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llama_lora_adapter_apply

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ngxson 2024-07-06 14:24:56 +02:00
parent 4e28ad40a0
commit 1b4ffbac47
4 changed files with 115 additions and 135 deletions
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3
common/common.cpp
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@ -2063,13 +2063,14 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) {
const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]);
float lora_scale = std::get<1>(params.lora_adapter[i]); float lora_scale = std::get<1>(params.lora_adapter[i]);
auto adapter = llama_lora_adapter_init(lctx, lora_adapter.c_str()); auto adapter = llama_lora_adapter_init(lctx, lora_adapter.c_str(), lora_scale);
if (adapter == nullptr) { if (adapter == nullptr) {
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
llama_free(lctx); llama_free(lctx);
llama_free_model(model); llama_free_model(model);
return std::make_tuple(nullptr, nullptr); return std::make_tuple(nullptr, nullptr);
} }
llama_lora_adapter_apply(lctx, adapter);
} }
if (params.ignore_eos) { if (params.ignore_eos) {

2
ggml/src/ggml.c
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@ -19339,7 +19339,7 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph
fprintf(fp, "digraph G {\n"); fprintf(fp, "digraph G {\n");
fprintf(fp, " newrank = true;\n"); fprintf(fp, " newrank = true;\n");
fprintf(fp, " rankdir = TB;\n"); fprintf(fp, " rankdir = LR;\n");
for (int i = 0; i < gb->n_nodes; i++) { for (int i = 0; i < gb->n_nodes; i++) {
struct ggml_tensor * node = gb->nodes[i]; struct ggml_tensor * node = gb->nodes[i];

6
include/llama.h
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@ -515,7 +515,11 @@ extern "C" {
// will be applied on top of the previous one // will be applied on top of the previous one
LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init( LLAMA_API struct llama_lora_adapter * llama_lora_adapter_init(
struct llama_context * ctx, struct llama_context * ctx,
const char * path_lora); const char * path_lora,
float scale);
LLAMA_API int32_t llama_lora_adapter_apply(
struct llama_context * ctx,
struct llama_lora_adapter * adapter);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear // Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector. // the currently loaded vector.

239
src/llama.cpp
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@ -2559,6 +2559,7 @@ struct llama_lora_adapter {
std::map<std::string, lora_weight> ab_map; std::map<std::string, lora_weight> ab_map;
std::vector<struct ggml_context *> ctxs; std::vector<struct ggml_context *> ctxs;
std::vector<ggml_backend_buffer_t> bufs; std::vector<ggml_backend_buffer_t> bufs;
float scale = 1.0f;
~llama_lora_adapter() { ~llama_lora_adapter() {
for (struct ggml_context * ctx : ctxs) { for (struct ggml_context * ctx : ctxs) {
@ -13495,10 +13496,6 @@ static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
return result; return result;
} }
// forward declaration
static int32_t llama_lora_patch_tensors(struct llama_context & lctx, struct ggml_context * ctx_build);
static int32_t llama_lora_restore_tensors(struct llama_context & lctx);
static struct ggml_cgraph * llama_build_graph( static struct ggml_cgraph * llama_build_graph(
llama_context & lctx, llama_context & lctx,
const llama_batch & batch, const llama_batch & batch,
@ -13542,11 +13539,6 @@ static struct ggml_cgraph * llama_build_graph(
llm.init(); llm.init();
if (!lctx.lora_adapters.empty()) {
