mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 06:39:25 +01:00
llama : control-vector -> adapter
This commit is contained in:
Georgi Gerganov
parent
f9b0e3b382
commit
7b5b594526
@ -385,6 +385,7 @@ extern "C" {
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} llama_chat_message;
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// lora adapter
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// TODO: rename to llama_adapter_lora
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struct llama_lora_adapter;
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// Helpers for getting default parameters
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@ -501,14 +502,20 @@ extern "C" {
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const char * fname_out,
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const llama_model_quantize_params * params);
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//
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// Adapters
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//
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// Load a LoRA adapter from file
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// The loaded adapter will be associated to the given model, and will be free when the model is deleted
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// TODO: rename to llama_adapter_lora_init
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LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
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struct llama_model * model,
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const char * path_lora);
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// Add a loaded LoRA adapter to given context
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// This will not modify model's weight
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// TODO: rename to llama_set_adapter_lora
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LLAMA_API int32_t llama_lora_adapter_set(
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struct llama_context * ctx,
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struct llama_lora_adapter * adapter,
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@ -516,16 +523,19 @@ extern "C" {
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// Remove a specific LoRA adapter from given context
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// Return -1 if the adapter is not present in the context
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// TODO: rename to llama_rm_adapter_lora
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LLAMA_API int32_t llama_lora_adapter_remove(
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struct llama_context * ctx,
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struct llama_lora_adapter * adapter);
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// Remove all LoRA adapters from given context
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// TODO: rename to llama_clear_adapter_lora
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LLAMA_API void llama_lora_adapter_clear(
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struct llama_context * ctx);
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// Manually free a LoRA adapter
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// Note: loaded adapters will be free when the associated model is deleted
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// TODO: rename to llama_adapter_lora_free
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LLAMA_API void llama_lora_adapter_free(struct llama_lora_adapter * adapter);
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// Apply a loaded control vector to a llama_context, or if data is NULL, clear
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@ -534,6 +544,7 @@ extern "C" {
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// to an n_embd x n_layers buffer starting from layer 1.
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// il_start and il_end are the layer range the vector should apply to (both inclusive)
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// See llama_control_vector_load in common to load a control vector.
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// TODO: rename to llama_adapter_vec_apply
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LLAMA_API int32_t llama_control_vector_apply(
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struct llama_context * lctx,
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const float * data,
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@ -12,7 +12,7 @@ add_library(llama
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llama-arch.cpp
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llama-batch.cpp
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llama-context.cpp
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llama-control-vector.cpp
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llama-adapter.cpp
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llama-grammar.cpp
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llama-kv-cache.cpp
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llama-mmap.cpp
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5
src/llama-adapter.cpp
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5
src/llama-adapter.cpp
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@ -0,0 +1,5 @@
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#include "llama-adapter.h"
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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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354
src/llama-adapter.h
Normal file
354
src/llama-adapter.h
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@ -0,0 +1,354 @@
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#pragma once
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#include "llama-impl.h"
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#include "ggml-cpp.h"
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#include "llama-model.h" // TODO: need only hparams
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#include <vector>
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#include <map>
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//
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// llama_adapter_vec
