4365 lines
151 KiB
C++
4365 lines
151 KiB
C++
/**
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* llama.cpp - git 3ebb00935f3f0522b75df49c2769ab1774b91380
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*
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* MIT License
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*
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* Copyright (c) 2023 Georgi Gerganov
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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// Defines fileno on msys:
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#ifndef _GNU_SOURCE
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#define _GNU_SOURCE
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#include <cstddef>
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#include <cstdint>
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#include <cstdio>
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#endif
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#include "llama-util.h"
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#include "llama.h"
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#include "ggml.h"
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#ifdef GGML_USE_CUBLAS
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#include "ggml-cuda.h"
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#elif defined(GGML_USE_CLBLAST)
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#include "ggml-opencl.h"
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#endif
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#ifdef GGML_USE_METAL
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#include "ggml-metal.h"
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#endif
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#ifdef GGML_USE_MPI
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#include "ggml-mpi.h"
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#endif
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#ifdef GGML_USE_K_QUANTS
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#ifndef QK_K
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#ifdef GGML_QKK_64
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#define QK_K 64
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#else
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#define QK_K 256
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#endif
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#endif
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#endif
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#include <array>
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#include <ctime>
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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 <cassert>
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#include <cstring>
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#include <climits>
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#include <memory>
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#include <algorithm>
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#include <initializer_list>
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#include <thread>
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#include <atomic>
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#include <mutex>
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#include <sstream>
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#include <numeric>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static void llama_log_internal(llama_log_level level, const char* format, ...);
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static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data);
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#define LLAMA_LOG_INFO(...) llama_log_internal(LLAMA_LOG_LEVEL_INFO , __VA_ARGS__)
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#define LLAMA_LOG_WARN(...) llama_log_internal(LLAMA_LOG_LEVEL_WARN , __VA_ARGS__)
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#define LLAMA_LOG_ERROR(...) llama_log_internal(LLAMA_LOG_LEVEL_ERROR, __VA_ARGS__)
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#if !defined(GGML_USE_CUBLAS) && !defined(GGML_USE_METAL)
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#include "ggml-alloc.h"
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#define LLAMA_USE_ALLOCATOR
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#else
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#define LLAMA_USE_SCRATCH
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#define LLAMA_MAX_SCRATCH_BUFFERS 16
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#endif
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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_3B,
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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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MODEL_70B,
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};
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static const size_t kB = 1024;
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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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typedef void (*offload_func_t)(struct ggml_tensor * tensor);
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void llama_nop(struct ggml_tensor * tensor) { // don't offload by default
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(void) tensor;
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}
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//
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// ggml helpers
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//
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static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph * graph, int n_threads) {
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struct ggml_cplan plan = ggml_graph_plan(graph, n_threads);
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if (plan.work_size > 0) {
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buf.resize(plan.work_size);
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plan.work_data = buf.data();
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}
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ggml_graph_compute(graph, &plan);
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}
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//
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// memory sizes (calculated for n_batch == 512)
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//
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static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0(int n_ctx)
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{
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static std::map<e_model, size_t> k_sizes = {
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{ MODEL_3B, ((size_t) n_ctx / 16ull + 92ull) * MB },
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{ MODEL_7B, ((size_t) n_ctx / 16ull + 100ull) * MB },
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{ MODEL_13B, ((size_t) n_ctx / 12ull + 120ull) * MB },
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{ MODEL_30B, ((size_t) n_ctx / 9ull + 160ull) * MB },
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{ MODEL_65B, ((size_t) n_ctx / 6ull + 256ull) * MB }, // guess
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{ MODEL_70B, ((size_t) n_ctx / 7ull + 164ull) * MB },
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};
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return k_sizes;
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}
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static const std::map<e_model, size_t> & MEM_REQ_SCRATCH1()
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{
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static std::map<e_model, size_t> k_sizes = {
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{ MODEL_3B, 128ull * MB },
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{ MODEL_7B, 160ull * MB },
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{ MODEL_13B, 192ull * MB },
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{ MODEL_30B, 256ull * MB },
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{ MODEL_65B, 384ull * MB }, // guess
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{ MODEL_70B, 304ull * MB },
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};
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return k_sizes;
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}
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// used to store the compute graph tensors + non-scratch data
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static const std::map<e_model, size_t> & MEM_REQ_EVAL()
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{
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static std::map<e_model, size_t> k_sizes = {
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{ MODEL_3B, 8ull * MB },
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{ MODEL_7B, 10ull * MB },
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{ MODEL_13B, 12ull * MB },
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{ MODEL_30B, 16ull * MB },
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{ MODEL_65B, 24ull * MB }, // guess
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{ MODEL_70B, 24ull * MB },
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};
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return k_sizes;
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}
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// amount of VRAM needed per batch size to hold temporary results
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// the values for 3b are not derived from testing but instead chosen conservatively
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static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_BASE()
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{
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static std::map<e_model, size_t> k_sizes = {
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{ MODEL_3B, 512ull * kB },
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{ MODEL_7B, 512ull * kB },
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{ MODEL_13B, 640ull * kB },
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{ MODEL_30B, 768ull * kB },
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{ MODEL_65B, 1280ull * kB },
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{ MODEL_70B, 1280ull * kB },
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};
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return k_sizes;
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}
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// amount of VRAM needed per batch size and context to hold temporary results
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// the values for 3b are not derived from testing but instead chosen conservatively
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static const std::map<e_model, size_t> & VRAM_REQ_SCRATCH_PER_CONTEXT()
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{
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static std::map<e_model, size_t> k_sizes = {
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{ MODEL_3B, 128ull },
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{ MODEL_7B, 128ull },
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{ MODEL_13B, 160ull },
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{ MODEL_30B, 208ull },
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{ MODEL_65B, 256ull },
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{ MODEL_70B, 256ull },
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};
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return k_sizes;
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}
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// default hparams (LLaMA 7B)
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struct llama_hparams {
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uint32_t n_vocab = 32000;
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uint32_t n_ctx = 512; // this is provided as user input?
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uint32_t n_embd = 4096;
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uint32_t n_mult = 256;
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uint32_t n_head = 32;
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uint32_t n_head_kv = 32;
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uint32_t n_layer = 32;
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uint32_t n_rot = 64;
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// LLaMAv2
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// TODO: load from model data hparams
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float f_ffn_mult = 1.0f;
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float f_rms_norm_eps = LLAMA_DEFAULT_RMS_EPS;
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float rope_freq_base = 10000.0f;
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float rope_freq_scale = 1.0f;
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enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
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bool operator!=(const llama_hparams & other) const {
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return static_cast<bool>(memcmp(this, &other, sizeof(llama_hparams))); // NOLINT
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}
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uint32_t n_gqa() const {
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return n_head/n_head_kv;
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}
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uint32_t n_embd_head() const {
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return n_embd/n_head;
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}
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uint32_t n_embd_gqa() const {
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return n_embd/n_gqa();
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}
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size_t kv_size() const {
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size_t result = 2ull;
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result *= (size_t) n_embd_gqa();
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result *= (size_t) n_ctx;
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result *= (size_t) n_layer;
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result *= sizeof(ggml_fp16_t);
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return result;
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}
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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 = NULL;
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struct ggml_tensor * v = NULL;
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struct ggml_context * ctx = NULL;
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llama_ctx_buffer buf;
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int n; // number of tokens currently in the cache
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~llama_kv_cache() {
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if (ctx) {
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ggml_free(ctx);
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}
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#ifdef GGML_USE_CUBLAS
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ggml_cuda_free_data(k);
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ggml_cuda_free_data(v);
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#endif // GGML_USE_CUBLAS
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}
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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_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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int n_gpu_layers;
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// context
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struct ggml_context * ctx = NULL;
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// the model memory buffer
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llama_ctx_buffer buf;
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// model memory mapped file
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std::unique_ptr<llama_mmap> mapping;
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// objects representing data potentially being locked in memory
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llama_mlock mlock_buf;
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llama_mlock mlock_mmap;
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// for quantize-stats only
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std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
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int64_t t_load_us = 0;
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int64_t t_start_us = 0;
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llama_vocab vocab;
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~llama_model() {
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if (ctx) {
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ggml_free(ctx);
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}
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#ifdef GGML_USE_CUBLAS
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for (size_t i = 0; i < tensors_by_name.size(); ++i) {
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ggml_cuda_free_data(tensors_by_name[i].second);
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}
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ggml_cuda_free_scratch();
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#elif defined(GGML_USE_CLBLAST)
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for (size_t i = 0; i < tensors_by_name.size(); ++i) {
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ggml_cl_free_data(tensors_by_name[i].second);
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}
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#endif
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}
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};
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struct llama_context {
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llama_context(const llama_model & model) : model(model), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
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~llama_context() {
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if (model_owner) {
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delete &model;
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}
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#ifdef GGML_USE_METAL
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if (ctx_metal) {
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ggml_metal_free(ctx_metal);
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}
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#endif
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#ifdef LLAMA_USE_ALLOCATOR
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if (alloc) {
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ggml_allocr_free(alloc);
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}
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#endif
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}
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std::mt19937 rng;
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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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const llama_model & model;
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bool model_owner = false;
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int64_t t_load_us;
