|
if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
|
|
int nx = tensor->ne[0];
|
|
if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
|
|
new_type = GGML_TYPE_Q8_0;
|
|
}
|
|
else if (new_type != GGML_TYPE_Q8_0) {
|
|
new_type = GGML_TYPE_Q6_K;
|
|
}
|
|
} else if (name.find("attn_v.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
|
new_type = *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_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
|
use_more_bits(*i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && *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) &&
|
|
(*i_attention_wv < n_attention_wv/8 || *i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
|
|
if (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;
|
|
}
|
|
++*i_attention_wv;
|
|
} else if (name.find("ffn_down.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
|
|
new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K
|
|
: model.arch != LLM_ARCH_FALCON || use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K
|
|
: GGML_TYPE_Q3_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
|
|
new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
|
|
if (model.arch == LLM_ARCH_FALCON) {
|
|
new_type = *i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K :
|
|
use_more_bits(*i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
|
|
} else {
|
|
if (use_more_bits(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
|
|
}
|
|
}
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(*i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && *i_feed_forward_w2 < 4) {
|
|
new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
++*i_feed_forward_w2;
|
|
} else if (name.find("attn_output.weight") != std::string::npos) {
|
|
if (model.arch != LLM_ARCH_FALCON) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
|
|
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_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) 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.weight") != std::string::npos || name.find("ffn_up.weight") != 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) {
|
|
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 k-quants\n", __func__, nx, ny, QK_K);
|
|
convert_incompatible_tensor = true;
|
|
}
|
|
}
|
|
if (convert_incompatible_tensor) {
|
|
if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
|
|
new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing.
|
|
LLAMA_LOG_WARN("F16 will be used for this tensor instead.\n");
|
|
} else if (name == tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
|
|
new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing.
|
|
LLAMA_LOG_WARN("Q4_0 will be used for this tensor instead.\n");
|
|
} else {
|
|
throw std::runtime_error("Unsupported tensor size encountered\n");
|
|
}
|
|
}
|
|
|
|
return new_type;
|
|
}
|
|
#endif
|
|
|
|
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
|
|
ggml_type quantized_type;
|
|
llama_ftype ftype = params->ftype;
|
|
|
|
switch (params->ftype) {
|
|
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break;
|
|
case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break;
|
|
|
|
#ifdef GGML_USE_K_QUANTS
|
|
// K-quants
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
|
|
#endif
|
|
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_loader ml(fname_inp, use_mmap);
|
|
if (ml.use_mmap) {
|
|
ml.mapping.reset(new llama_mmap(&ml.file, /* prefetch */ 0, ggml_is_numa()));
|
|
}
|
|
|
|
llama_model model;
|
|
llm_load_arch(ml, model);
|
|
llm_load_hparams(ml, model);
|
|
|
|
if (params->only_copy) {
|
|
ftype = model.ftype;
|
|
}
|
|
|
|
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.ctx_gguf);
|
|
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
|
|
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
|
|
|
|
#ifdef GGML_USE_K_QUANTS
|
|
int n_attention_wv = 0;
|
|
int n_feed_forward_w2 = 0;
|
|
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
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) {
|
|
++n_attention_wv;
|
|
}
|
|
else if (name.find("ffn_down.weight") != std::string::npos) {
|
|
++n_feed_forward_w2;
|
|
}
|
|
}
|
|
if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) {
|
|
LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n",
|
|
__func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer);
|
|
}
|
|
|
|
int i_attention_wv = 0;
|
|
int i_feed_forward_w2 = 0;
|
|
#endif
|
|
|
|
size_t total_size_org = 0;
|
|
size_t total_size_new = 0;
|
|
std::vector<int64_t> hist_all(1 << 4, 0);
|
|
|
|
