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8 Commits

Author SHA1 Message Date
Kawrakow 42f70cb2f6 Fix scalar version of Q5_K when QK_K = 64 (#2362)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-24 12:55:02 +03:00
Evan Jones 84e09a7d8b llama : add grammar-based sampling (#1773)
* llama, main : constrain sampling to grammar

* allow loading grammar from file

* fix whitespace errors

* handle & print parser errors

* add comments to grammar syntax and allow newlines where unambiguous

* add missing include

* support alternates in root rule

* fix bugs with empty token and EOS

* adjust JSON grammar

* remove swp file

* rewrite ternary expressions

Co-authored-by: Henri Vasserman <henv@hot.ee>

* use struct for grammar elements and add Unicode support

* add unicode escapes

* add inverse char ranges

* only sample full tokens (no peeking or truncation)

* llama : minor style changes

blindly applied in online editor - hopefully I didn't break something

* update help text

* add warning message if EOS is disabled

---------

Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-23 23:58:10 -04:00
Kawrakow 2f9cf974a0 Some more Q4_K and Q5_K speedup on CUDA (#2346)
* Faster Q5_K on CUDA

* Small Q5_K improvement on older GPUs

* Spped up Q4_K on CUDA

GTX1660: 29.5 ms/t -> 25.6 ms/t
RTX4080: 8.40 ms/t -> 8.25 ms/t

* Spped up Q4_K on CUDA

GTX1660: 36.7 ms/t -> 35.6 ms/t
RTX4080:  9.8 ms/t ->  9.5 ms/t

* Address PR comments

* Add some comments to satisfy PR reviewer

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-24 00:19:47 +03:00
IgnacioFDM 4f06592cc6 Add gqa parameter support to the server (#2351)
* Add gqa parameter support to the server
* Change help from stderr to stdout
2023-07-23 23:31:17 +03:00
Johannes Gäßler 70d26ac388 Fix __dp4a documentation (#2348) 2023-07-23 17:49:06 +02:00
wzy 57921ca6db common : n_threads == -1 uses std::thread::hardware_concurrency() (#2347)
* Fix #2345, fix incorrect n_threads

* Update examples/common.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-23 16:33:02 +03:00
slaren 3602ac4255 fix n_tasks (#2342)
ggml-ci
2023-07-23 15:19:39 +02:00
slaren 95a6c595e7 ggml: move op parameters from tensors to ggml_tensor::op_params (#2333)
* ggml: move op parameters from tensors to ggml_tensor::op_params

