1860 lines
59 KiB
C++
1860 lines
59 KiB
C++
#include "llama.h"
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#include "ggml.h"
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#include <cinttypes>
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#include <fstream>
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#include <random>
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#include <map>
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#include <unordered_map>
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#include <queue>
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#include <regex>
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#include <cassert>
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#include <cstring>
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#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
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#define WIN32_LEAN_AND_MEAN
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#include <Windows.h>
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#else
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#include <sys/types.h>
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#include <sys/mman.h>
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#include <unistd.h>
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#include <fcntl.h>
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#endif
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#define Min(X, Y) ((Y) > (X) ? (X) : (Y))
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#define Max(X, Y) ((Y) < (X) ? (X) : (Y))
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#define LLAMA_USE_SCRATCH
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#define LLAMA_MAX_SCRATCH_BUFFERS 16
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#define LLAMA_ASSERT(x) \
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do { \
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if (!(x)) { \
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fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
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abort(); \
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} \
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} while (0)
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// determine number of model parts based on the dimension
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static const std::unordered_map<int, int> LLAMA_N_PARTS = {
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{ 4096, 1 },
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{ 5120, 2 },
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{ 6656, 4 },
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{ 8192, 8 },
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};
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// available llama models
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enum e_model {
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MODEL_UNKNOWN,
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MODEL_7B,
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MODEL_13B,
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MODEL_30B,
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MODEL_65B,
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};
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static const size_t MB = 1024*1024;
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// computed for n_ctx == 2048
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// TODO: dynamically determine these sizes
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// needs modifications in ggml
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
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{ MODEL_7B, 512ull*MB },
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{ MODEL_13B, 512ull*MB },
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{ MODEL_30B, 512ull*MB },
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{ MODEL_65B, 512ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
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{ MODEL_7B, 512ull*MB },
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{ MODEL_13B, 512ull*MB },
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{ MODEL_30B, 512ull*MB },
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{ MODEL_65B, 512ull*MB },
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};
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// 2*n_embd*n_ctx*n_layer*sizeof(float16)
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static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
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{ MODEL_7B, 1026ull*MB },
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{ MODEL_13B, 1608ull*MB },
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{ MODEL_30B, 3124ull*MB },
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{ MODEL_65B, 5120ull*MB },
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};
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// this is mostly needed for temporary mul_mat buffers to dequantize the data
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// not actually needed if BLAS is disabled
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static const std::map<e_model, size_t> MEM_REQ_EVAL = {
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{ MODEL_7B, 768ull*MB },
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{ MODEL_13B, 1024ull*MB },
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{ MODEL_30B, 1280ull*MB },
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{ MODEL_65B, 1536ull*MB },
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};
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// default hparams (LLaMA 7B)
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struct llama_hparams {
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int32_t n_vocab = 32000;
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int32_t n_ctx = 512; // this is provided as user input?
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int32_t n_embd = 4096;
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int32_t n_mult = 256;
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int32_t n_head = 32;
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int32_t n_layer = 32;
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int32_t n_rot = 64;
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int32_t f16 = 1;
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};
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struct llama_layer {
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// normalization
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struct ggml_tensor * attention_norm;
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// attention
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struct ggml_tensor * wq;
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struct ggml_tensor * wk;
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struct ggml_tensor * wv;
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struct ggml_tensor * wo;
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// normalization
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struct ggml_tensor * ffn_norm;
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// ff
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struct ggml_tensor * w1;
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struct ggml_tensor * w2;
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struct ggml_tensor * w3;
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};
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struct llama_kv_cache {
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struct ggml_tensor * k;
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struct ggml_tensor * v;
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struct ggml_context * ctx;
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std::vector<uint8_t> buf;
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int n; // number of tokens currently in the cache
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};
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struct llama_model {
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e_model type = MODEL_UNKNOWN;
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llama_hparams hparams;
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struct ggml_tensor * tok_embeddings;
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struct ggml_tensor * norm;
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struct ggml_tensor * output;