std::vector<std::thread> workers;
|
|
workers.reserve(nthread);
|
|
std::mutex mutex;
|
|
|
|
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;
|
|
|
|
// populate the original tensors so we get an initial meta data
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
struct ggml_tensor * meta = ml.get_tensor_meta(i);
|
|
gguf_add_tensor(ctx_out, meta);
|
|
}
|
|
|
|
std::ofstream fout(fname_out, std::ios::binary);
|
|
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
|
|
|
|
const size_t meta_size = gguf_get_meta_size(ctx_out);
|
|
|
|
LLAMA_LOG_INFO("%s: meta size = %zu bytes\n", __func__, meta_size);
|
|
|
|
// placeholder for the meta data
|
|
::zeros(fout, meta_size);
|
|
|
|
for (int i = 0; i < ml.n_tensors; ++i) {
|
|
struct ggml_tensor * tensor = ml.get_tensor_meta(i);
|
|
|
|
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 tensors
|
|
quantize &= (tensor->n_dims == 2);
|
|
quantize &= params->quantize_output_tensor || name != "output.weight";
|
|
quantize &= !params->only_copy;
|
|
|
|
enum ggml_type new_type;
|
|
void * new_data;
|
|
size_t new_size;
|
|
|
|
if (quantize) {
|
|
new_type = quantized_type;
|
|
#ifdef GGML_USE_K_QUANTS
|
|
new_type = get_k_quant_type(
|
|
new_type, tensor, model, ftype, &i_attention_wv, n_attention_wv, &i_feed_forward_w2, n_feed_forward_w2
|
|
);
|
|
#endif
|
|
// 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 size_t nelements = ggml_nelements(tensor);
|
|
|
|
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_convert_tensor_internal(tensor, f32_conv_buf, workers, nelements, nthread);
|
|
f32_data = (float *) f32_conv_buf.data();
|
|
}
|
|
|
|
LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
|
|
fflush(stdout);
|
|
|
|
if (work.size() < nelements * 4) {
|
|
work.resize(nelements * 4); // upper bound on size
|
|
}
|
|
new_data = work.data();
|
|
std::array<int64_t, 1 << 4> hist_cur = {};
|
|
|
|
static const int chunk_size = 32 * 512;
|
|
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
|
|
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
|
|
if (nthread_use < 2) {
|
|
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
|
|
} else {
|
|
size_t counter = 0;
|
|
new_size = 0;
|
|
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements]() {
|
|
std::array<int64_t, 1 << 4> local_hist = {};
|
|
size_t local_size = 0;
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex);
|
|
size_t first = counter; counter += chunk_size;
|
|
if (first >= nelements) {
|
|
if (local_size > 0) {
|
|
for (int j=0; j<int(local_hist.size()); ++j) {
|
|
hist_cur[j] += local_hist[j];
|
|
}
|
|
new_size += local_size;
|
|
}
|
|
break;
|
|
}
|
|
lock.unlock();
|
|
size_t last = std::min(nelements, first + chunk_size);
|
|
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
|
|
}
|
|
};
|
|
for (int it = 0; it < nthread_use - 1; ++it) {
|
|
workers.emplace_back(compute);
|
|
}
|
|
compute();
|
|
for (auto & w : workers) { w.join(); }
|
|
workers.clear();
|
|
}
|
|
|
|
LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
|
|
int64_t tot_count = 0;
|
|
for (size_t i = 0; i < hist_cur.size(); i++) {
|
|
hist_all[i] += hist_cur[i];
|
|
tot_count += hist_cur[i];
|
|
}
|
|
|
|
if (tot_count > 0) {
|
|
for (size_t i = 0; i < hist_cur.size(); i++) {
|
|
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
|
|
}
|
|
}
|
|
LLAMA_LOG_INFO("\n");
|
|
}
|
|
total_size_org += ggml_nbytes(tensor);
|
|
total_size_new += new_size;
|
|
|
|
// update the gguf meta data as we go
|
|
gguf_set_tensor_type(ctx_out, name.c_str(), new_type);
|
|
gguf_set_tensor_data(ctx_out, 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);
|
|
}
|
|
|
|
// go back to beginning of file and write the updated meta data
|
|
{
|
|
fout.seekp(0);
|
|
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
|
|
gguf_get_meta_data(ctx_out, data.data());
|
|
fout.write((const char *) data.data(), data.size());
|
|
}
|
|
|
|
fout.close();
|
|
|
|
gguf_free(ctx_out);
|
|
|
|
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);
|
|
|
|
// print histogram for all tensors
|
|
{
|
|
int64_t sum_all = 0;
|
|
for (size_t i = 0; i < hist_all.size(); i++) {
|
|
sum_all += hist_all[i];
|
|
}
|
|
|
|
if (sum_all > 0) {
|
|
LLAMA_LOG_INFO("%s: hist: ", __func__);
|
|
for (size_t i = 0; i < hist_all.size(); i++) {
|
|
LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
|
|
}
|
|
LLAMA_LOG_INFO("\n");
|
|
}
|
|
}
|
|
}
|
|
|
|
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();
|
|
|
|
auto fin = std::ifstream(path_lora, std::ios::binary);
|
|
if (!fin) {
|
|
LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
|
|
return 1;
|
|
}
|
|
|
|
// verify magic and version
|
|
{
|
|
uint32_t magic;
|
|
fin.read((char *) &magic, sizeof(magic));
|
|
uint32_t format_version;
|
|
fin.read((char *) &format_version, sizeof(format_version));
|
|
|
|
if (format_version != 1) {
|
|
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int32_t lora_r;
|
|
int32_t lora_alpha;
|
|
fin.read((char *) &lora_r, sizeof(lora_r));
|
|
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
|
|
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);
|
|
|
|
// create a temporary ggml context to store the lora tensors
|
|
// todo: calculate size from biggest possible tensor
|
|
std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
|
|
struct ggml_init_params params;
|
|
params.mem_size = lora_buf.size();
|
|
params.mem_buffer = lora_buf.data();
|
|
params.no_alloc = false;
|
|
|
|
ggml_context * lora_ctx = ggml_init(params);
|
|
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
|
|
|
|
// create a name -> tensor map of the model to accelerate lookups
|
|
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
|
|
for (const auto & kv : model.tensors_by_name) {
|
|
model_tensors.insert(kv);
|
|
}
|
|
|
|
// load base model
|
|
std::unique_ptr<llama_model_loader> ml;
|
|
ggml_context * base_ctx = NULL;
|
|
std::vector<uint8_t> base_buf;
|
|
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));
|
|
|
|
size_t ctx_size;
|
|
size_t mmapped_size;
|
|
ml->calc_sizes(ctx_size, mmapped_size);
|
|
base_buf.resize(ctx_size);
|
|
|
|
ggml_init_params base_params;
|
|
base_params.mem_size = base_buf.size();
|
|
base_params.mem_buffer = base_buf.data();
|
|
base_params.no_alloc = ml->use_mmap;
|
|
|
|
base_ctx = ggml_init(base_params);
|
|
|
|
// maybe this should in llama_model_loader
|
|
if (ml->use_mmap) {
|
|
ml->mapping.reset(new llama_mmap(&ml->file, /* prefetch */ 0, ggml_is_numa()));
|
|
}
|
|
}
|
|
|
|
// read tensors and apply
|
|
bool warned = false;
|
|
int n_tensors = 0;
|
|
|
|
std::vector<uint8_t> work_buffer;
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ftype;
|
|
|
|
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
if (fin.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
}
|
|
|
|
std::string name;
|
|
{
|
|
char buf[1024];
|
|
fin.read(buf, length);
|
|
name = std::string(buf, length);
|
|
}
|
|
|
|
// check for lora suffix and get the type of tensor
|
|
const std::string lora_suffix = ".lora";
|
|
size_t pos = name.rfind(lora_suffix);
|
|
if (pos == std::string::npos) {
|
|
LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
|
|
return 1;
|
|
}
|
|
|
|
std::string lora_type = name.substr(pos + lora_suffix.length());
|
|
std::string base_name = name;
|
|
base_name.erase(pos);
|
|
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
|
|
|
|
if (model_tensors.find(base_name) == model_tensors.end()) {
|
|
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
|
|
return 1;
|
|
}
|
|
|
|
// create ggml tensor
|
|
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 false;
|
|
}
|
|
}
|
|
ggml_tensor * lora_tensor;
|
|
if (n_dims == 2) {
|
|
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
|
|
}
|
|
else {
|
|
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
|
|
return 1;
|
|
}
|
|
ggml_set_name(lora_tensor, "lora_tensor");
|
|
|
|
// load tensor data
|
|
size_t offset = fin.tellg();
|
|
size_t tensor_data_size = ggml_nbytes(lora_tensor);
|
|
offset = (offset + 31) & -32;
|
|
fin.seekg(offset);
|
|
fin.read((char*)lora_tensor->data, tensor_data_size);
|
|
|
|
lora_tensors[name] = lora_tensor;
|
|
|
|
// check if we have both A and B tensors and apply
|
|
if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
|
|
lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
|
|
|
|
ggml_tensor * dest_t = model_tensors[base_name];
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
offload_func_t offload_func_force_inplace = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
|
|
if (dest_t->type != GGML_TYPE_F16) {
|
|
throw std::runtime_error(format(
|
|
"%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
|
|
}
|
|
offload_func = ggml_cuda_assign_buffers;
|
|
offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
ggml_tensor * base_t;
|
|
if (ml) {
|
|
struct gguf_context * ctx_gguf = ml->ctx_gguf;
|
|
|
|
// load from base model
|
|
if (gguf_find_tensor(ctx_gguf, base_name.c_str()) < 0) {
|
|
// TODO: throw
|
|
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
|
|
return 1;
|
|
}
|
|
|
|
// TODO: not tested!! maybe not working!