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int64_t t_start_us;
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// key + value cache for the self attention
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struct llama_kv_cache kv_self;
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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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// reusable buffer for `struct ggml_graph_plan.work_data`
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std::vector<uint8_t> work_buffer;
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// memory buffers used to evaluate the model
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// TODO: move in llama_state
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llama_ctx_buffer buf_compute;
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#ifdef LLAMA_USE_ALLOCATOR
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llama_ctx_buffer buf_alloc;
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ggml_allocr * alloc = NULL;
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#endif
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#ifdef LLAMA_USE_SCRATCH
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llama_ctx_buffer 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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#endif
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#ifdef GGML_USE_METAL
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ggml_metal_context * ctx_metal = NULL;
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#endif
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#ifdef GGML_USE_MPI
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ggml_mpi_context * ctx_mpi = NULL;
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#endif
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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.addr, });
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}
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if (buf_last >= 0) {
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buf_max_size[buf_last] = std::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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struct llama_state {
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// We save the log callback globally
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llama_log_callback log_callback = llama_log_callback_default;
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void * log_callback_user_data = nullptr;
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};
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// global state
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static llama_state g_state;
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template <typename T>
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static T checked_mul(T a, T b) {
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T ret = a * b;
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if (a != 0 && ret / a != b) {
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throw std::runtime_error(format("overflow multiplying %llu * %llu",
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(unsigned long long) a, (unsigned long long) b));
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}
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return ret;
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}
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static size_t checked_div(size_t a, size_t b) {
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if (b == 0 || a % b != 0) {
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throw std::runtime_error(format("error dividing %zu / %zu", a, b));
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}
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return a / b;
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}
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static std::string llama_format_tensor_shape(const std::vector<uint32_t> & ne) {
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char buf[256];
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snprintf(buf, sizeof(buf), "%5u", ne.at(0));
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for (size_t i = 1; i < ne.size(); i++) {
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snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), " x %5u", ne.at(i));
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}
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return buf;
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}
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static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml_type type) {
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size_t size = ggml_type_size(type);
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for (uint32_t dim : ne) {
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size = checked_mul<size_t>(size, dim);
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}
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return size / ggml_blck_size(type);
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}
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struct llama_load_tensor {
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std::string name;
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enum ggml_type type = GGML_TYPE_F32;
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std::vector<uint32_t> ne;
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|
size_t file_off;
|
|
size_t size;
|
|
struct ggml_tensor * ggml_tensor = NULL;
|
|
uint8_t * data;
|
|
};
|
|
|
|
struct llama_load_tensors_map {
|
|
// tensors is kept in a separate vector to preserve file order
|
|
std::vector<llama_load_tensor> tensors;
|
|
std::unordered_map<std::string, size_t> name_to_idx;
|
|
};
|
|
|
|
enum llama_file_version {
|
|
LLAMA_FILE_VERSION_GGML,
|
|
LLAMA_FILE_VERSION_GGMF_V1, // added version field and scores in vocab
|
|
LLAMA_FILE_VERSION_GGJT_V1, // added padding
|
|
LLAMA_FILE_VERSION_GGJT_V2, // changed quantization format
|
|
LLAMA_FILE_VERSION_GGJT_V3, // changed Q4 and Q8 quantization format
|
|
};
|
|
|
|
struct llama_file_loader {
|
|
llama_file file;
|
|
llama_file_version file_version;
|
|
llama_hparams hparams;
|
|
llama_vocab vocab;
|
|
|
|
llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map)
|
|
: file(fname, "rb") {
|
|
LLAMA_LOG_INFO("llama.cpp: loading model from %s\n", fname);
|
|
read_magic();
|
|
read_hparams();
|
|
read_vocab();
|
|
read_tensor_metadata(tensors_map);
|
|
}
|
|
void read_magic() {
|
|
uint32_t magic = file.read_u32();
|
|
|
|
if (magic == LLAMA_FILE_MAGIC_GGML) {
|
|
file_version = LLAMA_FILE_VERSION_GGML;
|
|
return;
|
|
}
|
|
|
|
uint32_t version = file.read_u32();
|
|
|
|
switch (magic) {
|
|
case LLAMA_FILE_MAGIC_GGMF:
|
|
switch (version) {
|
|
case 1: file_version = LLAMA_FILE_VERSION_GGMF_V1; return;
|
|
}
|
|
break;
|
|
case LLAMA_FILE_MAGIC_GGJT:
|
|
switch (version) {
|
|
case 1: file_version = LLAMA_FILE_VERSION_GGJT_V1; return;
|
|
case 2: file_version = LLAMA_FILE_VERSION_GGJT_V2; return;
|
|
case 3: file_version = LLAMA_FILE_VERSION_GGJT_V3; return;
|
|
}
|
|
}
|
|
|
|
throw std::runtime_error(format("unknown (magic, version) combination: %08x, %08x; is this really a GGML file?",
|
|
magic, version));
|
|
}
|
|
void read_hparams() {
|
|
hparams.n_vocab = file.read_u32();
|
|
hparams.n_embd = file.read_u32();
|
|
hparams.n_mult = file.read_u32();
|
|
hparams.n_head = file.read_u32();
|
|
hparams.n_layer = file.read_u32();
|
|
hparams.n_rot = file.read_u32();
|
|
hparams.ftype = (enum llama_ftype) file.read_u32();
|
|
|
|
// LLaMAv2
|
|
// TODO: read from header
|
|
hparams.n_head_kv = hparams.n_head;
|
|
}
|
|
void read_vocab() {
|
|
vocab.id_to_token.resize(hparams.n_vocab);
|
|
|
|
for (uint32_t i = 0; i < hparams.n_vocab; i++) {
|
|
uint32_t len = file.read_u32();
|
|
std::string word = file.read_string(len);
|
|
|
|
float score = 0.0f;
|
|
file.read_raw(&score, sizeof(score));
|
|
|
|
vocab.token_to_id[word] = i;
|
|
|
|
auto & tok_score = vocab.id_to_token[i];
|
|
tok_score.tok = std::move(word);
|
|
tok_score.score = score;
|
|
}
|
|
}
|
|
void read_tensor_metadata(llama_load_tensors_map & tensors_map) {
|
|
while (file.tell() < file.size) {
|
|
llama_load_tensor tensor;
|
|
uint32_t n_dims = file.read_u32();
|
|
uint32_t name_len = file.read_u32();
|
|
tensor.type = (enum ggml_type) file.read_u32();
|
|
tensor.ne.resize(n_dims);
|
|
file.read_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * n_dims);
|
|
std::string name = file.read_string(name_len);
|
|
if (n_dims < 1 || n_dims > 2) {
|
|
throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims));
|
|
}
|
|
switch (tensor.type) {
|
|
case GGML_TYPE_F32:
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
break;
|
|
default: {
|
|
throw std::runtime_error(format("unrecognized tensor type %u\n", tensor.type));
|
|
}
|
|
}
|
|
|
|
// skip to the next multiple of 32 bytes
|
|
if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
|
|
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
|
|
}
|
|
|
|
tensor.file_off = file.tell();
|
|
tensor.name = name;
|
|
tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type);
|
|
file.seek(tensor.size, SEEK_CUR);
|
|
|
|
tensors_map.tensors.push_back(tensor);
|
|
tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1;
|
|
}
|
|
}
|
|
};
|
|
|
|
struct llama_file_saver {
|
|
llama_file file;
|
|
llama_file_loader * any_file_loader;
|
|
llama_file_saver(const char * fname, llama_file_loader * any_file_loader, enum llama_ftype new_ftype)
|
|
: file(fname, "wb"), any_file_loader(any_file_loader) {
|
|
LLAMA_LOG_INFO("llama.cpp: saving model to %s\n", fname);
|
|
write_magic();
|
|
write_hparams(new_ftype);
|
|
write_vocab();
|
|
}
|
|
void write_magic() {
|
|
file.write_u32(LLAMA_FILE_MAGIC); // magic
|
|
file.write_u32(LLAMA_FILE_VERSION); // version
|
|
}
|
|
void write_hparams(enum llama_ftype new_ftype) {
|
|
const llama_hparams & hparams = any_file_loader->hparams;
|
|
file.write_u32(hparams.n_vocab);
|
|
file.write_u32(hparams.n_embd);
|
|
file.write_u32(hparams.n_mult);
|
|
file.write_u32(hparams.n_head);
|
|
file.write_u32(hparams.n_layer);
|
|
file.write_u32(hparams.n_rot);
|
|
file.write_u32(new_ftype);
|
|
}
|
|
void write_vocab() {
|
|
if (any_file_loader->file_version == LLAMA_FILE_VERSION_GGML) {
|
|
LLAMA_LOG_WARN("llama.cpp: WARNING: input is an old file that doesn't have scores; will add dummy scores\n");
|
|
}
|
|
uint32_t n_vocab = any_file_loader->hparams.n_vocab;
|
|
for (uint32_t i = 0; i < n_vocab; i++) {
|
|
const auto & token_score = any_file_loader->vocab.id_to_token.at(i);
|
|
file.write_u32((uint32_t) token_score.tok.size());
|
|
file.write_raw(token_score.tok.data(), token_score.tok.size());
|
|
file.write_raw(&token_score.score, sizeof(token_score.score));
|
|
}
|
|
}
|
|
void write_tensor(llama_load_tensor & tensor, enum ggml_type new_type, const void * new_data, size_t new_size) {
|
|
switch (new_type) {
|
|
case GGML_TYPE_F32:
|
|
case GGML_TYPE_F16:
|
|
case GGML_TYPE_Q4_0:
|
|
case GGML_TYPE_Q4_1:
|
|
case GGML_TYPE_Q5_0:
|
|
case GGML_TYPE_Q5_1:
|
|
case GGML_TYPE_Q8_0:
|
|
case GGML_TYPE_Q2_K:
|
|
case GGML_TYPE_Q3_K:
|
|
case GGML_TYPE_Q4_K:
|
|
case GGML_TYPE_Q5_K:
|
|
case GGML_TYPE_Q6_K:
|
|
break;
|
|
default: LLAMA_ASSERT(false);
|
|
}
|
|
file.write_u32((uint32_t) tensor.ne.size());
|
|
file.write_u32((uint32_t) tensor.name.size());
|
|
file.write_u32(new_type);
|
|
file.write_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * tensor.ne.size());
|
|
file.write_raw(tensor.name.data(), tensor.name.size());
|
|
file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
|
|
LLAMA_ASSERT(new_size == llama_calc_tensor_size(tensor.ne, new_type));
|
|
file.write_raw(new_data, new_size);
|
|
}
|
|
};
|
|
|
|
struct llama_model_loader {
|
|
std::unique_ptr<llama_file_loader> file_loader;
|
|
llama_load_tensors_map tensors_map;
|
|
bool use_mmap;
|
|
size_t num_ggml_tensors_created = 0;
|
|
struct ggml_context * ggml_ctx = NULL;
|
|
std::unique_ptr<llama_mmap> mapping;
|
|
|
|
llama_model_loader(const std::string & fname_base, bool use_mmap) {
|
|
file_loader = std::unique_ptr<llama_file_loader>(new llama_file_loader(fname_base.c_str(), tensors_map));
|
|
if (!llama_mmap::SUPPORTED) {
|
|
use_mmap = false;
|
|
}
|
|
this->use_mmap = use_mmap;
|
|
}
|
|
|
|
void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const {
|
|
*ctx_size_p = *mmapped_size_p = 0;
|
|
for (const llama_load_tensor & lt : tensors_map.tensors) {
|
|
*ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
|
|
*(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size + 16;
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * get_tensor(const std::string & name, const std::vector<uint32_t> & ne, ggml_backend backend) {
|
|
auto it = tensors_map.name_to_idx.find(name);
|
|
if (it == tensors_map.name_to_idx.end()) {
|
|
throw std::runtime_error(std::runtime_error(format("llama.cpp: tensor '%s' is missing from model", name.c_str())));
|
|
}
|
|
llama_load_tensor & lt = tensors_map.tensors.at(it->second);
|
|
if (lt.ne != ne) {
|
|
throw std::runtime_error(format("llama.cpp: tensor '%s' has wrong shape; expected %s, got %s",
|
|
name.c_str(), llama_format_tensor_shape(ne).c_str(), llama_format_tensor_shape(lt.ne).c_str()));
|
|
}
|
|
|
|
return get_tensor_for(lt, backend);
|
|
}
|
|
|
|
struct ggml_tensor * get_tensor_for(llama_load_tensor & lt, ggml_backend backend) {
|
|
struct ggml_tensor * tensor;
|
|
if (backend != GGML_BACKEND_CPU) {
|
|
ggml_set_no_alloc(ggml_ctx, true);
|
|
}
|
|
if (lt.ne.size() == 2) {
|
|
tensor = ggml_new_tensor_2d(ggml_ctx, lt.type, lt.ne.at(0), lt.ne.at(1));
|
|
} else {
|
|
LLAMA_ASSERT(lt.ne.size() == 1);
|
|
tensor = ggml_new_tensor_1d(ggml_ctx, lt.type, lt.ne.at(0));
|
|
}
|
|
ggml_set_name(tensor, lt.name.c_str());
|
|
LLAMA_ASSERT(lt.ggml_tensor == NULL); // if this fails, we called get_tensor twice on the same tensor
|
|
|
|
if (backend != GGML_BACKEND_CPU) {
|
|
ggml_set_no_alloc(ggml_ctx, use_mmap);
|
|
}
|
|
tensor->backend = backend;
|
|
lt.ggml_tensor = tensor;
|
|
num_ggml_tensors_created++;
|
|
return tensor;
|
|
}
|
|
|
|
void done_getting_tensors() const {
|
|
if (num_ggml_tensors_created != tensors_map.tensors.size()) {
|
|
throw std::runtime_error(std::string("llama.cpp: file contained more tensors than expected"));
|
|
}
|
|
}
|
|
|
|
void load_all_data(llama_progress_callback progress_callback, void * progress_callback_user_data, llama_mlock * lmlock) {
|
|
size_t data_size = 0;
|
|
size_t prefetch_size = file_loader->file.size;
|
|
size_t lock_size = 0;
|
|
for (const llama_load_tensor & lt : tensors_map.tensors) {
|
|
data_size += lt.size;
|
|
if (lt.ggml_tensor->backend != GGML_BACKEND_CPU) {
|
|
prefetch_size -= lt.size;
|
|
}
|
|
}
|
|
|
|
if (use_mmap) {
|
|
mapping.reset(new llama_mmap(&file_loader->file, prefetch_size, ggml_is_numa()));
|
|
if (lmlock) {
|
|
lmlock->init(mapping->addr);
|
|
}
|
|
}
|
|
|
|
size_t done_size = 0;
|
|
for (llama_load_tensor & lt : tensors_map.tensors) {
|
|
if (progress_callback) {
|
|
progress_callback((float) done_size / data_size, progress_callback_user_data);
|
|
}
|
|
LLAMA_ASSERT(lt.ggml_tensor); // unused tensors should have been caught by load_data already
|
|
lt.data = (uint8_t *) lt.ggml_tensor->data;
|
|
|
|
// allocate temp buffer if not using mmap
|
|
if (!use_mmap && lt.data == NULL) {
|
|
GGML_ASSERT(lt.ggml_tensor->backend != GGML_BACKEND_CPU);
|
|
lt.data = (uint8_t*)malloc(ggml_nbytes(lt.ggml_tensor));
|
|
}
|
|
|
|
load_data_for(lt);
|
|
|
|
switch(lt.ggml_tensor->backend) {
|
|
case GGML_BACKEND_CPU:
|
|
lt.ggml_tensor->data = lt.data;
|
|
if (use_mmap && lmlock) {
|
|
lock_size += lt.size;
|
|
lmlock->grow_to(lock_size);
|
|
}
|
|
break;
|
|
#if defined(GGML_USE_CUBLAS)
|
|
case GGML_BACKEND_GPU:
|
|
case GGML_BACKEND_GPU_SPLIT:
|
|
ggml_cuda_transform_tensor(lt.data, lt.ggml_tensor);
|
|
if (!use_mmap) {
|
|
free(lt.data);
|
|
}
|
|
break;
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
case GGML_BACKEND_GPU:
|
|
ggml_cl_transform_tensor(lt.data, lt.ggml_tensor);
|
|
if (!use_mmap) {
|
|
free(lt.data);
|
|
}
|
|
break;
|
|
#endif
|
|
default:
|
|
continue;
|
|
}
|
|
|
|
done_size += lt.size;
|
|
}
|
|
}
|
|
|
|
void load_data_for(llama_load_tensor & lt) {
|
|
if (use_mmap) {
|
|
lt.data = (uint8_t *) mapping->addr + lt.file_off;
|
|
} else {
|
|
llama_file & file = file_loader->file;
|
|
file.seek(lt.file_off, SEEK_SET);
|
|
file.read_raw(lt.data, lt.size);
|
|
}
|
|
|
|
if (0) {
|
|
print_checksum(lt);
|
|
}
|
|
}
|
|
|
|
static void print_checksum(llama_load_tensor & lt) {
|
|
uint32_t sum = 0;
|
|
for (size_t i = 0; i < lt.size; i++) {
|
|
uint8_t byte = lt.data[i];
|
|
sum = byte + (sum << 6) + (sum << 16) - sum; // sdbm hash
|
|
}
|
|
LLAMA_LOG_INFO("%s checksum: %#08x (%s, size %zu)\n", lt.name.c_str(), sum,
|
|
llama_format_tensor_shape(lt.ne).c_str(), lt.size);
|
|
}
|
|
|
|
};
|
|
|
|
//
|
|
// kv cache
|
|
//
|
|
|
|
static bool kv_cache_init(
|
|
const struct llama_hparams & hparams,
|
|
struct llama_kv_cache & cache,
|
|
ggml_type wtype,
|
|
int n_ctx,
|
|
int n_gpu_layers) {
|
|
const int n_embd = hparams.n_embd_gqa();
|
|
const int n_layer = hparams.n_layer;
|
|
|
|
const int64_t n_mem = n_layer*n_ctx;
|
|
const int64_t n_elements = n_embd*n_mem;
|
|
|
|
cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB);
|
|
cache.n = 0;
|
|
|
|
struct ggml_init_params params;
|
|
params.mem_size = cache.buf.size;
|
|
params.mem_buffer = cache.buf.addr;
|
|
params.no_alloc = false;
|
|
|
|
cache.ctx = ggml_init(params);
|
|
|
|
if (!cache.ctx) {
|
|
LLAMA_LOG_ERROR("%s: failed to allocate memory for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
|
|
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
|
|
ggml_set_name(cache.k, "cache_k");
|
|
ggml_set_name(cache.v, "cache_v");
|
|
|
|
(void) n_gpu_layers;
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (n_gpu_layers > n_layer + 1) {
|
|
ggml_cuda_assign_buffers_no_scratch(cache.v);
|
|
}
|
|
if (n_gpu_layers > n_layer + 2) {
|
|
ggml_cuda_assign_buffers_no_scratch(cache.k);
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
return true;
|
|
}
|
|
|
|
struct llama_context_params llama_context_default_params() {
|
|
struct llama_context_params result = {
|
|
/*.seed =*/ LLAMA_DEFAULT_SEED,
|
|
/*.n_ctx =*/ 512,
|
|
/*.n_batch =*/ 512,
|
|
/*.n_gqa =*/ 1,
|
|
/*.rms_norm_eps =*/ LLAMA_DEFAULT_RMS_EPS,
|
|
/*.gpu_layers =*/ 0,
|
|
/*.main_gpu =*/ 0,
|
|
/*.tensor_split =*/ nullptr,
|
|
/*.rope_freq_base =*/ 10000.0f,
|
|
/*.rope_freq_scale =*/ 1.0f,
|
|
/*.progress_callback =*/ nullptr,
|
|
/*.progress_callback_user_data =*/ nullptr,
|
|
/*.low_vram =*/ false,
|
|
/*.mul_mat_q =*/ false,
|
|
/*.f16_kv =*/ true,
|
|
/*.logits_all =*/ false,
|
|
/*.vocab_only =*/ false,
|
|
/*.use_mmap =*/ true,
|
|
/*.use_mlock =*/ false,
|
|
/*.embedding =*/ false,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
struct llama_model_quantize_params llama_model_quantize_default_params() {
|
|
struct llama_model_quantize_params result = {
|
|
/*.nthread =*/ 0,
|
|
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
|
|
/*.allow_requantize =*/ false,
|
|
/*.quantize_output_tensor =*/ true,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
int llama_max_devices() {
|
|
return LLAMA_MAX_DEVICES;
|
|
}
|
|
|
|
bool llama_mmap_supported() {
|
|
return llama_mmap::SUPPORTED;
|
|
}
|
|
|
|
bool llama_mlock_supported() {
|
|
return llama_mlock::SUPPORTED;
|
|
}
|
|
|
|
void llama_backend_init(bool numa) {
|
|
ggml_time_init();
|
|
|
|
// needed to initialize f16 tables
|
|
{
|
|
struct ggml_init_params params = { 0, NULL, false };
|
|
struct ggml_context * ctx = ggml_init(params);
|
|
ggml_free(ctx);
|
|
}
|
|
|
|
if (numa) {
|
|
ggml_numa_init();
|
|
}
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_backend_init();
|
|
#endif
|
|
}
|
|
|
|
void llama_backend_free() {
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_backend_free();
|
|
#endif
|
|
}
|
|
|
|
int64_t llama_time_us() {
|
|
return ggml_time_us();
|
|
}
|
|
|
|
//
|
|
// model loading
|
|
//
|
|
|
|
static const char *llama_file_version_name(llama_file_version version) {
|
|
switch (version) {
|
|
case LLAMA_FILE_VERSION_GGML: return "'ggml' (old version with low tokenizer quality and no mmap support)";
