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https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 02:44:36 +00:00
YaRN : store rope scaling type as int32_t in memory (#5285)
* YaRN : store rope scaling type as int32_t in memory * llama : store mapped names as const char *
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@ -75,8 +75,7 @@ struct gpt_params {
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float yarn_beta_fast = 32.0f; // YaRN low correction dim
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float yarn_beta_slow = 1.0f; // YaRN high correction dim
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment
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// pinging @cebtenzzre
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int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED;
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// // sampling parameters
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struct llama_sampling_params sparams;
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24
llama.cpp
24
llama.cpp
@ -208,7 +208,7 @@ enum llm_arch {
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LLM_ARCH_UNKNOWN,
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};
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static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
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static std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GPT2, "gpt2" },
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@ -285,7 +285,7 @@ enum llm_kv {
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LLM_KV_TOKENIZER_RWKV,
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};
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static std::map<llm_kv, std::string> LLM_KV_NAMES = {
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static std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
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{ LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" },
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{ LLM_KV_GENERAL_ALIGNMENT, "general.alignment" },
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@ -346,7 +346,7 @@ struct LLM_KV {
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llm_arch arch;
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std::string operator()(llm_kv kv) const {
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return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str());
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return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]);
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}
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};
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@ -747,13 +747,13 @@ struct LLM_TN {
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// gguf helpers
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//
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static std::map<int8_t, std::string> LLAMA_ROPE_SCALING_TYPES = {
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static std::map<int32_t, const char *> LLAMA_ROPE_SCALING_TYPES = {
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{ LLAMA_ROPE_SCALING_NONE, "none" },
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{ LLAMA_ROPE_SCALING_LINEAR, "linear" },
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{ LLAMA_ROPE_SCALING_YARN, "yarn" },
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};
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static int8_t llama_rope_scaling_type_from_string(const std::string & name) {
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static int32_t llama_rope_scaling_type_from_string(const std::string & name) {
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for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
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if (kv.second == name) {
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return kv.first;
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@ -1415,6 +1415,7 @@ static const size_t GiB = 1024*MiB;
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struct llama_hparams {
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bool vocab_only;
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bool rope_finetuned;
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uint32_t n_vocab;
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uint32_t n_ctx_train; // context size the model was trained on
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uint32_t n_embd;
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@ -1434,8 +1435,7 @@ struct llama_hparams {
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float rope_freq_base_train;
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float rope_freq_scale_train;
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uint32_t n_yarn_orig_ctx;
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int8_t rope_scaling_type_train : 3;
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bool rope_finetuned : 1;
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int32_t rope_scaling_type_train;
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float f_clamp_kqv;
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float f_max_alibi_bias;
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@ -2701,7 +2701,7 @@ struct llama_model_loader {
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// load LLaMA models
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//
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static std::string llama_model_arch_name(llm_arch arch) {
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static const char * llama_model_arch_name(llm_arch arch) {
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auto it = LLM_ARCH_NAMES.find(arch);
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if (it == LLM_ARCH_NAMES.end()) {
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return "unknown";
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@ -3310,11 +3310,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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const auto & hparams = model.hparams;
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const auto & vocab = model.vocab;
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const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
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const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
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// hparams
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LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver));
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LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str());
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LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch));
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LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type));
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LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
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LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size());
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@ -3336,7 +3336,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
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LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
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LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
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LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str());
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LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type);
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LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
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LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
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LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx);
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@ -10735,7 +10735,7 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3
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int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
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return snprintf(buf, buf_size, "%s %s %s",
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llama_model_arch_name(model->arch).c_str(),
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llama_model_arch_name(model->arch),
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llama_model_type_name(model->type),
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llama_model_ftype_name(model->ftype).c_str());
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}
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2
llama.h
2
llama.h
@ -213,7 +213,7 @@ extern "C" {
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uint32_t n_batch; // prompt processing maximum batch size
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uint32_t n_threads; // number of threads to use for generation
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uint32_t n_threads_batch; // number of threads to use for batch processing
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int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
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// ref: https://github.com/ggerganov/llama.cpp/pull/2054
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float rope_freq_base; // RoPE base frequency, 0 = from model
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