* alibi: use memcpy for float params

* remove `src[1] = NULL` in ops
2023-07-23 14:36:02 +02:00
21 changed files with 1341 additions and 554 deletions
+4 -1
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@@ -323,6 +323,9 @@ llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
common.o: examples/common.cpp examples/common.h
$(CXX) $(CXXFLAGS) -c $< -o $@
grammar-parser.o: examples/grammar-parser.cpp examples/grammar-parser.h
$(CXX) $(CXXFLAGS) -c $< -o $@
libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
@@ -333,7 +336,7 @@ clean:
# Examples
#
main: examples/main/main.cpp build-info.h ggml.o llama.o common.o $(OBJS)
main: examples/main/main.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@echo
@echo '==== Run ./main -h for help. ===='
+1 -1
View File
@@ -401,7 +401,7 @@ Building the program with BLAS support may lead to some performance improvements
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
+2
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@@ -13,6 +13,8 @@ set(TARGET common)
add_library(${TARGET} OBJECT
common.h
common.cpp
grammar-parser.h
grammar-parser.cpp
)
if (BUILD_SHARED_LIBS)
+27
View File
@@ -117,6 +117,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_threads = std::stoi(argv[i]);
if (params.n_threads <= 0) {
params.n_threads = std::thread::hardware_concurrency();
}
} else if (arg == "-p" || arg == "--prompt") {
if (++i >= argc) {
invalid_param = true;
@@ -435,6 +438,28 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.input_suffix = argv[i];
} else if (arg == "--grammar") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.grammar = argv[i];
} else if (arg == "--grammar-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
break;
}
std::copy(
std::istreambuf_iterator<char>(file),
std::istreambuf_iterator<char>(),
std::back_inserter(params.grammar)
);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
gpt_print_usage(argc, argv, default_params);
@@ -511,6 +536,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " modifies the likelihood of token appearing in the completion,\n");
fprintf(stdout, " i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
fprintf(stdout, " or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n");
fprintf(stdout, " --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n");
fprintf(stdout, " --grammar-file FNAME file to read grammar from\n");
fprintf(stdout, " --cfg-negative-prompt PROMPT \n");
fprintf(stdout, " negative prompt to use for guidance. (default: empty)\n");
fprintf(stdout, " --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
+1
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@@ -63,6 +63,7 @@ struct gpt_params {
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
std::string input_prefix = ""; // string to prefix user inputs with
std::string input_suffix = ""; // string to suffix user inputs with
std::string grammar = ""; // optional BNF-like grammar to constrain sampling
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string lora_adapter = ""; // lora adapter path
+423
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@@ -0,0 +1,423 @@
#include "grammar-parser.h"
#include <cstdint>
#include <cwchar>
#include <string>
#include <utility>
#include <stdexcept>
#include <exception>
namespace grammar_parser {
// NOTE: assumes valid utf8 (but checks for overrun)
// copied from llama.cpp
std::pair<uint32_t, const char *> decode_utf8(const char * src) {
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t first_byte = static_cast<uint8_t>(*src);
uint8_t highbits = first_byte >> 4;
int len = lookup[highbits];
uint8_t mask = (1 << (8 - len)) - 1;
uint32_t value = first_byte & mask;
const char * end = src + len; // may overrun!
const char * pos = src + 1;
for ( ; pos < end && *pos; pos++) {
value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
}
return std::make_pair(value, pos);
}
uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) {
uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id));
return result.first->second;
}
uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) {
uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id;
return next_id;
}
void add_rule(
parse_state & state,
uint32_t rule_id,
const std::vector<llama_grammar_element> & rule) {
if (state.rules.size() <= rule_id) {
state.rules.resize(rule_id + 1);
}
state.rules[rule_id] = rule;
}
bool is_word_char(char c) {
return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');
}
std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
const char * pos = src;
const char * end = src + size;
uint32_t value = 0;
for ( ; pos < end && *pos; pos++) {
value <<= 4;
char c = *pos;
if ('a' <= c && c <= 'f') {
value += c - 'a' + 10;
} else if ('A' <= c && c <= 'F') {
value += c - 'A' + 10;
} else if ('0' <= c && c <= '9') {
value += c - '0';
} else {
break;
}
}
if (pos != end) {
throw std::runtime_error("expecting " + std::to_string(size) + " hex chars at " + src);
}
return std::make_pair(value, pos);
}
const char * parse_space(const char * src, bool newline_ok) {
const char * pos = src;
while (*pos == ' ' || *pos == '\t' || *pos == '#' ||
(newline_ok && (*pos == '\r' || *pos == '\n'))) {
if (*pos == '#') {
while (*pos && *pos != '\r' && *pos != '\n') {
pos++;
}
} else {
pos++;
}
}
return pos;
}
const char * parse_name(const char * src) {
const char * pos = src;
while (is_word_char(*pos)) {
pos++;
}
if (pos == src) {
throw std::runtime_error(std::string("expecting name at ") + src);
}
return pos;
}
std::pair<uint32_t, const char *> parse_char(const char * src) {
if (*src == '\\') {
switch (src[1]) {
case 'x': return parse_hex(src + 2, 2);
case 'u': return parse_hex(src + 2, 4);
case 'U': return parse_hex(src + 2, 8);
case 't': return std::make_pair('\t', src + 2);
case 'r': return std::make_pair('\r', src + 2);
case 'n': return std::make_pair('\n', src + 2);
case '\\':
case '"':
case '[':
case ']':
return std::make_pair(src[1], src + 2);
default:
throw std::runtime_error(std::string("unknown escape at ") + src);
}
} else if (*src) {
return decode_utf8(src);
}
throw std::runtime_error("unexpected end of input");
}
const char * parse_alternates(
parse_state & state,
const char * src,
const std::string & rule_name,
uint32_t rule_id,
bool is_nested);
const char * parse_sequence(
parse_state & state,
const char * src,
const std::string & rule_name,
std::vector<llama_grammar_element> & out_elements,
bool is_nested) {
size_t last_sym_start = out_elements.size();
