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https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
speculative : add grammar support (#2991)
* speculative : add grammar support * grammars : add json_arr.gbnf * grammar : add comments to new grammar file * grammar : remove one nested level * common : warm-up with 2 tokens - seems to work better * speculative : print draft token pieces * speculative : reuse grammar parser + better logs and comments * speculative : avoid grammar_mem * make : fix speculative build
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2
Makefile
2
Makefile
@ -495,7 +495,7 @@ baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS)
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beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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ifdef LLAMA_METAL
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@ -772,7 +772,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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{
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LOG("warming up the model with an empty run\n");
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const std::vector<llama_token> tmp = { llama_token_bos(lctx), };
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const std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
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llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads);
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llama_reset_timings(lctx);
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}
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@ -6,6 +6,7 @@
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#include "common.h"
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#include "llama.h"
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#include "grammar-parser.h"
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#include <cmath>
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#include <cstdio>
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@ -109,16 +110,35 @@ int main(int argc, char ** argv) {
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// used to determine end of generation
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bool has_eos = false;
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// grammar stuff
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struct llama_grammar * grammar_dft = NULL;
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struct llama_grammar * grammar_tgt = NULL;
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grammar_parser::parse_state parsed_grammar;
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// if requested - load the grammar, error checking is omitted for brevity
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if (!params.grammar.empty()) {
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parsed_grammar = grammar_parser::parse(params.grammar.c_str());
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// will be empty (default) if there are parse errors
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if (parsed_grammar.rules.empty()) {
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return 1;
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}
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std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
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grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
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}
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const auto t_dec_start = ggml_time_us();
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while (true) {
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LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
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// sample from the drafted tokens if any
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int i_dft = 0;
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while (true) {
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const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft);
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// sample from the target model
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const llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
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// remember which tokens were sampled - used for repetition penalties during sampling
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last_tokens.erase(last_tokens.begin());
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last_tokens.push_back(id);
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@ -134,8 +154,9 @@ int main(int argc, char ** argv) {
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++n_predict;
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// check if the draft matches the target
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if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
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LOG("drafted token %d accepted\n", id);
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LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
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++n_accept;
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++n_past_tgt;
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++n_past_dft;
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@ -145,6 +166,14 @@ int main(int argc, char ** argv) {
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}
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// the drafted token was rejected or we are out of drafted tokens
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if (i_dft < (int) drafted.size()) {
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LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n",
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i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str());
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} else {
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LOG("out of drafted tokens\n");
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}
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llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads);
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++n_past_dft;
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@ -158,7 +187,16 @@ int main(int argc, char ** argv) {
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break;
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}
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// sample n_draft tokens from the draft model picking the best token
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if (grammar_tgt) {
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if (grammar_dft) {
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llama_grammar_free(grammar_dft);
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}
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grammar_dft = llama_grammar_copy(grammar_tgt);
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LOG("copied target grammar to draft grammar\n");
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}
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// sample n_draft tokens from the draft model using greedy decoding
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int n_past_cur = n_past_dft;
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for (int i = 0; i < n_draft; ++i) {
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float * logits = llama_get_logits(ctx_dft);
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@ -170,25 +208,40 @@ int main(int argc, char ** argv) {
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llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
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if (grammar_dft != NULL) {
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llama_sample_grammar(ctx_dft, &cur_p, grammar_dft);
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}
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// computes softmax and sorts the candidates
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llama_sample_softmax(ctx_dft, &cur_p);
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for (int i = 0; i < 3; ++i) {
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LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p);
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LOG(" - draft candidate %3d: %6d (%8.3f) '%s'\n", i, cur_p.data[i].id, cur_p.data[i].p, llama_token_to_piece(ctx_dft, cur_p.data[i].id).c_str());
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}
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// too low probability, stop drafting
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// TODO: better logic?
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if (cur_p.data[0].p < 2*cur_p.data[1].p) {
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LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p);
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break;
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}
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drafted.push_back(cur_p.data[0].id);
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// drafted token
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const llama_token id = cur_p.data[0].id;
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drafted.push_back(id);
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++n_drafted;
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if (i < n_draft - 1) {
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// evaluate the drafted token on the draft model
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llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
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++n_past_cur;
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// no need to evaluate the last drafted token, since we won't use the result
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if (i == n_draft - 1) {
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break;
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}
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// evaluate the drafted token on the draft model
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llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads);
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++n_past_cur;
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if (grammar_dft != NULL) {
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llama_grammar_accept_token(ctx_dft, grammar_dft, id);
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}
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}
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@ -196,6 +249,7 @@ int main(int argc, char ** argv) {
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llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads);
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++n_past_tgt;
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// the first token is always proposed by the traget model before the speculation loop
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drafted.erase(drafted.begin());
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}
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@ -226,6 +280,10 @@ int main(int argc, char ** argv) {
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llama_free(ctx_dft);
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llama_free_model(model_dft);
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if (grammar_dft != NULL) {
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llama_grammar_free(grammar_dft);
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llama_grammar_free(grammar_tgt);
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}
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llama_backend_free();
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fprintf(stderr, "\n\n");
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34
grammars/json_arr.gbnf
Normal file
34
grammars/json_arr.gbnf
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@ -0,0 +1,34 @@
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# This is the same as json.gbnf but we restrict whitespaces at the end of the root array
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# Useful for generating JSON arrays
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root ::= arr
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value ::= object | array | string | number | ("true" | "false" | "null") ws
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arr ::=
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"[\n" ws (
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value
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(",\n" ws value)*
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)? "]"
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object ::=
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"{" ws (
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string ":" ws value
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("," ws string ":" ws value)*
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)? "}" ws
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array ::=
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"[" ws (
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value
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("," ws value)*
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)? "]" ws
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string ::=
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"\"" (
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[^"\\] |
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"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes
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)* "\"" ws
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number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws
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# Optional space: by convention, applied in this grammar after literal chars when allowed
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ws ::= ([ \t\n] ws)?
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19
llama.cpp
19
llama.cpp
@ -3850,6 +3850,25 @@ void llama_grammar_free(struct llama_grammar * grammar) {
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delete grammar;
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}
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struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
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llama_grammar * result = new llama_grammar{ grammar->rules, grammar->stacks, grammar->partial_utf8 };
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// redirect elements in stacks to point to new rules
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for (size_t is = 0; is < result->stacks.size(); is++) {
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for (size_t ie = 0; ie < result->stacks[is].size(); ie++) {
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for (size_t ir0 = 0; ir0 < grammar->rules.size(); ir0++) {
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for (size_t ir1 = 0; ir1 < grammar->rules[ir0].size(); ir1++) {
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if (grammar->stacks[is][ie] == &grammar->rules[ir0][ir1]) {
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result->stacks[is][ie] = &result->rules[ir0][ir1];
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}
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}
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}
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}
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}
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return result;
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}
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//
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// sampling
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//
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