llama : add llama_vocab, functions -> methods, naming (#11110)
* llama : functions -> methods (#11110) * llama : add struct llama_vocab to the API (#11156) ggml-ci * hparams : move vocab params to llama_vocab (#11159) ggml-ci * vocab : more pimpl (#11165) ggml-ci * vocab : minor tokenization optimizations (#11160) ggml-ci Co-authored-by: Diego Devesa <slarengh@gmail.com> * lora : update API names (#11167) ggml-ci * llama : update API names to use correct prefix (#11174) * llama : update API names to use correct prefix ggml-ci * cont ggml-ci * cont ggml-ci * minor [no ci] * vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (#11174) ggml-ci * vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174) ggml-ci --------- Co-authored-by: Diego Devesa <slarengh@gmail.com>
This commit is contained in:
Georgi Gerganov
GitHub
parent
c05e8c9934
commit
afa8a9ec9b
68 changed files with 5855 additions and 5400 deletions
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@ -1,5 +1,7 @@
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#include "llama-adapter.h"
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#include "llama-impl.h"
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#include "llama-mmap.h"
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#include "llama-model.h"
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#include <algorithm>
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@ -9,7 +11,7 @@
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// vec
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struct ggml_tensor * llama_control_vector::tensor_for(int il) const {
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struct ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
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if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
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return nullptr;
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}
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@ -17,7 +19,7 @@ struct ggml_tensor * llama_control_vector::tensor_for(int il) const {
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return tensors[il];
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}
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struct ggml_tensor * llama_control_vector::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
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struct ggml_tensor * llama_adapter_cvec::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
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ggml_tensor * layer_dir = tensor_for(il);
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if (layer_dir != nullptr) {
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cur = ggml_add(ctx, cur, layer_dir);
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@ -26,12 +28,12 @@ struct ggml_tensor * llama_control_vector::apply_to(struct ggml_context * ctx, s
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return cur;
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}
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static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
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bool llama_adapter_cvec::init(const llama_model & model) {
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const auto & hparams = model.hparams;
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GGML_ASSERT(cvec.tensors.empty());
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GGML_ASSERT(cvec.ctxs.empty());
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GGML_ASSERT(cvec.bufs.empty());
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GGML_ASSERT(tensors.empty());
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GGML_ASSERT(ctxs.empty());
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GGML_ASSERT(bufs.empty());
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// create a context for each buffer type
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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@ -50,7 +52,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
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}
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ctx_map[buft] = ctx;
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cvec.ctxs.emplace_back(ctx);
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ctxs.emplace_back(ctx);
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return ctx;
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}
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@ -59,21 +61,21 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
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};
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// make tensors
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cvec.tensors.reserve(hparams.n_layer);
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cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
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tensors.reserve(hparams.n_layer);
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tensors.push_back(nullptr); // there's never a tensor for layer 0
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for (size_t il = 1; il < hparams.n_layer; il++) {
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ggml_backend_buffer_type_t buft = llama_model_select_buft(model, il);
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ggml_backend_buffer_type_t buft = model.select_buft(il);
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ggml_context * ctx = ctx_for_buft(buft);
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if (!ctx) {
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LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
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return false;
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}
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ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
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cvec.tensors.push_back(tensor);
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tensors.push_back(tensor);
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}
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// allocate tensors / buffers and zero
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cvec.bufs.reserve(ctx_map.size());
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bufs.reserve(ctx_map.size());
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for (auto it : ctx_map) {
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ggml_backend_buffer_type_t buft = it.first;
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ggml_context * ctx = it.second;
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@ -83,14 +85,13 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const
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return false;
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}
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ggml_backend_buffer_clear(buf, 0);
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cvec.bufs.emplace_back(buf);
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bufs.emplace_back(buf);
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}
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return true;
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}
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int32_t llama_control_vector_apply(
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struct llama_control_vector & cvec,
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int32_t llama_adapter_cvec::apply(
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const llama_model & model,
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const float * data,
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size_t len,
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@ -101,8 +102,8 @@ int32_t llama_control_vector_apply(
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if (data == nullptr) {
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// disable the current control vector (but leave allocated for later)
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cvec.layer_start = -1;
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cvec.layer_end = -1;
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layer_start = -1;
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layer_end = -1;
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return 0;
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}
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@ -111,21 +112,21 @@ int32_t llama_control_vector_apply(
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return 1;
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}
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if (cvec.tensors.empty()) {
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if (!llama_control_vector_init(cvec, model)) {
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if (tensors.empty()) {
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if (!init(model)) {
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return 1;
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}
