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Merge pull request #65 from jmorganca/bindings

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call llama.cpp directly from go
This commit is contained in:
Michael Yang 2023-07-11 12:01:03 -07:00 committed by GitHub
parent abaf7d3bda 442dec1c6f
commit 62620914e9
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GPG key ID: 4AEE18F83AFDEB23
29 changed files with 35556 additions and 1296 deletions
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3
.gitignore vendored
View file

@ -3,8 +3,5 @@
.env
.venv
*.spec
build
dist
__pycache__
ollama
ggml-metal.metal

19
Makefile
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@ -1,19 +0,0 @@
default: ollama
.PHONY: llama
llama:
cmake -S llama -B llama/build -DLLAMA_METAL=on
cmake --build llama/build
.PHONY: ollama
ollama: llama
go build .
.PHONY: app
app: ollama
npm install --prefix app
npm run --prefix app make:sign
clean:
go clean
rm -rf llama/build

2
README.md
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@ -75,7 +75,7 @@ ollama run ~/Downloads/vicuna-7b-v1.3.ggmlv3.q4_1.bin
## Building
```
make
go build .
```
To run it start the server:

155
api/types.go
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@ -1,5 +1,7 @@
package api
import "runtime"
type PullRequest struct {
Model string `json:"model"`
}
@ -14,93 +16,76 @@ type GenerateRequest struct {
Model string `json:"model"`
Prompt string `json:"prompt"`
ModelOptions *ModelOptions `json:"model_opts,omitempty"`
PredictOptions *PredictOptions `json:"predict_opts,omitempty"`
}
type ModelOptions struct {
ContextSize int `json:"context_size,omitempty"`
Seed int `json:"seed,omitempty"`
NBatch int `json:"n_batch,omitempty"`
F16Memory bool `json:"memory_f16,omitempty"`
MLock bool `json:"mlock,omitempty"`
MMap bool `json:"mmap,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
Embeddings bool `json:"embeddings,omitempty"`
NUMA bool `json:"numa,omitempty"`
NGPULayers int `json:"gpu_layers,omitempty"`
MainGPU string `json:"main_gpu,omitempty"`
TensorSplit string `json:"tensor_split,omitempty"`
}
type PredictOptions struct {
Seed int `json:"seed,omitempty"`
Threads int `json:"threads,omitempty"`
Tokens int `json:"tokens,omitempty"`
TopK int `json:"top_k,omitempty"`
Repeat int `json:"repeat,omitempty"`
Batch int `json:"batch,omitempty"`
NKeep int `json:"nkeep,omitempty"`
TopP float64 `json:"top_p,omitempty"`
Temperature float64 `json:"temp,omitempty"`
Penalty float64 `json:"penalty,omitempty"`
F16KV bool
DebugMode bool
StopPrompts []string
IgnoreEOS bool `json:"ignore_eos,omitempty"`
TailFreeSamplingZ float64 `json:"tfs_z,omitempty"`
TypicalP float64 `json:"typical_p,omitempty"`
FrequencyPenalty float64 `json:"freq_penalty,omitempty"`
PresencePenalty float64 `json:"pres_penalty,omitempty"`
Mirostat int `json:"mirostat,omitempty"`
MirostatETA float64 `json:"mirostat_lr,omitempty"`
MirostatTAU float64 `json:"mirostat_ent,omitempty"`
PenalizeNL bool `json:"penalize_nl,omitempty"`
LogitBias string `json:"logit_bias,omitempty"`
PathPromptCache string
MLock bool `json:"mlock,omitempty"`
MMap bool `json:"mmap,omitempty"`
PromptCacheAll bool
PromptCacheRO bool
MainGPU string
TensorSplit string
}
var DefaultModelOptions ModelOptions = ModelOptions{
ContextSize: 512,
Seed: 0,
F16Memory: true,
MLock: false,
Embeddings: true,
MMap: true,
LowVRAM: false,
}
var DefaultPredictOptions PredictOptions = PredictOptions{
Seed: -1,
Threads: -1,
Tokens: 512,
Penalty: 1.1,
Repeat: 64,
Batch: 512,
NKeep: 64,
TopK: 90,
TopP: 0.86,
TailFreeSamplingZ: 1.0,
TypicalP: 1.0,
Temperature: 0.8,
FrequencyPenalty: 0.0,
PresencePenalty: 0.0,
Mirostat: 0,
MirostatTAU: 5.0,
MirostatETA: 0.1,
MMap: true,
StopPrompts: []string{"llama"},
Options `json:"options"`
}
type GenerateResponse struct {
Response string `json:"response"`
}
type Options struct {
Seed int `json:"seed,omitempty"`
// Backend options
UseNUMA bool `json:"numa,omitempty"`
// Model options
NumCtx int `json:"num_ctx,omitempty"`
NumBatch int `json:"num_batch,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"`
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
EmbeddingOnly bool `json:"embedding_only,omitempty"`
// Predict options
RepeatLastN int `json:"repeat_last_n,omitempty"`
RepeatPenalty float32 `json:"repeat_penalty,omitempty"`
FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
PresencePenalty float32 `json:"presence_penalty,omitempty"`
Temperature float32 `json:"temperature,omitempty"`
TopK int `json:"top_k,omitempty"`
TopP float32 `json:"top_p,omitempty"`
TFSZ float32 `json:"tfs_z,omitempty"`
TypicalP float32 `json:"typical_p,omitempty"`
Mirostat int `json:"mirostat,omitempty"`
MirostatTau float32 `json:"mirostat_tau,omitempty"`
MirostatEta float32 `json:"mirostat_eta,omitempty"`
NumThread int `json:"num_thread,omitempty"`
}
func DefaultOptions() Options {
return Options{
Seed: -1,
UseNUMA: false,
NumCtx: 512,
NumBatch: 512,
NumGPU: 1,
LowVRAM: false,
F16KV: true,
UseMMap: true,
UseMLock: false,
RepeatLastN: 512,
RepeatPenalty: 1.1,
FrequencyPenalty: 0.0,
PresencePenalty: 0.0,
Temperature: 0.8,
TopK: 40,
TopP: 0.9,
TFSZ: 1.0,
TypicalP: 1.0,
Mirostat: 0,
MirostatTau: 5.0,
MirostatEta: 0.1,
NumThread: runtime.NumCPU(),
}
}

1
go.mod
View file

@ -39,6 +39,7 @@ require (
golang.org/x/arch v0.3.0 // indirect
golang.org/x/crypto v0.10.0 // indirect
golang.org/x/net v0.10.0 // indirect
golang.org/x/sync v0.3.0
golang.org/x/sys v0.10.0 // indirect
golang.org/x/term v0.10.0
golang.org/x/text v0.10.0 // indirect

2
go.sum
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@ -99,6 +99,8 @@ golang.org/x/net v0.10.0/go.mod h1:0qNGK6F8kojg2nk9dLZ2mShWaEBan6FAoqfSigmmuDg=
golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20220722155255-886fb9371eb4/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.1.0/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.3.0 h1:ftCYgMx6zT/asHUrPw8BLLscYtGznsLAnjq5RH9P66E=
golang.org/x/sync v0.3.0/go.mod h1:FU7BRWz2tNW+3quACPkgCx/L+uEAv1htQ0V83Z9Rj+Y=
golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20201119102817-f84b799fce68/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
golang.org/x/sys v0.0.0-20210615035016-665e8c7367d1/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=

1
llama/.gitignore vendored
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@ -1 +0,0 @@
build

23
llama/CMakeLists.txt
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@ -1,23 +0,0 @@
cmake_minimum_required(VERSION 3.12)
project(binding)
include(FetchContent)
FetchContent_Declare(
llama_cpp
GIT_REPOSITORY https://github.com/ggerganov/llama.cpp.git
GIT_TAG 55dbb91
)
FetchContent_MakeAvailable(llama_cpp)
add_library(binding ${CMAKE_CURRENT_SOURCE_DIR}/binding/binding.cpp ${llama_cpp_SOURCE_DIR}/examples/common.cpp)
target_include_directories(binding PRIVATE ${llama_cpp_SOURCE_DIR}/examples)
target_link_libraries(binding llama ggml_static)
if (LLAMA_METAL)
configure_file(${llama_cpp_SOURCE_DIR}/ggml-metal.metal ${CMAKE_CURRENT_BINARY_DIR}/../../ggml-metal.metal COPYONLY)
endif()
add_custom_target(copy_libllama ALL COMMAND ${CMAKE_COMMAND} -E copy_if_different $<TARGET_FILE:llama> ${CMAKE_CURRENT_BINARY_DIR})
add_custom_target(copy_libggml_static ALL COMMAND ${CMAKE_COMMAND} -E copy_if_different $<TARGET_FILE:ggml_static> ${CMAKE_CURRENT_BINARY_DIR})