|
|
base_t = ml->create_tensor(base_ctx, base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
|
|
ml->load_data_for(base_t);
|
|
} else {
|
|
base_t = dest_t;
|
|
}
|
|
|
|
if (ggml_is_quantized(base_t->type)) {
|
|
if (!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;
|
|
}
|
|
}
|
|
|
|
ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
|
|
GGML_ASSERT(loraA->type == GGML_TYPE_F32);
|
|
ggml_set_name(loraA, "loraA");
|
|
|
|
ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
|
|
GGML_ASSERT(loraB->type == GGML_TYPE_F32);
|
|
ggml_set_name(loraB, "loraB");
|
|
|
|
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]);
|
|
return 1;
|
|
}
|
|
|
|
// w = w + BA*s
|
|
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
|
|
offload_func(BA);
|
|
ggml_set_name(BA, "BA");
|
|
|
|
if (scaling != 1.0f) {
|
|
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
|
|
ggml_set_name(scale_tensor, "scale_tensor");
|
|
|
|
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
|
|
offload_func(BA);
|
|
ggml_set_name(BA, "BA_scaled");
|
|
}
|
|
|
|
ggml_tensor * r;
|
|
if (base_t == dest_t) {
|
|
r = ggml_add_inplace(lora_ctx, dest_t, BA);
|
|
offload_func_force_inplace(r);
|
|
ggml_set_name(r, "r_add_inplace");
|
|
}
|
|
else {
|
|
r = ggml_add(lora_ctx, base_t, BA);
|
|
offload_func(r);
|
|
ggml_set_name(r, "r_add");
|
|
|
|
r = ggml_cpy(lora_ctx, r, dest_t);
|
|
offload_func(r);
|
|
ggml_set_name(r, "r_cpy");
|
|
}
|
|
|
|
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
|
|
ggml_build_forward_expand(gf, r);
|
|
|
|
ggml_graph_compute_helper(work_buffer, gf, n_threads);
|
|
|
|
// we won't need these tensors again, reset the context to save memory
|
|
ggml_free(lora_ctx);
|
|
lora_ctx = ggml_init(params);
|
|
lora_tensors.clear();
|
|
|
|
n_tensors++;
|
|
if (n_tensors % 4 == 0) {
|
|
LLAMA_LOG_INFO(".");
|
|
}
|
|
}
|
|
}
|
|
|
|
// TODO: this should be in a destructor, it will leak on failure
|
|
ggml_free(lora_ctx);
|
|
if (base_ctx) {
|
|
ggml_free(base_ctx);
|
|
}
|
|
|
|
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,
|
|
/*.main_gpu =*/ 0,
|
|
/*.tensor_split =*/ nullptr,
|
|
/*.progress_callback =*/ nullptr,
|
|
/*.progress_callback_user_data =*/ nullptr,
|
|
/*.vocab_only =*/ false,
|
|
/*.use_mmap =*/ true,
|
|
/*.use_mlock =*/ false,
|
|
};
|
|
|
|
#ifdef GGML_USE_METAL
|
|
result.n_gpu_layers = 1;
|
|
#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 =*/ 512,
|
|
/*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
|
|
/*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
|
|
/*.rope_freq_base =*/ 0.0f,
|
|
/*.rope_freq_scale =*/ 0.0f,
|
|
/*.mul_mat_q =*/ true,
|
|
/*.f16_kv =*/ true,
|
|
/*.logits_all =*/ false,
|
|
/*.embedding =*/ false,
|
|
};
|
|
|
|
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,
|
|
/*.allow_requantize =*/ false,
|
|
/*.quantize_output_tensor =*/ true,
|
|
/*.only_copy =*/ false,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
int llama_max_devices(void) {
|
|
return LLAMA_MAX_DEVICES;
|
|
}
|
|
|
|
bool llama_mmap_supported(void) {
|
|
return llama_mmap::SUPPORTED;
|
|
}
|
|
|
|
bool llama_mlock_supported(void) {
|
|
return llama_mlock::SUPPORTED;
|
|
}
|
|
|
|
void llama_backend_init(bool numa) {
|
|
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);
|
|
}
|
|
|
|
if (numa) {
|
|
ggml_numa_init();
|
|
}
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_backend_init();
|
|
#endif
|
|
}
|
|
|
|
void llama_backend_free(void) {
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_backend_free();
|
|
#endif
|
|
}
|
|
|
|
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");
|
|
}
|
|
}
|
|
};
|
|
}
|
|
|
|
if (!llama_model_load(path_model, *model, params.n_gpu_layers,
|
|
params.main_gpu, params.tensor_split,
|
|
params.use_mmap, params.use_mlock, params.vocab_only,
|
|
params.progress_callback, params.progress_callback_user_data)) {
|
|
LLAMA_LOG_ERROR("%s: failed to load model\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) {
|
|
return nullptr;
|
|
}
|
|
|
|
llama_context * ctx = new llama_context(*model);
|
|
|
|
const auto & hparams = model->hparams;
|
|
auto & cparams = ctx->cparams;
|
|
|
|
cparams.n_batch = params.n_batch;
|
|
cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
|
|
cparams.rope_freq_base = params.rope_freq_base == 0 ? hparams.rope_freq_base_train : params.rope_freq_base;
|
|
cparams.rope_freq_scale = params.rope_freq_scale == 0 ? hparams.rope_freq_scale_train : params.rope_freq_scale;
|
|
cparams.n_threads = params.n_threads;
|
|
cparams.n_threads_batch = params.n_threads_batch;
|
|
cparams.mul_mat_q = params.mul_mat_q;
|
|
|
|
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: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
|
|
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