|
|
case LLAMA_FILE_VERSION_GGMF_V1: return "ggmf v1 (old version with no mmap support)";
|
|
case LLAMA_FILE_VERSION_GGJT_V1: return "ggjt v1 (pre #1405)";
|
|
case LLAMA_FILE_VERSION_GGJT_V2: return "ggjt v2 (pre #1508)";
|
|
case LLAMA_FILE_VERSION_GGJT_V3: return "ggjt v3 (latest)";
|
|
}
|
|
|
|
return "unknown";
|
|
}
|
|
|
|
static const char *llama_ftype_name(enum llama_ftype ftype) {
|
|
switch (ftype) {
|
|
case LLAMA_FTYPE_ALL_F32: return "all F32";
|
|
case LLAMA_FTYPE_MOSTLY_F16: return "mostly F16";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_0: return "mostly Q4_0";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1: return "mostly Q4_1";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16:
|
|
return "mostly Q4_1, some F16";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_0: return "mostly Q5_0";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_1: return "mostly Q5_1";
|
|
case LLAMA_FTYPE_MOSTLY_Q8_0: return "mostly Q8_0";
|
|
// K-quants
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K: return "mostly Q2_K";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "mostly Q3_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "mostly Q3_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "mostly Q3_K - Large";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "mostly Q4_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "mostly Q4_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "mostly Q5_K - Small";
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "mostly Q5_K - Medium";
|
|
case LLAMA_FTYPE_MOSTLY_Q6_K: return "mostly Q6_K";
|
|
default: return "unknown, may not work";
|
|
}
|
|
}
|
|
|
|
static const char *llama_model_type_name(e_model type) {
|
|
switch (type) {
|
|
case MODEL_3B: return "3B";
|
|
case MODEL_7B: return "7B";
|
|
case MODEL_13B: return "13B";
|
|
case MODEL_30B: return "30B";
|
|
case MODEL_65B: return "65B";
|
|
case MODEL_70B: return "70B";
|
|
default: LLAMA_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
static void llama_model_load_internal(
|
|
const std::string & fname,
|
|
llama_model & model,
|
|
llama_vocab & vocab,
|
|
int n_ctx,
|
|
int n_batch,
|
|
int n_gqa,
|
|
float rms_norm_eps,
|
|
int n_gpu_layers,
|
|
int main_gpu,
|
|
const float * tensor_split,
|
|
const bool mul_mat_q,
|
|
float rope_freq_base,
|
|
float rope_freq_scale,
|
|
bool low_vram,
|
|
ggml_type memory_type,
|
|
bool use_mmap,
|
|
bool use_mlock,
|
|
bool vocab_only,
|
|
llama_progress_callback progress_callback,
|
|
void * progress_callback_user_data) {
|
|
|
|
model.t_start_us = ggml_time_us();
|
|
|
|
std::unique_ptr<llama_model_loader> ml(new llama_model_loader(fname, use_mmap));
|
|
|
|
vocab = std::move(ml->file_loader->vocab);
|
|
model.hparams = ml->file_loader->hparams;
|
|
model.n_gpu_layers = n_gpu_layers;
|
|
llama_file_version file_version = ml->file_loader->file_version;
|
|
|
|
auto & hparams = model.hparams;
|
|
|
|
// TODO: read from file
|
|
hparams.f_rms_norm_eps = rms_norm_eps;
|
|
|
|
{
|
|
switch (hparams.n_layer) {
|
|
case 26: model.type = e_model::MODEL_3B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
case 60: model.type = e_model::MODEL_30B; break;
|
|
case 80: model.type = e_model::MODEL_65B; break;
|
|
default:
|
|
{
|
|
if (hparams.n_layer < 32) {
|
|
model.type = e_model::MODEL_7B;
|
|
}
|
|
} break;
|
|
}
|
|
|
|
hparams.n_ctx = n_ctx;
|
|
|
|
// LLaMAv2
|
|
// TODO: temporary until GGUF
|
|
LLAMA_ASSERT(hparams.n_head % n_gqa == 0);
|
|
hparams.n_head_kv = hparams.n_head / n_gqa;
|
|
if (model.type == e_model::MODEL_65B && n_gqa == 8) {
|
|
LLAMA_LOG_WARN("%s: warning: assuming 70B model based on GQA == %d\n", __func__, n_gqa);
|
|
model.type = e_model::MODEL_70B;
|
|
hparams.f_ffn_mult = 1.3f; // from the params.json of the 70B model
|
|
}
|
|
|
|
hparams.rope_freq_base = rope_freq_base;
|
|
hparams.rope_freq_scale = rope_freq_scale;
|
|
}
|
|
|
|
// ref: https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/model.py#L194-L199
|
|
const uint32_t n_ff_raw = 2*(4*hparams.n_embd)/3;
|
|
const uint32_t n_ff_mult = hparams.f_ffn_mult*n_ff_raw;
|
|
const uint32_t n_ff = ((n_ff_mult + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
|
|
//const uint32_t n_ff = 28672;
|
|
|
|
{
|
|
LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(file_version));
|
|
LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
|
|
LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, hparams.n_ctx);
|
|
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
|
|
LLAMA_LOG_INFO("%s: n_mult = %u\n", __func__, hparams.n_mult);
|
|
LLAMA_LOG_INFO("%s: n_head = %u\n", __func__, hparams.n_head);
|
|
LLAMA_LOG_INFO("%s: n_head_kv = %u\n", __func__, hparams.n_head_kv);
|
|
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
|
|
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot); // a.k.a. n_embd_head, n_head_dim
|
|
LLAMA_LOG_INFO("%s: n_gqa = %u\n", __func__, hparams.n_gqa());
|
|
LLAMA_LOG_INFO("%s: rnorm_eps = %.1e\n", __func__, hparams.f_rms_norm_eps);
|
|
LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, n_ff);
|
|
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, hparams.rope_freq_base);
|
|
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, hparams.rope_freq_scale);
|
|
LLAMA_LOG_INFO("%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
|
|
LLAMA_LOG_INFO("%s: model size = %s\n", __func__, llama_model_type_name(model.type));
|
|
}
|
|
|
|
if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
|
|
if (hparams.ftype != LLAMA_FTYPE_ALL_F32 &&
|
|
hparams.ftype != LLAMA_FTYPE_MOSTLY_F16 &&
|
|
hparams.ftype != LLAMA_FTYPE_MOSTLY_Q8_0) {
|
|
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1405)"));
|
|
}
|
|
}
|
|
|
|
if (file_version < LLAMA_FILE_VERSION_GGJT_V3) {
|
|
if (hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
|
|
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ||
|
|
hparams.ftype == LLAMA_FTYPE_MOSTLY_Q8_0) {
|
|
throw std::runtime_error(format("this format is no longer supported (see https://github.com/ggerganov/llama.cpp/pull/1508)"));
|
|
}
|
|
}
|
|
|
|
if (vocab_only) {
|
|
return;
|
|
}
|
|
|
|
auto & ctx = model.ctx;
|
|
|
|
size_t ctx_size;
|
|
size_t mmapped_size;
|
|
ml->calc_sizes(&ctx_size, &mmapped_size);
|
|
LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MB\n", __func__, ctx_size/1024.0/1024.0);
|
|
|
|
// create the ggml context
|
|
{
|
|
model.buf.resize(ctx_size);
|
|
if (use_mlock) {
|
|
model.mlock_buf.init (model.buf.addr);
|
|
model.mlock_buf.grow_to(model.buf.size);
|
|
}
|
|
|
|
struct ggml_init_params params = {
|
|
/*.mem_size =*/ model.buf.size,
|
|
/*.mem_buffer =*/ model.buf.addr,
|
|
/*.no_alloc =*/ ml->use_mmap,
|
|
};
|
|
|
|
model.ctx = ggml_init(params);
|
|
if (!model.ctx) {
|
|
throw std::runtime_error(format("ggml_init() failed"));
|
|
}
|
|
}
|
|
|
|
(void) main_gpu;
|
|
(void) mul_mat_q;
|
|
#if defined(GGML_USE_CUBLAS)
|
|
LLAMA_LOG_INFO("%s: using CUDA for GPU acceleration\n", __func__);
|
|
ggml_cuda_set_main_device(main_gpu);
|
|
ggml_cuda_set_mul_mat_q(mul_mat_q);
|
|
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
|
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU_SPLIT
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
LLAMA_LOG_INFO("%s: using OpenCL for GPU acceleration\n", __func__);
|
|
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_GPU
|
|
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_GPU
|
|
#else
|
|
#define LLAMA_BACKEND_OFFLOAD GGML_BACKEND_CPU
|
|
#define LLAMA_BACKEND_OFFLOAD_SPLIT GGML_BACKEND_CPU
|
|
#endif
|
|
|
|
// prepare memory for the weights
|
|
size_t vram_weights = 0;
|
|
size_t vram_scratch = 0;
|
|
{
|
|
const uint32_t n_embd = hparams.n_embd;
|
|
const uint32_t n_embd_gqa = hparams.n_embd_gqa();
|
|
const uint32_t n_layer = hparams.n_layer;
|
|
const uint32_t n_vocab = hparams.n_vocab;
|
|
|
|
ml->ggml_ctx = ctx;
|
|
|
|
model.tok_embeddings = ml->get_tensor("tok_embeddings.weight", {n_embd, n_vocab}, GGML_BACKEND_CPU);
|
|
|
|
// "output" tensor
|
|
{
|
|
ggml_backend backend_norm;
|
|
ggml_backend backend_output;
|
|
if (n_gpu_layers > int(n_layer)) { // NOLINT
|
|
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
|
|
// on Windows however this is detrimental unless everything is on the GPU
|
|
#ifndef _WIN32
|
|
backend_norm = low_vram ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#else
|
|
backend_norm = low_vram || n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD;
|
|
#endif // _WIN32
|
|
|
|
backend_output = LLAMA_BACKEND_OFFLOAD_SPLIT;
|
|
} else {
|
|
backend_norm = GGML_BACKEND_CPU;
|
|
backend_output = GGML_BACKEND_CPU;
|
|
}
|
|
|
|
model.norm = ml->get_tensor("norm.weight", {n_embd}, backend_norm);
|
|
model.output = ml->get_tensor("output.weight", {n_embd, n_vocab}, backend_output);
|
|
if (backend_norm == GGML_BACKEND_GPU) {
|
|
vram_weights += ggml_nbytes(model.norm);
|
|
}
|
|
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
|
|
vram_weights += ggml_nbytes(model.output);
|
|
}
|
|
}
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
|
|
model.layers.resize(n_layer);
|
|
for (uint32_t i = 0; i < n_layer; ++i) {
|
|
const ggml_backend backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD; // NOLINT
|
|
const ggml_backend backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : LLAMA_BACKEND_OFFLOAD_SPLIT; // NOLINT
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
std::string layers_i = "layers." + std::to_string(i);
|
|
|
|
layer.attention_norm = ml->get_tensor(layers_i + ".attention_norm.weight", {n_embd}, backend);
|
|
|
|
layer.wq = ml->get_tensor(layers_i + ".attention.wq.weight", {n_embd, n_embd}, backend_split);
|
|
layer.wk = ml->get_tensor(layers_i + ".attention.wk.weight", {n_embd, n_embd_gqa}, backend_split);
|
|
layer.wv = ml->get_tensor(layers_i + ".attention.wv.weight", {n_embd, n_embd_gqa}, backend_split);
|
|
layer.wo = ml->get_tensor(layers_i + ".attention.wo.weight", {n_embd, n_embd}, backend_split);
|
|
|
|
layer.ffn_norm = ml->get_tensor(layers_i + ".ffn_norm.weight", {n_embd}, backend);
|
|
|
|
layer.w1 = ml->get_tensor(layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}, backend_split);
|
|
layer.w2 = ml->get_tensor(layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}, backend_split);
|
|
layer.w3 = ml->get_tensor(layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}, backend_split);
|
|
|
|
if (backend == GGML_BACKEND_GPU) {
|
|
vram_weights +=
|
|
ggml_nbytes(layer.attention_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
|
|
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
|
|
ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
|
|
}
|
|
}
|
|
}
|
|
|
|
ml->done_getting_tensors();
|
|
|
|
// print memory requirements
|
|
{
|
|
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
|
|
|
|
// this is the total memory required to run the inference
|
|
size_t mem_required =
|
|
ctx_size +
|
|
mmapped_size - vram_weights; // weights in VRAM not in memory
|
|
|
|
#ifndef LLAMA_USE_ALLOCATOR
|
|
mem_required +=
|
|
MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
|
|
MEM_REQ_SCRATCH1().at(model.type) +
|
|
MEM_REQ_EVAL().at(model.type);
|
|
#endif
|
|
|
|
// this is the memory required by one llama_state
|
|
const size_t mem_required_state =
|
|
scale*hparams.kv_size();
|
|
|
|
LLAMA_LOG_INFO("%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);
|
|
|
|
(void) vram_scratch;
|
|
(void) n_batch;
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (low_vram) {
|
|
LLAMA_LOG_INFO("%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__);
|
|
ggml_cuda_set_scratch_size(0); // disable scratch
|
|
} else {
|
|
const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type);
|
|
const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type);
|
|
vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context);
|
|
ggml_cuda_set_scratch_size(vram_scratch);
|
|
if (n_gpu_layers > 0) {
|
|
LLAMA_LOG_INFO("%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n",
|
|
__func__, vram_scratch_base / kB, vram_scratch_per_context,
|
|
(vram_scratch + MB - 1) / MB); // round up
|
|
}
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
|
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
|
|
|
LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
|
|
if (n_gpu_layers > (int) hparams.n_layer) {
|
|
LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
|
|
}
|
|
size_t vram_kv_cache = 0;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
const int max_backend_supported_layers = hparams.n_layer + 3;
|
|
const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3;
|
|
if (n_gpu_layers > (int) hparams.n_layer + 1) {
|
|
if (low_vram) {
|
|
LLAMA_LOG_INFO("%s: cannot offload v cache to GPU due to low VRAM option\n", __func__);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: offloading v cache to GPU\n", __func__);
|
|
vram_kv_cache += hparams.kv_size() / 2;
|
|
}
|
|
}
|
|
if (n_gpu_layers > (int) hparams.n_layer + 2) {
|
|
if (low_vram) {
|
|
LLAMA_LOG_WARN("%s: cannot offload k cache to GPU due to low VRAM option\n", __func__);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: offloading k cache to GPU\n", __func__);
|
|
vram_kv_cache += hparams.kv_size() / 2;
|
|
}
|
|
}
|
|
#elif defined(GGML_USE_CLBLAST)
|
|
const int max_backend_supported_layers = hparams.n_layer + 1;
|
|
const int max_offloadable_layers = hparams.n_layer + 1;
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n",
|
|
__func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
|
|
LLAMA_LOG_INFO("%s: total VRAM used: %zu MB\n",
|
|
__func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up
|
|
#else
|
|
(void) n_gpu_layers;
|
|
#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST)
|
|
}
|
|
|
|
// populate `tensors_by_name`
|
|
for (llama_load_tensor & lt : ml->tensors_map.tensors) {
|
|
model.tensors_by_name.emplace_back(lt.name, lt.ggml_tensor);
|
|
}
|
|
|
|
(void) tensor_split;
|
|
#if defined(GGML_USE_CUBLAS)
|
|
{
|
|
ggml_cuda_set_tensor_split(tensor_split);
|
|
}
|
|
#endif
|
|
|
|
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL);
|
|
|
|
if (progress_callback) {
|
|
progress_callback(1.0f, progress_callback_user_data);
|
|
}
|
|
|
|
model.mapping = std::move(ml->mapping);
|
|
|
|
// loading time will be recalculate after the first eval, so
|
|
// we take page faults deferred by mmap() into consideration
|
|
model.t_load_us = ggml_time_us() - model.t_start_us;
|
|
}
|
|
|
|
static bool llama_model_load(
|
|
const std::string & fname,
|
|
llama_model & model,
|
|
llama_vocab & vocab,
|
|
int n_ctx,
|
|
int n_batch,
|
|
int n_gqa,
|
|
float rms_norm_eps,
|
|
int n_gpu_layers,
|
|
int main_gpu,
|
|
const float * tensor_split,
|
|
const bool mul_mat_q,
|
|
float rope_freq_base,
|
|
float rope_freq_scale,
|
|
bool low_vram,
|
|
ggml_type memory_type,
|
|
bool use_mmap,
|
|
bool use_mlock,
|
|
bool vocab_only,
|
|
llama_progress_callback progress_callback,
|
|
void *progress_callback_user_data) {
|
|
try {
|
|
llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gqa, rms_norm_eps, n_gpu_layers,
|
|
main_gpu, tensor_split, mul_mat_q, rope_freq_base, rope_freq_scale, low_vram, memory_type,
|
|
use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
|
|
return true;
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error loading model: %s\n", err.what());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
static struct ggml_cgraph * llama_build_graph(
|
|
llama_context & lctx,
|
|
const llama_token * tokens,
|
|
const float * embd,
|
|
int n_tokens,
|
|
int n_past) {
|
|
|
|
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
|
|
|
|
const int N = n_tokens;
|
|
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
LLAMA_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_layer = hparams.n_layer;
|
|
const int64_t n_ctx = hparams.n_ctx;
|
|
const int64_t n_head = hparams.n_head;
|
|
const int64_t n_head_kv = hparams.n_head_kv;
|
|
const int64_t n_embd_head = hparams.n_embd_head();
|
|
const int64_t n_embd_gqa = hparams.n_embd_gqa();
|
|
|
|
LLAMA_ASSERT(n_embd_head == hparams.n_rot);
|
|
|
|
const float freq_base = hparams.rope_freq_base;
|
|
const float freq_scale = hparams.rope_freq_scale;
|
|
const float rms_norm_eps = hparams.f_rms_norm_eps;
|
|
|
|
const int n_gpu_layers = model.n_gpu_layers;
|
|
|
|
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.addr,
|
|
/*.no_alloc =*/ false,
|
|
};
|
|
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
params.no_alloc = true;
|
|
#endif
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
ggml_cgraph * gf = ggml_new_graph(ctx0);
|
|
|
|
struct ggml_tensor * cur;
|
|
struct ggml_tensor * inpL;
|
|
|
|
if (tokens) {
|
|
struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
ggml_allocr_alloc(lctx.alloc, inp_tokens);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
|
|
}
|
|
#else
|
|
memcpy(inp_tokens->data, tokens, N*ggml_element_size(inp_tokens));
|
|
#endif
|
|
ggml_set_name(inp_tokens, "inp_tokens");
|
|
|
|
inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
|
|
} else {
|
|
#ifdef GGML_USE_MPI
|
|
GGML_ASSERT(false && "not implemented");
|
|
#endif
|
|
|
|
inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N);
|
|
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
ggml_allocr_alloc(lctx.alloc, inpL);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
|
|
}
|
|
#else
|
|
memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL));
|
|
#endif
|
|
}
|
|
|
|
const int i_gpu_start = n_layer - n_gpu_layers;
|
|
(void) i_gpu_start;
|
|
|
|
// offload functions set the tensor output backend to GPU
|
|
// tensors are GPU-accelerated if any input or the output has been offloaded
|
|
//
|
|
// with the low VRAM option VRAM scratch is disabled in llama_load_model_internal
|
|
// in that case ggml_cuda_assign_buffers has no effect
|
|
offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
|
|
offload_func_t offload_func_kq = llama_nop;
|
|
offload_func_t offload_func_v = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (n_gpu_layers > n_layer) {
|
|
offload_func_nr = ggml_cuda_assign_buffers;
|
|
}
|
|
if (n_gpu_layers > n_layer + 1) {
|
|
offload_func_v = ggml_cuda_assign_buffers;
|
|
}
|
|
if (n_gpu_layers > n_layer + 2) {
|
|
offload_func_kq = ggml_cuda_assign_buffers;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
ggml_allocr_alloc(lctx.alloc, KQ_scale);
|
|
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
|
}
|
|
#else
|
|
ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd)/n_head));
|
|
#endif
|
|
ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
ggml_format_name(inpL, "layer_inp_%d", il);
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (il >= i_gpu_start) {
|
|
offload_func = ggml_cuda_assign_buffers;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
struct ggml_tensor * inpSA = inpL;
|
|
|
|
lctx.use_buf(ctx0, 0);
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_0");
|
|
|
|
// cur = cur*attention_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].attention_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "attention_norm_0");
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
// compute Q and K and RoPE them
|
|
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
|
|
offload_func_kq(tmpk);
|
|
ggml_set_name(tmpk, "tmpk");
|
|
|
|
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
|
|
offload_func_kq(tmpq);
|
|
ggml_set_name(tmpq, "tmpq");
|
|
|
|
struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
offload_func_kq(Kcur);
|
|
ggml_set_name(Kcur, "Kcur");
|
|
|
|
struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale);
|
|
offload_func_kq(Qcur);
|
|
ggml_set_name(Qcur, "Qcur");
|
|
|
|
// store key and value to memory
|
|
{
|
|
// compute the transposed [N, n_embd] V matrix
|
|
|
|
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
|
offload_func_v(tmpv);
|
|
ggml_set_name(tmpv, "tmpv");
|
|
|
|
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, N));
|
|
offload_func_v(Vcur);
|
|
ggml_set_name(Vcur, "Vcur");
|
|
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + n_past));
|
|
offload_func_kq(k);
|
|
ggml_set_name(k, "k");
|
|
|
|
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd_gqa,
|
|
( n_ctx)*ggml_element_size(kv_self.v),
|
|
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + n_past*ggml_element_size(kv_self.v));
|
|
offload_func_v(v);
|
|
ggml_set_name(v, "v");
|
|
|
|
// important: storing RoPE-ed version of K in the KV cache!