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//
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// TODO: rename to llama_adapter_vec
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struct llama_control_vector {
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std::vector<struct ggml_tensor *> tensors; // per layer
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std::vector<ggml_context_ptr> ctxs;
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std::vector<ggml_backend_buffer_ptr> bufs;
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int32_t layer_start = -1;
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int32_t layer_end = -1;
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struct ggml_tensor * 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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return tensors[il];
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}
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struct ggml_tensor * 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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}
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return cur;
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}
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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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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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// 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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auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
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auto it = ctx_map.find(buft);
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if (it == ctx_map.end()) {
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struct ggml_init_params params = {
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/*.mem_size =*/ model.hparams.n_layer*ggml_tensor_overhead(),
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context * ctx = ggml_init(params);
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if (!ctx) {
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return nullptr;
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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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return ctx;
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}
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return it->second;
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};
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// make tensors
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cvec.tensors.reserve(model.hparams.n_layer);
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cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
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for (size_t il = 1; il < model.hparams.n_layer; il++) {
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ggml_backend_buffer_type_t buft = select_buft(*model.dev_layer.at(il).buft_list,
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[&](ggml_context * ctx) {
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ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
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ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
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return ggml_add(ctx, cur, layer_dir);
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});
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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, model.hparams.n_embd);
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cvec.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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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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ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
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if (!buf) {
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LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
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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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}
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return true;
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}
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static int32_t llama_control_vector_apply(struct llama_control_vector & cvec, const llama_model & model, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
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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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return 0;
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}
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if (n_embd != (int) model.hparams.n_embd) {
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LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
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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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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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for (size_t il = 1; il < model.hparams.n_layer; il++) {
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assert(cvec.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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}
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}
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return 0;
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}
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//
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// llama_adapter_lora
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//
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// TODO: rename to llama_adapter_lora_weight
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struct llama_lora_weight {
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struct ggml_tensor * a = nullptr;
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struct ggml_tensor * b = nullptr;
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llama_lora_weight() = default;