705
llama/binding/binding.cpp
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@ -1,705 +0,0 @@
// MIT License
// Copyright (c) 2023 go-skynet authors
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
#include "common.h"
#include "llama.h"
#include "binding.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
#include <regex>
#include <sstream>
#include <string>
#include <vector>
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <signal.h>
#include <windows.h>
#endif
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) || \
defined(_WIN32)
void sigint_handler(int signo) {
if (signo == SIGINT) {
_exit(130);
}
}
#endif
int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings) {
gpt_params *params_p = (gpt_params *)params_ptr;
llama_context *ctx = (llama_context *)state_pr;
gpt_params params = *params_p;
if (params.seed <= 0) {
params.seed = time(NULL);
}
std::mt19937 rng(params.seed);
llama_init_backend(params.numa);
int n_past = 0;
// Add a space in front of the first character to match OG llama tokenizer
// behavior
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
// determine newline token
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
if (embd_inp.size() > 0) {
if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past,
params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
}
const int n_embd = llama_n_embd(ctx);
const auto embeddings = llama_get_embeddings(ctx);
for (int i = 0; i < n_embd; i++) {
res_embeddings[i] = embeddings[i];
}
return 0;
}
int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
int tokenSize, float *res_embeddings) {
gpt_params *params_p = (gpt_params *)params_ptr;
llama_context *ctx = (llama_context *)state_pr;
gpt_params params = *params_p;
for (int i = 0; i < tokenSize; i++) {
auto token_str = llama_token_to_str(ctx, tokens[i]);
if (token_str == nullptr) {
continue;
}
std::vector<std::string> my_vector;
std::string str_token(token_str); // create a new std::string from the char*
params_p->prompt += str_token;
}
return get_embeddings(params_ptr, state_pr, res_embeddings);
}
int eval(void *params_ptr, void *state_pr, char *text) {
gpt_params *params_p = (gpt_params *)params_ptr;
llama_context *ctx = (llama_context *)state_pr;
auto n_past = 0;
auto last_n_tokens_data =
std::vector<llama_token>(params_p->repeat_last_n, 0);
auto tokens = std::vector<llama_token>(params_p->n_ctx);
auto n_prompt_tokens =
llama_tokenize(ctx, text, tokens.data(), tokens.size(), true);
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
return 1;
}
// evaluate prompt
return llama_eval(ctx, tokens.data(), n_prompt_tokens, n_past,
params_p->n_threads);
}
int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug) {
gpt_params *params_p = (gpt_params *)params_ptr;
llama_context *ctx = (llama_context *)state_pr;
gpt_params params = *params_p;
const int n_ctx = llama_n_ctx(ctx);
if (params.seed <= 0) {
params.seed = time(NULL);
}
std::mt19937 rng(params.seed);
std::string path_session = params.path_prompt_cache;
std::vector<llama_token> session_tokens;
if (!path_session.empty()) {
if (debug) {
fprintf(stderr, "%s: attempting to load saved session from '%s'\n",
__func__, path_session.c_str());
}
// fopen to check for existing session
FILE *fp = std::fopen(path_session.c_str(), "rb");
if (fp != NULL) {
std::fclose(fp);
session_tokens.resize(n_ctx);
size_t n_token_count_out = 0;
if (!llama_load_session_file(
ctx, path_session.c_str(), session_tokens.data(),
session_tokens.capacity(), &n_token_count_out)) {
fprintf(stderr, "%s: error: failed to load session file '%s'\n",
__func__, path_session.c_str());
return 1;
}
session_tokens.resize(n_token_count_out);
llama_set_rng_seed(ctx, params.seed);
if (debug) {
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
__func__, (int)session_tokens.size());
}
} else {
if (debug) {
fprintf(stderr, "%s: session file does not exist, will create\n",
__func__);
}
}
}
std::vector<llama_token> embd_inp;
if (!params.prompt.empty() || session_tokens.empty()) {
// Add a space in front of the first character to match OG llama tokenizer
// behavior
params.prompt.insert(0, 1, ' ');
embd_inp = ::llama_tokenize(ctx, params.prompt, true);
} else {
embd_inp = session_tokens;
}
// debug message about similarity of saved session, if applicable
size_t n_matching_session_tokens = 0;
if (session_tokens.size()) {
for (llama_token id : session_tokens) {
if (n_matching_session_tokens >= embd_inp.size() ||
id != embd_inp[n_matching_session_tokens]) {
break;
}
n_matching_session_tokens++;
}
if (debug) {
if (params.prompt.empty() &&
n_matching_session_tokens == embd_inp.size()) {
fprintf(stderr, "%s: using full prompt from session file\n", __func__);
} else if (n_matching_session_tokens >= embd_inp.size()) {
fprintf(stderr, "%s: session file has exact match for prompt!\n",
__func__);
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
fprintf(stderr,
"%s: warning: session file has low similarity to prompt (%zu / "
"%zu tokens); will mostly be reevaluated\n",
__func__, n_matching_session_tokens, embd_inp.size());
} else {
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
__func__, n_matching_session_tokens, embd_inp.size());
}
}
}
// if we will use the cache for the full prompt without reaching the end of
// the cache, force reevaluation of the last token token to recalculate the
// cached logits
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
session_tokens.size() > embd_inp.size()) {
session_tokens.resize(embd_inp.size() - 1);
}
// number of tokens to keep when resetting context
if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) {
params.n_keep = (int)embd_inp.size();
}
// determine newline token
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
// TODO: replace with ring-buffer
std::vector<llama_token> last_n_tokens(n_ctx);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
bool need_to_save_session =
!path_session.empty() && n_matching_session_tokens < embd_inp.size();
int n_past = 0;
int n_remain = params.n_predict;
int n_consumed = 0;
int n_session_consumed = 0;
std::vector<llama_token> embd;
std::string res = "";
// do one empty run to warm up the model
{
const std::vector<llama_token> tmp = {
llama_token_bos(),
};
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
llama_reset_timings(ctx);
}
while (n_remain != 0) {
// predict
if (embd.size() > 0) {
// infinite text generation via context swapping
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the
// logits in batches
if (n_past + (int)embd.size() > n_ctx) {
const int n_left = n_past - params.n_keep;
// always keep the first token - BOS
n_past = std::max(1, params.n_keep);
// insert n_left/2 tokens at the start of embd from last_n_tokens
embd.insert(embd.begin(),
last_n_tokens.begin() + n_ctx - n_left / 2 - embd.size(),
last_n_tokens.end() - embd.size());
// stop saving session if we run out of context
path_session.clear();
// printf("\n---\n");
// printf("resetting: '");
// for (int i = 0; i < (int) embd.size(); i++) {
// printf("%s", llama_token_to_str(ctx, embd[i]));
// }
// printf("'\n");
// printf("\n---\n");
}
// try to reuse a matching prefix from the loaded session instead of
// re-eval (via n_past)
if (n_session_consumed < (int)session_tokens.size()) {
size_t i = 0;
for (; i < embd.size(); i++) {
if (embd[i] != session_tokens[n_session_consumed]) {
session_tokens.resize(n_session_consumed);
break;
}
n_past++;
n_session_consumed++;
if (n_session_consumed >= (int)session_tokens.size()) {
++i;
break;
}
}
if (i > 0) {
embd.erase(embd.begin(), embd.begin() + i);
}
}
// evaluate tokens in batches
// embd is typically prepared beforehand to fit within a batch, but not
// always
for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
int n_eval = (int)embd.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
n_past += n_eval;
}
if (embd.size() > 0 && !path_session.empty()) {
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
n_session_consumed = session_tokens.size();
}
}
embd.clear();
if ((int)embd_inp.size() <= n_consumed) {
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k =
params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t repeat_last_n =
params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
const float repeat_penalty = params.repeat_penalty;
const float alpha_presence = params.presence_penalty;
const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
const bool penalize_nl = params.penalize_nl;
// optionally save the session on first sample (for faster prompt loading
// next time)
if (!path_session.empty() && need_to_save_session &&
!params.prompt_cache_ro) {
need_to_save_session = false;
llama_save_session_file(ctx, path_session.c_str(),
session_tokens.data(), session_tokens.size());
}
llama_token id = 0;
{
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
it++) {
logits[it->first] += it->second;
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(
llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = {candidates.data(),
candidates.size(), false};
// Apply penalties
float nl_logit = logits[llama_token_nl()];
auto last_n_repeat =
std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
llama_sample_repetition_penalty(
ctx, &candidates_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, repeat_penalty);
llama_sample_frequency_and_presence_penalties(
ctx, &candidates_p,
last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
last_n_repeat, alpha_frequency, alpha_presence);
if (!penalize_nl) {
logits[llama_token_nl()] = nl_logit;
}
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau,
mirostat_eta, mirostat_m,
&mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(
ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}
// printf("`%d`", candidates_p.size);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
}
// add it to the context
embd.push_back(id);
// decrement remaining sampling budget
--n_remain;
// call the token callback, no need to check if one is actually
// registered, that will be handled on the Go side.
auto token_str = llama_token_to_str(ctx, id);
if (!tokenCallback(state_pr, (char *)token_str)) {
break;
}
} else {
// some user input remains from prompt or interaction, forward it to
// processing
while ((int)embd_inp.size() > n_consumed) {
embd.push_back(embd_inp[n_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[n_consumed]);
++n_consumed;
if ((int)embd.size() >= params.n_batch) {
break;
}
}
}
for (auto id : embd) {
res += llama_token_to_str(ctx, id);
}
// check for stop prompt
if (params.antiprompt.size()) {
std::string last_output;
for (auto id : last_n_tokens) {
last_output += llama_token_to_str(ctx, id);
}
// Check if each of the reverse prompts appears at the end of the output.
for (std::string &antiprompt : params.antiprompt) {
// size_t extra_padding = params.interactive ? 0 : 2;
size_t extra_padding = 2;
size_t search_start_pos =
last_output.length() >
static_cast<size_t>(antiprompt.length() + extra_padding)
? last_output.length() -
static_cast<size_t>(antiprompt.length() + extra_padding)
: 0;
if (last_output.find(antiprompt.c_str(), search_start_pos) !=
std::string::npos) {
goto end;
}
}
}
// end of text token
if (!embd.empty() && embd.back() == llama_token_eos()) {
break;
}
}
if (!path_session.empty() && params.prompt_cache_all &&
!params.prompt_cache_ro) {
if (debug) {
fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
__func__, path_session.c_str());
}
llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(),
session_tokens.size());
}
end:
#if defined(_WIN32)
signal(SIGINT, SIG_DFL);
#endif
if (debug) {
llama_print_timings(ctx);
llama_reset_timings(ctx);
}
strcpy(result, res.c_str());
return 0;
}
void llama_binding_free_model(void *state_ptr) {
llama_context *ctx = (llama_context *)state_ptr;
llama_free(ctx);
}
void llama_free_params(void *params_ptr) {
gpt_params *params = (gpt_params *)params_ptr;
delete params;
}
int load_state(void *ctx, char *statefile, char *modes) {
llama_context *state = (llama_context *)ctx;
const llama_context *constState = static_cast<const llama_context *>(state);
const size_t state_size = llama_get_state_size(state);
uint8_t *state_mem = new uint8_t[state_size];
{
FILE *fp_read = fopen(statefile, modes);
if (state_size != llama_get_state_size(constState)) {
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
return 1;
}
const size_t ret = fread(state_mem, 1, state_size, fp_read);
if (ret != state_size) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
return 1;
}
llama_set_state_data(
state, state_mem); // could also read directly from memory mapped file
fclose(fp_read);
}
return 0;
}
void save_state(void *ctx, char *dst, char *modes) {
llama_context *state = (llama_context *)ctx;
const size_t state_size = llama_get_state_size(state);
uint8_t *state_mem = new uint8_t[state_size];
// Save state (rng, logits, embedding and kv_cache) to file
{
FILE *fp_write = fopen(dst, modes);
llama_copy_state_data(
state, state_mem); // could also copy directly to memory mapped file
fwrite(state_mem, 1, state_size, fp_write);
fclose(fp_write);
}
}
void *llama_allocate_params(
const char *prompt, int seed, int threads, int tokens, int top_k,
float top_p, float temp, float repeat_penalty, int repeat_last_n,
bool ignore_eos, bool memory_f16, int n_batch, int n_keep,
const char **antiprompt, int antiprompt_count, float tfs_z, float typical_p,
float frequency_penalty, float presence_penalty, int mirostat,
float mirostat_eta, float mirostat_tau, bool penalize_nl,
const char *logit_bias, bool mlock, bool mmap, const char *maingpu,
const char *tensorsplit) {
gpt_params *params = new gpt_params;
params->seed = seed;
params->n_threads = threads;
params->n_predict = tokens;
params->repeat_last_n = repeat_last_n;
params->top_k = top_k;
params->top_p = top_p;
params->memory_f16 = memory_f16;
params->temp = temp;
params->use_mmap = mmap;
params->use_mlock = mlock;
params->repeat_penalty = repeat_penalty;
params->n_batch = n_batch;
params->n_keep = n_keep;
if (maingpu[0] != '\0') {
params->main_gpu = std::stoi(maingpu);
}
if (tensorsplit[0] != '\0') {
std::string arg_next = tensorsplit;
// split string by , and /
const std::regex regex{R"([,/]+)"};
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
if (i < split_arg.size()) {
params->tensor_split[i] = std::stof(split_arg[i]);
} else {
params->tensor_split[i] = 0.0f;
}
}
}
if (ignore_eos) {
params->logit_bias[llama_token_eos()] = -INFINITY;
}
for (int i = 0; i < antiprompt_count; i++) {
params->antiprompt.push_back(antiprompt[i]);
}
params->tfs_z = tfs_z;
params->typical_p = typical_p;
params->presence_penalty = presence_penalty;
params->mirostat = mirostat;
params->mirostat_eta = mirostat_eta;
params->mirostat_tau = mirostat_tau;
params->penalize_nl = penalize_nl;
std::stringstream ss(logit_bias);
llama_token key;
char sign;
std::string value_str;
if (ss >> key && ss >> sign && std::getline(ss, value_str) &&
(sign == '+' || sign == '-')) {
params->logit_bias[key] =
std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
}
params->frequency_penalty = frequency_penalty;
params->prompt = prompt;
return params;
}
void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
bool mlock, bool embeddings, bool mmap, bool low_vram,
bool vocab_only, int n_gpu_layers, int n_batch,
const char *maingpu, const char *tensorsplit, bool numa) {
// load the model
auto lparams = llama_context_default_params();
lparams.n_ctx = n_ctx;
lparams.seed = n_seed;
lparams.f16_kv = memory_f16;
lparams.embedding = embeddings;
lparams.use_mlock = mlock;
lparams.n_gpu_layers = n_gpu_layers;
lparams.use_mmap = mmap;
lparams.low_vram = low_vram;
lparams.vocab_only = vocab_only;
if (maingpu[0] != '\0') {
lparams.main_gpu = std::stoi(maingpu);
}
if (tensorsplit[0] != '\0') {
std::string arg_next = tensorsplit;
// split string by , and /
const std::regex regex{R"([,/]+)"};
std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
std::vector<std::string> split_arg{it, {}};
GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);
for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) {
if (i < split_arg.size()) {
lparams.tensor_split[i] = std::stof(split_arg[i]);
} else {
lparams.tensor_split[i] = 0.0f;
}
}
}
lparams.n_batch = n_batch;
llama_init_backend(numa);
void *res = nullptr;
try {
res = llama_init_from_file(fname, lparams);
} catch (std::runtime_error &e) {
fprintf(stderr, "failed %s", e.what());
return res;
}
return res;
}

