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Merge branch 'master' into gg/per-layer-kv
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ggml-ci
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@ggerganov
ggerganov committed Dec 7, 2023
2 parents 1a1a1c3 + 81bc921 commit 680a99e
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46 changes: 16 additions & 30 deletions Package.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -2,59 +2,45 @@

import PackageDescription

#if arch(arm) || arch(arm64)
let platforms: [SupportedPlatform]? = [
.macOS(.v12),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
]
let exclude: [String] = []
let resources: [Resource] = [
.process("ggml-metal.metal")
]
let additionalSources: [String] = ["ggml-metal.m"]
let additionalSettings: [CSetting] = [
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL")
]
#else
let platforms: [SupportedPlatform]? = nil
let exclude: [String] = ["ggml-metal.metal"]
let resources: [Resource] = []
let additionalSources: [String] = []
let additionalSettings: [CSetting] = []
#endif

let package = Package(
name: "llama",
platforms: platforms,
platforms: [
.macOS(.v12),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
],
products: [
.library(name: "llama", targets: ["llama"]),
],
targets: [
.target(
name: "llama",
path: ".",
exclude: exclude,
exclude: [],
sources: [
"ggml.c",
"llama.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",
] + additionalSources,
resources: resources,
"ggml-metal.m",
],
resources: [
.process("ggml-metal.metal")
],
publicHeadersPath: "spm-headers",
cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
.define("GGML_USE_ACCELERATE")
.define("GGML_USE_ACCELERATE"),
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL"),
// NOTE: NEW_LAPACK will required iOS version 16.4+
// We should consider add this in the future when we drop support for iOS 14
// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
// .define("ACCELERATE_NEW_LAPACK"),
// .define("ACCELERATE_LAPACK_ILP64")
] + additionalSettings,
],
linkerSettings: [
.linkedFramework("Accelerate")
]
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111 changes: 111 additions & 0 deletions common/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -278,6 +278,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--samplers") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = parse_samplers_input(argv[i]);
} else if (arg == "--sampling-seq") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = argv[i];
} else if (arg == "--top-p") {
if (++i >= argc) {
invalid_param = true;
Expand Down Expand Up @@ -682,6 +694,47 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
std::istreambuf_iterator<char>(),
std::back_inserter(sparams.grammar)
);
} else if (arg == "--override-kv") {
if (++i >= argc) {
invalid_param = true;
break;
}
char * sep = strchr(argv[i], '=');
if (sep == nullptr || sep - argv[i] >= 128) {
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
struct llama_model_kv_override kvo;
std::strncpy(kvo.key, argv[i], sep - argv[i]);
kvo.key[sep - argv[i]] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
} else {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
params.kv_overrides.push_back(kvo);
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
} else if ( log_param_single_parse( argv[i] ) ) {
Expand Down Expand Up @@ -725,6 +778,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
}
}

if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back(llama_model_kv_override());
params.kv_overrides.back().key[0] = 0;
}

return true;
}

Expand Down Expand Up @@ -765,6 +823,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --samplers samplers that will be used for generation in the order, separated by \';\', for example: \"top_k;tfs;typical;top_p;min_p;temp\"\n");
printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sparams.samplers_sequence.c_str());
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
Expand Down Expand Up @@ -858,6 +918,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str());
printf(" -ld LOGDIR, --logdir LOGDIR\n");
printf(" path under which to save YAML logs (no logging if unset)\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf("\n");
#ifndef LOG_DISABLE_LOGS
log_print_usage();
Expand Down Expand Up @@ -894,6 +957,48 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
GGML_UNREACHABLE();
}

//
// String parsing
//

std::string parse_samplers_input(std::string input) {
std::string output = "";
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, char> samplers_symbols {
{"top_k", 'k'},
{"top-k", 'k'},
{"top_p", 'p'},
{"top-p", 'p'},
{"nucleus", 'p'},
{"typical_p", 'y'},
{"typical-p", 'y'},
{"typical", 'y'},
{"min_p", 'm'},
{"min-p", 'm'},
{"tfs_z", 'f'},
{"tfs-z", 'f'},
{"tfs", 'f'},
{"temp", 't'},
{"temperature",'t'}
};
// expected format example: "temp;top_k;tfs_z;typical_p;top_p;min_p"
size_t separator = input.find(';');
while (separator != input.npos) {
std::string name = input.substr(0,separator);
input = input.substr(separator+1);
separator = input.find(';');

if (samplers_symbols.find(name) != samplers_symbols.end()) {
output += samplers_symbols[name];
}
}
if (samplers_symbols.find(input) != samplers_symbols.end()) {
output += samplers_symbols[input];
}
return output;
}