llama_lora_restore_tensors(lctx);
llama_lora_patch_tensors(lctx, llm.ctx0);
}
switch (model.arch) { switch (model.arch) {
case LLM_ARCH_LLAMA: case LLM_ARCH_LLAMA:
{ {
@ -18444,144 +18436,126 @@ static int llama_lora_adapter_init_internal(const struct llama_model & model, co
return 0; return 0;
} }
static int32_t llama_lora_restore_tensors(struct llama_context & lctx) { int32_t llama_lora_adapter_apply(struct llama_context * lctx, struct llama_lora_adapter * adapter) {
// TODO @ngxson : not ideal, but "const" is discarded to make it work GGML_ASSERT(!lctx->lora_adapters.empty());
struct llama_model & model = const_cast<struct llama_model &>(lctx.model); const struct llama_model & model = lctx->model;
if (!model.orig_tensors.empty()) { struct ggml_init_params ctx0_params = {
size_t i = 0; /*.mem_size =*/ lctx->buf_compute_meta.size(),
model.tok_embd = model.orig_tensors[i++]; /*.mem_buffer =*/ lctx->buf_compute_meta.data(),
model.type_embd = model.orig_tensors[i++]; /*.no_alloc =*/ true,
model.pos_embd = model.orig_tensors[i++]; };
model.tok_norm = model.orig_tensors[i++]; struct ggml_context * ctx0 = ggml_init(ctx0_params);
model.tok_norm_b = model.orig_tensors[i++];
model.output_norm = model.orig_tensors[i++];
model.output_norm_b = model.orig_tensors[i++];
model.output = model.orig_tensors[i++];
model.output_b = model.orig_tensors[i++];
model.output_norm_enc = model.orig_tensors[i++];
for (size_t il = 0; il < model.orig_layers.size(); il++) {
model.layers[il] = model.orig_layers[il]; // copy
}
}
return 0;
}
static int32_t llama_lora_patch_tensors(struct llama_context & lctx, struct ggml_context * ctx_build) { // apply lora for model tensors
GGML_ASSERT(!lctx.lora_adapters.empty()); struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
// TODO @ngxson : not ideal, but "const" is discarded to make it work std::vector<std::pair<struct ggml_tensor *, struct ggml_tensor *>> output_nodes;
struct llama_model & model = const_cast<struct llama_model &>(lctx.model); auto apply_lora = [&](struct llama_lora_adapter * adapter, struct ggml_tensor * model_tensor) {
if (model_tensor == nullptr) {
// save all original tensors
if (model.orig_tensors.empty()) {
model.orig_tensors.push_back(model.tok_embd);
model.orig_tensors.push_back(model.type_embd);
model.orig_tensors.push_back(model.pos_embd);
model.orig_tensors.push_back(model.tok_norm);
model.orig_tensors.push_back(model.tok_norm_b);
model.orig_tensors.push_back(model.output_norm);
model.orig_tensors.push_back(model.output_norm_b);
model.orig_tensors.push_back(model.output);
model.orig_tensors.push_back(model.output_b);
model.orig_tensors.push_back(model.output_norm_enc);
model.orig_layers.reserve(model.layers.size());
for (llama_layer layer : model.layers) {
model.orig_layers.push_back(layer); // copy
}
}
// patch tensors
auto patch_tensor = [&](struct llama_lora_adapter * adapter, struct ggml_tensor ** tensor) {
if (*tensor == nullptr) {
return; return;
} }
std::string name = ggml_get_name(*tensor); std::string name = ggml_get_name(model_tensor);
if (adapter->ab_map.find(name) != adapter->ab_map.end()) { if (adapter->ab_map.find(name) != adapter->ab_map.end()) {
auto lora_w = adapter->ab_map[name]; auto lora_w = adapter->ab_map[name];
struct ggml_tensor * cur = ggml_mul_mat(ctx_build, lora_w.a, lora_w.b); struct ggml_tensor * cur = ggml_mul_mat(ctx0, lora_w.a, lora_w.b);
cur = ggml_add(ctx_build, cur, *tensor); cur = ggml_scale_inplace(ctx0, cur, adapter->scale);
// TODO: scale cur = ggml_add(ctx0, cur, model_tensor);