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// MIT License
// Copyright (c) 2023 go-skynet authors
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
#ifdef __cplusplus
extern "C" {
#endif
#include <stdbool.h>
extern unsigned char tokenCallback(void *, char *);
int load_state(void *ctx, char *statefile, char *modes);
int eval(void *params_ptr, void *ctx, char *text);
void save_state(void *ctx, char *dst, char *modes);
void *load_model(const char *fname, int n_ctx, int n_seed, bool memory_f16,
bool mlock, bool embeddings, bool mmap, bool low_vram,
bool vocab_only, int n_gpu, int n_batch, const char *maingpu,
const char *tensorsplit, bool numa);
int get_embeddings(void *params_ptr, void *state_pr, float *res_embeddings);
int get_token_embeddings(void *params_ptr, void *state_pr, int *tokens,
int tokenSize, float *res_embeddings);
void *llama_allocate_params(
const char *prompt, int seed, int threads, int tokens, int top_k,
float top_p, float temp, float repeat_penalty, int repeat_last_n,
bool ignore_eos, bool memory_f16, int n_batch, int n_keep,
const char **antiprompt, int antiprompt_count, float tfs_z, float typical_p,
float frequency_penalty, float presence_penalty, int mirostat,
float mirostat_eta, float mirostat_tau, bool penalize_nl,
const char *logit_bias, bool mlock, bool mmap, const char *maingpu,
const char *tensorsplit);
void llama_free_params(void *params_ptr);
void llama_binding_free_model(void *state);
int llama_predict(void *params_ptr, void *state_pr, char *result, bool debug);
#ifdef __cplusplus
}
#endif