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llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
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};
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// TODO: rename to llama_adapter_lora
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struct llama_lora_adapter {
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struct llama_model * base_model;
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// map tensor name to lora_a_b
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std::unordered_map<std::string, struct llama_lora_weight> ab_map;
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std::vector<ggml_context_ptr> ctxs;
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std::vector<ggml_backend_buffer_ptr> bufs;
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float alpha;
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llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
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base_model->lora_adapters.insert(this);
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}
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llama_lora_weight * get_weight(struct ggml_tensor * w) {
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std::string name(w->name);
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auto pos = ab_map.find(name);
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if (ab_map.find(name) != ab_map.end()) {
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return &pos->second;
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}
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return nullptr;
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}
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~llama_lora_adapter() {
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auto pos = base_model->lora_adapters.find(this);
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if (pos != base_model->lora_adapters.end()) {
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base_model->lora_adapters.erase(pos);
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}
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}
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};
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static struct ggml_tensor * llama_get_model_tensor(const struct llama_model * model, const char * name) {
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auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
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[name](const std::pair<std::string, struct ggml_tensor *> & it) {
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return it.first == name;
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});
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if (it == model->tensors_by_name.end()) {
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return nullptr;
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}
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return it->second;
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}
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static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & 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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struct gguf_init_params meta_gguf_params = {
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/* .no_alloc = */ true,
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/* .ctx = */ &ctx_init,
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};
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gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };
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if (!ctx_gguf) {
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throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
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}
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ggml_context_ptr ctx { ctx_init };
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// check metadata
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{
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auto get_kv_str = [&](const std::string & key) -> std::string {
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int id = gguf_find_key(ctx_gguf.get(), key.c_str());
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return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
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};
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auto get_kv_f32 = [&](const std::string & key) -> float {
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int id = gguf_find_key(ctx_gguf.get(), key.c_str());
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return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
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};
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LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
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auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
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if (general_type != "adapter") {
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throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
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}
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auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
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auto general_arch = llm_arch_from_string(general_arch_str);
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if (general_arch != model->arch) {
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throw std::runtime_error("model arch and LoRA arch mismatch");
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}
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auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
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if (adapter_type != "lora") {
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throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
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}
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adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
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}