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std::vector<llama_layer> layers;
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// context
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struct ggml_context * ctx;
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// key + value cache for the self attention
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// TODO: move to llama_state
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struct llama_kv_cache kv_self;
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// the model memory buffer
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std::vector<uint8_t> buf;
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// model memory mapped file
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void * mm_addr = NULL;
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uint64_t mm_length = 0;
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// tensors
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int n_loaded;
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std::unordered_map<std::string, struct ggml_tensor *> tensors;
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};
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struct llama_vocab {
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using id = int32_t;
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using token = std::string;
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struct token_score {
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token tok;
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float score;
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};
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std::unordered_map<token, id> token_to_id;
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std::vector<token_score> id_to_token;
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};
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struct llama_context {
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std::mt19937 rng;
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int64_t t_load_us = 0;
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int64_t t_start_us = 0;
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bool has_evaluated_once = false;
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int64_t t_sample_us = 0;
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int64_t t_eval_us = 0;
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int64_t t_p_eval_us = 0;
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int32_t n_sample = 0; // number of tokens sampled
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int32_t n_eval = 0; // number of eval calls
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int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
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llama_model model;
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llama_vocab vocab;
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size_t mem_per_token = 0;
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// decode output (2-dimensional array: [n_tokens][n_vocab])
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std::vector<float> logits;
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bool logits_all = false;
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// input embedding (1-dimensional array: [n_embd])
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std::vector<float> embedding;
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// memory buffers used to evaluate the model
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// TODO: move in llama_state
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std::vector<uint8_t> buf_compute;
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std::vector<uint8_t> buf_scratch[LLAMA_MAX_SCRATCH_BUFFERS];
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int buf_last = 0;
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size_t buf_max_size[LLAMA_MAX_SCRATCH_BUFFERS] = { 0 };
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void use_buf(struct ggml_context * ctx, int i) {
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#if defined(LLAMA_USE_SCRATCH)
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size_t last_size = 0;
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if (i == -1) {
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last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
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} else {
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auto & buf = buf_scratch[i];
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last_size = ggml_set_scratch(ctx, { 0, buf.size(), buf.data(), });
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}
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if (buf_last >= 0) {
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buf_max_size[buf_last] = Max(buf_max_size[buf_last], last_size);
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}
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buf_last = i;
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#else
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(void) i;
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(void) ctx;
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#endif
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}
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size_t get_buf_max_mem(int i) const {
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#if defined(LLAMA_USE_SCRATCH)
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return buf_max_size[i];
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#else
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(void) i;
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return 0;
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#endif
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}
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};
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//
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// kv cache
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//
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static bool kv_cache_init(
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const struct llama_hparams & hparams,
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struct llama_kv_cache & cache,
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ggml_type wtype,
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int n_ctx) {
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const int n_embd = hparams.n_embd;
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const int n_layer = hparams.n_layer;
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const int64_t n_mem = (int64_t)n_layer*n_ctx;
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const int64_t n_elements = n_embd*n_mem;
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cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
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struct ggml_init_params params;
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params.mem_size = cache.buf.size();
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params.mem_buffer = cache.buf.data();
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params.no_alloc = false;
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cache.ctx = ggml_init(params);
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if (!cache.ctx) {
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fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
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return false;
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}
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cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
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return true;
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}
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static void kv_cache_free(struct llama_kv_cache & cache) {
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if (cache.ctx) {
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ggml_free(cache.ctx);
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cache.ctx = nullptr;
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}
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}
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struct llama_context_params llama_context_default_params() {
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struct llama_context_params result = {