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/**
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
#include "ggml.h"
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_CUDA_MAX_DEVICES 16
void ggml_init_cublas(void);
void ggml_cuda_set_tensor_split(const float * tensor_split);
void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);
// TODO: export these with GGML_API
void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
void ggml_cuda_free_data(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#ifdef __cplusplus
}
#endif

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/**
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
// An interface allowing to compute ggml_cgraph with Metal
//
// This is a fully functional interface that extends ggml with GPU support for Apple devices.
// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.)
//
// How it works?
//
// As long as your program can create and evaluate a ggml_cgraph on the CPU, you can use this
// interface to evaluate the same graph on the GPU. Instead of using ggml_graph_compute(), you
// use ggml_metal_graph_compute() (or ggml_vulkan_graph_compute(), etc.)
//
// You only need to make sure that all memory buffers that you used during the graph creation
// are mapped to the device memory with the ggml_metal_add_buffer() function. This mapping is
// used during the graph evaluation to determine the arguments of the compute kernels.
//
// Synchronization between device and host memory (for example for input and output tensors)
// is done with the ggml_metal_set_tensor() and ggml_metal_get_tensor() functions.
//
#pragma once
#include <stddef.h>
#include <stdbool.h>
// max memory buffers that can be mapped to the device
#define GGML_METAL_MAX_BUFFERS 16
struct ggml_tensor;
struct ggml_cgraph;
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_metal_context;
// number of command buffers to use
struct ggml_metal_context * ggml_metal_init(int n_cb);
void ggml_metal_free(struct ggml_metal_context * ctx);
// set the number of command buffers to use
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
// creates a mapping between a host memory buffer and a device memory buffer
// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
// - the mapping is used during computation to determine the arguments of the compute kernels
// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
// - max_size specifies the maximum size of a tensor and is used to create shared views such
// that it is guaranteed that the tensor will fit in at least one of the views
//
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size,
size_t max_size);
// set data from host memory into the device
void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// get data from the device into host memory
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
#ifdef __cplusplus
}
#endif

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/**
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#pragma once
#include "ggml.h"
#include <stdint.h>
#include <assert.h>
#include <stddef.h>
// Super-block size
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif
//
// Super-block quantization structures
//
// 2-bit quantization
// weight is represented as x = a * q + b
// 16 blocks of 16 elemenets each
// Effectively 2.5625 bits per weight
typedef struct {
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
uint8_t qs[QK_K/4]; // quants
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
} block_q2_K;
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
// 3-bit quantization
// weight is represented as x = a * q
// 16 blocks of 16 elemenets each
// Effectively 3.4375 bits per weight
#ifdef GGML_QKK_64
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[2];
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
#else
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[12]; // scales, quantized with 6 bits
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
#endif
// 4-bit quantization
// 16 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 4.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
#endif
// 5-bit quantization
// 16 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 5.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d; // super-block scale
int8_t scales[QK_K/16]; // 8-bit block scales
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif
// 6-bit quantization
// weight is represented as x = a * q
// 16 blocks of 16 elemenets each
// Effectively 6.5625 bits per weight
typedef struct {
uint8_t ql[QK_K/2]; // quants, lower 4 bits
uint8_t qh[QK_K/4]; // quants, upper 2 bits
int8_t scales[QK_K/16]; // scales, quantized with 8 bits
ggml_fp16_t d; // super-block scale
} block_q6_K;
static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
// This is only used for intermediate quantization and dot products
typedef struct {
float d; // delta
int8_t qs[QK_K]; // quants
int16_t bsums[QK_K/16]; // sum of quants in groups of 16
} block_q8_K;
static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
// Quantization
void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k);
void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k);
void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k);
void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k);
void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k);
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k);
void quantize_row_q2_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q3_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q4_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q5_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q6_K(const float * restrict x, void * restrict y, int k);
void quantize_row_q8_K(const float * restrict x, void * restrict y, int k);
// Dequantization
void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k);
void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k);
void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k);
void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k);
void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k);
void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k);
// Dot product
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
// Quantization with histogram collection
size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);