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int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
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// contexts for each buffer type
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
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auto it = ctx_map.find(buft);
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if (it == ctx_map.end()) {
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// add a new context
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struct ggml_init_params params = {
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/*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context * buft_ctx = ggml_init(params);
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if (!buft_ctx) {
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return nullptr;
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}
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ctx_map[buft] = buft_ctx;
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adapter.ctxs.emplace_back(buft_ctx);
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return buft_ctx;
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};
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return it->second;
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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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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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for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {
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std::string name(cur->name);
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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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} 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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} else {
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ab_map[name].b = cur;
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}
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} else {
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throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
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}
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}
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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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if (!w.a || !w.b) {
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throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
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}
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// device buft and device ctx
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auto * model_tensor = llama_get_model_tensor(model, 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");
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}
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struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
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// validate tensor shape
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if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
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throw std::runtime_error("tensor '" + name + "' has incorrect shape");
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}
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if (w.a->ne[1] != w.b->ne[0]) {
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throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
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}
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// save tensor to adapter
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struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
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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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}
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// allocate tensors / buffers and zero
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{
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adapter.ctxs.reserve(ctx_map.size());
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adapter.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_dev = it.second;
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ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };
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if (!buf) {
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throw std::runtime_error("failed to allocate buffer for lora adapter\n");
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}
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LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);
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adapter.bufs.emplace_back(std::move(buf));
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}
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}
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// set tensor data
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{
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llama_file gguf_file(path_lora, "rb");
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std::vector<uint8_t> read_buf;
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||||
auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
|
||||
size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
|
||||
size_t size = ggml_nbytes(orig);
|
||||
read_buf.resize(size);
|
||||
gguf_file.seek(offs, SEEK_SET);
|
||||
gguf_file.read_raw(read_buf.data(), size);
|
||||
ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
|
||||
};
|
||||
for (auto & it : adapter.ab_map) {
|
||||
auto orig = ab_map[it.first];
|
||||
auto dev = it.second;
|
||||
set_tensor(orig.a, dev.a);
|
||||
set_tensor(orig.b, dev.b);
|
||||
}
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
|
||||
}
|
||||
@ -4,7 +4,7 @@
|
||||
#include "llama-batch.h"
|
||||
#include "llama-model.h"
|
||||
#include "llama-kv-cache.h"
|
||||
#include "llama-control-vector.h"
|
||||
#include "llama-adapter.h"
|
||||
|
||||
#include "ggml-cpp.h"
|
||||
|
||||
@ -54,7 +54,7 @@ struct llama_context {
|
||||
const struct llama_model & model;
|
||||
|
||||
struct llama_cparams cparams;
|
