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llama : refactor llama_context, llama_kv_cache, llm_build_context (#12181)

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* llama : refactor llama_context, llama_kv_cache, llm_build_context

ggml-ci

* graph : don't mutate the KV cache during defrag

ggml-ci

* context : reduce virtuals + remove test function

ggml-ci

* context : move interface implementation to source file + factory

ggml-ci

* graph : move KV cache build functions to llama_context impl

ggml-ci

* graph : remove model reference from build_pooling

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* graph : remove llama_model reference

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* kv_cache : provide rope factors

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* graph : rework inputs to use only unique_ptr, remove attn input abstraction

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* context : remove llama_context_i abstraction

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* context : clean-up

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* graph : clean-up

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* llama : remove redundant keywords (struct, enum)

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* model : adapt gemma3

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* graph : restore same attention ops as on master

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* llama : remove TODO + fix indent

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This commit is contained in:
Georgi Gerganov
2025-03-13 12:35:44 +02:00
committed by GitHub
parent 2048b5913d
commit e0dbec0bc6
46 changed files with 13903 additions and 12190 deletions
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39
src/llama-adapter.cpp
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@ -4,14 +4,13 @@
#include "llama-mmap.h"
#include "llama-model.h"
#include <algorithm>
#include <map>
#include <cassert>
#include <stdexcept>
// vec
struct ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
return nullptr;
}
@ -19,7 +18,7 @@ struct ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
return tensors[il];
}
struct ggml_tensor * llama_adapter_cvec::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
ggml_tensor * llama_adapter_cvec::apply_to(ggml_context * ctx, ggml_tensor * cur, int il) const {
ggml_tensor * layer_dir = tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx, cur, layer_dir);
@ -40,7 +39,7 @@ bool llama_adapter_cvec::init(const llama_model & model) {
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 = {
ggml_init_params params = {
/*.mem_size =*/ hparams.n_layer*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
@ -91,7 +90,7 @@ bool llama_adapter_cvec::init(const llama_model & model) {
return true;
}
int32_t llama_adapter_cvec::apply(
bool llama_adapter_cvec::apply(
const llama_model & model,
const float * data,
size_t len,
@ -104,17 +103,17 @@ int32_t llama_adapter_cvec::apply(
// disable the current control vector (but leave allocated for later)
layer_start = -1;
layer_end = -1;
return 0;
return true;
}
if (n_embd != (int) hparams.n_embd) {
LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
return 1;
return false;
}
if (tensors.empty()) {
if (!init(model)) {
return 1;
return false;
}
}
@ -130,12 +129,12 @@ int32_t llama_adapter_cvec::apply(
}
}
return 0;
return true;
}
// lora
llama_adapter_lora_weight * llama_adapter_lora::get_weight(struct ggml_tensor * w) {
llama_adapter_lora_weight * llama_adapter_lora::get_weight(ggml_tensor * w) {
const std::string name(w->name);
const auto pos = ab_map.find(name);
@ -146,11 +145,11 @@ llama_adapter_lora_weight * llama_adapter_lora::get_weight(struct ggml_tensor *
return nullptr;
}
static void llama_adapter_lora_init_impl(struct llama_model & model, const char * path_lora, struct llama_adapter_lora & adapter) {
static void llama_adapter_lora_init_impl(llama_model & model, const char * path_lora, llama_adapter_lora & 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 = {
gguf_init_params meta_gguf_params = {
/* .no_alloc = */ true,
/* .ctx = */ &ctx_init,
};
@ -201,7 +200,7 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
auto it = ctx_map.find(buft);
if (it == ctx_map.end()) {
// add a new context
struct ggml_init_params params = {
ggml_init_params params = {
/*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
@ -264,7 +263,7 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");
}
struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
// validate tensor shape
if (is_token_embd) {
// expect B to be non-transposed, A and B are flipped; see llm_build_inp_embd()
@ -281,8 +280,8 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
}
// 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_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
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_adapter_lora_weight(tensor_a, tensor_b);
@ -308,7 +307,7 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
{
llama_file gguf_file(path_lora, "rb");
std::vector<uint8_t> read_buf;
auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
auto set_tensor = [&](ggml_tensor * orig, 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);
@ -327,8 +326,8 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
}
struct llama_adapter_lora * llama_adapter_lora_init(struct llama_model * model, const char * path_lora) {
struct llama_adapter_lora * adapter = new llama_adapter_lora();
llama_adapter_lora * llama_adapter_lora_init(llama_model * model, const char * path_lora) {
llama_adapter_lora * adapter = new llama_adapter_lora();
try {
llama_adapter_lora_init_impl(*model, path_lora, *adapter);
@ -342,6 +341,6 @@ struct llama_adapter_lora * llama_adapter_lora_init(struct llama_model * model,
return nullptr;
}
void llama_adapter_lora_free(struct llama_adapter_lora * adapter) {
void llama_adapter_lora_free(llama_adapter_lora * adapter) {
delete adapter;
}