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/**
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
// Internal header to be included only by llama.cpp.
// Contains wrappers around OS interfaces.
#ifndef LLAMA_UTIL_H
#define LLAMA_UTIL_H
#include <cstdio>
#include <cstdint>
#include <cerrno>
#include <cstring>
#include <cstdarg>
#include <cstdlib>
#include <climits>
#include <string>
#include <vector>
#include <stdexcept>
#ifdef __has_include
#if __has_include(<unistd.h>)
#include <unistd.h>
#if defined(_POSIX_MAPPED_FILES)
#include <sys/mman.h>
#endif
#if defined(_POSIX_MEMLOCK_RANGE)
#include <sys/resource.h>
#endif
#endif
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h> // for _fseeki64
#endif
#define LLAMA_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
LLAMA_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
LLAMA_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
}
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
LLAMA_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
LLAMA_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, len, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, len, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
LPSTR buf;
size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
if (!size) {
return "FormatMessageA failed";
}
std::string ret(buf, size);
LocalFree(buf);
return ret;
}
#endif
struct llama_mmap {
void * addr;
size_t size;
llama_mmap(const llama_mmap &) = delete;
#ifdef _POSIX_MAPPED_FILES
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
size = file->size;
int fd = fileno(file->fp);
int flags = MAP_PRIVATE;
// prefetch/readahead impairs performance on NUMA systems
if (numa) { prefetch = 0; }
#ifdef __linux__
if (prefetch) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ | PROT_WRITE, flags, fd, 0);
if (addr == MAP_FAILED) {
throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
}
if (prefetch > 0) {
// Advise the kernel to preload the mapped memory
if (madvise(addr, std::min(file->size, prefetch), MADV_WILLNEED)) {
fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n",
strerror(errno));
}
}
if (numa) {
// advise the kernel not to use readahead
// (because the next page might not belong on the same node)
if (madvise(addr, file->size, MADV_RANDOM)) {
fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n",
strerror(errno));
}
}
}
~llama_mmap() {
munmap(addr, size);
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
(void) numa;
size = file->size;
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
DWORD error = GetLastError();
if (hMapping == NULL) {
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
}
addr = MapViewOfFile(hMapping, FILE_MAP_COPY, 0, 0, 0);
error = GetLastError();
CloseHandle(hMapping);
if (addr == NULL) {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
#if _WIN32_WINNT >= _WIN32_WINNT_WIN8
if (prefetch) {
// Advise the kernel to preload the mapped memory
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
#pragma message("warning: You are building for pre-Windows 8; prefetch not supported")
#endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8
}
~llama_mmap() {
if (!UnmapViewOfFile(addr)) {
fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
static constexpr bool SUPPORTED = false;
llama_mmap(struct llama_file *, bool prefetch = true, bool numa = false) {
(void) prefetch;
(void) numa;
throw std::runtime_error(std::string("mmap not supported"));
}
#endif
};
// Represents some region of memory being locked using mlock or VirtualLock;
// will automatically unlock on destruction.
struct llama_mlock {
void * addr = NULL;
size_t size = 0;
bool failed_already = false;
llama_mlock() {}
llama_mlock(const llama_mlock &) = delete;
~llama_mlock() {
if (size) {
raw_unlock(addr, size);
}
}
void init(void * ptr) {
LLAMA_ASSERT(addr == NULL && size == 0);
addr = ptr;
}
void grow_to(size_t target_size) {
LLAMA_ASSERT(addr);
if (failed_already) {
return;
}
size_t granularity = lock_granularity();
target_size = (target_size + granularity - 1) & ~(granularity - 1);
if (target_size > size) {
if (raw_lock((uint8_t *) addr + size, target_size - size)) {
size = target_size;
} else {
failed_already = true;
}
}
}
#ifdef _POSIX_MEMLOCK_RANGE
static constexpr bool SUPPORTED = true;
size_t lock_granularity() {
return (size_t) sysconf(_SC_PAGESIZE);
}
#ifdef __APPLE__
#define MLOCK_SUGGESTION \
"Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
"decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
#else
#define MLOCK_SUGGESTION \
"Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
#endif
bool raw_lock(const void * addr, size_t size) {
if (!mlock(addr, size)) {
return true;
} else {
char* errmsg = std::strerror(errno);
bool suggest = (errno == ENOMEM);
// Check if the resource limit is fine after all
struct rlimit lock_limit;
if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
suggest = false;
if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
suggest = false;
fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
return false;
}
}
#undef MLOCK_SUGGESTION
void raw_unlock(void * addr, size_t size) {
if (munlock(addr, size)) {
fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
}
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
size_t lock_granularity() {
SYSTEM_INFO si;
GetSystemInfo(&si);
return (size_t) si.dwPageSize;
}
bool raw_lock(void * ptr, size_t len) {
for (int tries = 1; ; tries++) {
if (VirtualLock(ptr, len)) {
return true;
}
if (tries == 2) {
fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
len, size, llama_format_win_err(GetLastError()).c_str());
return false;
}
// It failed but this was only the first try; increase the working
// set size and try again.
SIZE_T min_ws_size, max_ws_size;
if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
return false;
}
// Per MSDN: "The maximum number of pages that a process can lock
// is equal to the number of pages in its minimum working set minus
// a small overhead."
// Hopefully a megabyte is enough overhead:
size_t increment = len + 1048576;
// The minimum must be <= the maximum, so we need to increase both:
min_ws_size += increment;
max_ws_size += increment;
if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
return false;
}
}
}
void raw_unlock(void * ptr, size_t len) {
if (!VirtualUnlock(ptr, len)) {
fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
static constexpr bool SUPPORTED = false;
size_t lock_granularity() {
return (size_t) 65536;
}
bool raw_lock(const void * addr, size_t len) {
fprintf(stderr, "warning: mlock not supported on this system\n");
return false;
}
void raw_unlock(const void * addr, size_t len) {}
#endif
};
// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
struct llama_buffer {
uint8_t * addr = NULL;
size_t size = 0;
llama_buffer() = default;
void resize(size_t len) {
#ifdef GGML_USE_METAL
free(addr);
int result = posix_memalign((void **) &addr, getpagesize(), len);
if (result == 0) {
memset(addr, 0, len);
}
else {
addr = NULL;
}
#else
delete[] addr;
addr = new uint8_t[len];
#endif
size = len;
}
~llama_buffer() {
#ifdef GGML_USE_METAL
free(addr);
#else
delete[] addr;
#endif
addr = NULL;
}
// disable copy and move
llama_buffer(const llama_buffer&) = delete;
llama_buffer(llama_buffer&&) = delete;
llama_buffer& operator=(const llama_buffer&) = delete;
llama_buffer& operator=(llama_buffer&&) = delete;
};
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
struct llama_ctx_buffer {
uint8_t * addr = NULL;
bool is_cuda;
size_t size = 0;
llama_ctx_buffer() = default;
void resize(size_t size) {
free();
addr = (uint8_t *) ggml_cuda_host_malloc(size);
if (addr) {
is_cuda = true;
}
else {
// fall back to pageable memory
addr = new uint8_t[size];
is_cuda = false;
}
this->size = size;
}
void free() {
if (addr) {
if (is_cuda) {
ggml_cuda_host_free(addr);
}
else {
delete[] addr;
}
}
addr = NULL;
}
~llama_ctx_buffer() {
free();
}
// disable copy and move
llama_ctx_buffer(const llama_ctx_buffer&) = delete;
llama_ctx_buffer(llama_ctx_buffer&&) = delete;
llama_ctx_buffer& operator=(const llama_ctx_buffer&) = delete;
llama_ctx_buffer& operator=(llama_ctx_buffer&&) = delete;
};
#else
typedef llama_buffer llama_ctx_buffer;
#endif
#endif