||||
struct llama_sbatch sbatch;
|
||||
struct llama_sbatch sbatch; // TODO: revisit if needed
|
||||
struct llama_kv_cache kv_self;
|
||||
struct llama_control_vector cvec;
|
||||
|
||||
|
||||
@ -1 +0,0 @@
|
||||
#include "llama-control-vector.h"
|
||||
@ -1,130 +0,0 @@
|
||||
#pragma once
|
||||
|
||||
#include "llama-impl.h"
|
||||
#include "ggml-cpp.h"
|
||||
|
||||
#include "llama-model.h" // TODO: need only hparams
|
||||
|
||||
#include <vector>
|
||||
#include <map>
|
||||
|
||||
struct llama_control_vector {
|
||||
std::vector<struct ggml_tensor *> tensors; // per layer
|
||||
std::vector<ggml_context_ptr> ctxs;
|
||||
std::vector<ggml_backend_buffer_ptr> bufs;
|
||||
|
||||
int32_t layer_start = -1;
|
||||
int32_t layer_end = -1;
|
||||
|
||||
struct ggml_tensor * tensor_for(int il) const {
|
||||
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
|
||||
return nullptr;
|
||||
}
|
||||
return tensors[il];
|
||||
}
|
||||
|
||||
struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
|
||||
ggml_tensor * layer_dir = tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx, cur, layer_dir);
|
||||
}
|
||||
return cur;
|
||||
}
|
||||
};
|
||||
|
||||
static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
|
||||
GGML_ASSERT(cvec.tensors.empty());
|
||||
GGML_ASSERT(cvec.ctxs.empty());
|
||||
GGML_ASSERT(cvec.bufs.empty());
|
||||
|
||||
// create a context for each buffer type
|
||||
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
||||
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
|
||||
auto it = ctx_map.find(buft);
|
||||
if (it == ctx_map.end()) {
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ model.hparams.n_layer*ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
ggml_context * ctx = ggml_init(params);
|
||||
if (!ctx) {
|
||||
return nullptr;
|
||||
}
|
||||
ctx_map[buft] = ctx;
|
||||
cvec.ctxs.emplace_back(ctx);
|
||||
return ctx;
|
||||
}
|
||||
return it->second;
|
||||
};
|
||||
|
||||
// make tensors
|
||||
cvec.tensors.reserve(model.hparams.n_layer);
|
||||
cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
|
||||
for (size_t il = 1; il < model.hparams.n_layer; il++) {
|
||||
ggml_backend_buffer_type_t buft = select_buft(*model.dev_layer.at(il).buft_list,
|
||||
[&](ggml_context * ctx) {
|
||||
ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
|
||||
ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
|
||||
return ggml_add(ctx, cur, layer_dir);
|
||||
});
|
||||
ggml_context * ctx = ctx_for_buft(buft);
|
||||
if (!ctx) {
|
||||
LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
|
||||
return false;
|
||||
}
|
||||
ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
|
||||
cvec.tensors.push_back(tensor);
|
||||
}
|
||||
|
||||
// allocate tensors / buffers and zero
|
||||
cvec.bufs.reserve(ctx_map.size());
|
||||
for (auto it : ctx_map) {
|
||||
ggml_backend_buffer_type_t buft = it.first;
|
||||
ggml_context * ctx = it.second;
|
||||
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
|
||||
if (!buf) {
|
||||
LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
|
||||
return false;
|
||||
}
|
||||
ggml_backend_buffer_clear(buf, 0);
|
||||
cvec.bufs.emplace_back(buf);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static int32_t llama_control_vector_apply(struct llama_control_vector & cvec, const llama_model & model, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
|
||||
if (data == nullptr) {
|
||||
// disable the current control vector (but leave allocated for later)
|
||||
cvec.layer_start = -1;
|
||||
cvec.layer_end = -1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
if (n_embd != (int) model.hparams.n_embd) {
|
||||
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (cvec.tensors.empty()) {
|
||||
if (!llama_control_vector_init(cvec, model)) {
|
||||
return 1;
|
||||
}
|
||||
}
|
||||
|
||||
cvec.layer_start = il_start;
|
||||
cvec.layer_end = il_end;
|
||||
|
||||
for (size_t il = 1; il < model.hparams.n_layer; il++) {
|
||||
assert(cvec.tensors[il] != nullptr);
|
||||
|
||||
const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
|
||||
if (off + n_embd <= len) {
|
||||
ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
|
||||
}
|
||||
}
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
218
src/llama.cpp
218
src/llama.cpp
@ -230,58 +230,10 @@ static const size_t kiB = 1024;
|
||||
static const size_t MiB = 1024*kiB;
|
||||
static const size_t GiB = 1024*MiB;
|
||||
|
||||
struct llama_lora_weight {
|
||||
struct ggml_tensor * a = nullptr;
|
||||
struct ggml_tensor * b = nullptr;
|
||||
llama_lora_weight() = default;
|
||||
llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
|
||||
};
|
||||
|
||||
struct llama_lora_adapter {
|
||||
struct llama_model * base_model;
|
||||
// map tensor name to lora_a_b
|
||||
std::unordered_map<std::string, struct llama_lora_weight> ab_map;
|
||||
std::vector<ggml_context_ptr> ctxs;
|
||||
std::vector<ggml_backend_buffer_ptr> bufs;
|
||||
|
||||
float alpha;
|
||||
|
||||
llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
|
||||
base_model->lora_adapters.insert(this);
|
||||
}
|
||||
|
||||
llama_lora_weight * get_weight(struct ggml_tensor * w) {
|
||||
std::string name(w->name);
|
||||
auto pos = ab_map.find(name);
|
||||
if (ab_map.find(name) != ab_map.end()) {
|
||||
return &pos->second;
|
||||
}
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
~llama_lora_adapter() {
|
||||
auto pos = base_model->lora_adapters.find(this);
|
||||
if (pos != base_model->lora_adapters.end()) {
|
||||
base_model->lora_adapters.erase(pos);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
static int llama_get_device_count(const llama_model & model) {
|
||||
return (int) model.devices.size();
|
||||
}
|
||||
|
||||
static struct ggml_tensor * llama_get_model_tensor(const struct llama_model * model, const char * name) {
|
||||
auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
|
||||
[name](const std::pair<std::string, struct ggml_tensor *> & it) {
|
||||
return it.first == name;
|
||||
});
|
||||
if (it == model->tensors_by_name.end()) {
|
||||
return nullptr;
|
||||
}
|
||||
return it->second;
|
||||
}
|
||||
|
||||
//
|
||||
// model loading and saving
|
||||
//
|
||||
@ -15884,172 +15836,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
||||
}
|
||||
}
|
||||
|
||||
static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
|
||||
LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