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/*.n_ctx =*/ 512,
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/*.n_parts =*/ -1,
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/*.seed =*/ 0,
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/*.f16_kv =*/ false,
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/*.logits_all =*/ false,
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/*.vocab_only =*/ false,
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/*.use_mlock =*/ false,
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/*.embedding =*/ false,
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/*.progress_callback =*/ nullptr,
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/*.progress_callback_user_data =*/ nullptr,
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};
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return result;
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}
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//
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// model loading
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//
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static void *mmap_file(const char *fname, uint64_t *mm_length) {
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#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
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HANDLE hFile = CreateFileA(fname,
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GENERIC_READ,
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FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE,
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NULL,
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OPEN_EXISTING,
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FILE_ATTRIBUTE_NORMAL | FILE_ATTRIBUTE_NOT_CONTENT_INDEXED,
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NULL);
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if (hFile == INVALID_HANDLE_VALUE) return 0;
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LARGE_INTEGER fileSize;
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fileSize.QuadPart = -1;
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GetFileSizeEx(hFile, &fileSize);
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int64_t length = fileSize.QuadPart;
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HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
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CloseHandle(hFile);
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if (!hMapping) return 0;
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void *addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
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CloseHandle(hMapping);
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if (!addr) return 0;
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#else
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int fd = open(fname, O_RDONLY);
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if (fd == -1) return 0;
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int64_t length = lseek(fd, 0, SEEK_END);
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void *addr = mmap(NULL, length, PROT_READ, MAP_SHARED, fd, 0);
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close(fd);
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if (addr == MAP_FAILED) return 0;
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#endif
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*mm_length = length;
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return addr;
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}
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static void munmap_file(void * addr, size_t length) {
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#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES)
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UnmapViewOfFile(addr);
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#else
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munmap(addr, length);
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#endif
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}
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static bool report_bad_magic(const char *path, uint32_t got, uint32_t want) {
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fprintf(stderr,
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"%s: invalid model file (bad magic [got %#x want %#x])\n"
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"\tyou most likely need to regenerate your ggml files\n"
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"\tthe benefit is you'll get 10-100x faster load times\n"
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"\tsee https://github.com/ggerganov/llama.cpp/issues/91\n"
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"\tuse convert-pth-to-ggml.py to regenerate from original pth\n"
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"\tuse migrate-ggml-2023-03-30-pr613.py if you deleted originals\n",
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path, got, want);
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return false;
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}
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static bool llama_model_load(
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const std::string & fname,
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llama_context & lctx,
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int n_ctx,
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int n_parts,
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ggml_type memory_type,
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bool vocab_only,
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llama_progress_callback progress_callback,
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void *progress_callback_user_data) {
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fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
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lctx.t_start_us = ggml_time_us();
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auto & model = lctx.model;
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auto & vocab = lctx.vocab;
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auto fin = std::ifstream(fname, std::ios::binary);
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if (!fin) {
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fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
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return false;
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}
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std::vector<char> f_buf(1024*1024);
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fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size());
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fin.seekg(0, fin.end);
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const size_t file_size = fin.tellg();
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fin.seekg(0);
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// verify magic
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{
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uint32_t magic;
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fin.read((char *) &magic, sizeof(magic));
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if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
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fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files or convert them with convert-unversioned-ggml-to-ggml.py!)\n",
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__func__, fname.c_str());
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return false;
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}
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if (magic != LLAMA_FILE_MAGIC) {
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return report_bad_magic(fname.c_str(), magic, LLAMA_FILE_MAGIC);
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}
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uint32_t format_version;
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fin.read((char *) &format_version, sizeof(format_version));
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if (format_version != LLAMA_FILE_VERSION) {
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fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
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__func__, fname.c_str(), format_version, LLAMA_FILE_VERSION);