//
// Model utils
//
Expand All @@ -908,6 +1013,12 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
mparams.kv_overrides = params.kv_overrides.data();
}

return mparams;
}
Expand Down
8 changes: 8 additions & 0 deletions common/common.h
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Expand Up @@ -86,6 +86,8 @@ struct gpt_params {
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string logdir = ""; // directory in which to save YAML log files

std::vector<llama_model_kv_override> kv_overrides;

// TODO: avoid tuple, use struct
std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
std::string lora_base = ""; // base model path for the lora adapter
Expand Down Expand Up @@ -144,6 +146,12 @@ std::string gpt_random_prompt(std::mt19937 & rng);

void process_escapes(std::string& input);

//
// String parsing
//

std::string parse_samplers_input(std::string input);

//
// Model utils
//
Expand Down
2 changes: 1 addition & 1 deletion common/grammar-parser.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@ namespace grammar_parser {
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
if (last_sym_start == out_elements.size()) {
throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos);
throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
}

// apply transformation to previous symbol (last_sym_start to end) according to
Expand Down
62 changes: 51 additions & 11 deletions common/sampling.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -99,6 +99,56 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
return std::string(result);
}

std::string llama_sampling_order_print(const llama_sampling_params & params) {
std::string result = "CFG -> Penalties ";
if (params.mirostat == 0) {
for (auto s : params.samplers_sequence) {
switch (s) {
case 'k': result += "-> top_k "; break;
case 'f': result += "-> tfs_z "; break;
case 'y': result += "-> typical_p "; break;
case 'p': result += "-> top_p "; break;
case 'm': result += "-> min_p "; break;
case 't': result += "-> temp "; break;
default : break;
}
}
} else {
result += "-> mirostat ";
}

return result;
}

// no reasons to expose this function in header
static void sampler_queue(
struct llama_context * ctx_main,
const llama_sampling_params & params,
llama_token_data_array & cur_p,
size_t & min_keep) {
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));

const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const std::string & samplers_sequence = params.samplers_sequence;

for (auto s : samplers_sequence) {
switch (s){
case 'k': llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case 'f': llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case 'y': llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case 'p': llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case 'm': llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case 't': llama_sample_temp (ctx_main, &cur_p, temp); break;
default : break;
}
}
}

llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
Expand All @@ -109,11 +159,6 @@ llama_token llama_sampling_sample(
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));

const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
Expand Down Expand Up @@ -188,12 +233,7 @@ llama_token llama_sampling_sample(
// temperature sampling
size_t min_keep = std::max(1, params.n_probs);

llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep);
llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep);
llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep);
llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep);
llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep);
llama_sample_temp (ctx_main, &cur_p, temp);
sampler_queue(ctx_main, params, cur_p, min_keep);

id = llama_sample_token(ctx_main, &cur_p);

Expand Down
36 changes: 20 additions & 16 deletions common/sampling.h
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Original file line number Diff line number Diff line change
Expand Up @@ -10,22 +10,23 @@

// sampling parameters
typedef struct llama_sampling_params {
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.10f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
int32_t n_prev = 64; // number of previous tokens to remember
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
int32_t top_k = 40; // <= 0 to use vocab size
float top_p = 0.95f; // 1.0 = disabled
float min_p = 0.05f; // 0.0 = disabled
float tfs_z = 1.00f; // 1.0 = disabled
float typical_p = 1.00f; // 1.0 = disabled
float temp = 0.80f; // 1.0 = disabled
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
float penalty_repeat = 1.10f; // 1.0 = disabled
float penalty_freq = 0.00f; // 0.0 = disabled
float penalty_present = 0.00f; // 0.0 = disabled
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp

std::string grammar; // optional BNF-like grammar to constrain sampling

Expand Down Expand Up @@ -80,6 +81,9 @@ std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama
// Print sampling parameters into a string
std::string llama_sampling_print(const llama_sampling_params & params);

// Print sampling order into a string
std::string llama_sampling_order_print(const llama_sampling_params & params);

// this is a common sampling function used across the examples for convenience
// it can serve as a starting point for implementing your own sampling function
// Note: When using multiple sequences, it is the caller's responsibility to call
Expand Down
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