|
|
|
|
ctx->rng = std::mt19937(params.seed);
|
|
ctx->logits_all = params.logits_all;
|
|
|
|
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
|
|
|
// reserve memory for context buffers
|
|
if (!hparams.vocab_only) {
|
|
if (!llama_kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, cparams.n_ctx, model->n_gpu_layers)) {
|
|
LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
{
|
|
const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
|
|
LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// resized during inference
|
|
if (params.logits_all) {
|
|
ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab);
|
|
} else {
|
|
ctx->logits.reserve(hparams.n_vocab);
|
|
}
|
|
|
|
if (params.embedding){
|
|
ctx->embedding.resize(hparams.n_embd);
|
|
}
|
|
|
|
{
|
|
static const size_t tensor_alignment = 32;
|
|
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
|
|
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
|
|
|
|
// create measure allocator
|
|
ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
|
|
|
|
// build worst-case graph
|
|
int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_batch);
|
|
int n_past = cparams.n_ctx - n_tokens;
|
|
llama_token token = llama_token_bos(ctx); // 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));
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (model->n_gpu_layers > 0) {
|
|
ggml_metal_log_set_callback(llama_log_callback_default, NULL);
|
|
|
|
ctx->ctx_metal = ggml_metal_init(1);
|
|
if (!ctx->ctx_metal) {
|
|
LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
|
|
llama_free(ctx);
|
|
return NULL;
|
|
}
|
|
//ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false);
|
|
//ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
|
|
}
|
|
#endif
|
|
// measure memory requirements for the graph
|
|
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
|
|
|
|
LLAMA_LOG_INFO("%s: compute buffer total size = %.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
|
|
|
|
// recreate allocator with exact memory requirements
|
|
ggml_allocr_free(ctx->alloc);
|
|
|
|
ctx->buf_alloc.resize(alloc_size);
|
|
ctx->alloc = ggml_allocr_new(ctx->buf_alloc.data, ctx->buf_alloc.size, tensor_alignment);
|
|
#ifdef GGML_USE_METAL
|
|
if (ctx->ctx_metal) {
|
|
//ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
|
|
}
|
|
#endif
|
|
#ifdef GGML_USE_CUBLAS
|
|
ggml_cuda_set_scratch_size(alloc_size);
|
|
LLAMA_LOG_INFO("%s: VRAM scratch buffer: %.2f MB\n", __func__, alloc_size / 1024.0 / 1024.0);
|
|
|
|
// calculate total VRAM usage
|
|
auto add_tensor = [](const ggml_tensor * t, size_t & size) {
|
|
if (t->backend == GGML_BACKEND_GPU || t->backend == GGML_BACKEND_GPU_SPLIT) {
|
|
size += ggml_nbytes(t);
|
|
}
|
|
};
|
|
size_t model_vram_size = 0;
|
|
for (const auto & kv : model->tensors_by_name) {
|
|
add_tensor(kv.second, model_vram_size);
|
|
}
|
|
|
|
size_t kv_vram_size = 0;
|
|
add_tensor(ctx->kv_self.k, kv_vram_size);
|
|
add_tensor(ctx->kv_self.v, kv_vram_size);
|
|
|
|
size_t ctx_vram_size = alloc_size + kv_vram_size;
|
|
size_t total_vram_size = model_vram_size + ctx_vram_size;
|
|
|
|
LLAMA_LOG_INFO("%s: total VRAM used: %.2f MB (model: %.2f MB, context: %.2f MB)\n", __func__,
|
|
total_vram_size / 1024.0 / 1024.0,
|
|
model_vram_size / 1024.0 / 1024.0,
|
|
ctx_vram_size / 1024.0 / 1024.0);
|
|
#endif
|
|
}
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (model->n_gpu_layers > 0) {
|
|
// this allocates all Metal resources and memory buffers
|
|
|
|
void * data_ptr = NULL;
|
|
size_t data_size = 0;
|
|
|
|
if (ctx->model.mapping) {
|
|
data_ptr = ctx->model.mapping->addr;
|
|
data_size = ctx->model.mapping->size;
|
|
} else {
|
|
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
|
data_size = ggml_get_mem_size (ctx->model.ctx);
|
|
}
|
|
|
|
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
|
|
|
|
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
|
|
|
|
#define LLAMA_METAL_CHECK_BUF(result) \
|
|
if (!(result)) { \
|
|
LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
|
|
llama_free(ctx); \
|
|
return NULL; \
|
|
}
|
|
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
|
|
#undef LLAMA_METAL_CHECK_BUF
|
|
}
|
|
#endif
|
|
}
|
|
|
|
#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;
|
|
}
|
|
|
|
int llama_n_ctx(const struct llama_context * ctx) {
|
|
return ctx->cparams.n_ctx;
|
|
}
|
|
|
|
enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
|
|
return model->vocab.type;
|
|
}
|
|
|
|
int llama_n_vocab(const struct llama_model * model) {
|
|
return model->vocab.id_to_token.size();
|
|
}
|
|
|
|
int llama_n_ctx_train(const struct llama_model * model) {
|
|
return model->hparams.n_ctx_train;
|
|
}
|
|
|
|
int llama_n_embd(const struct llama_model * model) {
|
|