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}
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cvec.layer_start = il_start;
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cvec.layer_end = il_end;
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layer_start = il_start;
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layer_end = il_end;
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for (size_t il = 1; il < hparams.n_layer; il++) {
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assert(cvec.tensors[il] != nullptr);
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assert(tensors[il] != nullptr);
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const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
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if (off + n_embd <= len) {
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ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
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ggml_backend_tensor_set(tensors[il], data + off, 0, n_embd * ggml_element_size(tensors[il]));
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}
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}
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@ -134,7 +135,7 @@ int32_t llama_control_vector_apply(
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// lora
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llama_lora_weight * llama_lora_adapter::get_weight(struct ggml_tensor * w) {
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llama_adapter_lora_weight * llama_adapter_lora::get_weight(struct ggml_tensor * w) {
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const std::string name(w->name);
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const auto pos = ab_map.find(name);
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@ -145,11 +146,7 @@ llama_lora_weight * llama_lora_adapter::get_weight(struct ggml_tensor * w) {
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return nullptr;
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}
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void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
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delete adapter;
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}
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static void llama_lora_adapter_init_impl(struct llama_model & model, const char * path_lora, struct llama_lora_adapter & adapter) {
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static void llama_adapter_lora_init_impl(struct llama_model & model, const char * path_lora, struct llama_adapter_lora & adapter) {
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LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
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ggml_context * ctx_init;
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@ -221,7 +218,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
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};
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// bundle lora_a and lora_b into pairs
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std::map<std::string, llama_lora_weight> ab_map;
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std::map<std::string, llama_adapter_lora_weight> ab_map;
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auto str_endswith = [](const std::string & str, const std::string & suffix) {
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return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
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};
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@ -231,14 +228,14 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
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if (str_endswith(name, ".lora_a")) {
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replace_all(name, ".lora_a", "");
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if (ab_map.find(name) == ab_map.end()) {
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ab_map[name] = llama_lora_weight(cur, nullptr);
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ab_map[name] = llama_adapter_lora_weight(cur, nullptr);
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} else {
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ab_map[name].a = cur;
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}
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} else if (str_endswith(name, ".lora_b")) {
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replace_all(name, ".lora_b", "");
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if (ab_map.find(name) == ab_map.end()) {
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ab_map[name] = llama_lora_weight(nullptr, cur);
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ab_map[name] = llama_adapter_lora_weight(nullptr, cur);
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} else {
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ab_map[name].b = cur;
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}
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@ -254,7 +251,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
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// add tensors
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for (auto & it : ab_map) {
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const std::string & name = it.first;
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llama_lora_weight & w = it.second;
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llama_adapter_lora_weight & w = it.second;
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bool is_token_embd = str_endswith(name, "token_embd.weight");
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if (!w.a || !w.b) {
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@ -262,7 +259,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
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}
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// device buft and device ctx
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auto * model_tensor = llama_model_get_tensor(model, name.c_str());
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const auto * model_tensor = model.get_tensor(name.c_str());
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if (!model_tensor) {
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throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");
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}
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@ -288,7 +285,7 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
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struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
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ggml_set_name(tensor_a, w.a->name);
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ggml_set_name(tensor_b, w.b->name);
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adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
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adapter.ab_map[name] = llama_adapter_lora_weight(tensor_a, tensor_b);
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}
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// allocate tensors / buffers and zero
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@ -330,11 +327,11 @@ static void llama_lora_adapter_init_impl(struct llama_model & model, const char
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LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
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}
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struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
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struct llama_lora_adapter * adapter = new llama_lora_adapter();
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struct llama_adapter_lora * llama_adapter_lora_init(struct llama_model * model, const char * path_lora) {
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struct llama_adapter_lora * adapter = new llama_adapter_lora();
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try {
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llama_lora_adapter_init_impl(*model, path_lora, *adapter);
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llama_adapter_lora_init_impl(*model, path_lora, *adapter);
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return adapter;
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} catch (const std::exception & err) {
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LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
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@ -344,3 +341,7 @@ struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model,
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return nullptr;
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}
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void llama_adapter_lora_free(struct llama_adapter_lora * adapter) {
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delete adapter;
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}
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