mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-13 14:29:52 +00:00
78ca9838ee
This is a breaking change that's going to give you three benefits: 1. Your inference commands should load 100x faster 2. You may be able to safely load models 2x larger 3. You can run many concurrent inference processes This was accomplished by changing the file format so we can mmap() weights directly into memory without having to read() or copy them thereby ensuring the kernel can make its file cache pages directly accessible to our inference processes; and secondly, that the file cache pages are much less likely to get evicted (which would force loads to hit disk) because they're no longer competing with memory pages that were needlessly created by gigabytes of standard i/o. The new file format supports single-file models like LLaMA 7b, and it also supports multi-file models like LLaMA 13B. Our Python tool now merges the foo.1, foo.2, etc. files back into a single file so that the C++ code which maps it doesn't need to reshape data every time. That's made llama.cpp so much simpler. Much of its load code has now been deleted. Furthermore, this change ensures that tensors are aligned properly on a 32-byte boundary. That opens the door to seeing if we can get additional performance gains on some microprocessors, by using ops that require memory alignment. Lastly note that both POSIX and the Windows platform are supported Fixes #91
1812 lines
57 KiB
C++
1812 lines
57 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 int n_mem = n_layer*n_ctx;
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const int 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) {
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fprintf(stderr,
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"%s: invalid model file (bad magic)\n"
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"you most likely need to regenerate your ggml files\n"
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"the benefit is you'll get 10-100x faster load times\n"
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"see https://github.com/ggerganov/llama.cpp/issues/91\n"
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"use convert-pth-to-ggml.py on your llama model files\n",
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path);
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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());
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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) {
|
|
tmp.resize(len);
|
|
fin.read(tmp.data(), len);
|
|
word.assign(tmp.data(), len);
|
|
} else {
|
|
word.clear();
|
|
}
|
|
|
|
float score;
|
|
fin.read((char *) &score, sizeof(score));
|
|
|
|
vocab.token_to_id[word] = i;
|
|
|
|
auto &tok_score = vocab.id_to_token[i];
|
|
tok_score.tok = word;
|
|
tok_score.score = score;
|
|
}
|
|
}
|
|
|
|
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 [%d, %d], 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
|
|
{
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
|
|
// store key and value to memory
|
|
if (N >= 1) {
|
|
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_1d(ctx0, kv_self.v, N*n_embd, (ggml_element_size(kv_self.v)*n_embd)*(il*n_ctx + n_past));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
// Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_rope(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
|
|
n_past, n_rot, 0),
|
|
0, 2, 1, 3);
|
|
|
|
// K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_rope(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),
|
|
n_past, n_rot, 1),
|
|
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);
|
|
|
|
// V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
|
|
struct ggml_tensor * V_trans =
|
|
ggml_cpy(ctx0,
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kv_self.v)*n_embd),
|
|
n_embd/n_head, n_head, n_past + N),
|
|
1, 2, 0, 3),
|
|
ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd/n_head, n_head));
|
|
|
|
// KQV = transpose(V) * KQ_soft_max
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
|
|
// 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);
|
|
|
|
//if (n_past%100 == 0) {
|
|
// ggml_graph_print (&gf);
|
|
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.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;
|
|
|
|
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);
|
|
|
|
float maxl = -std::numeric_limits<float>::infinity();
|
|
for (const auto & kv : logits_id) {
|
|
maxl = Max(maxl, kv.first);
|
|
}
|
|
|
|
// compute probs for the top k tokens
|
|
std::vector<float> probs;
|
|
probs.reserve(logits_id.size());
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
cumsum = 1.0/cumsum;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
probs[i] *= cumsum;
|
|
}
|
|
}
|
|
|
|
//printf("\n");
|
|
//for (int i = 0; i < (int) 10; i++) {
|
|
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).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());
|
|
}
|
|
|
|
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, &err)) {
|
|
fprintf(stderr, "%s\n", err);
|
|
free(err);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
}
|
|
|
|
// reserve memory for context buffers
|
|
{
|
|
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;
|
|
}
|
|
|
|
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();
|
|
}
|