|
||||
|
||||
ggml_context * ctx_init;
|
||||
struct gguf_init_params meta_gguf_params = {
|
||||
/* .no_alloc = */ true,
|
||||
/* .ctx = */ &ctx_init,
|
||||
};
|
||||
|
||||
gguf_context_ptr ctx_gguf { gguf_init_from_file(path_lora, meta_gguf_params) };
|
||||
if (!ctx_gguf) {
|
||||
throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
|
||||
}
|
||||
|
||||
ggml_context_ptr ctx { ctx_init };
|
||||
|
||||
// check metadata
|
||||
{
|
||||
auto get_kv_str = [&](const std::string & key) -> std::string {
|
||||
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
|
||||
return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf.get(), id));
|
||||
};
|
||||
auto get_kv_f32 = [&](const std::string & key) -> float {
|
||||
int id = gguf_find_key(ctx_gguf.get(), key.c_str());
|
||||
return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf.get(), id);
|
||||
};
|
||||
LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
|
||||
|
||||
auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
|
||||
if (general_type != "adapter") {
|
||||
throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
|
||||
}
|
||||
|
||||
auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
|
||||
auto general_arch = llm_arch_from_string(general_arch_str);
|
||||
if (general_arch != model->arch) {
|
||||
throw std::runtime_error("model arch and LoRA arch mismatch");
|
||||
}
|
||||
|
||||
auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
|
||||
if (adapter_type != "lora") {
|
||||
throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
|
||||
}
|
||||
|
||||
adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
|
||||
}
|
||||
|
||||
int n_tensors = gguf_get_n_tensors(ctx_gguf.get());
|
||||
|
||||
// contexts for each buffer type
|
||||
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
||||
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
|
||||
auto it = ctx_map.find(buft);
|
||||
if (it == ctx_map.end()) {
|
||||
// add a new context
|
||||
struct ggml_init_params params = {
|
||||
/*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
ggml_context * buft_ctx = ggml_init(params);
|
||||
if (!buft_ctx) {
|
||||
return nullptr;
|
||||
}
|
||||
ctx_map[buft] = buft_ctx;
|
||||
adapter.ctxs.emplace_back(buft_ctx);
|
||||
return buft_ctx;
|
||||
};
|
||||
return it->second;
|
||||
};
|
||||
|
||||
// bundle lora_a and lora_b into pairs
|
||||
std::map<std::string, llama_lora_weight> ab_map;
|
||||
auto str_endswith = [](const std::string & str, const std::string & suffix) {
|
||||
return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
|
||||
};
|
||||
for (ggml_tensor * cur = ggml_get_first_tensor(ctx.get()); cur; cur = ggml_get_next_tensor(ctx.get(), cur)) {
|
||||
std::string name(cur->name);
|
||||
if (str_endswith(name, ".lora_a")) {
|
||||
replace_all(name, ".lora_a", "");
|
||||
if (ab_map.find(name) == ab_map.end()) {
|
||||
ab_map[name] = llama_lora_weight(cur, nullptr);
|
||||
} else {
|
||||
ab_map[name].a = cur;
|
||||
}
|
||||
} else if (str_endswith(name, ".lora_b")) {
|
||||
replace_all(name, ".lora_b", "");
|
||||
if (ab_map.find(name) == ab_map.end()) {
|
||||
ab_map[name] = llama_lora_weight(nullptr, cur);
|
||||
} else {
|
||||
ab_map[name].b = cur;
|
||||
}
|
||||
} else {
|
||||
throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
|
||||
}
|
||||
}
|
||||
|
||||
// add tensors
|
||||
for (auto & it : ab_map) {
|
||||
const std::string & name = it.first;
|
||||
llama_lora_weight & w = it.second;
|
||||
|
||||
if (!w.a || !w.b) {
|
||||
throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
|
||||
}
|
||||
|
||||
// device buft and device ctx
|
||||
auto * model_tensor = llama_get_model_tensor(model, name.c_str());
|
||||
if (!model_tensor) {
|
||||
throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
|
||||
}
|
||||
struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
|
||||
// validate tensor shape
|
||||
if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
|
||||
throw std::runtime_error("tensor '" + name + "' has incorrect shape");
|
||||
}
|
||||
if (w.a->ne[1] != w.b->ne[0]) {
|
||||
throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
|
||||
}
|
||||
// save tensor to adapter
|
||||
struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
|
||||
struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
|
||||
ggml_set_name(tensor_a, w.a->name);
|
||||
ggml_set_name(tensor_b, w.b->name);
|
||||
adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
|
||||
}
|
||||
|
||||
// allocate tensors / buffers and zero
|
||||
{
|
||||
adapter.ctxs.reserve(ctx_map.size());
|
||||
adapter.bufs.reserve(ctx_map.size());
|
||||
for (auto & it : ctx_map) {
|
||||
ggml_backend_buffer_type_t buft = it.first;
|
||||
ggml_context * ctx_dev = it.second;
|
||||
ggml_backend_buffer_ptr buf { ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft) };
|
||||
if (!buf) {
|
||||
throw std::runtime_error("failed to allocate buffer for lora adapter\n");
|
||||
}
|
||||
LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get())/1024.0/1024.0);
|
||||
adapter.bufs.emplace_back(std::move(buf));
|
||||
}
|
||||
}
|
||||
|
||||
// set tensor data
|
||||
{
|
||||
llama_file gguf_file(path_lora, "rb");
|
||||
std::vector<uint8_t> read_buf;
|
||||
auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
|
||||
size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
|
||||
size_t size = ggml_nbytes(orig);
|
||||
read_buf.resize(size);
|
||||
gguf_file.seek(offs, SEEK_SET);
|
||||
gguf_file.read_raw(read_buf.data(), size);
|
||||
ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
|
||||
};
|
||||
for (auto & it : adapter.ab_map) {
|
||||
auto orig = ab_map[it.first];
|
||||
auto dev = it.second;
|
||||
set_tensor(orig.a, dev.a);
|
||||
set_tensor(orig.b, dev.b);
|
||||
}
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
|
||||
}
|
||||
|
||||
int32_t llama_lora_adapter_set(
|
||||
struct llama_context * ctx,
|
||||
struct llama_lora_adapter * adapter,
|
||||
@ -16077,10 +15863,6 @@ void llama_lora_adapter_clear(struct llama_context * ctx) {
|
||||
ctx->lora_adapters.clear();
|
||||
}
|
||||
|
||||
void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
|
||||
delete adapter;
|
||||
}
|
||||
|
||||
// TODO: tmp
|
||||
int32_t llama_control_vector_apply(
|
||||
struct llama_context * lctx,
|
||||
|
||||
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Reference in New Issue
Block a user