ggml_format_name(cur, "%s.merged", name.c_str()); ggml_format_name(cur, "%s.merged", name.c_str());
// LLAMA_LOG_INFO("LORA %p %s\n", cur, cur->name); ggml_build_forward_expand(gf, cur);
*tensor = cur; output_nodes.push_back({model_tensor, cur});
} }
}; };
for (auto adapter : lctx.lora_adapters) { apply_lora(adapter, model.tok_embd);
patch_tensor(adapter, &model.tok_embd); apply_lora(adapter, model.type_embd);
patch_tensor(adapter, &model.type_embd); apply_lora(adapter, model.pos_embd);
patch_tensor(adapter, &model.pos_embd); apply_lora(adapter, model.tok_norm);
patch_tensor(adapter, &model.tok_norm); apply_lora(adapter, model.tok_norm_b);
patch_tensor(adapter, &model.tok_norm_b); apply_lora(adapter, model.output_norm);
patch_tensor(adapter, &model.output_norm); apply_lora(adapter, model.output_norm_b);
patch_tensor(adapter, &model.output_norm_b); apply_lora(adapter, model.output);
patch_tensor(adapter, &model.output); apply_lora(adapter, model.output_b);
patch_tensor(adapter, &model.output_b); apply_lora(adapter, model.output_norm_enc);
patch_tensor(adapter, &model.output_norm_enc); for (const llama_layer & layer : model.layers) {
for (llama_layer & layer : model.layers) { apply_lora(adapter, layer.attn_norm);
patch_tensor(adapter, &layer.attn_norm); apply_lora(adapter, layer.attn_norm_b);
patch_tensor(adapter, &layer.attn_norm_b); apply_lora(adapter, layer.attn_norm_2);
patch_tensor(adapter, &layer.attn_norm_2); apply_lora(adapter, layer.attn_norm_2_b);
patch_tensor(adapter, &layer.attn_norm_2_b); apply_lora(adapter, layer.attn_q_norm);
patch_tensor(adapter, &layer.attn_q_norm); apply_lora(adapter, layer.attn_q_norm_b);
patch_tensor(adapter, &layer.attn_q_norm_b); apply_lora(adapter, layer.attn_k_norm);
patch_tensor(adapter, &layer.attn_k_norm); apply_lora(adapter, layer.attn_k_norm_b);
patch_tensor(adapter, &layer.attn_k_norm_b); apply_lora(adapter, layer.attn_out_norm);
patch_tensor(adapter, &layer.attn_out_norm); apply_lora(adapter, layer.attn_out_norm_b);
patch_tensor(adapter, &layer.attn_out_norm_b); apply_lora(adapter, layer.attn_q_a_norm);
patch_tensor(adapter, &layer.attn_q_a_norm); apply_lora(adapter, layer.attn_kv_a_norm);
patch_tensor(adapter, &layer.attn_kv_a_norm); apply_lora(adapter, layer.attn_sub_norm);
patch_tensor(adapter, &layer.attn_sub_norm); apply_lora(adapter, layer.attn_post_norm);
patch_tensor(adapter, &layer.attn_post_norm); apply_lora(adapter, layer.ffn_sub_norm);
patch_tensor(adapter, &layer.ffn_sub_norm); apply_lora(adapter, layer.attn_norm_cross);
patch_tensor(adapter, &layer.attn_norm_cross); apply_lora(adapter, layer.attn_norm_enc);
patch_tensor(adapter, &layer.attn_norm_enc);
patch_tensor(adapter, &layer.wq); apply_lora(adapter, layer.wq);
patch_tensor(adapter, &layer.wk); apply_lora(adapter, layer.wk);
patch_tensor(adapter, &layer.wv); apply_lora(adapter, layer.wv);
patch_tensor(adapter, &layer.wo); apply_lora(adapter, layer.wo);
patch_tensor(adapter, &layer.wqkv); apply_lora(adapter, layer.wqkv);
patch_tensor(adapter, &layer.wq_a); apply_lora(adapter, layer.wq_a);
patch_tensor(adapter, &layer.wq_b); apply_lora(adapter, layer.wq_b);
patch_tensor(adapter, &layer.wkv_a_mqa); apply_lora(adapter, layer.wkv_a_mqa);
patch_tensor(adapter, &layer.wkv_b); apply_lora(adapter, layer.wkv_b);
patch_tensor(adapter, &layer.bq); apply_lora(adapter, layer.bq);
patch_tensor(adapter, &layer.bk); apply_lora(adapter, layer.bk);
patch_tensor(adapter, &layer.bv); apply_lora(adapter, layer.bv);
patch_tensor(adapter, &layer.bo); apply_lora(adapter, layer.bo);