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// MIT License
// Copyright (c) 2023 go-skynet authors
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
package llama
// #cgo LDFLAGS: -Lbuild -lbinding -lllama -lm -lggml_static -lstdc++
// #cgo CXXFLAGS: -std=c++11
// #cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
// #include "binding/binding.h"
// #include <stdlib.h>
import "C"
/*
#cgo CPPFLAGS: -O3 -DNDEBUG=1
#cgo CXXFLAGS: -std=c++11
#cgo darwin CPPFLAGS: -DGGML_USE_METAL=1 -DGGML_METAL_NDEBUG=1
#cgo darwin LDFLAGS: -framework Accelerate -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
#include <stdlib.h>
#include "llama.h"
struct llama_sample_options
{
float repeat_penalty;
float frequency_penalty;
float presence_penalty;
float temperature;
int32_t top_k;
float top_p;
float tfs_z;
float typical_p;
int mirostat;
float mirostat_tau;
float mirostat_eta;
};
llama_token llama_sample(
struct llama_context *ctx,
struct llama_token_data *candidates,
size_t n_candidates,
const llama_token *last_tokens,
size_t n_last_tokens,
struct llama_sample_options *opts)
{
llama_token_data_array candidates_p = {
candidates,
n_candidates,
false,
};
llama_sample_repetition_penalty(
ctx, &candidates_p,
last_tokens, n_last_tokens,
opts->repeat_penalty);
llama_sample_frequency_and_presence_penalties(
ctx, &candidates_p,
last_tokens, n_last_tokens,
opts->frequency_penalty, opts->presence_penalty);
if (opts->temperature <= 0) {
return llama_sample_token_greedy(ctx, &candidates_p);
}
if (opts->mirostat == 1) {
int mirostat_m = 100;
float mirostat_mu = 2.0f * opts->mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, opts->temperature);
return llama_sample_token_mirostat(
ctx, &candidates_p,
opts->mirostat_tau, opts->mirostat_eta,
mirostat_m, &mirostat_mu);
} else if (opts->mirostat == 2) {
float mirostat_mu = 2.0f * opts->mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, opts->temperature);
return llama_sample_token_mirostat_v2(
ctx, &candidates_p,
opts->mirostat_tau, opts->mirostat_eta,
&mirostat_mu);
} else {
llama_sample_top_k(ctx, &candidates_p, opts->top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, opts->tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, opts->typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, opts->top_p, 1);
llama_sample_temperature(ctx, &candidates_p, opts->temperature);
return llama_sample_token(ctx, &candidates_p);
}
}
*/
import "C"
import (
"fmt"
"errors"
"io"
"os"
"strings"
"sync"
"unsafe"
"github.com/jmorganca/ollama/api"
)
type LLama struct {
ctx unsafe.Pointer
embeddings bool
contextSize int
type llama struct {
params *C.struct_llama_context_params
model *C.struct_llama_model
ctx *C.struct_llama_context
api.Options
}
func New(model string, mo ModelOptions) (*LLama, error) {
modelPath := C.CString(model)
defer C.free(unsafe.Pointer(modelPath))
ctx := C.load_model(modelPath, C.int(mo.ContextSize), C.int(mo.Seed), C.bool(mo.F16Memory), C.bool(mo.MLock), C.bool(mo.Embeddings), C.bool(mo.MMap), C.bool(mo.LowVRAM), C.bool(mo.VocabOnly), C.int(mo.NGPULayers), C.int(mo.NBatch), C.CString(mo.MainGPU), C.CString(mo.TensorSplit), C.bool(mo.NUMA))
if ctx == nil {
return nil, fmt.Errorf("failed loading model")
func New(model string, opts api.Options) (*llama, error) {
if _, err := os.Stat(model); err != nil {
return nil, err
}
ll := &LLama{ctx: ctx, contextSize: mo.ContextSize, embeddings: mo.Embeddings}
llm := llama{Options: opts}
return ll, nil
C.llama_backend_init(C.bool(llm.UseNUMA))
params := C.llama_context_default_params()
params.seed = C.uint(llm.Seed)
params.n_ctx = C.int(llm.NumCtx)
params.n_batch = C.int(llm.NumBatch)
params.n_gpu_layers = C.int(llm.NumGPU)
params.main_gpu = C.int(llm.MainGPU)
params.low_vram = C.bool(llm.LowVRAM)
params.f16_kv = C.bool(llm.F16KV)
params.logits_all = C.bool(llm.LogitsAll)
params.vocab_only = C.bool(llm.VocabOnly)
params.use_mmap = C.bool(llm.UseMMap)
params.use_mlock = C.bool(llm.UseMLock)
params.embedding = C.bool(llm.EmbeddingOnly)
llm.params = &params
cModel := C.CString(model)
defer C.free(unsafe.Pointer(cModel))
llm.model = C.llama_load_model_from_file(cModel, params)
llm.ctx = C.llama_new_context_with_model(llm.model, params)
// warm up the model
bos := []C.llama_token{C.llama_token_bos()}
C.llama_eval(llm.ctx, unsafe.SliceData(bos), C.int(len(bos)), 0, C.int(opts.NumThread))
C.llama_reset_timings(llm.ctx)
return &llm, nil
}
func (l *LLama) Free() {
C.llama_binding_free_model(l.ctx)
func (llm *llama) Close() {
defer C.llama_free_model(llm.model)
defer C.llama_free(llm.ctx)
C.llama_print_timings(llm.ctx)
}
func (l *LLama) Eval(text string, po PredictOptions) error {
input := C.CString(text)
if po.Tokens == 0 {
po.Tokens = 99999999
}
defer C.free(unsafe.Pointer(input))
reverseCount := len(po.StopPrompts)
reversePrompt := make([]*C.char, reverseCount)
var pass **C.char
for i, s := range po.StopPrompts {
cs := C.CString(s)
reversePrompt[i] = cs
pass = &reversePrompt[0]
defer C.free(unsafe.Pointer(cs))
func (llm *llama) Predict(prompt string, fn func(string)) error {
if tokens := llm.tokenize(prompt); tokens != nil {
return llm.generate(tokens, fn)
}
cLogitBias := C.CString(po.LogitBias)
defer C.free(unsafe.Pointer(cLogitBias))
return errors.New("llama: tokenize")
}
cMainGPU := C.CString(po.MainGPU)
defer C.free(unsafe.Pointer(cMainGPU))
func (llm *llama) tokenize(prompt string) []C.llama_token {
cPrompt := C.CString(prompt)
defer C.free(unsafe.Pointer(cPrompt))
cTensorSplit := C.CString(po.TensorSplit)
defer C.free(unsafe.Pointer(cTensorSplit))
params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
C.bool(po.IgnoreEOS), C.bool(po.F16KV),
C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), cLogitBias,
C.bool(po.MLock), C.bool(po.MMap), cMainGPU, cTensorSplit,
)
defer C.llama_free_params(params)
ret := C.eval(params, l.ctx, input)
if ret != 0 {
return fmt.Errorf("inference failed")
tokens := make([]C.llama_token, llm.NumCtx)
if n := C.llama_tokenize(llm.ctx, cPrompt, unsafe.SliceData(tokens), C.int(len(tokens)), true); n > 0 {
return tokens[:n]
}
return nil
}
func (l *LLama) Predict(text string, po PredictOptions) (string, error) {
if po.TokenCallback != nil {
setCallback(l.ctx, po.TokenCallback)
func (llm *llama) detokenize(tokens ...C.llama_token) string {
var sb strings.Builder
for _, token := range tokens {
sb.WriteString(C.GoString(C.llama_token_to_str(llm.ctx, token)))
}
input := C.CString(text)
if po.Tokens == 0 {
po.Tokens = 99999999
}
defer C.free(unsafe.Pointer(input))
out := make([]byte, po.Tokens)
reverseCount := len(po.StopPrompts)
reversePrompt := make([]*C.char, reverseCount)
var pass **C.char
for i, s := range po.StopPrompts {
cs := C.CString(s)
reversePrompt[i] = cs
pass = &reversePrompt[0]
defer C.free(unsafe.Pointer(cs))
}
cLogitBias := C.CString(po.LogitBias)
defer C.free(unsafe.Pointer(cLogitBias))
cMainGPU := C.CString(po.MainGPU)
defer C.free(unsafe.Pointer(cMainGPU))
cTensorSplit := C.CString(po.TensorSplit)
defer C.free(unsafe.Pointer(cTensorSplit))
params := C.llama_allocate_params(input, C.int(po.Seed), C.int(po.Threads), C.int(po.Tokens), C.int(po.TopK),
C.float(po.TopP), C.float(po.Temperature), C.float(po.Penalty), C.int(po.Repeat),
C.bool(po.IgnoreEOS), C.bool(po.F16KV),
C.int(po.Batch), C.int(po.NKeep), pass, C.int(reverseCount),
C.float(po.TailFreeSamplingZ), C.float(po.TypicalP), C.float(po.FrequencyPenalty), C.float(po.PresencePenalty),
C.int(po.Mirostat), C.float(po.MirostatETA), C.float(po.MirostatTAU), C.bool(po.PenalizeNL), cLogitBias,
C.bool(po.MLock), C.bool(po.MMap), cMainGPU, cTensorSplit,
)
defer C.llama_free_params(params)
ret := C.llama_predict(params, l.ctx, (*C.char)(unsafe.Pointer(&out[0])), C.bool(po.DebugMode))
if ret != 0 {
return "", fmt.Errorf("inference failed")
}
res := C.GoString((*C.char)(unsafe.Pointer(&out[0])))
res = strings.TrimPrefix(res, " ")
res = strings.TrimPrefix(res, text)
res = strings.TrimPrefix(res, "\n")
for _, s := range po.StopPrompts {
res = strings.TrimRight(res, s)
}
if po.TokenCallback != nil {
setCallback(l.ctx, nil)
}
return res, nil
return sb.String()
}
// CGo only allows us to use static calls from C to Go, we can't just dynamically pass in func's.
// This is the next best thing, we register the callbacks in this map and call tokenCallback from
// the C code. We also attach a finalizer to LLama, so it will unregister the callback when the
// garbage collection frees it.
func (llm *llama) generate(tokens []C.llama_token, fn func(string)) error {
var opts C.struct_llama_sample_options
opts.repeat_penalty = C.float(llm.RepeatPenalty)
opts.frequency_penalty = C.float(llm.FrequencyPenalty)
opts.presence_penalty = C.float(llm.PresencePenalty)
opts.temperature = C.float(llm.Temperature)
opts.top_k = C.int(llm.TopK)
opts.top_p = C.float(llm.TopP)
opts.tfs_z = C.float(llm.TFSZ)
opts.typical_p = C.float(llm.TypicalP)
opts.mirostat = C.int(llm.Mirostat)
opts.mirostat_tau = C.float(llm.MirostatTau)
opts.mirostat_eta = C.float(llm.MirostatEta)
// SetTokenCallback registers a callback for the individual tokens created when running Predict. It
// will be called once for each token. The callback shall return true as long as the model should
// continue predicting the next token. When the callback returns false the predictor will return.
// The tokens are just converted into Go strings, they are not trimmed or otherwise changed. Also
// the tokens may not be valid UTF-8.
// Pass in nil to remove a callback.
//
// It is save to call this method while a prediction is running.
func (l *LLama) SetTokenCallback(callback func(token string) bool) {
setCallback(l.ctx, callback)
}
pastTokens := deque[C.llama_token]{capacity: llm.RepeatLastN}
var (
m sync.Mutex
callbacks = map[uintptr]func(string) bool{}
)
for C.llama_get_kv_cache_token_count(llm.ctx) < C.int(llm.NumCtx) {
if retval := C.llama_eval(llm.ctx, unsafe.SliceData(tokens), C.int(len(tokens)), C.llama_get_kv_cache_token_count(llm.ctx), C.int(llm.NumThread)); retval != 0 {
return errors.New("llama: eval")
}
//export tokenCallback
func tokenCallback(statePtr unsafe.Pointer, token *C.char) bool {
m.Lock()
defer m.Unlock()
token, err := llm.sample(pastTokens, &opts)
switch {
case err != nil:
return err
case errors.Is(err, io.EOF):
return nil
}
if callback, ok := callbacks[uintptr(statePtr)]; ok {
return callback(C.GoString(token))
fn(llm.detokenize(token))
tokens = []C.llama_token{token}
pastTokens.PushLeft(token)
}
return true
return nil
}
// setCallback can be used to register a token callback for LLama. Pass in a nil callback to
// remove the callback.
func setCallback(statePtr unsafe.Pointer, callback func(string) bool) {
m.Lock()
defer m.Unlock()
func (llm *llama) sample(pastTokens deque[C.llama_token], opts *C.struct_llama_sample_options) (C.llama_token, error) {
numVocab := int(C.llama_n_vocab(llm.ctx))
logits := unsafe.Slice(C.llama_get_logits(llm.ctx), numVocab)
if callback == nil {
delete(callbacks, uintptr(statePtr))
} else {
callbacks[uintptr(statePtr)] = callback
candidates := make([]C.struct_llama_token_data, 0, numVocab)
for i := 0; i < numVocab; i++ {
candidates = append(candidates, C.llama_token_data{
id: C.int(i),
logit: logits[i],
p: 0,
})
}
token := C.llama_sample(
llm.ctx,
unsafe.SliceData(candidates), C.ulong(len(candidates)),
unsafe.SliceData(pastTokens.Data()), C.ulong(pastTokens.Len()),
opts)
if token != C.llama_token_eos() {
return token, nil
}
return 0, io.EOF
}