const char * pos = src;
while (*pos) {
if (*pos == '"') { // literal string
pos++;
last_sym_start = out_elements.size();
while (*pos != '"') {
auto char_pair = parse_char(pos);
pos = char_pair.second;
out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first});
}
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '[') { // char range(s)
pos++;
enum llama_gretype start_type = LLAMA_GRETYPE_CHAR;
if (*pos == '^') {
pos++;
start_type = LLAMA_GRETYPE_CHAR_NOT;
}
last_sym_start = out_elements.size();
while (*pos != ']') {
auto char_pair = parse_char(pos);
pos = char_pair.second;
enum llama_gretype type = last_sym_start < out_elements.size()
? LLAMA_GRETYPE_CHAR_ALT
: start_type;
out_elements.push_back({type, char_pair.first});
if (pos[0] == '-' && pos[1] != ']') {
auto endchar_pair = parse_char(pos + 1);
pos = endchar_pair.second;
out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});
}
}
pos = parse_space(pos + 1, is_nested);
} else if (is_word_char(*pos)) { // rule reference
const char * name_end = parse_name(pos);
uint32_t ref_rule_id = get_symbol_id(state, pos, name_end - pos);
pos = parse_space(name_end, is_nested);
last_sym_start = out_elements.size();
out_elements.push_back({LLAMA_GRETYPE_RULE_REF, ref_rule_id});
} else if (*pos == '(') { // grouping
// parse nested alternates into synthesized rule
pos = parse_space(pos + 1, true);
uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
pos = parse_alternates(state, pos, rule_name, sub_rule_id, true);
last_sym_start = out_elements.size();
// output reference to synthesized rule
out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
if (*pos != ')') {
throw std::runtime_error(std::string("expecting ')' at ") + pos);
}
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);
}
// apply transformation to previous symbol (last_sym_start to end) according to
// rewrite rules:
// S* --> S' ::= S S' |
// S+ --> S' ::= S S' | S
// S? --> S' ::= S |
uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
std::vector<llama_grammar_element> sub_rule;
// add preceding symbol to generated rule
sub_rule.insert(
sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
if (*pos == '*' || *pos == '+') {
// cause generated rule to recurse
sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
}
// mark start of alternate def
sub_rule.push_back({LLAMA_GRETYPE_ALT, 0});
if (*pos == '+') {
// add preceding symbol as alternate only for '+' (otherwise empty)
sub_rule.insert(
sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
}
sub_rule.push_back({LLAMA_GRETYPE_END, 0});
add_rule(state, sub_rule_id, sub_rule);
// in original rule, replace previous symbol with reference to generated rule
out_elements.resize(last_sym_start);
out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
pos = parse_space(pos + 1, is_nested);
} else {
break;
}
}
return pos;
}
const char * parse_alternates(
parse_state & state,
const char * src,
const std::string & rule_name,
uint32_t rule_id,
bool is_nested) {
std::vector<llama_grammar_element> rule;
const char * pos = parse_sequence(state, src, rule_name, rule, is_nested);
while (*pos == '|') {
rule.push_back({LLAMA_GRETYPE_ALT, 0});
pos = parse_space(pos + 1, true);
pos = parse_sequence(state, pos, rule_name, rule, is_nested);
}
rule.push_back({LLAMA_GRETYPE_END, 0});
add_rule(state, rule_id, rule);
return pos;
}
const char * parse_rule(parse_state & state, const char * src) {
const char * name_end = parse_name(src);
const char * pos = parse_space(name_end, false);
size_t name_len = name_end - src;
uint32_t rule_id = get_symbol_id(state, src, name_len);
const std::string name(src, name_len);
if (!(pos[0] == ':' && pos[1] == ':' && pos[2] == '=')) {
throw std::runtime_error(std::string("expecting ::= at ") + pos);
}
pos = parse_space(pos + 3, true);
pos = parse_alternates(state, pos, name, rule_id, false);
if (*pos == '\r') {
pos += pos[1] == '\n' ? 2 : 1;
} else if (*pos == '\n') {
pos++;
} else if (*pos) {
throw std::runtime_error(std::string("expecting newline or end at ") + pos);
}
return parse_space(pos, true);
}
parse_state parse(const char * src) {
try {
parse_state state;
const char * pos = parse_space(src, true);
while (*pos) {
pos = parse_rule(state, pos);
}
return state;
} catch (const std::exception & err) {
fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
return parse_state();
}
}
void print_grammar_char(FILE * file, uint32_t c) {
if (0x20 <= c && c <= 0x7f) {
fprintf(file, "%c", static_cast<char>(c));
} else {
// cop out of encoding UTF-8
fprintf(file, "<U+%04X>", c);
}
}
bool is_char_element(llama_grammar_element elem) {
switch (elem.type) {
case LLAMA_GRETYPE_CHAR: return true;
case LLAMA_GRETYPE_CHAR_NOT: return true;
case LLAMA_GRETYPE_CHAR_ALT: return true;
case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;
default: return false;
}
}
void print_rule_binary(FILE * file, const std::vector<llama_grammar_element> & rule) {
for (auto elem : rule) {
switch (elem.type) {
case LLAMA_GRETYPE_END: fprintf(file, "END"); break;
case LLAMA_GRETYPE_ALT: fprintf(file, "ALT"); break;
case LLAMA_GRETYPE_RULE_REF: fprintf(file, "RULE_REF"); break;
case LLAMA_GRETYPE_CHAR: fprintf(file, "CHAR"); break;
case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break;
case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break;
}
switch (elem.type) {
case LLAMA_GRETYPE_END:
case LLAMA_GRETYPE_ALT:
case LLAMA_GRETYPE_RULE_REF:
fprintf(file, "(%u) ", elem.value);
break;
case LLAMA_GRETYPE_CHAR:
case LLAMA_GRETYPE_CHAR_NOT:
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
case LLAMA_GRETYPE_CHAR_ALT:
fprintf(file, "(\"");
print_grammar_char(file, elem.value);
fprintf(file, "\") ");
break;
}
}
fprintf(file, "\n");
}
void print_rule(
FILE * file,
uint32_t rule_id,
const std::vector<llama_grammar_element> & rule,
const std::map<uint32_t, std::string> & symbol_id_names) {
if (rule.empty() || rule.back().type != LLAMA_GRETYPE_END) {
throw std::runtime_error(
"malformed rule, does not end with LLAMA_GRETYPE_END: " + std::to_string(rule_id));
}
fprintf(file, "%s ::= ", symbol_id_names.at(rule_id).c_str());
for (size_t i = 0, end = rule.size() - 1; i < end; i++) {
llama_grammar_element elem = rule[i];
switch (elem.type) {
case LLAMA_GRETYPE_END:
throw std::runtime_error(
"unexpected end of rule: " + std::to_string(rule_id) + "," +
std::to_string(i));
case LLAMA_GRETYPE_ALT:
fprintf(file, "| ");
break;
case LLAMA_GRETYPE_RULE_REF:
fprintf(file, "%s ", symbol_id_names.at(elem.value).c_str());