return model->hparams.n_embd;
|
|
}
|
|
|
|
float llama_rope_freq_scale_train(const struct llama_model * model) {
|
|
return model->hparams.rope_freq_scale_train;
|
|
}
|
|
|
|
int 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).c_str(),
|
|
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) {
|
|
return ggml_get_tensor(model->ctx, name);
|
|
}
|
|
|
|
int 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;
|
|
}
|
|
}
|
|
|
|
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, float scale, const char * path_base_model, int n_threads) {
|
|
try {
|
|
return llama_apply_lora_from_file_internal(ctx->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;
|
|
}
|
|
}
|
|
|
|
int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int 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;
|
|
}
|
|
}
|
|
|
|
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
|
return ctx->kv_self.head;
|
|
}
|
|
|
|
void llama_kv_cache_tokens_rm(struct llama_context * ctx, int32_t c0, int32_t c1) {
|
|
llama_kv_cache_tokens_rm(ctx->kv_self, c0, c1);
|
|
}
|
|
|
|
void llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
|
|
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) {
|
|
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_shift(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
|
|
llama_kv_cache_seq_shift(ctx->kv_self, seq_id, p0, p1, delta);
|
|
}
|
|
|
|
// Returns the *maximum* size of the state
|
|
size_t llama_get_state_size(const struct llama_context * ctx) {
|
|
// 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_logits_capacity = sizeof(size_t);
|
|
const size_t s_logits_size = sizeof(size_t);
|
|
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
|
|
const size_t s_embedding_size = sizeof(size_t);
|
|
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
|
|
const size_t s_kv_size = sizeof(size_t);
|
|
const size_t s_kv_ntok = sizeof(int);
|
|
const size_t s_kv = ctx->kv_self.buf.size;
|
|
|
|
const size_t s_total = (
|
|
+ s_rng_size
|
|
+ s_rng
|
|
+ s_logits_capacity
|
|
+ s_logits_size
|
|
+ s_logits
|
|
+ s_embedding_size
|
|
+ s_embedding
|
|
+ s_kv_size
|
|
+ s_kv_ntok
|
|
+ s_kv
|
|
);
|
|
|
|
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_copy_state_data(ctx, &data_ctx);
|
|
*
|
|
* buffer context:
|
|
* std::vector<uint8_t> buf(max_size, 0);
|
|
* llama_data_buffer_context data_ctx(&buf.data());
|
|
* llama_copy_state_data(ctx, &data_ctx);
|
|
*
|
|
*/
|
|
static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
|
|
// copy rng
|
|
{
|
|
std::stringstream rng_ss;
|
|
rng_ss << ctx->rng;
|
|
|
|
const size_t rng_size = rng_ss.str().size();
|
|
char rng_buf[LLAMA_MAX_RNG_STATE];
|
|
|
|
memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
|
|
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
|
|
|
|
data_ctx->write(&rng_size, sizeof(rng_size));
|
|
data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
|
|
}
|
|
|
|
// copy logits
|
|
{
|
|
const size_t logits_cap = ctx->logits.capacity();
|
|
const size_t logits_size = ctx->logits.size();
|
|
|
|
data_ctx->write(&logits_cap, sizeof(logits_cap));
|
|
data_ctx->write(&logits_size, sizeof(logits_size));
|
|
|
|
if (logits_size) {
|
|
data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
|
|
}
|
|
|
|
// If there is a gap between the size and the capacity, write padding
|
|
size_t padding_size = (logits_cap - logits_size) * sizeof(float);
|
|
if (padding_size > 0) {
|
|
std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
|
|
data_ctx->write(padding.data(), padding_size);
|
|
}
|
|
}
|
|
|
|
// copy embeddings
|
|
{
|
|
const size_t embedding_size = ctx->embedding.size();
|
|
|
|
data_ctx->write(&embedding_size, sizeof(embedding_size));
|
|
|
|
if (embedding_size) {
|
|
data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
|
|
}
|
|
}
|
|
|
|
// copy kv cache
|
|
{
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
const auto & cparams = ctx->cparams;
|
|
|
|
const auto n_layer = hparams.n_layer;
|
|
const auto n_embd = hparams.n_embd_gqa();
|
|
const auto n_ctx = cparams.n_ctx;
|
|
|
|
const size_t kv_buf_size = kv_self.buf.size;
|
|
const uint32_t kv_head = kv_self.head;
|
|
const uint32_t kv_size = kv_self.size;
|
|
|
|
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));
|
|
|
|
if (kv_buf_size) {
|
|
const size_t elt_size = ggml_element_size(kv_self.k);
|
|
|
|
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
|
|
ggml_cgraph gf{};
|
|
|
|
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
|
|
std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
|
|
kout3d->data = kout3d_data.data();
|
|
|
|
ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
|
|
std::vector<uint8_t> vout3d_data(ggml_nbytes(vout3d), 0);
|
|
vout3d->data = vout3d_data.data();
|
|
|
|