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
Qcur,
|
|
0, 2, 1, 3);
|
|
offload_func_kq(Q);
|
|
ggml_set_name(Q, "Q");
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_embd_gqa, il*n_ctx*ggml_element_size(kv_self.k)*n_embd_gqa),
|
|
n_embd_head, n_head_kv, n_past + N),
|
|
0, 2, 1, 3);
|
|
offload_func_kq(K);
|
|
ggml_set_name(K, "K");
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
offload_func_kq(KQ);
|
|
ggml_set_name(KQ, "KQ");
|
|
|
|
// KQ_scaled = KQ / sqrt(n_embd_head)
|
|
// KQ_scaled shape [n_past + N, N, n_head, 1]
|
|
struct ggml_tensor * KQ_scaled = ggml_scale_inplace(ctx0, KQ, KQ_scale);
|
|
offload_func_kq(KQ_scaled);
|
|
ggml_set_name(KQ_scaled, "KQ_scaled");
|
|
|
|
// KQ_masked = mask_past(KQ_scaled)
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
|
|
offload_func_kq(KQ_masked);
|
|
ggml_set_name(KQ_masked, "KQ_masked");
|
|
|
|
// KQ = soft_max(KQ_masked)
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
|
|
offload_func_v(KQ_soft_max);
|
|
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
|
|
|
// split cached V into n_head heads
|
|
struct ggml_tensor * V =
|
|
ggml_view_3d(ctx0, kv_self.v,
|
|
n_past + N, n_embd_head, n_head_kv,
|
|
n_ctx*ggml_element_size(kv_self.v),
|
|
n_ctx*ggml_element_size(kv_self.v)*n_embd_head,
|
|
n_ctx*ggml_element_size(kv_self.v)*n_embd_gqa*il);
|
|
offload_func_v(V);
|
|
ggml_set_name(V, "V");
|
|
|
|
#if 1
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
|
offload_func_v(KQV);
|
|
ggml_set_name(KQV, "KQV");
|
|
#else
|
|
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
|
|
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
|
|
// is there a better way?
|
|
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd_head, n_head));
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
|
|
#endif
|
|
|
|
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
offload_func_v(KQV_merged);
|
|
ggml_set_name(KQV_merged, "KQV_merged");
|
|
|
|
// cur = KQV_merged.contiguous().view(n_embd, N)
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
|
|
offload_func_v(cur);
|
|
ggml_set_name(cur, "KQV_merged_contiguous");
|
|
|
|
// projection (no bias)
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].wo,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_wo");
|
|
}
|
|
|
|
lctx.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
|
offload_func(inpFF);
|
|
ggml_set_name(inpFF, "inpFF");
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "rms_norm_1");
|
|
|
|
// cur = cur*ffn_norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "ffn_norm");
|
|
}
|
|
|
|
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
|
model.layers[il].w3,
|
|
cur);
|
|
offload_func(tmp);
|
|
ggml_set_name(tmp, "result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w1,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w1");
|
|
|
|
// SILU activation
|
|
cur = ggml_silu(ctx0, cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu");
|
|
|
|
cur = ggml_mul(ctx0, cur, tmp);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "silu_x_result_w3");
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
model.layers[il].w2,
|
|
cur);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "result_w2");
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, inpFF);
|
|
offload_func(cur);
|
|
ggml_set_name(cur, "inpFF_+_result_w2");
|
|
|
|
// input for next layer
|
|
inpL = cur;
|
|
}
|
|
|
|
lctx.use_buf(ctx0, 0);
|
|
|
|
// norm
|
|
{
|
|
cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps);
|
|
offload_func_nr(cur);
|
|
ggml_set_name(cur, "rms_norm_2");
|
|
|
|
// cur = cur*norm(broadcasted)
|
|
cur = ggml_mul(ctx0, cur, model.norm);
|
|
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
|
ggml_set_name(cur, "result_norm");
|
|
}
|
|
|
|
// lm_head
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
ggml_set_name(cur, "result_output");
|
|
|
|
lctx.use_buf(ctx0, -1);
|
|
|
|
// logits -> probs
|
|
//cur = ggml_soft_max_inplace(ctx0, cur);
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
|
|
if (mem_per_token == 0) {
|
|
mem_per_token = ggml_used_mem(ctx0)/N;
|
|
}
|
|
|
|
#if 0
|
|
LLAMA_LOG_INFO("\n%s: used_mem: eval ctx %.3f MB, scratch %.3f MB %.3f MB, work buf %.3f MB, n_past = %d, N = %d\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,
|
|
lctx.work_buffer.size()/1024.0/1024.0,
|
|
n_past, N);
|
|
#endif
|
|
|
|
ggml_free(ctx0);
|
|
|
|
return gf;
|
|
}
|
|
|
|
// evaluate the transformer
|
|
//
|
|
// - lctx: llama context
|
|
// - tokens: new batch of tokens to process
|
|
// - embd embeddings input
|
|
// - n_tokens number of tokens
|
|
// - 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 float * embd,
|
|
int n_tokens,
|
|
int n_past,
|
|
int n_threads,
|
|
const char * cgraph_fname) {
|
|
|
|
LLAMA_ASSERT((!tokens && embd) || (tokens && !embd));
|
|
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads);
|
|
#endif
|
|
|
|
const int N = n_tokens;
|
|
|
|
const auto & model = lctx.model;
|
|
const auto & hparams = model.hparams;
|
|
|
|
const auto & kv_self = lctx.kv_self;
|
|
|
|
LLAMA_ASSERT(!!kv_self.ctx);
|
|
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_vocab = hparams.n_vocab;
|
|
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
ggml_allocr_reset(lctx.alloc);
|
|
#endif
|
|
|
|
ggml_cgraph * gf = llama_build_graph(lctx, tokens, embd, n_tokens, n_past);
|
|
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
ggml_allocr_alloc_graph(lctx.alloc, gf);
|
|
#endif
|
|
|
|
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
|
|
|
|
// for big prompts, if BLAS is enabled, it is better to use only one thread
|
|
// otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
|
|
n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads;
|
|
|
|
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
|
|
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
|
|
|
|
LLAMA_ASSERT(strcmp(res->name, "result_output") == 0);
|
|
LLAMA_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
|
|
|
|
#if GGML_USE_MPI
|
|
const int64_t n_layer = hparams.n_layer;
|
|
ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer);
|
|
#endif
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (lctx.ctx_metal && N == 1) {
|
|
// TODO: disabled until #2413 is resolved
|
|
//if (!ggml_metal_if_optimized(lctx.ctx_metal)) {
|
|
// ggml_metal_graph_find_concurrency(lctx.ctx_metal, gf);
|
|
//}
|
|
ggml_metal_set_n_cb (lctx.ctx_metal, n_threads);
|
|
ggml_metal_graph_compute(lctx.ctx_metal, gf);
|
|
ggml_metal_get_tensor (lctx.ctx_metal, res);
|
|
if (!lctx.embedding.empty()) {
|
|
ggml_metal_get_tensor(lctx.ctx_metal, embeddings);
|
|
}
|
|
} else {
|
|
// IMPORTANT:
|
|
// Since we don't have efficient Matrix x Matrix Metal multiplication yet, we fallback to vanilla
|
|
// ggml_graph_compute(). It uses Apple's Accelerate CBLAS API which takes advantage of the ANE or the AMX
|
|
// coprocessor.
|
|
//
|
|
// When we implement Matrix x Matrix Metal multiplication, we can avoid this branch.
|
|
// But for now, we have focused only on Matrix x Vector Metal multiplication.
|
|
//
|
|
// TODO: avoid these syncs via shared memory (ref #1696)
|
|
//
|
|
if (lctx.ctx_metal) {
|
|
// We need to sync the GPU KV cache with the CPU KV cache
|
|
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.k);
|
|
ggml_metal_get_tensor(lctx.ctx_metal, kv_self.v);
|
|
}
|
|
|
|
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
|
|
}
|
|
#else
|
|
ggml_graph_compute_helper(lctx.work_buffer, gf, n_threads);
|
|
#endif
|
|
|
|
#if GGML_USE_MPI
|
|
ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer);
|
|
#endif
|
|
|
|
// update kv token count
|
|
lctx.kv_self.n = n_past + N;
|
|
|
|
if (cgraph_fname) {
|
|
ggml_graph_export(gf, cgraph_fname);
|
|
}
|
|
|
|
#ifdef GGML_PERF
|
|
// print timing information per ggml operation (for debugging purposes)
|
|
// requires GGML_PERF to be defined
|
|
ggml_graph_print(gf);
|
|
#endif
|
|
|
|
// plot the computation graph in dot format (for debugging purposes)
|
|
//if (n_past%100 == 0) {
|
|
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
|
|
//}
|
|
|
|
// extract logits
|
|
{
|
|
auto & logits_out = lctx.logits;
|
|
|
|
if (lctx.logits_all) {
|
|
logits_out.resize(n_vocab * N);
|
|
memcpy(logits_out.data(), (float *) ggml_get_data(res), 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(res) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
|
|
}
|
|
}
|
|
|
|
// extract embeddings
|
|
if (!lctx.embedding.empty()) {
|
|
auto & embedding_out = lctx.embedding;
|
|
|
|
embedding_out.resize(n_embd);
|
|
memcpy(embedding_out.data(), (float *) ggml_get_data(embeddings) + (n_embd*(N - 1)), sizeof(float)*n_embd);
|
|
}
|
|
|
|
// 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;
|
|
};
|
|
|
|
static_assert(std::is_trivially_copyable<llama_sp_symbol>::value, "llama_sp_symbol is not trivially copyable");
|
|
|
|
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 = std::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(sym);
|
|
}
|
|
|
|
// seed the work queue with all possible 2-character tokens.
|
|
for (size_t i = 1; i < symbols_.size(); ++i) {
|
|
try_add_bigram(i - 1, i);
|
|
}
|
|
|
|
// keep substituting the highest frequency pairs for as long as we can.
|
|
while (!work_queue_.empty()) {
|
|
auto bigram = work_queue_.top();
|
|
work_queue_.pop();
|
|
|
|
auto & left_sym = symbols_[bigram.left];
|
|
auto & right_sym = symbols_[bigram.right];
|
|
|
|
// if one of the symbols already got merged, skip it.
|
|
if (left_sym.n == 0 || right_sym.n == 0 ||
|
|
left_sym.n + right_sym.n != bigram.size) {
|
|
continue;
|
|
}
|
|
|
|
// merge the right sym into the left one
|
|
left_sym.n += right_sym.n;
|
|
right_sym.n = 0;
|
|
|
|
//LLAMA_LOG_INFO("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
|
|
|
|
// remove the right sym from the chain
|
|
left_sym.next = right_sym.next;
|
|
if (right_sym.next >= 0) {
|
|
symbols_[right_sym.next].prev = bigram.left;
|
|
}
|
|
|
|
// find more substitutions
|
|
try_add_bigram(left_sym.prev, bigram.left);
|
|
try_add_bigram(bigram.left, left_sym.next);
|
|
}
|
|
|
|
for (int i = 0; i != -1; i = symbols_[i].next) {
|
|
auto & symbol = symbols_[i];
|
|
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) {
|
|
// NOTE: old version, before #2420 - not sure what are the implications of this
|
|
//llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
|
|
llama_vocab::id token_id = vocab_.token_to_id.at(std::string(1, symbol.text[j]));
|
|
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.empty()) {
|
|
return output;
|
|
}
|
|
|
|
if (bos) {
|
|
output.push_back(llama_token_bos());
|
|
}
|
|
|
|
tokenizer.tokenize(text, output);
|
|
return output;
|
|
}
|
|
|
|
//
|
|
// grammar - internal
|
|
//
|
|
|
|
struct llama_grammar {
|
|
const std::vector<std::vector<llama_grammar_element>> rules;
|
|
std::vector<std::vector<const llama_grammar_element *>> stacks;
|
|
};
|
|
|
|
struct llama_grammar_candidate {
|
|
size_t index;
|
|
const uint32_t * code_points;
|
|
};
|
|
|
|
// NOTE: assumes valid utf8 (but checks for overrun)
|
|
// adds a terminating 0 for use as pointer
|
|
std::vector<uint32_t> decode_utf8(const char * src) {
|
|
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
|
|
const char * pos = src;
|
|
std::vector<uint32_t> code_points;
|
|
while (*pos != 0) {
|
|
uint8_t first_byte = static_cast<uint8_t>(*pos);
|
|
uint8_t highbits = first_byte >> 4;
|
|
int len = lookup[highbits];
|
|
uint8_t mask = (1 << (8 - len)) - 1;
|
|
uint32_t value = first_byte & mask;
|
|
const char * end = pos + len; // may overrun!