break;
case LLAMA_GRETYPE_CHAR:
fprintf(file, "[");
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_NOT:
fprintf(file, "[^");
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
if (i == 0 || !is_char_element(rule[i - 1])) {
throw std::runtime_error(
"LLAMA_GRETYPE_CHAR_RNG_UPPER without preceding char: " +
std::to_string(rule_id) + "," + std::to_string(i));
}
fprintf(file, "-");
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_ALT:
if (i == 0 || !is_char_element(rule[i - 1])) {
throw std::runtime_error(
"LLAMA_GRETYPE_CHAR_ALT without preceding char: " +
std::to_string(rule_id) + "," + std::to_string(i));
}
print_grammar_char(file, elem.value);
break;
}
if (is_char_element(elem)) {
switch (rule[i + 1].type) {
case LLAMA_GRETYPE_CHAR_ALT:
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
break;
default:
fprintf(file, "] ");
}
}
}
fprintf(file, "\n");
}
void print_grammar(FILE * file, const parse_state & state) {
try {
std::map<uint32_t, std::string> symbol_id_names;
for (auto kv : state.symbol_ids) {
symbol_id_names[kv.second] = kv.first;
}
for (size_t i = 0, end = state.rules.size(); i < end; i++) {
// fprintf(file, "%zu: ", i);
// print_rule_binary(file, state.rules[i]);
print_rule(file, i, state.rules[i], symbol_id_names);
// fprintf(file, "\n");
}
} catch (const std::exception & err) {
fprintf(stderr, "\n%s: error printing grammar: %s\n", __func__, err.what());
}
}
std::vector<const llama_grammar_element *> parse_state::c_rules() {
std::vector<const llama_grammar_element *> ret;
for (const auto & rule : rules) {
ret.push_back(rule.data());
}
return ret;
}
}
+29
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@@ -0,0 +1,29 @@
// Implements a parser for an extended Backus-Naur form (BNF), producing the
// binary context-free grammar format specified by llama.h. Supports character
// ranges, grouping, and repetition operators. As an example, a grammar for
// arithmetic might look like:
//
// root ::= expr
// expr ::= term ([-+*/] term)*
// term ::= num | "(" space expr ")" space
// num ::= [0-9]+ space
// space ::= [ \t\n]*
#pragma once
#include "llama.h"
#include <vector>
#include <map>
#include <cstdint>
#include <string>
namespace grammar_parser {
struct parse_state {
std::map<std::string, uint32_t> symbol_ids;
std::vector<std::vector<llama_grammar_element>> rules;
std::vector<const llama_grammar_element *> c_rules();
};
parse_state parse(const char * src);
void print_grammar(FILE * file, const parse_state & state);
}
+49
View File
@@ -6,6 +6,7 @@
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include "grammar-parser.h"
#include <cassert>
#include <cinttypes>
@@ -337,6 +338,31 @@ int main(int argc, char ** argv) {
fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
fprintf(stderr, "\n\n");
grammar_parser::parse_state parsed_grammar;
llama_grammar * grammar = NULL;
if (!params.grammar.empty()) {
parsed_grammar = grammar_parser::parse(params.grammar.c_str());
// will be empty (default) if there are parse errors
if (parsed_grammar.rules.empty()) {
return 1;
}
fprintf(stderr, "%s: grammar:\n", __func__);
grammar_parser::print_grammar(stderr, parsed_grammar);
fprintf(stderr, "\n");
{
auto it = params.logit_bias.find(llama_token_eos());
if (it != params.logit_bias.end() && it->second == -INFINITY) {
fprintf(stderr,
"%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
}
}
std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
}
// 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);
@@ -570,6 +596,10 @@ int main(int argc, char ** argv) {
logits[llama_token_nl()] = nl_logit;
}
if (grammar != NULL) {
llama_sample_grammar(ctx, &candidates_p, grammar);
}
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
@@ -595,6 +625,10 @@ int main(int argc, char ** argv) {
}
// printf("`%d`", candidates_p.size);
if (grammar != NULL) {
llama_grammar_accept_token(ctx, grammar, id);
}
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(id);
}
@@ -725,6 +759,18 @@ int main(int argc, char ** argv) {
}
if (n_past > 0) {
if (is_interacting) {
// reset grammar state if we're restarting generation
if (grammar != NULL) {
llama_grammar_free(grammar);
std::vector<const llama_grammar_element *> grammar_rules(
parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(),
parsed_grammar.symbol_ids.at("root"));
}
}
is_interacting = false;
}
}
@@ -756,6 +802,9 @@ int main(int argc, char ** argv) {
llama_free(ctx);
llama_free_model(model);
if (grammar != NULL) {
llama_grammar_free(grammar);
}
llama_backend_free();
return 0;
+47 -35
View File
@@ -601,47 +601,48 @@ struct llama_server_context
static void server_print_usage(const char *argv0, const gpt_params &params,
const server_params &sparams)
{
fprintf(stderr, "usage: %s [options]\n", argv0);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stderr, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stderr, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stderr, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
fprintf(stderr, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
fprintf(stderr, " not recommended: doubles context memory required and no measurable increase in quality\n");
fprintf(stdout, "usage: %s [options]\n", argv0);
fprintf(stdout, "\n");
fprintf(stdout, "options:\n");
fprintf(stdout, " -h, --help show this help message and exit\n");
fprintf(stdout, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stdout, " --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
fprintf(stdout, " not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_mlock_supported())
{
fprintf(stderr, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n");
}
if (llama_mmap_supported())
{
fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
fprintf(stdout, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
fprintf(stderr, " number of layers to store in VRAM\n");
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
fprintf(stdout, " -ngl N, --n-gpu-layers N\n");
fprintf(stdout, " number of layers to store in VRAM\n");
fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n");
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n");
fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n");
#endif
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, " -a ALIAS, --alias ALIAS\n");
fprintf(stderr, " set an alias for the model, will be added as `model` field in completion response\n");
fprintf(stderr, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port);