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
|
|
n_embd, kv_head, n_layer,
|
|
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
|
|
|
|
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
|
|
kv_head, n_embd, n_layer,
|
|
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
|
|
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
|
|
|
|
ggml_free(cpy_ctx);
|
|
|
|
// our data is now in the kout3d_data and vout3d_data buffers
|
|
// write them to file
|
|
data_ctx->write(kout3d_data.data(), kout3d_data.size());
|
|
data_ctx->write(vout3d_data.data(), vout3d_data.size());
|
|
}
|
|
|
|
for (uint32_t i = 0; i < kv_size; ++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_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
|
|
llama_data_buffer_context data_ctx(dst);
|
|
llama_copy_state_data_internal(ctx, &data_ctx);
|
|
|
|
return data_ctx.get_size_written();
|
|
}
|
|
|
|
// Sets the state reading from the specified source address
|
|
size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
|
|
uint8_t * inp = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
char rng_buf[LLAMA_MAX_RNG_STATE];
|
|
|
|
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
|
|
memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
|
|
|
|
std::stringstream rng_ss;
|
|
rng_ss.str(std::string(&rng_buf[0], rng_size));
|
|
rng_ss >> ctx->rng;
|
|
|
|
GGML_ASSERT(!rng_ss.fail());
|
|
}
|
|
|
|
// set logits
|
|
{
|
|
size_t logits_cap;
|
|
size_t logits_size;
|
|
|
|
memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap);
|
|
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
|
|
|
|
GGML_ASSERT(ctx->logits.capacity() == logits_cap);
|
|
|
|
if (logits_size) {
|
|
ctx->logits.resize(logits_size);
|
|
memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
|
|
}
|
|
|
|
inp += logits_cap * sizeof(float);
|
|
}
|
|
|
|
// set embeddings
|
|
{
|
|
size_t embedding_size;
|
|
|
|
memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
|
|
|
|
GGML_ASSERT(ctx->embedding.capacity() == embedding_size);
|
|
|
|
if (embedding_size) {
|
|
memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
|
|
inp += embedding_size * sizeof(float);
|
|
}
|
|
}
|
|
|
|
// set kv cache
|
|
{
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
const auto & cparams = ctx->cparams;
|
|
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_embd = hparams.n_embd_gqa();
|
|
const int n_ctx = cparams.n_ctx;
|
|
|
|
size_t kv_buf_size;
|
|
uint32_t kv_head;
|
|
uint32_t kv_size;
|
|
|
|
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);
|
|
|
|
if (kv_buf_size) {
|
|
GGML_ASSERT(kv_self.buf.size == kv_buf_size);
|
|
|
|
const size_t elt_size = ggml_element_size(kv_self.k);
|
|
|
|
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
|
|
ggml_cgraph gf{};
|
|
|
|
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
|
|
kin3d->data = (void *) inp;
|
|
inp += ggml_nbytes(kin3d);
|
|
|
|
ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_head, n_embd, n_layer);
|
|
vin3d->data = (void *) inp;
|
|
inp += ggml_nbytes(vin3d);
|
|
|
|
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
|
|
n_embd, kv_head, n_layer,
|
|
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
|
|
|
|
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
|
|
kv_head, n_embd, n_layer,
|
|
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
|
|
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
|
|
|
|
ggml_free(cpy_ctx);
|
|
}
|
|
|
|
ctx->kv_self.head = kv_head;
|
|
ctx->kv_self.size = kv_size;
|
|
|
|
ctx->kv_self.cells.resize(kv_size);
|
|
|
|
for (uint32_t i = 0; i < kv_size; ++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_get_state_size(ctx);
|
|
|
|
GGML_ASSERT(nread <= max_size);
|
|
|
|
return nread;
|
|
}
|
|
|
|
static bool llama_load_session_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_get_state_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_set_state_data(ctx, state_data.data());
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
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) {
|
|
try {
|
|
return llama_load_session_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;
|
|
}
|
|
}
|
|
|
|
bool llama_save_session_file(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_copy_state_data_internal(ctx, &data_ctx);
|
|
|
|
return true;
|
|
}
|
|
|
|
int llama_eval(
|
|
struct llama_context * ctx,
|
|
llama_token * tokens,
|
|
int32_t n_tokens,
|
|
int n_past) {
|
|
llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1);
|
|
|
|
const int ret = llama_decode_internal(*ctx, llama_batch_get_one(tokens, n_tokens, n_past, 0));
|
|
if (ret < 0) {
|
|
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
|
|
}
|
|
|
|
return ret;
|
|
}
|
|
|
|
int llama_eval_embd(
|
|
struct llama_context * ctx,