|
|
++pos;
|
|
for ( ; pos < end && *pos != 0; ++pos) {
|
|
value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
|
|
}
|
|
code_points.push_back(value);
|
|
}
|
|
code_points.push_back(0);
|
|
return code_points;
|
|
}
|
|
|
|
// returns true iff pos points to the end of one of the definitions of a rule
|
|
static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
|
|
switch (pos->type) {
|
|
case LLAMA_GRETYPE_END: return true;
|
|
case LLAMA_GRETYPE_ALT: return true;
|
|
default: return false;
|
|
}
|
|
}
|
|
|
|
// returns true iff chr satisfies the char range at pos (regular or inverse range)
|
|
// asserts that pos is pointing to a char range element
|
|
static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
|
|
const llama_grammar_element * pos,
|
|
const uint32_t chr) {
|
|
|
|
bool found = false;
|
|
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
|
|
LLAMA_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
|
|
|
|
do {
|
|
if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
|
|
// inclusive range, e.g. [a-z]
|
|
found = found || (pos->value <= chr && chr <= pos[1].value);
|
|
pos += 2;
|
|
} else {
|
|
// exact char match, e.g. [a] or "a"
|
|
found = found || pos->value == chr;
|
|
pos += 1;
|
|
}
|
|
} while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
|
|
|
|
return std::make_pair(found == is_positive_char, pos);
|
|
}
|
|
|
|
// transforms a grammar pushdown stack into N possible stacks, all ending
|
|
// at a character range (terminal element)
|
|
static void llama_grammar_advance_stack(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<const llama_grammar_element *> & stack,
|
|
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
|
|
|
|
if (stack.empty()) {
|
|
new_stacks.push_back(stack);
|
|
return;
|
|
}
|
|
|
|
const llama_grammar_element * pos = stack.back();
|
|
|
|
switch (pos->type) {
|
|
case LLAMA_GRETYPE_RULE_REF: {
|
|
const size_t rule_id = static_cast<size_t>(pos->value);
|
|
const llama_grammar_element * subpos = rules[rule_id].data();
|
|
do {
|
|
// init new stack without the top (pos)
|
|
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
|
|
if (!llama_grammar_is_end_of_sequence(pos + 1)) {
|
|
// if this rule ref is followed by another element, add that to stack
|
|
new_stack.push_back(pos + 1);
|
|
}
|
|
if (!llama_grammar_is_end_of_sequence(subpos)) {
|
|
// if alternate is nonempty, add to stack
|
|
new_stack.push_back(subpos);
|
|
}
|
|
llama_grammar_advance_stack(rules, new_stack, new_stacks);
|
|
while (!llama_grammar_is_end_of_sequence(subpos)) {
|
|
// scan to end of alternate def
|
|
subpos++;
|
|
}
|
|
if (subpos->type == LLAMA_GRETYPE_ALT) {
|
|
// there's another alternate def of this rule to process
|
|
subpos++;
|
|
} else {
|
|
break;
|
|
}
|
|
} while (true);
|
|
break;
|
|
}
|
|
case LLAMA_GRETYPE_CHAR:
|
|
case LLAMA_GRETYPE_CHAR_NOT:
|
|
new_stacks.push_back(stack);
|
|
break;
|
|
default:
|
|
// end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
|
|
// (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
|
|
// those
|
|
LLAMA_ASSERT(false);
|
|
}
|
|
}
|
|
|
|
// takes a set of possible pushdown stacks on a grammar, which are required to
|
|
// be positioned at a character range (see `llama_grammar_advance_stack`), and
|
|
// produces the N possible stacks if the given char is accepted at those
|
|
// positions
|
|
static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
|
const uint32_t chr) {
|
|
|
|
std::vector<std::vector<const llama_grammar_element *>> new_stacks;
|
|
|
|
for (const auto & stack : stacks) {
|
|
if (stack.empty()) {
|
|
continue;
|
|
}
|
|
|
|
auto match = llama_grammar_match_char(stack.back(), chr);
|
|
if (match.first) {
|
|
const llama_grammar_element * pos = match.second;
|
|
|
|
// update top of stack to next element, if any
|
|
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
|
|
if (!llama_grammar_is_end_of_sequence(pos)) {
|
|
new_stack.push_back(pos);
|
|
}
|
|
llama_grammar_advance_stack(rules, new_stack, new_stacks);
|
|
}
|
|
}
|
|
|
|
return new_stacks;
|
|
}
|
|
|
|
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
|
const std::vector<llama_grammar_candidate> & candidates);
|
|
|
|
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<const llama_grammar_element *> & stack,
|
|
const std::vector<llama_grammar_candidate> & candidates) {
|
|
|
|
std::vector<llama_grammar_candidate> rejects;
|
|
|
|
if (stack.empty()) {
|
|
// accept nothing; EOS is handled elsewhere
|
|
rejects.insert(rejects.end(), candidates.begin(), candidates.end());
|
|
return rejects;
|
|
}
|
|
|
|
const llama_grammar_element * stack_pos = stack.back();
|
|
|
|
std::vector<llama_grammar_candidate> next_candidates;
|
|
for (auto tok : candidates) {
|
|
if (llama_grammar_match_char(stack_pos, tok.code_points[0]).first) {
|
|
if (tok.code_points[1] != 0) {
|
|
next_candidates.push_back({ tok.index, tok.code_points + 1 });
|
|
}
|
|
} else {
|
|
rejects.push_back(tok);
|
|
}
|
|
}
|
|
|
|
auto stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
|
|
|
|
// update top of stack to next element, if any
|
|
std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
|
|
if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
|
|
stack_after.push_back(stack_pos_after);
|
|
}
|
|
std::vector<std::vector<const llama_grammar_element *>> next_stacks;
|
|
llama_grammar_advance_stack(rules, stack_after, next_stacks);
|
|
|
|
auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
|
|
for (auto tok : next_rejects) {
|
|
rejects.push_back({ tok.index, tok.code_points - 1 });
|
|
}
|
|
|
|
return rejects;
|
|
}
|
|
|
|
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
|
|
const std::vector<std::vector<llama_grammar_element>> & rules,
|
|
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
|
const std::vector<llama_grammar_candidate> & candidates) {
|
|
LLAMA_ASSERT(!stacks.empty()); // REVIEW
|
|
|
|
if (candidates.empty()) {
|
|
return std::vector<llama_grammar_candidate>();
|
|
}
|
|
|
|
auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
|
|
|
|
for (size_t i = 1, size = stacks.size(); i < size; ++i) {
|
|
rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
|
|
}
|
|
return rejects;
|
|
}
|
|
|
|
//
|
|
// grammar - external
|
|
//
|
|
|
|
struct llama_grammar * llama_grammar_init(
|
|
const llama_grammar_element ** rules,
|
|
size_t n_rules,
|
|
size_t start_rule_index) {
|
|
const llama_grammar_element * pos;
|
|
|
|
// copy rule definitions into vectors
|
|
std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
|
|
for (size_t i = 0; i < n_rules; i++) {
|
|
for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
|
|
vec_rules[i].push_back(*pos);
|
|
}
|
|
vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
|
|
}
|
|
|
|
// loop over alternates of start rule to build initial stacks
|
|
std::vector<std::vector<const llama_grammar_element *>> stacks;
|
|
pos = rules[start_rule_index];
|
|
do {
|
|
std::vector<const llama_grammar_element *> stack;
|
|
if (!llama_grammar_is_end_of_sequence(pos)) {
|
|
// if alternate is nonempty, add to stack
|
|
stack.push_back(pos);
|
|
}
|
|
llama_grammar_advance_stack(vec_rules, stack, stacks);
|
|
while (!llama_grammar_is_end_of_sequence(pos)) {
|
|
// scan to end of alternate def
|
|
pos++;
|
|
}
|
|
if (pos->type == LLAMA_GRETYPE_ALT) {
|
|
// there's another alternate def of this rule to process
|
|
pos++;
|
|
} else {
|
|
break;
|
|
}
|
|
} while (true);
|
|
|
|
return new llama_grammar{ std::move(vec_rules), std::move(stacks) };
|
|
}
|
|
|
|
void llama_grammar_free(struct llama_grammar * grammar) {
|
|
delete grammar;
|
|
}
|
|
|
|
//
|
|
// sampling
|
|
//
|
|
|
|
void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
assert(candidates->size > 0);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Sort the logits in descending order
|
|
if (!candidates->sorted) {
|
|
std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit > b.logit;
|
|
});
|
|
candidates->sorted = true;
|
|
}
|
|
|
|
float max_l = candidates->data[0].logit;
|
|
float cum_sum = 0.0f;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
float p = expf(candidates->data[i].logit - max_l);
|
|
candidates->data[i].p = p;
|
|
cum_sum += p;
|
|
}
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
candidates->data[i].p /= cum_sum;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
k = std::max(k, (int) min_keep);
|
|
k = std::min(k, (int) candidates->size);
|
|
|
|
// Sort scores in descending order
|
|
if (!candidates->sorted) {
|
|
auto comp = [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit > b.logit;
|
|
};
|
|
if (k == (int) candidates->size) {
|
|
std::sort(candidates->data, candidates->data + candidates->size, comp);
|
|
} else {
|
|
std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp);
|
|
}
|
|
candidates->sorted = true;
|
|
}
|
|
candidates->size = k;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
|
if (p >= 1.0f) {
|
|
return;
|
|
}
|
|
|
|
llama_sample_softmax(ctx, candidates);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute the cumulative probabilities
|
|
float cum_sum = 0.0f;
|
|
size_t last_idx = candidates->size;
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
cum_sum += candidates->data[i].p;
|
|
|
|
// Check if the running sum is at least p or if we have kept at least min_keep tokens
|
|
// we set the last index to i+1 to indicate that the current iterate should be included in the set
|
|
if (cum_sum >= p && i + 1 >= min_keep) {
|
|
last_idx = i + 1;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the top-p tokens
|
|
candidates->size = last_idx;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
|
|
if (z >= 1.0f || candidates->size <= 2) {
|
|
return;
|
|
}
|
|
|
|
llama_sample_softmax(nullptr, candidates);
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute the first and second derivatives
|
|
std::vector<float> first_derivatives(candidates->size - 1);
|
|
std::vector<float> second_derivatives(candidates->size - 2);
|
|
|
|
for (size_t i = 0; i < first_derivatives.size(); ++i) {
|
|
first_derivatives[i] = candidates->data[i].p - candidates->data[i + 1].p;
|
|
}
|
|
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
|
second_derivatives[i] = first_derivatives[i] - first_derivatives[i + 1];
|
|
}
|
|
|
|
// Calculate absolute value of second derivatives
|
|
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
|
second_derivatives[i] = abs(second_derivatives[i]);
|
|
}
|
|
|
|
// Normalize the second derivatives
|
|
{
|
|
const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
|
|
|
|
if (second_derivatives_sum > 1e-6f) {
|
|
for (float & value : second_derivatives) {
|
|
value /= second_derivatives_sum;
|
|
}
|
|
} else {
|
|
for (float & value : second_derivatives) {
|
|
value = 1.0f / second_derivatives.size();
|
|
}
|
|
}
|
|
}
|
|
|
|
float cum_sum = 0.0f;
|
|
size_t last_idx = candidates->size;
|
|
for (size_t i = 0; i < second_derivatives.size(); ++i) {
|
|
cum_sum += second_derivatives[i];
|
|
|
|
// Check if the running sum is greater than z or if we have kept at least min_keep tokens
|
|
if (cum_sum > z && i >= min_keep) {
|
|
last_idx = i;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the tokens above the tail location
|
|
candidates->size = last_idx;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
|
|
void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
|
|
// Reference implementation:
|
|
// https://github.com/huggingface/transformers/compare/main...cimeister:typical-sampling:typical-pr
|
|
if (p >= 1.0f) {
|
|
return;
|
|
}
|
|
|
|
// Compute the softmax of logits and calculate entropy
|
|
llama_sample_softmax(nullptr, candidates);
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
float entropy = 0.0f;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
entropy += -candidates->data[i].p * logf(candidates->data[i].p);
|
|
}
|
|
|
|
// Compute the absolute difference between negative log probability and entropy for each candidate
|
|
std::vector<float> shifted_scores;
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
float shifted_score = fabsf(-logf(candidates->data[i].p) - entropy);
|
|
shifted_scores.push_back(shifted_score);
|
|
}
|
|
|
|
// Sort tokens based on the shifted_scores and their corresponding indices
|
|
std::vector<size_t> indices(candidates->size);
|
|
std::iota(indices.begin(), indices.end(), 0);
|
|
|
|
std::sort(indices.begin(), indices.end(), [&](size_t a, size_t b) {
|
|
return shifted_scores[a] < shifted_scores[b];
|
|
});
|
|
|
|
// Compute the cumulative probabilities
|
|
float cum_sum = 0.0f;
|
|
size_t last_idx = indices.size();
|
|
|
|
for (size_t i = 0; i < indices.size(); ++i) {
|
|
size_t idx = indices[i];
|
|
cum_sum += candidates->data[idx].p;
|
|
|
|
// Check if the running sum is greater than typical or if we have kept at least min_keep tokens
|
|
if (cum_sum > p && i >= min_keep - 1) {
|
|
last_idx = i + 1;
|
|
break;
|
|
}
|
|
}
|
|
|
|
// Resize the output vector to keep only the locally typical tokens
|
|
std::vector<llama_token_data> new_candidates;
|
|
for (size_t i = 0; i < last_idx; ++i) {
|
|
size_t idx = indices[i];
|
|
new_candidates.push_back(candidates->data[idx]);
|
|
}
|
|
|
|
// Replace the data in candidates with the new_candidates data
|
|
std::copy(new_candidates.begin(), new_candidates.end(), candidates->data);
|
|
candidates->size = new_candidates.size();
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
for (size_t i = 0; i < candidates_p->size; ++i) {
|
|
candidates_p->data[i].logit /= temp;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty) {
|
|
if (last_tokens_size == 0 || penalty == 1.0f) {
|
|
return;
|
|
}
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
const auto * token_iter = std::find(last_tokens, last_tokens + last_tokens_size, candidates->data[i].id);
|
|
if (token_iter == last_tokens + last_tokens_size) {
|
|
continue;
|
|
}
|
|
|
|
// The academic publication that described this technique actually just only divided, but that would cause tokens with negative logits to become more likely, which is obviously wrong.
|
|
// This is common fix for this problem, which is to multiply by the penalty instead of dividing.