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return false;
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}
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}
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int n_ff = 0;
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// load hparams
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{
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auto & hparams = model.hparams;
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fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
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//fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
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fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
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fin.read((char *) &hparams.n_mult, sizeof(hparams.n_mult));
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fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
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fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
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fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
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fin.read((char *) &hparams.f16, sizeof(hparams.f16));
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hparams.n_ctx = n_ctx;
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n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
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if (n_parts < 1) {
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n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
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}
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// temp warning to tell the user to use "--n_parts"
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if (hparams.f16 == 4 && n_parts != 1) {
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fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts);
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fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
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}
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if (hparams.n_layer == 32) {
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model.type = e_model::MODEL_7B;
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}
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if (hparams.n_layer == 40) {
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model.type = e_model::MODEL_13B;
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}
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if (hparams.n_layer == 60) {
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model.type = e_model::MODEL_30B;
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}
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if (hparams.n_layer == 80) {
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model.type = e_model::MODEL_65B;
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}
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fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
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fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
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fprintf(stderr, "%s: n_embd = %d\n", __func__, hparams.n_embd);
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fprintf(stderr, "%s: n_mult = %d\n", __func__, hparams.n_mult);
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fprintf(stderr, "%s: n_head = %d\n", __func__, hparams.n_head);
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fprintf(stderr, "%s: n_layer = %d\n", __func__, hparams.n_layer);
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fprintf(stderr, "%s: n_rot = %d\n", __func__, hparams.n_rot);
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fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
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fprintf(stderr, "%s: n_ff = %d\n", __func__, n_ff);
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fprintf(stderr, "%s: n_parts = %d\n", __func__, n_parts);
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fprintf(stderr, "%s: type = %d\n", __func__, model.type);
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}
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// load vocab
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{
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std::string word;
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vocab.id_to_token.resize(model.hparams.n_vocab);
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std::vector<char> tmp(64);
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for (int i = 0; i < model.hparams.n_vocab; i++) {
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uint32_t len;
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fin.read((char *) &len, sizeof(len));
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word.resize(len);
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if (len > 0) {
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tmp.resize(len);
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fin.read(tmp.data(), len);
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word.assign(tmp.data(), len);
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} else {
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word.clear();
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}
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float score;
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fin.read((char *) &score, sizeof(score));
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vocab.token_to_id[word] = i;
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auto &tok_score = vocab.id_to_token[i];
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tok_score.tok = word;
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tok_score.score = score;
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}
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}
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|
if (vocab_only) {
|
|
return true;
|
|
}
|
|
|
|
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
|
|
// in order to save memory and also to speed up the computation
|
|
// wtype is for per-layer weights, while vtype is for other weights
|
|
ggml_type wtype, vtype;
|
|
switch (model.hparams.f16) {
|
|
case 0: wtype = vtype = GGML_TYPE_F32; break;
|
|
case 1: wtype = vtype = GGML_TYPE_F16; break;
|
|
case 2: wtype = vtype = GGML_TYPE_Q4_0; break;
|
|
case 3: wtype = vtype = GGML_TYPE_Q4_1; break;
|
|
case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break;
|
|
default:
|
|
{
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
|
|
__func__, fname.c_str(), model.hparams.f16);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// map model into memory
|
|
char *mm_addr = NULL;
|
|
model.mm_addr = mmap_file(fname.c_str(), &model.mm_length);
|
|
if (model.mm_addr == NULL) {
|
|
fprintf(stderr, "%s: failed to mmap '%s'\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
mm_addr = (char *)model.mm_addr;
|
|
fprintf(stderr, "%s: ggml map size = %6.2f MB\n", __func__, model.mm_length/(1024.0*1024.0));
|
|
|
|
auto & ctx = model.ctx;
|
|
|
|
size_t ctx_size = 0;
|
|
{
|
|
const auto &hparams = model.hparams;
|
|
const int n_layer = hparams.n_layer;
|
|
ctx_size += (5 + 10*n_layer)*256; // object overhead
|
|
fprintf(stderr, "%s: ggml ctx size = %6.2f KB\n", __func__, ctx_size/1024.0);
|
|
}
|
|
|
|
// print memory requirements
|
|
{
|
|
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
|
|
|
|
// this is the total memory required to run the inference
|
|
const size_t mem_required =
|
|
ctx_size +
|
|
model.mm_length +
|
|
MEM_REQ_SCRATCH0.at(model.type) +
|
|
MEM_REQ_SCRATCH1.at(model.type) +
|
|
MEM_REQ_EVAL.at (model.type);
|
|
|
|
// this is the memory required by one llama_state
|
|
const size_t mem_required_state =
|
|
scale*MEM_REQ_KV_SELF.at(model.type);
|
|
|
|
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per state)\n", __func__,
|
|
mem_required / 1024.0 / 1024.0, mem_required_state / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// create the ggml context
|
|
{
|
|
lctx.model.buf.resize(ctx_size);
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ lctx.model.buf.size(),
|
|
/*.mem_buffer =*/ lctx.model.buf.data(),
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
model.ctx = ggml_init(params);
|
|
if (!model.ctx) {
|
|
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// prepare memory for the weights
|
|
{
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