|
|
float * embd,
|
|
int32_t n_tokens,
|
|
int n_past) {
|
|
llama_kv_cache_tokens_rm(ctx->kv_self, n_past, -1);
|
|
|
|
llama_batch batch = { n_tokens, nullptr, embd, nullptr, nullptr, nullptr, n_past, 1, 0, };
|
|
|
|
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_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;
|
|
}
|
|
|
|
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,
|
|
/*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, int32_t embd) {
|
|
llama_batch batch = { -1, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };
|
|
|
|
if (embd) {
|
|
batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd);
|
|
} else {
|
|
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
|
|
}
|
|
|
|
batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens);
|
|
batch.seq_id = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_tokens);
|
|
batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
|
|
|
|
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.seq_id) free(batch.seq_id);
|
|
if (batch.logits) free(batch.logits);
|
|
}
|
|
|
|
int 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;
|
|
}
|
|
|
|
float * llama_get_logits(struct llama_context * ctx) {
|
|
return ctx->logits.data();
|
|
}
|
|
|
|
float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
|
|
return ctx->logits.data() + i*ctx->model.hparams.n_vocab;
|
|
}
|
|
|
|
float * llama_get_embeddings(struct llama_context * ctx) {
|
|
return ctx->embedding.data();
|
|
}
|
|
|
|
const char * llama_token_get_text(const struct llama_context * ctx, llama_token token) {
|
|
return ctx->model.vocab.id_to_token[token].text.c_str();
|
|
}
|
|
|
|
float llama_token_get_score(const struct llama_context * ctx, llama_token token) {
|
|
return ctx->model.vocab.id_to_token[token].score;
|
|
}
|
|
|
|
llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token) {
|
|
return ctx->model.vocab.id_to_token[token].type;
|
|
}
|
|
|
|
llama_token llama_token_bos(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.special_bos_id;
|
|
}
|
|
|
|
llama_token llama_token_eos(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.special_eos_id;
|
|
}
|
|
|
|
llama_token llama_token_nl(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.linefeed_id;
|
|
}
|
|
llama_token llama_token_prefix(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.special_prefix_id;
|
|
}
|
|
|
|
llama_token llama_token_middle(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.special_middle_id;
|
|
}
|
|
|
|
llama_token llama_token_suffix(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.special_suffix_id;
|
|
}
|
|
|
|
llama_token llama_token_eot(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.special_eot_id;
|
|
}
|
|
|
|
|
|
int llama_tokenize(
|
|
const struct llama_model * model,
|
|
const char * text,
|
|
int text_len,
|
|
llama_token * tokens,
|
|
int n_max_tokens,
|
|
bool add_bos) {
|
|
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos);
|
|
|
|
if (n_max_tokens < (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 = codepoints_from_utf8(text);
|
|
for (auto& unicode_sequence : unicode_sequences) {
|
|
decoded_text += unicode_to_bytes_bpe(codepoint_to_utf8(unicode_sequence));
|
|
}
|
|
|
|
return decoded_text;
|
|
}
|
|
|
|
// does not write null-terminator to buf
|
|
int llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int length) {
|
|
if (0 <= token && token < llama_n_vocab(model)) {
|
|
switch (llama_vocab_get_type(model->vocab)) {
|
|
case LLAMA_VOCAB_TYPE_SPM: {
|
|
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 -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_control_token(model->vocab, token)) {
|
|
;
|
|
} 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;
|
|
} else {
|
|
// TODO: for now we accept all unsupported token types,
|
|
// suppressing them like CONTROL tokens.
|
|
// GGML_ASSERT(false);
|
|
}
|
|
break;
|
|
}
|
|
case LLAMA_VOCAB_TYPE_BPE: {
|
|
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 -result.length();
|
|
}
|
|
memcpy(buf, result.c_str(), result.length());
|
|
return result.length();
|
|
} else if (llama_is_control_token(model->vocab, token)) {
|
|
;
|
|
} else {
|
|
// TODO: for now we accept all unsupported token types,
|
|
// suppressing them like CONTROL tokens.
|
|
// GGML_ASSERT(false);
|
|
}
|
|
break;
|
|
}
|
|
default:
|
|
GGML_ASSERT(false);
|
|
}
|
|
}
|
|
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\n", __func__, (timings.t_end_ms - timings.t_start_ms));
|
|
}
|
|
|
|
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 += "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()) + " | ";
|
|
|
|
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;
|
|
}
|
|
|
|
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);
|
|
}
|