|
|
if (candidates->data[i].logit <= 0) {
|
|
candidates->data[i].logit *= penalty;
|
|
} else {
|
|
candidates->data[i].logit /= penalty;
|
|
}
|
|
}
|
|
|
|
candidates->sorted = false;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens_p, size_t last_tokens_size, float alpha_frequency, float alpha_presence) {
|
|
if (last_tokens_size == 0 || (alpha_frequency == 0.0f && alpha_presence == 0.0f)) {
|
|
return;
|
|
}
|
|
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Create a frequency map to count occurrences of each token in last_tokens
|
|
std::unordered_map<llama_token, int> token_count;
|
|
for (size_t i = 0; i < last_tokens_size; ++i) {
|
|
token_count[last_tokens_p[i]]++;
|
|
}
|
|
|
|
// Apply frequency and presence penalties to the candidates
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
auto token_iter = token_count.find(candidates->data[i].id);
|
|
if (token_iter == token_count.end()) {
|
|
continue;
|
|
}
|
|
|
|
int count = token_iter->second;
|
|
candidates->data[i].logit -= float(count) * alpha_frequency + float(count > 0) * alpha_presence;
|
|
}
|
|
|
|
candidates->sorted = false;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
|
|
assert(ctx);
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
bool allow_eos = false;
|
|
for (const auto & stack : grammar->stacks) {
|
|
if (stack.empty()) {
|
|
allow_eos = true;
|
|
break;
|
|
}
|
|
}
|
|
|
|
const llama_token eos = llama_token_eos();
|
|
|
|
std::vector<std::vector<uint32_t>> candidates_decoded;
|
|
std::vector<llama_grammar_candidate> candidates_grammar;
|
|
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
const llama_token id = candidates->data[i].id;
|
|
const char * str = llama_token_to_str(ctx, id);
|
|
if (id == eos) {
|
|
if (!allow_eos) {
|
|
candidates->data[i].logit = -INFINITY;
|
|
}
|
|
} else if (*str == 0) {
|
|
candidates->data[i].logit = -INFINITY;
|
|
} else {
|
|
candidates_decoded.push_back(decode_utf8(str));
|
|
candidates_grammar.push_back({ i, candidates_decoded.back().data() });
|
|
}
|
|
}
|
|
|
|
const auto rejects =
|
|
llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
|
|
for (auto & reject : rejects) {
|
|
candidates->data[reject.index].logit = -INFINITY;
|
|
}
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
static void llama_log_softmax(float * array, size_t size) {
|
|
float max_l = *std::max_element(array, array + size);
|
|
float sum = 0.f;
|
|
for (size_t i = 0; i < size; ++i) {
|
|
float p = expf(array[i] - max_l);
|
|
sum += p;
|
|
array[i] = p;
|
|
}
|
|
|
|
for (size_t i = 0; i < size; ++i) {
|
|
array[i] = logf(array[i] / sum);
|
|
}
|
|
}
|
|
|
|
void llama_sample_classifier_free_guidance(
|
|
struct llama_context * ctx,
|
|
llama_token_data_array * candidates,
|
|
struct llama_context * guidance_ctx,
|
|
float scale) {
|
|
int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
assert(ctx);
|
|
auto n_vocab = llama_n_vocab(ctx);
|
|
assert(n_vocab == (int)candidates->size);
|
|
assert(!candidates->sorted);
|
|
|
|
std::vector<float> logits_base;
|
|
logits_base.reserve(candidates->size);
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
logits_base.push_back(candidates->data[i].logit);
|
|
}
|
|
llama_log_softmax(logits_base.data(), candidates->size);
|
|
|
|
float* logits_guidance = llama_get_logits(guidance_ctx);
|
|
llama_log_softmax(logits_guidance, n_vocab);
|
|
|
|
for (int i = 0; i < n_vocab; ++i) {
|
|
float logit_guidance = logits_guidance[i];
|
|
float logit_base = logits_base[i];
|
|
candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
}
|
|
|
|
llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu) {
|
|
assert(ctx);
|
|
auto N = float(llama_n_vocab(ctx));
|
|
int64_t t_start_sample_us;
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
llama_sample_softmax(nullptr, candidates);
|
|
|
|
// Estimate s_hat using the most probable m tokens
|
|
float s_hat = 0.0;
|
|
float sum_ti_bi = 0.0;
|
|
float sum_ti_sq = 0.0;
|
|
for (size_t i = 0; i < size_t(m - 1) && i < candidates->size - 1; ++i) {
|
|
float t_i = logf(float(i + 2) / float(i + 1));
|
|
float b_i = logf(candidates->data[i].p / candidates->data[i + 1].p);
|
|
sum_ti_bi += t_i * b_i;
|
|
sum_ti_sq += t_i * t_i;
|
|
}
|
|
s_hat = sum_ti_bi / sum_ti_sq;
|
|
|
|
// Compute k from the estimated s_hat and target surprise value
|
|
float epsilon_hat = s_hat - 1;
|
|
float k = powf((epsilon_hat * powf(2, *mu)) / (1 - powf(N, -epsilon_hat)), 1 / s_hat);
|
|
|
|
// Sample the next word X using top-k sampling
|
|
llama_sample_top_k(nullptr, candidates, int(k), 1);
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
llama_token X = llama_sample_token(ctx, candidates);
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute error as the difference between observed surprise and target surprise value
|
|
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
|
return candidate.id == X;
|
|
}));
|
|
float observed_surprise = -log2f(candidates->data[X_idx].p);
|
|
float e = observed_surprise - tau;
|
|
|
|
// Update mu using the learning rate and error
|
|
*mu = *mu - eta * e;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
return X;
|
|
}
|
|
|
|
llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
|
|
int64_t t_start_sample_us;
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
llama_sample_softmax(ctx, candidates);
|
|
|
|
// Truncate the words with surprise values greater than mu
|
|
candidates->size = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
|
return -log2f(candidate.p) > *mu;
|
|
}));
|
|
|
|
if (candidates->size == 0) {
|
|
candidates->size = 1;
|
|
}
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
// Normalize the probabilities of the remaining words
|
|
llama_sample_softmax(ctx, candidates);
|
|
|
|
// Sample the next word X from the remaining words
|
|
llama_token X = llama_sample_token(ctx, candidates);
|
|
t_start_sample_us = ggml_time_us();
|
|
|
|
// Compute error as the difference between observed surprise and target surprise value
|
|
size_t X_idx = std::distance(candidates->data, std::find_if(candidates->data, candidates->data + candidates->size, [&](const llama_token_data & candidate) {
|
|
return candidate.id == X;
|
|
}));
|
|
float observed_surprise = -log2f(candidates->data[X_idx].p);
|
|
float e = observed_surprise - tau;
|
|
|
|
// Update mu using the learning rate and error
|
|
*mu = *mu - eta * e;
|
|
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
return X;
|
|
}
|
|
|
|
llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
// Find max element
|
|
auto * max_iter = std::max_element(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
|
|
return a.logit < b.logit;
|
|
});
|
|
|
|
llama_token result = max_iter->id;
|
|
if (ctx) {
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
}
|
|
return result;
|
|
}
|
|
|
|
llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
|
|
assert(ctx);
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
llama_sample_softmax(nullptr, candidates);
|
|
|
|
std::vector<float> probs;
|
|
probs.reserve(candidates->size);
|
|
for (size_t i = 0; i < candidates->size; ++i) {
|
|
probs.push_back(candidates->data[i].p);
|
|
}
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
auto & rng = ctx->rng;
|
|
int idx = dist(rng);
|
|
|
|
llama_token result = candidates->data[idx].id;
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
ctx->n_sample++;
|
|
return result;
|
|
}
|
|
|
|
void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
if (token == llama_token_eos()) {
|
|
for (const auto & stack : grammar->stacks) {
|
|
if (stack.empty()) {
|
|
return;
|
|
}
|
|
}
|
|
LLAMA_ASSERT(false);
|
|
}
|
|
|
|
const char * str = llama_token_to_str(ctx, token);
|
|
// Note terminating 0 in decoded string
|
|
auto code_points = decode_utf8(str);
|
|
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
|
|
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
|
|
}
|
|
LLAMA_ASSERT(!grammar->stacks.empty());
|
|
|
|
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
|
|
}
|
|
|
|
//
|
|
// quantization
|
|
//
|
|
|
|
static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llama_buffer & output, const int nelements, const int nthread) {
|
|
if (output.size < nelements * sizeof(float)) {
|
|
output.resize(nelements * sizeof(float));
|
|
}
|
|
float * f32_output = (float *) output.addr;
|
|
|
|
ggml_type_traits_t qtype;
|
|
if (ggml_is_quantized(tensor.type)) {
|
|
qtype = ggml_internal_get_type_traits(tensor.type);
|
|
if (qtype.to_float == NULL) {
|
|
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type)));
|
|
}
|
|
} else if (tensor.type != GGML_TYPE_F16) {
|
|
throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor.type)));
|
|
}
|
|
|
|
if (nthread < 2) {
|
|
if (tensor.type == GGML_TYPE_F16) {
|
|
ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements);
|
|
} else if (ggml_is_quantized(tensor.type)) {
|
|
qtype.to_float(tensor.data, f32_output, nelements);
|
|
} else {
|
|
LLAMA_ASSERT(false); // unreachable
|
|
}
|
|
return;
|
|
}
|
|
|
|
auto block_size = tensor.type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor.type);
|
|
auto block_size_bytes = ggml_type_size(tensor.type);
|
|
|
|
LLAMA_ASSERT(nelements % block_size == 0);
|
|
auto nblocks = nelements / block_size;
|
|
auto blocks_per_thread = nblocks / nthread;
|
|
auto spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
|
|
|
|
std::vector<std::thread> workers;
|
|
for (auto tnum = 0, in_buff_offs = 0, out_buff_offs = 0; tnum < nthread; tnum++) {
|
|
auto thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
|
|
auto thr_elems = thr_blocks * block_size; // number of elements for this thread
|
|
auto thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
|
|
|
|
auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
|
|
if (typ == GGML_TYPE_F16) {
|
|
ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
|
|
} else {
|
|
qtype.to_float(inbuf, outbuf, nels);
|
|
}
|
|
};
|
|
workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems));
|
|
in_buff_offs += thr_block_bytes;
|
|
out_buff_offs += thr_elems;
|
|
}
|
|
for (auto & worker : workers) {
|
|
worker.join();
|
|
}
|
|
|
|
}
|
|
|
|
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
|
|
ggml_type quantized_type;
|
|
llama_ftype ftype = params->ftype;
|
|
int nthread = params->nthread;
|
|
|
|
switch (params->ftype) {
|
|
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q4_1: quantized_type = GGML_TYPE_Q4_1; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
|
|
case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break;
|
|
case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break;
|
|
|
|
#ifdef GGML_USE_K_QUANTS
|
|
// K-quants
|
|
case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
|
|
case LLAMA_FTYPE_MOSTLY_Q3_K_L: quantized_type = GGML_TYPE_Q3_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q4_K_M: quantized_type = GGML_TYPE_Q4_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
|
|
case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
|
|
case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
|
|
#endif
|
|
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
|
|
}
|
|
|
|
if (nthread <= 0) {
|
|
nthread = std::thread::hardware_concurrency();
|
|
}
|
|
|
|
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false));
|
|
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loader.get(), params->ftype);
|
|
|
|
#ifdef GGML_USE_K_QUANTS
|
|
int n_attention_wv = 0;
|
|
int n_feed_forward_w2 = 0;
|
|
for (auto& tensor : model_loader->tensors_map.tensors) {
|
|
if (tensor.name.find("attention.wv.weight") != std::string::npos) {
|
|
++n_attention_wv;
|
|
}
|
|
else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
|
|
++n_feed_forward_w2;
|
|
}
|
|
}
|
|
|
|
int i_attention_wv = 0;
|
|
int i_feed_forward_w2 = 0;
|
|
#endif
|
|
|
|
size_t total_size_org = 0;
|
|
size_t total_size_new = 0;
|
|
std::vector<int64_t> hist_all(1 << 4, 0);
|
|
|
|
std::vector<std::thread> workers;
|
|
std::mutex mutex;
|
|
|
|
auto use_more_bits = [] (int i_layer, int num_layers) -> bool {
|
|
return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2;
|
|
};
|
|
|
|
size_t idx = 0;
|
|
for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) {
|
|
llama_buffer read_data;
|
|
read_data.resize(tensor.size);
|
|
tensor.data = read_data.addr;
|
|
model_loader->load_data_for(tensor);
|
|
|
|
LLAMA_LOG_INFO("[%4zu/%4zu] %36s - %16s, type = %6s, ",
|
|
++idx, model_loader->tensors_map.tensors.size(),
|
|
tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(),
|
|
ggml_type_name(tensor.type));
|
|
|
|
// This used to be a regex, but <regex> has an extreme cost to compile times.
|
|
bool quantize = tensor.name.rfind("weight") == tensor.name.size() - 6; // ends with 'weight'?
|
|
|
|
// quantize only 2D tensors
|
|
quantize &= (tensor.ne.size() == 2);
|
|
quantize &= params->quantize_output_tensor || tensor.name != "output.weight";
|
|
quantize &= quantized_type != tensor.type;
|
|
|
|
enum ggml_type new_type;
|
|
void * new_data;
|
|
size_t new_size;
|
|
llama_buffer work;
|
|
|
|
if (!quantize) {
|
|
new_type = tensor.type;
|
|
new_data = tensor.data;
|
|
new_size = tensor.size;
|
|
LLAMA_LOG_INFO("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
|
|
} else {
|
|
new_type = quantized_type;
|
|
#ifdef GGML_USE_K_QUANTS
|
|
if (tensor.name == "output.weight") {
|
|
int nx = tensor.ne.at(0);
|
|
int ny = tensor.ne.at(1);
|
|
if (nx % QK_K == 0 && ny % QK_K == 0) {
|
|
new_type = GGML_TYPE_Q6_K;
|
|
}
|
|
} else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
|
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
|
use_more_bits(i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K;
|
|
else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) &&
|
|
(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K;
|
|
++i_attention_wv;
|
|
} else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
|
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
|
|
use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K;
|
|
//else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < n_feed_forward_w2/8) new_type = GGML_TYPE_Q6_K;
|
|
++i_feed_forward_w2;
|
|
} else if (tensor.name.find("attention.wo.weight") != std::string::npos) {
|
|
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
|
|
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
|
|
}
|
|
bool convert_incompatible_tensor = false;
|
|
if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
|
|
new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) {
|
|
int nx = tensor.ne.at(0);
|
|
int ny = tensor.ne.at(1);
|
|
if (nx % QK_K != 0 || ny % QK_K != 0) {
|
|
LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K);
|
|
convert_incompatible_tensor = true;
|
|
}
|
|
}
|
|
if (convert_incompatible_tensor) {
|
|
if (tensor.name == "output.weight") {
|
|
new_type = GGML_TYPE_F16; //fall back to F16 instead of just failing.
|
|
LLAMA_LOG_WARN("F16 will be used for this tensor instead.\n");
|
|
} else if (tensor.name == "tok_embeddings.weight") {
|
|
new_type = GGML_TYPE_Q4_0; //fall back to Q4_0 instead of just failing.
|
|
LLAMA_LOG_WARN("Q4_0 will be used for this tensor instead.\n");
|
|
} else {
|
|
throw std::runtime_error("Unsupported tensor size encountered\n");
|
|
}
|
|
}
|
|
#endif
|
|
|
|
float * f32_data;
|
|
size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
|
|
llama_buffer f32_conv_buf;
|
|
|
|
if (tensor.type == GGML_TYPE_F32) {
|
|
f32_data = (float *) tensor.data;
|
|
} else if (ggml_is_quantized(tensor.type) && !params->allow_requantize) {
|
|
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor.type)));
|
|
} else {
|
|
llama_convert_tensor_internal(tensor, f32_conv_buf, nelements, nthread);
|
|
f32_data = (float *) f32_conv_buf.addr;
|
|
}
|
|
|
|
LLAMA_LOG_INFO("quantizing to %s .. ", ggml_type_name(new_type));
|
|
fflush(stdout);
|
|
|
|
work.resize(nelements * 4); // upper bound on size
|
|
new_data = work.addr;
|
|
std::vector<int64_t> hist_cur(1 << 4, 0);
|
|
|
|
int chunk_size = 32 * 512;
|
|
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
|
|
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
|
|
if (nthread_use < 2) {
|
|
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
|
|
} else {
|
|
size_t counter = 0;
|
|
new_size = 0;
|
|
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements, chunk_size] () {
|
|
std::vector<int64_t> local_hist;
|
|
size_t local_size = 0;
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex);
|
|
size_t first = counter; counter += chunk_size;
|
|
if (first >= nelements) {
|
|
if (!local_hist.empty()) {
|
|
for (int j=0; j<int(local_hist.size()); ++j) {
|
|
hist_cur[j] += local_hist[j];
|
|
}
|
|
new_size += local_size;
|
|
}
|
|
break;
|
|
}
|
|
lock.unlock();
|
|
size_t last = std::min(nelements, first + chunk_size);
|
|
if (local_hist.empty()) {
|
|
local_hist.resize(hist_cur.size(), 0);
|
|
}
|
|
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
|
|
}
|
|
};
|
|