model.layers.resize(n_layer);
|
|
|
|
model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
|
|
|
|
model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
model.output = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
|
|
|
|
// map by name
|
|
model.tensors["tok_embeddings.weight"] = model.tok_embeddings;
|
|
|
|
model.tensors["norm.weight"] = model.norm;
|
|
model.tensors["output.weight"] = model.output;
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
|
|
layer.wq = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
layer.wk = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
layer.wv = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
layer.wo = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
|
|
|
|
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
|
|
|
|
layer.w1 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
|
|
layer.w2 = ggml_new_tensor_2d(ctx, wtype, n_ff, n_embd);
|
|
layer.w3 = ggml_new_tensor_2d(ctx, wtype, n_embd, n_ff);
|
|
|
|
// map by name
|
|
model.tensors["layers." + std::to_string(i) + ".attention_norm.weight"] = layer.attention_norm;
|
|
|
|
model.tensors["layers." + std::to_string(i) + ".attention.wq.weight"] = layer.wq;
|
|
model.tensors["layers." + std::to_string(i) + ".attention.wk.weight"] = layer.wk;
|
|
model.tensors["layers." + std::to_string(i) + ".attention.wv.weight"] = layer.wv;
|
|
model.tensors["layers." + std::to_string(i) + ".attention.wo.weight"] = layer.wo;
|
|
|
|
model.tensors["layers." + std::to_string(i) + ".ffn_norm.weight"] = layer.ffn_norm;
|
|
|
|
model.tensors["layers." + std::to_string(i) + ".feed_forward.w1.weight"] = layer.w1;
|
|
model.tensors["layers." + std::to_string(i) + ".feed_forward.w2.weight"] = layer.w2;
|
|
model.tensors["layers." + std::to_string(i) + ".feed_forward.w3.weight"] = layer.w3;
|
|
}
|
|
}
|
|
|
|
std::vector<uint8_t> tmp;
|
|
|
|
if (progress_callback) {
|
|
progress_callback(0.0, progress_callback_user_data);
|
|
}
|
|
|
|
fprintf(stderr, "%s: loading tensors from '%s'\n", __func__, fname.c_str());
|
|
|
|
// load weights
|
|
{
|
|
size_t total_size = 0;
|
|
model.n_loaded = 0;
|
|
|
|
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 nelements = 1;
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
nelements *= ne[i];
|
|
}
|
|
|
|
std::string name(length, 0);
|
|
fin.read(&name[0], length);
|
|
|
|
if (model.tensors.find(name.data()) == model.tensors.end()) {
|
|
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
auto tensor = model.tensors[name.data()];
|
|
|
|
if (ggml_nelements(tensor) != nelements) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%" PRId64 ", %" PRId64 "], expected [%d, %d]\n",
|
|
__func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
|
|
return false;
|
|
}
|
|
if (0) {
|
|
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
|
fprintf(stderr, "%24s - [%5d, %5d], type = %6s\n", name.data(), ne[0], ne[1], ftype_str[ftype]);
|
|
}
|
|
|
|
switch (ftype) {
|
|
case 0: // f32
|
|
case 1: // f16
|
|
break;
|
|
case 2: // q4_0
|
|
case 3: // q4_1
|
|
assert(ne[0] % 64 == 0);
|
|
break;
|
|
default:
|
|
fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype);
|
|
return false;
|
|
};
|
|
|
|
// load the tensor data into memory without copying or reading it
|
|
size_t offset = fin.tellg();
|
|
size_t tensor_data_size = ggml_nbytes(tensor);
|
|
offset = (offset + 31) & -32;
|
|
tensor->data = mm_addr + offset;
|
|
fin.seekg(offset + tensor_data_size);
|
|
total_size += tensor_data_size;
|
|
model.n_loaded++;
|
|
|
|
// progress
|
|
if (progress_callback) {
|
|
double current_progress = size_t(fin.tellg()) / double(file_size);
|
|
progress_callback(current_progress, progress_callback_user_data);
|
|
}
|
|
}
|
|
|
|
fin.close();
|
|
|
|
fprintf(stderr, "%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, model.n_loaded);
|
|
if (model.n_loaded == 0) {
|
|
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
|
|
} else if (model.n_loaded != (int) model.tensors.size()) {
|
|
fprintf(stderr, "%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// loading time will be recalculate after the first eval, so
|
|
// we take page faults deferred by mmap() into consideration
|
|
lctx.t_load_us = ggml_time_us() - lctx.t_start_us;
|
|
|
|
if (progress_callback) {
|
|
progress_callback(1.0, progress_callback_user_data);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// evaluate the transformer
|
|
//
|
|
// - lctx: llama context
|
|
// - tokens: new batch of tokens to process
|
|
// - n_past: the context size so far
|
|
// - n_threads: number of threads to use
|
|
//
|
|
static bool llama_eval_internal(
|
|
llama_context & lctx,
|
|
const llama_token * tokens,
|
|
const int n_tokens,
|
|
const int n_past,
|
|
const int n_threads) {
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
const int N = n_tokens;
|
|
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
|
|
auto & kv_self = model.kv_self;
|
|
|
|
LLAMA_ASSERT(!!kv_self.ctx);
|
|
|
|
const int n_embd = hparams.n_embd;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_ctx = hparams.n_ctx;
|
|
const int n_head = hparams.n_head;
|
|
const int n_vocab = hparams.n_vocab;
|
|
const int n_rot = hparams.n_embd/hparams.n_head;
|
|
|
|
auto & mem_per_token = lctx.mem_per_token;
|
|
auto & buf_compute = lctx.buf_compute;
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ buf_compute.size(),
|
|
/*.mem_buffer =*/ buf_compute.data(),
|
|
/*.no_alloc =*/ false,
|
|
};
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
// 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
|
|
ggml_cgraph gf = {};
|
|
gf.n_threads = N >= 32 && ggml_cpu_has_blas() ? 1 : n_threads;
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
|
|
|
struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd);
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
lctx.use_buf(ctx0, 0);
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// cur = attention_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].attention_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
|
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [N, n_embd] V matrix
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), n_embd, N));
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.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);
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.k)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
0, 2, 1, 3);
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd/n_head)
|
|
struct ggml_tensor * KQ_scaled =
|
|
ggml_scale(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head)));
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
// split cached V into n_head heads
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_past + N, n_embd/n_head, n_head,
|
|
n_ctx*ggml_element_size(kv_self.v),
|
|
n_ctx*ggml_element_size(kv_self.v)*n_embd/n_head,
|
|
il*n_ctx*ggml_element_size(kv_self.v)*n_embd);
|
|
|
|
#if 1
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
#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_past + N, n_embd/n_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);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
}
|
|
|
|
lctx.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpFF);
|
|
|
|
// cur = ffn_norm*cur
|
|
cur = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.layers[il].ffn_norm, cur),
|
|
cur);
|
|
}
|
|
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model.layers[il].w3,
|
|
cur);
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w1,
|
|
cur);
|
|
|
|
// SILU activation
|
|
cur = ggml_silu(ctx0, cur);
|
|
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w2,
|
|
cur);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
lctx.use_buf(ctx0, 0);
|
|
|
|
// used at the end to optionally extract the embeddings
|
|
struct ggml_tensor * embeddings = NULL;
|
|
|
|
// norm
|
|
{
|
|
|
|
inpL = ggml_rms_norm(ctx0, inpL);
|
|
|
|
// inpL = norm*inpL
|
|
inpL = ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.norm, inpL),
|
|
inpL);
|
|
|
|
embeddings = inpL;
|
|
}
|
|
|
|
// lm_head
|
|
inpL = ggml_mul_mat(ctx0, model.output, inpL);
|
|
|
|
lctx.use_buf(ctx0, -1);
|
|
|
|
// logits -> probs
|
|
//inpL = ggml_soft_max(ctx0, inpL);
|
|
|
|
// run the computation
|
|
ggml_build_forward_expand(&gf, inpL);
|
|
ggml_graph_compute (ctx0, &gf);
|
|
|
|
// print timing information per ggml operation (for debugging purposes)
|
|
// requires GGML_PERF to be defined
|
|
//ggml_graph_print(&gf);
|
|
|
|
// plot the computation graph in dot format (for debugging purposes)
|
|
//if (n_past%100 == 0) {
|
|
// ggml_graph_dump_dot(&gf, NULL, "llama.dot");
|
|
//}
|
|
|
|
//embd_w.resize(n_vocab*N);
|
|
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