fprintf(stderr, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
fprintf(stderr, "\n");
fprintf(stdout, " -m FNAME, --model FNAME\n");
fprintf(stdout, " model path (default: %s)\n", params.model.c_str());
fprintf(stdout, " -a ALIAS, --alias ALIAS\n");
fprintf(stdout, " set an alias for the model, will be added as `model` field in completion response\n");
fprintf(stdout, " --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n");
fprintf(stdout, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
fprintf(stdout, " --port PORT port to listen (default (default: %d)\n", sparams.port);
fprintf(stdout, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str());
fprintf(stdout, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
fprintf(stdout, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
fprintf(stdout, "\n");
}
static void server_params_parse(int argc, char **argv, server_params &sparams,
@@ -724,9 +725,19 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.n_ctx = std::stoi(argv[i]);
}
else if (arg == "-gqa" || arg == "--gqa")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_gqa = std::stoi(argv[i]);
}
else if (arg == "--rope-freq-base")
{
if (++i >= argc) {
if (++i >= argc)
{
invalid_param = true;
break;
}
@@ -734,7 +745,8 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
else if (arg == "--rope-freq-scale")
{
if (++i >= argc) {
if (++i >= argc)
{
invalid_param = true;
break;
}
+96 -39
View File
@@ -1073,10 +1073,12 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
uint16_t q16[8];
const uint8_t * q4 = (const uint8_t *)q16;
for (int i = ix; i < num_blocks_per_row; i += 2) {
const uint8_t * ql1 = x[i].qs + q_offset;
const uint8_t * ql2 = ql1 + 64;
const uint8_t * qh = x[i].qh + l0;
const float * y1 = yy + i*QK_K + y_offset;
const float * y2 = y1 + 128;
@@ -1092,15 +1094,25 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
float4 sum = {0.f, 0.f, 0.f, 0.f};
float smin = 0;
const uint16_t * q1 = (const uint16_t *)ql1;
const uint16_t * q2 = q1 + 32;
q16[0] = q1[0] & 0x0f0f;
q16[1] = q1[8] & 0x0f0f;
q16[2] = (q1[0] >> 4) & 0x0f0f;
q16[3] = (q1[8] >> 4) & 0x0f0f;
q16[4] = q2[0] & 0x0f0f;
q16[5] = q2[8] & 0x0f0f;
q16[6] = (q2[0] >> 4) & 0x0f0f;
q16[7] = (q2[8] >> 4) & 0x0f0f;
for (int l = 0; l < n; ++l) {
sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+ y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0));
sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+ y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0));
sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+ y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0));
sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+ y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0));
sum.x += y1[l+ 0] * (q4[l +0] + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+ y1[l+16] * (q4[l +2] + (qh[l+16] & (hm1 << 0) ? 16 : 0));
sum.y += y1[l+32] * (q4[l +4] + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+ y1[l+48] * (q4[l +6] + (qh[l+16] & (hm1 << 1) ? 16 : 0));
sum.z += y2[l+ 0] * (q4[l +8] + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+ y2[l+16] * (q4[l+10] + (qh[l+16] & (hm2 << 0) ? 16 : 0));
sum.w += y2[l+32] * (q4[l+12] + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+ y2[l+48] * (q4[l+14] + (qh[l+16] & (hm2 << 1) ? 16 : 0));
smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+ (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
}
@@ -1554,7 +1566,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const block_q4_K * bq4_K = (const block_q4_K *) vbq;
const int bq8_offset = QR4_K * (iqs / QI8_1); // 0, 2, 4, 6
// iqs is in 0...15. bq8_offset = 2 * (iqs/4) -> bq8_offset = 0, 2, 4, 6
const int bq8_offset = QR4_K * (iqs / (QI8_1/2));
float sumf_d = 0.0f;
float sumf_m = 0.0f;
@@ -1562,7 +1575,14 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
const float d = bq4_K->d;
const float dmin = bq4_K->dmin;
const int v = *((int *) &bq4_K->qs[sizeof(int) * iqs]);
// iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12
// iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44
// iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76
// iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108
const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * (iqs%4));
const int v1 = q4[0];
const int v2 = q4[4];
const uint16_t * scales = (const uint16_t *)bq4_K->scales;
uint16_t aux[2];
@@ -1580,13 +1600,19 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
for (int i = 0; i < QR4_K; ++i) {
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % QI8_1)]);
const float d8i = bq8i->d;
const int * q8 = (const int *)bq8i->qs + (iqs%4);
const int ui1 = q8[0];
const int ui2 = q8[4];
const int vi = (v >> (4*i)) & 0x0F0F0F0F;
const int vi1 = (v1 >> (4*i)) & 0x0F0F0F0F;
const int vi2 = (v2 >> (4*i)) & 0x0F0F0F0F;
sumf_d += d8i * (__dp4a(vi, ui, 0) * sc[i]); // SIMD dot product
sumf_m += d8i * (__dp4a(0x01010101, ui, 0) * m[i]); // multiply constant part of q4_K with sum of q8_1 values
const int dot1 = __dp4a(vi2, ui2, __dp4a(vi1, ui1, 0)); // SIMD dot product
const int dot2 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
sumf_d += d8i * (dot1 * sc[i]);
sumf_m += d8i * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values
}
return d*sumf_d - dmin*sumf_m;
@@ -1601,7 +1627,9 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
const block_q5_K * bq5_K = (const block_q5_K *) vbq;
const int bq8_offset = QR5_K * (iqs / QI8_1);
const int bq8_offset = QR5_K * (iqs / (QI8_1/2));
const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * (iqs%4));
const int * qh = (const int *)(bq5_K->qh + 4 * (iqs%4));
float sumf_d = 0.0f;
float sumf_m = 0.0f;
@@ -1609,28 +1637,48 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
const float d = bq5_K->d;
const float dmin = bq5_K->dmin;
const int vl = *((int *) &bq5_K->qs[sizeof(int) * iqs]);
const int vl1 = ql[0];
const int vl2 = ql[4];
const int vh = (*((int *) &bq5_K->qh[sizeof(int) * (iqs % (QI5_K/4))])) >> bq8_offset;
const int vh1 = qh[0] >> bq8_offset;
const int vh2 = qh[4] >> bq8_offset;
const uint16_t * scales = (const uint16_t *)bq5_K->scales;
uint16_t aux[2];
const int j = bq8_offset/2;
if (j < 2) {
aux[0] = scales[j+0] & 0x3f3f;
aux[1] = scales[j+2] & 0x3f3f;
} else {
aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2);
aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2);
}
const uint8_t * sc = (const uint8_t *)aux;
const uint8_t * m = sc + 2;
for (int i = 0; i < QR5_K; ++i) {