if ((int) workers.size() < nthread_use - 1) {
|
|
workers.resize(nthread_use - 1);
|
|
}
|
|
for (int it = 0; it < nthread_use - 1; ++it) {
|
|
workers[it] = std::thread(compute);
|
|
}
|
|
compute();
|
|
for (int it = 0; it < nthread_use - 1; ++it) {
|
|
workers[it].join();
|
|
}
|
|
}
|
|
|
|
LLAMA_LOG_INFO("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
|
|
int64_t tot_count = 0;
|
|
for (size_t i = 0; i < hist_cur.size(); i++) {
|
|
hist_all[i] += hist_cur[i];
|
|
tot_count += hist_cur[i];
|
|
}
|
|
|
|
if (tot_count > 0) {
|
|
for (size_t i = 0; i < hist_cur.size(); i++) {
|
|
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
|
|
}
|
|
}
|
|
LLAMA_LOG_INFO("\n");
|
|
}
|
|
total_size_org += tensor.size;
|
|
total_size_new += new_size;
|
|
file_saver.write_tensor(tensor, new_type, new_data, new_size);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
|
|
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
|
|
|
|
{
|
|
int64_t sum_all = 0;
|
|
for (size_t i = 0; i < hist_all.size(); i++) {
|
|
sum_all += hist_all[i];
|
|
}
|
|
|
|
if (sum_all > 0) {
|
|
LLAMA_LOG_INFO("%s: hist: ", __func__);
|
|
for (size_t i = 0; i < hist_all.size(); i++) {
|
|
LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
|
|
}
|
|
LLAMA_LOG_INFO("\n");
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
|
|
struct llama_model * llama_load_model_from_file(
|
|
const char * path_model,
|
|
struct llama_context_params params) {
|
|
ggml_time_init();
|
|
|
|
llama_model * model = new llama_model;
|
|
|
|
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
|
|
|
if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gqa, params.rms_norm_eps, params.n_gpu_layers,
|
|
params.main_gpu, params.tensor_split, params.mul_mat_q, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
|
|
memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
|
|
params.progress_callback_user_data)) {
|
|
LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
|
|
delete model;
|
|
return nullptr;
|
|
}
|
|
|
|
return model;
|
|
}
|
|
|
|
void llama_free_model(struct llama_model * model) {
|
|
delete model;
|
|
}
|
|
|
|
struct llama_context * llama_new_context_with_model(
|
|
struct llama_model * model,
|
|
struct llama_context_params params) {
|
|
|
|
if (!model) {
|
|
return nullptr;
|
|
}
|
|
|
|
llama_context * ctx = new llama_context(*model);
|
|
|
|
if (params.seed == LLAMA_DEFAULT_SEED) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
unsigned cur_percentage = 0;
|
|
if (params.progress_callback == NULL) {
|
|
params.progress_callback_user_data = &cur_percentage;
|
|
params.progress_callback = [](float progress, void * ctx) {
|
|
unsigned * cur_percentage_p = (unsigned *) ctx;
|
|
unsigned percentage = (unsigned) (100 * progress);
|
|
while (percentage > *cur_percentage_p) {
|
|
*cur_percentage_p = percentage;
|
|
LLAMA_LOG_INFO(".");
|
|
if (percentage >= 100) {
|
|
LLAMA_LOG_INFO("\n");
|
|
}
|
|
}
|
|
};
|
|
}
|
|
|
|
ctx->rng = std::mt19937(params.seed);
|
|
ctx->logits_all = params.logits_all;
|
|
|
|
ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
|
|
|
// reserve memory for context buffers
|
|
if (!params.vocab_only) {
|
|
if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) {
|
|
LLAMA_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
|
llama_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
{
|
|
const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v);
|
|
LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
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_vocab);
|
|
}
|
|
|
|
if (params.embedding){
|
|
ctx->embedding.resize(hparams.n_embd);
|
|
}
|
|
|
|
#ifdef LLAMA_USE_ALLOCATOR
|
|
{
|
|
static const size_t tensor_alignment = 32;
|
|
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
|
|
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
|
|
|
|
// create measure allocator
|
|
ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
|
|
|
|
// build worst-case graph
|
|
int n_tokens = std::min((int)hparams.n_ctx, params.n_batch);
|
|
int n_past = hparams.n_ctx - n_tokens;
|
|
llama_token token = llama_token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
|
|
ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
|
|
|
|
// measure memory requirements for the graph
|
|
size_t alloc_size = ggml_allocr_alloc_graph(ctx->alloc, gf) + tensor_alignment;
|
|
|
|
LLAMA_LOG_INFO("%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
|
|
|
|
// debug - for comparison with scratch buffer
|
|
//size_t prev_req =
|
|
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
|
|
// MEM_REQ_SCRATCH1().at(ctx->model.type) +
|
|
// MEM_REQ_EVAL().at(ctx->model.type);
|
|
//LLAMA_LOG_INFO("%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
|
|
|
|
// recreate allocator with exact memory requirements
|
|
ggml_allocr_free(ctx->alloc);
|
|
|
|
ctx->buf_alloc.resize(alloc_size);
|
|
ctx->alloc = ggml_allocr_new(ctx->buf_alloc.addr, ctx->buf_alloc.size, tensor_alignment);
|
|
}
|
|
#else
|
|
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
|
|
#endif
|
|
|
|
#ifdef LLAMA_USE_SCRATCH
|
|
ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
|
|
ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
|
|
#endif
|
|
}
|
|
|
|
#ifdef GGML_USE_METAL
|
|
if (params.n_gpu_layers > 0) {
|
|
// this allocates all Metal resources and memory buffers
|
|
ctx->ctx_metal = ggml_metal_init(1);
|
|
|
|
if (!ctx->ctx_metal) {
|
|
LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
|
|
llama_free(ctx);
|
|
return NULL;
|
|
}
|
|
|
|
void * data_ptr = NULL;
|
|
size_t data_size = 0;
|
|
|
|
if (params.use_mmap) {
|
|
data_ptr = ctx->model.mapping->addr;
|
|
data_size = ctx->model.mapping->size;
|
|
} else {
|
|
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
|
data_size = ggml_get_mem_size (ctx->model.ctx);
|
|
}
|
|
|
|
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
|
|
|
|
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
|
|
|
|
#define LLAMA_METAL_CHECK_BUF(result) \
|
|
if (!(result)) { \
|
|
LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
|
|
llama_free(ctx); \
|
|
return NULL; \
|
|
}
|
|
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
|
|
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0));
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.addr, ctx->kv_self.buf.size, 0));
|
|
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0));
|
|
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0));
|
|
#undef LLAMA_METAL_CHECK_BUF
|
|
}
|
|
#endif
|
|
|
|
#ifdef GGML_USE_MPI
|
|
ctx->ctx_mpi = ggml_mpi_init();
|
|
|
|
if (ggml_mpi_rank(ctx->ctx_mpi) > 0) {
|
|
// Enter a blocking eval loop with dummy input, letting rank=0 drive the process
|
|
const std::vector<llama_token> tmp(ctx->model.hparams.n_ctx, llama_token_bos());
|
|
while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {};
|
|
llama_backend_free();
|
|
exit(1);
|
|
}
|
|
#endif
|
|
|
|
return ctx;
|
|
}
|
|
|
|
struct llama_context * llama_init_from_file(
|
|
const char * path_model,
|
|
struct llama_context_params params) {
|
|
|
|
struct llama_model * model = llama_load_model_from_file(path_model, params);
|
|
if (!model) {
|
|
return nullptr;
|
|
}
|
|
struct llama_context * ctx = llama_new_context_with_model(model, params);
|
|
ctx->model_owner = true;
|
|
return ctx;
|
|
}
|
|
|
|
void llama_free(struct llama_context * ctx) {
|
|
delete ctx;
|
|
}
|
|
|
|
int llama_model_quantize(
|
|
const char * fname_inp,
|
|
const char * fname_out,
|
|
const llama_model_quantize_params *params) {
|
|
try {
|
|
llama_model_quantize_internal(fname_inp, fname_out, params);
|
|
return 0;
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) {
|
|
LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora);
|
|
|
|
const int64_t t_start_lora_us = ggml_time_us();
|
|
|
|
auto fin = std::ifstream(path_lora, std::ios::binary);
|
|
if (!fin) {
|
|
LLAMA_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_lora);
|
|
return 1;
|
|
}
|
|
|
|
// verify magic and version
|
|
{
|
|
uint32_t magic;
|
|
fin.read((char *) &magic, sizeof(magic));
|
|
if (magic != LLAMA_FILE_MAGIC_GGLA) {
|
|
LLAMA_LOG_ERROR("%s: bad file magic\n", __func__);
|
|
return 1;
|
|
}
|
|
uint32_t format_version;
|
|
fin.read((char *) &format_version, sizeof(format_version));
|
|
|
|
if (format_version != 1) {
|
|
LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ );
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int32_t lora_r;
|
|
int32_t lora_alpha;
|
|
fin.read((char *) &lora_r, sizeof(lora_r));
|
|
fin.read((char *) &lora_alpha, sizeof(lora_alpha));
|
|
float scaling = (float)lora_alpha / (float)lora_r;
|
|
|
|
LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling);
|
|
|
|
|
|
// create a temporary ggml context to store the lora tensors
|
|
// todo: calculate size from biggest possible tensor
|
|
std::vector<uint8_t> lora_buf(1024ull * 1024ull * 1024ull);
|
|
struct ggml_init_params params;
|
|
params.mem_size = lora_buf.size();
|
|
params.mem_buffer = lora_buf.data();
|
|
params.no_alloc = false;
|
|
|
|
ggml_context * lora_ctx = ggml_init(params);
|
|
std::unordered_map<std::string, struct ggml_tensor *> lora_tensors;
|
|
|
|
// create a name -> tensor map of the model to accelerate lookups
|
|
std::unordered_map<std::string, struct ggml_tensor*> model_tensors;
|
|
for (const auto & kv: model.tensors_by_name) {
|
|
model_tensors.insert(kv);
|
|
}
|
|
|
|
|
|
// load base model
|
|
std::unique_ptr<llama_model_loader> model_loader;
|
|
ggml_context * base_ctx = NULL;
|
|
llama_buffer base_buf;
|
|
if (path_base_model) {
|
|
LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model);
|
|
model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true));
|
|
|
|
size_t ctx_size;
|
|
size_t mmapped_size;
|
|
model_loader->calc_sizes(&ctx_size, &mmapped_size);
|
|
base_buf.resize(ctx_size);
|
|
|
|
ggml_init_params base_params;
|
|
base_params.mem_size = base_buf.size;
|
|
base_params.mem_buffer = base_buf.addr;
|
|
base_params.no_alloc = model_loader->use_mmap;
|
|
|
|
base_ctx = ggml_init(base_params);
|
|
|
|
model_loader->ggml_ctx = base_ctx;
|
|
|
|
// maybe this should in llama_model_loader
|
|
if (model_loader->use_mmap) {
|
|
model_loader->mapping.reset(new llama_mmap(&model_loader->file_loader->file, /* prefetch */ 0, ggml_is_numa()));
|
|
}
|
|
}
|
|
|
|
// read tensors and apply
|
|
bool warned = false;
|
|
int n_tensors = 0;
|
|
|
|
std::vector<uint8_t> work_buffer;
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ftype;
|
|
|
|
fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
|
|
fin.read(reinterpret_cast<char *>(&length), sizeof(length));
|
|
fin.read(reinterpret_cast<char *>(&ftype), sizeof(ftype));
|
|
if (fin.eof()) {
|
|
break;
|
|
}
|
|
|
|
int32_t ne[2] = { 1, 1 };
|
|
for (int i = 0; i < n_dims; ++i) {
|
|
fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
|
|
}
|
|
|
|
std::string name;
|
|
{
|
|
char buf[1024];
|
|
fin.read(buf, length);
|
|
name = std::string(buf, length);
|
|
}
|
|
|
|
// check for lora suffix and get the type of tensor
|
|
const std::string lora_suffix = ".lora";
|
|
size_t pos = name.rfind(lora_suffix);
|
|
if (pos == std::string::npos) {
|
|
LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str());
|
|
return 1;
|
|
}
|
|
|
|
std::string lora_type = name.substr(pos + lora_suffix.length());
|
|
std::string base_name = name;
|
|
base_name.erase(pos);
|
|
// LLAMA_LOG_INFO("%s: %s => %s (lora type %s) \n", __func__, name.c_str(),base_name.c_str(), lora_type.c_str());
|
|
|
|
if (model_tensors.find(base_name) == model_tensors.end()) {
|
|
LLAMA_LOG_ERROR("%s: unknown tensor '%s' in lora adapter\n", __func__, name.data());
|
|
return 1;
|
|
}
|
|
|
|
// create ggml tensor
|
|
ggml_type wtype;
|
|
switch (ftype) {
|
|
case 0: wtype = GGML_TYPE_F32; break;
|
|
case 1: wtype = GGML_TYPE_F16; break;
|
|
default:
|
|
{
|
|
LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n",
|
|
__func__, ftype);
|
|
return false;
|
|
}
|
|
}
|
|
ggml_tensor * lora_tensor;
|
|
if (n_dims == 2) {
|
|
lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]);
|
|
}
|
|
else {
|
|
LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims);
|
|
return 1;
|
|
}
|
|
ggml_set_name(lora_tensor, "lora_tensor");
|
|
|
|
// load tensor data
|
|
size_t offset = fin.tellg();
|
|
size_t tensor_data_size = ggml_nbytes(lora_tensor);
|
|
offset = (offset + 31) & -32;
|
|
fin.seekg(offset);
|
|
fin.read((char*)lora_tensor->data, tensor_data_size);
|
|
|
|
lora_tensors[name] = lora_tensor;
|
|
|
|
// check if we have both A and B tensors and apply
|
|
if (lora_tensors.find(base_name + ".loraA") != lora_tensors.end() &&
|
|
lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) {
|
|
|
|
ggml_tensor * dest_t = model_tensors[base_name];
|
|
|
|
offload_func_t offload_func = llama_nop;
|
|
offload_func_t offload_func_force_inplace = llama_nop;
|
|
|
|
#ifdef GGML_USE_CUBLAS
|
|
if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) {
|
|
if (dest_t->type != GGML_TYPE_F16) {
|
|
throw std::runtime_error(format(
|
|
"%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__));
|
|
}
|
|
offload_func = ggml_cuda_assign_buffers;
|
|
offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace;
|
|
}
|
|
#endif // GGML_USE_CUBLAS
|
|
|
|
ggml_tensor * base_t;
|
|
if (model_loader) {
|
|
// load from base model
|
|
if (model_loader->tensors_map.name_to_idx.find(base_name) == model_loader->tensors_map.name_to_idx.end()) {
|
|
LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str());
|
|
return 1;
|
|
}
|
|
size_t idx = model_loader->tensors_map.name_to_idx[base_name];
|
|
llama_load_tensor & lt = model_loader->tensors_map.tensors[idx];
|
|
base_t = model_loader->get_tensor(base_name, { (uint32_t)dest_t->ne[0], (uint32_t)dest_t->ne[1] }, GGML_BACKEND_CPU);
|
|
lt.data = (uint8_t *) lt.ggml_tensor->data;
|
|
model_loader->load_data_for(lt);
|
|
lt.ggml_tensor->data = lt.data;
|
|
}
|
|
else {
|
|
base_t = dest_t;
|
|
}
|
|
|
|
if (ggml_is_quantized(base_t->type)) {
|
|
if (!warned) {
|
|
LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, "
|
|
"use a f16 or f32 base model with --lora-base\n", __func__);
|
|
warned = true;
|
|
}
|
|
}
|
|
|
|
ggml_tensor * loraA = lora_tensors[base_name + ".loraA"];
|
|
GGML_ASSERT(loraA->type == GGML_TYPE_F32);
|
|
ggml_set_name(loraA, "loraA");
|
|
|
|
ggml_tensor * loraB = lora_tensors[base_name + ".loraB"];
|
|
GGML_ASSERT(loraB->type == GGML_TYPE_F32);
|
|
ggml_set_name(loraB, "loraB");
|
|
|
|
if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) {
|
|
LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");"
|
|
" are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]);
|
|
return 1;
|
|
}
|
|
|
|
// w = w + BA*s
|
|
ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB);
|
|
offload_func(BA);
|
|
ggml_set_name(BA, "BA");
|
|
|
|
if (scaling != 1.0f) {
|
|
ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling);
|
|
ggml_set_name(scale_tensor, "scale_tensor");
|
|
|
|
BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor);
|
|
offload_func(BA);
|
|
ggml_set_name(BA, "BA_scaled");
|
|
}
|
|
|
|
ggml_tensor * r;
|
|
if (base_t == dest_t) {
|
|
r = ggml_add_inplace(lora_ctx, dest_t, BA);
|
|
offload_func_force_inplace(r);
|
|
ggml_set_name(r, "r_add_inplace");
|
|
}
|
|
else {
|
|
r = ggml_add(lora_ctx, base_t, BA);
|
|
offload_func(r);
|
|
ggml_set_name(r, "r_add");
|
|
|
|
r = ggml_cpy(lora_ctx, r, dest_t);
|
|
offload_func(r);
|
|
ggml_set_name(r, "r_cpy");
|
|
}
|
|
|
|
struct ggml_cgraph gf = ggml_build_forward(r);
|
|
|
|
ggml_graph_compute_helper(work_buffer, &gf, n_threads);
|
|
|
|
// we won't need these tensors again, reset the context to save memory
|
|
ggml_free(lora_ctx);
|
|
lora_ctx = ggml_init(params);
|
|
lora_tensors.clear();
|
|
|
|
n_tensors++;
|
|
if (n_tensors % 4 == 0) {
|
|
LLAMA_LOG_INFO(".");
|
|
}
|
|
}
|
|
}
|
|
|
|
// TODO: this should be in a destructor, it will leak on failure
|
|
ggml_free(lora_ctx);
|
|
if (base_ctx) {
|
|
ggml_free(base_ctx);
|
|
}
|
|
|
|
const int64_t t_lora_us = ggml_time_us() - t_start_lora_us;
|
|
LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0);
|
|
|
|
return 0;
|
|
}
|
|
|
|
int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) {
|
|
try {
|
|
return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, const char * path_base_model, int n_threads) {
|
|
try {
|
|
return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
int llama_get_kv_cache_token_count(const struct llama_context * ctx) {
|
|
return ctx->kv_self.n;
|
|
}
|
|
|
|
#define LLAMA_MAX_RNG_STATE (64*1024)
|
|
|
|
void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
|
|
if (seed == LLAMA_DEFAULT_SEED) {
|
|
seed = time(NULL);
|
|
}
|
|
ctx->rng.seed(seed);
|
|
}
|
|
|
|
// Returns the *maximum* size of the state
|
|
size_t llama_get_state_size(const struct llama_context * ctx) {
|
|
// we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
|
|
// for reference, std::mt19937(1337) serializes to 6701 bytes.