|
|
|
// extract logits
|
|
{
|
|
auto & logits_out = lctx.logits;
|
|
|
|
if (lctx.logits_all) {
|
|
logits_out.resize(n_vocab * N);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
|
|
} else {
|
|
// return result for just the last token
|
|
logits_out.resize(n_vocab);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
|
}
|
|
}
|
|
|
|
// extract embeddings
|
|
if (lctx.embedding.size()) {
|
|
auto & embedding_out = lctx.embedding;
|
|
|
|
embedding_out.resize(n_embd);
|
|
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
|
|
}
|
|
|
|
if (mem_per_token == 0) {
|
|
mem_per_token = ggml_used_mem(ctx0)/N;
|
|
}
|
|
|
|
#if 0
|
|
printf("\n%s: used_mem = %.3f MB, scratch -- %.3f MB %.3f MB\n", __func__,
|
|
ggml_used_mem(ctx0)/1024.0/1024.0,
|
|
lctx.get_buf_max_mem(0)/1024.0/1024.0,
|
|
lctx.get_buf_max_mem(1)/1024.0/1024.0);
|
|
#endif
|
|
|
|
ggml_free(ctx0);
|
|
|
|
// measure the performance only for the single-token evals
|
|
if (N == 1) {
|
|
lctx.t_eval_us += ggml_time_us() - t_start_us;
|
|
lctx.n_eval++;
|
|
}
|
|
else if (N > 1) {
|
|
lctx.t_p_eval_us += ggml_time_us() - t_start_us;
|
|
lctx.n_p_eval += N;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// tokenizer
|
|
//
|
|
|
|
static size_t utf8_len(char src) {
|
|
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
|
|
uint8_t highbits = static_cast<uint8_t>(src) >> 4;
|
|
return lookup[highbits];
|
|
}
|
|
|
|
struct llama_sp_symbol {
|
|
using index = int;
|
|
index prev;
|
|
index next;
|
|
const char * text;
|
|
size_t n;
|
|
};
|
|
|
|
struct llama_sp_bigram {
|
|
struct comparator {
|
|
bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
|
|
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
|
|
}
|
|
};
|
|
using queue_storage = std::vector<llama_sp_bigram>;
|
|
using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
|
|
llama_sp_symbol::index left;
|
|
llama_sp_symbol::index right;
|
|
float score;
|
|
size_t size;
|
|
};
|
|
|
|
// original implementation:
|
|
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
|
|
struct llama_tokenizer {
|
|
llama_tokenizer(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()) {
|
|
llama_sp_symbol sym;
|
|
size_t char_len = Min(text.size() - offs, utf8_len(text[offs]));
|
|
sym.text = text.c_str() + offs;
|
|
sym.n = char_len;
|
|
offs += char_len;
|
|
sym.prev = index - 1;
|
|
sym.next = offs == text.size() ? -1 : index + 1;
|
|
index++;
|
|
symbols_.emplace_back(std::move(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;
|
|
|
|
//printf("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];
|
|
auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
|
|
|
|
if (token == vocab_.token_to_id.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 = static_cast<uint8_t>(symbol.text[j]) + 3;
|
|
output.push_back(token_id);
|
|
}
|
|
} else {
|
|
output.push_back((*token).second);
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
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_score = vocab_.id_to_token[(*token).second];
|
|
|
|
llama_sp_bigram bigram;
|
|
bigram.left = left;
|
|
bigram.right = right;
|
|
bigram.score = tok_score.score;
|
|
bigram.size = text.size();
|
|
work_queue_.push(bigram);
|
|
}
|
|
|
|
const llama_vocab & vocab_;
|
|
std::vector<llama_sp_symbol> symbols_;
|
|
llama_sp_bigram::queue work_queue_;
|
|
};
|
|
|
|
static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
|
|
llama_tokenizer tokenizer(vocab);
|
|
std::vector<llama_vocab::id> output;
|
|
|
|
if (text.size() == 0) {
|
|
return output;
|
|
}
|
|
|
|
if (bos) {
|
|
output.push_back(1);
|
|
}
|
|
|
|
tokenizer.tokenize(text, output);
|
|
return output;
|
|
}
|
|
|
|
//
|
|
// sampling
|
|
//
|
|
|
|
static void sample_top_k(std::vector<std::pair<float, llama_vocab::id>> & logits_id, int top_k) {
|
|
// find the top k tokens
|
|
std::partial_sort(
|
|
logits_id.begin(),
|
|
logits_id.begin() + top_k, logits_id.end(),
|
|
[](const std::pair<float, llama_vocab::id> & a, const std::pair<float, llama_vocab::id> & b) {
|
|
return a.first > b.first;
|
|
});
|
|
|
|
logits_id.resize(top_k);
|
|
}
|
|
|
|
static llama_vocab::id llama_sample_top_p_top_k(
|
|
llama_context & lctx,
|
|
const std::vector<llama_vocab::id> & last_n_tokens,
|
|
int top_k,
|
|
float top_p,
|
|
float temp,
|
|
float repeat_penalty) {
|
|
auto & rng = lctx.rng;
|
|
|
|
const int n_logits = lctx.model.hparams.n_vocab;
|
|
|
|
const auto & logits = lctx.logits;
|
|
const auto * plogits = logits.data() + logits.size() - n_logits;
|
|
|
|
if (temp <= 0) {
|
|
// select the token with the highest logit directly
|
|
float max_logit = plogits[0];
|
|
llama_vocab::id max_id = 0;
|
|
|
|
for (int i = 1; i < n_logits; ++i) {
|
|
if (plogits[i] > max_logit) {
|
|
max_logit = plogits[i];
|
|
max_id = i;
|
|
}
|
|
}
|
|
return max_id;
|
|
}
|
|
|
|
std::vector<std::pair<float, llama_vocab::id>> logits_id;
|
|
logits_id.reserve(n_logits);
|
|
|
|
{
|
|
const float scale = 1.0f/temp;
|
|
for (int i = 0; i < n_logits; ++i) {
|
|
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
|
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
|
if (std::find(last_n_tokens.begin(), last_n_tokens.end(), i) != last_n_tokens.end()) {
|
|
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
|
if (plogits[i] < 0.0f) {
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
|
} else {
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
|
}
|
|
} else {
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
|
|
}
|
|
}
|
|
}
|
|
|
|
sample_top_k(logits_id, top_k > 0 ? Min(top_k, n_logits) : n_logits);
|
|
|
|
// compute probs for the top k tokens
|
|
std::vector<float> probs;
|
|
probs.reserve(logits_id.size());
|
|
|
|
float maxl = logits_id[0].first;
|
|
double sum = 0.0;
|
|
for (const auto & kv : logits_id) {
|
|
const float p = expf(kv.first - maxl);
|
|
probs.push_back(p);
|
|
sum += p;
|
|
}
|
|
|
|
// normalize the probs
|
|
for (auto & p : probs) {
|
|
p /= sum;
|
|
}
|
|
|
|
if (top_p < 1.0) {
|
|
double cumsum = 0.0;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
cumsum += probs[i];
|
|
if (cumsum >= top_p) {
|
|
probs.resize(i + 1);
|
|
logits_id.resize(i + 1);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
//printf("\n");
|
|
//for (int i = 0; i < (int) 10; i++) {
|
|
// printf("%d: '%s' %f\n", i, lctx.vocab.id_to_token.at(logits_id[i].second).tok.c_str(), probs[i]);
|
|
//}
|
|
//printf("\n\n");
|
|
//exit(0);
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
int idx = dist(rng);
|
|
|
|
return logits_id[idx].second;
|
|
}
|
|
|
|
//
|
|
// quantization
|
|
//
|
|
|
|
// TODO: reuse code from the llama_model_load() somehow
|
|
static bool llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, int itype) {
|
|
ggml_type type = GGML_TYPE_Q4_1;
|
|
|
|
switch (itype) {
|
|
case 2: type = GGML_TYPE_Q4_0; break;
|
|
case 3: type = GGML_TYPE_Q4_1; break;
|
|
default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1;
|
|
};
|
|
|
|
if (type != GGML_TYPE_Q4_0 && type != GGML_TYPE_Q4_1) {
|
|
fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type);
|
|
return false;
|
|
}
|
|
|
|
llama_vocab vocab;
|
|
|
|
printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
|
|
|
|
auto finp = std::ifstream(fname_inp, std::ios::binary);
|
|
if (!finp) {
|
|
fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str());
|
|
return false;
|
|
}
|
|
|
|
auto fout = std::ofstream(fname_out, std::ios::binary);
|
|
if (!fout) {
|
|
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str());
|
|
return false;
|
|
}
|
|
|
|
// verify magic
|
|
{
|
|
uint32_t magic;
|
|
finp.read((char *) &magic, sizeof(magic));
|
|
if (magic == LLAMA_FILE_MAGIC_UNVERSIONED) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
|
|
__func__, fname_inp.c_str());
|
|
return false;
|
|
}
|
|
if (magic != LLAMA_FILE_MAGIC) {
|
|
return report_bad_magic(fname_inp.c_str(), magic, LLAMA_FILE_MAGIC);
|
|
}
|
|
|
|
fout.write((char *) &magic, sizeof(magic));
|
|
|
|
uint32_t format_version;
|
|
finp.read((char *) &format_version, sizeof(format_version));
|
|
|
|
if (format_version != LLAMA_FILE_VERSION) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
|
|
__func__, fname_inp.c_str(), format_version, LLAMA_FILE_VERSION);
|
|
return false;
|
|
}
|
|
|
|
fout.write((char *) &format_version, sizeof(format_version));
|
|
}
|
|
|
|
llama_hparams hparams;
|
|
|
|
// load hparams
|
|
{
|
|
finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
|
//finp.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
|
|
finp.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
|
|
finp.read((char *) &hparams.n_mult, sizeof(hparams.n_mult));
|
|
finp.read((char *) &hparams.n_head, sizeof(hparams.n_head));
|
|
finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
|
|
finp.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
|
|
finp.read((char *) &hparams.f16, sizeof(hparams.f16));
|
|
|
|
printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
|
printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx);
|
|
printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
|
|
printf("%s: n_mult = %d\n", __func__, hparams.n_mult);
|
|
printf("%s: n_head = %d\n", __func__, hparams.n_head);
|
|
printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
|
|
printf("%s: f16 = %d\n", __func__, hparams.f16);
|
|
|
|
fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
|
|
//fout.write((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
|
|
fout.write((char *) &hparams.n_embd, sizeof(hparams.n_embd));
|
|
fout.write((char *) &hparams.n_mult, sizeof(hparams.n_mult));
|
|
fout.write((char *) &hparams.n_head, sizeof(hparams.n_head));
|
|
fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer));
|
|
fout.write((char *) &hparams.n_rot, sizeof(hparams.n_rot));
|
|
fout.write((char *) &itype, sizeof(hparams.f16));
|
|
}
|
|
|
|
// load vocab
|
|
{
|
|
const int32_t n_vocab = hparams.n_vocab;
|
|
|
|
if (n_vocab != hparams.n_vocab) {
|
|
fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
|
|
__func__, fname_inp.c_str(), n_vocab, hparams.n_vocab);
|
|
return false;
|
|
}
|
|
|
|
std::vector<char> word(32);
|
|
vocab.id_to_token.resize(n_vocab);
|
|
for (int i = 0; i < n_vocab; i++) {
|
|
uint32_t len;
|
|
finp.read ((char *) &len, sizeof(len));
|
|
fout.write((char *) &len, sizeof(len));
|
|
|
|
word.resize(len);
|
|
finp.read ((char *) &word[0], len);
|
|
fout.write((char *) &word[0], len);
|
|
|
|
float score;
|
|
finp.read ((char *) &score, sizeof(score));
|
|
fout.write((char *) &score, sizeof(score));
|
|
|
|
vocab.token_to_id[word.data()] = i;
|
|
|
|
auto &tok_score = vocab.id_to_token[i];
|
|
tok_score.tok = word.data();
|
|
tok_score.score = score;
|
|
}
|
|
}
|
|
|
|
// load weights
|
|
{
|
|
size_t total_size_org = 0;
|
|
size_t total_size_new = 0;
|
|
|
|
std::vector<float> work;
|
|
|
|
std::vector<uint8_t> data_u8;
|
|
std::vector<ggml_fp16_t> data_f16;
|
|
std::vector<float> data_f32;
|
|
|
|
std::vector<int64_t> hist_all(1 << 4, 0);
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ftype;
|
|
|
|
finp.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
finp.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
finp.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
|
|
if (finp.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t nelements = 1;
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
finp.read (reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
nelements *= ne[i];
|
|
}
|
|
|
|
std::string name(length, 0);
|
|
finp.read (&name[0], length);
|
|
|
|
{
|
|
// ensure tensor data is aligned
|
|
uint64_t offset = finp.tellg();
|
|
offset = (offset + 31) & -32;
|
|
finp.seekg(offset);
|
|
}
|
|
|
|
{
|
|
static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", };
|
|
printf("%48s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]);
|
|
}
|
|
|
|
// regexes of tensor names to be quantized
|
|
const std::vector<std::string> k_names = {
|
|
".*weight",
|
|
};
|
|
|
|
bool quantize = false;
|
|
for (const auto & s : k_names) {
|
|
if (std::regex_match(name, std::regex(s))) {
|
|
quantize = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// quantize only 2D tensors
|
|
quantize &= (n_dims == 2);
|
|
|
|
if (quantize) {
|
|
if (ftype != 0 && ftype != 1) {
|
|
fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype);
|
|
return false;
|
|
}
|
|
|
|
if (ftype == 1) {
|
|
data_f16.resize(nelements);
|
|
finp.read(reinterpret_cast<char *>(data_f16.data()), nelements * sizeof(ggml_fp16_t));
|
|
data_f32.resize(nelements);
|
|
for (int i = 0; i < nelements; ++i) {
|
|
data_f32[i] = ggml_fp16_to_fp32(data_f16[i]);
|
|
}
|
|
} else {
|
|
data_f32.resize(nelements);
|
|
finp.read(reinterpret_cast<char *>(data_f32.data()), nelements * sizeof(float));
|
|
}
|
|
|
|
ftype = itype;
|
|
} else {
|
|
const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t);
|
|
|
|
data_u8.resize(nelements*bpe);
|
|
finp.read(reinterpret_cast<char *>(data_u8.data()), nelements * bpe);
|
|
}
|
|
|
|
fout.write(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
fout.write(reinterpret_cast<char *>(&length), sizeof(length));
|
|
fout.write(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fout.write(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
}
|
|
fout.write(&name[0], length);
|
|
|
|
{
|
|
// ensure tensor data is aligned
|
|
uint64_t offset = fout.tellp();
|
|
offset = (offset + 31) & -32;
|
|
fout.seekp(offset);
|
|
}
|
|
|
|
if (quantize) {
|
|
printf("quantizing .. ");
|
|
work.resize(nelements); // for quantization
|
|
|
|
size_t cur_size = 0;
|
|
std::vector<int64_t> hist_cur(1 << 4, 0);
|
|
|
|
switch (type) {
|
|
case GGML_TYPE_Q4_0:
|
|
{
|
|
cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
|
|
} break;
|
|
case GGML_TYPE_Q4_1:
|
|
{
|
|
cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data());
|
|
} break;
|
|
default:
|
|
{
|
|
fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
fout.write(reinterpret_cast<char *>(work.data()), cur_size);
|
|
total_size_new += cur_size;
|
|
|
|
printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
|
|
for (int i = 0; i < (int) hist_cur.size(); ++i) {
|
|
hist_all[i] += hist_cur[i];
|
|
}
|
|
|
|
for (int i = 0; i < (int) hist_cur.size(); ++i) {
|
|
printf("%5.3f ", hist_cur[i] / float(nelements));
|
|
}
|
|
printf("\n");
|
|
} else {
|
|
printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
|
|
fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
|
|
total_size_new += data_u8.size();
|
|
}
|
|
|
|
total_size_org += nelements * sizeof(float);
|
|
}
|
|
|
|
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
|
|
printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
|
|
|
|
{
|
|
int64_t sum_all = 0;
|
|
for (int i = 0; i < (int) hist_all.size(); ++i) {
|
|
sum_all += hist_all[i];
|
|
}
|
|
|
|
printf("%s: hist: ", __func__);
|
|
for (int i = 0; i < (int) hist_all.size(); ++i) {
|
|
printf("%5.3f ", hist_all[i] / float(sum_all));
|
|
}
|
|
printf("\n");
|
|
}
|
|
}
|
|
|
|
finp.close();
|
|
fout.close();
|
|
|
|
return true;
|
|
}
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
|
|
struct llama_context * llama_init_from_file(
|
|
const char * path_model,
|
|
struct llama_context_params params) {
|
|
ggml_time_init();
|
|
|
|
llama_context * ctx = new llama_context;
|
|
|
|
if (params.seed <= 0) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
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;
|
|
|
|
if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_parts, memory_type,
|
|
params.vocab_only, params.progress_callback,
|
|
params.progress_callback_user_data)) {
|
|
fprintf(stderr, "%s: failed to load model\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
if (params.use_mlock) {
|
|
char *err;
|
|
if (!ggml_mlock(ctx->model.ctx,
|
|
ctx->model.mm_addr,
|
|
ctx->model.mm_length,
|
|
&err)) {
|
|
fprintf(stderr, "%s\n", err);
|
|
free(err);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
// reserve memory for context buffers
|
|
if (!params.vocab_only) {
|
|
if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx)) {
|
|
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
{
|
|
const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v);
|
|
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
const auto & hparams = ctx->model.hparams;
|
|
|
|
// resized during inference
|
|
if (params.logits_all) {
|
|
ctx->logits.reserve(hparams.n_ctx*hparams.n_vocab);
|
|
} else {
|
|
ctx->logits.reserve(hparams.n_ctx);
|
|
}
|
|
|
|
if (params.embedding){
|
|
ctx->embedding.resize(hparams.n_embd);
|
|
}
|
|
|
|
ctx->buf_compute.resize(MEM_REQ_EVAL.at(ctx->model.type));
|
|
|
|
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0.at(ctx->model.type));
|
|
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1.at(ctx->model.type));
|
|
}
|
|
|
|
return ctx;
|
|
}
|
|
|
|
void llama_free(struct llama_context * ctx) {
|
|
kv_cache_free(ctx->model.kv_self);
|
|
|
|
if (ctx->model.ctx) {
|
|
ggml_free(ctx->model.ctx);
|
|
}
|
|
|
|
if (ctx->model.mm_addr) {
|
|
munmap_file(ctx->model.mm_addr, ctx->model.mm_length);
|
|
}
|
|
|
|
delete ctx;
|
|
}
|
|
|
|
int llama_model_quantize(
|
|
const char * fname_inp,
|
|
const char * fname_out,
|
|
int itype) {
|
|
if (!llama_model_quantize_internal(fname_inp, fname_out, itype)) {
|
|
fprintf(stderr, "%s: failed to quantize\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
// Returns the KV cache that will contain the context for the
|
|
// ongoing prediction with the model.