const int isc = bq8_offset + i;
uint8_t sc, m;
get_scale_min_k4(isc, bq5_K->scales, sc, m);
const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
const int ui = *((int*) &bq8i->qs[sizeof(int) * (iqs % QI8_1)]);
const float d8i = bq8i->d;
const int * q8 = (const int *)bq8i->qs + (iqs%4);
const int ui1 = q8[0];
const int ui2 = q8[4];
const int vil = (vl >> (4*i)) & 0x0F0F0F0F;
const int vil1 = (vl1 >> (4*i)) & 0x0F0F0F0F;
const int vil2 = (vl2 >> (4*i)) & 0x0F0F0F0F;
const int vih = ((vh >> i) << 4) & 0x10101010;
const int vih1 = ((vh1 >> i) << 4) & 0x10101010;
const int vih2 = ((vh2 >> i) << 4) & 0x10101010;
const int vi = vil | vih;
const int vi1 = vil1 | vih1;
const int vi2 = vil2 | vih2;
const int dot1 = __dp4a(vi2, ui2, __dp4a(vi1, ui1, 0)); // SIMD dot product
const int dot2 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0));
sumf_d += d8i * (dot1 * sc[i]);
sumf_m += d8i * (dot2 * m[i]);
sumf_d += d8i * (__dp4a(vi, ui, 0) * sc); // SIMD dot product
sumf_m += d8i * (__dp4a(0x01010101, ui, 0) * m); // multiply constant part of q5_K with sum of q8_1 values
}
return d*sumf_d - dmin*sumf_m;
@@ -2306,7 +2354,10 @@ static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI4_K, block_q4_K, vec_dot_q4_K_q8_1>
// Note: we use QI4_K/2 instead of QI4_K to make the dot product template require 4 groups of quants to be processed per
// kernel call instead of 2. This results in a better perfmance because the cost of computing the k-quant scales
// is better amortized.
mul_mat_vec_q<QK_K, QI4_K/2, block_q4_K, vec_dot_q4_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
@@ -2315,7 +2366,10 @@ static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, float *
const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
const dim3 block_nums(1, block_num_y, 1);
const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
mul_mat_vec_q<QK_K, QI5_K, block_q5_K, vec_dot_q5_K_q8_1>
// Note: we use QI5_K/2 instead of QI5_K to make the dot product template require 4 groups of quants to be processed per
// kernel call instead of 2. This results in a better perfmance because the cost of computing the k-quant scales
// is better amortized.
mul_mat_vec_q<QK_K, QI5_K/2, block_q5_K, vec_dot_q5_K_q8_1>
<<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
}
@@ -2742,6 +2796,7 @@ inline void ggml_cuda_op_mul(
(void) dst;
(void) src0_ddq_i;
(void) i02;
(void) i1;
}
inline void ggml_cuda_op_gelu(
@@ -3037,15 +3092,15 @@ inline void ggml_cuda_op_rope(
const int64_t ne00 = src0->ne[0];
const int64_t i01_diff = i01_high - i01_low;
const int n_past = ((int32_t *) src1->data)[0];
const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2];
const int n_ctx = ((int32_t *) src1->data)[3];
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
const int n_ctx = ((int32_t *) dst->op_params)[3];
// RoPE alteration for extended context
float freq_base, freq_scale;
memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale;
@@ -3061,6 +3116,7 @@ inline void ggml_cuda_op_rope(
rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main);
}
(void) src1;
(void) dst;
(void) src0_ddq_i;
(void) src1_ddf_i;
@@ -3079,11 +3135,12 @@ inline void ggml_cuda_op_diag_mask_inf(
const int64_t ne01 = src0->ne[1];
const int64_t i01_diff = i01_high - i01_low;
const int n_past = ((int32_t *) src1->data)[0];
const int n_past = ((int32_t *) dst->op_params)[0];
// compute
diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main);
(void) src1;
(void) dst;
(void) src0_ddq_i;
(void) src1_ddf_i;
@@ -3803,7 +3860,7 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
size_t offset = 0;
if (tensor->op == GGML_OP_VIEW) {
memcpy(&offset, tensor->src[2]->data, sizeof(size_t));
memcpy(&offset, tensor->op_params, sizeof(size_t));
}
extra = ggml_cuda_alloc_temp_tensor_extra();
extra->data_device[g_main_device] = src0_ddc + offset;
+10 -10
View File
@@ -585,7 +585,7 @@ void ggml_metal_graph_compute(
encoder = [command_buffer computeCommandEncoder];
}
const int n_past = ((int32_t *)(src1->data))[0];
const int n_past = ((int32_t *)(dst->op_params))[0];
[encoder setComputePipelineState:ctx->pipeline_diag_mask_inf];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@@ -850,9 +850,10 @@ void ggml_metal_graph_compute(
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int n_past = ((int32_t *) src1->data)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) dst->op_params)[1];
float max_bias;
memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
if (__builtin_popcount(n_head) != 1) {
GGML_ASSERT(false && "only power-of-two n_head implemented");
@@ -890,15 +891,14 @@ void ggml_metal_graph_compute(
encoder = [command_buffer computeCommandEncoder];
}
const int n_dims = ((int32_t *) src1->data)[1];
const int mode = ((int32_t *) src1->data)[2];
const int n_past = ((int32_t *)(src1->data))[0];
const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
float freq_base;
float freq_scale;
memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
[encoder setComputePipelineState:ctx->pipeline_rope];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
+201 -465
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File diff suppressed because it is too large Load Diff
+4
View File
@@ -199,6 +199,7 @@
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_SRC 6
#define GGML_MAX_NAME 48
#define GGML_MAX_OP_PARAMS 32
#define GGML_DEFAULT_N_THREADS 4
@@ -418,6 +419,9 @@ extern "C" {
// compute data
enum ggml_op op;
// op params - allocated as int32_t for alignment
int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(uint32_t)];
bool is_param;
struct ggml_tensor * grad;
+6
View File
@@ -0,0 +1,6 @@
root ::= (expr "=" ws term "\n")+
expr ::= term ([-+*/] term)*
term ::= ident | num | "(" ws expr ")" ws
ident ::= [a-z] [a-z0-9_]* ws
num ::= [0-9]+ ws
ws ::= [ \t\n]*
+13
View File
@@ -0,0 +1,13 @@
# Specifies chess moves as a list in algebraic notation, using PGN conventions
# Force first move to "1. ", then any 1-2 digit number after, relying on model to follow the pattern
root ::= "1. " move " " move "\n" ([1-9] [0-9]? ". " move " " move "\n")+
move ::= (pawn | nonpawn | castle) [+#]?
# piece type, optional file/rank, optional capture, dest file & rank
nonpawn ::= [NBKQR] [a-h]? [1-8]? "x"? [a-h] [1-8]
# optional file & capture, dest file & rank, optional promotion
pawn ::= ([a-h] "x")? [a-h] [1-8] ("=" [NBKQR])?
castle ::= "O-O" "-O"?
+7
View File
@@ -0,0 +1,7 @@
# A probably incorrect grammar for Japanese
root ::= jp-char+ ([ \t\n] jp-char+)*