|
|
const size_t s_rng_size = sizeof(size_t);
|
|
const size_t s_rng = LLAMA_MAX_RNG_STATE;
|
|
const size_t s_logits_capacity = sizeof(size_t);
|
|
const size_t s_logits_size = sizeof(size_t);
|
|
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
|
|
const size_t s_embedding_size = sizeof(size_t);
|
|
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
|
|
const size_t s_kv_size = sizeof(size_t);
|
|
const size_t s_kv_ntok = sizeof(int);
|
|
const size_t s_kv = ctx->kv_self.buf.size;
|
|
|
|
const size_t s_total = (
|
|
+ s_rng_size
|
|
+ s_rng
|
|
+ s_logits_capacity
|
|
+ s_logits_size
|
|
+ s_logits
|
|
+ s_embedding_size
|
|
+ s_embedding
|
|
+ s_kv_size
|
|
+ s_kv_ntok
|
|
+ s_kv
|
|
);
|
|
|
|
return s_total;
|
|
}
|
|
|
|
/** copy state data into either a buffer or file depending on the passed in context
|
|
*
|
|
* file context:
|
|
* llama_file file("/path", "wb");
|
|
* llama_data_file_context data_ctx(&file);
|
|
* llama_copy_state_data(ctx, &data_ctx);
|
|
*
|
|
* buffer context:
|
|
* std::vector<uint8_t> buf(max_size, 0);
|
|
* llama_data_buffer_context data_ctx(&buf.data());
|
|
* llama_copy_state_data(ctx, &data_ctx);
|
|
*
|
|
*/
|
|
void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
|
|
// copy rng
|
|
{
|
|
std::stringstream rng_ss;
|
|
rng_ss << ctx->rng;
|
|
|
|
const size_t rng_size = rng_ss.str().size();
|
|
char rng_buf[LLAMA_MAX_RNG_STATE];
|
|
|
|
memset(&rng_buf[0], 0, LLAMA_MAX_RNG_STATE);
|
|
memcpy(&rng_buf[0], rng_ss.str().data(), rng_ss.str().size());
|
|
|
|
data_ctx->write(&rng_size, sizeof(rng_size));
|
|
data_ctx->write(&rng_buf[0], LLAMA_MAX_RNG_STATE);
|
|
}
|
|
|
|
// copy logits
|
|
{
|
|
const size_t logits_cap = ctx->logits.capacity();
|
|
const size_t logits_size = ctx->logits.size();
|
|
|
|
data_ctx->write(&logits_cap, sizeof(logits_cap));
|
|
data_ctx->write(&logits_size, sizeof(logits_size));
|
|
|
|
if (logits_size) {
|
|
data_ctx->write(ctx->logits.data(), logits_size * sizeof(float));
|
|
}
|
|
|
|
// If there is a gap between the size and the capacity, write padding
|
|
size_t padding_size = (logits_cap - logits_size) * sizeof(float);
|
|
if (padding_size > 0) {
|
|
std::vector<uint8_t> padding(padding_size, 0); // Create a buffer filled with zeros
|
|
data_ctx->write(padding.data(), padding_size);
|
|
}
|
|
}
|
|
|
|
// copy embeddings
|
|
{
|
|
const size_t embedding_size = ctx->embedding.size();
|
|
|
|
data_ctx->write(&embedding_size, sizeof(embedding_size));
|
|
|
|
if (embedding_size) {
|
|
data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
|
|
}
|
|
}
|
|
|
|
// copy kv cache
|
|
{
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_embd = hparams.n_embd_gqa();
|
|
const int n_ctx = hparams.n_ctx;
|
|
|
|
const size_t kv_size = kv_self.buf.size;
|
|
const int kv_ntok = llama_get_kv_cache_token_count(ctx);
|
|
|
|
data_ctx->write(&kv_size, sizeof(kv_size));
|
|
data_ctx->write(&kv_ntok, sizeof(kv_ntok));
|
|
|
|
if (kv_size) {
|
|
const size_t elt_size = ggml_element_size(kv_self.k);
|
|
|
|
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
|
|
ggml_cgraph gf{};
|
|
|
|
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
|
|
std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
|
|
kout3d->data = kout3d_data.data();
|
|
|
|
ggml_tensor * vout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
|
|
std::vector<uint8_t> vout3d_data(ggml_nbytes(vout3d), 0);
|
|
vout3d->data = vout3d_data.data();
|
|
|
|
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
|
|
n_embd, kv_ntok, n_layer,
|
|
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
|
|
|
|
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
|
|
kv_ntok, n_embd, n_layer,
|
|
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
|
|
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
|
|
|
|
ggml_free(cpy_ctx);
|
|
|
|
// our data is now in the kout3d_data and vout3d_data buffers
|
|
// write them to file
|
|
data_ctx->write(kout3d_data.data(), kout3d_data.size());
|
|
data_ctx->write(vout3d_data.data(), vout3d_data.size());
|
|
}
|
|
}
|
|
}
|
|
|
|
size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
|
|
llama_data_buffer_context data_ctx(dst);
|
|
llama_copy_state_data_internal(ctx, &data_ctx);
|
|
|
|
return data_ctx.get_size_written();
|
|
}
|
|
|
|
// Sets the state reading from the specified source address
|
|
size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
|
|
uint8_t * inp = src;
|
|
|
|
// set rng
|
|
{
|
|
size_t rng_size;
|
|
char rng_buf[LLAMA_MAX_RNG_STATE];
|
|
|
|
memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
|
|
memcpy(&rng_buf[0], inp, LLAMA_MAX_RNG_STATE); inp += LLAMA_MAX_RNG_STATE;
|
|
|
|
std::stringstream rng_ss;
|
|
rng_ss.str(std::string(&rng_buf[0], rng_size));
|
|
rng_ss >> ctx->rng;
|
|
|
|
LLAMA_ASSERT(rng_ss.fail() == false);
|
|
}
|
|
|
|
// set logits
|
|
{
|
|
size_t logits_cap;
|
|
size_t logits_size;
|
|
|
|
memcpy(&logits_cap, inp, sizeof(logits_cap)); inp += sizeof(logits_cap);
|
|
memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
|
|
|
|
LLAMA_ASSERT(ctx->logits.capacity() == logits_cap);
|
|
|
|
if (logits_size) {
|
|
ctx->logits.resize(logits_size);
|
|
memcpy(ctx->logits.data(), inp, logits_size * sizeof(float));
|
|
}
|
|
|
|
inp += logits_cap * sizeof(float);
|
|
}
|
|
|
|
// set embeddings
|
|
{
|
|
size_t embedding_size;
|
|
|
|
memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
|
|
|
|
LLAMA_ASSERT(ctx->embedding.capacity() == embedding_size);
|
|
|
|
if (embedding_size) {
|
|
memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
|
|
inp += embedding_size * sizeof(float);
|
|
}
|
|
}
|
|
|
|
// set kv cache
|
|
{
|
|
const auto & kv_self = ctx->kv_self;
|
|
const auto & hparams = ctx->model.hparams;
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_embd = hparams.n_embd_gqa();
|
|
const int n_ctx = hparams.n_ctx;
|
|
|
|
size_t kv_size;
|
|
int kv_ntok;
|
|
|
|
memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
|
|
memcpy(&kv_ntok, inp, sizeof(kv_ntok)); inp += sizeof(kv_ntok);
|
|
|
|
if (kv_size) {
|
|
LLAMA_ASSERT(kv_self.buf.size == kv_size);
|
|
|
|
const size_t elt_size = ggml_element_size(kv_self.k);
|
|
|
|
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
|
|
ggml_cgraph gf{};
|
|
|
|
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_ntok, n_layer);
|
|
kin3d->data = (void *) inp;
|
|
inp += ggml_nbytes(kin3d);
|
|
|
|
ggml_tensor * vin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.v->type, kv_ntok, n_embd, n_layer);
|
|
vin3d->data = (void *) inp;
|
|
inp += ggml_nbytes(vin3d);
|
|
|
|
ggml_tensor * k3d = ggml_view_3d(cpy_ctx, kv_self.k,
|
|
n_embd, kv_ntok, n_layer,
|
|
elt_size*n_embd, elt_size*n_embd*n_ctx, 0);
|
|
|
|
ggml_tensor * v3d = ggml_view_3d(cpy_ctx, kv_self.v,
|
|
kv_ntok, n_embd, n_layer,
|
|
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
|
|
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
|
|
|
|
ggml_free(cpy_ctx);
|
|
}
|
|
|
|
ctx->kv_self.n = kv_ntok;
|
|
}
|
|
|
|
const size_t nread = inp - src;
|
|
const size_t max_size = llama_get_state_size(ctx);
|
|
|
|
LLAMA_ASSERT(nread <= max_size);
|
|
|
|
return nread;
|
|
}
|
|
|
|
static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
llama_file file(path_session, "rb");
|
|
|
|
// sanity checks
|
|
{
|
|
const uint32_t magic = file.read_u32();
|
|
const uint32_t version = file.read_u32();
|
|
|
|
if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
|
|
LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
|
|
return false;
|
|
}
|
|
|
|
llama_hparams session_hparams;
|
|
file.read_raw(&session_hparams, sizeof(llama_hparams));
|
|
|
|
if (session_hparams != ctx->model.hparams) {
|
|
LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// load the prompt
|
|
{
|
|
const uint32_t n_token_count = file.read_u32();
|
|
|
|
if (n_token_count > n_token_capacity) {
|
|
LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
|
|
return false;
|
|
}
|
|
|
|
file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
|
|
*n_token_count_out = n_token_count;
|
|
}
|
|
|
|
// restore the context state
|
|
{
|
|
const size_t n_state_size_cur = file.size - file.tell();
|
|
const size_t n_state_size_max = llama_get_state_size(ctx);
|
|
|
|
if (n_state_size_cur > n_state_size_max) {
|
|
LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
|
|
return false;
|
|
}
|
|
|
|
std::vector<uint8_t> state_data(n_state_size_max);
|
|
file.read_raw(state_data.data(), n_state_size_cur);
|
|
|
|
llama_set_state_data(ctx, state_data.data());
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
|
|
try {
|
|
return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
|
|
} catch (const std::exception & err) {
|
|
LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
|
|
return false;
|
|
}
|
|
}
|
|
|
|
bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
|
|
llama_file file(path_session, "wb");
|
|
|
|
file.write_u32(LLAMA_SESSION_MAGIC);
|
|
file.write_u32(LLAMA_SESSION_VERSION);
|
|
|
|
file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
|
|
|
|
// save the prompt
|
|
file.write_u32((uint32_t) n_token_count);
|
|
file.write_raw(tokens, sizeof(llama_token) * n_token_count);
|
|
|
|
// save the context state using stream saving
|
|
llama_data_file_context data_ctx(&file);
|
|
llama_copy_state_data_internal(ctx, &data_ctx);
|
|
|
|
return true;
|
|
}
|
|
|
|
int llama_eval(
|
|
struct llama_context * ctx,
|
|
const llama_token * tokens,
|
|
int n_tokens,
|
|
int n_past,
|
|
int n_threads) {
|
|
if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) {
|
|
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// get a more accurate load time, upon first eval
|
|
// TODO: fix this
|
|
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_eval_embd(
|
|
struct llama_context * ctx,
|
|
const float * embd,
|
|
int n_tokens,
|
|
int n_past,
|
|
int n_threads) {
|
|
if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) {
|
|
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
// get a more accurate load time, upon first eval
|
|
// TODO: fix this
|
|
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_eval_export(struct llama_context * ctx, const char * fname) {
|
|
const int n_batch = 1;
|
|
const int n_ctx = 512 - n_batch;
|
|
|
|
const std::vector<llama_token> tmp(n_batch, llama_token_bos());
|
|
|
|
if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) {
|
|
LLAMA_LOG_ERROR("%s: failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
int llama_tokenize_with_model(
|
|
const struct llama_model * model,
|
|
const char * text,
|
|
llama_token * tokens,
|
|
int n_max_tokens,
|
|
bool add_bos) {
|
|
auto res = llama_tokenize(model->vocab, text, add_bos);
|
|
|
|
if (n_max_tokens < (int) res.size()) {
|
|
LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
|
|
return -((int) res.size());
|
|
}
|
|
|
|
for (size_t i = 0; i < res.size(); i++) {
|
|
tokens[i] = res[i];
|
|
}
|
|
|
|
return res.size();
|
|
}
|
|
|
|
int llama_tokenize(
|
|
struct llama_context * ctx,
|
|
const char * text,
|
|
llama_token * tokens,
|
|
int n_max_tokens,
|
|
bool add_bos) {
|
|
return llama_tokenize_with_model(&ctx->model, text, tokens, n_max_tokens, add_bos);
|
|
}
|
|
|
|
int llama_n_vocab_from_model(const struct llama_model * model) {
|
|
return model->vocab.id_to_token.size();
|
|
}
|
|
|
|
int llama_n_ctx_from_model(const struct llama_model * model) {
|
|
return model->hparams.n_ctx;
|
|
}
|
|
|
|
int llama_n_embd_from_model(const struct llama_model * model) {
|
|
return model->hparams.n_embd;
|
|
}
|
|
|
|
int llama_n_vocab(const struct llama_context * ctx) {
|
|
return ctx->model.vocab.id_to_token.size();
|
|
}
|
|
|
|
int llama_n_ctx(const struct llama_context * ctx) {
|
|
return ctx->model.hparams.n_ctx;
|
|
}
|
|
|
|
int llama_n_embd(const struct llama_context * ctx) {
|
|
return ctx->model.hparams.n_embd;
|
|
}
|
|
|
|
int llama_get_vocab_from_model(
|
|
const struct llama_model * model,
|
|
const char * * strings,
|
|
float * scores,
|
|
int capacity) {
|
|
int n = std::min(capacity, (int) model->vocab.id_to_token.size());
|
|
for (int i = 0; i<n; ++i) {
|
|
strings[i] = model->vocab.id_to_token[i].tok.c_str();
|
|
scores[i] = model->vocab.id_to_token[i].score;
|
|
}
|
|
return n;
|
|
}
|
|
|
|
int llama_get_vocab(
|
|
const struct llama_context * ctx,
|
|
const char * * strings,
|
|
float * scores,
|
|
int capacity) {
|
|
return llama_get_vocab_from_model(&ctx->model, strings, scores, capacity);
|
|
}
|
|
|
|
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_with_model(const struct llama_model * model, llama_token token) {
|
|
if (token >= llama_n_vocab_from_model(model)) {
|
|
return nullptr;
|
|
}
|
|
|
|
return model->vocab.id_to_token[token].tok.c_str();
|
|
}
|
|
|
|
const char * llama_token_to_str(const struct llama_context * ctx, llama_token token) {
|
|
return llama_token_to_str_with_model(&ctx->model, token);
|
|
}
|
|
|
|
llama_token llama_token_bos() {
|
|
return 1;
|
|
}
|
|
|
|
llama_token llama_token_eos() {
|
|
return 2;
|
|
}
|
|
|
|
llama_token llama_token_nl() {
|
|
return 13;
|
|
}
|
|
|
|
struct llama_timings llama_get_timings(struct llama_context * ctx) {
|
|
struct llama_timings result = {
|
|
/*.t_start_ms =*/ 1e-3 * ctx->t_start_us,
|
|
/*.t_end_ms =*/ 1.00 * ggml_time_ms(),
|
|
/*.t_load_ms =*/ 1e-3 * ctx->t_load_us,
|
|
/*.t_sample_ms =*/ 1e-3 * ctx->t_sample_us,
|
|
/*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
|
|
/*.t_eval_ms =*/ 1e-3 * ctx->t_eval_us,
|
|
|
|
/*.n_sample =*/ std::max(1, ctx->n_sample),
|
|
/*.n_p_eval =*/ std::max(1, ctx->n_p_eval),
|
|
/*.n_eval =*/ std::max(1, ctx->n_eval),
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
void llama_print_timings(struct llama_context * ctx) {
|
|
const llama_timings timings = llama_get_timings(ctx);
|
|
|
|
LLAMA_LOG_INFO("\n");
|
|
LLAMA_LOG_INFO("%s: load time = %8.2f ms\n", __func__, timings.t_load_ms);
|
|
LLAMA_LOG_INFO("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
|
|
__func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
|
|
LLAMA_LOG_INFO("%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
|
|
__func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
|
|
LLAMA_LOG_INFO("%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
|
|
__func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
|
|
LLAMA_LOG_INFO("%s: total time = %8.2f ms\n", __func__, (timings.t_end_ms - timings.t_start_ms));
|
|
}
|
|
|
|
void llama_reset_timings(struct llama_context * ctx) {
|
|
ctx->t_start_us = ggml_time_us();
|
|
ctx->t_sample_us = ctx->n_sample = 0;
|
|
ctx->t_eval_us = ctx->n_eval = 0;
|
|
ctx->t_p_eval_us = ctx->n_p_eval = 0;
|
|
}
|
|
|
|
const char * llama_print_system_info(void) {
|
|
static std::string s;
|
|
|
|
s = "";
|
|
s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
|
|
s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
|
|
s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
|
|
s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
|
|
s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
|
|
s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
|
|
s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
|
|
s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
|
|
s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
|
|
s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
|
|
s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
|
|
s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
|
|
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
|
|
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
|
|
|
|
return s.c_str();
|
|
}
|
|
|
|
// For internal test use
|
|
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx) {
|
|
return ctx->model.tensors_by_name;
|
|
}
|
|
|
|
|
|
void llama_log_set(llama_log_callback log_callback, void * user_data) {
|
|
g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
|
|
g_state.log_callback_user_data = user_data;
|
|
}
|
|
|
|
#if defined(_MSC_VER) && !defined(vsnprintf)
|
|
#define vsnprintf _vsnprintf
|
|
#endif
|
|
|
|
static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) {
|
|
va_list args_copy;
|
|
va_copy(args_copy, args);
|
|
char buffer[128];
|
|
int len = vsnprintf(buffer, 128, format, args);
|
|
if (len < 128) {
|
|
g_state.log_callback(level, buffer, g_state.log_callback_user_data);
|
|
} else {
|
|
char* buffer2 = new char[len+1];
|
|
vsnprintf(buffer2, len+1, format, args_copy);
|
|
buffer2[len] = 0;
|
|
g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
|
|
delete[] buffer2;
|
|
}
|
|
va_end(args_copy);
|
|
}
|
|
|
|
static void llama_log_internal(llama_log_level level, const char * format, ...) {
|
|
va_list args;
|
|
va_start(args, format);
|
|
llama_log_internal_v(level, format, args);
|
|
va_end(args);
|
|
}
|
|
|
|
static void llama_log_callback_default(llama_log_level level, const char * text, void * user_data) {
|
|
(void) level;
|
|
(void) user_data;
|
|
fputs(text, stderr);
|
|
fflush(stderr);
|
|
}
|