|
|
const uint8_t * llama_get_kv_cache(struct llama_context * ctx) {
|
|
return ctx->model.kv_self.buf.data();
|
|
}
|
|
|
|
// Returns the size of the KV cache
|
|
size_t llama_get_kv_cache_size(struct llama_context * ctx) {
|
|
return ctx->model.kv_self.buf.size();
|
|
}
|
|
|
|
int llama_get_kv_cache_token_count(struct llama_context * ctx) {
|
|
return ctx->model.kv_self.n;
|
|
}
|
|
|
|
// Sets the KV cache containing the current context for the model
|
|
void llama_set_kv_cache(
|
|
struct llama_context * ctx,
|
|
const uint8_t * kv_cache,
|
|
size_t n_size,
|
|
int n_token_count) {
|
|
// Make sure we have the same kv cache setup
|
|
LLAMA_ASSERT(ctx->model.kv_self.buf.size() == n_size);
|
|
memcpy(ctx->model.kv_self.buf.data(), kv_cache, n_size);
|
|
ctx->model.kv_self.n = n_token_count;
|
|
}
|
|
|
|
int llama_eval(
|
|
struct llama_context * ctx,
|
|
const llama_token * tokens,
|
|
int n_tokens,
|
|
int n_past,
|
|
int n_threads) {
|
|
if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
|
|
fprintf(stderr, "%s: failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
// get a more accurate load time, upon first eval
|
|
if (!ctx->has_evaluated_once) {
|
|
ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
|
|
ctx->has_evaluated_once = true;
|
|
}
|
|
return 0;
|
|
}
|
|
|
|
int llama_tokenize(
|
|
struct llama_context * ctx,
|
|
const char * text,
|
|
llama_token * tokens,
|
|
int n_max_tokens,
|
|
bool add_bos) {
|
|
auto res = llama_tokenize(ctx->vocab, text, add_bos);
|
|
|
|
if (n_max_tokens < (int) res.size()) {
|
|
fprintf(stderr, "%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();
|
|
}
|
|
|
|
int llama_n_vocab(struct llama_context * ctx) {
|
|
return ctx->vocab.id_to_token.size();
|
|
}
|
|
|
|
int llama_n_ctx(struct llama_context * ctx) {
|
|
return ctx->model.hparams.n_ctx;
|
|
}
|
|
|
|
int llama_n_embd(struct llama_context * ctx) {
|
|
return ctx->model.hparams.n_embd;
|
|
}
|
|
|
|
float * llama_get_logits(struct llama_context * ctx) {
|
|
return ctx->logits.data();
|
|
}
|
|
|
|
float * llama_get_embeddings(struct llama_context * ctx) {
|
|
return ctx->embedding.data();
|
|
}
|
|
|
|
const char * llama_token_to_str(struct llama_context * ctx, llama_token token) {
|
|
if (token >= llama_n_vocab(ctx)) {
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx->vocab.id_to_token[token].tok.c_str();
|
|
}
|
|
|
|
llama_token llama_token_bos() {
|
|
return 1;
|
|
}
|
|
|
|
llama_token llama_token_eos() {
|
|
return 2;
|
|
}
|
|
|
|
llama_token llama_sample_top_p_top_k(
|
|
llama_context * ctx,
|
|
const llama_token * last_n_tokens_data,
|
|
int last_n_tokens_size,
|
|
int top_k,
|
|
float top_p,
|
|
float temp,
|
|
float repeat_penalty) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
llama_token result = 0;
|
|
|
|
// TODO: avoid this ...
|
|
const auto last_n_tokens = std::vector<llama_token>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_size);
|
|
|
|
result = llama_sample_top_p_top_k(
|
|
*ctx,
|
|
last_n_tokens,
|
|
top_k,
|
|
top_p,
|
|
temp,
|
|
repeat_penalty);
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
|
|
return result;
|
|
}
|
|
|
|
|
|
void llama_print_timings(struct llama_context * ctx) {
|
|
const int64_t t_end_us = ggml_time_us();
|
|
|
|
const int32_t n_sample = Max(1, ctx->n_sample);
|
|
const int32_t n_eval = Max(1, ctx->n_eval);
|
|
const int32_t n_p_eval = Max(1, ctx->n_p_eval);
|
|
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0);
|
|
fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample);
|
|
fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval);
|
|
fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval);
|
|
fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0);
|
|
}
|
|
|
|
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 += "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 += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
|
|
|
|
return s.c_str();
|
|
}
|
|
|
|
// For internal test use
|
|
std::unordered_map<std::string, struct ggml_tensor *>& llama_internal_get_tensor_map(struct llama_context * ctx) {
|
|
return ctx->model.tensors;
|
|
}
|