jp-char ::= hiragana | katakana | punctuation | cjk
hiragana ::= [ぁ-ゟ]
katakana ::= [ァ-ヿ]
punctuation ::= [、-〾]
cjk ::= [一-鿿]
+29
View File
@@ -0,0 +1,29 @@
# Grammar for subset of JSON - doesn't support full string or number syntax
root ::= object
value ::= object | array | string | number | boolean | "null"
object ::=
"{" ws (
string ":" ws value
("," ws string ":" ws value)*
)? "}"
array ::=
"[" ws (
value
("," ws value)*
)? "]"
string ::=
"\"" (
[^"\\] |
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
)* "\"" ws
# Only plain integers currently
number ::= "-"? [0-9]+ ws
boolean ::= ("true" | "false") ws
# Optional space: by convention, applied in this grammar after literal chars when allowed
ws ::= ([ \t\n] ws)?
+4
View File
@@ -0,0 +1,4 @@
root ::= item+
# Excludes various line break characters
item ::= "- " [^\r\n\x0b\x0c\x85\u2028\u2029]+ "\n"
+2 -3
View File
@@ -3297,8 +3297,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
#else
uint8_t aux8[QK_K];
int8_t aux8[QK_K];
int16_t aux16[16];
float sums [8];
memset(sums, 0, 8*sizeof(float));
@@ -3308,7 +3307,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const uint8_t * restrict q4 = x[i].qs;
const uint8_t * restrict hm = x[i].qh;
const int8_t * restrict q8 = y[i].qs;
uint8_t * restrict a = aux8;
int8_t * restrict a = aux8;
for (int l = 0; l < 32; ++l) {
a[l+ 0] = q4[l] & 0xF;
a[l+32] = q4[l] >> 4;
+337
View File
@@ -1965,6 +1965,279 @@ static std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, co
return output;
}
//
// grammar - internal
//
struct llama_grammar {
const std::vector<std::vector<llama_grammar_element>> rules;
std::vector<std::vector<const llama_grammar_element *>> stacks;
};
struct llama_grammar_candidate {
size_t index;
const uint32_t * code_points;
};
// NOTE: assumes valid utf8 (but checks for overrun)
// adds a terminating 0 for use as pointer
std::vector<uint32_t> decode_utf8(const char * src) {
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
const char * pos = src;
std::vector<uint32_t> code_points;
while (*pos != 0) {
uint8_t first_byte = static_cast<uint8_t>(*pos);
uint8_t highbits = first_byte >> 4;
int len = lookup[highbits];
uint8_t mask = (1 << (8 - len)) - 1;
uint32_t value = first_byte & mask;
const char * end = pos + len; // may overrun!
++pos;
for ( ; pos < end && *pos != 0; ++pos) {
value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
}
code_points.push_back(value);
}
code_points.push_back(0);
return code_points;
}
// returns true iff pos points to the end of one of the definitions of a rule
static bool llama_grammar_is_end_of_sequence(const llama_grammar_element * pos) {
switch (pos->type) {
case LLAMA_GRETYPE_END: return true;
case LLAMA_GRETYPE_ALT: return true;
default: return false;
}
}
// returns true iff chr satisfies the char range at pos (regular or inverse range)
// asserts that pos is pointing to a char range element
static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
const llama_grammar_element * pos,
const uint32_t chr) {
bool found = false;
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
LLAMA_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
do {
if (pos[1].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
// inclusive range, e.g. [a-z]
found = found || (pos->value <= chr && chr <= pos[1].value);
pos += 2;
} else {
// exact char match, e.g. [a] or "a"
found = found || pos->value == chr;
pos += 1;
}
} while (pos->type == LLAMA_GRETYPE_CHAR_ALT);
return std::make_pair(found == is_positive_char, pos);
}
// transforms a grammar pushdown stack into N possible stacks, all ending
// at a character range (terminal element)
static void llama_grammar_advance_stack(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<const llama_grammar_element *> & stack,
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
if (stack.empty()) {
new_stacks.push_back(stack);
return;
}
const llama_grammar_element * pos = stack.back();
switch (pos->type) {
case LLAMA_GRETYPE_RULE_REF: {
const size_t rule_id = static_cast<size_t>(pos->value);
const llama_grammar_element * subpos = rules[rule_id].data();
do {
// init new stack without the top (pos)
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(pos + 1)) {
// if this rule ref is followed by another element, add that to stack
new_stack.push_back(pos + 1);
}
if (!llama_grammar_is_end_of_sequence(subpos)) {
// if alternate is nonempty, add to stack
new_stack.push_back(subpos);
}
llama_grammar_advance_stack(rules, new_stack, new_stacks);
while (!llama_grammar_is_end_of_sequence(subpos)) {
// scan to end of alternate def
subpos++;
}
if (subpos->type == LLAMA_GRETYPE_ALT) {
// there's another alternate def of this rule to process
subpos++;
} else {
break;
}
} while (true);
break;
}
case LLAMA_GRETYPE_CHAR:
case LLAMA_GRETYPE_CHAR_NOT:
new_stacks.push_back(stack);
break;
default:
// end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
// (LLAMA_GRETYPE_CHAR_ALT, LLAMA_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
// those
LLAMA_ASSERT(false);
}
}
// takes a set of possible pushdown stacks on a grammar, which are required to
// be positioned at a character range (see `llama_grammar_advance_stack`), and
// produces the N possible stacks if the given char is accepted at those
// positions
static std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const uint32_t chr) {
std::vector<std::vector<const llama_grammar_element *>> new_stacks;
for (const auto & stack : stacks) {
if (stack.empty()) {
continue;
}
auto match = llama_grammar_match_char(stack.back(), chr);
if (match.first) {
const llama_grammar_element * pos = match.second;
// update top of stack to next element, if any
std::vector<const llama_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(pos)) {
new_stack.push_back(pos);
}
llama_grammar_advance_stack(rules, new_stack, new_stacks);
}
}
return new_stacks;
}
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const std::vector<llama_grammar_candidate> & candidates);
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<const llama_grammar_element *> & stack,
const std::vector<llama_grammar_candidate> & candidates) {
std::vector<llama_grammar_candidate> rejects;
if (stack.empty()) {
// accept nothing; EOS is handled elsewhere
rejects.insert(rejects.end(), candidates.begin(), candidates.end());
return rejects;
}
const llama_grammar_element * stack_pos = stack.back();
std::vector<llama_grammar_candidate> next_candidates;