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/**
* llama.cpp - git 5bf2a2771886ee86137e01dbc7492f78fb392066
*
* MIT License
*
* Copyright (c) 2023 Georgi Gerganov
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#ifndef LLAMA_H
#define LLAMA_H
#include "ggml.h"
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES
#else
#define LLAMA_MAX_DEVICES 1
#endif // GGML_USE_CUBLAS
#include <stddef.h>
#include <stdint.h>
#include <stdbool.h>
#ifdef LLAMA_SHARED
# if defined(_WIN32) && !defined(__MINGW32__)
# ifdef LLAMA_BUILD
# define LLAMA_API __declspec(dllexport)
# else
# define LLAMA_API __declspec(dllimport)
# endif
# else
# define LLAMA_API __attribute__ ((visibility ("default")))
# endif
#else
# define LLAMA_API
#endif
#ifdef __GNUC__
# define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
#elif defined(_MSC_VER)
# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
#else
# define DEPRECATED(func, hint) func
#endif
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
#define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_FILE_VERSION 3
#define LLAMA_FILE_MAGIC LLAMA_FILE_MAGIC_GGJT
#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
#endif
#ifdef __cplusplus
extern "C" {
#endif
//
// C interface
//
// TODO: show sample usage
//
struct llama_model;
struct llama_context;
typedef int llama_token;
typedef struct llama_token_data {
llama_token id; // token id
float logit; // log-odds of the token
float p; // probability of the token
} llama_token_data;
typedef struct llama_token_data_array {
llama_token_data * data;
size_t size;
bool sorted;
} llama_token_data_array;
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
// called with a progress value between 0 and 1, pass NULL to disable
llama_progress_callback progress_callback;
// context pointer passed to the progress callback
void * progress_callback_user_data;
// Keep the booleans together to avoid misalignment during copy-by-value.
bool low_vram; // if true, reduce VRAM usage at the cost of performance
bool f16_kv; // use fp16 for KV cache
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
bool use_mlock; // force system to keep model in RAM
bool embedding; // embedding mode only
};
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
// LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed
// LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed
LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q2_K = 10,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_S = 11,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_M = 12,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_L = 13,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_K_S = 14,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q4_K_M = 15,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_S = 16,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q5_K_M = 17,// except 1d tensors
LLAMA_FTYPE_MOSTLY_Q6_K = 18,// except 1d tensors
};
// model quantization parameters
typedef struct llama_model_quantize_params {
int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
enum llama_ftype ftype; // quantize to this llama_ftype
bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool quantize_output_tensor; // quantize output.weight
} llama_model_quantize_params;
// performance timing information
struct llama_timings {
double t_start_ms;
double t_end_ms;
double t_load_ms;
double t_sample_ms;
double t_p_eval_ms;
double t_eval_ms;
int32_t n_sample;
int32_t n_p_eval;
int32_t n_eval;
};
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
LLAMA_API bool llama_mmap_supported();
LLAMA_API bool llama_mlock_supported();
// TODO: not great API - very likely to change
// Initialize the llama + ggml backend
// If numa is true, use NUMA optimizations
// Call once at the start of the program
LLAMA_API void llama_backend_init(bool numa);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free();
LLAMA_API int64_t llama_time_us();
LLAMA_API struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_context_params params);
LLAMA_API void llama_free_model(struct llama_model * model);
LLAMA_API struct llama_context * llama_new_context_with_model(
struct llama_model * model,
struct llama_context_params params);
// Various functions for loading a ggml llama model.
// Allocate (almost) all memory needed for the model.
// Return NULL on failure
LLAMA_API DEPRECATED(struct llama_context * llama_init_from_file(
const char * path_model,
struct llama_context_params params),
"please use llama_load_model_from_file combined with llama_new_context_with_model instead");
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
// Returns 0 on success
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
const llama_model_quantize_params * params);
// Apply a LoRA adapter to a loaded model
// path_base_model is the path to a higher quality model to use as a base for
// the layers modified by the adapter. Can be NULL to use the current loaded model.
// The model needs to be reloaded before applying a new adapter, otherwise the adapter
// will be applied on top of the previous one
// Returns 0 on success
LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
struct llama_context * ctx,
const char * path_lora,
const char * path_base_model,
int n_threads),
"please use llama_model_apply_lora_from_file instead");
LLAMA_API int llama_model_apply_lora_from_file(
const struct llama_model * model,
const char * path_lora,
const char * path_base_model,
int n_threads);
// Returns the number of tokens in the KV cache
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens
LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
// Copies the state to the specified destination address.
// Destination needs to have allocated enough memory.
// Returns the number of bytes copied
LLAMA_API size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst);
// Set the state reading from the specified address
// Returns the number of bytes read
LLAMA_API size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src);
// Save/load session file
LLAMA_API 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);
LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
// Run the llama inference to obtain the logits and probabilities for the next token.
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls
// Returns 0 on success
LLAMA_API int llama_eval(
struct llama_context * ctx,
const llama_token * tokens,
int n_tokens,
int n_past,
int n_threads);
// Same as llama_eval, but use float matrix input directly.
LLAMA_API int llama_eval_embd(
struct llama_context * ctx,
const float * embd,
int n_tokens,
int n_past,
int n_threads);
// Export a static computation graph for context of 511 and batch size of 1
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
// parameters here to keep things simple
// IMPORTANT: do not use for anything else other than debugging and testing!
LLAMA_API int llama_eval_export(struct llama_context * ctx, const char * fname);
// Convert the provided text into tokens.
// The tokens pointer must be large enough to hold the resulting tokens.
// Returns the number of tokens on success, no more than n_max_tokens
// Returns a negative number on failure - the number of tokens that would have been returned
// TODO: not sure if correct
LLAMA_API int llama_tokenize(
struct llama_context * ctx,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
LLAMA_API int llama_n_embd (const struct llama_context * ctx);
// Get the vocabulary as output parameters.
// Returns number of results.
LLAMA_API int llama_get_vocab(
const struct llama_context * ctx,
const char * * strings,
float * scores,
int capacity);
// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
// Can be mutated in order to change the probabilities of the next token
// Rows: n_tokens
// Cols: n_vocab
LLAMA_API float * llama_get_logits(struct llama_context * ctx);
// Get the embeddings for the input
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(); // end-of-sentence
LLAMA_API llama_token llama_token_nl(); // next-line
// Sampling functions
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
LLAMA_API 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);
/// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
/// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
/// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
/// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
/// @params smooth_factor Smooth factor between guidance logits and original logits. 1.0f means only use guidance logits. 0.0f means only original logits.
LLAMA_API void llama_sample_classifier_free_guidance(
struct llama_context * ctx,
llama_token_data_array * candidates,
struct llama_context * guidance_ctx,
float scale,
float smooth_factor);
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
/// @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
/// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
/// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
/// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
/// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
/// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
/// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
/// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
/// @details Selects the token with the highest probability.
LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
/// @details Randomly selects a token from the candidates based on their probabilities.
LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
// Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
LLAMA_API void llama_print_timings(struct llama_context * ctx);
LLAMA_API void llama_reset_timings(struct llama_context * ctx);
// Print system information
LLAMA_API const char * llama_print_system_info(void);
#ifdef __cplusplus
}
#endif
// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
#ifdef LLAMA_API_INTERNAL
#include <vector>
#include <string>
struct ggml_tensor;
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
#endif
#endif // LLAMA_H

9
llama/llama_cublas.go
View file

@ -1,9 +0,0 @@
//go:build cublas
// +build cublas
package llama
/*
#cgo LDFLAGS: -lcublas -lcudart -L/usr/local/cuda/lib64/
*/
import "C"

2
llama/llama_metal.go
View file

@ -1,2 +0,0 @@
//go:build metal
package llama

9
llama/llama_openblas.go
View file

@ -1,9 +0,0 @@
//go:build openblas
// +build openblas
package llama
/*
#cgo LDFLAGS: -lopenblas
*/
import "C"