patch_tensor(adapter, &layer.bqkv); apply_lora(adapter, layer.bqkv);
patch_tensor(adapter, &layer.ffn_norm); apply_lora(adapter, layer.ffn_norm);
patch_tensor(adapter, &layer.ffn_norm_b); apply_lora(adapter, layer.ffn_norm_b);
patch_tensor(adapter, &layer.ffn_post_norm); apply_lora(adapter, layer.ffn_post_norm);
patch_tensor(adapter, &layer.layer_out_norm); apply_lora(adapter, layer.layer_out_norm);
patch_tensor(adapter, &layer.layer_out_norm_b); apply_lora(adapter, layer.layer_out_norm_b);
patch_tensor(adapter, &layer.ffn_norm_exps); apply_lora(adapter, layer.ffn_norm_exps);
patch_tensor(adapter, &layer.ffn_norm_enc); apply_lora(adapter, layer.ffn_norm_enc);
patch_tensor(adapter, &layer.ffn_gate); apply_lora(adapter, layer.ffn_gate);
patch_tensor(adapter, &layer.ffn_down); apply_lora(adapter, layer.ffn_down);
patch_tensor(adapter, &layer.ffn_up); apply_lora(adapter, layer.ffn_up);
patch_tensor(adapter, &layer.ffn_gate_enc); apply_lora(adapter, layer.ffn_gate_enc);
patch_tensor(adapter, &layer.ffn_down_enc); apply_lora(adapter, layer.ffn_down_enc);
patch_tensor(adapter, &layer.ffn_up_enc); apply_lora(adapter, layer.ffn_up_enc);
patch_tensor(adapter, &layer.ffn_gate_inp); apply_lora(adapter, layer.ffn_gate_inp);
patch_tensor(adapter, &layer.ffn_gate_exps); apply_lora(adapter, layer.ffn_gate_exps);
patch_tensor(adapter, &layer.ffn_down_exps); apply_lora(adapter, layer.ffn_down_exps);
patch_tensor(adapter, &layer.ffn_up_exps); apply_lora(adapter, layer.ffn_up_exps);
patch_tensor(adapter, &layer.ffn_gate_inp_shexp); apply_lora(adapter, layer.ffn_gate_inp_shexp);
patch_tensor(adapter, &layer.ffn_gate_shexp); apply_lora(adapter, layer.ffn_gate_shexp);
patch_tensor(adapter, &layer.ffn_down_shexp); apply_lora(adapter, layer.ffn_down_shexp);
patch_tensor(adapter, &layer.ffn_up_shexp); apply_lora(adapter, layer.ffn_up_shexp);
patch_tensor(adapter, &layer.ffn_gate_b); apply_lora(adapter, layer.ffn_gate_b);
patch_tensor(adapter, &layer.ffn_down_b); apply_lora(adapter, layer.ffn_down_b);
patch_tensor(adapter, &layer.ffn_up_b); apply_lora(adapter, layer.ffn_up_b);
patch_tensor(adapter, &layer.ffn_act); apply_lora(adapter, layer.ffn_act);
}
} }
// merge lora to model weight
ggml_status res = ggml_backend_sched_graph_compute(lctx->sched, gf);
if (res == GGML_STATUS_SUCCESS) {
for (auto & out : output_nodes) {
struct ggml_tensor * model_tensor = out.first;
struct ggml_tensor * merged_tensor = out.second;
ggml_backend_tensor_copy(merged_tensor, model_tensor);
ggml_set_name(model_tensor, merged_tensor->name);
}
LLAMA_LOG_ERROR("%s: merged %ld lora weights to model\n", __func__, output_nodes.size());
} else {
LLAMA_LOG_ERROR("%s: compute error while merging lora weights to model, result = %d\n", __func__, res);
return res;
}
ggml_free(ctx0);
return 0; return 0;
} }
@ -19362,9 +19336,10 @@ uint32_t llama_model_quantize(
} }
} }
struct llama_lora_adapter * llama_lora_adapter_init(struct llama_context * ctx, const char * path_lora) { struct llama_lora_adapter * llama_lora_adapter_init(struct llama_context * ctx, const char * path_lora, float scale) {
try { try {
struct llama_lora_adapter * adapter = new llama_lora_adapter; struct llama_lora_adapter * adapter = new llama_lora_adapter;
adapter->scale = scale;
int res = llama_lora_adapter_init_internal(ctx->model, path_lora, *adapter); int res = llama_lora_adapter_init_internal(ctx->model, path_lora, *adapter);
if (res == 0) { if (res == 0) {
ctx->lora_adapters.push_back(adapter); ctx->lora_adapters.push_back(adapter);