for (auto tok : candidates) {
if (llama_grammar_match_char(stack_pos, tok.code_points[0]).first) {
if (tok.code_points[1] != 0) {
next_candidates.push_back({ tok.index, tok.code_points + 1 });
}
} else {
rejects.push_back(tok);
}
}
auto stack_pos_after = llama_grammar_match_char(stack_pos, 0).second;
// update top of stack to next element, if any
std::vector<const llama_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
if (!llama_grammar_is_end_of_sequence(stack_pos_after)) {
stack_after.push_back(stack_pos_after);
}
std::vector<std::vector<const llama_grammar_element *>> next_stacks;
llama_grammar_advance_stack(rules, stack_after, next_stacks);
auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
for (auto tok : next_rejects) {
rejects.push_back({ tok.index, tok.code_points - 1 });
}
return rejects;
}
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const std::vector<llama_grammar_candidate> & candidates) {
LLAMA_ASSERT(!stacks.empty()); // REVIEW
if (candidates.empty()) {
return std::vector<llama_grammar_candidate>();
}
auto rejects = llama_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);
for (size_t i = 1, size = stacks.size(); i < size; ++i) {
rejects = llama_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
}
return rejects;
}
//
// grammar - external
//
struct llama_grammar * llama_grammar_init(
const llama_grammar_element ** rules,
size_t n_rules,
size_t start_rule_index) {
const llama_grammar_element * pos;
// copy rule definitions into vectors
std::vector<std::vector<llama_grammar_element>> vec_rules(n_rules);
for (size_t i = 0; i < n_rules; i++) {
for (pos = rules[i]; pos->type != LLAMA_GRETYPE_END; pos++) {
vec_rules[i].push_back(*pos);
}
vec_rules[i].push_back({LLAMA_GRETYPE_END, 0});
}
// loop over alternates of start rule to build initial stacks
std::vector<std::vector<const llama_grammar_element *>> stacks;
pos = rules[start_rule_index];
do {
std::vector<const llama_grammar_element *> stack;
if (!llama_grammar_is_end_of_sequence(pos)) {
// if alternate is nonempty, add to stack
stack.push_back(pos);
}
llama_grammar_advance_stack(vec_rules, stack, stacks);
while (!llama_grammar_is_end_of_sequence(pos)) {
// scan to end of alternate def
pos++;
}
if (pos->type == LLAMA_GRETYPE_ALT) {
// there's another alternate def of this rule to process
pos++;
} else {
break;
}
} while (true);
return new llama_grammar{ std::move(vec_rules), std::move(stacks) };
}
void llama_grammar_free(struct llama_grammar * grammar) {
delete grammar;
}
//
// sampling
//
@@ -2250,6 +2523,47 @@ void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, l
}
}
void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
assert(ctx);
const int64_t t_start_sample_us = ggml_time_us();
bool allow_eos = false;
for (const auto & stack : grammar->stacks) {
if (stack.empty()) {
allow_eos = true;
break;
}
}
const llama_token eos = llama_token_eos();
std::vector<std::vector<uint32_t>> candidates_decoded;
std::vector<llama_grammar_candidate> candidates_grammar;
for (size_t i = 0; i < candidates->size; ++i) {
const llama_token id = candidates->data[i].id;
const char * str = llama_token_to_str(ctx, id);
if (id == eos) {
if (!allow_eos) {
candidates->data[i].logit = -INFINITY;
}
} else if (*str == 0) {
candidates->data[i].logit = -INFINITY;
} else {
candidates_decoded.push_back(decode_utf8(str));
candidates_grammar.push_back({ i, candidates_decoded.back().data() });
}
}
const auto rejects =
llama_grammar_reject_candidates(grammar->rules, grammar->stacks, candidates_grammar);
for (auto & reject : rejects) {
candidates->data[reject.index].logit = -INFINITY;
}
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
static void llama_log_softmax(float * array, size_t size) {
float max_l = *std::max_element(array, array + size);
float sum = 0.f;
@@ -2425,6 +2739,29 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
return result;
}
void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
const int64_t t_start_sample_us = ggml_time_us();
if (token == llama_token_eos()) {
for (const auto & stack : grammar->stacks) {
if (stack.empty()) {
return;
}
}
LLAMA_ASSERT(false);
}
const char * str = llama_token_to_str(ctx, token);
// Note terminating 0 in decoded string
auto code_points = decode_utf8(str);
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
}
LLAMA_ASSERT(!grammar->stacks.empty());
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
//
// quantization
//
+49
View File
@@ -141,6 +141,40 @@ extern "C" {
bool quantize_output_tensor; // quantize output.weight
} llama_model_quantize_params;
// grammar types
struct llama_grammar;
// grammar element type
enum llama_gretype {
// end of rule definition
LLAMA_GRETYPE_END = 0,
// start of alternate definition for rule
LLAMA_GRETYPE_ALT = 1,
// non-terminal element: reference to rule
LLAMA_GRETYPE_RULE_REF = 2,
// terminal element: character (code point)
LLAMA_GRETYPE_CHAR = 3,
// inverse char(s) ([^a], [^a-b] [^abc])
LLAMA_GRETYPE_CHAR_NOT = 4,
// modifies a preceding LLAMA_GRETYPE_CHAR or LLAMA_GRETYPE_CHAR_ALT to
// be an inclusive range ([a-z])
LLAMA_GRETYPE_CHAR_RNG_UPPER = 5,
// modifies a preceding LLAMA_GRETYPE_CHAR or
// LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
LLAMA_GRETYPE_CHAR_ALT = 6,
};
typedef struct llama_grammar_element {
enum llama_gretype type;
uint32_t value; // Unicode code point or rule ID
} llama_grammar_element;
// performance timing information
struct llama_timings {
double t_start_ms;
@@ -333,6 +367,15 @@ extern "C" {
LLAMA_API llama_token llama_token_eos(); // end-of-sentence
LLAMA_API llama_token llama_token_nl(); // next-line
// Grammar
//
LLAMA_API struct llama_grammar * llama_grammar_init(
const llama_grammar_element ** rules,
size_t n_rules,
size_t start_rule_index);
LLAMA_API void llama_grammar_free(struct llama_grammar * grammar);
// Sampling functions
/// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
@@ -367,6 +410,9 @@ extern "C" {
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 Apply constraints from grammar
LLAMA_API void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar);
/// @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.
@@ -388,6 +434,9 @@ extern "C" {
/// @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);
/// @details Accepts the sampled token into the grammar
LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
// Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
LLAMA_API void llama_print_timings(struct llama_context * ctx);