98
llama/options.go
View file

@ -1,98 +0,0 @@
// MIT License
// Copyright (c) 2023 go-skynet authors
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
// The above copyright notice and this permission notice shall be included in all
// copies or substantial portions of the Software.
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
package llama
type ModelOptions struct {
ContextSize int
Seed int
NBatch int
F16Memory bool
MLock bool
MMap bool
VocabOnly bool
LowVRAM bool
Embeddings bool
NUMA bool
NGPULayers int
MainGPU string
TensorSplit string
}
type PredictOptions struct {
Seed, Threads, Tokens, TopK, Repeat, Batch, NKeep int
TopP, Temperature, Penalty float64
F16KV bool
DebugMode bool
StopPrompts []string
IgnoreEOS bool
TailFreeSamplingZ float64
TypicalP float64
FrequencyPenalty float64
PresencePenalty float64
Mirostat int
MirostatETA float64
MirostatTAU float64
PenalizeNL bool
LogitBias string
TokenCallback func(string) bool
MLock, MMap bool
MainGPU string
TensorSplit string
}
type PredictOption func(p *PredictOptions)
type ModelOption func(p *ModelOptions)
var DefaultModelOptions ModelOptions = ModelOptions{
ContextSize: 512,
Seed: 0,
F16Memory: false,
MLock: false,
Embeddings: false,
MMap: true,
LowVRAM: false,
}
var DefaultOptions PredictOptions = PredictOptions{
Seed: -1,
Threads: 4,
Tokens: 128,
Penalty: 1.1,
Repeat: 64,
Batch: 512,
NKeep: 64,
TopK: 40,
TopP: 0.95,
TailFreeSamplingZ: 1.0,
TypicalP: 1.0,
Temperature: 0.8,
FrequencyPenalty: 0.0,
PresencePenalty: 0.0,
Mirostat: 0,
MirostatTAU: 5.0,
MirostatETA: 0.1,
MMap: true,
}

104
llama/utils.go Normal file
View file

@ -0,0 +1,104 @@
package llama
type node[T any] struct {
t T
next *node[T]
prev *node[T]
}
type deque[T any] struct {
head *node[T]
tail *node[T]
size int
capacity int
}
func (d *deque[T]) Empty() bool {
return d.size == 0
}
func (d *deque[T]) Len() int {
return d.size
}
func (d *deque[T]) Cap() int {
return d.capacity
}
func (d *deque[T]) Push(t T) {
if d.capacity > 0 && d.size >= d.capacity {
d.PopLeft()
}
n := node[T]{t: t}
if d.head != nil {
n.next = d.head
d.head.prev = &n
d.head = &n
} else {
d.head = &n
d.tail = &n
}
d.size++
}
func (d *deque[T]) PushLeft(t T) {
if d.capacity > 0 && d.size >= d.capacity {
d.Pop()
}
n := node[T]{t: t}
if d.tail != nil {
n.prev = d.tail
d.tail.next = &n
d.tail = &n
} else {
d.head = &n
d.tail = &n
}
d.size++
}
func (d *deque[T]) Pop() *T {
if d.Empty() {
return nil
}
head := d.head
d.head = head.next
if d.head != nil {
d.head.prev = nil
} else {
d.tail = nil
}
d.size--
return &head.t
}
func (d *deque[T]) PopLeft() *T {
if d.Empty() {
return nil
}
tail := d.tail
d.tail = tail.prev
if d.tail != nil {
d.tail.next = nil
} else {
d.head = nil
}
d.size--
return &tail.t
}
func (d *deque[T]) Data() (data []T) {
for n := d.head; n != nil; n = n.next {
data = append(data, n.t)
}
return data
}

137
server/routes.go
View file

@ -11,12 +11,12 @@ import (
"net/http"
"os"
"path"
"runtime"
"strings"
"text/template"
"github.com/gin-gonic/gin"
"github.com/lithammer/fuzzysearch/fuzzy"
"golang.org/x/sync/errgroup"
"github.com/jmorganca/ollama/api"
"github.com/jmorganca/ollama/llama"
@ -36,14 +36,10 @@ func cacheDir() string {
}
func generate(c *gin.Context) {
var req api.GenerateRequest
if req.ModelOptions == nil {
req.ModelOptions = &api.DefaultModelOptions
req := api.GenerateRequest{
Options: api.DefaultOptions(),
}
if req.PredictOptions == nil {
req.PredictOptions = &api.DefaultPredictOptions
}
if err := c.ShouldBindJSON(&req); err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
return
@ -60,15 +56,12 @@ func generate(c *gin.Context) {
req.Model = path.Join(cacheDir(), "models", req.Model+".bin")
}
modelOpts := getModelOpts(req)
modelOpts.NGPULayers = 1 // hard-code this for now
model, err := llama.New(req.Model, modelOpts)
llm, err := llama.New(req.Model, req.Options)
if err != nil {
c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
defer model.Free()
defer llm.Close()
templateNames := make([]string, 0, len(templates.Templates()))
for _, template := range templates.Templates() {
@ -87,43 +80,41 @@ func generate(c *gin.Context) {
}
ch := make(chan string)
model.SetTokenCallback(func(token string) bool {
ch <- token
return true
})
predictOpts := getPredictOpts(req)
go func() {
g, _ := errgroup.WithContext(c.Request.Context())
g.Go(func() error {
defer close(ch)
_, err := model.Predict(req.Prompt, predictOpts)
if err != nil {
panic(err)
}
}()
c.Stream(func(w io.Writer) bool {
token, ok := <-ch
if !ok {
return false
}
resp := api.GenerateResponse{
Response: token,
}
bts, err := json.Marshal(resp)
if err != nil {
return false
}
bts = append(bts, '\n')
if _, err := w.Write(bts); err != nil {
return false
}
return true
return llm.Predict(req.Prompt, func(s string) {
ch <- s
})
})
g.Go(func() error {
c.Stream(func(w io.Writer) bool {
s, ok := <-ch
if !ok {
return false
}
bts, err := json.Marshal(api.GenerateResponse{Response: s})
if err != nil {
return false
}
bts = append(bts, '\n')
if _, err := w.Write(bts); err != nil {
return false
}
return true
})
return nil
})
if err := g.Wait(); err != nil && !errors.Is(err, io.EOF) {
c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
return
}
}
func Serve(ln net.Listener) error {
@ -195,53 +186,3 @@ func matchRankOne(source string, targets []string) (bestMatch string, bestRank i
return
}
func getModelOpts(req api.GenerateRequest) llama.ModelOptions {
var opts llama.ModelOptions
opts.ContextSize = req.ModelOptions.ContextSize
opts.Seed = req.ModelOptions.Seed
opts.F16Memory = req.ModelOptions.F16Memory
opts.MLock = req.ModelOptions.MLock
opts.Embeddings = req.ModelOptions.Embeddings
opts.MMap = req.ModelOptions.MMap
opts.LowVRAM = req.ModelOptions.LowVRAM
opts.NBatch = req.ModelOptions.NBatch
opts.VocabOnly = req.ModelOptions.VocabOnly
opts.NUMA = req.ModelOptions.NUMA
opts.NGPULayers = req.ModelOptions.NGPULayers
opts.MainGPU = req.ModelOptions.MainGPU
opts.TensorSplit = req.ModelOptions.TensorSplit
return opts
}
func getPredictOpts(req api.GenerateRequest) llama.PredictOptions {
var opts llama.PredictOptions
if req.PredictOptions.Threads == -1 {
opts.Threads = runtime.NumCPU()
} else {
opts.Threads = req.PredictOptions.Threads
}
opts.Seed = req.PredictOptions.Seed
opts.Tokens = req.PredictOptions.Tokens
opts.Penalty = req.PredictOptions.Penalty
opts.Repeat = req.PredictOptions.Repeat
opts.Batch = req.PredictOptions.Batch
opts.NKeep = req.PredictOptions.NKeep
opts.TopK = req.PredictOptions.TopK
opts.TopP = req.PredictOptions.TopP
opts.TailFreeSamplingZ = req.PredictOptions.TailFreeSamplingZ
opts.TypicalP = req.PredictOptions.TypicalP
opts.Temperature = req.PredictOptions.Temperature
opts.FrequencyPenalty = req.PredictOptions.FrequencyPenalty
opts.PresencePenalty = req.PredictOptions.PresencePenalty
opts.Mirostat = req.PredictOptions.Mirostat
opts.MirostatTAU = req.PredictOptions.MirostatTAU
opts.MirostatETA = req.PredictOptions.MirostatETA
opts.MMap = req.PredictOptions.MMap
return opts
}