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lookup: fibonacci hashing, fix crashes (#8548)
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@ -37,11 +37,18 @@ struct llama_ngram {
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}
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};
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struct llama_token_hash_function {
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size_t operator()(const llama_token token) const {
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// see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
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return token * 11400714819323198485llu;
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}
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};
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struct llama_ngram_hash_function {
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size_t operator()(const llama_ngram & ngram) const {
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size_t hash = 0;
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for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
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hash ^= std::hash<llama_token>{}(ngram.tokens[i]);
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size_t hash = llama_token_hash_function{}(ngram.tokens[0]);
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for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
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hash ^= llama_token_hash_function{}(ngram.tokens[i]);
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}
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return hash;
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}
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@ -31,7 +31,6 @@ int main(int argc, char ** argv){
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// load the model
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
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// tokenize the prompt
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std::vector<llama_token> inp;
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@ -65,7 +64,7 @@ int main(int argc, char ** argv){
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}
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const int n_input = inp.size();
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const int n_ctx = params.n_ctx;
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const int n_ctx = llama_n_ctx(ctx);
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int n_drafted = 0;
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int n_accept = 0;
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@ -39,7 +39,6 @@ int main(int argc, char ** argv){
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// load the model
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
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// tokenize the prompt
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std::vector<llama_token> inp;
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